Breast cancer is the most frequently encountered medical hazard for women in their forties, affecting one in every eight women. It is the greatest cause of death worldwide, and early detection and diagnosis of the disease are extremely challenging. Breast cancer currently exceeds all other female cancers, including ovarian cancer. Researchers can use access to healthcare records to find previously unknown healthcare trends. According to the National Cancer Institute (NCI), breast cancer mortality rates can be lowered if the disease is detected early. The novelty of our work is to develop an optimized stacking ensemble learning (OSEL) model capable of early breast cancer prediction. A dataset from the University of California, Irvine repository was used, and comparisons to modern classifier models were undertaken. The implementation analyses reveal the unique approach’s efficacy and superiority when compared to existing contemporary categorization models (AdaBoostM1, gradient boosting, stochastic gradient boosting, CatBoost, and XGBoost). In every classification task, predictive models may be used to predict the class level, and the current research explores a range of predictive models. It is better to integrate multiple classification algorithms to generate a set of prediction models capable of predicting each class level with 91–99% accuracy. On the breast cancer Wisconsin dataset, the suggested OSEL model attained a maximum accuracy of 99.45%, much higher than any single classifier. Thus, the study helps healthcare professionals find breast cancer and prevent it from happening.

Cloud computing offers dynamic, scalable, and virtualized computing resources to end users over the internet. Load balancing is crucial for efficient resource use, distributing workloads across multiple resources to prevent overloading. Load balancing is crucial for resource utilization and processing time reduction, but traditional algorithms are often stuck at local maxima, leading to unequal allocation and performance decline. A metaheuristic based algorithm is proposed to dynamically adjust load distribution, ensuring resilience and sensitivity to changing workloads while managing energy consumption. This research presents a Rock Hyrax-based load balancing algorithm that addresses local maxima and power efficiency issues using QoS parameters. The algorithm’s performance is evaluated qualitatively and statistically, considering both static and dynamic modes of jobs and virtual machines. Comparing it with existing scheduling algorithms, the algorithm reduces makespan by 10%–15% and total energy consumption in data centers by 8%–13%. These results demonstrate the effectiveness of the Rock Hyrax-based load balancing algorithm in improving performance and energy efficiency in data centers, highlighting its potential impact on optimizing resource allocation and enhancing overall system performance.

Data centers are producing a lot of data as cloud-based smart grids replace traditional grids. The number of automated systems has increased rapidly, which in turn necessitates the rise of cloud computing. Cloud computing helps enterprises offer services cheaply and efficiently. Despite the challenges of managing resources, longer response plus processing time, and higher energy consumption, more people are using cloud computing. Fog computing extends cloud computing. It adds cloud services that minimize traffic, increase security, and speed up processes. Cloud and fog computing help smart grids save energy by aggregating and distributing the submitted requests. The paper discusses a load-balancing approach in Smart Grid using Rock Hyrax Optimization (RHO) to optimize response time and energy consumption. The proposed algorithm assigns tasks to virtual machines for execution and shuts off unused virtual machines, reducing the energy consumed by virtual machines. The proposed model is implemented on the CloudAnalyst simulator, and the results demonstrate that the proposed method has a better and quicker response time with lower energy requirements as compared with both static and dynamic algorithms. The suggested algorithm reduces processing time by 26%, response time by 15%, energy consumption by 29%, cost by 6%, and delay by 14%.

Utility-based computing popularly known as “cloud computing” offers several computing services to the users. Due to the proliferation in the users of cloud computing, there is an unprecedented increase in the demand for computation resources to execute cloud services. Thus, there is a requirement to investigate currently available resources like virtual machines, CPU, RAM, and storage to allocate cloud services. The allocation and QoS of cloud services are highly dependent on allocation schemes. The optimized solutions allocate resources to submitted jobs to reduce the overall cost to the end-users/service provider without degrading the performance of virtual machines. The allocation techniques also consider the harvesting of energy consumption required for running the cloud services. In this paper, we have utilized a Rock Hyrax-based optimization technique to allocate resources to the submitted jobs with reduced energy consumption. The proposed Rock Hyrax algorithm has been simulated on the CloudSim simulator for various scenarios. The performance of the proposed algorithm has been measured over various Quality of Service (QoS) parameters such as makespan, energy efficiency, response time, throughput, and cost. The gathered results validate the proposed algorithm that improves the QoS parameters by 3%-8% as compared to algorithms when both jobs and resources are considered to be dynamic in nature.