Integrating Neurocomputing with Artificial Intelligence E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 456

Veröffentlichungsjahr: 2025

Beschreibung

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.

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.

Leseprobe

Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Integrating Neurocomputing with Artificial Intelligence

Edited by

Abhishek Kumar

Pramod Singh Rathore

Sachin Ahuja

and

Umesh Kumar Lilhore

This edition first published 2025 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Library of Congress Cataloging-in-Publication Data

ISBN 9781394335688

Cover image: Generated with AI using Adobe FireflyCover design by Russell Richardson

Foreword

The convergence of neurocomputing and artificial intelligence (AI) marks a transformative era in computational sciences. As AI continues to evolve, its intersection with neurocomputing has paved the way for brain-inspired models, cognitive computing, and adaptive intelligence, leading to ground-breaking applications across various industries. This book, Integrating Neurocomputing with Artificial Intelligence, provides a timely and comprehensive exploration of this emerging domain, offering insights into both foundational theories and cutting-edge advancements.

In the current technological landscape, AI has made significant strides in machine learning, deep learning, reinforcement learning, and natural language processing. However, despite these advancements, conventional AI systems often struggle with energy efficiency, real-time adaptability, and cognitive reasoning, areas where neurocomputing plays a crucial role. Neurocomputing, inspired by the structure and function of biological neural networks, provides novel computational paradigms that aim to mimic the brain’s learning, perception, and decision-making abilities. This book delves into the integration of these fields, showcasing how neuromorphic computing, brain-inspired AI, and hybrid models can create more efficient, intelligent, and sustainable systems.

The chapters in this volume bring together leading researchers, engineers, and industry experts, presenting a multidisciplinary perspective on topics ranging from neuromorphic architectures, spiking neural networks (SNNs), bio-inspired computing, and hybrid AI models to their applications in healthcare, robotics, autonomous systems, cybersecurity, and smart environments. This compilation not only highlights state-of-the-art research but also underscores the challenges and opportunities that lie ahead in building more adaptive, interpretable, and scalable AI systems.

As industries increasingly adopt AI-driven solutions, the need for brain-like intelligence, real-time decision-making, and computational efficiency has never been more critical. This book serves as an essential resource for academicians, professionals, and students seeking to understand and contribute to the rapidly evolving field of AI-integrated neurocomputing.

I commend the editors and contributors for their remarkable effort in compiling this insightful volume. I am confident that Integrating Neurocomputing with Artificial Intelligence will inspire researchers, innovators, and practitioners to explore new frontiers in intelligent computing, ultimately shaping the future of AI-driven technologies.

Dr. Rashmi Agrawal

Professor and Associate Dean, School of Computer Applications, Manav Rachna International Institute of Research and Studies (MRIIRS), Faridabad, India

Preface

This book is organized into seventeen chapters. In Chapter 1, energy management in modern smart grids requires intelligent decision-making systems that optimize energy distribution and consumption. This chapter explores the synergy between fog computing and AI-driven energy models, presenting an architecture that enhances energy distribution efficiency. Using machine learning and neural networks, the authors demonstrate an advanced cloud-fog-based decision-making framework that ensures seamless energy optimization while addressing latency issues.

In Chapter 2, neural networks have revolutionized language processing and text analytics, yet challenges remain in integrating temporal dependencies efficiently. This chapter introduces a hybrid approach that combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for superior performance in natural language processing (NLP) tasks. Through detailed simulations, the authors provide insights into architecture design, training techniques, and performance benchmarking.

In Chapter 3, this chapter explores the use of Industry 4.0 is driven by autonomous mobile robots, which require secure, real-time decision-making systems to navigate industrial environments safely. This chapter presents a cyber-physical security framework that leverages Software-Defined Networking (SDN) and Internet of Robotic Things (IoRT) to enhance robotic safety. The proposed system enables secure communication, real-time threat mitigation, and automated attack detection.

In Chapter 4, in this research chapter, Medical diagnostics have greatly benefited from AI-powered feature extraction and pattern recognition techniques. This chapter explores a hybrid neuro-fuzzy computing framework designed for disease classification and medical image analysis. The authors present a linguistic fuzzification model that enhances disease detection accuracy, offering significant contributions to biomedical AI applications.

In Chapter 5, the book chapter affords brief and general information regarding Advancements in neuromorphic vision systems are transforming robotic automation. This chapter presents an AI-powered neuromorphic vision-based control system for robotic drilling applications, improving precision, speed, and adaptive learning. The authors discuss sensor integration, event-based vision processing, and real-time control strategies, making this work highly relevant to industrial robotics and automation.

In Chapter 6, autonomous vehicles require real-time decision-making models that mimic human cognitive functions. This chapter discusses neuromorphic AI frameworks that enhance path planning, perception, and adaptive control in self-driving cars. The proposed neural engineering architecture integrates spiking neural networks and reinforcement learning to improve vehicle maneuverability and collision avoidance.

In Chapter 7, brain-Computer Interfaces (BCI) enable direct neural communication with machines, leading to significant advancements in humanoid robotics and assistive technologies. This chapter introduces an adaptive BCI system for humanoid robot control, focusing on steady-state visual evoked potentials (SSVEPs) and real-time signal processing techniques for enhanced human-robot interaction.

In Chapter 8, decision-making is a fundamental AI application across industries. This chapter explores deep learning-based decision-making models that improve operational performance in business, healthcare, and logistics. The authors present an Artificial Neural Network (ANN)-based framework, focusing on predictive analytics, optimization, and intelligent automation.

In Chapter 9, speech recognition plays a critical role in human-computer interaction and automated language translation. This chapter presents an AI-powered speech recognition framework leveraging Natural Language Processing (NLP) and deep learning. The authors discuss acoustic modeling, error correction, and real-time voice scoring, highlighting practical applications in education and AI-driven assistants.

In Chapter 10, in this chapter user give AI-driven medical imaging has enhanced early detection of ocular diseases. This chapter introduces deep learning-based neurocomputing models for classifying ophthalmological disorders using Optical Coherence Tomography (OCT) images. The proposed YOLOv3 and ResNet50 architectures improve diagnostic accuracy, offering valuable insights for automated medical analysis.

In Chapter 11, data security is critical in modern communication systems. This chapter presents an innovative multi-image steganography model using Deep Convolutional Neural Networks (CNNs). The approach introduces private keys for encrypted image transmission, ensuring high security, robustness against steganalysis, and effective information concealment.

In Chapter 12, biodiversity conservation benefits from AI-driven species classification models. This chapter presents a deep learning-based framework for automated honey bee subspecies identification, utilizing morphometric analysis and image processing. The proposed model significantly improves classification accuracy, demonstrating the potential of AI in entomology and ecological research.

In Chapter 13, neural networks have revolutionized speech recognition and acoustic modeling. This chapter explores spiking neural networks (SNNs) for automatic speech recognition (ASR), presenting models for large vocabulary speech processing and phoneme recognition. The authors analyze energy-efficient deep SNNs, making significant contributions to speech technology advancements.

In Chapter 14, this chapter engages in discusses Brain-Computer Interfaces (BCIs) enable direct neural interaction with humanoid robots, transforming rehabilitation and assistive technology. This chapter discusses a brainwave-controlled robotic system based on steady-state visual evoked potentials (SSVEPs), showcasing its applications in robotic control and cognitive computing.

In Chapter 15, this book chapter presents Medical data augmentation using Generative Adversarial Networks (GANs) improves diabetes prediction models. This chapter presents a GAN-based approach for simulating glucose monitoring data, enhancing machine learning models for hypoglycemia detection and personalized diabetes care.

In Chapter 16, in this study, Neuromorphic computing offers brain-inspired AI solutions for high-performance computing and edge intelligence. This chapter presents an in-depth analysis of spiking neural networks, reservoir computing, and quasi-backpropagation algorithms, highlighting their impact on neuromorphic hardware and AI applications.

In Chapter 17, this chapter explains the purpose of Quantum computing is reshaping AI by enabling parallel computation and probabilistic learning. This chapter explores the integration of quantum machine learning with neural networks, focusing on quantum-enhanced reinforcement learning, quantum annealing, and quantum convolutional networks (QCNNs).

Dr. Abhishek Kumar

Department of Computer Science and Engineering, Chandigarh University, Punjab, India

Dr. Pramod Singh Rathore

Department of Computer and Communication Engineering, Manipal University Jaipur, India

Dr. Sachin Ahuja

Department of Computer Science and Engineering, Chandigarh University, Punjab, India

Dr. Umesh Kumar Lilhore

Department of Computer Science and Engineering, Galgotias University, Greater Noida, UP, India

2Construction and Simulation of Hybrid Neural Network and LSTM to Language Process Model

Kiran Sree Pokkuluri1*, Ramakrishna Kolikipogu2, K.S. Chakradhar3, Rama Devi P.4 and Mamta5

1Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Bhimavaram, India

2Department of Information Technology, Chaitanya Bharathi Institute of Technology (A), Hyderabad, India

3Department of ECE, Mohan Babu University, Tirupathi, India

4Department of English, KLEF, Deemed to be University, Guntur, Andhra Pradesh, India

5Dept. of CSE, Chandigarh University, Punjab, India

Abstract

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. Parameter optimization, text characteristics, and language competence increase TLP model accuracy. The suggested model outperforms the literature reference model with experimental findings on data sets showing an accuracy of 93.0%.

Keywords: CNN, LSTM, AI, machine learning, deep learning

2.1 Introduction

Many industries rely on text language processing, including those dealing with public opinion on networks, crisis PR, brand marketing, and many more. Netizens’ sentiments, opinions, and inclinations regarding current social procedures, policy execution, and goods and services are reflected in the massive amounts of user comment data collected on online media [1, 2]. Both academics and businesses have invested a lot of time and energy into studying network review data analysis due to its high practical utility [3, 4]. In order to assist visitors in selecting their preferred vacation spot, researchers have examined the language processing issue using data from travel blogs [5, 6]. Comment data classification into negative, positive, and neutral categories is a key field of natural language processing research [7, 8]. Language processing faces significant obstacles in a network setting due to the nonstandard nature of text expression, which includes the use of acronyms, network neologisms, spelling and grammatical flaws, and other issues [9, 10]. Traditional machine learning techniques, deep learning algorithms, and dictionary-based approaches are the mainstays of language processing issue solving [1, 11]. Yadav et al., offer a convolutional neural network language processing model that combines words, parts of expression, effective dictionary entries, and other external information [12]. This approach examines emotive and linguistic information to improve network text processing accuracy [2, 13]. Training the word vectors using the word vector learning model is first. Adding part of language and affective words creates feature data to eliminate word ambiguity and convey emotion [14, 15]. Natural language processing, semantic segmentation, image classification, and target identification all use convolutional neural networks to learn feature representations from sample data on their own [16, 17]. This research first looks at a model that uses network depth and width to increase performance, and then it looks at a model that uses an attention mechanism to boost performance even more. The model is a convolutional neural network [18, 19]. The current special model structure is summarized and analyzed to conclude this research. Text feature-language knowledge-convolutional neural network (LSTM) models, hybrid CNNs, and convolutional neural networks (CNNs) could enhance text language processing [20, 21]. Accuracy of TLP models is improved by optimization of parameters, text features, and language competency. train a traditional convolutional neural network (CNN) to classify images (LENETS). When it comes to processing text, the neural network method has also been quite effective. The phrase segmentation class was the pioneer in using convolutional neural networks to text categorization using deep learning. Following the generation of sentence vectors using LSTM cyclic neural networks, discourse vectors were generated in alignment with the sentence vectors, and sentiment categorization was performed at the discourse level [22, 23].

More in-depth study on neural network topology has been carried out by researchers as it greatly influences the model’s impact. For instance, the Cola Emotional Classification Neural Network Model integrates the greatest aspects of many models including attention, Hybrid CNN and LSTM in an effort to overcome the limitations of individual neural network models. An NN model for character-level classification is suggested using a combination of Hybrid CNN and LSTM, taking into account the features of brief text categorization. Word vectors are a key component of neural network models; their capacity to represent text information is a key component of the model’s efficacy. Google’s 2013-word vector training tool, Word2vec, uses the CBOW and SKIP DRAM word embedding models to form the backbone of deep learning models used in NLP. Scientists refined the word vector training model to meet the demands of interpreting emotional information via language.

Model for embedding sentiment words to make the language processing framework work better, SSWE is utilized to train word vectors. Emotion dictionary and remote supervised data were used to train the word vectors that carry emotional information. One may use word and training vector, part-of-speech chain, and word disambiguation to enhance the capability of word vector text representation [24]. An important part of language processing is the ability to represent and make use of textual emotional qualities. Many different emotional traits and their combinations have been the subject of academic research. This research presents a model for a multiattention CNN that uses word, part-of-speech, and word-position attention matrices to analyze a target emotion. For the purpose of sentiment analysis on Chinese microblogs, a multilayer convolutional neural network model is suggested. This model incorporates many elements of emotion information, including words, part of expression, and word position. For object-level sentiment categorization, experts recommend using a convolutional neural network, which is a model that incorporates both object attentional mechanisms and part-of-speech information. Several types of convolutional neural networks have been suggested by researchers as a result of work on deep learning theory. Figure 2.1 shows the results of a literature search for model recognition rates on classification tasks, which were then sorted in order to facilitate model quality comparisons. In lieu of testing on ImageNet, the appreciation rate on CIFAR-100 or the MNIST dataset is provided, as some models fail to do so. One of these metrics is the TOP-1 recognition rate, which measures how likely it is that the CNN model’s categorization prediction would be accurate.