Recent advancements in perfect difference networks for image recognition: a survey and analysis
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Ramakant Bhardwaj
Abstract
In recent years, significant advancements in deep learning have transformed image recognition tasks, leading to the development of various architectures aimed at enhancing feature extraction and learning capabilities. Among these novel approaches, the perfect difference network (PDN) stands out, utilizing perfect difference encoding to augment representation power. This survey presents an exhaustive analysis of recent progress in PDNs, specifically focusing on their application in image recognition tasks. The study covers the theoretical foundations of PDNs, encompassing perfect difference encoding and attention mechanisms. It also highlights the latest developments in PDNs such as refined encoding schemes, improved attention mechanisms, and effective transfer learning strategies. To assess the performance of PDNs, a comprehensive comparative analysis is conducted against state-of-the-art architectures, employing diverse image datasets. Additionally, the survey provides real-world case studies and applications that demonstrate the practical implications of PDNs. The conclusion includes discussions on existing challenges, limitations, and potential future directions, underscoring the promising role of PDNs in advancing image recognition capabilities. This survey serves as a valuable resource for researchers and practitioners, offering insights into the current state of PDNs and their practical implications in advancing image recognition technologies.
Abstract
In recent years, significant advancements in deep learning have transformed image recognition tasks, leading to the development of various architectures aimed at enhancing feature extraction and learning capabilities. Among these novel approaches, the perfect difference network (PDN) stands out, utilizing perfect difference encoding to augment representation power. This survey presents an exhaustive analysis of recent progress in PDNs, specifically focusing on their application in image recognition tasks. The study covers the theoretical foundations of PDNs, encompassing perfect difference encoding and attention mechanisms. It also highlights the latest developments in PDNs such as refined encoding schemes, improved attention mechanisms, and effective transfer learning strategies. To assess the performance of PDNs, a comprehensive comparative analysis is conducted against state-of-the-art architectures, employing diverse image datasets. Additionally, the survey provides real-world case studies and applications that demonstrate the practical implications of PDNs. The conclusion includes discussions on existing challenges, limitations, and potential future directions, underscoring the promising role of PDNs in advancing image recognition capabilities. This survey serves as a valuable resource for researchers and practitioners, offering insights into the current state of PDNs and their practical implications in advancing image recognition technologies.
Chapters in this book
- Frontmatter I
- Contents V
- Contributing authors IX
- Synchronizing neural networks, machine learning for medical diagnosis, and patient representation: looping advanced optimization strategies assisting experts for complex mechanisms behind health and disease detection 1
- The future of predictive health: evaluating the role of neural network based hybrid models in healthcare 19
- An overview of new trends on deep learning models for diabetes risk prediction 47
- A study on the detection and diagnosis of cervical cancer using machine and deep learning models 57
- Sentiments and opinions shared on social media during the COVID-19 pandemic using machine learning techniques 71
- Combining decision tree and Bayesian networks for improved predictive analytics 91
- Emerging trends in hybrid information systems modeling in artificial intelligence 115
- Hybrid approaches for improving cybersecurity and network intrusion system 153
- IoT security enhancement through blockchain solutions 167
- Securing cloud data exchange related to IoT devices: key challenges and its machine learning solutions 177
- Hybrid information systems for modeling traffic management and control 201
- Integrative hybrid information systems for enhanced traffic maintenance and control in Bangalore: a synchronized approach 223
- A comprehensive study for weapon detection technologies for surveillance under different YoloV8 models on primary data 241
- Strategic design of asymmetric graphene and ReS2 field-effect transistors using nonlinear optimization and machine learning 269
- Recent advancements in perfect difference networks for image recognition: a survey and analysis 307
- Image to text to speech: a web-based application using optical character recognition and speech synthesis 329
- Biomimicry and nature-inspired solutions for environmental sustainability 343
- Intelligent analysis of flowers and knowledge generation: an empirical study for agriculture 4.0 355
- Harnessing the power of hybrid models for supply chain management and optimization 407
- Optimizing long short-term memory networks for univariate time series forecasting: a comprehensive guide 427
- Optimizing bidirectional long short-term memory networks for univariate time series forecasting: a comprehensive guide 443
- Optimizing convolutional neural networks for univariate time series forecasting: a comprehensive guide 459
- Optimizing gated recurrent unit networks for univariate time series forecasting: a comprehensive guide 473
- Artificial intelligence-based diagnosis and treatment of childhood bronchial allergies 491
- Index 501
Chapters in this book
- Frontmatter I
- Contents V
- Contributing authors IX
- Synchronizing neural networks, machine learning for medical diagnosis, and patient representation: looping advanced optimization strategies assisting experts for complex mechanisms behind health and disease detection 1
- The future of predictive health: evaluating the role of neural network based hybrid models in healthcare 19
- An overview of new trends on deep learning models for diabetes risk prediction 47
- A study on the detection and diagnosis of cervical cancer using machine and deep learning models 57
- Sentiments and opinions shared on social media during the COVID-19 pandemic using machine learning techniques 71
- Combining decision tree and Bayesian networks for improved predictive analytics 91
- Emerging trends in hybrid information systems modeling in artificial intelligence 115
- Hybrid approaches for improving cybersecurity and network intrusion system 153
- IoT security enhancement through blockchain solutions 167
- Securing cloud data exchange related to IoT devices: key challenges and its machine learning solutions 177
- Hybrid information systems for modeling traffic management and control 201
- Integrative hybrid information systems for enhanced traffic maintenance and control in Bangalore: a synchronized approach 223
- A comprehensive study for weapon detection technologies for surveillance under different YoloV8 models on primary data 241
- Strategic design of asymmetric graphene and ReS2 field-effect transistors using nonlinear optimization and machine learning 269
- Recent advancements in perfect difference networks for image recognition: a survey and analysis 307
- Image to text to speech: a web-based application using optical character recognition and speech synthesis 329
- Biomimicry and nature-inspired solutions for environmental sustainability 343
- Intelligent analysis of flowers and knowledge generation: an empirical study for agriculture 4.0 355
- Harnessing the power of hybrid models for supply chain management and optimization 407
- Optimizing long short-term memory networks for univariate time series forecasting: a comprehensive guide 427
- Optimizing bidirectional long short-term memory networks for univariate time series forecasting: a comprehensive guide 443
- Optimizing convolutional neural networks for univariate time series forecasting: a comprehensive guide 459
- Optimizing gated recurrent unit networks for univariate time series forecasting: a comprehensive guide 473
- Artificial intelligence-based diagnosis and treatment of childhood bronchial allergies 491
- Index 501
