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"This book will cover topics on many new mathematical models in smart and sustainable cities using big data, visualization tools in mathematical modelling, machine learning based mathematical modelling, models for detecting anomalies in big sensor data, data security and blockchain for big data, mathematical models for epidemic data analysis, ICT platforms for analyzing data in smart cities, privacy and ethics in data analysis, mathematical models in urban environment management and much more"--
A smart city utilizes ICT technologies to improve the working effectiveness, share various data with the citizens, and enhance political assistance and societal wellbeing. The fundamental needs of a smart and sustainable city are utilizing smart technology for enhancing municipal activities, expanding monetary development, and improving citizens’ standards of living. The Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities discusses new mathematical models in smart and sustainable cities using big data, visualization tools in mathematical modeling, machine learning-based mathematical modeling, and more. It further delves into privacy and ethics in data analysis. Covering topics such as deep learning, optimization-based data science, and smart city automation, this premier reference source is an excellent resource for mathematicians, statisticians, computer scientists, civil engineers, government officials, students and educators of higher education, librarians, researchers, and academicians.
The research in this book looks at the likelihood and level of use of implemented technological components with regard to the adoption of different precision agricultural technologies. To identify the variables affecting farmers' choices to embrace more precise technology, zero-inflated Poisson and negative binomial count data regression models are utilized. Outcomes from the count data analysis of a random sample of various farm operators show that various aspects, including farm dimension, farmer demographics, soil texture, urban impacts, farmer position of liabilities, and position of the farm in a state, were significantly associated with the approval severity and likelihood of precision farming technologies. Technical topics discussed in the book include: Precision agriculture Machine learning Wireless sensor networks IoT Deep learning
The main goal of the book is to explore the idea behind data modeling in smart agriculture using information and communication technologies and tools to make agricultural practices more functional, fruitful and profitable. The research in the book looks at the likelihood and level of use of implemented technological components with regard to the adoption of different precision agricultural technologies. To identify the variables affecting farmers’ choices to embrace more precise technology, zero-inflated Poisson and negative binomial count data regression models were utilized. Outcomes from the count data analysis of a random sample of various farm operators show that various aspects, including farm dimension, farmer demographics, soil texture, urban impacts, farmer position of liabilities, and position of the farm in a state, were significantly associated with the approval severity and likelihood of precision farming technologies. Farm management information systems (FMIS) have constantly advanced in complexity as they have incorporated new technology, the most recent of which is the internet. However, few FMIS have fully tapped into the internet’s possibilities, and the newly developing idea of precision agriculture receives little or no support in the FMIS that are now being sold. FMIS for precision agriculture must meet a few more criteria beyond those of regular FMIS, which increases the technological complexity of these systems’ deployment in a number of ways. In order to construct an FMIS that meet these extra needs, the authors here evaluated various cutting-edge web-based methods. The goal was to determine the requirements that precision agriculture placed on FMIS.
The persistent challenge of inequitable access to quality services plagues diverse age groups, creating a glaring gap in our pursuit of inclusive well-being. Despite the revolutionary strides in digital health and artificial intelligence (AI), the promise of universal accessibility remains unfulfilled. The disparities demand a comprehensive understanding of obstacles hindering inclusivity, setting the stage for a transformative solution. Inclusivity and Accessibility in Digital Health is a groundbreaking exploration that is a beacon of change in the healthcare narrative. This book transcends conventional boundaries, offering innovative frameworks, case studies, and empirical research. It delves into the transformative potential of AI and digital health, presenting actionable insights to tailor healthcare services, manage diseases, and elevate overall well-being. Aligned with the United Nations Sustainable Development Goals, this book inspires researchers, healthcare professionals, policymakers, and tech enthusiasts to harness the power of technology for an inclusive healthcare revolution.
The surge in technological advancements, coupled with the exponential growth of data, has left marketers grappling with the need for a paradigm shift. The once-established methods of consumer engagement are now overshadowed by the complexities of the digital age, demanding a profound understanding of artificial intelligence (AI) and data analytics. The gap between academic knowledge and practical applications in the field of marketing has widened, leaving industry professionals, educators, and students seeking a comprehensive resource to navigate the intricacies of this transformative era. AI-Driven Marketing Research and Data Analytics is a groundbreaking book that serves as a beacon for marketers, educators, and industry leaders alike. With a keen focus on the symbiotic relationship between AI, data analytics, and marketing research, this book bridges the gap between theory and practice. It not only explores the historical evolution of marketing but also provides an innovative examination of how AI and data analytics are reshaping the landscape. Through real-time case studies, ethical considerations, and in-depth insights, the book offers a holistic solution to the challenges faced by marketing professionals in the digital age.
Academic scholars and industry professionals alike face the formidable challenge of staying informed about emerging trends and innovations in cloud computing. The expansive realm of cloud technology has been the catalyst for several transformative changes across industries, offering unparalleled opportunities for optimization and innovation. However, even seasoned experts may find themselves daunted by the intricate web of new technologies, including green cloud computing, edge computing, cryptography in the cloud, load balancing strategies, and cloud analytics insights. Emerging Trends in Cloud Computing: Analytics, Scalability, and Service Models provides academic scholars and industry professionals with a comprehensive exploration of these critical cloud computing topics and more. This invaluable resource provides clarity and insight, serving as a guiding beacon in the ever-evolving world of cloud technology. Whether you're seeking to understand the intricacies of cloud security solutions, the nuances of scalability in cloud computing, or the various service models in the cloud, this book empowers you to navigate this dynamic field with confidence and expertise.
Modern businesses face the challenge of how to most effectively harness the power of Artificial Intelligence (AI) to enhance customer engagement and streamline operations. The proliferation of AI tools like ChatGPT offers immense potential. Yet, businesses often need help to navigate the complexities of implementation and maximize the benefits. This gap between AI's promise and its practical application highlights the need for a comprehensive resource that offers practical insights and innovative strategies. Real-Time Data Decisions With AI and ChatGPT Techniques is a groundbreaking book that addresses this critical challenge. By providing a detailed analysis of ChatGPT and other AI tools, this book equips businesses with the knowledge and strategies needed to leverage AI effectively. From algorithmic enhancements to real-world applications, each chapter offers valuable insights and actionable recommendations, making this book an indispensable guide for businesses seeking to capitalize on AI's transformative potential.
Image processing integrates and extracts data from photos for a variety of uses. Applications for image processing are useful in many different disciplines. A few examples include remote sensing, space applications, industrial applications, medical imaging, and military applications. Imaging systems come in many different varieties, including those used for chemical, optical, thermal, medicinal, and molecular imaging. To extract the accurate picture values, scanning methods and statistical analysis must be used for image analysis. Thrust Technologies’ Effect on Image Processing provides insights into image processing and the technologies that can be used to enhance additional information within an image. The book is also a useful resource for researchers to grow their interest and understanding in the burgeoning fields of image processing. Covering key topics such as image augmentation, artificial intelligence, and cloud computing, this premier reference source is ideal for computer scientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.