Human Behavior Analysis Using Machine Learning

Abstract

The study and analysis of human behavior have been longstanding challenges in various fields such as psychology, sociology, and anthropology. With the advent of advanced technologies and the proliferation of data, machine learning has emerged as a powerful tool for deciphering and understanding complex human behaviors. This research aims to explore the application of machine learning techniques in the analysis of human behavior, leveraging diverse datasets and cutting-edge algorithms to gain insights into individual and collective actions. The research methodology involves the collection of multidimensional data sources, including but not limited to social media interactions, physiological measurements, and environmental factors. These datasets are preprocessed and integrated to create a comprehensive representation of human behavior. Machine learning models, ranging from traditional statistical methods to deep learning architectures, are then applied to uncover patterns, correlations, and predictive insights from the data. Machine learning algorithms can analyze patterns in behavior to identify potential signs of mental health issues, enabling early intervention and treatment. Tracking behavioral data can help in managing chronic conditions by providing insights into lifestyle choices, adherence to treatment plans, and overall well-being. Machine learning models can identify unusual patterns of behavior, which is crucial for security purposes. This can include recognizing suspicious activities in public spaces or flagging unusual behavior in online environments. The purpose of this study is to explore the challenges of multiple attribute decision-making when dealing with intuitionist fuzzy information. In this scenario, the attribute weights are not entirely known, and the attribute values are represented by intuitionist fuzzy numbers. To determine the attribute weights, an optimization model is constructed based on the traditional grey relational analysis (GRA) fundamental principles. The proposed method involves calculating the grey relation degree between each alternative and the positive-ideal solution and negative-ideal solution. This degree is then used to define a relative relational degree, which enables the ranking of all alternatives simultaneously with respect to both the positive-ideal solution (PIS) and negative-ideal solution (NIS). From the result 22 is ranked at first position and 45 is ranked at fifth position