This research explores the impact of malware on the digital world and presents an innovative system to detect and classify malware instances. The suggested system combines a random forest (RF) classifier and gray wolf optimizer (GWO) to identify and detect malware effectively. Therefore, the suggested system is called RFGWO-Mal. The RFGWO-Mal system employs the GWO for feature selection in binary and multiclass classification scenarios. Then, the RFGWO-Mal system uses a novel self-union feature selection approach, combining features from different subsets of binary and multiclass classification extracted using the GWO optimizer. The RF classifier is then applied for classifying malware and benign data. The comprehensive Obfuscated-MalMem2022 dataset was utilized to evaluate the suggested RFGWO-Mal system, which has been implanted using Python. The suggested RFGWO-Mal system achieves significantly improved results using the novel self-union feature selection approach. Specifically, the RFGWO-Mal system achieves an outstanding accuracy of 99.95% in binary classification and maintains a high accuracy of 86.57% with multiclass classification. The findings underscore the achievement of a self-union feature selection approach in enhancing the performance of malware detection systems, providing a valuable contribution to cybersecurity.

This research paper explores the efficacy of two nature-inspired optimization algorithms, the whale optimization algorithm (WOA) and Harris Hawks optimization (HHO), for feature selection in the context of intrusion detection and prevention systems (IDPS). Leveraging the NSL-KDD dataset as a benchmark, our study employs Python for implementation and uses decision tree (DT) as the classification model. The objective is to assess the impact of the HHO and WOA optimization techniques on the performance of IDPS through feature selection. The WOA and HHO techniques were able to lessen the features from 40 to 16 and 13, respectively. Results indicate that DT integrated with HHO achieves an impressive accuracy of 97.59%, outperforming the WOA-enhanced model, which attains an accuracy of 97.5%. This study contributes valuable insights into the comparative effectiveness of WOA and HHO optimization algorithms in enhancing the accuracy of IDPSs, shedding light on their potential applications in the realm of cybersecurity.

This paper explores the integration of Artificial Intelligence into 6G networks, focusing on optimizing communication, resource allocation, and enhancing security. As communication systems transition from 5G to 6G, Artificial Intelligence’s role in addressing the increasing complexity of network management becomes pivotal. The paper reviews key AI technologies such as machine learning, deep learning, and reinforcement learning, demonstrating their applications in predictive maintenance, traffic management, and energy efficiency optimization. It also highlights how Artificial Intelligence enables intelligent network slicing, spectrum management, and resource allocation through dynamic algorithms. Furthermore, Artificial Intelligence-driven solutions are presented for addressing security concerns in 6G networks, focusing on intrusion detection, anomaly detection, and blockchain-based decentralized security. Challenges such as computational overhead, data privacy, and ethical concerns in implementing Artificial Intelligence in 6G systems are also discussed, along with future directions, including quantum AI and federated learning. This paper provides a comprehensive analysis of how Artificial Intelligence can enhance the capabilities of 6G networks, ensuring improved performance, security, and scalability for future communication technologies.

Insomnia is a common sleep-related neuropsychological disorder that can lead to a range of problems, including cognitive deficits, emotional distress, negative thoughts, and a sense of insufficient sleep. This study proposes a providing computational dynamic cognitive model (PCDCM) insight into providing cognitive mechanisms of insomnia and consequent cognitive deficits. Since the support providing is significantly dynamic and it includes substantial changes as demanding condition happen. From this perspective the underlying model covers integrating of both coping strategies, provision preferences and adaptation concepts. The model was found to produce realistic behavior that could clarify conditions for providing support to handle insomnia individuals, which was done by employing simulation experiments under various negative events, personality resources, altruistic attitude and personality attributes. Simulation results show that, a person with bonadaptation and either problem focused or emotion focused coping can provide different social support based on his personality resources, personality attributes, and knowledge level, whereas a person with maladaptation regardless the coping strategies cannot provide any type of social support. Moreover, person with close tie tends to provide instrumental, emotional, and companionship support than from weak tie. Finally, a mathematical analysis was used to examine the possible equilibria of the model.

In this article, we have built a prototype of a decentralized IoT based biometric face detection framework for cities that are under lockdown during COVID-19 outbreaks. To impose restrictions on public movements, we have utilized face detection using three-layered edge computing architecture. We have built a deep learning framework of multi-task cascading to recognize the face. For the face detection proposal we have compared with the state of the art methods on various benchmarking dataset such as FDDB and WIDER FACE. Furthermore, we have also conducted various experiments on latency and face detection load on three-layer and cloud computing architectures. It shows that our proposal has an edge over cloud computing architecture.

Falls are a major concern, particularly for elderly individuals and vulnerable populations, often leading to severe injuries if not detected promptly. Traditional sensor-based fall detection methods suffer from limitations such as discomfort, maintenance challenges, and susceptibility to false readings. To address these issues, we propose a hybrid deep learning-based system that leverages video-based techniques for accurate and efficient fall detection. Our approach integrates YOLOv8 for real-time human detection with Time-Space Transformers for temporal motion analysis, ensuring robust recognition of fall incidents while minimizing false positives and negatives. The proposed system is evaluated on two benchmark datasets, CUCAFall and DiverseFALL10500, which contain diverse and challenging environmental scenarios. Experimental results demonstrate that our model outperforms existing state-of-the-art methods, achieving a mean Average Precision (mAP) of 0.9955 on CUCAFall and 0.950 on DiverseFALL10500, along with F1-scores exceeding 0.998 for all classes in certain configurations. These results confirm the model’s ability to distinguish fall events from normal activities accurately. Furthermore, the system maintains computational efficiency, making it suitable for real-time deployment in healthcare and smart home environments. By enhancing spatial and temporal awareness, our hybrid approach significantly improves fall detection reliability, ensuring timely intervention and enhancing safety for at-risk individuals.