Somanath Tripathy – författare

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9 produkter

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9 produkter

Applications of Machine Learning in Big-Data Analytics and Cloud Computing
AvTalal Ashraf Butt,Sumit Kundum. fl.
E-bok
PDF, Engelska, 2022
745 kr
Läs direkt efter köp
Cloud Computing and Big Data technologies have become the new descriptors of the digital age. The global amount of digital data has increased more than nine times in volume in just five years and by 2030 its volume may reach a staggering 65 trillion gigabytes. This explosion of data has led to opportunities and transformation in various areas such as healthcare, enterprises, industrial manufacturing and transportation. New Cloud Computing and Big Data tools endow researchers and analysts with novel techniques and opportunities to collect, manage and analyze the vast quantities of data. In Cloud and Big Data Analytics, the two areas of Swarm Intelligence and Deep Learning are a developing type of Machine Learning techniques that show enormous potential for solving complex business problems. Deep Learning enables computers to analyze large quantities of unstructured and binary data and to deduce relationships without requiring specific models or programming instructions.
This book introduces the state-of-the-art trends and advances in the use of Machine Learning in Cloud and Big Data Analytics. The book will serve as a reference for Data Scientists, systems architects, developers, new researchers and graduate level students in Computer and Data science. The book will describe the concepts necessary to understand current Machine Learning issues, challenges and possible solutions as well as upcoming trends in Big Data Analytics.
Applications of Machine Learning in Big-Data Analytics and Cloud Computing
AvTalal Ashraf Butt,Sumit Kundum. fl.
E-bok
Engelska, 2022
743 kr
Läs direkt efter köp
Cloud Computing and Big Data technologies have become the new descriptors of the digital age. The global amount of digital data has increased more than nine times in volume in just five years and by 2030 its volume may reach a staggering 65 trillion gigabytes. This explosion of data has led to opportunities and transformation in various areas such as healthcare, enterprises, industrial manufacturing and transportation. New Cloud Computing and Big Data tools endow researchers and analysts with novel techniques and opportunities to collect, manage and analyze the vast quantities of data. In Cloud and Big Data Analytics, the two areas of Swarm Intelligence and Deep Learning are a developing type of Machine Learning techniques that show enormous potential for solving complex business problems. Deep Learning enables computers to analyze large quantities of unstructured and binary data and to deduce relationships without requiring specific models or programming instructions.
This book introduces the state-of-the-art trends and advances in the use of Machine Learning in Cloud and Big Data Analytics. The book will serve as a reference for Data Scientists, systems architects, developers, new researchers and graduate level students in Computer and Data science. The book will describe the concepts necessary to understand current Machine Learning issues, challenges and possible solutions as well as upcoming trends in Big Data Analytics.
Federated Learning
Security and Privacy
AvMinghong Fang,Harsh Kasyapm. fl.
E-bok
PDF, Engelska, 2025
433 kr
Läs direkt efter köp
As data becomes more abundant and widespread across personal devices, the need for secure, privacy-aware machine learning is growing. Federated Learning (FL) offers a promising solution, enabling smart devices to collaboratively train models without sharing raw data. Yet, despite its benefits, FL faces serious risks from poisoning and inference attacks.This book begins by introducing the fundamentals of machine learning, along with core deep learning architectures. Based on this foundation, it introduces the concept of Federated Learning (FL), which is a decentralised approach that enables collaborative model training without sharing raw data. The book provides an in-depth exploration of FL's various forms, system architectures, and practical applications. A significant emphasis is placed on the growing security and privacy concerns in FL, particularly poisoning (both data poisoning and model poisoning) and inference attacks. It discusses state-of-the-art mitigation strategies, such as Byzantine-robust aggregation and inference-resistant techniques, supported with practical implementation insights.This book uniquely bridges foundational concepts with advanced topics in Federated Learning, offering a comprehensive view of its vulnerabilities and their mitigation. By combining theory with practical implementation of attacks and mitigation techniques, it serves as a valuable resource for researchers, practitioners, and students aiming to build secure, privacy-preserving collaborative machine learning systems.This book is unique due to its end-to-end coverage of Federated Learning (FL), from foundational machine and deep learning concepts to real-time deployment of FL along with security and privacy challenges associated. It both explains theory and offers hands-on implementation of attacks and defenses. This practical approach, combined with a clear structure and real-world relevance, makes it ideal for both academic and industry audiences. Promotional emphasis should highlight the book's focus on actionable insights, its relevance to privacy-preserving and secure AI, and its utility as a learning and reference tool for building secure collaborative learning systems.
Federated Learning
Security and Privacy
AvMinghong Fang,Harsh Kasyapm. fl.
E-bok
Engelska, 2025
433 kr
Läs direkt efter köp
As data becomes more abundant and widespread across personal devices, the need for secure, privacy-aware machine learning is growing. Federated Learning (FL) offers a promising solution, enabling smart devices to collaboratively train models without sharing raw data. Yet, despite its benefits, FL faces serious risks from poisoning and inference attacks.This book begins by introducing the fundamentals of machine learning, along with core deep learning architectures. Based on this foundation, it introduces the concept of Federated Learning (FL), which is a decentralised approach that enables collaborative model training without sharing raw data. The book provides an in-depth exploration of FL's various forms, system architectures, and practical applications. A significant emphasis is placed on the growing security and privacy concerns in FL, particularly poisoning (both data poisoning and model poisoning) and inference attacks. It discusses state-of-the-art mitigation strategies, such as Byzantine-robust aggregation and inference-resistant techniques, supported with practical implementation insights.This book uniquely bridges foundational concepts with advanced topics in Federated Learning, offering a comprehensive view of its vulnerabilities and their mitigation. By combining theory with practical implementation of attacks and mitigation techniques, it serves as a valuable resource for researchers, practitioners, and students aiming to build secure, privacy-preserving collaborative machine learning systems.This book is unique due to its end-to-end coverage of Federated Learning (FL), from foundational machine and deep learning concepts to real-time deployment of FL along with security and privacy challenges associated. It both explains theory and offers hands-on implementation of attacks and defenses. This practical approach, combined with a clear structure and real-world relevance, makes it ideal for both academic and industry audiences. Promotional emphasis should highlight the book's focus on actionable insights, its relevance to privacy-preserving and secure AI, and its utility as a learning and reference tool for building secure collaborative learning systems.
Federated Learning
Security and Privacy
AvSomanath Tripathy,Harsh Kasyapm. fl.
Inbunden, Engelska, 2025
1 000 kr
Skickas inom 10-15 vardagar
As data becomes more abundant and widespread across personal devices, the need for secure, privacy-aware machine learning is growing. Federated Learning (FL) offers a promising solution, enabling smart devices to collaboratively train models without sharing raw data. Yet, despite its benefits, FL faces serious risks from poisoning and inference attacks.This book begins by introducing the fundamentals of machine learning, along with core deep learning architectures. Based on this foundation, it introduces the concept of Federated Learning (FL), which is a decentralised approach that enables collaborative model training without sharing raw data. The book provides an in-depth exploration of FL’s various forms, system architectures, and practical applications. A significant emphasis is placed on the growing security and privacy concerns in FL, particularly poisoning (both data poisoning and model poisoning) and inference attacks. It discusses state-of-the-art mitigation strategies, such as Byzantine-robust aggregation and inference-resistant techniques, supported with practical implementation insights.This book uniquely bridges foundational concepts with advanced topics in Federated Learning, offering a comprehensive view of its vulnerabilities and their mitigation. By combining theory with practical implementation of attacks and mitigation techniques, it serves as a valuable resource for researchers, practitioners, and students aiming to build secure, privacy-preserving collaborative machine learning systems.This book is unique due to its end-to-end coverage of Federated Learning (FL), from foundational machine and deep learning concepts to real-time deployment of FL along with security and privacy challenges associated. It both explains theory and offers hands-on implementation of attacks and defenses. This practical approach, combined with a clear structure and real-world relevance, makes it ideal for both academic and industry audiences. Promotional emphasis should highlight the book’s focus on actionable insights, its relevance to privacy-preserving and secure AI, and its utility as a learning and reference tool for building secure collaborative learning systems.
Del 13146 - Lecture Notes in Computer Science
Information Systems Security
17th International Conference, ICISS 2021, Patna, India, December 16–20, 2021, Proceedings
AvSomanath Tripathy,Rudrapatna K. Shyamasundarm. fl.
Häftad, Engelska, 2021
738 kr
Skickas inom 10-15 vardagar
This book constitutes the proceedings of the 17th International Conference on Information Systems Security, ICISS 2021, held in Patna, India, during December 16-20, 2021.The 9 regular papers, 2 short papers and 4 work-in-progress papers included in this volume were carefully reviewed and selected from a total of 48 submissions. The papers were organized in topical sections named: attack detection, malware identification, data security in distributed systems, and applied cryptography.
Information Systems Security
17th International Conference, ICISS 2021, Patna, India, December 16–20, 2021, Proceedings
AvRajiv Ranjan,Rudrapatna K. Shyamasundarm. fl.
E-bok
Engelska, 2021
894 kr
Läs direkt efter köp
This book constitutes the proceedings of the 17th International Conference on Information Systems Security, ICISS 2021, held in Patna, India, during December 16-20, 2021.
The 9 regular papers, 2 short papers and 4 work-in-progress papers included in this volume were carefully reviewed and selected from a total of 48 submissions. The papers were organized in topical sections named: attack detection, malware identification, data security in distributed systems, and applied cryptography.
Applications of Machine Learning in Big-Data Analytics and Cloud Computing
AvSubhendu Kumar Pani,Somanath Tripathym. fl.
Häftad, Engelska, 2024
632 kr
Skickas inom 10-15 vardagar
Cloud Computing and Big Data technologies have become the new descriptors of the digital age. The global amount of digital data has increased more than nine times in volume in just five years and by 2030 its volume may reach a staggering 65 trillion gigabytes. This explosion of data has led to opportunities and transformation in various areas such as healthcare, enterprises, industrial manufacturing and transportation. New Cloud Computing and Big Data tools endow researchers and analysts with novel techniques and opportunities to collect, manage and analyze the vast quantities of data. In Cloud and Big Data Analytics, the two areas of Swarm Intelligence and Deep Learning are a developing type of Machine Learning techniques that show enormous potential for solving complex business problems. Deep Learning enables computers to analyze large quantities of unstructured and binary data and to deduce relationships without requiring specific models or programming instructions.This book introduces the state-of-the-art trends and advances in the use of Machine Learning in Cloud and Big Data Analytics. The book will serve as a reference for Data Scientists, systems architects, developers, new researchers and graduate level students in Computer and Data science. The book will describe the concepts necessary to understand current Machine Learning issues, challenges and possible solutions as well as upcoming trends in Big Data Analytics.
Applications of Machine Learning in Big-Data Analytics and Cloud Computing
AvSubhendu Kumar Pani,Somanath Tripathym. fl.
Inbunden, Engelska, 2021
1 417 kr
Skickas inom 10-15 vardagar
Cloud Computing and Big Data technologies have become the new descriptors of the digital age. The global amount of digital data has increased more than nine times in volume in just five years and by 2030 its volume may reach a staggering 65 trillion gigabytes. This explosion of data has led to opportunities and transformation in various areas such as healthcare, enterprises, industrial manufacturing and transportation. New Cloud Computing and Big Data tools endow researchers and analysts with novel techniques and opportunities to collect, manage and analyze the vast quantities of data. In Cloud and Big Data Analytics, the two areas of Swarm Intelligence and Deep Learning are a developing type of Machine Learning techniques that show enormous potential for solving complex business problems. Deep Learning enables computers to analyze large quantities of unstructured and binary data and to deduce relationships without requiring specific models or programming instructions.This book introduces the state-of-the-art trends and advances in the use of Machine Learning in Cloud and Big Data Analytics. The book will serve as a reference for Data Scientists, systems architects, developers, new researchers and graduate level students in Computer and Data science. The book will describe the concepts necessary to understand current Machine Learning issues, challenges and possible solutions as well as upcoming trends in Big Data Analytics.

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Somanath Tripathy – författare

Applications of Machine Learning in Big-Data Analytics and Cloud Computing

Applications of Machine Learning in Big-Data Analytics and Cloud Computing

Federated Learning

Federated Learning

Federated Learning

Information Systems Security

Information Systems Security

Applications of Machine Learning in Big-Data Analytics and Cloud Computing

Applications of Machine Learning in Big-Data Analytics and Cloud Computing

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