Because of the flaws of the present university attendance system, which has always been time intensive, not accurate, and a hard process to follow. It, therefore, becomes imperative to eradicate or minimize the deficiencies identified in the archaic method. The identification of human face systems has evolved into a significant element in autonomous attendance-taking systems due to their ease of adoption and dependable and polite engagement. Face recognition technology has drastically altered the field of Convolution Neural Networks (CNN) however it has challenges of high computing costs for analyzing information and determining the best specifications (design) for each problem. Thus, this study aims to enhance CNN’s performance using Genetic Algorithm (GA) for an automated face-based University attendance system. The improved face recognition accuracy with CNN-GA got 96.49% while the face recognition accuracy with CNN got 92.54%.

Introduction: Lung cancer is one of the main causes of the rising death rate among the expanding population. For patients with lung cancer to have a higher chance of survival and fewer deaths, early categorization is essential. The goal of thisresearch is to enhance machine learning to increase the precision and quality of lung cancer classification.Methods: The dataset was obtained from an open-source database and was utilized for testing and training. The suggested system used a CT scan picture as its input image, and it underwent a variety of image processing operations, including segmentation, contrast enhancement, and feature extraction.Results: The training process produces a chameleon swarm-based supportvector machine that can identify between benign, malignant, and normal nodules.Conclusion: The performance of the system is evaluated in terms of false-positive rate (FPR), sensitivity, specificity, recognition time and recognition accuracy.

Load balancing plays a critical role in optimizing resource utilization and enhancing performance in cloud computing systems. As cloud environments grow in scale and complexity, efficient load balancing mechanisms become increasingly vital. This paper presents a comprehensive review of load balancing techniques in cloud computing systems, with a focus on their applicability, advantages, and limitations. The review encompasses both static and dynamic load balancing approaches, evaluating their effectiveness in addressing the challenges posed by cloud infrastructure, such as heterogeneity, scalability, and variability in workload demands. Furthermore, the review examines load balancing algorithms considering factors such as resource utilization, response time, fault tolerance, and energy efficiency. Additionally, the impact of load balancing on cloud performance metrics, including throughput, latency, and scalability, is analyzed. This review aims to provide insights into the state-of-the-art load balancing strategies and serve as a valuable resource for researchers, practitioners, and system designers involved in the development and optimization of cloud computing systems.

Gender identification from videos is a challenging task with significant real-world applications, such as video content analysis and social behavior research. In this study, we propose a novel approach, the White Shark Optimizer-Support Vector Machine (WSO-SVM), tailored specifically for gender identification from video data. The WSO-SVM integrates the White Shark Optimizer, a bio-inspired optimization algorithm mimicking the hunting behavior of white sharks, with the Support Vector Machine, a powerful machine learning technique for classification. By combining these two methods, we aim to exploit the advantages of both algorithms and enhance gender identification accuracy. To evaluate the performance of the WSO-SVM in gender identification, the work conducted extensive experiments using a diverse dataset of video clips containing individuals of various genders and backgrounds. The work compared the results with conventional SVM-based gender identification and state-of-the-art methods. The findings demonstrate that the WSO-SVM achieves superior accuracy in gender identification compared to traditional SVM-based approaches. The WSO-SVM's ability to efficiently explore the solution space and select optimal SVM parameters contributes to its improved performance. Moreover, the WSO-SVM exhibits robustness in handling variations in lighting conditions, poses, and facial expressions, making it well-suited for real-world video-based gender identification tasks. The outcomes derived from the SVM approach demonstrate that WSO-SVM produced an average FPR of 7.14%, Sensitivity of 93.06%, Specificity of 92.86%, Precision of 91.0%, and overall accuracy of 93.00% in 45.83 s with a recognition time of 45.83 s.

A mobile agent is a software entity that migrates from one host to another, performing data collection and software configuration tasks on behalf of users in distributed networks. However, studies have shown that the existing pull – all data strategy of mobile agent migration pattern moves data from one host to the next host along with the mobile agent, thereby resulting in data accumulation which lowers the performance of the mobile agent in terms of transmission time and network load. This research developed an optimized mobile agent migration pattern for the pull-all data strategy in distributed networks using path strategy of Ant Colony Optimization (ACO) technique. The obtained results shows that the optimized pull-all data migration pattern developed produced lower network load and transmission time than the existing pull-all data migration pattern.