Soumya Ranjan Nayak – författare

The evolution of deep learning models, combined with with advances in the Internet of Things and sensor technology, has gained more importance for weather forecasting, plant disease detection, underground water detection, soil quality, crop condition monitoring, and many other issues in the field of agriculture. agriculture. Deep Learning for Sustainable Agriculture discusses topics such as the impactful role of deep learning during the analysis of sustainable agriculture data and how deep learning can help farmers make better decisions. It also considers the latest deep learning techniques for effective agriculture data management, as well as the standards established by international organizations in related fields. The book provides advanced students and professionals in agricultural science and engineering, geography, and geospatial technology science with an in-depth explanation of the relationship between agricultural inference and the decision-support amenities offered by an advanced mathematical evolutionary algorithm.

Intelligent Fractal-Based Image Analysis: Application in Pattern Recognition and Machine Vision provides insights into the current strengths and weaknesses of different applications as well as research findings on fractal graphics in engineering and science applications. The book aims to improve the exchange of ideas and coherence between various core computing methods and highlights the relevance of related application areas for advanced as well as novice-user application. The book presents core concepts, methodological aspects, and advanced feature opportunities, focusing on major, real-time applications in engineering and health science. It will appeal to researchers, data scientists, industry professionals, and graduate students.Fractals are infinite, complex patterns used in modeling physical and dynamic systems. Fractal theory research has increased across different fields of applications including engineering science, health science, and social science. Recent literature shows the vital role fractals play in digital image analysis, specifically in biomedical image processing. Fractal graphics is an interdisciplinary field that deals with how computers can be used to gain high-level understanding from digital images. Integrating artificial intelligence with fractal characteristics has resulted in new interdisciplinary research in the fields of pattern recognition and image processing analysis.

Smart Sensor Networks (WSNs) using AI have left a mark on the lives of all by aiding in various sectors, such as manufacturing, education, healthcare, and monitoring of the environment and industries. This book covers recent AI applications and explores aspects of modern sensor technologies and the systems needed to operate them.

The book reviews the fundamental concepts of gathering, processing, and analyzing different AI-based models and methods. It covers recent WSN techniques for the purpose of effective network management on par with the standards laid out by international organizations in related fields and focuses on both core concepts along with major applicational areas.

The book will be used by technical developers, academicians, data sciences, industrial professionals, researchers, and students interested in the latest innovations on problem-oriented processing techniques in sensor networks using IoT and evolutionary computer applications for Industry 4.0.

Smart Sensor Networks (WSNs) using AI have left a mark on the lives of all by aiding in various sectors, such as manufacturing, education, healthcare, and monitoring of the environment and industries. This book covers recent AI applications and explores aspects of modern sensor technologies and the systems needed to operate them.

The book reviews the fundamental concepts of gathering, processing, and analyzing different AI-based models and methods. It covers recent WSN techniques for the purpose of effective network management on par with the standards laid out by international organizations in related fields and focuses on both core concepts along with major applicational areas.

The book will be used by technical developers, academicians, data sciences, industrial professionals, researchers, and students interested in the latest innovations on problem-oriented processing techniques in sensor networks using IoT and evolutionary computer applications for Industry 4.0.

The book aims to develop methodologies and throw light on the advances in empirical research of various machine learning systems through data mining and parallel programming using GPU approaches. It reviews concepts of existing machine learning and deep learning techniques and how these can be implemented in GPU computing with CUDA architecture. The book also discusses modern machine learning techniques for effective big data management in accordance with worldwide standards in the field.

Covering diverse areas, this publication is meant for academicians, data scientists, industrial professionals, researchers and students interested in uncovering the latest innovations in the field.

The book aims to develop methodologies and throw light on the advances in empirical research of various machine learning systems through data mining and parallel programming using GPU approaches. It reviews concepts of existing machine learning and deep learning techniques and how these can be implemented in GPU computing with CUDA architecture. The book also discusses modern machine learning techniques for effective big data management in accordance with worldwide standards in the field.

Covering diverse areas, this publication is meant for academicians, data scientists, industrial professionals, researchers and students interested in uncovering the latest innovations in the field.

This edited book brings together leading researchers, academic scientists and research scholars to put forward and share their experiences and research results on all aspects of an inspection system for detection analysis for various machine vision applications. It also provides a premier interdisciplinary platform to present and discuss the most recent innovations, trends, methodology, applications, and concerns as well as practical challenges encountered and solutions adopted in the inspection system in terms of image processing and analytics of machine vision for real and industrial application.

Machine vision inspection systems (MVIS) utilized all industrial and non-industrial applications where the execution of their utilities based on the acquisition and processing of images. MVIS can be applicable in industry, governmental, defense, aerospace, remote sensing, medical, and academic/education applications but constraints are different. MVIS entails acceptable accuracy, high reliability, high robustness, and low cost. Image processing is a well-defined transformation between human vision and image digitization, and their techniques are the foremost way to experiment in the MVIS. The digital image technique furnishes improved pictorial information by processing the image data through machine vision perception. Digital image pro­cessing has widely been used in MVIS applications and it can be employed to a wide diversity of problems particularly in Non-Destructive testing (NDT), presence/absence detection, defect/fault detection (weld, textile, tiles, wood, etc.,), automated vision test & measurement, pattern matching, optical character recognition & verification (OCR/OCV), barcode reading and traceability, medical diagnosis, weather forecasting, face recognition, defence and space research, etc. This edited book is designed to address various aspects of recent methodologies, concepts and research plan out to the readers for giving more depth insights for perusing research on machine vision using image processing techniques.

This edited book brings together leading researchers, academic scientists and research scholars to put forward and share their experiences and research results on all aspects of an inspection system for detection analysis for various machine vision applications. It also provides a premier interdisciplinary platform to present and discuss the most recent innovations, trends, methodology, applications, and concerns as well as practical challenges encountered and solutions adopted in the inspection system in terms of image processing and analytics of machine vision for real and industrial application.

Machine vision inspection systems (MVIS) utilized all industrial and non-industrial applications where the execution of their utilities based on the acquisition and processing of images. MVIS can be applicable in industry, governmental, defense, aerospace, remote sensing, medical, and academic/education applications but constraints are different. MVIS entails acceptable accuracy, high reliability, high robustness, and low cost. Image processing is a well-defined transformation between human vision and image digitization, and their techniques are the foremost way to experiment in the MVIS. The digital image technique furnishes improved pictorial information by processing the image data through machine vision perception. Digital image pro­cessing has widely been used in MVIS applications and it can be employed to a wide diversity of problems particularly in Non-Destructive testing (NDT), presence/absence detection, defect/fault detection (weld, textile, tiles, wood, etc.,), automated vision test & measurement, pattern matching, optical character recognition & verification (OCR/OCV), barcode reading and traceability, medical diagnosis, weather forecasting, face recognition, defence and space research, etc. This edited book is designed to address various aspects of recent methodologies, concepts and research plan out to the readers for giving more depth insights for perusing research on machine vision using image processing techniques.

Machine Vision Inspection Systems (MVIS) is a multidisciplinary research field that emphasizes image processing, machine vision and, pattern recognition for industrial applications. Inspection techniques are generally used in destructive and non-destructive evaluation industry. Now a day''s the current research on machine inspection gained more popularity among various researchers, because the manual assessment of the inspection may fail and turn into false assessment due to a large number of examining while inspection process.

This volume 2 covers machine learning-based approaches in MVIS applications and it can be employed to a wide diversity of problems particularly in Non-Destructive testing (NDT), presence/absence detection, defect/fault detection (weld, textile, tiles, wood, etc.), automated vision test & measurement, pattern matching, optical character recognition & verification (OCR/OCV), natural language processing, medical diagnosis, etc. This edited book is designed to address various aspects of recent methodologies, concepts, and research plan out to the readers for giving more depth insights for perusing research on machine vision using machine learning-based approaches.

Machine Vision Inspection Systems (MVIS) is a multidisciplinary research field that emphasizes image processing, machine vision and, pattern recognition for industrial applications. Inspection techniques are generally used in destructive and non-destructive evaluation industry. Now a day''s the current research on machine inspection gained more popularity among various researchers, because the manual assessment of the inspection may fail and turn into false assessment due to a large number of examining while inspection process.

This volume 2 covers machine learning-based approaches in MVIS applications and it can be employed to a wide diversity of problems particularly in Non-Destructive testing (NDT), presence/absence detection, defect/fault detection (weld, textile, tiles, wood, etc.), automated vision test & measurement, pattern matching, optical character recognition & verification (OCR/OCV), natural language processing, medical diagnosis, etc. This edited book is designed to address various aspects of recent methodologies, concepts, and research plan out to the readers for giving more depth insights for perusing research on machine vision using machine learning-based approaches.