Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manufacturing environments, enabling scalable and flexible access to remote data centers over the internet. In these environments, Virtual Machines (VMs) are employed to manage workloads, with their optimal placement on Physical Machines (PMs) being crucial for maximizing resource utilization. However, achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives, particularly in scenarios involving inter-VM communication dependencies, which are common in smart manufacturing applications. This manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, enhanced with improved mutation and crossover operators, to efficiently place VMs. This approach aims to minimize the impact on networking devices during inter-VM communication while enhancing resource utilization. The proposed algorithm is benchmarked against other multi-objective algorithms, such as Multi-Objective Evolutionary Algorithm with Decomposition (MOEA/D), demonstrating its superiority in optimizing resource allocation in cloud-based environments for smart manufacturing.

Face recognition has emerged as one of the most prominent applications of image analysis and understanding, gaining considerable attention in recent years. This growing interest is driven by two key factors: its extensive applications in law enforcement and the commercial domain, and the rapid advancement of practical technologies. Despite the significant advancements, modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions, occlusion, and diverse facial postures. In such scenarios, human perception is still well above the capabilities of present technology. Using the systematic mapping study, this paper presents an in-depth review of face detection algorithms and face recognition algorithms, presenting a detailed survey of advancements made between 2015 and 2024. We analyze key methodologies, highlighting their strengths and restrictions in the application context. Additionally, we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications, size, diversity, and complexity. By analyzing these algorithms and datasets, this survey works as a valuable resource for researchers, identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.

The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet of Things (IoT) applications, particularly in terms of ultra-reliable, secure, and energy-efficient communication. This study explores the integration of Reconfigurable Intelligent Surfaces (RIS) into IoT networks to enhance communication performance. Unlike traditional passive reflector-based approaches, RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes, addressing critical IoT challenges such as energy efficiency, limited communication range, and double-fading effects in backscatter communication. We propose a novel computational framework that combines RIS functionality with Physical Layer Security (PLS) mechanisms, optimized through the algorithm known as Deep Deterministic Policy Gradient (DDPG). This framework adaptively adapts RIS configurations and transmitter beamforming to reduce key challenges, including imperfect channel state information (CSI) and hardware limitations like quantized RIS phase shifts. By optimizing both RIS settings and beamforming in real-time, our approach outperforms traditional methods by significantly increasing secrecy rates, improving spectral efficiency, and enhancing energy efficiency. Notably, this framework adapts more effectively to the dynamic nature of wireless channels compared to conventional optimization techniques, providing scalable solutions for large-scale RIS deployments. Our results demonstrate substantial improvements in communication performance setting a new benchmark for secure, efficient and scalable 6G communication. This work offers valuable insights for the future of IoT networks, with a focus on computational optimization, high spectral efficiency and energy-aware operations.

Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks. However, existing methods often fail in dynamic and high-demand environments, leading to resource bottlenecks and increased energy consumption. This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management (QIARM) model, which introduces novel algorithms inspired by quantum principles for enhanced resource allocation. QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically. In addition, an energy-aware scheduling module minimizes power consumption by selecting optimal configurations based on energy metrics. The simulation was carried out in a 360-minute environment with eight distinct scenarios. This study introduces a novel quantum-inspired resource management framework that achieves up to 98% task offload success and reduces energy consumption by 20%, addressing critical challenges of scalability and efficiency in dynamic fog computing environments.