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Revolutionizing Industrial Automation Through the Convergence of Artificial Intelligence and the Internet of Things
With the development of innovative technologies, the study of industrial automation is obtaining extraordinary attention from academia, governments, researchers, and various industrial communities. The technical innovations in advanced technologies such as deep learning, blockchain, artificial intelligence of things (AIOT), and more have unlocked the potential for bringing intelligent automation and efficiency to the industries to control their various operations. These new technologies provide an efficient and secure way for industries to manage and develop products intelligently. Revolutionizing Industrial Automation Through the Convergence of Artificial Intelligence and the Internet of Th...
Revolutionizing Industrial Automation Through the Convergence of Artificial Intelligence and the Internet of Things
"This book's aim is to include recent advancements in exploring and developing Artificial Intelligence of Things -powered strategies and mechanisms for future industrial automation and transforming industrial functions and architectures to help and improve various industrial operations"--
Climate Change and the Microbiome
This book highlights the impact of climate change on the soil microbiome and its subsequent effects on plant health, soil-plant dynamics, and the ecosphere. It also discusses emerging ideas to counteract these effects, e.g., through agricultural applications of functional microbes, to ensure a sustainable ecosystem. Climate change is altering the soil microbiome distributions and thus the interactions in microbiome and plant‐soil microorganism. Improvement of our understanding of microbe-microbe and plant-microbe interaction under changing climatic conditions is essential, because the overall impact of these interactions under varying adverse environmental conditions is lacking. This book ...
Fungal diversity, ecology and control management
This edited volume provides comprehensive and latest information on the fungal biodiversity in its morphological characters, bioactive molecules, pathogenicity and virulence, and its impacts on crop production and sustainable management of agricultural productivity towards resolving global food security issues. The increasing number of infectious fungal diseases are regarded as threats to agricultural productivity and global food security. The efforts done by scientists to inventories the fungal diversity and identification of fungal species contributing as pathogens towards many plant and human diseases have been compiled in the present volume. The identification of the potential fungal pat...
Soil Nitrogen Ecology
This book highlights the latest discoveries about the nitrogen cycle in the soil. It introduces the concept of nitrogen fixation and covers important aspects of nitrogen in soil and ecology such as its distribution and occurrence, soil microflora and fauna and their role in N-fixation. The importance of plant growth-promoting microbes for a sustainable agriculture, e.g. arbuscular mycorrhizae in N-fixation, is discussed as well as perspectives of metagenomics, microbe-plant signal transduction in N-ecology and related aspects. This book enables the reader to bridge the main gaps in knowledge and carefully presents perspectives on the ecology of biotransformations of nitrogen in soil.
Advancements in Interdisciplinary Research
This volume constitutes selected and revised papers presented at the First International Conference on Advancements in Interdisciplinary Research, AIR 2022, held in Allahabad, India, in May 2022. The 49 papers were thoroughly reviewed and selected from the 252 submissions. They are organized in topical sections on novel technologies enabled secured privacy models and optimized networking infrastructures toward secure industries; developments towards sustainable healthcare sector; machine learning and deep learning enabled applications in different sectors; robotics and computer vision for intelligent automation in industries; trending technologies: frameworks and applications focusing real life issues.
Convergence of Deep Learning and Internet of Things: Computing and Technology
Digital technology has enabled a number of internet-enabled devices that generate huge volumes of data from different systems. This large amount of heterogeneous data requires efficient data collection, processing, and analytical methods. Deep Learning is one of the latest efficient and feasible solutions that enable smart devices to function independently with a decision-making support system. Convergence of Deep Learning and Internet of Things: Computing and Technology contributes to technology and methodology perspectives in the incorporation of deep learning approaches in solving a wide range of issues in the IoT domain to identify, optimize, predict, forecast, and control emerging IoT systems. Covering topics such as data quality, edge computing, and attach detection and prediction, this premier reference source is a comprehensive resource for electricians, communications specialists, mechanical engineers, civil engineers, computer scientists, students and educators of higher education, librarians, researchers, and academicians.
Multidisciplinary Applications of Deep Learning-Based Artificial Emotional Intelligence
Emotional intelligence has emerged as an important area of research in the artificial intelligence field as it covers a wide range of real-life domains. Though machines may never need all the emotional skills that people need, there is evidence to suggest that machines require at least some of these skills to appear intelligent when interacting with people. To understand how deep learning-based emotional intelligence can be applied and utilized across industries, further study on its opportunities and future directions is required. Multidisciplinary Applications of Deep Learning-Based Artificial Emotional Intelligence explores artificial intelligence applications, such as machine and deep le...
Controlling Epidemics With Mathematical and Machine Learning Models
Communicable diseases have been an important part of human history. Epidemics afflicted populations, causing many deaths before gradually fading away and emerging again years after. Epidemics of infectious diseases are occurring more often, and spreading faster and further than ever, in many different regions of the world. The scientific community, in addition to its accelerated efforts to develop an effective treatment and vaccination, is also playing an important role in advising policymakers on possible non-pharmacological approaches to limit the catastrophic impact of epidemics using mathematical and machine learning models. Controlling Epidemics With Mathematical and Machine Learning Mo...
Constraint Decision-Making Systems in Engineering
In recent years, most applications deal with constraint decision-making systems as problems are based on imprecise information and parameters. It is difficult to understand the nature of data based on applications and it requires a specific model for understanding the nature of the system. Further research on constraint decision-making systems in engineering is required. Constraint Decision-Making Systems in Engineering derives and explores several types of constraint decisions in engineering and focuses on new and innovative conclusions based on problems, robust and efficient systems, and linear and non-linear applications. Covering topics such as fault detection, data mining techniques, and knowledge-based management, this premier reference source is an essential resource for engineers, managers, computer scientists, students and educators of higher education, librarians, researchers, and academicians.
