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    (Artificial) neural networks
    (Elsevier, 2024) Tareq, Wadhah Zeyad Tareq
    Buying a new or used car is one of the challenges faced by people in most parts of the world. A car's price can be affected by many factors such as economic and political crises. With the high demand for cars and the different crises across the world, predicting car prices has become impossible and incompetent. This study aims to find the overall amount that customers can offer to purchase a car. The suggested model uses artificial neural networks (ANN) to predict customer budgets based on different features. Moreover, this chapter provides a guideline for the authors and researchers to understand ANN's basic concepts and ideas to be used later in different areas. The suggested model was trained using 500 samples of customers from different countries, with different ages and different annual salaries. The proposed model obtains the highest accuracy of 95%. © 2024 Elsevier Inc. All rights reserved.
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    Classification and clustering
    (Elsevier, 2024) Tareq, Wadhah Zeyad Tareq; Davud, Muhammed
    Data analysis is the process of understanding or extracting patterns from raw data. One of the widely used methods in data analysis is machine learning. Machine learning is a system or model that can learn from raw data to make decisions without human intervention. Classification and clustering are the most popular machine learning technologies for the analysis of data. Each technology involves many algorithms that aim to categorize objects into classes depending on the object's features. In this chapter, we introduce a guide to both classification and clustering technology by applying different algorithms to different datasets. The classification dataset differs from the clustering dataset. The reason here is to explain the suitable type of data for each technology. For clustering, the k-means clustering algorithm is applied. For classification, the decision tree algorithm is applied. The results showed the efficiency of different machine learning algorithms for data analysis and decision-making. © 2024 Elsevier Inc. All rights reserved.
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    Deep learning
    (Elsevier, 2024) Tareq, Wadhah Zeyad Tareq
    Extracting features from raw data such as videos and soundtracks is difficult and almost impossible. Handcrafted design and feature engineering are hard tasks and need experimentation, evaluation, and creativity. Feature engineering requires exploring the data and the impact of each feature before selecting them. This exploration process requires significant time and effort. Deep learning is a powerful approach to extracting features by transforming raw data into numerical features. This process occurs by using nodes known as neural networks such as the human brain. Neural networks contain multiple layers, and each layer can extract several features from the input data. In this chapter, the convolutional neural network is used to extract features from video games. These features describe the state or observation of the game to enable an intelligent agent to make decisions. The deep learning algorithm combined with the reinforcement learning approach to build a deep reinforcement learning agent able to play different games using game screenshots and game scores just like a human player. The results showed that the agent is able to enhance its performance after several steps, which proves the efficiency of feature extraction using deep learning algorithms. © 2024 Elsevier Inc. All rights reserved.