Machine Learning for Environmental Monitoring in Wireless Sensor Networks

Parikshit N. Mahalle

Today, data fuels everything we do in a highly connected world. However, traditional environmental monitoring methods often fail to provide timely and accurate data for effective decision-making in today's rapidly changing ecosystems. The reliance on manual data collection and outdated technologies results in gaps in data coverage, making it challenging to detect and respond to environmental changes in real time. Additionally, integration between monitoring systems and advanced data analysis tools is necessary to derive actionable insights from collected data. As a result, environmental managers and policymakers face significant challenges in effectively monitoring, managing, and conserving natural resources in a rapidly evolving environment. Machine Learning for Environmental Monitoring in Wireless Sensor Networks offers a comprehensive solution to the limitations of traditional environmental monitoring methods. By harnessing the power of Wireless Sensor Networks (WSNs) and advanced machine learning algorithms, this book presents a novel approach to ecological monitoring that enables real-time, high-resolution data collection and analysis. By integrating WSNs and machine learning, environmental stakeholders can gain deeper insights into complex ecological processes, allowing for more informed decision-making and proactive management of natural resources.

  • Format: Hardback
  • ISBN: 9798369339404
  • Publication Date: Jul 2024
  • Availability: Not Yet Available - Pre-Order Now

Mitigating Cyber Threats Through Machine Learning

Ajmery Sultana

In today's digital landscape, cyber threats are becoming increasingly sophisticated, challenging traditional cybersecurity measures. Organizations face the daunting task of protecting their systems and data from cyber attacks that can result in financial loss, reputational damage, and even compromise of sensitive information. While effective to a certain extent, traditional cybersecurity approaches are often reactive and need help keeping up with the dynamic nature of modern threats. Mitigating Cyber Threats Through Machine Learning offers a comprehensive solution by exploring the integration of machine learning to fortify and enhance cybersecurity measures. This book aims to advance the field and empower readers to adopt responsible and privacy-conscious approaches by providing insights into the practical applications and challenges of integrating machine learning into cybersecurity. It benefits cybersecurity professionals seeking to enhance digital defenses and researchers and academics interested in staying abreast of the latest developments.

  • Format: Hardback
  • ISBN: 9798369331163
  • Publication Date: Jun 2024
  • Availability: Not Yet Available - Pre-Order Now

Cutting-Edge Techniques for Effective Data Mining and Machine Learning

Kenwright

In the dynamic realm of data mining and machine learning, staying ahead requires more than just knowledge—it demands understanding the latest advancements, practical applications, ethical considerations, and future trends. The gap between theory and practice poses a challenge, and the potential biases, privacy concerns, and lack of interpretability in algorithms further complicate matters. Cutting-Edge Techniques for Effective Data Mining and Machine Learning delves into the algorithms of data-driven decision-making, gaining an understanding of the methodologies propelling the field forward. This book bridges theoretical concepts and practical applications, offering a holistic perspective that connects the theoretical underpinnings of advanced algorithms with their tangible impact on real-world scenarios. This book emphasizes cross-disciplinary collaboration, and features contributions from diverse industries and disciplines. These diverse voices dive into how data mining and machine learning transcend conventional boundaries, fostering innovation and insights across sectors. Ethical considerations are at the forefront, addressing potential biases, privacy concerns, and fairness issues inherent in these technologies. The book sheds light on these challenges and presents strategies for responsible artificial intelligence (AI) practices. This book is ideal for researchers, practitioners, educators, and students.

  • Format: Hardback
  • ISBN: 9798369316061
  • Publication Date: Jun 2024
  • Availability: Not Yet Available - Pre-Order Now

Reshaping Environmental Science Through Machine Learning and IoT

Gupta

In the face of escalating environmental challenges such as climate change, air and water pollution, and natural disasters, traditional approaches to understanding and addressing these issues have yet to be proven sufficient. Academic scholars are compelled to seek innovative solutions that marry digital intelligence and natural ecosystems. Reshaping Environmental Science Through Machine Learning and IoT serves as a comprehensive exploration into the transformative potential of Machine Learning (ML) and the Internet of Things (IoT) to address critical environmental challenges. The book establishes a robust foundation in ML and IoT, explaining their relevance to environmental science. As the narrative unfolds, it delves into diverse applications, providing theoretical insights alongside practical knowledge. From interpreting weather patterns to predicting air and water quality, the book navigates through the intricate web of environmental complexities. Notably, it unveils approaches to disaster management, waste sorting, and climate change monitoring, showcasing the symbiotic relationship between digital intelligence and natural ecosystems. This book is ideal for audiences from students and researchers to data scientists and disaster management professionals with a nuanced understanding of IoT, ML, and Artificial Intelligence (AI).

  • Format: Hardback
  • ISBN: 9798369323519
  • Publication Date: Jun 2024
  • Availability: Not Yet Available - Pre-Order Now

Machine Learning and Computer Vision for Renewable Energy

Acharjya

As the world grapples with the urgent need for sustainable energy solutions, the limitations of traditional approaches to renewable energy forecasting become increasingly evident. The demand for more accurate predictions in net load forecasting, line loss predictions, and the seamless integration of hybrid solar and battery storage systems is more critical than ever. In response to this challenge, advanced Artificial Intelligence (AI) techniques are emerging as a solution, promising to revolutionize the renewable energy landscape. Machine Learning and Computer Vision for Renewable Energy presents a deep exploration of AI modeling, analysis, performance prediction, and control approaches dedicated to overcoming the pressing issues in renewable energy systems. Transitioning from the complexities of energy prediction to the promise of advanced technology, the book sets its sights on the game-changing potential of computer vision (CV) in the realm of renewable energy. Amidst the struggle to enhance sustainability across industries, CV technology emerges as a powerful ally, collecting invaluable data from digital photos and videos. This data proves instrumental in achieving better energy management, predicting factors affecting renewable energy, and optimizing overall sustainability. Readers, including researchers, academicians, and students, will find themselves immersed in a comprehensive understanding of the AI approaches and CV methodologies that hold the key to resolving the challenges faced by renewable energy systems.

  • Format: Hardback
  • ISBN: 9798369323557
  • Publication Date: May 2024
  • Availability: Not Yet Available - Pre-Order Now

AI, Machine Learning, and IoT for Communication and Medical Applications

Altaf O Mulani

The integration of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) into communication and healthcare systems presents a complex array of challenges. From data security and privacy concerns to ethical dilemmas and the need for regulatory frameworks, the adoption of these transformative technologies requires careful consideration and expertise. Additionally, the rapid pace of technological advancement often needs to improve the understanding and implementation of these innovations, leading to inefficiencies and missed opportunities in communication and medical practices. AI, Machine Learning, and IoT for Communication and Medical Applications offers a comprehensive solution to these challenges, providing a deep dive into the applications, challenges, and implications of AI, ML, and IoT in communication and healthcare. By combining theoretical insights with practical examples, this book equips professionals and researchers with the knowledge and tools needed to navigate the complexities of these technologies. With a focus on practical applications and real-world scenarios, this book is a valuable resource for implementing AI, ML, and IoT solutions in communication systems and medical practices.

  • Format: Hardback
  • ISBN: 9798369338643
  • Publication Date: May 2024
  • Availability: Not Yet Available - Pre-Order Now

Machine Learning Techniques and Industry Applications

Pramod Kumar Srivastava

In today's rapidly evolving world, the exponential growth of data poses a significant challenge. As data volumes increase, traditional methods of analysis and decision-making become inadequate. This surge in data complexity calls for innovative solutions that efficiently extract meaningful insights. Machine learning has emerged as a powerful tool to address this challenge, offering algorithms and techniques to analyze large datasets and uncover hidden patterns, trends, and correlations. Machine Learning Techniques and Industry Applications demystifies machine learning through detailed explanations, examples, and case studies, making it accessible to a broad audience. Whether you're a student, researcher, or practitioner, this book equips you with the knowledge and skills needed to harness the power of machine learning to address diverse challenges. From e-government to healthcare, cyber-physical systems to agriculture, this book explores how machine learning can drive innovation and sustainable development.

  • Format: Hardback
  • ISBN: 9798369352717
  • Publication Date: May 2024
  • Availability: Not Yet Available - Pre-Order Now

Algorithmic Mathematics in Machine Learning

Bastian Bohn

  • Format: Paperback
  • ISBN: 9781611977875
  • Publication Date: Apr 2024
  • Availability: Not Yet Available - Pre-Order Now

Methodologies, Frameworks, and Applications of Machine Learning

Srivastava

Technology is constantly evolving, and machine learning is positioned to become a pivotal tool with the power to transform industries and revolutionize everyday life. This book underscores the urgency of leveraging the latest machine learning methodologies and theoretical advancements, all while harnessing a wealth of realistic data and affordable computational resources. Machine learning is no longer confined to theoretical domains; it is now a vital component in healthcare, manufacturing, education, finance, law enforcement, and marketing, ushering in an era of data-driven decision-making. Academic scholars seeking to unlock the potential of machine learning in the context of Industry 5.0 and advanced IoT applications will find that the groundbreaking book, Methodologies, Frameworks, and Applications of Machine Learning , introduces an unmissable opportunity to delve into the forefront of modern research and application. This book offers a wealth of knowledge and practical insights across a wide array of topics, ranging from conceptual frameworks and methodological approaches to the application of probability theory, statistical techniques, and machine learning in domains as diverse as e-government, healthcare, cyber-physical systems, and sustainable development, this comprehensive guide equips you with the tools to navigate the complexities of Industry 5.0 and the Internet of Things (IoT).

  • Format: Hardback
  • ISBN: 9798369310625
  • Publication Date: Apr 2024
  • Availability: Not Yet Available - Pre-Order Now

Modeling, Simulation, and Control of AI Robotics and Autonomous Systems

Tanupriya Choudhury

The chasm between the physical capabilities of Intelligent Robotics and Autonomous Systems (IRAS) and their cognitive potential presents a formidable challenge. While these machines exhibit astonishing strength, precision, and speed, their intelligence and adaptability lag far behind. This inherent limitation obstructs the realization of autonomous systems that could reshape industries, from self-driving vehicles to industrial automation. The solution to this dilemma is unveiled within the pages of Modeling, Simulation, and Control of AI Robotics and Autonomous Systems . Find within the pages of this book answers for the cognitive deficit within IRAS. While these systems boast remarkable physical capabilities, their potential for intelligent decision-making and adaptation remains stunted, thereby bringing innovation to a halt. Solving this issue would mean the re-acceleration of multiple industries that could utilize automation to prevent humans from needing to do work that is dangerous, and could revolutionize transportation, and more.

  • Format: Hardback
  • ISBN: 9798369319628
  • Publication Date: Apr 2024
  • Availability: Not Yet Available - Pre-Order Now

Using Machine Learning to Detect Emotions and Predict Human Psychology

Rai

In the realm of analyzing human emotions through Artificial Intelligence (AI), a myriad of challenges persist. From the intricate nuances of emotional subtleties to the broader concerns of ethical considerations, privacy implications, and the ongoing battle against bias, AI faces a complex landscape when venturing into the understanding of human emotions. These challenges underscore the intricate balance required to navigate the human psyche with accuracy. The book, Using Machine Learning to Detect Emotions and Predict Human Psychology , serves as a guide for innovative solutions in the field of emotion detection through AI. It explores facial expression analysis, where AI decodes real-time emotions through subtle cues such as eyebrow movements and micro-expressions. In speech and voice analysis, the book unveils how AI processes vocal nuances to discern emotions, considering elements like tone, pitch, and language intricacies. Additionally, the power of text analysis is of great importance, revealing how AI extracts emotional tones from diverse textual communications. By weaving these systems together, the book offers a holistic solution to the challenges faced by AI in understanding the complex landscape of human emotions.

  • Format: Hardback
  • ISBN: 9798369319109
  • Publication Date: Apr 2024
  • Availability: In Stock - Despatched Within 5 Working Days

Applying Machine Learning Techniques to Bioinformatics

Lilhore

Why are cutting-edge data science techniques such as bioinformatics, few-shot learning, and zero-shot learning underutilized in the world of biological sciences?. In a rapidly advancing field, the failure to harness the full potential of these disciplines limits scientists' ability to unlock critical insights into biological systems, personalized medicine, and biomarker identification. This untapped potential hinders progress and limits our capacity to tackle complex biological challenges. The solution to this issue lies within the pages of Applying Machine Learning Techniques to Bioinformatics . This book serves as a powerful resource, offering a comprehensive analysis of how these emerging disciplines can be effectively applied to the realm of biological research. By addressing these challenges and providing in-depth case studies and practical implementations, the book equips researchers, scientists, and curious minds with the knowledge and techniques needed to navigate the ever-changing landscape of bioinformatics and machine learning within the biological sciences.

  • Format: Hardback
  • ISBN: 9798369318225
  • Publication Date: Apr 2024
  • Availability: Not Yet Available - Pre-Order Now

Machine Learning for Asset Pricing and Management

Henry Schellhorn

  • Format: Paperback
  • ISBN: 9781611977899
  • Publication Date: Apr 2024
  • Availability: Not Yet Available - Pre-Order Now

Innovative Machine Learning Applications for Cryptography

Ruth

Data security is paramount in our modern world, and the symbiotic relationship between machine learning and cryptography has recently taken center stage. The vulnerability of traditional cryptosystems to human error and evolving cyber threats is a pressing concern. The stakes are higher than ever, and the need for innovative solutions to safeguard sensitive information is undeniable. Innovative Machine Learning Applications for Cryptography emerges as a steadfast resource in this landscape of uncertainty. Machine learning's prowess in scrutinizing data trends, identifying vulnerabilities, and constructing adaptive analytical models offers a compelling solution. The book explores how machine learning can automate the process of constructing analytical models, providing a continuous learning mechanism to protect against an ever-increasing influx of data. This book goes beyond theoretical exploration, and provides a comprehensive resource designed to empower academic scholars, specialists, and students in the fields of cryptography, machine learning, and network security. Its broad scope encompasses encryption, algorithms, security, and more unconventional topics like Quantum Cryptography, Biological Cryptography, and Neural Cryptography. By examining data patterns and identifying vulnerabilities, it equips its readers with actionable insights and strategies that can protect organizations from the dire consequences of security breaches.

  • Format: Hardback
  • ISBN: 9798369316429
  • Publication Date: Mar 2024
  • Availability: In Stock - Despatched Within 5 Working Days

Machine and Deep Learning Techniques for Emotion Detection

Mritunjay Rai

Computer understanding of human emotions has become crucial and complex within the era of digital interaction and artificial intelligence. Emotion detection, a field within AI, holds promise for enhancing user experiences, personalizing services, and revolutionizing industries. However, navigating this landscape requires a deep understanding of machine and deep learning techniques and the interdisciplinary challenges accompanying them. Machine and Deep Learning Techniques for Emotion Detection offer a comprehensive solution to this pressing problem. Designed for academic scholars, practitioners, and students, it is a guiding light through the intricate terrain of emotion detection. By blending theoretical insights with practical implementations and real-world case studies, our book equips readers with the knowledge and tools needed to advance the frontier of emotion analysis using machine and deep learning methodologies.

  • Format: Hardback
  • ISBN: 9798369341438
  • Publication Date: Mar 2024
  • Availability: Not Yet Available - Pre-Order Now

Uncertain Spatiotemporal Data Management for the Semantic Web

Bai

In the world of data management, one of the most formidable challenges faced by academic scholars is the effective handling of spatiotemporal data within the semantic web. As our world continues to change dynamically with time, nearly every aspect of our lives, from environmental monitoring to urban planning and beyond, is intrinsically linked to time and space. This synergy has given rise to an avalanche of spatiotemporal data, and the pressing question is how to manage, model, and query this voluminous information effectively. The existing approaches often fall short in addressing the intricacies and uncertainties that come with spatiotemporal data, leaving scholars struggling to unlock its full potential. Uncertain Spatiotemporal Data Management for the Semantic Web is the definitive solution to the challenges faced by academic scholars in the realm of spatiotemporal data. This book offers a visionary approach to an all-encompassing guide in modeling and querying spatiotemporal data using innovative technologies like XML and RDF. Through a meticulously crafted set of chapters, this book sheds light on the nuances of spatiotemporal data and also provides practical solutions that empower scholars to navigate the complexities of this domain effectively.

  • Format: Hardback
  • ISBN: 9781668491089
  • Publication Date: Mar 2024
  • Availability: In Stock - Despatched Within 5 Working Days

Plunkett's Artificial Intelligence (AI) & Machine Learning Industry Almanac 2024

Jack W. Plunkett

  • Format: Paperback
  • ISBN: 9781647880231
  • Publication Date: Mar 2024
  • Availability: Not Yet Available - Pre-Order Now

Machine Learning and Data Science Techniques for Effective Government Service Delivery

Olalekan Samuel Ogunleye

In our data-rich era, extracting meaningful insights from the vast amount of information has become a crucial challenge, especially in government service delivery where informed decisions are paramount. Traditional approaches struggle with the enormity of data, highlighting the need for a new approach that integrates data science and machine learning. The book, Machine Learning and Data Science Techniques for Effective Government Service Delivery , becomes a vital resource in this transformation, offering a deep understanding of these technologies and their applications. Within the complex landscape of modern governance, this book stands as a solution-oriented guide. Recognizing data's value in the 21st century, it navigates the world of data science and machine learning, enhancing the mechanics of government service. By addressing citizens' evolving needs, these advanced methods counter inefficiencies in traditional systems. Tailored for experts across technology, academia, and government, the book bridges theory and practicality. Covering foundational concepts and innovative applications, it explores the potential of data-driven decision-making for a more efficient and citizen-centric government future.

  • Format: Hardback
  • ISBN: 9781668497166
  • Publication Date: Mar 2024
  • Availability: Not Yet Available - Pre-Order Now

Deep Learning, Reinforcement Learning, and the Rise of Intelligent Systems

Uddin

The applications of rapidly advancing intelligent systems are so varied that many are still yet to be discovered. There is often a disconnect between experts in computer science, artificial intelligence, machine learning, robotics, and other specialties, which inhibits the potential for the expansion of this technology and its many benefits. A resource that encourages interdisciplinary collaboration is needed to bridge the gap between these respected leaders of their own fields. Deep Learning, Reinforcement Learning, and the Rise of Intelligent Systems represents an exploration of the forefront of artificial intelligence, navigating the complexities of this field and its many applications. This guide expertly navigates through the intricate domains of deep learning and reinforcement learning, offering an in-depth journey through foundational principles, advanced methodologies, and cutting-edge algorithms shaping the trajectory of intelligent systems. The book covers an introduction to artificial intelligence and its subfields, foundational aspects of deep learning, a demystification of the architecture of neural networks, the mechanics of backpropagation, and the intricacies of critical elements such as activation and loss functions. The book serves as a valuable educational resource for professionals. Its structured approach makes it an ideal reference for students, researchers, and industry professionals.

  • Format: Hardback
  • ISBN: 9798369317389
  • Publication Date: Mar 2024
  • Availability: In Stock - Despatched Within 5 Working Days

Handbook of Research on Integrating Machine Learning Into HPC-Based Simulations and Analytics

Belgacem Ben Youssef

Researchers are increasingly using machine learning (ML) models to analyze data and simulate complex systems and phenomena. Small-scale computing systems used for training, validation, and testing of these ML models are no longer sufficient for grand-challenge problems characterized by large volumes of data generated at a much higher rate than before, surpassing by far the computing capabilities currently available in many cyberinfrastructure platforms. By associating high-performance computing (HPC) with ML environments, scientists and engineers would be able to enhance not only the scalability but also the performance of their predictive ML models. The Handbook of Research on Integrating Machine Learning Into HPC-Based Simulations and Analytics presents recent research efforts in designing and using ML techniques on HPC systems and discusses some of the results achieved thus far by cutting-edge relevant contributions. Covering topics such as data analytics, deep learning, and networking, this major reference work is ideal for computer scientists, academicians, engineers, researchers, scholars, practitioners, librarians, instructors, and students.

  • Format: Hardback
  • ISBN: 9781668437957
  • Publication Date: Mar 2024
  • Availability: Not Yet Available - Pre-Order Now