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"It was a wonderful experience interacting with you and appreciate the way you have planned and executed the whole publication process within the agreed timelines.”
Subrat SaurabhAuthor of Kuch Woh PalDr. Rahul Sharma, working as Assistant Professor – Senior Scale in the Department of Artificial Intelligence & Machine Learning, at Manipal University, Jaipur, Rajasthan. Prior to that he has more than 8+ years of teaching experience in several universities and engineering colleges as Assistant Professor in Department of Computer Science and Engineering at Parul Institute of Technology, Parul University Vadodara, Gujarat, Chameli Devi Group of Institution, Indore and Dr. A.P.J. Abdul Kalam University, he completed B.E. (CSE) from Patel College of Science and Technology, Indore (M.P.) and MRead More...
Dr. Rahul Sharma, working as Assistant Professor – Senior Scale in the Department of Artificial Intelligence & Machine Learning, at Manipal University, Jaipur, Rajasthan. Prior to that he has more than 8+ years of teaching experience in several universities and engineering colleges as Assistant Professor in Department of Computer Science and Engineering at Parul Institute of Technology, Parul University Vadodara, Gujarat, Chameli Devi Group of Institution, Indore and Dr. A.P.J. Abdul Kalam University, he completed B.E. (CSE) from Patel College of Science and Technology, Indore (M.P.) and M.Tech (NM&IS) from SCSIT, DAVV, Indore (M.P.), PhD degree in Computer Science and Engineering from Rabindranath Tagore University Bhopal (M.P.). His research includes Computer Network, Network Security, Cryptography, Data Mining and Web Programming’s. He is having 20+ research publications in reputed international journal, International- National conferences and 6 - patents (3 published- 3 Registered). He also has qualified GATE (CSE) in 2015. He is publishing more than 35+ books in the field of computer science and Engineering.
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Mastering the Machine: ML for the Real World explores the practical challenges and strategies for implementing machine learning systems beyond controlled research environments. While academic ML often focuses on clean datasets and benchmark accuracy, real-world applications must deal with messy, incomplete, and constantly evolving data. The book emphasizes that success in production ML is less about achieving the highest model accuracy and more about building
Mastering the Machine: ML for the Real World explores the practical challenges and strategies for implementing machine learning systems beyond controlled research environments. While academic ML often focuses on clean datasets and benchmark accuracy, real-world applications must deal with messy, incomplete, and constantly evolving data. The book emphasizes that success in production ML is less about achieving the highest model accuracy and more about building systems that are scalable, reliable, interpretable, and aligned with business goals. Key themes include the importance of data quality and preprocessing, as most real-world effort goes into cleaning, balancing, and engineering features rather than model selection alone. The text highlights data drift, concept drift, and feedback loops, showing how models degrade over time without proper monitoring and retraining. It also covers model evaluation, stressing that accuracy is insufficient for imbalanced datasets and that fairness, interpretability, and business KPIs must guide decision-making. Overall, the work positions machine learning as not just a technical challenge but a socio-technical system requiring collaboration among data scientists, engineers, and domain experts.
Mastering the Machine: ML for the Real World explores the practical challenges and strategies for implementing machine learning systems beyond controlled research environments. While academic ML often focuses on clean datasets and benchmark accuracy, real-world applications must deal with messy, incomplete, and constantly evolving data. The book emphasizes that success in production ML is less about achieving the highest model accuracy and more about building
Mastering the Machine: ML for the Real World explores the practical challenges and strategies for implementing machine learning systems beyond controlled research environments. While academic ML often focuses on clean datasets and benchmark accuracy, real-world applications must deal with messy, incomplete, and constantly evolving data. The book emphasizes that success in production ML is less about achieving the highest model accuracy and more about building systems that are scalable, reliable, interpretable, and aligned with business goals. Key themes include the importance of data quality and preprocessing, as most real-world effort goes into cleaning, balancing, and engineering features rather than model selection alone. The text highlights data drift, concept drift, and feedback loops, showing how models degrade over time without proper monitoring and retraining. It also covers model evaluation, stressing that accuracy is insufficient for imbalanced datasets and that fairness, interpretability, and business KPIs must guide decision-making. Overall, the work positions machine learning as not just a technical challenge but a socio-technical system requiring collaboration among data scientists, engineers, and domain experts.
High-Performance Computer Networking: Routing, Switching & Security is a concise and practical resource that explores the essential principles and technologies behind building fast, reliable, and secure networks. It begins with fundamental networking concepts and key performance metrics such as throughput, latency, jitter, and quality of service (QoS), which are crucial for evaluating and optimizing network performance.
The book systematically covers n
High-Performance Computer Networking: Routing, Switching & Security is a concise and practical resource that explores the essential principles and technologies behind building fast, reliable, and secure networks. It begins with fundamental networking concepts and key performance metrics such as throughput, latency, jitter, and quality of service (QoS), which are crucial for evaluating and optimizing network performance.
The book systematically covers network models (OSI and TCP/IP), topologies, and types (LAN, WAN, MAN), providing a solid understanding of how networks are structured. It delves into routing protocols like RIP, OSPF, EIGRP, and BGP, explaining their operations, configurations, and performance aspects. Switching technologies such as VLANs, STP, MAC tables, LACP, and port security are also thoroughly addressed, emphasizing how Layer 2 and Layer 3 switching enhances network efficiency and scalability.
Security is a central focus, with in-depth discussions on securing routing protocols, DHCP snooping, Dynamic ARP Inspection, IP Source Guard, and control plane policing. The book also explores practical configurations and threat mitigation strategies.
The text introduces advanced and emerging technologies like SDN, cloud networking, IoT, and 5G, preparing readers for modern networking challenges. This book is ideal for students, professionals, and network engineers aiming for high-performance and secure network design.
High-Performance Computer Networking: Routing, Switching & Security is a concise and practical resource that explores the essential principles and technologies behind building fast, reliable, and secure networks. It begins with fundamental networking concepts and key performance metrics such as throughput, latency, jitter, and quality of service (QoS), which are crucial for evaluating and optimizing network performance.
The book systematically covers n
High-Performance Computer Networking: Routing, Switching & Security is a concise and practical resource that explores the essential principles and technologies behind building fast, reliable, and secure networks. It begins with fundamental networking concepts and key performance metrics such as throughput, latency, jitter, and quality of service (QoS), which are crucial for evaluating and optimizing network performance.
The book systematically covers network models (OSI and TCP/IP), topologies, and types (LAN, WAN, MAN), providing a solid understanding of how networks are structured. It delves into routing protocols like RIP, OSPF, EIGRP, and BGP, explaining their operations, configurations, and performance aspects. Switching technologies such as VLANs, STP, MAC tables, LACP, and port security are also thoroughly addressed, emphasizing how Layer 2 and Layer 3 switching enhances network efficiency and scalability.
Security is a central focus, with in-depth discussions on securing routing protocols, DHCP snooping, Dynamic ARP Inspection, IP Source Guard, and control plane policing. The book also explores practical configurations and threat mitigation strategies.
The text introduces advanced and emerging technologies like SDN, cloud networking, IoT, and 5G, preparing readers for modern networking challenges. This book is ideal for students, professionals, and network engineers aiming for high-performance and secure network design.
"Modern Approaches to Digital Forensics” explores the cutting-edge techniques, tools, and methodologies transforming digital investigations in the era of rapid technological growth. As cybercrimes become more sophisticated and digital environments more complex, traditional forensic methods often fall short. This book bridges the gap by introducing readers to the next generation of forensic practices tailored for today’s challenges.
It cover
"Modern Approaches to Digital Forensics” explores the cutting-edge techniques, tools, and methodologies transforming digital investigations in the era of rapid technological growth. As cybercrimes become more sophisticated and digital environments more complex, traditional forensic methods often fall short. This book bridges the gap by introducing readers to the next generation of forensic practices tailored for today’s challenges.
It covers cloud forensics, where investigators face decentralized storage, jurisdictional complications, and data volatility. With increasing reliance on smartphones and tablets, the book delves into mobile forensics, highlighting tools used to recover app data, messages, and metadata. It also discusses the integration of AI and machine learning, enabling automated evidence analysis, anomaly detection, and predictive profiling.
Ideal for students, cybersecurity professionals, and law enforcement, this book provides a comprehensive guide to contemporary forensic science. It emphasizes legal considerations, chain-of-custody protocols, and future directions, preparing readers to face the evolving landscape of cyber investigations with confidence and competence.
"Modern Approaches to Digital Forensics” explores the cutting-edge techniques, tools, and methodologies transforming digital investigations in the era of rapid technological growth. As cybercrimes become more sophisticated and digital environments more complex, traditional forensic methods often fall short. This book bridges the gap by introducing readers to the next generation of forensic practices tailored for today’s challenges.
It cover
"Modern Approaches to Digital Forensics” explores the cutting-edge techniques, tools, and methodologies transforming digital investigations in the era of rapid technological growth. As cybercrimes become more sophisticated and digital environments more complex, traditional forensic methods often fall short. This book bridges the gap by introducing readers to the next generation of forensic practices tailored for today’s challenges.
It covers cloud forensics, where investigators face decentralized storage, jurisdictional complications, and data volatility. With increasing reliance on smartphones and tablets, the book delves into mobile forensics, highlighting tools used to recover app data, messages, and metadata. It also discusses the integration of AI and machine learning, enabling automated evidence analysis, anomaly detection, and predictive profiling.
Ideal for students, cybersecurity professionals, and law enforcement, this book provides a comprehensive guide to contemporary forensic science. It emphasizes legal considerations, chain-of-custody protocols, and future directions, preparing readers to face the evolving landscape of cyber investigations with confidence and competence.
In the world of programming, data structures serve as the backbone for developing efficient algorithms. This section explores fundamental and advanced data structures, focusing on how they support and enhance algorithmic problem-solving. Understanding the right data structure to use is crucial for optimizing performance, memory usage, and code clarity.
We begin with core structures such as arrays, linked lists, stacks, and queues, examining how they op
In the world of programming, data structures serve as the backbone for developing efficient algorithms. This section explores fundamental and advanced data structures, focusing on how they support and enhance algorithmic problem-solving. Understanding the right data structure to use is crucial for optimizing performance, memory usage, and code clarity.
We begin with core structures such as arrays, linked lists, stacks, and queues, examining how they operate, where they are used, and their time and space complexities. Moving deeper, we cover hierarchical and non-linear structures like trees (binary trees, binary search trees, heaps, tries) and graphs (directed, undirected, weighted, unweighted), which play a critical role in solving complex real-world problems like searching, scheduling, and network modeling.
Special attention is given to hash tables and sets, which provide near-constant time access and are essential for fast lookups and caching. Advanced data structures such as segment trees, Fenwick trees, disjoint sets, and suffix arrays are also introduced for readers interested in competitive programming and systems-level design.
This section emphasizes the practical application of each structure with examples in common programming languages, illustrating how to implement, manipulate, and choose the most suitable data structure for a given problem.
In the world of programming, data structures serve as the backbone for developing efficient algorithms. This section explores fundamental and advanced data structures, focusing on how they support and enhance algorithmic problem-solving. Understanding the right data structure to use is crucial for optimizing performance, memory usage, and code clarity.
We begin with core structures such as arrays, linked lists, stacks, and queues, examining how they op
In the world of programming, data structures serve as the backbone for developing efficient algorithms. This section explores fundamental and advanced data structures, focusing on how they support and enhance algorithmic problem-solving. Understanding the right data structure to use is crucial for optimizing performance, memory usage, and code clarity.
We begin with core structures such as arrays, linked lists, stacks, and queues, examining how they operate, where they are used, and their time and space complexities. Moving deeper, we cover hierarchical and non-linear structures like trees (binary trees, binary search trees, heaps, tries) and graphs (directed, undirected, weighted, unweighted), which play a critical role in solving complex real-world problems like searching, scheduling, and network modeling.
Special attention is given to hash tables and sets, which provide near-constant time access and are essential for fast lookups and caching. Advanced data structures such as segment trees, Fenwick trees, disjoint sets, and suffix arrays are also introduced for readers interested in competitive programming and systems-level design.
This section emphasizes the practical application of each structure with examples in common programming languages, illustrating how to implement, manipulate, and choose the most suitable data structure for a given problem.
"Introduction to Machine Learning with Python" is a practical guide designed for students, educators, and aspiring data scientists who want to build a strong foundation in machine learning using Python. This book presents complex concepts in a simplified manner, making it accessible even for beginners with basic programming knowledge. Covering essential topics such as data preprocessing, supervised and unsupervised learning, model evaluation, and real-world pr
"Introduction to Machine Learning with Python" is a practical guide designed for students, educators, and aspiring data scientists who want to build a strong foundation in machine learning using Python. This book presents complex concepts in a simplified manner, making it accessible even for beginners with basic programming knowledge. Covering essential topics such as data preprocessing, supervised and unsupervised learning, model evaluation, and real-world project applications, the book uses clear explanations and hands-on coding examples with libraries like scikit-learn and pandas. Each chapter builds logically upon the last, helping readers not only understand theory but also apply it through practical experiments. With a strong focus on conceptual clarity and Python-based implementation, this book serves as a bridge between academic learning and real-world machine learning applications. Whether you're a student, instructor, or self-learner, this book will help you confidently step into the world of machine learning with Python.
"Introduction to Machine Learning with Python" is a practical guide designed for students, educators, and aspiring data scientists who want to build a strong foundation in machine learning using Python. This book presents complex concepts in a simplified manner, making it accessible even for beginners with basic programming knowledge. Covering essential topics such as data preprocessing, supervised and unsupervised learning, model evaluation, and real-world pr
"Introduction to Machine Learning with Python" is a practical guide designed for students, educators, and aspiring data scientists who want to build a strong foundation in machine learning using Python. This book presents complex concepts in a simplified manner, making it accessible even for beginners with basic programming knowledge. Covering essential topics such as data preprocessing, supervised and unsupervised learning, model evaluation, and real-world project applications, the book uses clear explanations and hands-on coding examples with libraries like scikit-learn and pandas. Each chapter builds logically upon the last, helping readers not only understand theory but also apply it through practical experiments. With a strong focus on conceptual clarity and Python-based implementation, this book serves as a bridge between academic learning and real-world machine learning applications. Whether you're a student, instructor, or self-learner, this book will help you confidently step into the world of machine learning with Python.
A computer network connects devices like computers and smartphones, enabling data sharing. These networks can be local (LAN) or wide-reaching (WAN), with the Internet being the largest example.
Network security is crucial for protecting data from unauthorized access, cyber-attacks, and breaches. Key components of network security include:
Firewalls: They filter incoming and outgoing traffic, blocking unauthorized access.
Encryption: It scra
A computer network connects devices like computers and smartphones, enabling data sharing. These networks can be local (LAN) or wide-reaching (WAN), with the Internet being the largest example.
Network security is crucial for protecting data from unauthorized access, cyber-attacks, and breaches. Key components of network security include:
Firewalls: They filter incoming and outgoing traffic, blocking unauthorized access.
Encryption: It scrambles data, ensuring privacy by making it unreadable to unauthorized users.
Authentication and Access Control: These methods verify the identity of users, allowing only authorized access.
IDS/IPS: Intrusion Detection and Prevention Systems monitor and block suspicious activities.
VPN (Virtual Private Network): A VPN encrypts the internet connection, ensuring secure online activity, especially on public networks.
Effective network security is vital to protect sensitive data, prevent cyber-attacks, and maintain safe communication in a connected world.
A computer network connects devices like computers and smartphones, enabling data sharing. These networks can be local (LAN) or wide-reaching (WAN), with the Internet being the largest example.
Network security is crucial for protecting data from unauthorized access, cyber-attacks, and breaches. Key components of network security include:
Firewalls: They filter incoming and outgoing traffic, blocking unauthorized access.
Encryption: It scra
A computer network connects devices like computers and smartphones, enabling data sharing. These networks can be local (LAN) or wide-reaching (WAN), with the Internet being the largest example.
Network security is crucial for protecting data from unauthorized access, cyber-attacks, and breaches. Key components of network security include:
Firewalls: They filter incoming and outgoing traffic, blocking unauthorized access.
Encryption: It scrambles data, ensuring privacy by making it unreadable to unauthorized users.
Authentication and Access Control: These methods verify the identity of users, allowing only authorized access.
IDS/IPS: Intrusion Detection and Prevention Systems monitor and block suspicious activities.
VPN (Virtual Private Network): A VPN encrypts the internet connection, ensuring secure online activity, especially on public networks.
Effective network security is vital to protect sensitive data, prevent cyber-attacks, and maintain safe communication in a connected world.
"Data Science and Machine Learning: Mathematical and Statistical Methods" is a comprehensive guide that emphasizes the theoretical foundations of data science and machine learning. The book is ideal for students, researchers, and professionals who aim to build a strong mathematical understanding of core concepts in these rapidly growing fields. It bridges the gap between theory and practice by combining mathematical rigor with practical applications.
T
"Data Science and Machine Learning: Mathematical and Statistical Methods" is a comprehensive guide that emphasizes the theoretical foundations of data science and machine learning. The book is ideal for students, researchers, and professionals who aim to build a strong mathematical understanding of core concepts in these rapidly growing fields. It bridges the gap between theory and practice by combining mathematical rigor with practical applications.
The text delves deeply into essential topics such as probability theory, linear algebra, calculus, and statistical inference — all of which form the backbone of data science. These concepts are not just introduced but are thoroughly explored with clear explanations, proofs, and illustrative examples. A significant portion of the book is dedicated to regression analysis, classification methods, clustering techniques, and dimensionality reduction, which are fundamental tools in machine learning.
One of the key strengths of the book is its focus on the mathematical intuition behind machine learning algorithms. Readers are guided through the derivation of algorithms like linear regression, logistic regression, support vector machines, principal component analysis, and k-means clustering. It also introduces more advanced topics such as Bayesian methods, kernel methods, and elements of deep learning from a mathematical viewpoint.
The book introduces readers to the core principles and methodologies used to identify, preserve, analyze, and present digital evidence. It is structured to support students, law enforcement personnel, and cybersecurity professionals in understanding how to conduct forensically sound investigations.
The book begins by outlining the fundamentals of digital forensics, including the types and sources of digital evidence such as hard drives, mobile devices,
The book introduces readers to the core principles and methodologies used to identify, preserve, analyze, and present digital evidence. It is structured to support students, law enforcement personnel, and cybersecurity professionals in understanding how to conduct forensically sound investigations.
The book begins by outlining the fundamentals of digital forensics, including the types and sources of digital evidence such as hard drives, mobile devices, cloud storage, and networks. It emphasizes the importance of following proper legal and ethical procedures to ensure that evidence is admissible in court. Key topics include forensic imaging, file system analysis, metadata examination, and log analysis.
One of the strengths of this book is its focus on a methodical approach to investigations. It presents models and frameworks such as the digital forensic process model, which includes identification, acquisition, analysis, and reporting phases. The authors provide detailed explanations of forensic techniques along with examples and exercises that reinforce hands-on skills.
The book also discusses current tools and technologies used in the field, helping readers become familiar with widely adopted forensic software and command-line utilities. Real-world case studies and challenges provide context and illustrate the application of concepts in criminal and corporate investigations.
The book introduces readers to the core principles and methodologies used to identify, preserve, analyze, and present digital evidence. It is structured to support students, law enforcement personnel, and cybersecurity professionals in understanding how to conduct forensically sound investigations.
The book begins by outlining the fundamentals of digital forensics, including the types and sources of digital evidence such as hard drives, mobile devices,
The book introduces readers to the core principles and methodologies used to identify, preserve, analyze, and present digital evidence. It is structured to support students, law enforcement personnel, and cybersecurity professionals in understanding how to conduct forensically sound investigations.
The book begins by outlining the fundamentals of digital forensics, including the types and sources of digital evidence such as hard drives, mobile devices, cloud storage, and networks. It emphasizes the importance of following proper legal and ethical procedures to ensure that evidence is admissible in court. Key topics include forensic imaging, file system analysis, metadata examination, and log analysis.
One of the strengths of this book is its focus on a methodical approach to investigations. It presents models and frameworks such as the digital forensic process model, which includes identification, acquisition, analysis, and reporting phases. The authors provide detailed explanations of forensic techniques along with examples and exercises that reinforce hands-on skills.
The book also discusses current tools and technologies used in the field, helping readers become familiar with widely adopted forensic software and command-line utilities. Real-world case studies and challenges provide context and illustrate the application of concepts in criminal and corporate investigations.
"Data Science and Machine Learning: Mathematical and Statistical Methods" is a comprehensive guide that emphasizes the theoretical foundations of data science and machine learning. The book is ideal for students, researchers, and professionals who aim to build a strong mathematical understanding of core concepts in these rapidly growing fields. It bridges the gap between theory and practice by combining mathematical rigor with practical applications.
T
"Data Science and Machine Learning: Mathematical and Statistical Methods" is a comprehensive guide that emphasizes the theoretical foundations of data science and machine learning. The book is ideal for students, researchers, and professionals who aim to build a strong mathematical understanding of core concepts in these rapidly growing fields. It bridges the gap between theory and practice by combining mathematical rigor with practical applications.
The text delves deeply into essential topics such as probability theory, linear algebra, calculus, and statistical inference — all of which form the backbone of data science. These concepts are not just introduced but are thoroughly explored with clear explanations, proofs, and illustrative examples. A significant portion of the book is dedicated to regression analysis, classification methods, clustering techniques, and dimensionality reduction, which are fundamental tools in machine learning.
One of the key strengths of the book is its focus on the mathematical intuition behind machine learning algorithms. Readers are guided through the derivation of algorithms like linear regression, logistic regression, support vector machines, principal component analysis, and k-means clustering. It also introduces more advanced topics such as Bayesian methods, kernel methods, and elements of deep learning from a mathematical viewpoint.
This book takes a comprehensive approach to cybersecurity, covering a wide range of topics, including advanced hacking techniques, malware analysis, cryptography, threat intelligence, ethical hacking, AI-driven security, and cloud security. By integrating theoretical foundations with practical applications, this book bridges the gap between academia and industry, providing both students and professionals with a solid understanding of real-world cybersecurity c
This book takes a comprehensive approach to cybersecurity, covering a wide range of topics, including advanced hacking techniques, malware analysis, cryptography, threat intelligence, ethical hacking, AI-driven security, and cloud security. By integrating theoretical foundations with practical applications, this book bridges the gap between academia and industry, providing both students and professionals with a solid understanding of real-world cybersecurity challenges.
The content is structured to guide readers from fundamental concepts to advanced topics, making it suitable for cybersecurity enthusiasts at all levels. Through case studies, hands-on exercises, and real-world attack scenarios, this book aims to foster a deep understanding of both offensive and defensive security techniques.
Our goal is to empower cybersecurity professionals, ethical hackers, researchers, and students with the tools and knowledge necessary to protect digital assets and mitigate cyber risks. As we move toward an increasingly connected world, staying ahead of cyber adversaries requires continuous learning, innovation, and collaboration.
This book takes a comprehensive approach to cybersecurity, covering a wide range of topics, including advanced hacking techniques, malware analysis, cryptography, threat intelligence, ethical hacking, AI-driven security, and cloud security. By integrating theoretical foundations with practical applications, this book bridges the gap between academia and industry, providing both students and professionals with a solid understanding of real-world cybersecurity c
This book takes a comprehensive approach to cybersecurity, covering a wide range of topics, including advanced hacking techniques, malware analysis, cryptography, threat intelligence, ethical hacking, AI-driven security, and cloud security. By integrating theoretical foundations with practical applications, this book bridges the gap between academia and industry, providing both students and professionals with a solid understanding of real-world cybersecurity challenges.
The content is structured to guide readers from fundamental concepts to advanced topics, making it suitable for cybersecurity enthusiasts at all levels. Through case studies, hands-on exercises, and real-world attack scenarios, this book aims to foster a deep understanding of both offensive and defensive security techniques.
Our goal is to empower cybersecurity professionals, ethical hackers, researchers, and students with the tools and knowledge necessary to protect digital assets and mitigate cyber risks. As we move toward an increasingly connected world, staying ahead of cyber adversaries requires continuous learning, innovation, and collaboration.
Fundamentals of Data Structures is a comprehensive book that provides a solid foundation in data structures, an essential subject in computer science and software development. The book covers the fundamental concepts, principles, and techniques required to organize, manage, and store data efficiently. It introduces readers to various data structures such as arrays, linked lists, stacks, queues, trees, graphs, and hash tables.
The book emphasizes both t
Fundamentals of Data Structures is a comprehensive book that provides a solid foundation in data structures, an essential subject in computer science and software development. The book covers the fundamental concepts, principles, and techniques required to organize, manage, and store data efficiently. It introduces readers to various data structures such as arrays, linked lists, stacks, queues, trees, graphs, and hash tables.
The book emphasizes both theoretical concepts and practical applications, ensuring a balanced approach to learning. It explains data structures using clear definitions, diagrams, and step-by-step algorithms. The efficiency of different data structures is analyzed using time and space complexity, helping readers understand when and where to apply each structure.
Key topics include searching and sorting algorithms, recursion, memory management, and advanced structures like AVL trees, B-trees, and heaps. The book also highlights real-world applications of data structures in databases, operating systems, and artificial intelligence.
Designed for beginners and intermediate learners, the book includes numerous examples, exercises, and programming problems to reinforce concepts. It is useful for students pursuing computer science, engineering, and information technology courses. The book is also a valuable reference for professionals looking to enhance their understanding of data structures.
Fundamentals of Data Structures is a comprehensive book that provides a solid foundation in data structures, an essential subject in computer science and software development. The book covers the fundamental concepts, principles, and techniques required to organize, manage, and store data efficiently. It introduces readers to various data structures such as arrays, linked lists, stacks, queues, trees, graphs, and hash tables.
The book emphasizes both t
Fundamentals of Data Structures is a comprehensive book that provides a solid foundation in data structures, an essential subject in computer science and software development. The book covers the fundamental concepts, principles, and techniques required to organize, manage, and store data efficiently. It introduces readers to various data structures such as arrays, linked lists, stacks, queues, trees, graphs, and hash tables.
The book emphasizes both theoretical concepts and practical applications, ensuring a balanced approach to learning. It explains data structures using clear definitions, diagrams, and step-by-step algorithms. The efficiency of different data structures is analyzed using time and space complexity, helping readers understand when and where to apply each structure.
Key topics include searching and sorting algorithms, recursion, memory management, and advanced structures like AVL trees, B-trees, and heaps. The book also highlights real-world applications of data structures in databases, operating systems, and artificial intelligence.
Designed for beginners and intermediate learners, the book includes numerous examples, exercises, and programming problems to reinforce concepts. It is useful for students pursuing computer science, engineering, and information technology courses. The book is also a valuable reference for professionals looking to enhance their understanding of data structures.
In the ever-evolving landscape of technology, cyber forensics and investigation have become critical disciplines that play a vital role in safeguarding digital assets and ensuring justice in cyberspace. With the rise in cybercrimes such as hacking, identity theft, digital fraud, and cyber terrorism, the need for effective forensic methodologies and investigative techniques has never been more crucial.
This book delves into the fundamental and adva
In the ever-evolving landscape of technology, cyber forensics and investigation have become critical disciplines that play a vital role in safeguarding digital assets and ensuring justice in cyberspace. With the rise in cybercrimes such as hacking, identity theft, digital fraud, and cyber terrorism, the need for effective forensic methodologies and investigative techniques has never been more crucial.
This book delves into the fundamental and advanced aspects of cyber forensics, equipping readers with the necessary knowledge to analyze digital evidence, trace cybercriminal activities, and enforce cybersecurity protocols. It aims to bridge the gap between theoretical concepts and practical applications, offering insights into real-world cyber investigations and the latest forensic tools and technologies.
The field of cyber forensics is not just for law enforcement officers and security professionals; it is equally relevant for students, researchers, and anyone interested in understanding digital security. As cyber threats continue to grow in complexity, this book serves as a comprehensive guide, shedding light on the methodologies used in cyber investigations and the legal frameworks governing digital evidence.
In the ever-evolving landscape of technology, cyber forensics and investigation have become critical disciplines that play a vital role in safeguarding digital assets and ensuring justice in cyberspace. With the rise in cybercrimes such as hacking, identity theft, digital fraud, and cyber terrorism, the need for effective forensic methodologies and investigative techniques has never been more crucial.
This book delves into the fundamental and adva
In the ever-evolving landscape of technology, cyber forensics and investigation have become critical disciplines that play a vital role in safeguarding digital assets and ensuring justice in cyberspace. With the rise in cybercrimes such as hacking, identity theft, digital fraud, and cyber terrorism, the need for effective forensic methodologies and investigative techniques has never been more crucial.
This book delves into the fundamental and advanced aspects of cyber forensics, equipping readers with the necessary knowledge to analyze digital evidence, trace cybercriminal activities, and enforce cybersecurity protocols. It aims to bridge the gap between theoretical concepts and practical applications, offering insights into real-world cyber investigations and the latest forensic tools and technologies.
The field of cyber forensics is not just for law enforcement officers and security professionals; it is equally relevant for students, researchers, and anyone interested in understanding digital security. As cyber threats continue to grow in complexity, this book serves as a comprehensive guide, shedding light on the methodologies used in cyber investigations and the legal frameworks governing digital evidence.
Cloud computing and network storage have become integral to modern digital infrastructure, transforming how data is processed, stored, and accessed. With the increasing demand for scalable and cost-effective computing solutions, cloud-based technologies offer flexibility, efficiency, and security across various domains. Network storage, on the other hand, ensures reliable data management, enabling seamless access and collaboration in an interconnected world.
Cloud computing and network storage have become integral to modern digital infrastructure, transforming how data is processed, stored, and accessed. With the increasing demand for scalable and cost-effective computing solutions, cloud-based technologies offer flexibility, efficiency, and security across various domains. Network storage, on the other hand, ensures reliable data management, enabling seamless access and collaboration in an interconnected world.
This work aims to provide a comprehensive understanding of cloud computing and network storage, covering fundamental concepts, architectures, and emerging trends. It is designed for students, professionals, and researchers seeking to explore virtualization, distributed systems, storage technologies, and security challenges.
By bridging theoretical knowledge with practical applications, this work aspires to equip readers with the necessary skills to navigate and innovate within this rapidly evolving field. We hope it serves as a valuable resource for those eager to contribute to the advancement of cloud technologies.
Cloud computing and network storage have become integral to modern digital infrastructure, transforming how data is processed, stored, and accessed. With the increasing demand for scalable and cost-effective computing solutions, cloud-based technologies offer flexibility, efficiency, and security across various domains. Network storage, on the other hand, ensures reliable data management, enabling seamless access and collaboration in an interconnected world.
Cloud computing and network storage have become integral to modern digital infrastructure, transforming how data is processed, stored, and accessed. With the increasing demand for scalable and cost-effective computing solutions, cloud-based technologies offer flexibility, efficiency, and security across various domains. Network storage, on the other hand, ensures reliable data management, enabling seamless access and collaboration in an interconnected world.
This work aims to provide a comprehensive understanding of cloud computing and network storage, covering fundamental concepts, architectures, and emerging trends. It is designed for students, professionals, and researchers seeking to explore virtualization, distributed systems, storage technologies, and security challenges.
By bridging theoretical knowledge with practical applications, this work aspires to equip readers with the necessary skills to navigate and innovate within this rapidly evolving field. We hope it serves as a valuable resource for those eager to contribute to the advancement of cloud technologies.
Artificial Intelligence (AI) has evolved from a futuristic concept to a driving force behind countless innovations. At its core, AI relies on data science—the art and science of extracting insights from data to build intelligent systems. In an age where data is ubiquitous and continuously growing, the ability to harness and interpret it has become essential.
In this book, we embark on a journey through the intersection of data science and AI, exp
Artificial Intelligence (AI) has evolved from a futuristic concept to a driving force behind countless innovations. At its core, AI relies on data science—the art and science of extracting insights from data to build intelligent systems. In an age where data is ubiquitous and continuously growing, the ability to harness and interpret it has become essential.
In this book, we embark on a journey through the intersection of data science and AI, exploring how data is transformed into actionable insights and powerful machine learning models. From the basics of data pre-processing to the complexities of deep learning and neural networks, we will cover the techniques that power AI systems today. Along the way, we will also examine the ethical considerations that must guide responsible AI development.
Whether you are new to data science or an experienced practitioner, this book will equip you with the knowledge to unlock the potential of AI through data.
Artificial Intelligence (AI) has evolved from a futuristic concept to a driving force behind countless innovations. At its core, AI relies on data science—the art and science of extracting insights from data to build intelligent systems. In an age where data is ubiquitous and continuously growing, the ability to harness and interpret it has become essential.
In this book, we embark on a journey through the intersection of data science and AI, exp
Artificial Intelligence (AI) has evolved from a futuristic concept to a driving force behind countless innovations. At its core, AI relies on data science—the art and science of extracting insights from data to build intelligent systems. In an age where data is ubiquitous and continuously growing, the ability to harness and interpret it has become essential.
In this book, we embark on a journey through the intersection of data science and AI, exploring how data is transformed into actionable insights and powerful machine learning models. From the basics of data pre-processing to the complexities of deep learning and neural networks, we will cover the techniques that power AI systems today. Along the way, we will also examine the ethical considerations that must guide responsible AI development.
Whether you are new to data science or an experienced practitioner, this book will equip you with the knowledge to unlock the potential of AI through data.
A book on Computer Networks with Routing Approach would typically explore the foundational concepts, protocols, and algorithms that enable data transmission and management within modern computer networks, focusing particularly on the role of routing in ensuring efficient communication.
The book would begin with an introduction to computer networks, covering the basics of networking, types of networks (e.g., LAN, WAN), and the OSI and TCP/IP models. It
A book on Computer Networks with Routing Approach would typically explore the foundational concepts, protocols, and algorithms that enable data transmission and management within modern computer networks, focusing particularly on the role of routing in ensuring efficient communication.
The book would begin with an introduction to computer networks, covering the basics of networking, types of networks (e.g., LAN, WAN), and the OSI and TCP/IP models. It would explain the importance of routing in these networks, where routers are used to forward data packets between different network segments. The book would then delve into the two primary types of routing: static routing, where routes are manually configured, and dynamic routing, which uses algorithms to adapt to network changes.
The book would also cover advanced topics such as link-state routing, distance-vector routing, explaining how each works and the trade-offs between them in terms of scalability, reliability, and efficiency. Additionally, the text would explore concepts of convergence, network topology, and load balancing, providing real-world case studies to illustrate how routing techniques are applied in large-scale networks, including the Internet.
A book on Computer Networks with Routing Approach would typically explore the foundational concepts, protocols, and algorithms that enable data transmission and management within modern computer networks, focusing particularly on the role of routing in ensuring efficient communication.
The book would begin with an introduction to computer networks, covering the basics of networking, types of networks (e.g., LAN, WAN), and the OSI and TCP/IP models. It
A book on Computer Networks with Routing Approach would typically explore the foundational concepts, protocols, and algorithms that enable data transmission and management within modern computer networks, focusing particularly on the role of routing in ensuring efficient communication.
The book would begin with an introduction to computer networks, covering the basics of networking, types of networks (e.g., LAN, WAN), and the OSI and TCP/IP models. It would explain the importance of routing in these networks, where routers are used to forward data packets between different network segments. The book would then delve into the two primary types of routing: static routing, where routes are manually configured, and dynamic routing, which uses algorithms to adapt to network changes.
The book would also cover advanced topics such as link-state routing, distance-vector routing, explaining how each works and the trade-offs between them in terms of scalability, reliability, and efficiency. Additionally, the text would explore concepts of convergence, network topology, and load balancing, providing real-world case studies to illustrate how routing techniques are applied in large-scale networks, including the Internet.
Deep Learning for Natural Language Processing is a comprehensive guide that explores the intersection of deep learning techniques and natural language processing (NLP).
The book begins with an overview of fundamental NLP concepts and progresses into advanced deep learning architectures, including feedforward neural networks, recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and attention mechanisms. Each chapter offers cle
Deep Learning for Natural Language Processing is a comprehensive guide that explores the intersection of deep learning techniques and natural language processing (NLP).
The book begins with an overview of fundamental NLP concepts and progresses into advanced deep learning architectures, including feedforward neural networks, recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and attention mechanisms. Each chapter offers clear explanations of the theory behind the models, followed by practical code examples using popular deep learning frameworks like TensorFlow and PyTorch.
Readers will learn how to preprocess text data, build word embeddings, and work with sequence models to handle real-world language data. The authors also cover cutting-edge techniques such as transformers, BERT, and GPT, which have revolutionized NLP tasks. Emphasizing both theory and hands-on practice, this book is ideal for students, researchers, and professionals looking to deepen their understanding of deep learning methods in the context of language.
By the end of the book, readers will be equipped with the skills to tackle a wide range of NLP problems using deep learning, and will gain a deeper appreciation for the powerful combination of deep learning and natural language processing in transforming industries like healthcare, finance, and customer service.
Deep Learning for Natural Language Processing is a comprehensive guide that explores the intersection of deep learning techniques and natural language processing (NLP).
The book begins with an overview of fundamental NLP concepts and progresses into advanced deep learning architectures, including feedforward neural networks, recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and attention mechanisms. Each chapter offers cle
Deep Learning for Natural Language Processing is a comprehensive guide that explores the intersection of deep learning techniques and natural language processing (NLP).
The book begins with an overview of fundamental NLP concepts and progresses into advanced deep learning architectures, including feedforward neural networks, recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and attention mechanisms. Each chapter offers clear explanations of the theory behind the models, followed by practical code examples using popular deep learning frameworks like TensorFlow and PyTorch.
Readers will learn how to preprocess text data, build word embeddings, and work with sequence models to handle real-world language data. The authors also cover cutting-edge techniques such as transformers, BERT, and GPT, which have revolutionized NLP tasks. Emphasizing both theory and hands-on practice, this book is ideal for students, researchers, and professionals looking to deepen their understanding of deep learning methods in the context of language.
By the end of the book, readers will be equipped with the skills to tackle a wide range of NLP problems using deep learning, and will gain a deeper appreciation for the powerful combination of deep learning and natural language processing in transforming industries like healthcare, finance, and customer service.
"Advanced Computer Networks: A Practical Approach" offers a comprehensive exploration of modern networking concepts, tailored for both aspiring professionals and seasoned experts. This book delves into the intricate architecture of advanced networks, emphasizing practical applications and real-world scenarios.
Readers will gain a thorough understanding of the OSI and TCP/IP models, alongside in-depth discussions of key protocols such as BGP, OSPF, and
"Advanced Computer Networks: A Practical Approach" offers a comprehensive exploration of modern networking concepts, tailored for both aspiring professionals and seasoned experts. This book delves into the intricate architecture of advanced networks, emphasizing practical applications and real-world scenarios.
Readers will gain a thorough understanding of the OSI and TCP/IP models, alongside in-depth discussions of key protocols such as BGP, OSPF, and MPLS. Each chapter integrates hands-on labs and case studies, enabling readers to design and implement efficient network solutions, from subnetting and VLANs to advanced security measures.
With a strong focus on network performance and management, the book covers Quality of Service (QoS) strategies, traffic monitoring tools, and troubleshooting techniques, ensuring a well-rounded skill set. Additionally, readers will explore cutting-edge topics like Software-Defined Networking (SDN) and Network Function Virtualization (NFV), equipping them for the evolving landscape of cloud networking.
Packed with practical exercises and illustrative diagrams, this book bridges theory and practice, making complex concepts accessible. Ideal for self-study or as a supplementary resource in academic courses, "Advanced Computer Networks" is your essential guide to mastering the technologies and techniques that drive today’s digital communication landscape.
"Advanced Computer Networks: A Practical Approach" offers a comprehensive exploration of modern networking concepts, tailored for both aspiring professionals and seasoned experts. This book delves into the intricate architecture of advanced networks, emphasizing practical applications and real-world scenarios.
Readers will gain a thorough understanding of the OSI and TCP/IP models, alongside in-depth discussions of key protocols such as BGP, OSPF, and
"Advanced Computer Networks: A Practical Approach" offers a comprehensive exploration of modern networking concepts, tailored for both aspiring professionals and seasoned experts. This book delves into the intricate architecture of advanced networks, emphasizing practical applications and real-world scenarios.
Readers will gain a thorough understanding of the OSI and TCP/IP models, alongside in-depth discussions of key protocols such as BGP, OSPF, and MPLS. Each chapter integrates hands-on labs and case studies, enabling readers to design and implement efficient network solutions, from subnetting and VLANs to advanced security measures.
With a strong focus on network performance and management, the book covers Quality of Service (QoS) strategies, traffic monitoring tools, and troubleshooting techniques, ensuring a well-rounded skill set. Additionally, readers will explore cutting-edge topics like Software-Defined Networking (SDN) and Network Function Virtualization (NFV), equipping them for the evolving landscape of cloud networking.
Packed with practical exercises and illustrative diagrams, this book bridges theory and practice, making complex concepts accessible. Ideal for self-study or as a supplementary resource in academic courses, "Advanced Computer Networks" is your essential guide to mastering the technologies and techniques that drive today’s digital communication landscape.
"Cloud Computing and Its Applications" explores the transformative impact of cloud technology across various industries. This comprehensive guide delves into the fundamental concepts of cloud computing, including infrastructure, platforms, and software as services. It examines key deployment models—public, private, and hybrid clouds—highlighting their unique advantages and challenges.
The book also investigates real-world applications, show
"Cloud Computing and Its Applications" explores the transformative impact of cloud technology across various industries. This comprehensive guide delves into the fundamental concepts of cloud computing, including infrastructure, platforms, and software as services. It examines key deployment models—public, private, and hybrid clouds—highlighting their unique advantages and challenges.
The book also investigates real-world applications, showcasing how businesses leverage cloud solutions for enhanced efficiency, scalability, and cost-effectiveness. Case studies illustrate successful cloud implementations in sectors like healthcare, finance, and education, demonstrating innovation and competitive advantage.
Additionally, the text addresses critical considerations such as security, compliance, and data management, equipping readers with the knowledge to navigate the complexities of cloud adoption. Ideal for students, professionals, and decision-makers, this book serves as a valuable resource for understanding the dynamics of cloud computing and its potential to reshape the digital landscape.
"Cloud Computing and Its Applications" explores the transformative impact of cloud technology across various industries. This comprehensive guide delves into the fundamental concepts of cloud computing, including infrastructure, platforms, and software as services. It examines key deployment models—public, private, and hybrid clouds—highlighting their unique advantages and challenges.
The book also investigates real-world applications, show
"Cloud Computing and Its Applications" explores the transformative impact of cloud technology across various industries. This comprehensive guide delves into the fundamental concepts of cloud computing, including infrastructure, platforms, and software as services. It examines key deployment models—public, private, and hybrid clouds—highlighting their unique advantages and challenges.
The book also investigates real-world applications, showcasing how businesses leverage cloud solutions for enhanced efficiency, scalability, and cost-effectiveness. Case studies illustrate successful cloud implementations in sectors like healthcare, finance, and education, demonstrating innovation and competitive advantage.
Additionally, the text addresses critical considerations such as security, compliance, and data management, equipping readers with the knowledge to navigate the complexities of cloud adoption. Ideal for students, professionals, and decision-makers, this book serves as a valuable resource for understanding the dynamics of cloud computing and its potential to reshape the digital landscape.
In an era defined by rapid technological advancement, High-Performance Computing (HPC) stands at the forefront of innovation, enabling us to solve problems once deemed insurmountable. From weather forecasting and climate modeling to drug discovery and complex simulations, HPC empowers researchers and industries to push the boundaries of what is possible.
This book aims to demystify the world of HPC, providing a thorough exploration of its fundamental c
In an era defined by rapid technological advancement, High-Performance Computing (HPC) stands at the forefront of innovation, enabling us to solve problems once deemed insurmountable. From weather forecasting and climate modeling to drug discovery and complex simulations, HPC empowers researchers and industries to push the boundaries of what is possible.
This book aims to demystify the world of HPC, providing a thorough exploration of its fundamental concepts, architectures, and programming models. We cover essential topics such as parallel computing, cluster management, and performance optimization, making it accessible to both newcomers and experienced practitioners.
Our goal is not only to impart knowledge but also to inspire a deeper understanding of how HPC can be leveraged to address real-world challenges. Each chapter is designed to build upon the last, guiding readers through practical examples and case studies that illustrate the transformative power of HPC.
We are grateful to the community of researchers, educators, and practitioners whose contributions have shaped this field. Their work serves as a testament to the potential of HPC to drive scientific discovery and technological progress.
As you embark on this journey through the realms of High-Performance Computing, we hope you find inspiration, knowledge, and the tools to harness this incredible technology.
In an era defined by rapid technological advancement, High-Performance Computing (HPC) stands at the forefront of innovation, enabling us to solve problems once deemed insurmountable. From weather forecasting and climate modeling to drug discovery and complex simulations, HPC empowers researchers and industries to push the boundaries of what is possible.
This book aims to demystify the world of HPC, providing a thorough exploration of its fundamental c
In an era defined by rapid technological advancement, High-Performance Computing (HPC) stands at the forefront of innovation, enabling us to solve problems once deemed insurmountable. From weather forecasting and climate modeling to drug discovery and complex simulations, HPC empowers researchers and industries to push the boundaries of what is possible.
This book aims to demystify the world of HPC, providing a thorough exploration of its fundamental concepts, architectures, and programming models. We cover essential topics such as parallel computing, cluster management, and performance optimization, making it accessible to both newcomers and experienced practitioners.
Our goal is not only to impart knowledge but also to inspire a deeper understanding of how HPC can be leveraged to address real-world challenges. Each chapter is designed to build upon the last, guiding readers through practical examples and case studies that illustrate the transformative power of HPC.
We are grateful to the community of researchers, educators, and practitioners whose contributions have shaped this field. Their work serves as a testament to the potential of HPC to drive scientific discovery and technological progress.
As you embark on this journey through the realms of High-Performance Computing, we hope you find inspiration, knowledge, and the tools to harness this incredible technology.
Data mining is the process of extracting knowledge or insights from large amounts of data using various statistical and computational techniques. The data can be structured, semi-structured, or unstructured, and it can be stored in various forms such as databases, data warehouses, and data lakes. The primary goal of data mining is to discover hidden patterns and relationships in the data that can be used to make informed decisions or predictions. This involves
Data mining is the process of extracting knowledge or insights from large amounts of data using various statistical and computational techniques. The data can be structured, semi-structured, or unstructured, and it can be stored in various forms such as databases, data warehouses, and data lakes. The primary goal of data mining is to discover hidden patterns and relationships in the data that can be used to make informed decisions or predictions. This involves exploring the data using various techniques such as clustering, classification, regression analysis, association rule mining, and anomaly detection. Data mining has a wide range of applications across various industries, including marketing, finance, healthcare, and telecommunications. For example, in marketing, data mining can be used to identify customer segments and target marketing campaigns, while in healthcare, it can be used to identify risk factors for diseases and develop personalized treatment plans.
Welcome to the world of Python, where limitless possibilities in programming await! Python Unleashed is the ultimate book for both beginners and experienced developers who aspire to harness the full potential of this versatile programming language. Whether you're a seasoned coder looking to expand your skillset or a newcomer eager to dive into the world of programming, this book will take you on an exhilarating journey through Python's diverse landscape.
Welcome to the world of Python, where limitless possibilities in programming await! Python Unleashed is the ultimate book for both beginners and experienced developers who aspire to harness the full potential of this versatile programming language. Whether you're a seasoned coder looking to expand your skillset or a newcomer eager to dive into the world of programming, this book will take you on an exhilarating journey through Python's diverse landscape.
Inside Python Unleashed, you'll embark on an immersive learning experience that covers the language's fundamentals, advanced concepts, and real-world applications. Step-by-step, you'll build a solid foundation in Python programming, gaining confidence in your abilities to tackle complex challenges and build sophisticated applications.
Big data analytics describes the process of uncovering trends, patterns, and correlations in large amounts of raw data to help make data-informed decisions.
Big data analytics describes the process of uncovering trends, patterns, and correlations in large amounts of raw data to help make data-informed decisions.
This book is a comprehensive introduction to the principles and technologies that underlie computer networks. It is intended for students and professionals who seek to gain a solid foundation in the basics of network architecture, topology, and protocols, The book begins by introducing the fundamental concepts of computer networks, including network types, architectures, and topologies. It then moves on to explore the different layers of the OSI and TCP/IP mod
This book is a comprehensive introduction to the principles and technologies that underlie computer networks. It is intended for students and professionals who seek to gain a solid foundation in the basics of network architecture, topology, and protocols, The book begins by introducing the fundamental concepts of computer networks, including network types, architectures, and topologies. It then moves on to explore the different layers of the OSI and TCP/IP models, providing a detailed explanation of the protocols and services that operate at each layer, The book also covers important topics such as network addressing, routing, and switching, as well as common network applications and services such as email, file sharing, and web browsing., Throughout the book, the authors emphasize practical examples and real-world applications, helping readers understand how network technology is used in various industries and scenarios. They also provide tips and best practices to help readers optimize their network performance, enhance security, and troubleshoot common issues, By the end of the book, readers will have a solid understanding of the principles and technologies that underlie computer networks, as well as the ability to design, build, and manage basic network configurations,Whether you are a student looking to gain a strong foundation in network technology or a professional seeking to enhance your skills, this book is an essential guide to the basics of computer networks.
cloud computing is the delivery of computing services — including servers, storage, databases, networking, software, analytics, and intelligence — over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale.
cloud computing is the delivery of computing services — including servers, storage, databases, networking, software, analytics, and intelligence — over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale.
Business is the act of doing something productive to serve someone’s
needs, and thus earn a living, and make the world a better place. Business
activities are recorded on paper or using electronic media, and then these
records become data. There is more data from customers’ responses and
on the industry as a whole. All this data can be analyzed and mined using
special tools and techniques to generate patterns and intelligence, w
Business is the act of doing something productive to serve someone’s
needs, and thus earn a living, and make the world a better place. Business
activities are recorded on paper or using electronic media, and then these
records become data. There is more data from customers’ responses and
on the industry as a whole. All this data can be analyzed and mined using
special tools and techniques to generate patterns and intelligence, which
reflect how the business is functioning. These ideas can then be fed back
into the business so that it can evolve to become more effective and efficient
in serving customer needs.
This book offers how Wireless computing mobile services can be used in order to simplify the mobile users. The main contribution of this book is enhancingWireless computing mobile application development stages as analysis, design, development and test. how to interact with application very easily, and developed android apllications.
This book offers how Wireless computing mobile services can be used in order to simplify the mobile users. The main contribution of this book is enhancingWireless computing mobile application development stages as analysis, design, development and test. how to interact with application very easily, and developed android apllications.
Cyber security is widely viewed as a matter of pressing national importance. Many elements of cyberspace are notoriously vulnerable to an expanding range of attacks by a spectrum of hackers, criminals, terrorists, and state actors. For example, government agencies and private-sector companies, both large and small, suffer from cyber thefts of sensitive information, cyber vandalism (e.g., defacing of websites), and denial-of-service attacks. The nation’s
Cyber security is widely viewed as a matter of pressing national importance. Many elements of cyberspace are notoriously vulnerable to an expanding range of attacks by a spectrum of hackers, criminals, terrorists, and state actors. For example, government agencies and private-sector companies, both large and small, suffer from cyber thefts of sensitive information, cyber vandalism (e.g., defacing of websites), and denial-of-service attacks. The nation’s critical infrastructure, including the electric power grid, air traffic control system, financial systems, and communication networks, depends extensively on information technology for its operation. National policymakers have become increasingly concerned that adversaries backed by considerable resources will attempt to exploit the cyber vulnerabilities in the critical infrastructure, thereby inflicting substantial harm on the nation. Numerous policy proposals have been advanced, and a number of bills have been introduced in Congress to tackle parts of the cyber security challenge. This book is designed to serve as the textbook for a semester course devoted to cyber security. It is focused on helping students acquire the skills sought in the professional workforce.
Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.
Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.
The purpose of this book is to provide an introductory text for understanding the C++
language and to empower the reader to write C++ programs. The book also introduces
reader to the paradigm of object oriented programming. The main strength of
this book is that it is written by Teacher for students but will be equally useful for
intermediate level programmers, and software development professional. The author is in
the best position to ide
The purpose of this book is to provide an introductory text for understanding the C++
language and to empower the reader to write C++ programs. The book also introduces
reader to the paradigm of object oriented programming. The main strength of
this book is that it is written by Teacher for students but will be equally useful for
intermediate level programmers, and software development professional. The author is in
the best position to identify and address issues and areas where a student needs maximum
help in understanding the C++ concepts and developing programming skills.
Beginning Software Engineering demystifies the software engineering methodologies and techniques that professional developers use to design and build robust, efficient, and consistently reliable software. Free of jargon and assuming no previous programming, development, or management experience, this accessible guide explains important concepts and techniques that can be applied to any programming language. Each chapter ends with exercises that let you test yo
Beginning Software Engineering demystifies the software engineering methodologies and techniques that professional developers use to design and build robust, efficient, and consistently reliable software. Free of jargon and assuming no previous programming, development, or management experience, this accessible guide explains important concepts and techniques that can be applied to any programming language. Each chapter ends with exercises that let you test your understanding and help you elaborate on the chapter's main concepts. Everything you need to understand waterfall, prototype, agile, RAD, Programming, and many other development models is inside!
The Internet of Things is a network of billions of embedded sensors, actuators, and computers. With basic programming skills, these devices can be used to build a wide range of helpful systems. This practical guide shows you where to start when creating your own IoT projects.
The IoT refers to the network interconnection of all everyday objects, which are often equipped with some kind of intelligence; it is a platform for teams that communicate electro
The Internet of Things is a network of billions of embedded sensors, actuators, and computers. With basic programming skills, these devices can be used to build a wide range of helpful systems. This practical guide shows you where to start when creating your own IoT projects.
The IoT refers to the network interconnection of all everyday objects, which are often equipped with some kind of intelligence; it is a platform for teams that communicate electronically and share specific information and data with the world around them, combining physical and digital components to create new products.
The Internet of Things—a networked world of connected devices, objects, and people. In this book, Samuel Greengard offers a guided tour through this emerging world and how it will change the way we live and work. Greengard explains that the Internet of Things (IoT) is still in its early stages.
Welcome to the world of object-oriented programming using C++ and Java. Get ready for an exciting tour!
While teaching OOPs, C++ and Java to graduate and postgraduate students and practicing professionals from the industry for several years, our experience shows that:
There are good programmers both in C++ and Java but very few among them understand the underlying OOPs concepts.
The text books available treat C++, Java and OOPs concepts
Welcome to the world of object-oriented programming using C++ and Java. Get ready for an exciting tour!
While teaching OOPs, C++ and Java to graduate and postgraduate students and practicing professionals from the industry for several years, our experience shows that:
There are good programmers both in C++ and Java but very few among them understand the underlying OOPs concepts.
The text books available treat C++, Java and OOPs concepts as different and separate and students get exposed to these concepts in a compartmentalized manner.
The study of OOPs languages, analysis and design and software development are all treated separately thereby sacrificing the integrated OOPs approach.
C++ is a highly evolved language and is used in systems.
cloud computing is the delivery of computing services — including servers, storage, databases, networking, software, analytics, and intelligence — over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale.
cloud computing infrastructures, developers can drastically reduce the time and the cost of application development. You can quickly build, deploy, and scale applica
cloud computing is the delivery of computing services — including servers, storage, databases, networking, software, analytics, and intelligence — over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale.
cloud computing infrastructures, developers can drastically reduce the time and the cost of application development. You can quickly build, deploy, and scale applications using cloud-native technologies and approaches for web, mobile, and API.
What is the purpose of the cloud?
The cloud enables users to access the same files and applications from almost any device, because the computing and storage takes place on servers in a data center, instead of locally on the user device.
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