The rapid growth of sophisticated Android malware (AM) threats is significant, as Android devices often store private and sensitive personal and financial information. These threats allow stealing of data, interference with device functioning, and network compromise. One of the greatest difficulties in efficient interception systems is ensuring a high level of detection accuracy for distinguishable AM variants. This study focuses on developing a robust Android malware detection model via machine learning (ML) and deep learning (DL) techniques. The model combines ML classifiers, which consist of logistic regression (LR) and decision trees (DTs), and a DL classifier, an artificial neural network (ANN). The model was implemented via an open-source data mining program called Orange. The NATICUSdroid dataset was used to train and test the model, which was measured in terms of accuracy, precision, recall, F-measure and AUC. The experimental findings revealed that the ANN performed the best (accuracy: 98.0%, precision/recall/F-measure: 98.0%, AUC: 0.997) and was better than the LR (accuracy: 96.1%, AUC: 0.989) and DT (accuracy: 96.0%, AUC: 0.971) methods. The results highlight the high potential of DL-based approaches, especially ANNs, to detect Android malware and reinforce their suitability for enhancing mobile security systems.

Computational intelligence (CI) and artificial intelligence (AI) have incredible roles to play in the development of smart and sustainable healthcare systems by facilitating the integration of smart technologies with conventional medical procedures. The Internet of Things (IoT) and CI healthcare systems rely heavily on data collection and machine learning since miniature devices represent the foundation and paradigm shift to sustainable healthcare. With these advancements in AI techniques, we can reduce our environmental impact, while simultaneously enhancing the quality of our services. Widespread use of these devices for innovative IoT applications, however, generates massive amounts of data, which can significantly strain processing power. There is still a need for an efficient and sustainable model in the area of disease predictions, such as lung cancer, blood cancer, and breast cancer. The fundamental purpose of this research is to prove the efficacy of a secure Internet of Medical Things (IoMT) in the detection and management of breast cancer via the use of gated recurrent units (GRUs), which are a more recent version of recurrent neural networks (RNNs). The blockchain has been employed to achieve the secure IoMT. Unlike long short-term memory units, they do not have a cell state of their own. Therefore, we have combined GRU with RNN to achieve the best results. When training a GRU-RNN classifier, it is typically necessary to collect tagged IoT data from many sources, which raises significant concerns about the confidentiality of the data. To verify the model, the experiment is performed on Wisconsin Diagnostic Breast Cancer (WDBC). The experimental result shows that the GRU-RNN has been archived 95% in terms of the accuracy metric, and the efficacy of the proposed IoMT model is superior to the existing approach in terms of accuracy, precision, and recall.

The increasing sophistication of cyber threats necessitates the development of advanced security mechanisms to protect modern networks. Among these threats, spoofing attacks pose a significant risk by enabling malicious actors to impersonate legitimate entities. To address this challenge, we propose a novel Transformer-based deep learning framework designed for the effective detection of spoofing attacks. The core of our novel model is a Transformer neural network, enhanced with a custom attention mechanism to improve feature extraction and classification accuracy. To enhance model interpretability and foster trust in AI-driven security systems, we integrate Explainable AI (XAI) techniques, specifically SHAP analysis, allowing for a deeper understanding of feature contributions in decision-making. The proposed model utilized the CIC IoMT2024 dataset, a benchmark with limited prior research on spoofing attack detection. Further, our approach incorporates comprehensive data preprocessing techniques and employs over-sampling using the synthetic minority oversampling technique (smote) and cleaning using (tomek) these techniques are integrated into links smotetomek to mitigate class imbalance, ensuring a more representative training dataset. The proposed framework is evaluated using benchmark dataset datasets, demonstrating high binary classification performance in spoofing attacks through key metrics such as accuracy, confusion matrix analysis, and other classification benchmarks. The proposed model archived an exact result with Accuracy 99.71%. The findings highlight the potential of Transformer-based architectures in cybersecurity applications, paving the way for real-time threat detection and adaptive defense mechanisms.

This study aimed to explore the sustainable aesthetic and functional dimensions of environmental waste in the context of interior design applications by students. Employing both descriptive and applied methodologies, a series of artistic works derived from environmental waste, specifically metal and wood, were conceptualized and executed by art education students. These works, totaling 11 artistic models in interior design, underwent chemical treatment as part of the process. An evaluation card, assessed by arbitrators, was utilized to gauge the standards of aesthetic and functional sustainability inherent in the artworks. Technical data were collected and subsequently analyzed using SPSS software, which facilitated the calculation of arithmetic averages, standard deviations, and t-tests to ascertain the extent to which sustainability standards were met within the aesthetic and functional dimensions of the works. The study findings indicated that the average response scores for the aesthetic and functional dimensions, pertaining to the achievement of sustainability for wood and metal waste, were notably high. This underscores the potential of producing artful works suitable for interior design applications within the spaces of the College of Education. With an average score of 3.984, students exhibited positive engagement with the aesthetic and functional aspects of their artistic products, indicative of their considerable ethical significance. This augurs well for the feasibility of attaining sustainability through the recycling of wood and metal waste. Furthermore, this research underscores the necessity of integrating aesthetic, environmental, and social values in achieving sustainable aesthetic and functional environmental values within the interior design curriculum. This integration demands a comprehensive understanding of user expectations, technological advancements, and the cultural background, customs, and traditions of both users and society at large.

Augmented reality and virtual reality are changing the healthcare sector, allowing for more immersive and interactive solutions for remote patient care and medical training. These technologies have opened new possibilities for telemedicine—new ways of delivering treatments in real-time and monitoring patients, for example. However, the challenges for AR/VR systems include the real-time integration of data from IoT-enabled medical devices and ensuring the security and privacy of sensitive patient data. This paper presents a cutting-edge AI-enhanced framework for AR/VR in remote healthcare to address these critical issues. It integrates edge computing to ensure minimal latency and system responsiveness; blockchain technology to secure the transaction of data; and federated learning to train AI models while maintaining the privacy of patients. Leveraging the advanced capabilities of the HTC VIVE Pro 2 kit, the framework supports several applications, including remote physiotherapy, surgical guidance, and real-time health monitoring. All the developed applications are tested in simulated real-world scenarios to check the performance of the system in terms of latency, adaptability, security, and user satisfaction. The experiments conducted show vast improvements in terms of data transmission efficiency, system robustness, and user engagement as a whole, proving that the proposed solution is very feasible and scalable. Hence, the research conducted signifies the significance of integrating AI, IoT, and AR/VR to address healthcare issues, paving the way for future breakthroughs. This research contributes to a new frontier for patient-centric, data-driven, and highly interactive healthcare systems by laying a secure and intelligent base platform for remote care.

The Internet of Medical Things (IoMT) supports health monitoring and data access and exchange in real time, but devices constrained in terms of energy, memory, and processing resources make traditional cryptographic protocols not applicable. This work presents a lightweight authentication framework that compares three symmetric encryption algorithms, TinyAES, SPECK, and PRESENT, designed for resource-limited IoMT systems. The method consists of four standalone phases: user registration, login authentication, session key exchange, and secure data transfer. The proposed approach was evaluated on the ESP32 and Raspberry Pi platforms, demonstrating up to 56% execution gain, 58% energy reduction, and 42% memory savings over ECC-based schemes. TinyAES achieves the best trade-off among all compared algorithms and is recommended for secure and sustainable medical IoT applications.