Although the transition between the first three industrial revolutions took more than a century, Industry 4.0 is progressing quickly. The emergence of digitalization has been rapid thanks to the development of cutting-edge technologies. Though we are witnessing this rapid technological decentralization and interconnectivity at present, organizations and researchers are already discussing Industry 5.0 where full integration of the human side of business and intelligent systems is expected. In this scenario, it is essential to look forward to such strategic workplaces that allow a combination of humans and technology to assure a high degree of automation merged with the cognitive skills of business leaders. Managing Technology Integration for Human Resources in Industry 5.0 provides insights into the impact of the Industrial Revolution 4.0 on human resources. It provides insights for both industry and academia to assist them in teaching and training the next generation leaders through universities and corporate training. Covering topics such as business performance, human technology integration, and digitalization, this premier reference source is an essential resource for human resource managers, IT managers, organizational executives and leaders, entrepreneurs, students and educators of higher education, librarians, researchers, and academicians.

With recent advancements in electronics, specifically nanoscale devices, new technologies are being implemented to improve the properties of automated systems. However, conventional materials are failing due to limited mobility, high leakage currents, and power dissipation. To mitigate these challenges, alternative resources are required to advance electronics further into the nanoscale domain. Carbon nanotube field-effect transistors are a potential solution yet lack the information and research to be properly utilized. Major Applications of Carbon Nanotube Field-Effect Transistors (CNTFET) is a collection of innovative research on the methods and applications of converting semiconductor devices from micron technology to nanotechnology. The book provides readers with an updated status on existing CNTs, CNTFETs, and their applications and examines practical applications to minimize short channel effects and power dissipation in nanoscale devices and circuits. While highlighting topics including interconnects, digital circuits, and single-wall CNTs, this book is ideally designed for electrical engineers, electronics engineers, students, researchers, academicians, industry professionals, and practitioners working in nanoscience, nanotechnology, applied physics, and electrical and electronics engineering.

Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance. The Handbook of Research on Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches.

As the progression of the internet continues, society is finding easier, quicker ways of simplifying their needs with the use of technology. With the growth of lightweight devices, such as smart phones and wearable devices, highly configured hardware is in heightened demand in order to process the large amounts of raw data that are acquired. Connecting these devices to fog computing can reduce bandwidth and latency for data transmission when associated with centralized cloud solutions and uses machine learning algorithms to handle large amounts of raw data. The risks that accompany this advancing technology, however, have yet to be explored. Architecture and Security Issues in Fog Computing Applications is a pivotal reference source that provides vital research on the architectural complications of fog processing and focuses on security and privacy issues in intelligent fog applications. While highlighting topics such as machine learning, cyber-physical systems, and security applications, this publication explores the architecture of intelligent fog applications enabled with machine learning. This book is ideally designed for IT specialists, software developers, security analysts, software engineers, academicians, students, and researchers seeking current research on network security and wireless systems.

Step up your Excel skills with our 6-page laminated guide focusing on tips and tricks for using data efficiently while ensuring data quality. Curtis Frye, author of multiple books on Excel, creator of many Lynda.com videos and an experienced corporate trainer used his experience and knowledge to cover the most relevant functions for users at different levels. This is the second in the Excel 2016 series. Suggested uses: Workplace -- Kept conveniently at your desk for easy reference; Company Training -- reduce help-desk calls and keep productivity flowing for a team or for your entire company; Students/Teachers/Parents -- help for classroom or homework; College Professors/Students -- offers a range of guides for different levels.