Emerging trends in hybrid information systems modeling in artificial intelligence
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A. Sakshi
Abstract
This chapter provides a comprehensive overview of emerging trends and challenges in developing hybrid information systems for artificial intelligence (AI) applications. Hybrid AI combines multiple techniques like machine learning, rule-based systems, and symbolic AI to create more robust and versatile solutions. The chapter elucidates the growing adoption of ensemble learning to improve model performance by aggregating diverse base model predictions. Combining neural networks with rulebased systems for enhanced decision-making capabilities is also explored. Additionally, the fusion of reinforcement learning and expert systems to enable agents that leverage both exploration-driven learning and domain knowledge is analyzed, along with associated techniques and challenges. For natural language processing, the chapter examines hybrid models capable of deeper language understanding and generation. Within computer vision, augmenting deep learning with traditional techniques for improved efficiency and transparency is discussed. Furthermore, the importance of explainable AI to interpret hybrid system outputs is emphasized, along with emerging techniques. Finally, the chapter addresses benchmarking complex hybrid models, including specialized evaluation metrics beyond accuracy and tailored benchmark datasets. Overall, it provides indispensable insights into the state of the art, open challenges, and interdisciplinary research shaping the future of hybrid AI.
Abstract
This chapter provides a comprehensive overview of emerging trends and challenges in developing hybrid information systems for artificial intelligence (AI) applications. Hybrid AI combines multiple techniques like machine learning, rule-based systems, and symbolic AI to create more robust and versatile solutions. The chapter elucidates the growing adoption of ensemble learning to improve model performance by aggregating diverse base model predictions. Combining neural networks with rulebased systems for enhanced decision-making capabilities is also explored. Additionally, the fusion of reinforcement learning and expert systems to enable agents that leverage both exploration-driven learning and domain knowledge is analyzed, along with associated techniques and challenges. For natural language processing, the chapter examines hybrid models capable of deeper language understanding and generation. Within computer vision, augmenting deep learning with traditional techniques for improved efficiency and transparency is discussed. Furthermore, the importance of explainable AI to interpret hybrid system outputs is emphasized, along with emerging techniques. Finally, the chapter addresses benchmarking complex hybrid models, including specialized evaluation metrics beyond accuracy and tailored benchmark datasets. Overall, it provides indispensable insights into the state of the art, open challenges, and interdisciplinary research shaping the future of hybrid AI.
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
