Hybrid Information Systems

Non-Linear Optimization Strategies with Artificial Intelligence

Ramakant Bhardwaj, Pushan Kumar Dutta, Pethuru Raj, Abhishek Kumar, Kavita Saini, Alfonso González Briones, Mohammed K.A. Kaabar

• Publisher: De Gruyter
• Year: 2024
• Print ISBN: 9783111329796
• eBook ISBN: 9783111331188
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

The book provides comprehensive and cognitive approach to building and deploying sophisticated information systems. The book utilizes non-linear optimization techniques, fuzzy logic, and rough sets to model various real-world use cases for the digital era. The hybrid information system modeling handles both qualitative and quantitative data and can effectively handle uncertainty and imprecision in the data. The combination of non-linear optimization mechanisms, fuzzy logic, and rough sets provides a robust foundation for next-generation information systems that can fulfill the demands of adaptive, aware, and adroit software applications for the knowledge era. The book emphasizes the importance of the hybrid approach, which combines the strengths of both mathematical and AI techniques, to achieve a more comprehensive and effective modeling process. Hybrid information system modeling techniques combine different approaches, such as fuzzy logic, rough sets, and neural networks, to create models that can handle the complexity and uncertainty of real-world problems. These techniques provide a powerful tool for modeling and analyzing complex systems, and the applications of hybrid information system modeling demonstrate their potential for solving real-world problems in various fields.

Table of contents

• Title Page
• Copyright
• Contents
• Contributing authors
• 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
• The future of predictive health: evaluating the role of neural network based hybrid models in healthcare
• An overview of new trends on deep learning models for diabetes risk prediction
• A study on the detection and diagnosis of cervical cancer using machine and deep learning models
• Sentiments and opinions shared on social media during the COVID-19 pandemic using machine learning techniques
• Combining decision tree and Bayesian networks for improved predictive analytics
• Emerging trends in hybrid information systems modeling in artificial intelligence
• Hybrid approaches for improving cybersecurity and network intrusion system
• IoT security enhancement through blockchain solutions
• Securing cloud data exchange related to IoT devices: key challenges and its machine learning solutions
• Hybrid information systems for modeling traffic management and control
• Integrative hybrid information systems for enhanced traffic maintenance and control in Bangalore: a synchronized approach
• A comprehensive study for weapon detection technologies for surveillance under different YoloV8 models on primary data
• Strategic design of asymmetric graphene and ReS2 field-effect transistors using nonlinear optimization and machine learning
• Recent advancements in perfect difference networks for image recognition: a survey and analysis
• Image to text to speech: a web-based application using optical character recognition and speech synthesis
• Biomimicry and nature-inspired solutions for environmental sustainability
• Intelligent analysis of flowers and knowledge generation: an empirical study for agriculture 4.0
• Harnessing the power of hybrid models for supply chain management and optimization
• Optimizing long short-term memory networks for univariate time series forecasting: a comprehensive guide
• Optimizing bidirectional long short-term memory networks for univariate time series forecasting: a comprehensive guide
• Optimizing convolutional neural networks for univariate time series forecasting: a comprehensive guide
• Optimizing gated recurrent unit networks for univariate time series forecasting: a comprehensive guide
• Artificial intelligence-based diagnosis and treatment of childhood bronchial allergies
• Index