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Submit a ProposalIntegrating Neurocomputing with Artificial Intelligence
One Line Description
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.
Audience
Students, educators, policymakers, and those working in healthcare, robotics, and finance looking for innovative solutions based on AI and neurocomputing techniques
Students, educators, policymakers, and those working in healthcare, robotics, and finance looking for innovative solutions based on AI and neurocomputing techniques
Description
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 neurocomputings 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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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 neurocomputings 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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Author / Editor Details
Abhishek Kumar, PhD is a professor and Assistant Director in the Computer Science and Engineering Department at Chandigarh University, Punjab with over 13 years of teaching experience. He has published over 170 peer-reviewed papers, seven books, and one patent and edited over 50 volumes. His research interests include artificial intelligence, renewable energy systems, image processing, and data mining.
Abhishek Kumar, PhD is a professor and Assistant Director in the Computer Science and Engineering Department at Chandigarh University, Punjab with over 13 years of teaching experience. He has published over 170 peer-reviewed papers, seven books, and one patent and edited over 50 volumes. His research interests include artificial intelligence, renewable energy systems, image processing, and data mining.
Abhishek Kumar, PhD is a professor and Assistant Director in the Computer Science and Engineering Department at Chandigarh University, Punjab with over 13 years of teaching experience. He has published over 170 peer-reviewed papers, seven books, and one patent and edited over 50 volumes. His research interests include artificial intelligence, renewable energy systems, image processing, and data mining.
Sachin Ahuja, PhD is the Executive Director of Engineering at Chandigarh University with extensive research and academic experience. He has served in key academic positions at various reputed higher education institutes, guiding several master’s and doctoral scholars in areas including artificial intelligence, machine learning, and data mining.
Umesh Kumar Lilhore, PhD is affiliated with Galgotias University where he actively engages in academic leadership, research, and mentoring. He has published over 100 scholarly articles and is a senior member of the Institue for Electrical and Electronics Engineers. His areas of expertise include artificial intelligence, machine learning, Internet of Things (IoT), cloud computing, and cybersecurity.
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Table of Contents
Foreword
Preface
1. Integrating Fag Computing with AI Model on Decision Making for Distribution of Energy Management
Prajwal Hegde N., Parvathi C., Ajay Malpani, D. Suganthi and Priya Batta
1.1 Introduction
1.2 Methodology
1.2.1 Energy Management Using a Cloud-Fog Hierarchical Architecture
1.2.2 Units Terminal
1.2.3 Operating Fog Layers
1.2.4 Operation of the Cloud Layer
1.3 Modelling of Different Distribution Network Stakeholders
1.3.1 Consumers’ Usefulness Model
1.4 Results
1.4.1 Operating a Fog Computing System
1.4.2 Computing Operation Cloud
1.5 Conclusions
References
2. Construction and Simulation of Hybrid Neural Network and LSTM to Language Process Model
Kiran Sree Pokkuluri, Ramakrishna Kolikipogu, K.S. Chakradhar, Rama Devi P. and Mamta
2.1 Introduction
2.2 Convolutional Neural Network
2.2.1 Operation Unit and Basic CNN
2.2.2 Standard CNN Model
2.2.3 Model of Stacking Structure
2.2.4 Structure Model for Networks Within Networks
2.2.5 Model for the Attention Mechanism
2.2.6 Model for Free-Motion Learning
2.3 Construction and Simulation of Hybrid CNN-LSTM Language Processing Models
2.3.1 Language Dispensation Model Construction: Hybrid CNN/LSTM
2.3.2 An LSTM and CNN-Based Hybrid Model for Language Processing is Being Simulated
2.4 Conclusion
References
3. An Approach to Ensure the Safety of Industry 4.0 Mobile Robots
P. Balaji Srikaanth, Rajeshwari M. Hegde, Ramachandra V. Ballary, Poornachandran R., R. Senthamil Selvan and Amandeep Kaur
3.1 Introduction
3.2 Methodology
3.2.1 Mobile Robots IN Smart Enterprises
3.2.2 Cyber-Physical Systems
3.2.3 Internet of Robotic Things
3.2.4 Using SDN to Improve Cyber-Physical System Security for Mobile Robotics Industry 4.0
3.3 Proposed Real-Time Attack of Data Classification
3.3.1 Auto-Manufacturing IORT and COBOTS
3.3.2 Attacking Node Termination for Human Security
3.4 Results
3.5 Conclusion
References
4. Feature Extrusion and Categorization of Disease by Hybrid Neuro-Fuzzy Computing
Manideep Yenugula, K.S. Chakradhar, Makhan Kumbhkar, D. Victorseelan and Rupinder Karur
4.1 Introduction
4.2 Methods and Materials
4.2.1 Procedure for Linguistic Fuzzing
4.2.2 Principal Component Analysis
4.3 Features Extraction Model-Based Linguistic Neuro-Fuzzy
4.4 Results
4.5 Data Analysis Using Statistics
4.6 Conclusion
References
5. AI Based Neuromorphic Vision to Control the Robotic Drilling Machine
Venkat Namdev Ghodke, Rajeshwari M. Hegde, Ramachandra V. Ballary and R. Senthamil Selvan
5.1 Introduction
5.2 Setup for Robotic Drilling
5.2.1 Geometrical Tool and Hand-Eye Calibration
5.3 A Sensor for Neuromorphic Vision
5.4 Multi-View Neuromorphic Event-Based Work Piece Localization
5.5 Hole Detection with Neuromorphic Events
5.6 Robotic Vision Controller
5.7 Results and Experiment Validation
5.7.1 Protocol and Preparation for Experiments
5.7.2 Localize 6-DOF Work Piece
5.7.3 Finding Neuromorphic Holes
5.7.4 Performance Drilling Nutplate Holes
5.8 Conclusion
References
6. Design and Development of AI Neuromorphic to Control the Autonomous Driving System
J. Balamurugan, Mohammed Mahaboob Basha, Mamatha Bai B. G., J. A. Jevin, Rakesh Bharti and R. Senthamil Selvan
6.1 Introduction
6.2 Methodology
6.2.1 An Architecture for Neural Engineering
6.2.1.1 First Principle—Image
6.2.1.2 Principle 2—Metamorphosis
6.2.2 Kinematic Bike Model
6.2.3 Detectors of Paths
6.2.4 Virtual Setting for Simulation
6.3 Results
6.3.1 Controller for Pure-Pursuit
6.3.2 Stanley Controls
6.4 Discussion
References
7. Design of Brain-Computer Interface System to Develop Humanoid Robot
R. Raffik, K. Senthilkumar, A. Sakira Parveen, K. Akila, B. Sabitha and P. Magudapathi
7.1 Introduction
7.2 Methodology
7.2.1 Proposed BCI Telepresence System Structure
7.2.2 Participants
7.2.3 Electroencephalography
7.2.4 Calibration Session
7.2.5 Feedback Session
7.2.6 EEG Signal Filtering
7.2.7 Demonstration-Based Programming
7.3 Results
7.4 Discussion
7.5 Conclusion
References
8. AI-Based Neural Network Used to Enhance the Decision-Making System to Improve Operational Performance
G. Naga Rama Devi, Manthena Swapna Kumari, Vijaykumar S. Biradar, Manish Maheshwari, Subramanian Selvakumar and Jenita Subash
8.1 Introduction
8.2 Methodology
8.3 Conceptual Model
8.3.1 A Model for SEM Research
8.3.2 Artificial Neural Network Studies
8.4 Results
8.4.1 Data Gathering and Sample
8.4.2 ANN Implementation
8.5 Conclusion
8.5.1 Contribution to Theory
8.5.2 Methodological and Empirical Contributions
References
9. Simulation and Implementation of English Speech Recognition by NLP
K. Kavita, K. Suresh Kumar, Sridevi Dasam and Kiran Sree Pokkuluri
9.1 Introduction
9.2 Methodology
9.2.1 Practice of Oral English Using Speech Recognition
9.2.2 Error Correction and Voice Scoring
9.2.3 Deep Learning in NLP-Specific Applications
9.2.3.1 Application Process
9.2.3.2 Evaluation of Practical Metrics
9.3 Result
9.4 Conclusion
References
10. Deep Learning-Based Neuro Computing to Classify and Diagnosis of Ophthalmology by OCT
D. Arul Pon Daniel, Santhana Sagaya Mary A. and S. Chidambaranathan
10.1 Introduction
10.2 Methodology
10.2.1 The Training and Labeling of Images
10.2.2 Progress in Intelligent System Development
10.2.3 Evaluation of Performance
10.3 Results
10.4 Discussion
10.5 Conclusions
Bibliography
11. Deep CNN-Based Multi-Image Steganography: Private Key
S. Pavan Kumar Reddy, K. Suresh Kumar, Madhu G.C. and Pavitar Parkash Singh
11.1 Introduction
11.2 Works in a Related Field
11.3 Methodology
11.3.1 Net Concealment
11.3.2 Network Reveals
11.3.3 Training
11.4 Results
11.4.1 Analysis Model
11.4.2 Steganalysis Robustness
11.4.3 Noise Effects
11.5 Conclusion
References
12. Automatic Classification of Honey Bee Subspecies by AI-Based Neural Network
B. Sai Chandana, Ravindra Changala, R. Sivaraman and Anand Bhat B.
12.1 Introduction
12.2 Methodology
12.2.1 Morphometrical Analysis, Colony Samples, and Wing Pictures
12.2.2 Utilizing AI for Image Processing
12.2.3 Models for Recognition and Instruction
12.2.4 Evaluation
12.3 Results
12.3.1 Analysis of the Model
12.3.2 Evaluation Using the Morphometric Approach
12.4 Discussion
References
13. Acoustic Modeling and Evaluation of Speech Recognition by Neural Networks
Y. Ramadevi, K. Suresh Kumar, Venkata Pavankumar and G.N.R. Prasad
13.1 Introduction
13.2 Related Work
13.2.1 Spiking Neural Networks
13.2.2 Automatic Speech Recognition with Large Vocabulary
13.2.3 Spiking Neural Network Speech Recognition
13.3 Methodology
13.3.1 Model of Spiking Neurons
13.3.2 Neurocoding Arrangement
13.3.3 Deep SNN Training with Tandem Learning
13.3.4 The SNN-Based Acoustic Model
13.4 Results and Discussion
13.4.1 TIMIT Corpus Phone Recognition
13.4.2 Librispeech Corpus Experiments Using LVCSR
13.4.3 SNN-Based ASR Systems Energy Efficiency
13.5 Conclusion
Bibiliography
14. Brain–Computer Interface for Humanoid Robot Control Adaptation
B. Sai Chandana, K.S. Chakradhar, T. Rajasanthosh Kumar and Makhan Kumbhkar
14.1 Introduction
14.2 The System Architecture
14.2.1 BCI Based on SSVEP
14.2.2 Adaptable Hierarchy
14.2.3 Robot and Software for Robots
14.3 Procedure for Experimentation
14.4 Results
14.5 Conclusion
References
15. Evaluation and Validation of Type 1 Diabetes Clinical Data by GAN
Robin Rohit Vincent, Senthilkumar Moorthy, F. Nisha and Soumya
15.1 Introduction
15.1.1 Modern Technology
15.1.2 Metabolic Syndrome
15.2 Methodology
15.2.1 Gathering and Preparing Data
15.2.2 Networks of Generative Adversaries
15.2.3 Enhanced Nighttime Data-Based Sugar Lows Predictor
15.3 Methods of Evaluation
15.4 Results
15.5 Discussion
15.6 Conclusion
References
16. Exploring Neuromorphic Computing with Deep Learning: Unveiling Opportunities, Applications, and Overcoming Challenges
Yogesh Kumar Sharma, Smitha, Shaik Saddam Hussain and Leena Arya
16.1 Introduction
16.2 Neuromorphic Deep Learning Algorithms
16.2.1 Spiking Neural Networks
16.2.2 Spike-Based Quasi-Backpropagation
16.2.3 Mapping with a Pretrained Model
16.2.4 Reservoir Computing
16.2.5 Evolutionary Approaches
16.2.6 Non-Deep Learning Algorithms
16.3 Neuromorphic vs. Deep Learning Algorithms
16.4 Areas of High-Impact Studies
16.4.1 Neuromorphic Hardware
16.4.2 Neuroscience
16.4.3 Epidemiological Simulations
16.4.3.1 Mobility
16.4.4 High-Energy Physics
16.4.5 Power Electronics
16.4.6 Health Sciences
16.4.7 Smart Automation
16.5 Challenges and Opportunities
16.6 Conclusion
References
17. Quantum Neurocomputing: Bridging the Frontiers of Quantum Computing and Neural Networks
Smitha, Yogesh Kumar Sharma, Muniraju Naidu Vadlamudi and Leena Arya
17.1 Introduction
17.2 Quantum Computation
17.3 Quantum Machine Learning Technique
17.3.1 Applying Machine Learning Techniques in Quantum Computers
17.3.2 Quantum-Enhanced Reinforcement Learning
17.3.3 Quantum Annealing
17.4 Quantum Neural Networks
17.4.1 Quantum Perceptrons
17.4.2 Quantum Networks
17.4.3 Quantum Associative Memory
17.4.4 Quantum Convolution Neural Network
17.4.5 Dissipative Quantum Neural Network
17.5 Conclusion and Future Directions
References
Index
Back to Top
Foreword
Preface
1. Integrating Fag Computing with AI Model on Decision Making for Distribution of Energy Management
Prajwal Hegde N., Parvathi C., Ajay Malpani, D. Suganthi and Priya Batta
1.1 Introduction
1.2 Methodology
1.2.1 Energy Management Using a Cloud-Fog Hierarchical Architecture
1.2.2 Units Terminal
1.2.3 Operating Fog Layers
1.2.4 Operation of the Cloud Layer
1.3 Modelling of Different Distribution Network Stakeholders
1.3.1 Consumers’ Usefulness Model
1.4 Results
1.4.1 Operating a Fog Computing System
1.4.2 Computing Operation Cloud
1.5 Conclusions
References
2. Construction and Simulation of Hybrid Neural Network and LSTM to Language Process Model
Kiran Sree Pokkuluri, Ramakrishna Kolikipogu, K.S. Chakradhar, Rama Devi P. and Mamta
2.1 Introduction
2.2 Convolutional Neural Network
2.2.1 Operation Unit and Basic CNN
2.2.2 Standard CNN Model
2.2.3 Model of Stacking Structure
2.2.4 Structure Model for Networks Within Networks
2.2.5 Model for the Attention Mechanism
2.2.6 Model for Free-Motion Learning
2.3 Construction and Simulation of Hybrid CNN-LSTM Language Processing Models
2.3.1 Language Dispensation Model Construction: Hybrid CNN/LSTM
2.3.2 An LSTM and CNN-Based Hybrid Model for Language Processing is Being Simulated
2.4 Conclusion
References
3. An Approach to Ensure the Safety of Industry 4.0 Mobile Robots
P. Balaji Srikaanth, Rajeshwari M. Hegde, Ramachandra V. Ballary, Poornachandran R., R. Senthamil Selvan and Amandeep Kaur
3.1 Introduction
3.2 Methodology
3.2.1 Mobile Robots IN Smart Enterprises
3.2.2 Cyber-Physical Systems
3.2.3 Internet of Robotic Things
3.2.4 Using SDN to Improve Cyber-Physical System Security for Mobile Robotics Industry 4.0
3.3 Proposed Real-Time Attack of Data Classification
3.3.1 Auto-Manufacturing IORT and COBOTS
3.3.2 Attacking Node Termination for Human Security
3.4 Results
3.5 Conclusion
References
4. Feature Extrusion and Categorization of Disease by Hybrid Neuro-Fuzzy Computing
Manideep Yenugula, K.S. Chakradhar, Makhan Kumbhkar, D. Victorseelan and Rupinder Karur
4.1 Introduction
4.2 Methods and Materials
4.2.1 Procedure for Linguistic Fuzzing
4.2.2 Principal Component Analysis
4.3 Features Extraction Model-Based Linguistic Neuro-Fuzzy
4.4 Results
4.5 Data Analysis Using Statistics
4.6 Conclusion
References
5. AI Based Neuromorphic Vision to Control the Robotic Drilling Machine
Venkat Namdev Ghodke, Rajeshwari M. Hegde, Ramachandra V. Ballary and R. Senthamil Selvan
5.1 Introduction
5.2 Setup for Robotic Drilling
5.2.1 Geometrical Tool and Hand-Eye Calibration
5.3 A Sensor for Neuromorphic Vision
5.4 Multi-View Neuromorphic Event-Based Work Piece Localization
5.5 Hole Detection with Neuromorphic Events
5.6 Robotic Vision Controller
5.7 Results and Experiment Validation
5.7.1 Protocol and Preparation for Experiments
5.7.2 Localize 6-DOF Work Piece
5.7.3 Finding Neuromorphic Holes
5.7.4 Performance Drilling Nutplate Holes
5.8 Conclusion
References
6. Design and Development of AI Neuromorphic to Control the Autonomous Driving System
J. Balamurugan, Mohammed Mahaboob Basha, Mamatha Bai B. G., J. A. Jevin, Rakesh Bharti and R. Senthamil Selvan
6.1 Introduction
6.2 Methodology
6.2.1 An Architecture for Neural Engineering
6.2.1.1 First Principle—Image
6.2.1.2 Principle 2—Metamorphosis
6.2.2 Kinematic Bike Model
6.2.3 Detectors of Paths
6.2.4 Virtual Setting for Simulation
6.3 Results
6.3.1 Controller for Pure-Pursuit
6.3.2 Stanley Controls
6.4 Discussion
References
7. Design of Brain-Computer Interface System to Develop Humanoid Robot
R. Raffik, K. Senthilkumar, A. Sakira Parveen, K. Akila, B. Sabitha and P. Magudapathi
7.1 Introduction
7.2 Methodology
7.2.1 Proposed BCI Telepresence System Structure
7.2.2 Participants
7.2.3 Electroencephalography
7.2.4 Calibration Session
7.2.5 Feedback Session
7.2.6 EEG Signal Filtering
7.2.7 Demonstration-Based Programming
7.3 Results
7.4 Discussion
7.5 Conclusion
References
8. AI-Based Neural Network Used to Enhance the Decision-Making System to Improve Operational Performance
G. Naga Rama Devi, Manthena Swapna Kumari, Vijaykumar S. Biradar, Manish Maheshwari, Subramanian Selvakumar and Jenita Subash
8.1 Introduction
8.2 Methodology
8.3 Conceptual Model
8.3.1 A Model for SEM Research
8.3.2 Artificial Neural Network Studies
8.4 Results
8.4.1 Data Gathering and Sample
8.4.2 ANN Implementation
8.5 Conclusion
8.5.1 Contribution to Theory
8.5.2 Methodological and Empirical Contributions
References
9. Simulation and Implementation of English Speech Recognition by NLP
K. Kavita, K. Suresh Kumar, Sridevi Dasam and Kiran Sree Pokkuluri
9.1 Introduction
9.2 Methodology
9.2.1 Practice of Oral English Using Speech Recognition
9.2.2 Error Correction and Voice Scoring
9.2.3 Deep Learning in NLP-Specific Applications
9.2.3.1 Application Process
9.2.3.2 Evaluation of Practical Metrics
9.3 Result
9.4 Conclusion
References
10. Deep Learning-Based Neuro Computing to Classify and Diagnosis of Ophthalmology by OCT
D. Arul Pon Daniel, Santhana Sagaya Mary A. and S. Chidambaranathan
10.1 Introduction
10.2 Methodology
10.2.1 The Training and Labeling of Images
10.2.2 Progress in Intelligent System Development
10.2.3 Evaluation of Performance
10.3 Results
10.4 Discussion
10.5 Conclusions
Bibliography
11. Deep CNN-Based Multi-Image Steganography: Private Key
S. Pavan Kumar Reddy, K. Suresh Kumar, Madhu G.C. and Pavitar Parkash Singh
11.1 Introduction
11.2 Works in a Related Field
11.3 Methodology
11.3.1 Net Concealment
11.3.2 Network Reveals
11.3.3 Training
11.4 Results
11.4.1 Analysis Model
11.4.2 Steganalysis Robustness
11.4.3 Noise Effects
11.5 Conclusion
References
12. Automatic Classification of Honey Bee Subspecies by AI-Based Neural Network
B. Sai Chandana, Ravindra Changala, R. Sivaraman and Anand Bhat B.
12.1 Introduction
12.2 Methodology
12.2.1 Morphometrical Analysis, Colony Samples, and Wing Pictures
12.2.2 Utilizing AI for Image Processing
12.2.3 Models for Recognition and Instruction
12.2.4 Evaluation
12.3 Results
12.3.1 Analysis of the Model
12.3.2 Evaluation Using the Morphometric Approach
12.4 Discussion
References
13. Acoustic Modeling and Evaluation of Speech Recognition by Neural Networks
Y. Ramadevi, K. Suresh Kumar, Venkata Pavankumar and G.N.R. Prasad
13.1 Introduction
13.2 Related Work
13.2.1 Spiking Neural Networks
13.2.2 Automatic Speech Recognition with Large Vocabulary
13.2.3 Spiking Neural Network Speech Recognition
13.3 Methodology
13.3.1 Model of Spiking Neurons
13.3.2 Neurocoding Arrangement
13.3.3 Deep SNN Training with Tandem Learning
13.3.4 The SNN-Based Acoustic Model
13.4 Results and Discussion
13.4.1 TIMIT Corpus Phone Recognition
13.4.2 Librispeech Corpus Experiments Using LVCSR
13.4.3 SNN-Based ASR Systems Energy Efficiency
13.5 Conclusion
Bibiliography
14. Brain–Computer Interface for Humanoid Robot Control Adaptation
B. Sai Chandana, K.S. Chakradhar, T. Rajasanthosh Kumar and Makhan Kumbhkar
14.1 Introduction
14.2 The System Architecture
14.2.1 BCI Based on SSVEP
14.2.2 Adaptable Hierarchy
14.2.3 Robot and Software for Robots
14.3 Procedure for Experimentation
14.4 Results
14.5 Conclusion
References
15. Evaluation and Validation of Type 1 Diabetes Clinical Data by GAN
Robin Rohit Vincent, Senthilkumar Moorthy, F. Nisha and Soumya
15.1 Introduction
15.1.1 Modern Technology
15.1.2 Metabolic Syndrome
15.2 Methodology
15.2.1 Gathering and Preparing Data
15.2.2 Networks of Generative Adversaries
15.2.3 Enhanced Nighttime Data-Based Sugar Lows Predictor
15.3 Methods of Evaluation
15.4 Results
15.5 Discussion
15.6 Conclusion
References
16. Exploring Neuromorphic Computing with Deep Learning: Unveiling Opportunities, Applications, and Overcoming Challenges
Yogesh Kumar Sharma, Smitha, Shaik Saddam Hussain and Leena Arya
16.1 Introduction
16.2 Neuromorphic Deep Learning Algorithms
16.2.1 Spiking Neural Networks
16.2.2 Spike-Based Quasi-Backpropagation
16.2.3 Mapping with a Pretrained Model
16.2.4 Reservoir Computing
16.2.5 Evolutionary Approaches
16.2.6 Non-Deep Learning Algorithms
16.3 Neuromorphic vs. Deep Learning Algorithms
16.4 Areas of High-Impact Studies
16.4.1 Neuromorphic Hardware
16.4.2 Neuroscience
16.4.3 Epidemiological Simulations
16.4.3.1 Mobility
16.4.4 High-Energy Physics
16.4.5 Power Electronics
16.4.6 Health Sciences
16.4.7 Smart Automation
16.5 Challenges and Opportunities
16.6 Conclusion
References
17. Quantum Neurocomputing: Bridging the Frontiers of Quantum Computing and Neural Networks
Smitha, Yogesh Kumar Sharma, Muniraju Naidu Vadlamudi and Leena Arya
17.1 Introduction
17.2 Quantum Computation
17.3 Quantum Machine Learning Technique
17.3.1 Applying Machine Learning Techniques in Quantum Computers
17.3.2 Quantum-Enhanced Reinforcement Learning
17.3.3 Quantum Annealing
17.4 Quantum Neural Networks
17.4.1 Quantum Perceptrons
17.4.2 Quantum Networks
17.4.3 Quantum Associative Memory
17.4.4 Quantum Convolution Neural Network
17.4.5 Dissipative Quantum Neural Network
17.5 Conclusion and Future Directions
References
Index
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