Recent developments in e-commerce and e-payment systems have led to a rise in financial fraud incidents, particularly credit card fraud. Software tools to identify credit card theft are essential. Critical characteristics of credit card fraud are crucial in utilizing Machine Learning (ML) for credit card fraud identification and must be selected carefully. This study suggests a An Efficient Machine Learning Algorithm for Reliable Credit Card Fraud Identification (EMLA-RCCFI) was constructed using ML, which utilizes the Genetic Algorithm (GA) to select features. Once the optimum characteristics are determined, the suggested detecting module utilizes the subsequent ML-based classifications. The proposed EMLA-RCCFI system is assessed using a dataset produced by European cardholders to confirm its efficacy. Based on the results, the suggested EMLA-RCCFI method surpassed existing systems regarding accuracy, precision, and F score.

Wireless Sensor Networks (WSNs) are a fundamental component of the Internet of Things (IoT), used in diverse applications to detect environmental conditions and send information to the Internet. WSNs are susceptible to congestion issues, leading to increased packet loss, extended delays, and reduced throughput. This research introduces a Fuzzy Logic-based Cross-Layered Optimization Model (FL-CLOM) for WSNs to tackle the problem. FL-CLOM is developed by including the signal-to-noise ratio of the wireless channels in the Transmission Control Protocol (TCP) approach, bridging the transmission layer and Media Access Control (MAC) layer. A fuzzy logic system is created by integrating fuzzy control with congestion control to dynamically manage the queue size in crowded nodes and minimize the effects of external uncertainties. Various simulations were conducted using MATLAB and NS-2.34 to compare the suggested FL-CLOM to conventional methods. The results indicate that FL-CLOM efficiently adjusts to queue size changes and demonstrates rapid convergence, reduced average delay, reduced packet loss, and increased throughput.

Natural Language Processing NLP, or Natural Language Processing, is an area of artificial intelligence (AI) that concentrates on the interaction between computers and human language. Its goal is to develop algorithms and models that enable computers to understand, interpret, and generate human language in a meaningful manner. NLP aims to bridge the gap between human language and computer language, enabling computers to process and comprehend natural language data effectively. This field encompasses a range of tasks, including speech recognition, language translation, sentiment analysis, text summarization, question answering, and many others. These tasks rely on machine learning models that are trained using large amounts of annotated data. media monitoring, information retrieval, customer support chatbots, and many other areas where understanding human language is crucial. As research and development in NLP progress, we can anticipate the emergence of more advanced and sophisticated models that will further enhance the capabilities of language processing and understanding by computers. This advancement holds exciting possibilities for human-computer interaction. NLP research has seamless communication and empowering us to extract valuable knowledge from textual data on a large scale. technique employed in the process of multi-criteria decision-making and prioritizing alternatives. Its purpose is to provide a structured method for assessing and ranking different options in the presence of multiple criteria that may conflict with one another. MOORA is widely utilized across various domains, including operations research, engineering, and management. Alternative: Programming language, Linguistic theory, Hardware platform, Knowledge Representation

Data clustering is frequently utilized in the early stages of analyzing big data. It enables the examination of massive datasets encompassing diverse types of data, with the aim of revealing undiscovered correlations, concealed patterns, and other valuable information that can be leveraged. The assessment of algorithms designed for handling large-scale data poses a significant research challenge across various fields. Evaluating the performance of different algorithms in processing massive data can yield diverse or even contradictory results, a phenomenon that remains insufficiently explored. This paper seeks to address this issue by proposing a solution framework for evaluating clustering algorithms, with the objective of reconciling divergent or conflicting evaluation outcomes. “The multicriteria decision making (MCDM) method” is used to assess the clustering algorithms. Using the EDAS rating system, the report examines six alternative clustering algorithms “the KM algorithm, EM algorithm, filtered clustering (FC), farthest-first (FF) algorithm, make density-based clustering (MD), and hierarchical clustering (HC)”—against, six clustering external measures. The Expectation Maximization (EM) algorithm has an ASi value of 0.048021 and is ranked 5th among the clustering algorithms. The Farthest-First (FF) Algorithm has an ASi value of 0.753745 and is ranked 2nd. The Filtered Clustering (FC) algorithm has an ASi value of 0.055173 and is ranked 4th. The Hierarchical Clustering (HC) algorithm has the highest ASi value of 0.929506 and is ranked 1st. The Make Density-Based Clustering (MD) algorithm has an ASi value of 0.011219 and is ranked 6th. Lastly, the K-Means Algorithm has an ASi value of 0.055376 and is ranked 3rd. These ASi values provide an assessment of each algorithm’s overall performance, and the rankings offer a comparative analysis of their performance. Based on the result, we observe that the Hierarchical Clustering algorithm achieves the highest ASi value and is ranked first, indicating its superior performance compared to the other algorithms.