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Integrating Neurocomputing with Artificial Intelligence

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  • Дата: 11-06-2025, 21:02
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Название: Integrating Neurocomputing with Artificial Intelligence
Автор: Abhishek Kumar, Pramod Singh Rathore, Sachin Ahuja, Umesh Kumar Lilhore
Издательство: Wiley-Scrivener
Год: 2025
Страниц: 327
Язык: английский
Формат: pdf (true)
Размер: 33.6 MB

Integrating Neurocomputing with Artificial Intelligence provides unparalleled insights into the cutting-edge convergence of neuroscience and computing, enriched with real-world case studies and expert analyses that harness the transformative potential of neurocomputing in various disciplines.

Integrating Neurocomputing with Artificial Intelligence is a comprehensive volume that delves into the forefront of the neurocomputing landscape, offering a rich tapestry of insights and cutting-edge innovations. This volume unfolds as a carefully curated collection of research, showcasing multidimensional perspectives on the intersection of neuroscience and computing. Readers can expect a deep exploration of fundamental theories, methodologies, and breakthrough applications that span the spectrum of neurocomputing.

AI and Machine Learning, Deep Learning is where it is at right now. More and more academics are paying attention to it since it is a relatively young topic that has grown rapidly in the last time. In the current years, there has been a steady improvement in the presentation of CNN models on Deep Learning problems; these models are among the most significant classical structures in the field. Image classification, semantic separation, target identification, and natural language processing employ convolutional neural networks to autonomously learn sample data feature representations. After examining the typical CNN model’s structure to improve performance through system depth and width, this paper examines a model that improves performance even more through an attention mechanism. This study finishes with a summary and analysis of the existing special model structure. A CNN model, hybrid CNN, and LSTM that incorporate text features with language knowledge may improve text language processing.

Throughout the book, readers will find a wealth of case studies and real-world examples that exemplify how neurocomputing is being harnessed to address complex challenges across different disciplines. Experts and researchers in the field contribute their expertise, presenting in-depth analyses, empirical findings, and forward-looking projections. Integrating Neurocomputing with Artificial Intelligence serves as a gateway to this fascinating domain, offering a comprehensive exploration of neurocomputing’s foundations, contemporary developments, ethical considerations, and future trajectories. It embodies a collective endeavor to drive progress and unlock the potential of neurocomputing, setting the stage for a future where Artificial Intelligence is not merely artificial, but profoundly inspired by the elegance and efficiency of the human brain.

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