As the healthcare sector increasingly integrates Artificial Intelligence (AI) technologies to improve operational effectiveness, diagnosis, and therapy, the environmental footprint of these innovations has become a growing concern. High energy consumption, electronic waste, and carbon emissions associated with the deployment and training of AI models pose sustainability challenges that must be addressed. This paper investigates the concept and application of Green AI in healthcare, aiming to balance technological advancement with environmental responsibility. This systematic review explores key themes, including green computing practices, the adoption of energy-efficient AI models, and the use of renewable energy sources within healthcare settings. It identifies a range of healthcare applications employing Green AI, highlights emerging trends, and emphasizes the growing importance of environmental awareness in AI development. Furthermore, the study examines enabling tools and techniques, outlines barriers to adoption, and highlights how Green AI can help streamline processes, reduce resource waste, and promote environmentally friendly medical procedures like telemedicine. The review also discusses the significance of policy frameworks, international initiatives, and cross-sector collaboration in promoting environmentally responsible AI deployment. Finally, the paper presents practical implications and outlines future research directions to guide the sustainable evolution of AI in healthcare.

In the era of rapid technological advancement, many organizations shift their data to the cloud. At the same time, valuable data should be made available to various stakeholders when required for analysis, storage, and data consumption. Conversely, to attain security and preserve privacy while data is present on the cloud, data sharing involves significant challenges. In this paper, the authors have integrated cryptography technologies with probabilistic frameworks and machine learning (ML) to introduce a unique model that assists in secure data distribution. The model describes the rules and protocol based on which data can be allocated to various parties from the cloud. The proposed HyMLCPP (Hybrid Machine Learning Cryptography Predictive Probabilistic) model facilitates minimizing the computational time in digital image encryption. Further, it enhances data distribution and sharing practices among users while safeguarding privacy by integrating differential privacy techniques and ML models. The proposed model minimizes the risk by simultaneously detecting vulnerabilities in the cloud-IoT environment. The experimental outcomes demonstrate the proficiency of the proposed model over different self-generated and standard data sets. The proposed model was tested by the authors on standard datasets such as the CICIDS 2017 Dataset, Kaggle Dataset, AIND DDoS Dataset , and CTU-13 Dataset for DDoS attack detection. The proposed model shows accuracy and precision values of about 100% and 99.98% respectively, for standard and self-generated data sets. The proposed HyMLCPP model was tested using various accuracy measurement parameters such as F1 score, recall value , and precision score . The probabilistic features of the proposed model tend to identify the future events as a DDoS assault, which further makes the HyMLCPP model unique and stronger in comparison to the prior works that have proved the effectiveness.

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.

Background: To ensure the quality, dependability, and optimal functioning of software systems, software defect detection (SDD) is a crucial component of software development. Conventional techniques frequently depend on single-modal data sources, which might restrict the range and efficiency of fault identification. Aim: This research investigates the importance of AI-based multimodality, which combines and examines a range of data sources, including source code, design documents, execution logs, and test results. Multimodal frameworks can detect correlations, process complex and diverse data sources, and offer a comprehensive knowledge of software behaviour by utilising artificial intelligence (AI). This capability allows for more comprehensive and precise defect detection at different levels of software development, from design and implementation to testing and deployment. Moreover, AI-based multimodality enables proactive defect prevention techniques, strengthens fault prediction, and advances root cause investigation. Methods: In this context, this study demonstrates how multimodal approaches can revolutionise SDD by tackling the drawbacks of unimodal approaches in the software industry. Results: In addition to demonstrating its effectiveness in comparison to conventional methodologies, it analyses the difficulties in implementing AI-based SDD using multimodality. Conclusion: Further, this paper highlights the implications of AI-based multimodality for producing software systems that are dependable, efficient, and of high quality.

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.

Abstract 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.

Abstract 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.

Summary 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.

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.

Editorial article Since the publication of Agile Manifesto in 2001, agile methods have transited from a grass- root initiative among enthusiastic advocates and developers to a mainstream software development approach adopted by both small and large companies worldwide. Meanwhile research on agile methods has grown rapidly and steadily into an established research area, evidenced by dedicated conferences (e.g., XP conference series, research track of previous Agile Conference series), special issues and sections in top Information Systems and Software Engineering journals. However, practitioners and consultants have largely driven the advancement in agile field, and agile research has lagged behind practice in the past. Has this situation changed as both agile methods and research community become increasingly mature? To be able to answer such questions, there is a constant need to check what interest agile practitioners and what agile researchers are investigating, to make sure that the states of the art and practice are aligned properly.

It is important to identify modules that are fault prone or exhibit evidence of high cognitive complexity as these modules require corrective actions such as increased source code inspection, refactoring or performing more exhaustive testing. This ca

Abstract The Agile Software Development paradigm has become increasingly popular in the last few years, since it claims lower costs, better productivity, better quality and better business satisfaction. Supply chain management (SCM) is a complex software development project. Owing to its scope and uncertain, complex and unstable requirements, it is not possible to develop it with predictable software development process models. Agile methodologies are targeted toward such kinds of problems that involve change and uncertainty, and are adaptive rather than predictive. How an agile process is introduced will significantly impact the implementation success of the process change. The objective of this paper is to analyze the agile development methodologies and management approach used in developing a complex software project. This further demonstrates how to overcome risks and barriers in each development phase of such complex inventive software projects. It also provides a set of guidelines regarding how the agile methodologies can be adopted, combined and used in these kinds of complex software projects. These findings have implications for software engineers and managers developing software by agile methods. Copyright © 2011 John Wiley & Sons, Ltd.

Application of Information Technology (IT) has had a significant impact on all aspects of business. Due to technology, the ease with which software can be pirated is increasing and is leading to increased concern for copyright protection. This paper reviews and discusses software piracy issues from a global perspective and reports the findings of a survey concerning the impact of sectors like government, private and academic in Turkey. Although software piracy has long been attracting the interest of academics, no quantitative research has ever been realized in this field in the country. Elsewhere also, most of the software piracy-related studies are from individuals' perspectives and are limited to students, academics, cost, and attitudes. Very few have reported findings related to IT professionals and organizations. The survey was conducted among IT managers of large-scale organizations from different sectors such as the government, private and academic community. Based on the survey of 162 IT managers, the results indicated that sectors have significant impact on software piracy to some extent.