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Modern understanding of human actions is focused primarily on universal characteristics of visual images. In recent decades, conventional human activity identification has been largely focused on the global characteristics of an image, introducing multiple files containing such as edge characteristics, shape characteristics, statistical features, or converting characteristics that classify human behavior. This human behavior is examined with maximum accuracy in machine learning. Machine-learning intelligence (AI) framework offers algorithms to better learn and improve from the experience with no clear programming of the obtained and operated data. This stated that the application of artificial intelligence (AI) plays a vital role in human behavior prediction. Hence, this chapter provides brief knowledge about the techniques of brain computing and human behavior analysis through machine learning. The learning orientation continues to look for data correlations and to make informed choices with experience or evidence, such as facts, direct knowledge, or feedback, depending on the data in the near term. The primary objective is to allow computers to automatically learn without human involvement or assistance and to modify behavior accordingly. There are numerous techniques to examine the brain activity and human behavior in machine learning, but throughout this chapter, we will also concentrate on in-depth brain computing learning techniques with deep learning algorithms because in-depth learning has publicized interface and analytical benefits. Many BCIs (Brain Computer Interfaces) have implemented deep learning techniques for brain computing due to its benefits and the pre-processing stage of all feature engineering steps does not take time. For example, it begins working explicitly on brain relevant data signals to understand the distinguishing back propagation data information. For human behavior, computing and analyzing the motions or actions of all body parameters (hands, eyes, legs, heads, etc.) are important in human behavior prediction or computing, hence for this purpose also deep learning engine of machine learning is in boom of artificial intelligence. In earlier days, the storage of data in computer and performance of the innovation of deep learning in the area of bio-parameter analysis was limited by the software system, but now-a-days, the use of advanced and developed computer system and cloud helps store large amounts of data that inherited the growth of deep learning in computing and analyzing of human behavior. This chapter also gives an idea about the type of deep models called Deep Neural Networks (DNNs). According to the research, DNNs can directly work on raw inputs to automate the functions. This chapter explains the identification process for human behavior (users) using activities, actions, and behavior patterns, intra- and inter-active content. Researchers Actor Almeida et al. created a deep learning architecture based on long-term memory networks (LSTMs) that model the behavior of interactivity. This chapter will give you a brief of machine learning usage and techniques for brain and human behavior computing with its merits and demerits along with brief knowledge of human behavior prediction algorithm. Abbreviations/Acronyms: Artificial Intelligence (AI), Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Conventional Neural Networks (CNN), Brain Computer Interface (BCI), Functional Magnetic Resonance Imaging (fMRI), Functional Near-Infrared Spectroscopy (fNRI), Magnetoencephalography (MEG), Electroencephalogram (EEG), Long Short Tem Memory Networks (LSTMs). |
