Aryan Chaudhary – författare

Digital analytics is an emerging new trend used in education to measure, collect, analyze, and report on data about learners and their contexts and to understand and optimize learning and learning environments. Taking into consideration the UN''s Sustainable Development Goal 4, which aims to "ensure inclusive and equitable quality education and promote lifelong learning opportunities for all," this new book looks at digital technologies as a means to foster sustainable educational innovations for improving the teaching, learning, and assessment from K-12 to higher education. It demonstrates how artificial intelligence, deep learning, cloud computing, big data, and machine learning can be used to assess, evaluate, record, and predict student progress, participation, performance, personalization, and empowerment in academic and curricular activities.

Digital analytics is an emerging new trend used in education to measure, collect, analyze, and report on data about learners and their contexts and to understand and optimize learning and learning environments. Taking into consideration the UN''s Sustainable Development Goal 4, which aims to "ensure inclusive and equitable quality education and promote lifelong learning opportunities for all," this new book looks at digital technologies as a means to foster sustainable educational innovations for improving the teaching, learning, and assessment from K-12 to higher education. It demonstrates how artificial intelligence, deep learning, cloud computing, big data, and machine learning can be used to assess, evaluate, record, and predict student progress, participation, performance, personalization, and empowerment in academic and curricular activities.

Advances on Mathematical Modeling and Optimization with Its Applications discusses optimization, equality, and inequality constraints and their application in the versatile optimizing domain. It further covers non-linear optimization methods such as global optimization, and gradient-based non-linear optimization, and their applications.

The text is primarily for senior undergraduate and graduate students, and academic researchers in the fields of mathematics, statistics, and computer science.

Advances on Mathematical Modeling and Optimization with Its Applications discusses optimization, equality, and inequality constraints and their application in the versatile optimizing domain. It further covers non-linear optimization methods such as global optimization, and gradient-based non-linear optimization, and their applications.

The text is primarily for senior undergraduate and graduate students, and academic researchers in the fields of mathematics, statistics, and computer science.

The book showcases how advanced cybersecurity and forensic techniques can be applied to various computational issues. It further covers the advanced exploitation tools that are used in the domain of ethical hacking and penetration testing.

• Focuses on tools used in performing mobile and SIM forensics, static and dynamic memory analysis, and deep web forensics

• Covers advanced tools in the domain of data hiding and steganalysis

• Discusses the role and application of artificial intelligence and big data in cybersecurity

• Elaborates on the use of advanced cybersecurity and forensics techniques in computational issues

• Includes numerous open-source tools such as NMAP, Autopsy, and Wireshark used in the domain of digital forensics

The text is primarily written for senior undergraduates, graduate students, and academicresearchers, in the fields of computer science, electrical engineering, cybersecurity,and forensics.

The book showcases how advanced cybersecurity and forensic techniques can be applied to various computational issues. It further covers the advanced exploitation tools that are used in the domain of ethical hacking and penetration testing.

• Focuses on tools used in performing mobile and SIM forensics, static and dynamic memory analysis, and deep web forensics

• Covers advanced tools in the domain of data hiding and steganalysis

• Discusses the role and application of artificial intelligence and big data in cybersecurity

• Elaborates on the use of advanced cybersecurity and forensics techniques in computational issues

• Includes numerous open-source tools such as NMAP, Autopsy, and Wireshark used in the domain of digital forensics

The text is primarily written for senior undergraduates, graduate students, and academicresearchers, in the fields of computer science, electrical engineering, cybersecurity,and forensics.

The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and ML as well. Among a variety of topics, the book examines:

Operations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.

The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and ML as well. Among a variety of topics, the book examines:

Operations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.

The model-based approach for carrying out the classification and identification of tasks has led to progression of the machine learning paradigm in diversified fields of technology. Deep Learning Applications in Operations Research presents the varied applications of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomenon. Such fields as object classification, speech recognition, and face detection have sought extensive applications of artificial intelligence (AI) and machine learning as well. The application of AI and ML has also become increasingly common in the domains of agriculture, health sectors, and insurance.

Operations research is the branch of mathematics used to perform many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects to aid in decision making. Arriving at the proper decision depends on a number of factors; this book examines how AI and ML can be used to model equations and define constraints to solve problems more easily and discover proper and valid solutions. This book also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost. Case studies examine how to streamline operations and unearth data to make better business decisions. The concepts presented in this book can bring about and guide unique research directions to the future application of AI-enabled technologies.

The model-based approach for carrying out the classification and identification of tasks has led to progression of the machine learning paradigm in diversified fields of technology. Deep Learning Applications in Operations Research presents the varied applications of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomenon. Such fields as object classification, speech recognition, and face detection have sought extensive applications of artificial intelligence (AI) and machine learning as well. The application of AI and ML has also become increasingly common in the domains of agriculture, health sectors, and insurance.

Operations research is the branch of mathematics used to perform many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects to aid in decision making. Arriving at the proper decision depends on a number of factors; this book examines how AI and ML can be used to model equations and define constraints to solve problems more easily and discover proper and valid solutions. This book also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost. Case studies examine how to streamline operations and unearth data to make better business decisions. The concepts presented in this book can bring about and guide unique research directions to the future application of AI-enabled technologies.

Technology is an indispensable part of the business world with firms using digital platforms to gain/create, maintain and deliver customer experience, especially post-pandemic. Digitization involves the comprehensive integration of digital technology into all facets of a company’s operations, yielding heightened value for customers. This book, Digital Transformation in the Customer Experience, looks at how digital transformation can help both service and manufacturing firms to deliver better customer experience.

This book will enhance readers’ understanding of the disruptive technologies that have brought businesses to their current digital state, including social media, artificial intelligence (AI), big data, machine learning, and the Internet of Things (IoT). Numerous examples and exhibits are employed to illustrate the book’s comprehensive digital framework. It presents case studies of digital implementation across diverse industries and provides implementation templates for businesses. Designed to support careers in consulting and the execution of digital strategies within companies, the book covers all essential aspects. To offer insights into practical digital transformation in real-world scenarios, the book incorporates case studies and examples spanning various industries. Its organized structure makes it accessible to students, instructors, and junior and mid-level executives. Furthermore, the book seeks to disseminate novel technical concepts and features that can be integrated into daily life for the betterment of society.

The book addresses important questions such as the relevance of e-service quality in customer satisfaction and loyalty, impact and effect of adware, malware and spyware on the digital environment, the Internet of Everything in marketing, digital entertainment, digital transformation in healthcare and more. The book also covers the impact of digitalization on education, finance and banking as well as the hospitality industry. Sensitive topics like the influence of Facebook on consumer engagement and the impact of social media marketing communications on teenagers’ attitudes, etc. are also addressed in the book.

Technology is an indispensable part of the business world with firms using digital platforms to gain/create, maintain and deliver customer experience, especially post-pandemic. Digitization involves the comprehensive integration of digital technology into all facets of a company’s operations, yielding heightened value for customers. This book, Digital Transformation in the Customer Experience, looks at how digital transformation can help both service and manufacturing firms to deliver better customer experience.

This book will enhance readers’ understanding of the disruptive technologies that have brought businesses to their current digital state, including social media, artificial intelligence (AI), big data, machine learning, and the Internet of Things (IoT). Numerous examples and exhibits are employed to illustrate the book’s comprehensive digital framework. It presents case studies of digital implementation across diverse industries and provides implementation templates for businesses. Designed to support careers in consulting and the execution of digital strategies within companies, the book covers all essential aspects. To offer insights into practical digital transformation in real-world scenarios, the book incorporates case studies and examples spanning various industries. Its organized structure makes it accessible to students, instructors, and junior and mid-level executives. Furthermore, the book seeks to disseminate novel technical concepts and features that can be integrated into daily life for the betterment of society.

The book addresses important questions such as the relevance of e-service quality in customer satisfaction and loyalty, impact and effect of adware, malware and spyware on the digital environment, the Internet of Everything in marketing, digital entertainment, digital transformation in healthcare and more. The book also covers the impact of digitalization on education, finance and banking as well as the hospitality industry. Sensitive topics like the influence of Facebook on consumer engagement and the impact of social media marketing communications on teenagers’ attitudes, etc. are also addressed in the book.

In this internet age of security challenges and threats from cybercrime, enhanced security measures are necessary. The zero trust model—the IT security model that requires strict identity verification for every person and device trying to access resources on a private network—helps to meet these everincreasing and evolving security challenges. This new volume offers a comprehensive overview of the zero trust security model and its application in the field of cybersecurity, covering the principles, technologies, and best practices for implementing a zero trust approach, equipping readers with the knowledge and tools to secure their digital environments effectively. This book stands out by providing a holistic view of the zero trust security model, combining practical guidance for professionals with educational insights for both professionals and students and offering real-world examples and case studies that bridge the gap between learning and implementation.

In this internet age of security challenges and threats from cybercrime, enhanced security measures are necessary. The zero trust model—the IT security model that requires strict identity verification for every person and device trying to access resources on a private network—helps to meet these everincreasing and evolving security challenges. This new volume offers a comprehensive overview of the zero trust security model and its application in the field of cybersecurity, covering the principles, technologies, and best practices for implementing a zero trust approach, equipping readers with the knowledge and tools to secure their digital environments effectively. This book stands out by providing a holistic view of the zero trust security model, combining practical guidance for professionals with educational insights for both professionals and students and offering real-world examples and case studies that bridge the gap between learning and implementation.