Application of Machine Learning Models in Agricultural and Meteorological Sciences
Application of Machine Learning Models in Agricultural and Meteorological Sciences

Author: Mohammad Ehteram

Publisher: Springer Nature

Published: 2023-03-21

Total Pages: 201

ISBN-13: 9811997330

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This book is a comprehensive guide for agricultural and meteorological predictions. It presents advanced models for predicting target variables. The different details and conceptions in the modelling process are explained in this book. The models of the current book help better agriculture and irrigation management. The models of the current book are valuable for meteorological organizations. Meteorological and agricultural variables can be accurately estimated with this book's advanced models. Modelers, researchers, farmers, students, and scholars can use the new optimization algorithms and evolutionary machine learning to better plan and manage agriculture fields. Water companies and universities can use this book to develop agricultural and meteorological sciences. The details of the modeling process are explained in this book for modelers. Also this book introduces new and advanced models for predicting hydrological variables. Predicting hydrological variables help water resource planning and management. These models can monitor droughts to avoid water shortage. And this contents can be related to SDG6, clean water and sanitation. The book explains how modelers use evolutionary algorithms to develop machine learning models. The book presents the uncertainty concept in the modeling process. New methods are presented for comparing machine learning models in this book. Models presented in this book can be applied in different fields. Effective strategies are presented for agricultural and water management. The models presented in the book can be applied worldwide and used in any region of the world. The models of the current books are new and advanced. Also, the new optimization algorithms of the current book can be used for solving different and complex problems. This book can be used as a comprehensive handbook in the agricultural and meteorological sciences. This book explains the different levels of the modeling process for scholars.

Application of Machine Learning in Agriculture
Application of Machine Learning in Agriculture

Author: Mohammad Ayoub Khan

Publisher: Academic Press

Published: 2022-05-14

Total Pages: 332

ISBN-13: 0323906680

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Application of Machine Learning in Smart Agriculture is the first book to present a multidisciplinary look at how technology can not only improve agricultural output, but the economic efficiency of that output as well. Through a global lens, the book approaches the subject from a technical perspective, providing important knowledge and insights for effective and efficient implementation and utilization of machine learning. As artificial intelligence techniques are being used to increase yield through optimal planting, fertilizing, irrigation, and harvesting, these are only part of the complex picture which must also take into account the economic investment and its optimized return. The performance of machine learning models improves over time as the various mathematical and statistical models are proven. Presented in three parts, Application of Machine Learning in Smart Agriculture looks at the fundamentals of smart agriculture; the economics of the technology in the agricultural marketplace; and a diverse representation of the tools and techniques currently available, and in development. This book is an important resource for advanced level students and professionals working with artificial intelligence, internet of things, technology and agricultural economics. - Addresses the technology of smart agriculture from a technical perspective - Reveals opportunities for technology to improve and enhance not only yield and quality, but the economic value of a food crop - Discusses physical instruments, simulations, sensors, and markets for machine learning in agriculture

Advances in Computational Intelligence
Advances in Computational Intelligence

Author: Ignacio Rojas

Publisher: Springer Nature

Published: 2023-11-03

Total Pages: 723

ISBN-13: 3031430859

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This two-volume set LNCS 14134 and LNCS 14135 constitutes the refereed proceedings of the 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, held in Ponta Delgada, Portugal, during June 19–21, 2023. The 108 full papers presented in this two-volume set were carefully reviewed and selected from 149 submissions. The papers in Part I are organized in topical sections on advanced topics in computational intelligence; advances in artificial neural networks; ANN HW-accelerators; applications of machine learning in biomedicine and healthcare; and applications of machine learning in time series analysis. The papers in Part II are organized in topical sections on deep learning and applications; deep learning applied to computer vision and robotics; general applications of artificial intelligence; interaction with neural systems in both health and disease; machine learning for 4.0 industry solutions; neural networks in chemistry and material characterization; ordinal classification; real world applications of BCI systems; and spiking neural networks: applications and algorithms.

Deep Learning for Sustainable Agriculture
Deep Learning for Sustainable Agriculture

Author: Ramesh Chandra Poonia

Publisher: Academic Press

Published: 2022-01-09

Total Pages: 408

ISBN-13: 0323903622

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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. - Introduces new deep learning models developed to address sustainable solutions for issues related to agriculture - Provides reviews on the latest intelligent technologies and algorithms related to the state-of-the-art methodologies of monitoring and mitigation of sustainable agriculture - Illustrates through case studies how deep learning has been used to address a variety of agricultural diseases that are currently on the cutting edge - Delivers an accessible explanation of artificial intelligence algorithms, making it easier for the reader to implement or use them in their own agricultural domain

Uncertainty in Computational Intelligence-Based Decision Making
Uncertainty in Computational Intelligence-Based Decision Making

Author: Ali Ahmadian

Publisher: Elsevier

Published: 2024-09-16

Total Pages: 340

ISBN-13: 044321476X

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Uncertainty in Computational Intelligence-Based Decision-Making focuses on techniques for reasoning and decision-making under uncertainty that are used to solve issues in artificial intelligence (AI). It covers a wide range of subjects, including knowledge acquisition and automated model construction, pattern recognition, machine learning, natural language processing, decision analysis, and decision support systems, among others. The first chapter of this book provides a thorough introduction to the topics of causation in Bayesian belief networks, applications of uncertainty, automated model construction and learning, graphic models for inference and decision making, and qualitative reasoning. The following chapters examine the fundamental models of computational techniques, computational modeling of biological and natural intelligent systems, including swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems, and evolutionary computation. They also examine decision making and analysis, expert systems, and robotics in the context of artificial intelligence and computer science. - Provides readers a thorough understanding of the uncertainty that arises in artificial intelligence (AI), computational intelligence (CI) paradigms, and algorithms - Encourages readers to put concepts into practice and solve complex real-world problems using CI development frameworks like decision support systems and visual decision design - Provides a comprehensive overview of the techniques used in computational intelligence, uncertainty, and decision

Advancements in Climate and Smart Environment Technology
Advancements in Climate and Smart Environment Technology

Author: Mabrouki, Jamal

Publisher: IGI Global

Published: 2024-05-06

Total Pages: 309

ISBN-13:

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The world faces escalating environmental and healthcare challenges, from climate change to managing natural resources and providing efficient medical services. These issues are complex, often requiring intricate modeling and intervention from domain experts. Traditional analytical methods need help to cope with the complexity and scale of these challenges, leading to inefficiencies and suboptimal outcomes. There is a pressing need for innovative solutions that can enhance our ability to address these issues effectively. Advancements in Climate and Smart Environment Technology present a compelling solution to these pressing problems. By leveraging the power of artificial intelligence (AI) techniques, we offer a path toward more efficient and effective solutions in environmental engineering, healthcare management, and natural resource conservation. AI provides the tools to model complex systems, optimize processes, and make informed decisions without constant expert intervention, thus revolutionizing these fields. This book is a comprehensive guide for scholars, researchers, and practitioners in various fields related to environmental and healthcare sciences. It explores the applications of AI in areas such as innovative environments, sustainable agriculture, climate change mitigation, and healthcare delivery.

Data Science and Interdisciplinary Research: Recent Trends and Applications
Data Science and Interdisciplinary Research: Recent Trends and Applications

Author: Brojo Kishore Mishra

Publisher: Bentham Science Publishers

Published: 2023-09-27

Total Pages: 260

ISBN-13: 9815079018

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Data Science and Interdisciplinary Research: Recent Trends and Applications is a compelling edited volume that offers a comprehensive exploration of the latest advancements in data science and interdisciplinary research. Through a collection of 10 insightful chapters, this book showcases diverse models of machine learning, communications, signal processing, and data analysis, illustrating their relevance in various fields. Key Themes: Advanced Rainfall Prediction: Presents a machine learning model designed to tackle the challenging task of predicting rainfall across multiple countries, showcasing its potential to enhance weather forecasting. Efficient Cloud Data Clustering: Explains a novel computational approach for clustering large-scale cloud data, addressing the scalability of cloud computing and data analysis. Secure In-Vehicle Communication: Explores the critical topic of secure communication in in-vehicle networks, emphasizing message authentication and data integrity. Smart Irrigation 4.0: Details a decision model designed for smart irrigation, integrating agricultural sensor data reliability analysis to optimize water usage in precision agriculture. Smart Electricity Monitoring: Highlights machine learning-based smart electricity monitoring and fault detection systems, contributing to the development of smart cities. Enhanced Learning Environments: Investigates the effectiveness of mobile learning in higher education, shedding light on the role of technology in shaping modern learning environments. Coastal Socio-Economy Study: Presents a case study on the socio-economic conditions of coastal fishing communities, offering insights into the livelihoods and challenges they face. Signal Noise Removal: Shows filtering techniques for removing noise from ECG signals, enhancing the accuracy of medical data analysis and diagnosis. Deep Learning in Biomedical Research: Explores deep learning techniques for biomedical research, particularly in the realm of gene identification using Next Generation Sequencing (NGS) data. Medical Diagnosis through Machine Learning: Concludes with a chapter on breast cancer detection using machine learning concepts, demonstrating the potential of AI-driven diagnostics.

Eddy Covariance Method For Scientific, Regulatory, and Commercial Applications
Eddy Covariance Method For Scientific, Regulatory, and Commercial Applications

Author: George Burba

Publisher: LI-COR Biosciences

Published: 2022-09-06

Total Pages: 702

ISBN-13: 0578977141

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Eddy covariance method is a modern high-precision technique for direct measurements of the movement of gases, water vapor, heat, and momentum between the surface and the atmosphere. It is used for measurements of carbon sequestration and emission rates in natural, agricultural, industrial, and urban environments, atmospheric exchange rates of greenhouse gases, direct mea­surements of evapotranspirative water loss, heat exchange, turbulence rates, and momentum fluxes. The wide range of applications includes numerous fundamental and applied sciences, regulatory and industrial monitoring, multiple aspects of agricultural management, as well as carbon trading and offsets, corporate sustainability and neutrality, among many others. The book “Eddy Covariance Method for Scientific, Regulatory, and Commercial Applications” has been created to familiarize the reader with the general theoretical principles, requirements, applica­tions, planning, processing, and analysis steps of the eddy covariance method. It is intended to assist readers in furthering their understanding of the method and provide refer­ences such as academic textbooks, flux network guidelines, and journal papers. In particular, it is designed to help scientific, industrial, agricultural, and regulatory projects and monitoring programs with experiment design and field deployment of the eddy covariance method. Some of the topics covered in “Eddy Covariance Method for Scientific, Regulatory, and Commercial Applications” include: · Overview of eddy covariance principles · Planning and design of an eddy covariance experiment · Implementation of an eddy covariance experiment · Processing and analysis of eddy covariance data · Networking multiple flux stations · Alternative flux methods · Useful resources, training and knowledge base · Example of planning, design and implementation of a comprehensive automated flux station

Machine Learning Applications
Machine Learning Applications

Author: Indranath Chatterjee

Publisher: John Wiley & Sons

Published: 2023-12-08

Total Pages: 244

ISBN-13: 1394173342

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Machine Learning Applications Practical resource on the importance of Machine Learning and Deep Learning applications in various technologies and real-world situations Machine Learning Applications discusses methodological advancements of machine learning and deep learning, presents applications in image processing, including face and vehicle detection, image classification, object detection, image segmentation, and delivers real-world applications in healthcare to identify diseases and diagnosis, such as creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader’s active learning. Composed of 13 chapters, this book also introduces real-world applications of machine and deep learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space exploration. The book describes the importance of each subject area and detail why they are so important to us from a societal and human perspective. Edited by two highly qualified academics and contributed to by established thought leaders in their respective fields, Machine Learning Applications includes information on: Content based medical image retrieval (CBMIR), covering face and vehicle detection, multi-resolution and multisource analysis, manifold and image processing, and morphological processing Smart medicine, including machine learning and artificial intelligence in medicine, risk identification, tailored interventions, and association rules AI and robotics application for transportation and infrastructure (e.g., autonomous cars and smart cities), along with global warming and climate change Identifying diseases and diagnosis, drug discovery and manufacturing, medical imaging diagnosis, personalized medicine, and smart health records With its practical approach to the subject, Machine Learning Applications is an ideal resource for professionals working with smart technologies such as machine and deep learning, AI, IoT, and other wireless communications; it is also highly suitable for professionals working in robotics, computer vision, cyber security and more.

Machine Learning with SVM and Other Kernel Methods
Machine Learning with SVM and Other Kernel Methods

Author: K.P. Soman

Publisher: PHI Learning Pvt. Ltd.

Published: 2009-02-02

Total Pages: 495

ISBN-13: 8120334353

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Support vector machines (SVMs) represent a breakthrough in the theory of learning systems. It is a new generation of learning algorithms based on recent advances in statistical learning theory. Designed for the undergraduate students of computer science and engineering, this book provides a comprehensive introduction to the state-of-the-art algorithm and techniques in this field. It covers most of the well known algorithms supplemented with code and data. One Class, Multiclass and hierarchical SVMs are included which will help the students to solve any pattern classification problems with ease and that too in Excel. KEY FEATURES  Extensive coverage of Lagrangian duality and iterative methods for optimization  Separate chapters on kernel based spectral clustering, text mining, and other applications in computational linguistics and speech processing  A chapter on latest sequential minimization algorithms and its modifications to do online learning  Step-by-step method of solving the SVM based classification problem in Excel.  Kernel versions of PCA, CCA and ICA The CD accompanying the book includes animations on solving SVM training problem in Microsoft EXCEL and by using SVMLight software . In addition, Matlab codes are given for all the formulations of SVM along with the data sets mentioned in the exercise section of each chapter.