Cloud computing has revolutionized the modes of computing. With huge success and diverse benefits, the paradigm faces several challenges as well. Power consumption, dynamic resource scaling, and over- and under-provisioning issues are challenges for the cloud computing paradigm. The research has been carried out in cloud computing for resource utilization prediction to overcome over- and under-provisioning issues. Over-provisioning of resources consumes more energy and leads to high costs. However, under-provisioning induces Service Level Agreement (SLA) violation and Quality of Service (QoS) degradation. Most of the existing mechanisms focus on single resource utilization prediction, such as memory, CPU, storage, network, or servers allocated to cloud applications but overlook the correlation among resources. This research focuses on multi-resource utilization prediction using Functional Link Neural Network (FLNN) with hybrid Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The proposed technique is evaluated on Google cluster traces data. Experimental results show that the proposed model yields better accuracy as compared to traditional techniques.

<p style="text-align: justify;">The outbreak of Coronavirus disease (COVID-19) has shaken the world, forcing countries to implement a state of emergency, including the education system. Students have been forced to remain in hostels or houses since they cannot get to university campuses. As a result of this predicament, university authorities have no option but to implement an online learning environment. Specifically, Saudi universities have faced numerous difficulties in bringing the online learning systems to continue the educational process. On the other hand, students faced difficulties to cope with such circumstances (complete online learning) without any preparation or backup plan. According to the findings of the literature research, students experienced difficulties that were difficult to overcome. The aim of this study was to determine the challenges that first-year students of the University faced. The present research got a total of 234 valid responses from the participants. The findings indicate that respondents were not fully prepared in this situation in terms of physical, environmental, and psychological readiness, with some variances in viewpoints depending on their gender and age. Respondents expressed concern about the effect of lockdown on their ability to perform well academically. In this study, the researchers found that switching suddenly to an all-online alternative cause significant obstacles for students. It was determined that the present blended learning model, which utilizes online learning to support face-to-face instruction, has encountered a critical challenge when it comes towards replacing it, particularly with underprepared learners.</p>

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Breast adenocarcinoma is the most common of all cancers that occur in women. According to the United States of America survey, more than 282,000 breast cancer patients are registered each year; most of them are women. Detection of cancer at its early stage saves many lives. Each cell contains the genetic code in the form of gene sequences. Changes in the gene sequences may lead to cancer. Replication and/or recombination in the gene base sometimes lead to a permanent change in the nucleotide sequence of the genome, called a mutation. Cancer driver mutations can lead to cancer. The proposed study develops a framework for the early detection of breast adenocarcinoma using machine learning techniques. Every gene has a specific sequence of nucleotides. A total of 99 genes are identified in various studies whose mutations can lead to breast adenocarcinoma. This study uses the dataset taken from 4127 human samples, including men and women from more than 12 cohorts. A total of 6170 mutations in gene sequences are used in this study. Decision Tree, Random Forest, and Gaussian Naïve Bayes are applied to these gene sequences using three evaluation methods: independent set testing, self-consistency testing, and tenfold cross-validation testing. Evaluation metrics such as accuracy, specificity, sensitivity, and Mathew’s correlation coefficient are calculated. The decision tree algorithm obtains the best accuracy of 99% for each evaluation method.

Autonomous learning has been identified as an effective mechanism for learning. Its importance in learning has mainly been studied by placing its diverse elements and functions in supporting and encouraging learner autonomy. The Outbreak of COVID-19 has signified its significance in the learning process. This study aims to investigate the learners' potential ability of the practices of autonomous learning during the implementation of E-learning. The research was carried out using a quantitative technique and a questionnaire design. An online questionnaire was used to collect data. Data were analyzed using descriptive statistics.  The results of the data analysis suggested that the learners who took part in the research appeared to have a clear viewpoint on the potential ability of autonomous learning.