Non-convex optimization problems often challenge gradient-based algorithms, such as Gradient Descent. Neural network training, a prominent application of gradient-based methods, heavily relies on their computational efficiency. However, the cost function in neural network training is typically non-convex, causing gradient-based algorithms to become trapped in local minima due to their limited exploration of the solution space. In contrast, global optimization algorithms, such as swarm-based methods, provide better exploration but introduce significant computational overhead. To address these challenges, we propose Multi-Agent Gradient Descent (MAGD), a novel algorithm that combines the efficiency of gradient-based methods with enhanced exploration capabilities. MAGD initializes multiple agents, each representing a candidate solution, and independently updates their positions using gradient-based techniques without inter-agent communication. The number of agents is dynamically adjusted by removing underperforming agents to minimize computational cost. MAGD offers a cost-effective solution for non-convex optimization problems, including but not limited to neural network training. We benchmark MAGD against traditional Gradient Descent (GD), Adam, and Swarm-Based Gradient Descent (SBGD), demonstrating that MAGD achieves superior solution quality without a significant increase in computational complexity. MAGD outperforms these methods on 20 benchmark mathematical optimization functions and 20 real-world classification and regression datasets for training shallow neural networks.

Human Activity Recognition (HAR) plays a vital role in healthcare, smart home, and security applications. Recent advances in sensor technology and machine learning have substantially enhanced HAR capabilities, particularly for continuous monitoring of elderly individuals. Time-series imaging and convolutional neural networks (CNNs) have been effectively applied to this task. In this study, we adapt these techniques to the underexplored and challenging GOTOV dataset and further develop a novel framework, PWMF-ResMiniNet, a lightweight yet highly accurate solution forHARoptimized for resource-constrained edge computing environments. The framework introduces two key innovations. First, the Precision-Weighted Model Fusion (PWMF) algorithm performs decision-level fusion of multiple sensor-specific CNNs by assigning adaptive per-class weights based on each model’s validation precision, achieving state-of-the-art accuracy with zero additional computational cost while remaining interpretable and theoretically sound. Second, the ResMiniNet CNN architecture, inspired by the ResNet model, reduces trainable parameters by approximately 75% while maintaining competitive accuracy. Combined with Gramian angular field imaging, PWMF-ResMiniNet achieves over 90% F1-score on the GOTOV dataset—the highest reported accuracy on this dataset to date—and demonstrates consistent superiority over state-of-the-art methods such as HARDenseCNN and HART on the PAMAP2 and WISDM 2019 datasets. Moreover, the study discusses the limitations of the proposed framework and current HAR approaches, identifies open research challenges, and highlights promising directions for future investigation.

Human Activity Recognition (HAR) plays a vital role in healthcare, smart home, and security applications. Recent advances in sensor technology and machine learning have substantially enhanced HAR capabilities, particularly for continuous monitoring of elderly individuals. Time-series imaging and convolutional neural networks (CNNs) have been effectively applied to this task. In this study, we adapt these techniques to the underexplored and challenging GOTOV dataset and further develop a novel framework, PWMF-ResMiniNet, a lightweight yet highly accurate solution for HAR optimized for resource-constrained edge computing environments. The framework introduces two key innovations. First, the Precision-Weighted Model Fusion (PWMF) algorithm performs decision-level fusion of multiple sensor-specific CNNs by assigning adaptive per-class weights based on each model’s validation precision, achieving state-of-the-art accuracy with zero additional computational cost while remaining interpretable and theoretically sound. Second, the ResMiniNet CNN architecture, inspired by the ResNet model, reduces trainable parameters by approximately 75% while maintaining competitive accuracy. Combined with Gramian angular field imaging, PWMF-ResMiniNet achieves over 90% F1-score on the GOTOV dataset—the highest reported accuracy on this dataset to date—and demonstrates consistent superiority over state-of-the-art methods such as HARDenseCNN and HART on the PAMAP2 and WISDM 2019 datasets. Moreover, the study discusses the limitations of the proposed framework and current HAR approaches, identifies open research challenges, and highlights promising directions for future investigation.

This paper delves into a crucial yet overlooked facet of smart parking systems: critical decision-making. While existing research extensively covers routine processes such as car routing and scheduling, the focus on pivotal decisions has been lacking. Despite numerous studies, only 23 papers directly address critical decision-making, often not as their primary focus. This scarcity underscores the untapped potential for exploration in this domain. By conducting a comprehensive review, this paper not only highlights the existing research landscape but also identifies significant gaps. Moreover, it serves as a pioneering effort to shed light on the importance of critical decision-making in smart parking systems. By providing valuable insights and analysis, this review lays the groundwork for future research endeavors, encouraging scholars to delve deeper into this unexplored territory. In summary, this paper fills a notable void in the literature, paving the way for further advancements and understanding in the realm of smart parking system decision-making.