# Cheating Detection in Online Exams Using Deep Learning and Machine Learning
## 1 Introduction  
Efforts to detect unethical behaviors in online distance education exam environments are of great importance in terms of protecting the academic integrity of learners and the reputation of the relevant institutions. Behaviors such as cheating behaviors, plagiarism, plagiarism, dishonesty, lying, making excuses, etc. are contrary to the achievements targeted in the education and training processes and constitute a major problem for the relevant institutions. Analyzing online exams with artificial intelligence (AI) methods can reveal the academic honesty of learners studying in relevant educational institutions and provide a forward-looking prediction for institution administrators and educators. Machine learning (ML), which aims to develop and evaluate models, is a science that extracts meaningful information from large amounts of data using algorithms and as a data analysis technique [1]. Machine learning and deep learning (DL) are subsets of AI that reveal the perfect change in object detection and classification in video or images [2]. By automatically analyzing this data, AI algorithms can identify complex patterns that may be difficult for humans to detect. ML models have undoubtedly made remarkable advances in various fields, demonstrating their ability to make accurate predictions and automate decision-making processes [3]. ML offers flexible models for identifying patterns and relationships among complex distributions and making meaningful decisions from data. It can be categorized into supervised ML, which develops predictive models given training data whose answers are known in advance, and unsupervised ML, which aims to group observation data or create simplified representations of the main structures of data [4]. It is the science that gives the ability of classification, prediction, and pattern recognition based on models emerging from existing data and without any programming; it is defined as the potential to select effective features on data. As a data analysis method, it produces appropriate outputs from data-centered modeling algorithms [1]. DL is a broader term used to describe ML techniques based on multilayer neural networks (deep architectures). DL can be unsupervised, semi-supervised, or supervised [5]. DL, a branch of AI and ML, is currently in the process of regaining its relevance of the last decade, with significant success in areas such as banking, transportation, life science, and entertainment [6]. The use of useful learning architectures depends on increasing training data [7]. For larger neural networks, their performance continues to improve as the algorithms are trained on larger amounts of data. Most other ML methods have reached their performance limit, as using larger amounts of data for training does not increase the speed of operation. DL also allows for scaling [8]. DL provides methods that reinforce the learning of data representations across multiple layers of computational models. These methods have significantly advanced the state of the art in many different areas, such as object detection, visual object recognition, speech recognition, inference from data, and many more. DL discovers complex structures in large datasets by using the back-propagation algorithm and changes in the representation of internal parameters in the previous layer [9]. DL offers an effective structure on its own and incorporates classification into the architecture. It also offers more realistic approaches to real-world problems by providing a non-linear structure to the training data with the power of activation functions [10]. When the literature is examined, DL applications have been examined mostly in the fields of health and engineering. It has been seen that image separation and classification are widely used in the field of engineering. In the field of health, diagnosis is based on image, classification, and prediction. However, DL is a promising approach with many potential applications in industry and manufacturing [11].