This volume, Understanding AI Decisions, Computer Vision, and Management Analytics, brings together diverse yet interconnected explorations of how intelligent systems interpret data, generate insights, and support strategic action. The chapters collectively highlight not only the technical sophistication of contemporary AI models—ranging from machine learning and deep learning to reinforcement learning—but also their managerial, ethical, and societal implications.

Enhancing Customer Experience.
In the current world, customer experience has become the key competitive advantage among the players in industries. NLP hence is central to improving customer satisfaction since it aims at optimizing the interactions between institutions and customers. Most of the engagement models, including the chatbots and virtual assistants, apply NLP in real-time to meet customers’ requirements. These agents can include quickly answering general questions, recommending products, and solving various types of technological problems.
Sentiment analysis tools derived from NLP help companies determine the feelings of customers through feedback in forms of reviews, social media activities, and feedback surveys. Customers’ emotions and opinions about a certain product are significant factors that help organizations to improve on the delivery of the products and services and the way they are marketed. Further, NLP is useful in other customer care related activities such as handling most of the response frequently asked by the customers, thus cutting down on response time and at the same time, providing the customers with consistent service.
Contents.
Preface.
Chapter 1 Applications of NLP in Customer Service. Sentiment Analysis, and Market Research.
Chapter 2 Applications of Sentiment Analysis on Customer Sendee by Mediation of Market Research in the Horn of Africa.
Chapter 3 Entertainment Revolution and Applications of Machine Learning Innovations.
Chapter 4 Predictive Modelling of Drivers' Positions in Formula 1 Racing Using Ensemble Machine Learning.
Chapter 5 Smart Logistics: Designing and Implementing AI-Driven Decision Support Systems.
Chapter 6 Deep Learning-Based Poly Cystic Ovarian Syndrome Disease Prediction.
Chapter 7 Deep Learning in Medical Imaging: Improving Diagnostics of Brain Tumors Using Neural Networks via Classification Task.
Chapter 8 Presenting the Methodologies, Challenges, and Opportunities in Underwater Target Recognition.
Chapter 9 Towards Secure Smart Contracts: An AI-Enhanced Framework for Vulnerability Detection.
Compilation of References.
About the Contributors.
Index.
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