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.

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.

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.

Traffic flow estimation is critical for road traffic management. However, the traditional systems measuring procedures demand a significant amount of time and money to continually collect the essential data and keep the associated hardware, like cameras and sensors. On the other hand, deep learning approaches give a scientifically solid framework for modeling the ambiguity and complicated relationships between multiple variables. Accordingly, this study utilized deep learning networks and built a model to estimate traffic flows using anticipated journey times. Stacked Autoencoders (SAEs), Gated Recurrent Units (GRUs) and Long Short-Time Memory units (LSTMs) techniques were specifically used to train the model. A number of experiments were carried out using various time sequence values and compared the estimated traffic flows produced by the suggested model and the actual ones collected from real sensors. The results demonstrate that the suggested model is capable of capturing the general trend of actual traffic flows. Our research proposes building a model to estimate traffic flows using anticipated journey times to capture the general trend of actual traffic loads using a deep learning model. Different experiments are completed using various time sequence values and comparing the estimated traffic flows produced by the suggested models and real data sensors. This work provides a solution using a deep learning model to estimate traffic flow that provides a consummate for e-businesses to use this approach to reduce their transportation cost and increase their customer satisfaction.

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.

Abstract Component‐based software development (CBSD) is an emerging technology that integrates existing software components to swiftly develop and deploy big and complex software systems with little engineering effort, money, and time. CBSD, on the other hand, has difficulties with security trust, particularly dependability. When a system provides the desired outcomes while causing no harm to the environment, it is said to be dependable. Dependability encompasses several attributes, including availability, confidentiality, integrity, reliability, safety, and maintainability. Developing dependable component software is achieved by embedding dependability attributes in CBSD. Thus, the CBSD model must address the dependability attributes. Hence, the objectives of this work are: (1) to propose a model for developing a dependable system using component‐based software development approach (hereafter the model is referred to as MDDS‐CBSD), which aims to mitigate software component vulnerabilities, and (2) to assess the proposed model. The best‐practice method was used to frame the CBSD architecture phases and processes, as well as embed the six dependability attributes. The MDDS‐CBSD architecture was evaluated using expert opinion. The MDDS‐CBSD was also used to develop an information and communications technology (ICT) portal using an empirical study method. Vulnerability Assessment Tools were used to assess the developed ICT portal's dependability. The MDDS‐CBSD may be used to create web application systems and to protect them from attacks. Model developers may use CBSD to describe and assess dependability attributes at any point during the model development process. The reliability of this model can also let companies utilise CBSD with confidence.

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.

Driven by several issues from earlier commercial public alerting projects, this paper investigates people's opinions in regard to the current and expected deployments of mobile location-based services under national emergency alerting and warning systems. In particular, the paper examines general public perspective of the importance of utilising the services in different types of emergency events, categorised as natural and human-caused. A survey was carried out to fulfil the requirements of the investigation. The findings clearly denoted significant differences between the mean ranks of all emergency types in the two categories, providing evidence that the importance of utilising LBS is perceived differently by the public for different emergency event types. It is expected that such validated criterion of investigation would help systems' designers to narrow down their selection of emergency event types to only those with extremely high significance to the public, hence avoiding the possibility of ending up with people opting out from the system as a consequence of being continuously bombarded by notifications for emergency events including minor ones.