Alok Mishra

• Alzoubi, Yehia & Mishra, Alok (2026). Assessing Privacy Risks of Proprietary and Open-Weight Large Language Models: A GDPR-Oriented Comparative Analysis. Journal of Responsible Technology. 27(September). doi: 10.1016/j.jrt.2026.100187. Fulltekst i vitenarkiv Vis sammendrag

  This study examines the growing privacy risks posed by Large Language Models (LLMs) through a structured, General Data Protection Regulation (GDPR)-oriented comparative analysis. As LLMs increasingly process personal data across sectors, understanding their alignment with the proposed GDPR-based evaluation dimensions is essential for responsible adoption and risk mitigation. Using a multi-criteria evaluation framework informed by the literature, six leading LLMs (GPT-4/5, Claude 3, Gemini, Llama 3, Mistral, and Falcon) were assessed across six GDPR-based evaluation dimensions. Weighted scoring enabled a transparent comparison of privacy governance maturity across the evaluated models. Findings indicate strong performance in security and risk-based controls, particularly among proprietary models, while notable gaps remain in Purpose Limitation, Data Minimization, and documentation of lawful bases. Proprietary models generally demonstrated higher privacy governance maturity, whereas open-weight models benefited from community scrutiny but lacked structured documentation and enterprise accountability. Data Subject Rights exhibited mixed support across all evaluated models, reflecting technical challenges in implementing access, rectification, and erasure within machine learning contexts. This study contributes an actionable framework for organizations, policymakers, and researchers seeking to align Artificial Intelligence (AI) deployment with GDPR requirements. Future research should focus on longitudinal assessments of evolving LLM policies, the development of AI-specific regulatory guidance to address ambiguities in the GDPR, and the creation of standardized benchmarks for evaluating privacy governance in foundation models.

• Mishra, Alok & Alzoubi, Yehia Ibrahim (2026). Software Architecture Quality Attributes in IoT-Based Smart City Systems. International journal of software engineering and knowledge engineering. ISSN 0218-1940. 36(09), s. 1254–1254. doi: 10.1142/s0218194025300040. Fulltekst i vitenarkiv Vis sammendrag

  Achieving and guaranteeing different software quality attributes is based on software architecture. This architecture encompasses the gathered criteria for the product, which serve as a guide outlining the quality attributes important to all project participants. It also includes techniques for measurement and control. Despite the noticeable increase in Internet of Things (IoT)-based smart city systems, there is a lack of research on the quality attributes of their software architecture. To fulfill this demand, this study offers a review of components and services made to address specific quality attributes crucial to IoT-based smart city systems. We identified and discussed several quality attributes, including scalability, performance, security and privacy, flexibility, interoperability, citizen engagement and reliability. These attributes were then mapped to relevant parts of the IoT-based smart city software architecture. Moreover, issues for each of these quality attributes were discussed. The findings of this research provide insightful guidance for creating, implementing and improving IoT-based smart systems to meet the evolving needs of the smart city sector. Subsequent investigations could concentrate on offering all-encompassing legal and regulatory structures, security and privacy protocols and optimal approaches for every smart city application separately.

• Alzoubi, Yehia Ibrahim & Mishra, Alok (2026). Green artificial intelligence in health applications. Artificial Intelligence in Medicine. ISSN 0933-3657. 178. doi: 10.1016/j.artmed.2026.103442. Fulltekst i vitenarkiv
• Mishra, Alok; Alzoubi, Yehia Ibrahim; Iftikhar, Muhammad Hassan & Khan, Amama Bushra Jahangir (2026). Empirical insights into supply chain management integration with product lifecycle management: benefits and challenges. Engineering Management in Production and Services. ISSN 2543-6597. 18(1), s. 87–106. doi: 10.2478/emj-2026-0007. Fulltekst i vitenarkiv Vis sammendrag

  Integration of product lifecycle management systems with supply chain management processes has proven challenging for companies that attempt it, with limited empirical research available on this topic. Consequently, this study aimed to explore whether implementing product lifecycle management systems can enhance supply chain management within an organisation. To obtain relevant data, this study employed a single qualitative case study design, using in-depth semi-structured interviews and on-site observations conducted at ABC TECH to examine how Sovelia PLM supports supply chain processes. The data was collected from a company in Northern Europe that uses Sovelia lifecycle management software. Findings showed that this software generally aids in enhancing supply chain process integration, traceability, and structure. However, it is design-centric, which makes it more difficult to adequately address the supply chain management department’s operational requirements. These insights may enhance the decision-making processes and guide actionable strategies for seamless integration. The findings call for a more holistic product lifecycle management system that encapsulates both design and supply chain functionalities, thereby ensuring cohesive operations and improved communication within the company’s supply chain network.

• Choudhary, Arbind Kumar; Mishra, Kamta Nath; Lal, Rajesh Kumar & Mishra, Alok (2026). Enhanced EEG-based detection of major depressive disorder using maximum likelihood estimation and machine learning. Journal of Big Data. 13(1). doi: 10.1186/s40537-026-01380-1. Fulltekst i vitenarkiv
• Shameem, Mohammad; Gangwar, Hemlata; Niazi, Mahmood; Mahmood, Sajjad & Mishra, Alok (2026). Exploring the impact of personality traits and cognitive diversity on knowledge-sharing behaviour, team innovation climate, and team agility in Agile Software Development. Cognition, Technology & Work. ISSN 1435-5558. doi: 10.1007/s10111-026-00858-5. Fulltekst i vitenarkiv Vis sammendrag

  Context Knowledge-sharing has been considered one of the most significant factors in the success of Agile Software Development (ASD) projects. Although its importance has been widely acknowledged in literature, prior studies have not sufficiently explored its intermediate relationships with personality traits, team innovation climate, and team agility in enhancing team dynamism within ASD. Objective This study proposes and validates a theoretical model to examine the relationships among personality traits, knowledge-sharing behaviour, team innovation climate, and team agility. Methods Data were collected from 361 agile practitioners through a questionnaire survey. The collected data were analyzed using descriptive statistics, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). Results The findings indicate that extraversion, agreeableness, and openness to experience positively influence knowledgesharing behaviour, whereas neuroticism and conscientiousness are negatively related. Furthermore, knowledge-sharing behaviour positively influences team innovation climate and team agility. In addition, a positive relationship is observed between cognitive diversity and knowledge-sharing behaviour among agile practitioners in self-organizing teams. Conclusion This study highlights the essential role of personality traits and cognitive diversity in shaping knowledge-sharing behaviour, which in turn enhances team agility and fosters an innovative climate in agile software projects. The findings provide a valuable knowledge base for industry practitioners and researchers by clarifying how personality traits and cognitive diversity influence team agility and collaboration within agile development teams.

• Pathak, Mayank; Mishra, Kamta Nath; Singh, Satya Prakash & Mishra, Alok (2026). A hybrid machine learning and cryptography-based predictive probability model for enhancing security and privacy in cloud-IoT environment. Computers & Security. ISSN 0167-4048. 166. doi: 10.1016/j.cose.2026.104870. Fulltekst i vitenarkiv
• Rao, Rajwant Singh; Banjare, Karishma & Mishra, Alok (2026). A Lightweight Hybrid Approach to Practical Code Smell Detection. IEEE Software. ISSN 0740-7459. doi: 10.1109/MS.2026.3671868. Fulltekst i vitenarkiv
• Mishra, Alok; Alzoubi, Yehia Ibrahim & Ibrahim, Thaer (2026). Earthquake response framework with IoT-based e-government services. European Journal of Sustainable Development Research (EJOSDR). 10(1). doi: 10.29333/ejosdr/17275. Fulltekst i vitenarkiv
• Kundu, Shrabanti; Mishra, Deepti & Mishra, Alok (2025). A Proposal on an AI-Based Framework for Software Defect Detection Using Multimodality in Software Industries. International Symposium on Empirical Software Engineering and Measurement. ISSN 1949-3770. 2025, s. 508–513. doi: 10.1109/ESEM64174.2025.00020. Fulltekst i vitenarkiv
• Rao, Rajwant Singh; Dewangan, Seema & Mishra, Alok (2025). An Empirical Evaluation of Ensemble Models for Python Code Smell Detection. Applied Sciences. 15(13). doi: 10.3390/app15137472. Fulltekst i vitenarkiv
• Alzoubi, Yehia Ibrahim & Mishra, Alok (2025). A comparative analysis of e-government services in ten African countries. eJournal of eDemocracy and Open Government. 17(3), s. 172–200. doi: 10.29379/jedem.v17i3.1107. Fulltekst i vitenarkiv
• Rao, Rajwant Singh; Kashyap, Akash; Yadav, Mehul & Mishra, Alok (2025). MESNET: integrating lightweight CNNs and real-time carbon tracking for sustainable image classification. Discover Sustainability. 6(1). doi: 10.1007/s43621-025-02347-7. Fulltekst i vitenarkiv
• Alzoubi, Yehia Ibrahim; Mishra, Alok & Aljaafreh, Ali (2025). Blockchain based on proof-of-work consensus algorithm: Evolution and future potential. Energy Nexus. 20(December 2025). doi: 10.1016/j.nexus.2025.100577. Fulltekst i vitenarkiv Vis sammendrag

  The Proof-of-Work (PoW) consensus algorithm has been a foundational element of blockchain technology, designed to establish decentralized security, enable trustless transactions, and ensure tamper-proof record-keeping. Despite the emergence of various other consensus algorithms, PoW remains widely adopted, though it has been criticized for its high resource consumption and inefficiency. Over the past five years, PoW has remained the focus of numerous studies, underscoring its ongoing relevance. Given its sustained use, it is important to examine both the current state and prospects of PoW. This study conducts a thorough literature review of publications indexed in Scopus up to July 2025, tracing the evolution of PoW and its potential trajectory. The analysis indicates that PoW is likely to continue its prominence, particularly in public blockchain applications, in the foreseeable future. However, it's important to note that this study is limited by the availability of research in commonly accessed databases at the time of writing. Future investigations could expand the scope by incorporating additional databases and leveraging the latest developments in this rapidly evolving field.

• Mawrides, Emmanuel Kafti; Mishra, Alok & Jæger, Bjørn (2025). Blockchain technology for sustainable supply chains in the fishing industry : a systematic review. Cleaner Logistics and Supply Chain (CLSC). 17(December). doi: 10.1016/j.clscn.2025.100277. Fulltekst i vitenarkiv Vis sammendrag

  This study presents a systematic review on the application of blockchain in fish supply chains. It addresses the current state of blockchain in fish supply chains, enablers and challenges to its adoption. The review delves into the fishing industry, focusing on empirical works on the intersection of blockchain and sustainability where there is limited research. The key findings illustrate that the themes of transparency, traceability, trust, and information sharing are dominant in the blockchain and fish supply chains domain. By synthesizing and critically assessing existing empirical research, the paper further sheds light on the interplay between blockchain and sustainable fish supply chains, further examining the facilitators that drive blockchain adoption. The findings demonstrate real-time visibility, authentication and security, cost reduction and blockchain’s decentralized nature as the main facilitators. Conversely, the study investigates challenges that hinder the seamless integration of blockchain in fish supply chains under the three categories of technology, organizational and regulatory frameworks. High costs of investments, resistance to change, environmental concerns and challenges such as forking, scalability issues, garbage in garbage out and integration issues emerge as the predominant barriers to adoption of blockchain in supply chain contexts. The paper further discusses smart contracts, token economics and consensus mechanisms and their impact on sustainability in this domain. There is need for further research into the social aspects of sustainability that have limited empirical research compared to governance and environmental sustainability for fish supply chains.

• Alzoubi, Yehia Ibrahim & Mishra, Alok (2025). ChatGPT technology contribution in business innovation, policy and management. Journal of Science and Technology Policy Management. ISSN 2053-4620. doi: 10.1108/JSTPM-10-2023-0173. Fulltekst i vitenarkiv
• Mishra, Kamta Nath; Lal, Rajesh Kumar; Barwal, Paras Nath & Mishra, Alok (2025). Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework. Applied Sciences. 15(13). doi: 10.3390/app15137485. Fulltekst i vitenarkiv
• El-Bayaa, Nourhan; Alzoubi, Yehia Ibrahim & Mishra, Alok (2025). Adoption ChatGPT in higher education settings: Potentials, challenges, and paving the way for future advancements. Edelweiss Applied Science and Technology. ISSN 2576-8484. 9(5), s. 2940–2964. doi: 10.55214/25768484.v9i5.7618. Fulltekst i vitenarkiv Vis sammendrag

  Several artificial intelligence generative language models have been developed recently. Many people in different contexts have started using them, such as education, industry, and even content creation. This study focuses on investigating how higher education students have been using ChatGPT as an example of a generative artificial intelligence tool. The context of the study was chosen due to the many voices against or in favor of using ChatGPT in higher education institutions. We deployed a mixed method to collect and analyze the data, including open-ended and closed-ended question surveys, to investigate the motivations and future expectations of students who have been using the ChatGPT tool. The research methodology comprises two main components: the initial phase involves conducting statistical analyses on questions about each of the 15 adoption factors. Responses concerning each factor were meticulously compiled and transferred into an Excel spreadsheet. Subsequently, the frequency of responses for each choice was tallied to discern prevailing trends and preferences among respondents. The second phase focuses on analyzing the open-ended questions' responses that forecast the future of ChatGPT as perceived by the participants. The data gathered from open-ended questions was analyzed thematically using Miles and Huberman's approach. Our findings reveal that students generally find using ChatGPT to be a helpful tool in their higher education institutions. The findings show that ease of use, perceived value, trialability, observability, relative advantage, social impact, and network effect are the most important factors behind their adoption decision. The findings also revealed that a significant number of students are still confused about ethical concerns and report the need for regulations when using ChatGPT. These findings are important for educators, policymakers, and technology designers who want to make the most of generative AI in education. Future research, however, is yet to be conducted to understand its ethical implications, long-term effects, and how to best incorporate it into teaching practices.

• Alzoubi, Yehia Ibrahim & Mishra, Alok (2025). Differential privacy and artificial intelligence: potentials, challenges, and future avenues. EURASIP Journal on Information Security. 2025(1). doi: 10.1186/s13635-025-00203-9. Fulltekst i vitenarkiv Vis sammendrag

  Privacy preservation has become an increasingly critical concern in applications where data serves as a cornerstone for decision-making and innovation. Researchers and developers are dedicated to identifying and mitigating emerging risks while improving the privacy of existing systems. Artificial intelligence technologies can dynamically detect and address privacy concerns. Differential privacy, with its strong and verifiable assurances, is critical for addressing rising concerns about data privacy in the age of big data and advanced analytics. Combining differential privacy with AI has been identified as a solution for balancing data usage for insights while maintaining individual privacy. However, research in this field is still scarce due to the recent widespread application of artificial intelligence in many industries. This paper reviews current literature, professional websites, and other online resources to determine the potential, challenges, and future directions of combining differential privacy with AI. The key opportunities identified in this study include enhancing privacy (reported in 27% of the reviewed papers), promoting responsible AI (21%), facilitating data sharing (14.5%), and minimizing AI model biases (12.5%). Several concerns, however, require additional exploration, including accuracy trade-offs, computational complexity, regulatory restrictions, expertise, data usability, scalability constraints, and bias concerns. Given that this combination is still a relatively new field, AI developers and users need to stay current on differential privacy research and implement appropriate measures.

• Rao, Rajwant Singh; Dewangan, Seema; Mishra, Alok & Gupta, Manjari (2025). A study for method-level code smells detection using machine learning algorithms. Systems and Soft Computing (SASC). 7. Fulltekst i vitenarkiv
• Rao, Rajwant Singh; Dewangan, Seema & Mishra, Alok (2025). Optimized Python Code Smell Detection: A Novel Approach with Ensemble Learning Models. Applied Sciences. Fulltekst i vitenarkiv
• Alzoubi, Yehia Ibrahim & Mishra, Alok (2024). Techniques to alleviate blockchain bloat: Potentials, challenges, and recommendations. Computers and Electrical Engineering. ISSN 0045-7906. 116. doi: 10.1016/j.compeleceng.2024.109216. Fulltekst i vitenarkiv Vis sammendrag

  Blockchain (BC) bloat, characterized by excessive data growth on BC networks, presents a pressing challenge. The need for a comprehensive study addressing BC bloat solutions arises due to the scarcity of knowledge in this emerging area. This study advances knowledge along with practical insights and inspires innovative approaches to address BC bloat. This research explores various techniques proposed to combat BC bloat. Two technique themes are identified: on-chain and off-chain techniques. On-chain techniques focus on updating the consensus mechanism, block size, or database partition. This study identified five on-chain techniques: structural modification, pruning, sharding, ephemeral BC, and zk-SNARKs. Off-chain techniques focus on processing in parallel, bundling transactions, or handling transactions off-chain. Further, three off-chain techniques identified are off-chain storage, historical data storage, and light clients’ techniques. These recommendations aim to optimize resources use, enhance scalability, and maintain data integrity. However, these may compromise several BC capabilities, such as security, privacy, and data loss. The applicability of these techniques for resource-constrained devices is also explored. Future research should include further investigation into the performance and applicability of these techniques, especially in resource-constrained environments like IoT and fog computing.

• Alzoubi, Yehia Ibrahim & Mishra, Alok (2024). Green artificial intelligence initiatives: Potentials and challenges. Journal of Cleaner Production. ISSN 0959-6526. 468. doi: 10.1016/j.jclepro.2024.143090. Fulltekst i vitenarkiv Vis sammendrag

  Recently, the widespread adoption of artificial intelligence, particularly generative AI technology, has surged across various industries. However, a notable drawback of this technology is its significant energy consumption during model training and operation, which poses challenges to sustainability goals and the environment. Consequently, various initiatives have emerged to promote what is termed "green artificial intelligence," aiming to mitigate these environmental impacts. Nevertheless, research discussing these initiatives remains scarce. Hence, this study aims to identify green artificial intelligence initiatives that contribute to environmental friendliness. This paper has comprehensively reviewed the existing literature, professional websites, and expert blogs to identify and analyze available green AI initiatives. This paper has identified 55 such initiatives, broadly categorized into six themes: cloud optimization, model efficiency, carbon footprinting, sustainability-focused AI development, open-source initiatives, and green AI research and community. This study discusses the strengths and limitations of each initiative to offer a comprehensive overview. The findings provide valuable insights, particularly for industries interested in green artificial intelligence and green technology in general. While some tools have been recognized and studied, comprehensive research and analysis are still required to empirically evaluate the majority of other tools due to their early stages of development in this field.

• Alzoubi, Yehia Ibrahim; Mishra, Alok & Kulakli, Atik (2024). Critical Concerns of Deploying Blockchain in Internet of Things Applications. TEM JOURNAL - Technology, Education, Management, Informatics. ISSN 2217-8309. 13(2), s. 856–865. doi: 10.18421/TEM132-02. Fulltekst i vitenarkiv Vis sammendrag

  The Internet of Things (IoT) and blockchain are both recognized as cutting-edge, popular technologies. In contrast to IoT, which pertains to the spread of linked equipment via supplying information over the Internet, blockchain offers innovative data storage and management avenues. Even as blockchain demands real-time data application and IoT specifies mechanisms to securely store and handle data overflows, a combination of the two seems promising. Blockchain, a technology created with the cryptocurrency Bitcoin, could meet the needs of the IoT. However, combining blockchain with the IoT might present a multitude of issues due to the features of blockchain and IoT technology. Although many articles have been released on the blockchain and the IoT, the concerns with this combination are still vague and dispersed. In light of this, this study seeks to give an overview of the problems that have the biggest impact on blockchain-based IoT by reviewing the pertinent peer-reviewed articles. This article also addresses some suggestions for lessening the impact of these concerns. The study considered peer-reviewed articles published within the last five years, focusing on topics related to blockchain and IoT integration. We identified 44 articles for this review. The study's significance is that it not only expands the scholarly understanding of this complex intersection between blockchain and IoT but also provides actionable insights that can drive innovation and enhance the reliability and security of IoT ecosystems in practical settings.

• Mishra, Kamta Nath; Mishra, Alok; Barwal, Paras Nath & Lal, Rajesh Kumar (2024). Natural Language Processing and Machine Learning-Based Solution of Cold Start Problem Using Collaborative Filtering Approach. Electronics (Basel). 13(21). doi: 10.3390/electronics13214331. Fulltekst i vitenarkiv Vis sammendrag

  In today’s digital era, the abundance of online services presents users with a daunting array of choices, spanning from streaming platforms to e-commerce websites, leading to decision fatigue. Recommendation algorithms play a pivotal role in aiding users in navigating this plethora of options, among which collaborative filtering (CF) stands out as a prevalent technique. However, CF encounters several challenges, including scalability issues, privacy implications, and the well-known cold start problem. This study endeavors to mitigate the cold start problem by harnessing the capabilities of natural language processing (NLP) applied to user-generated reviews. A unique methodology is introduced, integrating both supervised and unsupervised NLP approaches facilitated by sci-kit learn, utilizing benchmark datasets across diverse domains. This study offers scientific contributions through its novel approach, ensuring rigor, precision, scalability, and real-world relevance. It tackles the cold start problem in recommendation systems by combining natural language processing (NLP) with machine learning and collaborative filtering techniques, addressing data sparsity effectively. This study emphasizes reproducibility and accuracy while proposing an advanced solution that improves personalization in recommendation models. The proposed NLP-based strategy enhances the quality of user-generated content, consequently refining the accuracy of Collaborative Filtering-Based Recommender Systems (CFBRSs). The authors conducted experiments to test the performance of the proposed approach on benchmark datasets like MovieLens, Jester, Book-Crossing, Last.fm, Amazon Product Reviews, Yelp, Netflix Prize, Goodreads, IMDb (Internet movie Database) Data, CiteULike, Epinions, and Etsy to measure global accuracy, global loss, F-1 Score, and AUC (area under curve) values. Assessment through various techniques such as random forest, Naïve Bayes, and Logistic Regression on heterogeneous benchmark datasets indicates that random forest is the most effective method, achieving an accuracy rate exceeding 90%. Further, the proposed approach received a global accuracy above 95%, a global loss of 1.50%, an F-1 Score of 0.78, and an AUC value of 92%. Furthermore, the experiments conducted on distributed and global differential privacy (GDP) further optimize the system’s efficacy.

• Mishra, Alok; Alzoubi, Yehia Ibrahim & Gavrilović, Nebojša (2024). Quality attributes of software architecture in IoT-based agricultural systems. Smart Agricultural Technology. 8(100523). doi: 10.1016/j.atech.2024.100523. Fulltekst i vitenarkiv Vis sammendrag

  Software architecture forms the cornerstone for achieving and ensuring various software quality attributes. It encompasses the collected requirements of the product, serving as a blueprint that delineates quality features for all project stakeholders, along with methods for measurement and control. Despite the significant increase in IoT-based agricultural systems, there is a dearth of studies on the quality elements of their software architecture. To address this need, this study offers an overview of components and services tailored to address specific quality attributes pertinent to agriculture systems. It identifies, investigates, and presents quality attributes influencing the design of software architecture for IoT-based agriculture systems. This paper identified and discussed several quality attributes, including performance, scalability, flexibility, interoperability, productivity, extensibility, and security, and mapped them to corresponding components of the IoT-based agriculture software architecture. Also, several issues were identified and discussed for the software architecture quality of IoT-based agriculture systems, such as real-time processing and interoperability due to the various devices and protocols utilized in these systems. The findings of this study offer valuable insights for developing, executing, and refining IoT-based agricultural systems to fulfill the changing requirements of the agriculture industry.

• Yadav, Pravin Singh; Rao, Rajwant Singh; Mishra, Alok & Gupta, Manjari (2024). Machine Learning-Based Methods for Code Smell Detection: A Survey. Applied Sciences. 14(14). doi: 10.3390/app14146149. Fulltekst i vitenarkiv Vis sammendrag

  Code smells are early warning signs of potential issues in software quality. Various techniques are used in code smell detection, including the Bayesian approach, rule-based automatic antipattern detection, antipattern identification utilizing B-splines, Support Vector Machine direct, SMURF (Support Vector Machines for design smell detection using relevant feedback), and immune-based detection strategy. Machine learning (ML) has taken a great stride in this area. This study includes relevant studies applying ML algorithms from 2005 to 2024 in a comprehensive manner for the survey to provide insight regarding code smell, ML algorithms frequently applied, and software metrics. Forty-two pertinent studies allow us to assess the efficacy of ML algorithms on selected datasets. After evaluating various studies based on open-source and project datasets, this study evaluated additional threats and obstacles to code smell detection, such as the lack of standardized code smell definitions, the difficulty of feature selection, and the challenges of handling large-scale datasets. The current studies only considered a few factors in identifying code smells, while in this study, several potential contributing factors to code smells are included. Several ML algorithms are examined, and various approaches, datasets, dataset languages, and software metrics are presented. This study provides the potential of ML algorithms to produce better results and fills a gap in the body of knowledge by providing class-wise distributions of the ML algorithms. Support Vector Machine, J48, Naive Bayes, and Random Forest models are the most common for detecting code smells. Researchers can find this study helpful in better anticipating and taking care of software development design and implementation issues. The findings from this study, which highlight the practical implications of ML algorithms in software quality improvement, will help software engineers fix problems during software design and development to ensure software quality.

• Kadhim, Yezi Ali; Guzel, Mehmet Serdar & Mishra, Alok (2024). A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification. Diagnostics (Basel). 14(14). doi: 10.3390/diagnostics14141469. Fulltekst i vitenarkiv Vis sammendrag

  Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep learning techniques, which were based on a convolutional neural network (CNN) or autoencoder, to extract features and combine them with the next step of the meta-heuristic algorithm in order to select optimal features using the particle swarm optimization (PSO) algorithm. This combination sought to reduce the dimensionality of the datasets while maintaining the original performance of the data. This is considered an innovative method and ensures highly accurate classification results across various medical datasets. Several classifiers were employed to predict the diseases. The COVID-19 dataset found that the highest accuracy was 99.76% using the combination of CNN-PSO-SVM. In comparison, the brain tumor dataset obtained 99.51% accuracy, the highest accuracy derived using the combination method of autoencoder-PSO-KNN.

• Aubaid, Asmaa M.; Mishra, Alok & Mishra, Atul (2024). Machine learning and rule-based embedding techniques for classifying text documents. International Journal of System Assurance Engineering and Management. ISSN 0975-6809. 15, s. 5637–5652. doi: 10.1007/s13198-024-02555-w. Fulltekst i vitenarkiv Vis sammendrag

  Rapid expansion of electronic document archives and the proliferation of online information have made it incredibly difficult to categorize text documents. Classification helps in information retrieval from a conceptual framework. This study addresses the challenge of efficiently categorizing text documents amidst the vast electronic document landscape. Employing machine learning models and a novel document categorization method, W2vRule, we compare its performance with traditional methods. Emphasizing the importance of tuning hyperparameters for optimal performance, the research recommends the W2vRule, a word-to-vector rule-based framework, for improved association-based text classification. The study used the Reuters Newswire dataset. Findings show that W2vRule and machine learning can effectively tell apart important categories. Rule-based approaches perform better than Naive Bayes, BayesNet, Decision Tables, and others in terms of performance metrics.

• Alzoubi, Yehia Ibrahim; Mishra, Alok & Topcu, Ahmet Ercan (2024). Research trends in deep learning and machine learning for cloud computing security. Artificial Intelligence Review. ISSN 0269-2821. 57(132). doi: 10.1007/s10462-024-10776-5. Fulltekst i vitenarkiv Vis sammendrag

  Deep learning and machine learning show effectiveness in identifying and addressing cloud security threats. Despite the large number of articles published in this field, there remains a dearth of comprehensive reviews that synthesize the techniques, trends, and challenges of using deep learning and machine learning for cloud computing security. Accordingly, this paper aims to provide the most updated statistics on the development and research in cloud computing security utilizing deep learning and machine learning. Up to the middle of December 2023, 4051 publications were identified after we searched the Scopus database. This paper highlights key trend solutions for cloud computing security utilizing machine learning and deep learning, such as anomaly detection, security automation, and emerging technology's role. However, challenges such as data privacy, scalability, and explainability, among others, are also identified as challenges of using machine learning and deep learning for cloud security. The findings of this paper reveal that deep learning and machine learning for cloud computing security are emerging research areas. Future research directions may include addressing these challenges when utilizing machine learning and deep learning for cloud security. Additionally, exploring the development of algorithms and techniques that comply with relevant laws and regulations is essential for effective implementation in this domain.

• Yadav, Pravin Singh; Rao, Rajwant Singh & Mishra, Alok (2024). An Evaluation of Multi-Label Classification Approaches for Method-Level Code Smells Detection. IEEE Access. 12, s. 53664–53676. doi: 10.1109/ACCESS.2024.3387856. Fulltekst i vitenarkiv Vis sammendrag

  (1) Background: Code smell is the most popular and reliable method for detecting potential errors in code. In real-world circumstances, a single source code may have multiple code smells. Multi-label code smell detection is a popular research study. However, limited studies are available on it, and there is a need for a standardized classifier for reliably identifying various multi-label code smells that belong to the method-level code smell category. The primary goal of this study is to develop a rule-based method for detecting multi-label code smells. (2) Methods: Binary Relevance, Label Powerset, and Classifier Chain methods are utilized with tree based single-label algorithms, including some ensemble algorithms in this research paper. The chi-square feature selection technique is applied to select relevant features. The proposed model is trained using 10-fold cross-validation, Random Search cross-validation parameter tuning, and different performance measures are used to evaluate the model. (3) Results: The proposed model achieves 99.54% of the best jaccard accuracy for detecting method-level code smells using the Classifier Chain method with the Decision Tree. The Decision Tree model incorporating a multi-label classifier outperforms alternative approaches to multi-label classification. Single-label classifiers produced better results after considering the correlation factor. (4) Conclusion: This study will facilitate scientists and programmers by providing a systematic method for detecting various code smells in software projects and saving time and effort during code reviews by detecting multiple problems simultaneously. After detecting multi-label code smell, programmers can create more organized, easier-to-understand, and trustworthy programs.

• Alzoubi, Yehia Ibrahim; Mishra, Alok; Topcu, Ahmet Ercan & Cibikdiken, Ali Osman (2024). Generative Artificial Intelligence Technology for Systems Engineering Research: Contribution and Challenges. International Journal of Industrial Engineering and Management (IJIEM). ISSN 2217-2661. 15(2), s. 169–179. doi: 10.24867/IJIEM-2024-2-355. Fulltekst i vitenarkiv Vis sammendrag

  The advancement of artificial intelligence technology in recent years has had a significant impact on various industries, including the field of systems engineering. Generative Artificial Intelligence (AI), like OpenAI's ChatGPT, is one such tool that has garnered attention. While this technology offers researchers in systems engineering intriguing possibilities, it also introduces certain risks to the traditional research framework. The aim of this paper is to investigate the advantages and drawbacks associated with embracing generative AI. We conducted a comprehensive literature review utilizing resources like Google Scholar, Web of Science, and the Scopus database, along with professional websites and white papers. The analysis highlights the potential benefits of generative AI in systems engineering research, including data processing, analysis, hypothesis formulation, prediction and forecasting, and collaboration enhancement. However, it also underscores various risks, such as potential data bias, the generation of human-like text, potential loss of analytical capabilities, and difficulties in analyzing output from these AI tools. As emphasized in this paper, numerous concerns still need to be addressed regarding the use of generative AI tools due to their relatively new nature and evolving capabilities.

• Almashhadani, Mohammed; Mishra, Alok & Yazici, Ali (2024). Software Maintenance Practices Using Agile Methods Towards Cloud Environment: A Systematic Mapping. Journal of Software: Evolution and Process (JSEP). ISSN 2047-7473. doi: 10.1002/smr.2698. Fulltekst i vitenarkiv Vis sammendrag

  Agile methods have emerged to overcome the obstacles of structured methodologies, such as the waterfall, prototype, spiral, and so on. There are studies showing the usefulness of agile approaches in software development. However, studies on Agile maintenance are very limited in number. Regardless of the chosen methodology, software maintenance can be carried out in either a local (on-the-premise) or global (distributed) environment. In a local environment, the software maintenance team is co-located on the same premises, while in a global environment, the team is geographically dispersed from the customer. The main objective of this Systematic Mapping (SM) study is to identify the practices useful for software maintenance using the Agile approaches in the Cloud environment. We have conducted a comprehensive search in well-known digital databases and examined the articles that map to the pre-defined inclusion criteria. The study selected and analyzed 48 articles out of 320 published between 2000 and 2022. The findings of the mapping study reveal that Agile can resolve the major issues faced in traditional software maintenance, making the role of this approach significant in global/distributed software maintenance. Cloud computing plays a vital role in software maintenance. Most of the studies highlight the application of XP- and Scrum-based Agile maintenance models. The study found a need for more Agile maintenance solutions in the cloud, highlighting the importance of agile in software maintenance, both locally and globally. Irrespective of the environment, Cloud computing provides a centralized platform for collaboration and communication, while also offering scalability and flexibility to adapt to diverse infrastructure needs. This allows agile maintenance practices to be implemented across both local and global environments, leveraging the cloud's capabilities to overcome geographical and infrastructural challenges.

• Alzoubi, Yehia Ibrahim & Mishra, Alok (2024). Blockchain consensus mechanisms comparison in fog computing: A systematic review. ICT Express. 10(2), s. 342–373. doi: 10.1016/j.icte.2024.02.008. Fulltekst i vitenarkiv Vis sammendrag

  Numerous consensus mechanisms have been suggested to cater to the specific characteristics of fog computing. To comprehensively understand their unique features, performance, and applications in fog computing, it is crucial to conduct a systematic analysis of these mechanisms. For this study, 79 relevant articles were carefully selected based on predefined criteria. Among these articles, 35 employed work-proof-based consensus mechanisms, 24 utilized voting-based mechanisms, and 22 adopted capability-based mechanisms. Among the 26 identified consensus mechanisms, proof of work remains the most prevalent one. It is important to note that the scope of this paper is limited to the research available in the predominant databases at the time of writing. Future research may expand to include additional databases and more recent literature in this domain.

• Yadav, Pravin Singh; Rao, Rajwant Singh; Mishra, Alok & Gupta, Manjari (2024). Ensemble Methods with Feature Selection and Data Balancing for Improved Code Smells Classification Performance. Engineering Applications of Artificial Intelligence. ISSN 0952-1976. 139(A). doi: 10.1016/j.engappai.2024.109527. Fulltekst i vitenarkiv Vis sammendrag

  Code smells are software flaws that make it challenging to comprehend, develop, and maintain the software. Identifying and removing code smells is crucial for software quality. This study examines the effectiveness of several machine-learning models before and after applying feature selection and data balancing on code smell datasets. Extreme Gradient Boosting, Gradient Boosting, Adaptive Boosting, Random Forest, Artificial Neural Network (ANN), and Ensemble model of Bagging, and the two best-performing Boosting techniques are used to predict code smell. This study proposes an enhanced approach, which is an ensemble model of the Bagging and Boosting classifier (EMBBC) that incorporates feature selection and data balancing techniques to predict code smells. Four publicly available code smell datasets, Blob Class, Data Class, Long Parameter List, and Switch Statement, were considered for the experimental work. Classes of datasets are balanced using the Synthetic Minority Over-Sampling Technique (SMOTE). A feature selection method called Recursive Feature Elimination with Cross-Validation (RFECV) is used. This study shows that the ensemble model of Bagging and the two best-performing Boosting techniques performs better in Blob Class, Data Class, and Long Parameter List datasets with the highest accuracy of 99.21%, 99.21%, and 97.62%, respectively. In the Switch Statement dataset, the ANN model provides a higher accuracy of 92.86%. Since the proposed model uses only seven features and still provides better results than others, it could be helpful to detect code smells for software engineers and practitioners in less computational time, improving the system's overall performance.

• Mishra, Kamta Nath; Mishra, Alok; Ray, Soumya; Kumari, Anjali & Waris, Saad Misbah (2024). Enhancing cancer detection and prevention mechanisms using advanced machine learning approaches. Informatics in Medicine Unlocked (IMU). 50. doi: 10.1016/j.imu.2024.101579. Fulltekst i vitenarkiv Vis sammendrag

  Cancer is a major global health challenge, emphasizing the critical need for early detection to enhance patient outcomes. This study thoroughly investigates the applications of advanced machine learning methods for cancer detection and prevention, aiming to develop robust algorithms that can accurately identify cancerous cells and assess cancer severity based on key parameters. The authors synthesize insights from previous cancer detection and prevention research through an in-depth literature review. The study sets specific objectives, including creating and evaluating innovative algorithms for classifying cancer cells. The authors employed machine learning techniques to analyze parameters, such as cell size, shape, nucleus characteristics, and additional factors like cell texture, mitosis count, tumour progression, metastasis, gene expression patterns, and biological markers. This methodology is distinguished by its effective use of diverse data types and automated feature extraction to improve cancer detection and prediction accuracy. Advanced machine learning methods enhance the precision and reliability of current cancer cell classification algorithms. The research underscores the importance of timely and accurate cancer detection, which enables early intervention and significantly improves patient survival rates. The results and discussion section meticulously analyzes the findings, demonstrating the approach's effectiveness in accurately identifying cancerous cells and assessing cancer severity. This study is a valuable resource for medical professionals, supporting early-stage cancer detection and classification of cancer cells at different stages. The proposed algorithms show promising results in improving the accuracy and efficiency of cancer detection systems, paving the way for future advancements in cancer research and offering essential insights for healthcare practitioners and researchers.

• Mishra, Alok; Jabar, Thr Satar; Alzoubi, Yehia Ibrahim & Mishra, Kamta Nath (2023). Enhancing privacy-preserving mechanisms in Cloud storage: A novel conceptual framework. Concurrency and Computation: Practice and Experience. ISSN 1532-0626. 35(26). doi: 10.1002/cpe.7831. Fulltekst i vitenarkiv Vis sammendrag

  Data privacy is critical for users who want to use Cloud storage services. There is a significant focus on Cloud service providers to address this need. However, in the evolving dynamic cyber-space, privacy infractions are rising and pose threats to Cloud storage infrastructures. Several studies developed various models and techniques to ensure the privacy of Cloud storage contents. However, these models came with several shortages in the privacy-preserving attributes they cover. Thus, this article identified a comprehensive set of Cloud data storage privacy-preserving attributes to propose a flexible and efficient framework to handle the privacy problem. This framework uses a multi-layer encryption storage structure and a one-time password authentication technique. The findings of this article intend to help future communities to enhance existing techniques or develop new research-based practical alternatives. Since Cloud computing is a rapidly growing technology, new privacy vulnerabilities emerge daily. Future research might confirm the findings of this article and test the suggested framework in different contexts.

• Alzoubi, Yehia Ibrahim & Mishra, Alok (2023). Enterprise architecture contribution in distributed agile software development. Systems Engineering. ISSN 1098-1241. doi: 10.1002/sys.21739. Fulltekst i vitenarkiv Vis sammendrag

  Geographically distributed agile development may experience a high failure rate due to communication issues, which has a negative influence on project performance. One suggested solution in the literature is to enhance both communication and project performance by implementing agile enterprise architecture. However, the empirical evidence that supports this claim is still scarce. To address this gap, this study empirically explores the role of agile enterprise architecture as an artifact in distributed agile development. The findings of an in-depth qualitative case study from a dispersed agile development organization that involves teams distributed over three locations are used in this work. Over 2 months, data was gathered by interviewing 12 key members of the team and watching three Sprint sessions of agile software development. Text analysis qualitative approach was used to analyze the data. The findings imply that agile enterprise architecture has a positive effect on distributed agile software development communication, quality, and functionality. Agile enterprise architecture may also support on-time completion, but a trade-off with on-budget may be necessary. These findings provide valuable insights, frameworks, and best practices that support organizations in achieving greater agility, collaboration, and success in their distributed software development initiatives. As this is one of the first studies to look at the influence of agile enterprise architecture on distributed agile software development communication and performance, further research is needed to confirm and expand on the conclusions of this study.

• Alzoubi, Yehia Ibrahim & Mishra, Alok (2023). Green Blockchain – A Move Towards Sustainability. Journal of Cleaner Production. ISSN 0959-6526. 430. doi: 10.1016/j.jclepro.2023.139541. Fulltekst i vitenarkiv Vis sammendrag

  In recent years, blockchain technology has seen significant growth and widespread adoption in various industries. However, one major drawback of blockchain investments is their substantial energy consumption, which has negative impacts on both the economy and the environment. The main cause of concern is the generation of atmospheric carbon emissions resulting from excessive energy usage. This research study aims to identify blockchain networks and systems that assert themselves as environmentally friendly and determine which of them produces the least amount of carbon emissions, such as Cardano, Tezos, and Bitgreen. This has been accomplished by following a comprehensive hybrid literature review. Our study has identified 23 blockchain networks that consume significantly less power and release fewer carbon dioxide emissions compared to the Bitcoin network. Some of these environmentally friendly networks include Algorand, Fantom, MobileCoin, and Electroneum. Additionally, we have found various projects and organizations that support greener blockchain initiatives, such as the Renewable Energy Certificate Mechanism, Green Digital Finance Alliance, GreenTrust, and the Energy Web Foundation. While several projects in this area have been recognized and examined, comprehensive research and analysis are still needed to provide empirical evidence regarding the power consumption and carbon dioxide emissions of these claimed environmentally friendly blockchains. This is due to the relatively early stage of development in this field.

• Mishra, Deepti; Mishra, Alok & Abdalhamid, Samia (2023). Facilitators and inhibitors of Agile methods adoption: Practitioners view. Systems Engineering. ISSN 1098-1241. 26(6), s. 891–907. doi: 10.1002/sys.21702. Fulltekst i vitenarkiv Vis sammendrag

  This study provides empirical evidence to the body of knowledge in Agile methods adoption in small, medium, and large organizations in the global context. This research explores facilitators and inhibitors of Agile methods adoption in software development organizations. A survey was conducted among Agile professionals to gather survey data from 52 software organizations in seven countries across the world. This study found many facilitators of Agile adoption to be significant such as customers’ dominant issues, encouragement, project champion, highly competent team, use of tools, etc. Similarly a correlation analysis revealed multiple inhibitors as significant: absence of a full set of right Agile practices, absence of customer presence, absence of tracking mechanisms during Agile progress, and failure to determine the role of the client. The present study identifies that an Agile team with high expertise and competence leads to higher quality in software, customer satisfaction along with return on investment (ROI) while a small Agile team increases ease in handling changing requirements, customer satisfaction, reduced delivery time, and increased ROI. Frequent delivery accelerates better control over work, adds to software quality, customer satisfaction, and in shortening delivery time along with increase ROI. It has also been observed that providing essential features early leads to increase in software quality and customer satisfaction. This study confirms that active customer focus leads to better control over work. Further, absence of customer decreases dealing with changing requirements, and customer satisfaction while absence of progress tracking lowers customer satisfaction.

• Mohammed, Almashhadani; Mishra, Alok; Ali, Yazici & Muhammad, Younas (2023). Challenges in Agile Software Maintenance for Local and Global Development: An Empirical Assessment. Information. 14(5). doi: 10.3390/info14050261. Fulltekst i vitenarkiv Vis sammendrag

  Agile methods have gained wide popularity recently due to their characteristics in software development. Despite the success of agile methods in the software maintenance process, several challenges have been reported. In this study, we investigate the challenges that measure the impact of agile methods in software maintenance in terms of quality factors. A survey was conducted to collect data from agile practitioners to establish their opinions about existing challenges. As a result of the statistical analysis of the data from the survey, it has been observed that there are moderately effective challenges in manageability, scalability, communication, collaboration, and transparency. Further research is required to validate software maintenance challenges in agile methods.

• Mishra, Alok & Alzoubi, Yehia Ibrahim (2023). Structured software development versus agile software development: a comparative analysis. International Journal of System Assurance Engineering and Management. ISSN 0975-6809. 14. doi: 10.1007/s13198-023-01958-5. Fulltekst i vitenarkiv Vis sammendrag

  The use of agile methodology has become widespread in organizations that previously relied on traditional or structured software development methods, such as the waterfall approach. Successful completion rates for agile initiatives are at 40%, whereas only 15% of waterfall projects were successfully completed. To take advantage of the benefits of each method, many organizations are using a hybrid approach that combines Agile with Waterfall and structured software development methodologies. Rather than asking which methodology is superior, it is more productive to determine which one is most effective for a particular project. Agile projects have a failure rate of 10%, while waterfall projects fail 30% of the time. This study aims to compare and contrast agile and waterfall methodologies in order to create a decision tree for selecting the most suitable method for a software development project. Several cases and scenarios are examined using the hybrid development approach. Despite many advantages of Agile methodologies, a combination approach may be necessary, as Waterfall may be required for certain projects or development phases. This study explores the feasibility of combining Waterfall and Agile methodologies in software development management.

• Mishra, Atul & Mishra, Alok (2023). Identifying and Analyzing Reduplication Multiword Expressions in Hindi Text Using Machine Learning. TEM JOURNAL - Technology, Education, Management, Informatics. ISSN 2217-8309. 12(3), s. 1732–1741. doi: 10.18421/TEM123-56. Fulltekst i vitenarkiv Vis sammendrag

  The task of identifying and analyzing Reduplication Multiword Expressions (RMWEs) in Natural Language Processing (NLP) involves extracting repeated words from various text forms and classifying them into Onomatopoeic, non-Onomatopoeic, partial, or semantic types. With the increasing use of low-resource languages in news, opinions, comments, hashtags, reviews, posts, and journals, this study proposes a machine learning-based RMWE identification method for Hindi text. The method employs linguistic patterns and statistical data, along with a proposed threshold boundary detection in statistical filtering. The Jaccard distance of dissimilarity and Sorensen Dice Coefficient of Similarity are used for semantic relation analysis. The proposed approach was evaluated using the publicly available Hindi corpus from IITB, measuring performance between two consecutive thresholds with the lowest error and highest recall. This study proposes an effective method for Indian computational linguistics, with experimental results highlighting its viability and utility, and providing a blueprint for current procedures.

• Rao, Rajwant Singh; Dewangan, Seema; Mishra, Alok & Gupta, Manjari (2023). A study of dealing class imbalance problem with machine learning methods for code smell severity detection using PCA-based feature selection technique. Scientific Reports. 13(1), s. 1–18. doi: 10.1038/s41598-023-43380-8. Fulltekst i vitenarkiv Vis sammendrag

  Detecting code smells may be highly helpful for reducing maintenance costs and raising source code quality. Code smells facilitate developers or researchers to understand several types of design flaws. Code smells with high severity can cause significant problems for the software and may cause challenges for the system's maintainability. It is quite essential to assess the severity of the code smells detected in software, as it prioritizes refactoring efforts. The class imbalance problem also further enhances the difficulties in code smell severity detection. In this study, four code smell severity datasets (Data class, God class, Feature envy, and Long method) are selected to detect code smell severity. In this work, an effort is made to address the issue of class imbalance, for which, the Synthetic Minority Oversampling Technique (SMOTE) class balancing technique is applied. Each dataset's relevant features are chosen using a feature selection technique based on principal component analysis. The severity of code smells is determined using five machine learning techniques: K-nearest neighbor, Random forest, Decision tree, Multi-layer Perceptron, and Logistic Regression. This study obtained the 0.99 severity accuracy score with the Random forest and Decision tree approach with the Long method code smell. The model's performance is compared based on its accuracy and three other performance measurements (Precision, Recall, and F-measure) to estimate severity classification models. The impact of performance is also compared and presented with and without applying SMOTE. The results obtained in the study are promising and can be beneficial for paving the way for further studies in this area.

• Ehsan, Adeel; Catal, Cagatay & Mishra, Alok (2022). Detecting Malware by Analyzing App Permissions on Android Platform: A Systematic Literature Review. Sensors. 22(20), s. 1–18. doi: 10.3390/s22207928. Fulltekst i vitenarkiv Vis sammendrag

  Smartphone adaptation in society has been progressing at a very high speed. Having the ability to run on a vast variety of devices, much of the user base possesses an Android phone. Its popularity and flexibility have played a major role in making it a target of different attacks via malware, causing loss to users, both financially and from a privacy perspective. Different malware and their variants are emerging every day, making it a huge challenge to come up with detection and preventive methodologies and tools. Research has spawned in various directions to yield effective malware detection mechanisms. Since malware can adopt different ways to attack and hide, accurate analysis is the key to detecting them. Like any usual mobile app, malware requires permission to take action and use device resources. There are 235 total permissions that the Android app can request on a device. Malware takes advantage of this to request unnecessary permissions, which would enable those to take malicious actions. Since permissions are critical, it is important and challenging to identify if an app is exploiting permissions and causing damage. The focus of this article is to analyze the identified studies that have been conducted with a focus on permission analysis for malware detection. With this perspective, a systematic literature review (SLR) has been produced. Several papers have been retrieved and selected for detailed analysis. Current challenges and different analyses were presented using the identified articles. View Full-Text Keywords: malware detection, static analysis, hybrid analysis, permissions analysis

• Akbar, Muhammad Azeem; Khan, Arif Ali; Mahmood, Sajjad & Mishra, Alok (2022). SRCMIMM: the software requirements change management and implementation maturity model in the domain of global software development industry. Information Technology and Management. ISSN 1385-951X. 24(3), s. 195–219. doi: 10.1007/s10799-022-00364-w. Fulltekst i vitenarkiv Vis sammendrag

  The software industry has widely adopted global software development (GSD) to gain economic benefits. Organizations that engage in GSD face various challenges, the majority being associated with requirements change management (RCM). The key motive of this study is to develop a requirement change management and implementation maturity model (SRCMIMM) for the GSD industry that could help the practitioners to assess and manage their RCM activities. A systematic literature review and questionnaire survey approach are used to identify and validate the critical success factors (CSFs), critical challenges (CCHs), and the related best practices of the RCM process. The investigated CSFs and CCHs are classified into five maturity levels based on the concepts of the existing maturity models in other domains, practitioners’ feedback, and academic research. Every maturity level comprises different CSFs and CCHs that can help assess and manage a firm's RCM capability. To evaluate the effectiveness of the proposed model, four case studies are conducted in different GSD firms. The SRCMIMM has been developed to assist GSD organizations in improving their RCM process in efficiency and effectiveness.

• Guzel, Mehmet Serdar; Bostanci, Erkan & Mishra, Alok (2022). Mobile robot navigation with swarm intelligence using a decentralized strategy, 2022 IEEE/SICE International Symposium on System Integration (SII 2022) : January 9-12, 2022 (Virtual Conference) Narvik, Norway. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665445405. s. 838–843. doi: 10.1109/SII52469.2022.9708908. Fulltekst i vitenarkiv Vis sammendrag

  Collective navigation of swarm robots is one of the most topical problems in the field of robotics. In this context, more than one robot must have the ability to simultaneously perform different tasks in a certain coordination and plan. In the solution of the related problem, it has been tried to ensure that the robots communicate and move in coordination over a central communication architecture. The most important handicap of central architectures is that the communication mechanism of the whole system collapses in case the related unit fails because the system is connected to a single unit. In this context, a novel decentralized architecture has been implemented and tested for physical robots. Several experiments have been conducted to verify the performance of the proposed architecture.

• Jorayeva, Manzura; Akbulut, Akhan; Catal, Cagatay & Mishra, Alok (2022). Deep learning-based defect prediction for mobile applications. Sensors. 22(13), s. 1–18. doi: 10.3390/s22134734. Fulltekst i vitenarkiv Vis sammendrag

  Smartphones have enabled the widespread use of mobile applications. However, there are unrecognized defects of mobile applications that can affect businesses due to a negative user experience. To avoid this, the defects of applications should be detected and removed before release. This study aims to develop a defect prediction model for mobile applications. We performed cross-project and within-project experiments and also used deep learning algorithms, such as convolutional neural networks (CNN) and long short term memory (LSTM) to develop a defect prediction model for Android-based applications. Based on our within-project experimental results, the CNN-based model provides the best performance for mobile application defect prediction with a 0.933 average area under ROC curve (AUC) value. For cross-project mobile application defect prediction, there is still room for improvement when deep learning algorithms are preferred. Keywords: software defect prediction; software fault prediction; mobile application; Android applications; deep learning; machine learning.

• Balli, Cagla; Guzel, Mehmet Serdar; Bostanci, Erkan & Mishra, Alok (2022). Sentimental analysis of Twitter users from Turkish content with natural language processing. Computational Intelligence and Neuroscience. ISSN 1687-5265. 2022(Special Issue), s. 1–17. doi: 10.1155/2022/2455160. Fulltekst i vitenarkiv Vis sammendrag

  Artificial Intelligence has guided technological progress in recent years; it has shown significant development with increased academic studies on Machine Learning and the high demand for this field in the sector. In addition to the advancement of technology day by day, the pandemic, which has become a part of our lives since early 2020, has led to social media occupying a larger place in the lives of individuals. Therefore, social media posts have become an excellent data source for the field of sentiment analysis. The main contribution of this study is based on the Natural Language Processing method, which is one of the machine learning topics in the literature. Sentiment analysis classification is a solid example for machine learning tasks that belongs to human-machine interaction. It is essential to make the computer understand people emotional situation with classifiers. There are a limited number of Turkish language studies in the literature. Turkish language has different types of linguistic features from English. Since Turkish is an agglutinative language, it is challenging to make sentiment analysis with that language. This paper aims to perform sentiment analysis of several machine learning algorithms on Turkish language datasets that are collected from Twitter. In this research, besides using public dataset that belongs to Beyaz (2021) to get more general results, another dataset is created to understand the impact of the pandemic on people and to learn about public opinions. Therefore, a custom dataset, namely, SentimentSet (Balli 2021), was created, consisting of Turkish tweets that were filtered with words such as pandemic and corona by manually marking as positive, negative, or neutral. Besides, SentimentSet could be used in future researches as benchmark dataset. Results show classification accuracy of not only up to ∼87% with test data from datasets of both datasets and trained models, but also up to ∼84% with small “Sample Test Data” generated by the same methods as SentimentSet dataset. These research results contributed to indicating Turkish language specific sentiment analysis that is dependent on language specifications.

• Sivari, Esra; Güzel, Mehmet Serdar; Bostanci, Erkan & Mishra, Alok (2022). A novel hybrid machine learning based system to classify shoulder implant manufacturers. Healthcare. 10(3), s. 1–17. doi: 10.3390/healthcare10030580. Fulltekst i vitenarkiv Vis sammendrag

  It is necessary to know the manufacturer and model of a previously implanted shoulder prosthesis before performing Total Shoulder Arthroplasty operations, which may need to be performed repeatedly in accordance with the need for repair or replacement. In cases where the patient’s previous records cannot be found, where the records are not clear, or the surgery was conducted abroad, the specialist should identify the implant manufacturer and model during preoperative X-ray controls. In this study, an auxiliary expert system is proposed for classifying manufacturers of shoulder implants on the basis of X-ray images that is automated, objective, and based on hybrid machine learning models. In the proposed system, ten different hybrid models consisting of a combination of deep learning and machine learning algorithms were created and statistically tested. According to the experimental results, an accuracy of 95.07% was achieved using the DenseNet201 + Logistic Regression model, one of the proposed hybrid machine learning models (p < 0.05). The proposed hybrid machine learning algorithms achieve the goal of low cost and high performance compared to other studies in the literature. The results lead the authors to believe that the proposed system could be used in hospitals as an automatic and objective system for assisting orthopedists in the rapid and effective determination of shoulder implant types before performing revision surgery. Keywords: machine learning; hybrid models; shoulder implants; X-ray images.

• Abdulwahid, Huda M. & Mishra, Alok (2022). Deployment optimization algorithms in wireless sensor networks for smart cities : a systematic mapping study. Sensors. 22(14), s. 1–30. doi: 10.3390/s22145094. Fulltekst i vitenarkiv Vis sammendrag

  In recent years, different types of monitoring systems have been designed for various applications, in order to turn the urban environments into smart cities. Most of these systems consist of wireless sensor networks (WSN)s, and the designing of these systems has faced many problems. The first and most important problem is sensor node deployment. The main function of WSNs is to gather the required information, process it, and send it to remote places. A large number of sensor nodes were deployed in the monitored area, so finding the best deployment algorithm that achieves maximum coverage and connectivity with the minimum number of sensor nodes is the significant point of the research. This paper provides a systematic mapping study that includes the latest recent studies, which are focused on solving the deployment problem using optimization algorithms, especially heuristic and meta-heuristic algorithms in the period (2015–2022). It was found that 35% of these studies updated the swarm optimization algorithms to solve the deployment problem. This paper will be helpful for the practitioners and researchers, in order to work out new algorithms and seek objectives for the sensor deployment. A comparison table is provided, and the basic concepts of a smart city and WSNs are presented. Finally, an overview of the challenges and open issues are illustrated. Keywords: smart city; wireless sensor network (WSN); deployment; coverage; connectivity; meta-heuristic.

• Jorayeva, Manzura; Akbulut, Akhan; Catal, Cagatay & Mishra, Alok (2022). Machine learning-based software defect prediction for mobile applications : a systematic literature review. Sensors. 22(7), s. 1–17. doi: 10.3390/s22072551. Fulltekst i vitenarkiv Vis sammendrag

  Software defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules effectively. While a few software defect prediction models have been developed for mobile applications, a systematic overview of these studies is still missing. Therefore, we carried out a Systematic Literature Review (SLR) study to evaluate how machine learning has been applied to predict faults in mobile applications. This study defined nine research questions, and 47 relevant studies were selected from scientific databases to respond to these research questions. Results show that most studies focused on Android applications (i.e., 48%), supervised machine learning has been applied in most studies (i.e., 92%), and object-oriented metrics were mainly preferred. The top five most preferred machine learning algorithms are Naïve Bayes, Support Vector Machines, Logistic Regression, Artificial Neural Networks, and Decision Trees. Researchers mostly preferred Object-Oriented metrics. Only a few studies applied deep learning algorithms including Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), and Deep Neural Networks (DNN). This is the first study that systematically reviews software defect prediction research focused on mobile applications. It will pave the way for further research in mobile software fault prediction and help both researchers and practitioners in this field. Keywords: software defect prediction; software fault prediction; mobile application; review; systematic literature review; deep learning; machine learning

• Kadhim, Yezi Ali; Kahn, Muhammad Umer & Mishra, Alok (2022). Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets. Sensors. 22(22), s. 1–21. doi: 10.3390/s22228999. Fulltekst i vitenarkiv Vis sammendrag

  Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors. Keywords: deep learning, CNN, auto-encoder, ant colony optimization, COVID-19, brain tumor

• Alkhateeb, Ahmed; Catal, Cagatay; Kar, Gorkem & Mishra, Alok (2022). Hybrid blockchain platforms for the Internet of Things (IoT) : a systematic literature review. Sensors. 22(4), s. 1–19. doi: 10.3390/s22041304. Fulltekst i vitenarkiv Vis sammendrag

  In recent years, research into blockchain technology and the Internet of Things (IoT) has grown rapidly due to an increase in media coverage. Many different blockchain applications and platforms have been developed for different purposes, such as food safety monitoring, cryptocurrency exchange, and secure medical data sharing. However, blockchain platforms cannot store all the generated data. Therefore, they are supported with data warehouses, which in turn is called a hybrid blockchain platform. While several systems have been developed based on this idea, a current state-of-the-art systematic overview on the use of hybrid blockchain platforms is lacking. Therefore, a systematic literature review (SLR) study has been carried out by us to investigate the motivations for adopting them, the domains at which they were used, the adopted technologies that made this integration effective, and, finally, the challenges and possible solutions. This study shows that security, transparency, and efficiency are the top three motivations for adopting these platforms. The energy, agriculture, health, construction, manufacturing, and supply chain domains are the top domains. The most adopted technologies are cloud computing, fog computing, telecommunications, and edge computing. While there are several benefits of using hybrid blockchains, there are also several challenges reported in this study. View Full-Text Keywords: Internet of Things, blockchain, cloud computing, integration, hybrid blockchains, systematic literature review

• Dewangan, Seema; Rao, Rajwant Singh; Mishra, Alok & Gupta, Manjari (2022). Code smell detection using ensemble machine learning algorithms. Applied Sciences. 12(20), s. 1–22. doi: 10.3390/app122010321. Fulltekst i vitenarkiv Vis sammendrag

  Code smells are the result of not following software engineering principles during software development, especially in the design and coding phase. It leads to low maintainability. To evaluate the quality of software and its maintainability, code smell detection can be helpful. Many machine learning algorithms are being used to detect code smells. In this study, we applied five ensemble machine learning and two deep learning algorithms to detect code smells. Four code smell datasets were analyzed: the Data class, the God class, the Feature-envy, and the Long-method datasets. In previous works, machine learning and stacking ensemble learning algorithms were applied to this dataset and the results found were acceptable, but there is scope of improvement. A class balancing technique (SMOTE) was applied to handle the class imbalance problem in the datasets. The Chi-square feature extraction technique was applied to select the more relevant features in each dataset. All five algorithms obtained the highest accuracy—100% for the Long-method dataset with the different selected sets of metrics, and the poorest accuracy, 91.45%, was achieved by the Max voting method for the Feature-envy dataset for the selected twelve sets of metrics. Keywords: code smell, code smell detection, ensemble method, deep learning, Chi-square feature extraction technique, SMOTE class balancing technique

• Alzoubi, Yehia Ibrahim; Gill, Asif Qumer & Mishra, Alok (2022). A systematic review of the purposes of Blockchain and fog computing integration: classification and open issues. Journal of Cloud Computing. 11(1), s. 1–36. doi: 10.1186/s13677-022-00353-y. Fulltekst i vitenarkiv Vis sammendrag

  The fog computing concept was proposed to help cloud computing for the data processing of Internet of Things (IoT) applications. However, fog computing faces several challenges such as security, privacy, and storage. One way to address these challenges is to integrate blockchain with fog computing. There are several applications of blockchain-fog computing integration that have been proposed, recently, due to their lucrative benefits such as enhancing security and privacy. There is a need to systematically review and synthesize the literature on this topic of blockchain-fog computing integration. The purposes of integrating blockchain and fog computing were determined using a systematic literature review approach and tailored search criteria established from the research questions. In this research, 181 relevant papers were found and reviewed. The results showed that the authors proposed the combination of blockchain and fog computing for several purposes such as security, privacy, access control, and trust management. A lack of standards and laws may make it difficult for blockchain and fog computing to be integrated in the future, particularly in light of newly developed technologies like quantum computing and artificial intelligence. The findings of this paper serve as a resource for researchers and practitioners of blockchain-fog computing integration for future research and designs. Keywords: blockchain, cloud computing, fog computing, integration, Internet of Things (IoT), security

• Kumar, Kuldeep; Sumit, -; Kumar, Sandeep; Singh, Lalit Kumar & Mishra, Alok (2022). Predicting reliability of software in industrial systems using a Petri net based approach : a case study on a safety system used in nuclear power plant. Information and Software Technology. ISSN 0950-5849. 146(June), s. 1–12. doi: 10.1016/j.infsof.2022.106895. Fulltekst i vitenarkiv Vis sammendrag

  Context: Software reliability prediction in the early stages of development can be propitious in many ways. The combinatorial models used to predict reliability using architectures such as fault trees, binary decision diagrams, etc. have limitations in modeling complex system behavior. On the other hand, state-based models such as Markov chains suffer from the state-space explosion problem, and they need transition probability among different system states to measure reliability. These probabilities are usually assumed or are obtained from the operational profile for which the system should be used in the field. Objective: The objective of this paper is to present a method for predicting the reliability of software in industrial systems using a generalized stochastic Petri nets based approach. The key idea is to violate the assumption of state transition probabilities in the Markov chain. The state transition probabilities are calculated using Petri net transitions’ throughput by performing stationary analysis under the consideration to identify and handle dead markings in the Petri net. Method: Initially, a generalized stochastic Petri net of the system under consideration is generated from the standard system's specification. Thereafter, dead markings are identified in the Petri net which are further removed to perform steady-state analysis. At last, a Markov model is generated based on the reachability graph of the Petri net, which is further used to predict the system reliability. Results: The presented method has been applied to a safety-critical system, Shut Down System-1, of a nuclear power plant, which is operational in the Canada Deuterium Uranium reactor. The predicted reliability of the system using this method is 99.99966% which has been validated using the specified system requirements. To further validate and generalize the results, sensitivity analysis is performed by varying different system parameters. Conclusions: The method discussed in this paper presents a step of performing structural analysis on the Petri net of the system under consideration to identify and handle dead markings on the Petri net. It further handles the issue of assuming transition probabilities among the system states by calculating them using Petri net transitions’ throughput. Keywords: software reliability, safety-critical systems, petri net, reliability model

• Mishra, Alok; Alzoubi, Yehia Ibrahim; Anwar, Memoona Javeria & Gill, Asif Qumer (2022). Attributes impacting cybersecurity policy development : an evidence from seven nations. Computers & Security. ISSN 0167-4048. 120(September), s. 1–23. doi: 10.1016/j.cose.2022.102820. Fulltekst i vitenarkiv Vis sammendrag

  Cyber threats have risen as a result of the growing usage of the Internet. Organizations must have effective cybersecurity policies in place to respond to escalating cyber threats. Individual users and corporations are not the only ones who are affected by cyber-attacks; national security is also a serious concern. Different nations' cybersecurity rules make it simpler for cybercriminals to carry out damaging actions while making it tougher for governments to track them down. Hence, a comprehensive cybersecurity policy is needed to enable governments to take a proactive approach to all types of cyber threats. This study investigates cybersecurity regulations and attributes used in seven nations in an attempt to fill this research gap. This paper identified fourteen common cybersecurity attributes such as telecommunication, network, Cloud computing, online banking, E-commerce, identity theft, privacy, and smart grid. Some nations seemed to focus, based on the study of key available policies, on certain cybersecurity attributes more than others. For example, the USA has scored the highest in terms of online banking policy, but Canada has scored the highest in terms of E-commerce and spam policies. Identifying the common policies across several nations may assist academics and policymakers in developing cybersecurity policies. A survey of other nations' cybersecurity policies might be included in the future research.

• Dogan, Gulin; Akbulut, Fatma Patlar; Catal, Cagatay & Mishra, Alok (2022). Stress detection using experience sampling : a systematic mapping study. International Journal of Environmental Research and Public Health (IJERPH). ISSN 1661-7827. 19(9), s. 1–39. doi: 10.3390/ijerph19095693. Fulltekst i vitenarkiv Vis sammendrag

  Stress has been designated the “Health Epidemic of the 21st Century” by the World Health Organization and negatively affects the quality of individuals’ lives by detracting most body systems. In today’s world, different methods are used to track and measure various types of stress. Among these techniques, experience sampling is a unique method for studying everyday stress, which can affect employees’ performance and even their health by threatening them emotionally and physically. The main advantage of experience sampling is that evaluating instantaneous experiences causes less memory bias than traditional retroactive measures. Further, it allows the exploration of temporal relationships in subjective experiences. The objective of this paper is to structure, analyze, and characterize the state of the art of available literature in the field of surveillance of work stress via the experience sampling method. We used the formal research methodology of systematic mapping to conduct a breadth-first review. We found 358 papers between 2010 and 2021 that are classified with respect to focus, research type, and contribution type. The resulting research landscape summarizes the opportunities and challenges of utilizing the experience sampling method on stress detection for practitioners and academics.

• Mishra, Alok; Alzoubi, Yehia Ibrahim; Gill, Asif Qumer & Anwar, Memoona Javeria (2022). Cybersecurity enterprises policies : a comparative study. Sensors. 22(2), s. 1–35. doi: 10.3390/s22020538. Fulltekst i vitenarkiv Vis sammendrag

  Cybersecurity is a critical issue that must be prioritized not just by enterprises of all kinds, but also by national security. To safeguard an organization’s cyberenvironments, information, and communication technologies, many enterprises are investing substantially in cybersecurity these days. One part of the cyberdefense mechanism is building an enterprises’ security policies library, for consistent implementation of security controls. Significant and common cybersecurity policies of various enterprises are compared and explored in this study to provide robust and comprehensive cybersecurity knowledge that can be used in various enterprises. Several significant common security policies were identified and discussed in this comprehensive study. This study identified 10 common cybersecurity policy aspects in five enterprises: healthcare, finance, education, aviation, and e-commerce. We aimed to build a strong infrastructure in each business, and investigate the security laws and policies that apply to all businesses in each sector. Furthermore, the findings of this study reveal that the importance of cybersecurity requirements differ across multiple organizations. The choice and applicability of cybersecurity policies are determined by the type of information under control and the security requirements of organizations in relation to these policies. Keywords: cybersecurity (CS), cybersecurity polices, cyberspace, enterprise(s), information and communication technology (ICT)

• Kassahun, Ayalew; Bloo, Robbin; Catal, Cagatay & Mishra, Alok (2022). Dairy farm management information systems. Electronics (Basel). 11(2), s. 1–18. doi: 10.3390/electronics11020239. Fulltekst i vitenarkiv Vis sammendrag

  Nowadays, different types of farm management information systems (FMIS) are being used in practice in several sectors of farming, such as dairy, arable, fruits, vegetables, and meat farming. The goal of this research is to identify, evaluate, and synthesize existing FMISs in the Dutch dairy sector and present the state–of–the–art. We performed a multivocal literature review (MLR) to find sources both in scientific and grey literature. A grey literature search was adopted because most of the FMISs were not reported in the scientific literature. To support and improve the effectiveness of the MLR process, an online survey was first sent to Dutch dairy farmers to identify the FMISs that are being used in practice. With the help of the MLR process, we identified 50 FMISs used by Dutch dairy farmers. We identified 33 features of these FMISs and listed the advantages and disadvantages of the FMISs. Keywords: grey literature, multivocal literature review, precision agriculture, smart agriculture, smart systems

• Mishra, Alok; Abdalhamid, Samia; Mishra, Deepti & Ostrovska, Sofiya (2021). Organizational issues in embracing Agile methods : an empirical assessment. International Journal of System Assurance Engineering and Management. ISSN 0975-6809. 12(6), s. 1420–1433. doi: 10.1007/s13198-021-01350-1. Fulltekst i vitenarkiv Vis sammendrag

  This study provides empirical evidence to the body of knowledge in Agile methods adoption in small, medium and large organizations in international context. This research explores the factors involved in the adoption of Agile methods in software development organizations. A survey was conducted among Agile professionals to gather survey data from 52 software organizations in seven countries across the world. Statistical techniques are applied towards empirical assessment. Organizational culture, team structure and management support are found to be crucial success factors whereas lack of management support, a large organization size and traditional organizational culture are found to be detrimental for the adoption of Agile approach in an organization. The selection of an appropriate Agile method depends on the project size and, for each size, there are specific methods preferred by different enterprises. Providing better control over the work is viewed as the primary advantage of the Agile methods within large and small organizations, while for the medium-size organizations, the priority is switched to coping with changing user requirements. Majority of the respondents did not consider embracing agile methods as a reason for project failure which indicates that Agile methods are, indeed, beneficial.

• Mishra, Alok; Shatnawi, Raed; Catal, Cagatay & Akbulut, Akhan (2021). Techniques for calculating software product metrics threshold values : a systematic mapping study. Applied Sciences. 11(23), s. 1–25. doi: 10.3390/app112311377. Fulltekst i vitenarkiv Vis sammendrag

  Several aspects of software product quality can be assessed and measured using product metrics. Without software metric threshold values, it is difficult to evaluate different aspects of quality. To this end, the interest in research studies that focus on identifying and deriving threshold values is growing, given the advantage of applying software metric threshold values to evaluate various software projects during their software development life cycle phases. The aim of this paper is to systematically investigate research on software metric threshold calculation techniques. In this study, electronic databases were systematically searched for relevant papers; 45 publications were selected based on inclusion/exclusion criteria, and research questions were answered. The results demonstrate the following important characteristics of studies: (a) both empirical and theoretical studies were conducted, a majority of which depends on empirical analysis; (b) the majority of papers apply statistical techniques to derive object-oriented metrics threshold values; (c) Chidamber and Kemerer (CK) metrics were studied in most of the papers, and are widely used to assess the quality of software systems; and (d) there is a considerable number of studies that have not validated metric threshold values in terms of quality attributes. From both the academic and practitioner points of view, the results of this review present a catalog and body of knowledge on metric threshold calculation techniques. The results set new research directions, such as conducting mixed studies on statistical and quality-related studies, studying an extensive number of metrics and studying interactions among metrics, studying more quality attributes, and considering multivariate threshold derivation. Keywords: object-oriented metrics, software metrics, object-oriented measures; thresholds; systematic mapping, software quality

• Colakoglu, Fatima Nur; Yazici, Ali & Mishra, Alok (2021). Software product quality metrics : a systematic mapping study. IEEE Access. 9, s. 44647–44670. doi: 10.1109/ACCESS.2021.3054730. Fulltekst i vitenarkiv Vis sammendrag

  In the current competitive world, producing quality products has become a prominent factor to succeed in business. In this respect, defining and following the software product quality metrics (SPQM) to detect the current quality situation and continuous improvement of systems have gained tremendous importance. Therefore, it is necessary to review the present studies in this area to allow for the analysis of the situation at hand, as well as to enable us to make predictions regarding the future research areas. The present research aims to analyze the active research areas and trends on this topic appearing in the literature during the last decade. A Systematic Mapping (SM) study was carried out on 70 articles and conference papers published between 2009 and 2019 on SPQM as indicated in their titles and abstract. The result is presented through graphics, explanations, and the mind mapping method. The outputs include the trend map between the years 2009 and 2019, knowledge about this area and measurement tools, issues determined to be open to development in this area, and conformity between conference papers, articles and internationally valid quality models. This study may serve as a foundation for future studies that aim to contribute to the development in this crucial field. Future SM studies might focus on this subject for measuring the quality of network performance and new technologies such as Artificial Intelligence (AI), Internet of things (IoT), Cloud of Things (CoT), Machine Learning, and Robotics.

• Gangwar, Arvind Kumar; Kumar, Sandeep & Mishra, Alok (2021). A paired learner-based approach for concept drift detection and adaptation in software defect prediction. Applied Sciences. 11(14), s. 1–24. doi: 10.3390/app11146663. Fulltekst i vitenarkiv Vis sammendrag

  The early and accurate prediction of defects helps in testing software and therefore leads to an overall higher-quality product. Due to drift in software defect data, prediction model performances may degrade over time. Very few earlier works have investigated the significance of concept drift (CD) in software-defect prediction (SDP). Their results have shown that CD is present in software defect data and tha it has a significant impact on the performance of defect prediction. Motivated from this observation, this paper presents a paired learner-based drift detection and adaptation approach in SDP that dynamically adapts the varying concepts by updating one of the learners in pair. For a given defect dataset, a subset of data modules is analyzed at a time by both learners based on their learning experience from the past. A difference in accuracies of the two is used to detect drift in the data. We perform an evaluation of the presented study using defect datasets collected from the SEACraft and PROMISE data repositories. The experimentation results show that the presented approach successfully detects the concept drift points and performs better compared to existing methods, as is evident from the comparative analysis performed using various performance parameters such as number of drift points, ROC-AUC score, accuracy, and statistical analysis using Wilcoxon signed rank test. Keywords: concept drift; naive Bayes; random forest; software defect prediction; software quality assurance.

• Pande, Akshara; Pant, Vivekanand; Gupta, Manjari & Mishra, Alok (2021). Design patterns discovery in source code : novel technique using substring match. TEM JOURNAL - Technology, Education, Management, Informatics. ISSN 2217-8309. 10(3), s. 1166–1174. doi: 10.18421/TEM103-21. Fulltekst i vitenarkiv Vis sammendrag

  The role of design pattern mining is a very significant strategy of re-engineering as with the help of detection one could easily understand complex systems. Of course, identifying a design pattern is not always a simple task. Additionally, pattern recovering methods often encounter problems dealing with space outburst for extensive systems. This paper introduces a new way to discover a design pattern based on an Impact Analysis matrix followed by substring match. UML diagrams corresponding to codes are created using Visual Paradigm Enterprise. Impact Analysis matrices of these UML diagrams are converted to string format. Considering system code string as main string and design pattern string as a substring, the main string is further decomposed. A substring match technique is developed here to discover design patterns in the source code. Overall, this procedure has the potential to convert the representation of system design and design pattern in ingenious shapes. In addition, this method has the advantage of moderation in the size. Therefore, this approach is beneficial for Software professionals and researchers due to its simplicity. Keywords : Design patterns, UML diagrams, Visual Paradigm Enterprise software, Impact Analysis Matrix, Substring match.

• Gosala, Bethany; Chowdhuri, Sripriya Roy; Singh, Jyotika; Gupta, Manjari & Mishra, Alok (2021). Automatic classification of UML class diagrams using deep learning technique : convolutional neural network. Applied Sciences. 11(9), s. 1–14. doi: 10.3390/app11094267. Fulltekst i vitenarkiv Vis sammendrag

  Unified Modeling Language (UML) includes various types of diagrams that help to study, analyze, document, design, or develop any software efficiently. Therefore, UML diagrams are of great advantage for researchers, software developers, and academicians. Class diagrams are the most widely used UML diagrams for this purpose. Despite its recognition as a standard modeling language for Object-Oriented software, it is difficult to learn. Although there exist repositories that aids the users with the collection of UML diagrams, there is still much more to explore and develop in this domain. The objective of our research was to develop a tool that can automatically classify the images as UML class diagrams and non-UML class diagrams. Earlier research used Machine Learning techniques for classifying class diagrams. Thus, they are required to identify image features and investigate the impact of these features on the UML class diagrams classification problem. We developed a new approach for automatically classifying class diagrams using the approach of Convolutional Neural Network under the domain of Deep Learning. We have applied the code on Convolutional Neural Networks with and without the Regularization technique. Our tool receives JPEG/PNG/GIF/TIFF images as input and predicts whether it is a UML class diagram image or not. There is no need to tag images of class diagrams as UML class diagrams in our dataset. Keywords: Unified Modeling Language, Machine Learning (ML); Object-Oriented modeling, Deep Learning (DL), Convolutional Neural Networks (CNN)

• Senirkentli, Güler Burcu; Ekinci, Fatih; Bostanci, Erkan; Güzel, Mehmet Serdar; Dağli, Özlem & Karim, Ahmed M. [Vis alle 7 forfattere av denne artikkelen] (2021). Proton therapy for mandibula plate phantom. Healthcare. 9(2), s. 1–12. doi: 10.3390/healthcare9020167. Fulltekst i vitenarkiv Vis sammendrag

  Purpose: In this study, the required dose rates for optimal treatment of tumoral tissues when using proton therapy in the treatment of defective tumours seen in mandibles has been calculated. We aimed to protect the surrounding soft and hard tissues from unnecessary radiation as well as to prevent complications of radiation. Bragg curves of therapeutic energized protons for two different mandible (molar and premolar) plate phantoms were computed and compared with similar calculations in the literature. The results were found to be within acceptable deviation values. Methods: In this study, mandibular tooth plate phantoms were modelled for the molar and premolar areas and then a Monte Carlo simulation was used to calculate the Bragg curve, lateral straggle/range and recoil values of protons remaining in the therapeutic energy ranges. The mass and atomic densities of all the jawbone layers were selected and the effect of layer type and thickness on the Bragg curve, lateral straggle/range and the recoil were investigated. As protons move through different layers of density, lateral straggle and increases in the range were observed. A range of energies was used for the treatment of tumours at different depths in the mandible phantom. Results: Simulations revealed that as the cortical bone thickness increased, Bragg peak position decreased between 0.47–3.3%. An increase in the number of layers results in a decrease in the Bragg peak position. Finally, as the proton energy increased, the amplitude of the second peak and its effect on Bragg peak position decreased. Conclusion: These findings should guide the selection of appropriate energy levels in the treatment of tumour structures without damaging surrounding tissues. View Full-Text Keywords: proton treatment, biomaterials, bragg peak, mandible plate phantom, dental tumour, paediatric dentistry

• Dewangan, Seema; Rao, Rajwant Singh; Mishra, Alok & Gupta, Manjari (2021). A novel approach for code smell detection : an empirical study. IEEE Access. 9, s. 162869–162883. doi: 10.1109/ACCESS.2021.3133810. Fulltekst i vitenarkiv Vis sammendrag

  Code smells detection helps in improving understandability and maintainability of software while reducing the chances of system failure. In this study, six machine learning algorithms have been applied to predict code smells. For this purpose, four code smell datasets (God-class, Data-class, Feature-envy, and Long-method) are considered which are generated from 74 open-source systems. To evaluate the performance of machine learning algorithms on these code smell datasets, 10-fold cross validation technique is applied that predicts the model by partitioning the original dataset into a training set to train the model and test set to evaluate it. Two feature selection techniques are applied to enhance our prediction accuracy. The Chi-squared and Wrapper-based feature selection techniques are used to improve the accuracy of total six machine learning methods by choosing the top metrics in each dataset. Results obtained by applying these two feature selection techniques are compared. To improve the accuracy of these algorithms, grid search-based parameter optimization technique is applied. In this study, 100% accuracy was obtained for the Long-method dataset by using the Logistic Regression algorithm with all features while the worst performance 95.20 % was obtained by Naive Bayes algorithm for the Long-method dataset using the chi-square feature selection technique.

• Shatnawi, Raed & Mishra, Alok (2021). An empirical study on software fault prediction using product and process metrics. International Journal of Information Technologies and Systems Approach. ISSN 1935-570X. 14(1), s. 62–78. doi: 10.4018/IJITSA.2021010104. Fulltekst i vitenarkiv Vis sammendrag

  Product and process metrics are measured from the development and evolution of software. Metrics are indicators of software fault-proneness and advanced models using machine learning can be provided to the development team to select modules for further inspection. Most fault-proneness classifiers were built from product metrics. However, the inclusion of process metrics adds evolution as a factor to software quality. In this work, the authors propose a process metric measured from the evolution of software to predict fault-proneness in software models. The process metrics measures change-proneness of modules (classes and interfaces). Classifiers are trained and tested for five large open-source systems. Classifiers were built using product metrics alone and using a combination of product and the proposed process metric. The classifiers evaluation shows improvements whenever the process metrics were used. Evolution metrics are correlated with quality of software and helps in improving software quality prediction for future releases.

• Karim, Ahmad M. & Mishra, Alok (2021). Novel COVID-19 recognition framework based on conic functions classifier. I Garg, Lalit; Chakraborty, Chinmay; Mahmoudi, Saïd & Sohmen, Victor S. Sohmen (Red.), Healthcare informatics for fighting COVID-19 and future epidemics. Springer. ISSN 9783030727512. s. 1–10. doi: 10.1007/978-3-030-72752-9_1. Fulltekst i vitenarkiv Vis sammendrag

  The new coronavirus has been declared as a global emergency. The first case was officially declared in Wuhan, China, during the end of 2019. Since then, the virus has spread to nearly every continent, and case numbers continue to rise. The scientists and engineers immediately responded to the virus and presented techniques, devices and treatment approaches to fight back and eliminate the virus. Machine learning is a popular scientific tool and is applied to several medical image recognition problems, involving tumour recognition, cancer detection, organ transplantation and COVID-19 diagnosis. It is proved that machine learning presents robust, fast and accurate results in various medical image recognition problems. Generally, machine learning-based frameworks consist of two stages: feature extraction and classification. In the feature extraction, overwhelmingly unsupervised learning techniques are applied to reduce the input data’s size. This step extracts appropriate features by reducing the computational time and increasing the performance of the classifiers. A classifier is the second step that aims to categorise the input. Within the proposed step, the unsupervised part relies on the feature extraction by using local binary patterns (LBP), followed by feature selection relying on factor analysis technique. The LBP is a kind of visual descriptor, mainly applied for image recognition problem. The aim of using LBP is to analyse the input COVID-19 image and extract salient features. Furthermore, factor analysis is a statistical technique applied to define variability among observed variables in less unnoticed variables named factors. The factor analysis applied to the LBP wavelet aims to select sensitive features from input data (LBP output) and reduce the size input. In the last stage, conic functions classifier is applied to classify two sets of data, categorising the extracted features by using LBP and factor analysis as positive or negative COVID-19 cases. The proposed solution aims to diagnose COVID-19 by using LBP and factor analysis, based on conic functions classifier. The conic functions classifier presents remarkable results compared with these popular classifiers and state-of-the-art studies presented in the literature. Keywords: COVID-19, conic functions classifier, LBP, factor analysis

• Prajapati, Amarjeet; Parashar, Anshu; Sunita, - & Mishra, Alok (2021). Entropy-driven global best selection in particle swarm optimization for many-objective software package restructuring. Complexity. ISSN 1076-2787. 2021(Special Issue), s. 1–11. doi: 10.1155/2021/3974635. Fulltekst i vitenarkiv Vis sammendrag

  Many real-world optimization problems usually require a large number of conflicting objectives to be optimized simultaneously to obtain solution. It has been observed that these kinds of many-objective optimization problems (MaOPs) often pose several performance challenges to the traditional multi-objective optimization algorithms. To address the performance issue caused by the different types of MaOPs, recently, a variety of many-objective particle swarm optimization (MaOPSO) has been proposed. However, external archive maintenance and selection of leaders for designing the MaOPSO to real-world MaOPs are still challenging issues. This work presents a MaOPSO based on entropy-driven global best selection strategy (called EMPSO) to solve the many-objective software package restructuring (MaOSPR) problem. EMPSO makes use of the entropy and quality indicator for the selection of global best particle. To evaluate the performance of the proposed approach, we applied it over the five MaOSPR problems. We compared it with eight variants of MaOPSO, which are based on eight different global best selection strategies. The results indicate that the proposed EMPSO is competitive with respect to the existing global best selection strategies based on variants of MaOPSO approaches.

• Mishra, Alok & Mishra, Deepti (2021). Sustainable software engineering : curriculum development based on ACM/IEEE guidelines. I Calero, Coral; Moraga, Mª Ángeles & Piattini, Mario (Red.), Software sustainability. Springer. ISSN 9783030699703. s. 269–285. doi: 10.1007/978-3-030-69970-3_11. Fulltekst i vitenarkiv Vis sammendrag

  Climate change risk and environmental degradation are the most critical issues of our society. Our technology-influenced daily lifestyle involves many types of software and apps which are used by society at large, and their use is increasing more than ever before. Sustainability is a significant topic for future professionals and more so for software engineers due to its impact on society. It is crucial to motivate and raise concern among students and faculty members regarding sustainability by including it in the Software Engineering (SE) curriculum. This chapter discusses how sustainability can be included in various courses of the SE curriculum by considering ACM/IEEE curriculum guidelines for the SE curriculum, literature review, and various viewpoints so that SE students can attain knowledge on sustainable software engineering. It also includes an assessment of key competences in sustainability for proposed units in the SE curriculum.

• Abdalla, Reem & Mishra, Alok (2021). Agent-oriented software engineering methodologies : analysis and future directions. Complexity. ISSN 1076-2787. 2021, s. 1–21. doi: 10.1155/2021/1629419. Fulltekst i vitenarkiv Vis sammendrag

  The Internet of Things (IoT) facilitates in building cyber-physical systems, which are significant for Industry 4.0. Agent-based computing represents effective modeling, programming, and simulation paradigm to develop IoT systems. Agent concepts, techniques, methods, and tools are being used in evolving IoT systems. Over the last years, in particular, there has been an increasing number of agent approaches proposed along with an ever-growing interest in their various implementations. Yet a comprehensive and full-fledged agent approach for developing related projects is still lacking despite the presence of agent-oriented software engineering (AOSE) methodologies. One of the moves towards compensating for this issue is to compile various available methodologies, ones that are comparable to the evolution of the unified modeling language (UML) in the domain of object-oriented analysis and design. These have become de facto standards in software development. In line with this objective, the present research attempts to comprehend the relationship among seven main AOSE methodologies. More specifically, we intend to assess and compare these seven approaches by conducting a feature analysis through examining the advantages and limitations of each competing process, structural analysis, and a case study evaluation method. This effort is made to address the significant characteristics of AOSE approaches. The main objective of this study is to conduct a comprehensive analysis of selected AOSE methodologies and provide a proposal of a draft unified approach that drives strengths (best) of these methodologies towards advancement in this area.

• Ibrahim, Thaer & Mishra, Alok (2021). A conceptual design of smart management system for flooding disaster. International Journal of Environmental Research and Public Health (IJERPH). ISSN 1661-7827. 18(16), s. 1–14. doi: 10.3390/ijerph18168632. Fulltekst i vitenarkiv Vis sammendrag

  Disasters pose a real threat to the lives and property of citizens; therefore, it is necessary to reduce their impact to the minimum possible. In order to achieve this goal, a framework for enhancing the current disaster management system was proposed, called the smart disaster management system. The smart aspect of this system is due to the application of the principles of information and communication technology, especially the Internet of Things. All participants and activities of the proposed system were clarified by preparing a conceptual design by using The Unified Modeling Language diagrams. This effort was made to overcome the lack of citizens’ readiness towards the use of information and communication technology as well as increase their readiness towards disasters. This study aims to develop conceptual design that can facilitate in development of smart management system for flooding disaster. This will assist in the design process of the Internet of Things systems in this regard. View Full-Text Keywords: information and communication technology, smart disaster management system, Internet of Things, flood, disaster

• Gavrilović, Nebojša & Mishra, Alok (2020). Software architecture of the internet of things (IoT) for smart city, healthcare and agriculture : analysis and improvement directions. Journal of Ambient Intelligence and Humanized Computing. ISSN 1868-5137. 12, s. 1315–1336. doi: 10.1007/s12652-020-02197-3. Fulltekst i vitenarkiv Vis sammendrag

  Internet of things (IoT) enables organizations to automate the process and improves service delivery through Internet technology and transferring the data at the cloud level. IoT does not allow the use of a universal software architecture for different fields in which it is used, but needs to be adjusted according to the requirements of users. This paper presents an analysis of currently available types of software architectures of the IoT systems in the field of smart cities, healthcare, and agriculture. It provides a proposal for solutions and improvements of different software architecture types, interactions between identified software architecture elements that will provide better performance and simplicity. The novelty of the study is the analysis of different types of IoT software architecture such as: layered, service-oriented and cloud-based software architecture application in these areas of IoT. Based on the analysis, the study proposed the type of software architecture of the IoT system for the relevant area of application (smart city, healthcare, and agriculture). Specific points of research are: analysis of different types of software architecture applied in IoT systems, identification of functionalities available in IoT systems through different types of software architecture, the proposal for enhancement of the above functionalities, and proposal of software architecture that is most relevant to the IoT system of a particular area.

• Aubaid, Asmaa M. & Mishra, Alok (2020). A rule-based approach to embedding techniques for text document classification. Applied Sciences. 10(11), s. 1–22. doi: 10.3390/app10114009. Fulltekst i vitenarkiv Vis sammendrag

  With the growth of online information and sudden expansion in the number of electronic documents provided on websites and in electronic libraries, there is difficulty in categorizing text documents. Therefore, a rule-based approach is a solution to this problem; the purpose of this study is to classify documents by using a rule-based. This paper deals with the rule-based approach with the embedding technique for a document to vector (doc2vec) files. An experiment was performed on two data sets Reuters-21578 and the 20 Newsgroups to classify the top ten categories of these data sets by using a document to vector rule-based (D2vecRule). Finally, this method provided us a good classification result according to the F-measures and implementation time metrics. In conclusion, it was observed that our algorithm document to vector rule-based (D2vecRule) was good when compared with other algorithms such as JRip, One R, and ZeroR applied to the same Reuters-21578 dataset. Keywords: text classification; rule-based; word embedding; Doc2vec.

• Ateşoğulları, Dilara & Mishra, Alok (2020). Automation testing tools : a comparative view. International Journal on Information Technologies and Security (IJITS). ISSN 1313-8251. 12(4), s. 63–76. doi: 10.3390/su12135236. Fulltekst i vitenarkiv Vis sammendrag

  Effective software testing leads to assurance towards high quality in software development. Automation testing tool facilitates in faster testing process in testing stage thus completion and implementation of software on time. One of the most significant issues for automation is to select the automation-testing tool and the appropriate framework. The objective of this paper is to assess and compare twenty-one available automation-testing tools on twenty attributes in comprehensive manner. This study will assist software testing professionals and researchers towards further insight in this area. Keywords: test automation, automation tools, software testing, automation test frameworks, test automation types

• Andersen, Terje; Jæger, Bjørn & Mishra, Alok (2020). Circularity in waste electrical and electronic equipment (WEEE) directive. Comparison of a manufacturer's Danish and Norwegian operations. Sustainability. 12(13), s. 1–15. doi: 10.3390/su12135236. Fulltekst i vitenarkiv Vis sammendrag

  Waste electrical and electronic equipment (WEEE) as a reverse supply chain (RSC) has a low degree of circularity, mainly focusing on recovering or recycling. Targets to increase the circularity have recently been introduced in the EU WEEE directive. In this case study, we have investigated how WEEE is handled within an electric and electronic (EE) equipment manufacturer. The case study includes findings from two different Nordic countries, Norway and Denmark, with interviews of six stakeholders. The case study shows that there are significant differences in how the case company fulfills its extended producer responsibility (EPR), especially related to reporting. The study also found that there is a mismatch between the ambitions in the WEEE directive and a company’s approach related to circularity in the end-of-life phase of an EE product. Based on the results of this case study and from the literature we propose recommendations on alignment with other directives and on a common information regime within the WEEE RSC. Keywords: waste electrical and electronic equipment (WEEE); product information flow; reverse supply chain; manufacturer; circularity.

• Yu, Liguo & Mishra, Alok (2020). Customer Engagement Strategies of China's E-Commerce Industry and Their Impact on China's Economy Transition. I Semeradova, Tereza & Weinlich, Petr (Red.), Impacts of Online Advertising on Business Performance. IGI Global. ISSN 9781799816188. doi: 10.4018/978-1-7998-1618-8.ch004. Fulltekst i vitenarkiv Vis sammendrag

  In the past 15 years, China's e-commerce industry has grown tremendously and become the world's largest e-commerce market. This chapter studies some popular e-commerce companies of China, including Alibaba, JD, and Pindoudou. Specifically, the customer engagement strategies of these companies are described and analyzed. This chapter helps readers understand how various customer engagement strategies of China's e-commerce companies have facilitated the change of their consumers' shopping habits—switching from offline shopping centers to online marketplaces and from cash-based payment to credit-based payment. The success of these customer engagement strategies has contributed to the performance improvement of e-commerce companies and could ultimately assist China to transfer from a production economy to a consumption economy. Moreover, these customer engagement strategies developed in China might also be applied to other e-businesses or marketplaces of different regions, especially developing countries that have a similar economy structure as China.

• Mishra, Alok & Otaiwi, Ziadoon (2020). DevOps and software quality : a systematic mapping. Computer Science Review. ISSN 1574-0137. 38(November), s. 1–14. doi: 10.1016/j.cosrev.2020.100308. Fulltekst i vitenarkiv Vis sammendrag

  Quality pressure is one of the factors affecting processes for software development in its various stages. DevOps is one of the proposed solutions to such pressure. The primary focus of DevOps is to increase the deployment speed, frequency and quality. DevOps is a mixture of different developments and operations to its multitudinous ramifications in software development industries, DevOps have attracted the interest of many researchers. There are considerable literature surveys on this critical innovation in software development, yet, little attention has been given to DevOps impact on software quality. This research is aimed at analyzing the implications of DevOps features on software quality. DevOps can also be referred to a change in organization cultures aimed at removal of gaps between the development and operations of an organization. The adoption of DevOps in an organization provides many benefits including quality but also brings challenges to an organization. This study presents systematic mapping of the impact of DevOps on software quality. The results of this study provide a better understanding of DevOps on software quality for both professionals and researchers working in this area. The study shows research was mainly focused in automation, culture, continuous delivery, fast feedback of DevOps. There is need of further research in many areas of DevOps (for instance: measurement, development of metrics of different stages to assess its performance, culture, practices toward ensuring quality assurance, and quality factors such as usability, efficiency, software maintainability and portability). Keywords: DevOps, development, operations, software, software quality, automation, measurement, systematic mapping

• Mishra, Alok & Mishra, Deepti (2020). Sustainable software engineering education curricula development. International Journal on Information Technologies and Security (IJITS). ISSN 1313-8251. 12(2), s. 47–56. Fulltekst i vitenarkiv Vis sammendrag

  Climate change risk and environmental degradation are the most critical issues of our society. Our technology influenced daily life style involves many software and apps which are used by large society and their use is increasing than ever before. Sustainability is a significant topic for future professionals and more so for Information Technology (IT) professionals and software engineers due to its impact on the society. It is significant to motivate and raise concern among students and faculty members regarding sustainability by including it into Software Engineering curriculum. Key words: Sustainability, Sustainable Software Engineering, Curricula, Software Engineering.

• Mishra, Deepti & Mishra, Alok (2020). Sustainability inclusion in informatics curriculum development. Sustainability. 12(14), s. 1–17. doi: 10.3390/su12145769. Fulltekst i vitenarkiv Vis sammendrag

  Background: Presently, sustainability is a crucial issue for human beings due to many disasters owing to climate change. Information Technology (IT) is now part of everyday life in society due to the proliferation of gadgets such as mobile phones, apps, computers, information systems, web-based systems, etc. (2) Methods: The analysis is based on recent ACM/IEEE curriculum guidelines for IT, a rigorous literature review as well as various viewpoints and their relevance for sustainability-oriented curriculum development; it also includes an assessment of key competencies in sustainability for proposed units in the IT curriculum. (3) Results: Sustainability is a critical subject for prospective IT professionals. Therefore, it is imperative to motivate and raise awareness among students and the faculty community regarding sustainability through its inclusion in the Informatics curriculum. This paper focuses on how sustainability can be included in various courses of the Informatics curriculum. It also considers recent ACM/IEEE curriculum guidelines for IT professionals, which assert that IT students should explore IT strategies required for developing a culture of green and sustainable IT. (4) Conclusions: This paper provides guidelines for IT curriculum development by incorporating sustainable elements in courses, so that future IT professionals can learn and practice sustainability in order to develop a sustainable society. Keywords: curricula; environment; information technology (IT); informatics; sustainability.

• Jæger, Bjørn; Bruckenberger, Sophie A. & Mishra, Alok (2020). Critical success factors for ERP consultancies : a case study. Scandinavian Journal of Information Systems (SJIS). ISSN 0905-0167. 32(2), s. 169–202. Fulltekst i vitenarkiv Vis sammendrag

  ERP implementation is a complex and expensive project for organisations because it touches upon social, behavioural and technical issues. This article presents an in-depth case study of an ERP consultancy in order to comprehensively understand ERP implementation from the consultancy’s perspective. Interviews were conducted with consultants, project managers, senior managers and other employees. The interview responses indicate that both the client and the consultancy measure success in terms of quantifiable units of time, cost and scope. In addition, qualitative measures of client maturity and client satisfaction are central to achieving project success for the consultancy when implementing ERP systems. Through incorporating critical success factors (CSFs) in the project methodology, the consultancy can ensure repeatable implementation processes, which contribute to a higher probability of success for both the consultancy and its clients. This study contributes by identifying consultancy CSFs: project management, IT environment, training and education, change management, and maintenance and support. Furthermore, the client’s maturity, and the management of client expectations are identified as unique CSFs for the ERP consultancy. This study will be beneficial for ERP consultancies, practitioners and researchers.

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• Mishra, Alok (2026). IoT-based Agriculture Software Architecture Quality Attributes. Fulltekst i vitenarkiv
• Mishra, Alok (2025). Artificial Intelligence Applications in Manufacturing. Fulltekst i vitenarkiv
• Mishra, Alok (2025). Increasing Trust in Seafood by Blockchain-A Pilot Study. Fulltekst i vitenarkiv
• Mishra, Alok (2025). Green Artificial Intelligence: Sustainability Focused AI Development. Fulltekst i vitenarkiv
• Mishra, Alok (2025). Green Artificial Intelligence-Why we Need. Fulltekst i vitenarkiv
• Mishra, Alok (2024). Research Collaborations – Opportunities and Challenges. Fulltekst i vitenarkiv
• Mishra, Alok (2024). AI-enhanced Software Engineering. Fulltekst i vitenarkiv
• Mishra, Alok (2024). DRL028 Blockchain Applications in Supply Chain Management. Fulltekst i vitenarkiv
• Mishra, Alok (2023). “Business Analytics for Management Decision Making”. Fulltekst i vitenarkiv
• Mishra, Deepti & Mishra, Alok (2023). Projects and Research Collaboration with Indian Institutions. Fulltekst i vitenarkiv
• Mayank, Pathak; Mishra, Kamta Nath; Singh, S.P. & Mishra, Alok (2023). An Automated Smart Centralized Vehicle Security System for Controlling the Vehicle Thefts/Hacking Using IOT and Facial Recognition. Fulltekst i vitenarkiv
• Mishra, Alok (2023). Emerging Research Areas in Software Engineering for Doctoral Students and Young Researchers. Fulltekst i vitenarkiv
• Mishra, Alok; Khan, Arif Ali; Iribarne, Luis & Mishra, Deepti (2022). Multimedia software engineering: challenges and opportunities. Multimedia Tools and Applications. ISSN 1380-7501. 81(25), s. 35533–35533. doi: 10.1007/s11042-022-13890-0. Fulltekst i vitenarkiv
• Mishra, Alok (2022). Sustainability in software engineering. Fulltekst i vitenarkiv
• Mishra, Alok (2022). Additive Manufacturing Ramifications On Supply Chain Resilience. Fulltekst i vitenarkiv
• Mishra, Alok (2022). Emerging trends in software engineering. Fulltekst i vitenarkiv

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