Predicting Customer Lifetime Value (CLV) is one of the most critical tasks that businesses undertake in order to improve customer retention and optimize marketing strategies. The present paper proposes a predictive mo...
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The rapid evolution of smartphone technology and the diverse range of available models have made selecting a cost-effective mobile phone a complex decision for consumers. Although brand, internal memory, camera qualit...
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In software development, system integrity is a measure of the impact code changes have on them. It is determined by the team's comprehension. However, rapid evolution of change commits and interaction in complex c...
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Recommendation systems are the subset of data filtering techniques and focus on providing personalized suggestions to the users. The systems rely on the data to provide insightful suggestions. Over the years, recommen...
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The primary objective is to develop a robust system for precise object identification on retail shelves, accurate item counting, and product categorization through class detection. This multifaceted approach directly ...
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Credit card fraud is an essential problem in the economic industry;thus, its detection is solved with the help of the developed methods in order to minimize the overall loses and to improve the confidence of clients. ...
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Reliability prediction in automotive systems undoubted represents a substantial part of safety and customer satisfaction. a new graph-based probabilistic method and machine learning algorithm for the automotive system...
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Understanding and recognition of human emotions are very crucial in various fields. This paper proposes a new approach to show the different feelings that are hidden using multi-modalities like video, audio, and textu...
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Healthcare resource management is essential for ensuring the quality of patient care. However, it can be a complex and costly task. This work addresses the patient admission scheduling (PAS) problem, a complex aspect ...
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This study presents a comparative analysis of the Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithms in the context of stock trading, focusing on historical stock pric...
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