Forthcoming Networks and Sustainability in the AIoT Era Second International Conference FoNeS-AIoT 2024 - Volume 2 /
Forthcoming Networks and Sustainability in the AIoT Era Second International Conference FoNeS-AIoT 2024 - Volume 2 / [electronic resource] :
edited by Jawad Rasheed, Adnan M. Abu-Mahfouz, Muhammad Fahim.
- 1st ed. 2024.
- VIII, 424 p. 212 illus., 173 illus. in color. online resource.
- Lecture Notes in Networks and Systems, 1036 2367-3389 ; .
- Lecture Notes in Networks and Systems, 1036 .
Analyzing the Economic Viability and Design of Solar Powered Water Pumps for Farming Irrigation Case Study Conducted in Somalia -- Evaluation of No load Losses in the Single sheet Double sheet and Triple sheet Step Lap Joints of the Transformer Core -- Modeling and Simulation of a Hybrid Electrical Grid for Reliability and Power Quality Enchantment -- Lane Segmentation and Turn Prediction Using CNN and SVM Approach -- Internet of Things Data Privacy And Security Based On Blockchain Technology -- Enhancing Biogeographical Ancestry Prediction with Deep Learning A Long Short Term Memory Approach -- Stacking Ensemble for Pill Image Classification.
This book introduces a groundbreaking approach to enhancing IoT device security, providing a comprehensive overview of its applications and methodologies. Covering a wide array of topics, from crime prediction to cyberbullying detection, from facial recognition to analyzing email spam, it addresses diverse challenges in contemporary society. Aimed at researchers, practitioners, and policymakers, this book equips readers with practical tools to tackle real-world issues using advanced machine learning algorithms. Whether you're a data scientist, law enforcement officer, or urban planner, this book is a valuable resource for implementing predictive models and enhancing public safety measures. It is a comprehensive guide for implementing machine learning solutions across various domains, ensuring optimal performance and reliability. Whether you're delving into IoT security or exploring the potential of AI in urban landscapes, this book provides invaluable insights and tools to navigate the evolving landscape of technology and data science. The book provides a comprehensive overview of the challenges and solutions in contemporary cybersecurity. Through case studies and practical examples, readers gain a deeper understanding of the security concerns surrounding IoT devices and learn how to mitigate risks effectively. The book's interdisciplinary approach caters to a diverse audience, including academics, industry professionals, and government officials, who seek to address the growing cybersecurity threats in IoT environments. Key uses of this book include implementing robust security measures for IoT devices, conducting research on machine learning algorithms for attack detection, and developing policies to enhance cybersecurity in IoT ecosystems. By leveraging advanced machine learning techniques, readers can effectively detect and mitigate cyber threats, ensuring the integrity and reliability of IoT systems. Overall, this book is a valuable resource for anyone involved in designing, implementing, or regulating IoT devices and systems.
9783031628818
Computational intelligence.
Artificial intelligence.
Engineering--Data processing.
Computational Intelligence.
Artificial Intelligence.
Data Engineering.
Q342
006.3
Analyzing the Economic Viability and Design of Solar Powered Water Pumps for Farming Irrigation Case Study Conducted in Somalia -- Evaluation of No load Losses in the Single sheet Double sheet and Triple sheet Step Lap Joints of the Transformer Core -- Modeling and Simulation of a Hybrid Electrical Grid for Reliability and Power Quality Enchantment -- Lane Segmentation and Turn Prediction Using CNN and SVM Approach -- Internet of Things Data Privacy And Security Based On Blockchain Technology -- Enhancing Biogeographical Ancestry Prediction with Deep Learning A Long Short Term Memory Approach -- Stacking Ensemble for Pill Image Classification.
This book introduces a groundbreaking approach to enhancing IoT device security, providing a comprehensive overview of its applications and methodologies. Covering a wide array of topics, from crime prediction to cyberbullying detection, from facial recognition to analyzing email spam, it addresses diverse challenges in contemporary society. Aimed at researchers, practitioners, and policymakers, this book equips readers with practical tools to tackle real-world issues using advanced machine learning algorithms. Whether you're a data scientist, law enforcement officer, or urban planner, this book is a valuable resource for implementing predictive models and enhancing public safety measures. It is a comprehensive guide for implementing machine learning solutions across various domains, ensuring optimal performance and reliability. Whether you're delving into IoT security or exploring the potential of AI in urban landscapes, this book provides invaluable insights and tools to navigate the evolving landscape of technology and data science. The book provides a comprehensive overview of the challenges and solutions in contemporary cybersecurity. Through case studies and practical examples, readers gain a deeper understanding of the security concerns surrounding IoT devices and learn how to mitigate risks effectively. The book's interdisciplinary approach caters to a diverse audience, including academics, industry professionals, and government officials, who seek to address the growing cybersecurity threats in IoT environments. Key uses of this book include implementing robust security measures for IoT devices, conducting research on machine learning algorithms for attack detection, and developing policies to enhance cybersecurity in IoT ecosystems. By leveraging advanced machine learning techniques, readers can effectively detect and mitigate cyber threats, ensuring the integrity and reliability of IoT systems. Overall, this book is a valuable resource for anyone involved in designing, implementing, or regulating IoT devices and systems.
9783031628818
Computational intelligence.
Artificial intelligence.
Engineering--Data processing.
Computational Intelligence.
Artificial Intelligence.
Data Engineering.
Q342
006.3
