Information technology has revolutionized data collection and analysis, offering unprecedented opportunities to study human behavior. Various information registers, the internet of things, and electronic demographic platforms that collect and analyze user data from various online sources provide a unique opportunity to predict human behavior using machine learning methods. This study applies machine learning to analyze textual data derived from diverse sources: demographic data, scientific articles, employee documents, and social media content. The primary goal is to identify a person's area of interest and predict their behavior. We propose using Support Vector Machines (SVM) as a robust and versatile machine learning algorithm for text data analysis. SVM's ability to handle diverse data types makes it well-suited for analyzing complex human behavior patterns. By classifying documents into relevant topics, SVM can help assess how employee behavior aligns with organizational goals and performance metrics. This research aims to contribute to human behavior analysis by demonstrating the effectiveness of machine learning techniques, particularly SVM, in extracting meaningful insights from textual data.

This research delves into the utilization of machine learning algorithms to address the urgent challenge of assisting students in navigating a highly competitive job market. Recognizing the limitations of conventional methods in delivering effective guidance for securing job opportunities, there is a growing imperative to integrate advanced technology. Our model using Machine Learning (ML) algorithms offers customized solutions and emphasizes the algorithms that exhibit the highest effectiveness within this context. In the contemporary employment, achieving success extends beyond mere academic credentials, necessitating a holistic grasp of industry trends and in-demand skills. Through the application of machine learning, a fresh approach is presented, encompassing the gathering, and preprocessing of diverse data that encompasses skill proficiencies. This data forms the bedrock upon which ML algorithms operate, predicting and enhancing students’ likelihood of securing favorable job placements. The proposed work focuses on the careful selection of suitable machine learning algorithms, with special attention given to classification techniques such as Linear Regression, Random Forest, Decision Tree Classifier, K-nearest neighbors Classifier, and ensembled models. By meticulous evaluation and Ensemble Technique, these algorithms unearth intricate patterns within the data, deciphering the multifaceted factors influencing job placement outcomes. By deconstructing the performance of each algorithm, the report provides valuable insights into their strengths and potential synergies.

Sleep is a critical biological process required for physical recovery, cognitive function, emotional regulation, and sound health. Conventional techniques for evaluating the quality of sleep are usually costly and intrusive, especially when they use sleep clinics and advanced sensors. Instead of using several factors to predict sleep quality, the majority of earlier studies only employed one factor and a short dataset. Their results were less accurate since they did not apply machine learning to look into the cause of poor sleep quality. This paper initiates a machine-learning (ML) based method for assessing and predicting sleep quality using a larger dataset and the Pittsburgh Sleep Quality Index (PSQI). To find the best machine learning model for predicting sleep quality, the proposed system tests eight classifiers. The results show that the Cat Boost classifier outperforms other models, with an accuracy value of 90.1%, precision value of 87%, recall value of 88%, and f1-score value of 87%. The proposed prediction model also outperformed previous works in terms of accuracy, precision, and recall by 12%, 8%, and 11%, respectively. This paper also describes a web application with features such as personalized sleep quality prediction, result checking, improvement suggestions, and doctor consultation services. According to the review results, up to 65 percent of users agreed that the proposed sleep quality assistance web application features were appropriate and necessary.

The increase in value of security and privacy is compounded by the rapid advancements in the digital landscape sprouting new problems in information security. This research explores the use of artificial intelligence (AI) to enhance cybersecurity and to strengthen data protection. This research aims to first assess and critically evaluate the potential of applying AI methods to improve predicting, mitigating, and resolving cyber threats while addressing important ethical issues. Specifically, it wants to determine AI’s advantages compared to traditional cybersecurity ways and the plausible technological risks and ethical implications associated with its use. We show that AI tools, especially machine learning and deep learning, can greatly aid the threat detection and response automation. The rise of AI, however, brings forth new vulnerabilities and necessitates stronger ethical frameworks to preclude their misuse. This study offers a balanced view of potential with AI and hazards. The results emphasize the importance of AI in securing both the cybersecurity and data protection portfolio, and urge strongly for ethical standards to be met and the research to be continued in order to mitigate risks and promote responsible AI integration.

Recent technological advancements have fueled a notable increase in credit card usage, consequently amplifying the prevalence of credit card fraud in both offline and online transactions. Although measures such as PIN codes, embedded chips, and supplementary keys like tokens have enhanced credit card security, financial institutions are compelled to bolster their usage controls and deploy real-time monitoring systems to promptly identify and mitigate suspicious activities. This study explores the utilization of ensemble methods, incorporating the k-nearest neighbors (KNN), Random Forest (RF), and Logistic Regression (LR) models, along with the Isolation Forest (iForest) algorithm, to enhance the efficacy of credit card fraud detection. Additionally, automated parameter optimization using GridSearchCV is employed to fine-tune the iForest model parameters. By integrating multiple classifiers into an ensemble approach and automating parameter tuning for the iForest model, our research aims to provide a robust solution capable of adapting to varying datasets and improving fraud detection accuracy. Through empirical analysis and comparison of individual models with the ensemble approach, we underscore the significance of ensemble learning and parameter optimization in enhancing fraud detection capabilities, thereby contributing to the advancement of financial security measures in the realm of credit card transactions.