Background: Utilizing cloud technology in the healthcare sector has completely changed how patient data is stored, retrieved, and shared while improving scalability and costefficiency. In this environment, however, it is crucial to ensure network security. Objective: Intrusion Detection Systems (IDS) continuously monitor network traffic and system activities to detect and neutralize possible threats. This helps preserve patient privacy and the reliability of healthcare delivery systems. We proposed Machine learning classifiers. Methods: The machine learning classifiers used four feature selection techniques: Logistic Regression, naive Baye, Decision Tree, and K-nearest neighbors with the Adam optimization method. Finally, we used the Django framework to deploy the trained model. Results: In experimental work, the NSL-KDD dataset and CICIDS-2017 dataset are used. The models were trained and tested using the same datasets to ensure consistency in data preparation. Conclusion: When the classification accuracy of the four distinct classifiers is compared, the suggested optimized Decision Tree classifier performs better.

: Artificial intelligence (AI) related technologies are being employed more and more in a range of industries to increase automation and improve productivity. The increasing volumes of data and advancements in high-performance computing have led to a sharp increase in the application of these methods in recent years. AI technology has been widely applied in the field of hardware design, notably in the design of digital and analogue integrated circuits (ICs), to address challenges such as rising networked devices, aggressive time-to-market, and everincreasing design complexity. However, very little attention has been paid to the issues and problems related to the design of integrated circuits. The authors of this article review the stateof- the-art in AI for circuit design and optimization. AI offers knowledge-based technologies that give challenges a foundation and structure. A technology known as AI makes it possible for machines to mimic human behavior. Data in all formats, including unstructured, semistructured, and structured, can be processed by AI. It is crucial to incorporate all of the features and levels of the many CAD programmes into a single, cohesive environment for creation, as was mentioned in the section that came before this one. Consequently, the application of AI automation helps to enhance the effectiveness and efficiency of CAD's performance.

: After decades of introducing the concept of virtual reality, the expansion, and significant advances of technologies and innovations, such as 6g, edge computing, the internet of things, robotics, artificial intelligence, blockchain, quantum computing, and digital twins, the world is on the cusp of a new revolution. By moving through the three stages of the digital twin, digital native, and finally surrealist, the metaverse has created a new vision of the future of human and societal life so that we are likely to face the next generation of societies (perhaps society 6) in the not too distant future. However, until then, the reality has been that the metaverse is still in its infancy, perhaps where the internet was in 1990. There is still no single definition, few studies have been conducted, there is no comprehensive and complete paradigm or clear framework, and due to the high financial volume of technology giants, most of these studies have focused on profitable areas such as gaming and entertainment. The motivation and purpose of this article are to introduce a prospective metaverse paradigm based on the revised reality-virtuality continuum and provide a new supporting taxonomy with the three dimensions of interaction, immersion, and extent of world knowledge to develop and strengthen the theoretical foundations of the metaverse and help researchers. Furthermore, there is still no comprehensive and agreed-upon conceptual framework for the metaverse. To this end, by reviewing the research literature, discovering the important components of technological building blocks, especially digital twins, and presenting a new concept called meta-twins, a prospective conceptual framework based on the revised reality-virtuality continuum with a new supporting taxonomy was presented.

Background: Considering the singularity of collaborative filtering algorithms in recommending learning resources and the problem that existing knowledge graph convolutional networks cannot deeply mine the neighbourhood information of learning resource nodes in application scenarios with less neighbourhood information, a multi-domain fusion convolutional network learning resource recommendation model based on knowledge graphs is proposed here. Objective: This study aimed to improve the accuracy and personalization of recommendations of filtering algorithms. Methods: First, the model mapped learner nodes, learning resources, and their neighbour nodes into low-dimensional dense vectors. Second, the multi-domain fusion layer and the multidomain aggregator were used to obtain the fused multi-domain learning resource vector. Finally, the learner vector and the multi-domain learning resource vector were fed into the prediction layer to calculate the interaction probability. Results: To verify the effectiveness of the algorithm, we conducted a comparative experiment using the publicly available datasets MOOPer and MOOCCubeX. The experimental results showed that the proposed model outperformed baseline models, such as CKE, MKR, KGCN, DEKGCN, and KGIN, in terms of evaluation metrics, such as AUC, ACC, and F1. At the same time, when the neighborhood information was limited, the AUC, ACC, and F1 values of the proposed model still maintained the optimal value. Conclusion: Compared to the optimal baseline model, the effectiveness of the proposed model has been proven.

Background: Various methods can be used to solve dual fully fuzzy linear systems. The main choice involves identifying which method produces the most accurate and efficient solutions. Objective: This article describes a procedure to solve the dual fully fuzzy linear systems based on the Jacobi method, which is done using a computer program. Methods: The Jacobi method is an approximate method used to solve dual fully fuzzy linear systems involving the arithmetic concept of triangular fuzzy numbers. The process of the Jacobi method begins with inputting the initial value to obtain a solution with a relatively small error. This requires that the equations of the system be diagonally dominant. Results: The solution obtained from a dual fully fuzzy linear system using the Jacobi method is a single solution that has a minimal error. Conclusion: The solutions of the given examples can be obtained easily by applying the Jacobi method algorithm using MATLAB.

Background: The increasing integration of robotic systems across various sectors has highlighted the critical need for robust cybersecurity measures to safeguard these systems against cyber threats. Objective: This research presents a novel Real-Time Intrusion Detection System (IDS) framework specifically designed to enhance the cybersecurity of robotic systems. Methods: The proposed IDS framework monitors network traffic and continuously identifies potential threats in real time. A testbed is set up using an AlphaBot robotic device and a server machine to perform experiments under both normal and attack conditions. Network traffic data is captured in real-time using tools like Wireshark, generating raw datasets from actual data exchanges between the robotic device and the server. The dataset undergoes preprocessing, including feature extraction, data cleaning, and normalization. This processed dataset is then used to train machine learning algorithms, such as Decision Trees, K-Nearest Neighbors, and Random Forest, designed to identify patterns distinguishing between normal and malicious activities. Results: The IDS framework is tested on the AlphaBot robotic device and server machine, demonstrating effective results in real-world conditions. The system achieved an accuracy rate of 96.61% in distinguishing between normal and attack traffic, highlighting its robustness and practicality. Conclusion: The proposed real-time IDS framework shows promise in enhancing the cybersecurity of robotic systems by effectively identifying potential threats in real time.

Abstract: The use of robot arms has significantly increased due to their numerous advantages. Initially confined to controlled environments such as factories, homes, operating rooms, and laboratories, these robots have now expanded into open spaces. This expansion includes gardens, farms, forests, job sites, urban areas, and even underwater settings, where they interact with diverse elements including humans, animals, plants, and environmental factors. The integration of outdoor robot arms into shared natural environments worldwide is crucial for the advancement of the upcoming intelligent era. However, this integration presents new challenges due to the dynamic and unpredictable nature of these environments, necessitating the development of new rules and guidelines. One significant concern is the safety of both the robot arms and the non-intelligent entities they interact with, alongside human safety considerations. This paper reviews recent literature on outdoor robot arms in shared natural environments, analyzing studies from multiple perspectives. It categorizes the distribution of publications by working places, including gardens, job sites, farms, urban areas, forests, beaches and seas, and other locations. The analysis also examines the primary targets and obstacles faced by these robots, such as plants, elements, animals, and humans. Finally, the paper discusses critical considerations for the successful coexistence of robot arms with the diverse elements of nature. These considerations include sustainable integration, balancing technological advancement with environmental preservation, ethical considerations, adaptability to dynamic environments, and ensuring safety in diverse settings. Addressing these aspects is essential for the harmonious and effective deployment of robotic arms in outdoor environments.

Background: The pervasive adoption of edge computing is reshaping real-time big data analysis, smart city management, intelligent transportation, and various other domains. Its appeal lies in its distributed nature, decentralization, low latency, mobile support, and spatial awareness. However, this ubiquity exposes data to security threats, jeopardizing privacy and integrity. Consequently, access control assumes paramount importance in securing data sharing within edge computing and blockchain technologies. Methods: This research addresses this critical issue by conducting a comprehensive study on access control techniques within the context of edge computing and blockchain for secure data sharing. Our methodology commences with an exhaustive review of relevant articles, aiming to identify and expound upon gaps in existing research. Subsequently, we perform a meticulous analysis of access control methods, mechanisms, and performance metrics, seeking to establish a holistic understanding of the landscape. Results: The culmination of this research effort is a multifaceted contribution. We distill insights from a diverse range of access control schemes, shedding light on their nuances and effectiveness. Our analysis extends to evaluating the performance metrics vital for ensuring robust access control. Through this research, we also pinpoint critical research gaps within traditional access control methods, creating a foundation for innovative approaches to address the evolving challenges within edge computing and blockchain environments. Conclusion: In conclusion, this research venture paves the way for secure data sharing in edge computing and blockchain by offering a thorough examination of access control. The findings from this study are anticipated to guide future developments in access control techniques and facilitate the evolution of secure, privacy-conscious, and efficient data sharing practices in the dynamic landscape of digital technology.

Abstract: Path planning is a way to define the motion of an autonomous surface vehicle (ASV) in any existing obstacle environment to enable the vehicle's movement by setting directions to avoid that can react to the obstacles in the vehicle's path. A good, planned path perceives the environment to the extent of uncertainty and tries to build or adapt its change in the path of motion. Efficient path planning algorithms are needed to alleviate deficiencies, which are to be modified using the deterministic path that leads the ASV to reach a goal or a desired location while finding an optimal solution has become a challenge in the field of optimization along with a collision-free path, making path planning a critical thinker. The traditional algorithms have a lot of training and computation, making it difficult in a realistic environment. This review paper explores the different techniques available in path planning and collision avoidance of ASV in a dynamic environment. The objective of good path planning and collision avoidance for a dynamic environment is compared effectively with the existing obstacle’s movement of different vehicles. Different path planning technical approaches are compared with their performance and collision avoidance for unmanned vehicles in marine environments by early researchers. This paper gives us a clear idea for developing an effective path planning technique to overcome marine accidents in the dynamic ocean environment while choosing the shortest, obstacle-free path for Autonomous Surface Vehicles that can reduce risk and enhance the safety of unmanned vehicle movement in a harsh ocean environment.