Nowadays, many organizations and individual users are employing cloud services extensively due to their efficiency, reliability and low cost. A key aspect for cloud data centers is to achieve management methods to reduce energy consumption, increasing the profit and reducing the environmental impact, which is critical in the deployment of leading-edge technologies today such as blockchain and digital finances, IoT, online gaming and video streaming. In this review, various clustering, optimization, and machine learning methods used in cloud resource allocation to increase the energy efficiency and performance are analyzed, compared and classified. Specifically, on the one hand, we discuss how clustering methods and optimization techniques are widely applied in energy management due to their capacity to provide solutions for energy consumption reduction. On the other hand, we study how multi-objective optimization methods focus on reducing energy consumption as well as service level agreement (SLA) violation, and improving quality of services (QoS) simultaneously. Also, we discuss how optimization methods such as the firefly algorithm, whale optimization algorithm (WOA), particle swarm optimization (PSO) and genetic algorithm (GA) provide the highest performance in the field. Moreover, we analyze how machine learning methods such as deep neural network (DNN), random forest, and support vector machine (SVM) are applied to the prediction of energy consumption in the cloud, showing an accurate performance in this prediction. Nevertheless, we study how the existing methods still have limitations of low convergence, trap into local optima and overfitting.

Diagnosing brain tumors is a time-consuming process requiring radiologist expertise. With the growing patient population and increased data volume, conventional procedures have become expensive and ineffective. Scholars have explored algorithms for detecting and classifying brain tumors, focusing on precision and efficiency. Deep learning methodologies are being used to create automated systems that can diagnose or segment brain tumors with precision and efficiency, particularly in brain cancer classification. This approach facilitates transfer learning models in medical imaging. The present study undertakes an evaluation of three foundational models in the domain of computer vision, namely AlexNet, VGG16, and ResNet-50. The VGG16 and ResNet-50 models demonstrated praiseworthy performance, thereby instigating the amalgamation of these models into a groundbreaking hybrid VGG16–ResNet-50 model. The amalgamated model was subsequently implemented on the dataset, yielding a remarkable accuracy of 99.98%, sensitivity of 99.98%, and specificity of 99.98% with an F1 score of 99.98%. Based on a comparative analysis with alternative models, it can be deduced that the suggested framework exhibits a commendable level of dependability in facilitating the timely identification of diverse cerebral neoplasms.

Glaucoma is a highly perilous ocular disease that significantly impacts human visual acuity. This is a retinal condition that causes damage to the Optic Nerve Head (ONH) and can lead to permanent blindness if detected in a late stage. The prevention of permanent blindness is contingent upon the timely identification and intervention of glaucoma during its initial stages. This paper introduces a convolutional neural network (CNN) model that utilizes specific architectural designs to identify early-stage glaucoma by analyzing fundus images. This study utilizes publicly accessible datasets, including the Online Retinal Fundus Image Database for Glaucoma Analysis and Research (ORIGA), Structured Analysis of the Retina (STARE), and Retinal Fundus Glaucoma Challenge (REFUGE). The retinal fundus images are fed into AlexNet, VGG16, ResNet50, and InceptionV3 models for the purpose of classifying glaucoma. The ResNet50 and InceptionV3 models, both of which demonstrated a superior performance, were merged to create a hybrid model. The ORIGA dataset achieved high accuracy with an F1 Score of 97.4%, while the STARE dataset achieved higher accuracy with an F1 Score of 99.1%. The REFUGE dataset also showed excellent performance, with an F1 Score of 99.2%. The proposed methodology has established a reliable glaucoma diagnostic system, aiding ophthalmologists and physicians in conducting accurate mass screenings and diagnosing glaucoma.