Ensemble multifeatured deep learning methodology has emerged as a powerful approach to overcome the limitations of single deep learning models in terms of generalization, robustness, and performance. This survey provides an extended review of ensemble multifeatured deep learning models, and their applications, challenges, and future directions. We explore potential applications of these models across various domains, including computer vision, medical imaging, natural language processing, and speech recognition. By combining the strengths of multiple models and features, ensemble multifeatured deep learning models have demonstrated improved performance and adaptability in diverse problem settings. We also discuss the challenges associated with these models, such as model interpretability, computational complexity, ensemble model selection, adversarial robustness, and personalized and federated learning. This survey highlights recent advancements in addressing these challenges and emphasizes the importance of continued research in tackling these issues to enable widespread adoption of ensemble multifeatured deep learning models. It provides an outlook on future research directions, focusing on the development of new algorithms, frameworks, and hardware architectures that can efficiently handle the large-scale computations required by these models. Moreover, it underlines the need for a better understanding of the trade-offs between model complexity, accuracy, and computational resources to optimize the design and deployment of ensemble multifeatured deep learning models.

Systems for monitoring air quality are essential for reducing the negative consequences of air pollution, but creating real-time systems encounters several challenges. The accuracy and effectiveness of these systems can be greatly improved by integrating federated learning and multi-access edge computing (MEC) technology. This paper critically reviews the state-of-the-art methodologies for federated learning and MEC-enabled air quality monitoring systems. It discusses the immense benefits of federated learning, including privacy-preserving model training, and MEC, such as reduced latency and improved response times, for air quality monitoring applications. Additionally, it highlights the challenges and requirements for developing and implementing real-time air quality monitoring systems, such as data quality, security, and privacy, as well as the need for interpretable and explainable AI-powered models. By leveraging such advanced techniques and technologies, air monitoring systems can overcome various challenges and deliver accurate, reliable, and timely air quality predictions. Moreover, this article provides an in-depth analysis and assessment of the state-of-the-art techniques and emphasizes the need for further research to develop more practical and affordable AI-powered decentralized systems with improved performance and data quality and security while ensuring the ethical and responsible use of the data to support informed decision making and promote sustainability.

The rise of smart cities, smart homes, and smart health powered by the Internet of Things (IoT) presents significant challenges in design, deployment, and security. The seamless data processing across a complex network of interconnected devices in unprotected conditions makes it vulnerable to potential breaches, underscoring the need for robust security at various levels of the network. Traditional security methods based on statistics often struggle to comprehend data patterns and provide the desired level of security. This work proposes a novel hybrid framework that combines Whale Optimization and Deep Learning with a trust-index to identify malicious nodes engaging in various attacks such as DoS, DDoS, Drop attack, and Tamper Attacks, thus enhancing IoT node security. The developed framework first calculates a trust-index score for IoT nodes based on drop attack, tamper attack, replay attack, and multiple-max attack. Subsequently, it utilizes the trust index score in the Optimized Neural Network (ONN) model to effectively identify the malicious IoT node. The neural network optimization is achieved through a fitness function that determines optimal weights using the Whale Optimization Algorithm. The proposed framework has been tested across varying network sizes, comprising 100, 500, and 1000 nodes. The resulting outcomes were evaluated against benchmark security methods such as Logical regression (LR),Random Forest (RF), Support Vector Machine (SVM), Bayesian models, ANN, Elephant herding optimization (EHO),and Lion algorithm (LA) using metrics like specificity, sensitivity, accuracy, precision, False Positive Rate (FPR), False Negative Rate (FNR), False Discovery Rate (FDR), Error, F1 score, Matthews Correlation Coefficient (MCC), and Negative Predictive Value (NPV). The results reveal a notable enhancement in accuracy (26.63%, 13.04%, 17.78%, 30.52%, 22.45%, 4.26%, and 2.24%) for a 100-node network when compared to the benchmark security methods. Furthermore, the proposed framework consistently demonstrates strong performance even when applied to larger IoT networks with a higher node count.