Data stealing is a major concern on the internet as hackers and criminals have begun using simple tricks to hack social networks and violate privacy. Cyber-attack methods are progressively modern, and obstructing the attack is increasingly troublesome, regardless of whether countermeasures are taken. The Dark Web especially presents challenges to information privacy and security due to anonymous behaviors and the unavailability of data. To better understand and prevent cyberattacks, it is vital to have a forecast of cyberattacks, proper safety measures, and viable use of cyber-intelligence that empowers these activities. Dark Web Pattern Recognition and Crime Analysis Using Machine Intelligence discusses cyberattacks, security, and safety measures to protect data and presents the shortcomings faced by researchers and practitioners due to the unavailability of information about the Dark Web. Attacker techniques in these Dark Web environments are highlighted, along with intrusion detection practices and crawling of hidden content. Covering a range of topics such as malware and fog computing, this reference work is ideal for researchers, academicians, practitioners, industry professionals, computer scientists, scholars, instructors, and students.

The success of any organization is largely dependent on positive feedback and repeat business from patrons. By utilizing acquired marketing data, business professionals can more accurately assess practices, services, and products that their customers find appealing. The Handbook of Research on Intelligent Techniques and Modeling Applications in Marketing Analytics features innovative research and implementation practices of analytics in marketing research. Highlighting various techniques in acquiring and deciphering marketing data, this publication is a pivotal reference for professionals, managers, market researchers, and practitioners interested in the observation and utilization of data on marketing trends to promote positive business practices.

The fourth edition of this best-selling guide to Prolog and Artificial Intelligence has been updated to include key developments in the field while retaining its lucid approach to these topics. New and extended topics include Constraint Logic Programming, abductive reasoning and partial order planning. Divided into two parts, the first part of the book introduces the programming language Prolog, while the second part teaches Artificial Intelligence using Prolog as a tool for the implementation of AI techniques. This textbook is meant to teach Prolog as a practical programming tool and so it concentrates on the art of using the basic mechanisms of Prolog to solve interesting problems. The fourth edition has been fully revised and extended to provide an even greater range of applications, making it a self-contained guide to Prolog, AI or AI Programming for students and professional programmers.

Translation and communication between cultures can sometimes be a difficult process. Image-based assessments can offer a way for large populations to be tested on different subjects without having to create multiple testing programs. Cross-Cultural Analysis of Image-Based Assessments: Emerging Research and Opportunities is an innovative resource that offers insight into the application of visual assessments across a global and intercultural context. Highlighting applicable topics which include visual literacy, psychological assessments, assessment development, and equivalency measurements, this publication is ideal for psychologists, therapists, and researchers who would like to stay current on the most efficient way to test multi-cultural populations in various fields of knowledge.

Using a multi-disciplinary and comparative approach, this study examines emerging and innovative attempts to tackle privacy and legal issues in cloud computing such as personal data privacy, security and intellectual property protection. An international team of legal scholars, computer science researchers, regulators and practitioners present original and critical responses to the growing challenges posed by cloud computing. They analyze the specific legal implications pertaining to jurisdiction, biomedical practice and information ownership, as well as issues of regulatory control, competition and cross-border regulation. Law academics, practitioners and regulators will find this book to be a valuable, practical and accessible resource, as will computer science scholars interested in cloud computing issues. Contributors: H. Chang, A.S.Y. Cheung, A. Chiu, K.P. Chow, E.S. Dove, X. Fan, Y. Joly, T.S.-H. Kaan, B.M. Knoppers, J. Kong, G. Master, J.-P. Moiny, C. Reed, D.N. Staiger, G.Y. Tian, R.H. Weber, P.K. Yu

In today's digital world, the huge amount of data being generated is unstructured, messy, and chaotic in nature. Dealing with such data, and attempting to unfold the meaningful information, can be a challenging task. Feature engineering is a process to transform such data into a suitable form that better assists with interpretation and visualization. Through this method, the transformed data is more transparent to the machine learning models, which in turn causes better prediction and analysis of results. Data science is crucial for the data scientist to assess the trade-offs of their decisions regarding the effectiveness of the machine learning model implemented. Investigating the demand in this area today and in the future is a necessity. The Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science provides an in-depth analysis on both the theoretical and the latest empirical research findings on how features can be extracted and transformed from raw data. The chapters will introduce feature engineering and the recent concepts, methods, and applications with the use of various data types, as well as examine the latest machine learning applications on the data. While highlighting topics such as detection, tracking, selection techniques, and prediction models using data science, this book is ideally intended for research scholars, big data scientists, project developers, data analysts, and computer scientists along with practitioners, researchers, academicians, and students interested in feature engineering and its impact on data.

The highly dynamic world of information technology service management stresses the benefits of the quick and correct implementation of IT services. A disciplined approach relies on a separate set of assumptions and principles as an agile approach, both of which have complicated implementation processes as well as copious benefits. Combining these two approaches to enhance the effectiveness of each, while difficult, can yield exceptional dividends. Balancing Agile and Disciplined Engineering and Management Approaches for IT Services and Software Products is an essential publication that focuses on clarifying theoretical foundations of balanced design methods with conceptual frameworks and empirical cases. Highlighting a broad range of topics including business trends, IT service, and software development, this book is ideally designed for software engineers, software developers, programmers, information technology professionals, researchers, academicians, and students.

Food is a necessary aspect of human life, and agriculture is crucial to any country's global economy. Because the food business is essential to both a country's economy and global economy, artificial intelligence (AI)-based smart solutions are needed to assure product quality and food safety. The agricultural sector is constantly under pressure to boost crop output as a result of population growth. This necessitates the use of AI applications. Artificial Intelligence Applications in Agriculture and Food Quality Improvement discusses the application of AI, machine learning, and data analytics for the acceleration of the agricultural and food sectors. It presents a comprehensive view of how these technologies and tools are used for agricultural process improvement, food safety, and food quality improvement. Covering topics such as diet assessment research, crop yield prediction, and precision farming, this premier reference source is an essential resource for food safety professionals, quality assurance professionals, agriculture specialists, crop managers, agricultural engineers, food scientists, computer scientists, AI specialists, students, libraries, government officials, researchers, and academicians.

Research in the domains of learning analytics and educational data mining has prototyped an approach where methodologies from data science and machine learning are used to gain insights into the learning process by using large amounts of data. As many training and academic institutions are maturing in their data-driven decision making, useful, scalable, and interesting trends are emerging. Organizations can benefit from sharing information on those efforts. Applying Data Science and Learning Analytics Throughout a Learner's Lifespan examines novel and emerging applications of data science and sister disciplines for gaining insights from data to inform interventions into learners' journeys and interactions with academic institutions. Data is collected at various times and places throughout a learner's lifecycle, and the learners and the institution should benefit from the insights and knowledge gained from this data. Covering topics such as learning analytics dashboards, text network analysis, and employment recruitment, this book is an indispensable resource for educators, computer scientists, faculty of higher education, government officials, educational administration, students of higher education, pre-service teachers, business professionals, researchers, and academicians.

In the very near future, "smart" technologies and "big data" will allow us to make large-scale and sophisticated interventions in politics, culture, and everyday life. Technology will allow us to solve problems in highly original ways and create new incentives to get more people to do the right thing. But how will such "solutionism" affect our society, once deeply political, moral, and irresolvable dilemmas are recast as uncontroversial and easily manageable matters of technological efficiency? What if some such problems are simply vices in disguise? What if some friction in communication is productive and some hypocrisy in politics necessary? The temptation of the digital age is to fix everything--from crime to corruption to pollution to obesity--by digitally quantifying, tracking, or gamifying behavior. But when we change the motivations for our moral, ethical, and civic behavior we may also change the very nature of that behavior. Technology, Evgeny Morozov proposes, can be a force for improvement--but only if we keep solutionism in check and learn to appreciate the imperfections of liberal democracy. Some of those imperfections are not accidental but by design.
Arguing that we badly need a new, post-Internet way to debate the moral consequences of digital technologies, "To Save Everything, Click Here" warns against a world of seamless efficiency, where everyone is forced to wear Silicon Valley's digital straitjacket.