Neural Networks Uncorked: Unraveling Alcoholism Through Machine Learning is a pioneering collection of scientific journal articles that explores the intersection of advanced machine learning techniques and alcoholism research. This comprehensive volume delves into the utilization of neural networks, genetics, and psychiatric evaluations to enhance the understanding of alcoholism's complex nature. Key studies presented include a deep convolutional neural network achieving 96% accuracy in diagnosing alcoholism from brain images and innovative methods using EEG signals and transfer learning for early detection. The book also examines the role of serotonin receptors in alcoholism and their association with major depressive disorder and suicide, providing insights into neurotransmitter deficits linked to these conditions. Additionally, it addresses chronic alcohol abuse's effects on sleep architecture, offering a granular view of physiological distortions and potential treatment paths. From genomic studies to the importance of mental health screening in primary care, this collection showcases the blend of computational advancements and genetic research in revolutionizing alcoholism diagnostics and therapy. Essential for researchers, clinicians, and students, Neural Networks Uncorked provides both inspiration and knowledge to contribute to this critical field.

Quality Research Publishing is dedicated to making knowledge more accessible to the general public by curating a topical compilation of the latest research on various subjects and providing plain language summaries.

Each of the articles in this book is available individually and digitally without cost. However, we believe it is important for the contextualizing and sharing of educational and scientific work to curate this research in a way that is understandable and helpful to the average person seeking deeper knowledge of a particular subject.

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