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The distribution of available resources is an essential aspect of cloud computing systems. It entails allocating computer resources to various programmes and users in order to guarantee that the available resources are utilized in an efficient, effective, and equitable manner. The optimization of resource allocation in cloud computing systems presents a number of challenges, such as heterogeneity, multi-objective optimization, large-scale optimization, dynamic optimization, and user satisfaction. Other challenges include privacy and security, large-scale optimization, and dynamic optimization. Researchers have made tremendous progress in designing algorithms, models, and frameworks to address these concerns despite the difficulties they face in doing so. In this overview of the relevant literature, we focus on research that investigates methods for resource allocation and optimization in cloud computing systems. We provide a brief synopsis of the most important findings from these studies and provide them in table form, drawing attention to the study methodologies, algorithms, and optimization strategies that were applied. In addition, we address potential future research areas and outline the obstacles that researchers encounter when attempting to optimize the allocation of resources in cloud computing systems. The purpose of this literature review is to offer a complete overview of the current state of research in resource allocation and optimization in cloud computing systems, and it may also serve as a valuable reference for researchers and practitioners working in this subject.

Machine learning techniques are being widely used to identify and respond to unusual events in industrial controls systems (ICS), where they play a vital role in preventing potential catastrophes. This paper reviews the various techniques that are used in anomaly detection in these systems. The paper discusses the definition of an anomaly detection process and provides a comprehensive review of the various techniques involved in this area. It also explores the applications of machine learning and statistical techniques in this domain. Some of the techniques that are commonly used in this area include clustering, decision trees and random forests, and control charts. The paper also covers the applications and challenges of anomaly detection in different industrial control systems such as water treatment plants, power grid systems, and chemical plants. Case studies are presented to demonstrate the effectiveness of learning-based techniques in identifying anomalies in these facilities. The paper also presents an evaluation of the performance of various machine learning techniques in performing anomaly detection. The evaluation metrics that are used in these experiments include false positive rate, accuracy, recall, area under receiver characteristic curve, and F1 score. The paper concludes by providing a summary of the findings of the review and the future directions of the investigation in anomaly detection for industrial control systems. The paper offers valuable insights into the latest state-of-art techniques in this area, and it can help practitioners and researchers make informed decisions when it comes to choosing the appropriate ones for their specific projects.

Credit cards are widely used and accepted in the financial sector all over the world. The latest trend is to use electronic payments and to go cashless. Unfortunately, these credit card-based online transactions and cashless payments invite online fraudsters, who then attack all forms of online payment, including shopping sites and banking services. According to polls, approximately 4 billion people are presently affected by credit card fraud detection, and by 2025, that figure is projected to increase to 8 billion. Concern for its detection has increased as a result of this worrying pace. Both research scholars and industry experts have contributed their effort in this area for this goal. When considering the credit card detection method, its detection largely becomes a difficult problem. Due mostly to its unstable nature and dependence on customer behaviour, and secondarily because the dataset is readily available and easily accessible. This causes the dataset to become imbalanced, which makes it harder for a researcher to find instances of credit card fraud. Implementations of data mining algorithms are suitable for overcoming such difficulties. As a result, applying the proposed thesis necessitates using the random forest, decision trees, logistic regression, and Naive Bayes. The paper also proposes the use of a stacking algorithm, which integrates the basic theories of decision trees, logistic regression, and random forests, in addition to datamining techniques. According to experimental study of the aforementioned classifiers, the stacking algorithm produced an optimum model with exact precisions and generated the greatest accuracy of 97.78%.