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Leveraging futuristic machine learni...

Abdul Hamid, Abu Bakar, (1967-)

Leveraging futuristic machine learning and next-generational security for e-governance

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Leveraging futuristic machine learning and next-generational security for e-governanceRajeev Kumar, Abu Bakar Abdul Hamid, Noor Inayah Binti Ya'akub, Tadiwa Elisha Nyamasvisva, Rajesh Kumar Tiwari, editors.
其他作者:	Tiwari, Rajesh Kumar.
出版者:	Hershey, Pennsylvania :IGI Global Scientific Publishing,2025.
面頁冊數:	1 online resource (xx, 335 p.) :ill.
標題:	Deep learning (Machine learning)Industrial applications.
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-7883-0
ISBN:	9798369378854$q(ebook)

Leveraging futuristic machine learning and next-generational security for e-governance
Leveraging futuristic machine learning and next-generational security for e-governance[electronic resource] /Rajeev Kumar, Abu Bakar Abdul Hamid, Noor Inayah Binti Ya'akub, Tadiwa Elisha Nyamasvisva, Rajesh Kumar Tiwari, editors. - Hershey, Pennsylvania :IGI Global Scientific Publishing,2025. - 1 online resource (xx, 335 p.) :ill. - Advances in electronic government, digital divide, and regional development (AEGDDRD) book series. - Advances in electronic government, digital divide, and regional development (AEGDDRD) book series..

Includes bibliographical references and index.

Preface -- Chapter 1. Bridging E-Governance and Sustainability: Unveiling Synergies for a Sustainable Future -- Chapter 2. The Next Generation of Greentech Trends and Innovations Shaping Sustainable E-Governance -- Chapter 3. A Primer for Governance -- Chapter 4. Machine Learning for Traffic Management, Object Detection, and Collision Avoidance in Autonomous Driving for Smart City Governance -- Chapter 5. Cutting-Edge Deep Learning Approaches for Human Pose Estimationand Activity Recognition in Smart Cities -- Chapter 6. Approaches of Deep Learning Used in Cyber Security and Cyber-Crime -- Chapter 7. Cybersecurity Affected Social Life Using Online Transactions in India -- Chapter 8. Cyber-Physical Systems for Enhancement in Security While Using Cyber -- Chapter 9. Security of Linear Regression Models -- Chapter 10. Optimizing Image Encryption Efficiency Through Advanced Deep Learning Architectures and Techniques -- Chapter 11. Machine Learning for Healthcare Fraud Detection: A Comprehensive Review Literature -- Chapter 12. Impact of the COVID-19 Pandemic on Healthcare Fraud: A Comprehensive Literature Review -- Compilation of References -- About the Contributors -- Index.

"In an era defined by rapid technological advancement and a pressing need for effective governance, the intersection of machine learning and cybersecurity has emerged as a pivotal area of exploration and innovation. E-governance serves as a vital framework for enhancing the delivery of public services, increasing governmental transparency, and fostering citizen engagement. However, as governments increasingly rely on digital infrastructures, they expose themselves to a myriad of cyber threats that can undermine public trust and security. The contemporary landscape of e-governance must not only adapt to the wave of new digital tools but also ensure the security and integrity of the data that underpins them. Leveraging Futuristic Machine Learning and Next-Generational Security for e-Governance brings together a comprehensive collection of insights and research from leading experts in the fields of artificial intelligence, cybersecurity, andpublic administration. The contributions to this volume encompass theoretical frameworks, case studies, and practical applications that showcase the transformative potential of integrating machine learning with next-generation security solutions. With this resource, researchers, practitioners, and academics can work toward a new age where e-governance thrives at the nexus of machine learning and cybersecurity."--

Mode of access: World Wide Web.

ISBN: 9798369378854$q(ebook)Subjects--Topical Terms:

974324
Deep learning (Machine learning)
--Industrial applications.Subjects--Index Terms:

Activity Recognition.Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: Q325.73 / .L48 2025eb

Dewey Class. No.: 352.3/8028558

Leveraging futuristic machine learning and next-generational security for e-governance
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490 1 $a Advances in electronic government, digital divide, and regional development (AEGDDRD) book series
504 $a Includes bibliographical references and index.
505 0 $a Preface -- Chapter 1. Bridging E-Governance and Sustainability: Unveiling Synergies for a Sustainable Future -- Chapter 2. The Next Generation of Greentech Trends and Innovations Shaping Sustainable E-Governance -- Chapter 3. A Primer for Governance -- Chapter 4. Machine Learning for Traffic Management, Object Detection, and Collision Avoidance in Autonomous Driving for Smart City Governance -- Chapter 5. Cutting-Edge Deep Learning Approaches for Human Pose Estimationand Activity Recognition in Smart Cities -- Chapter 6. Approaches of Deep Learning Used in Cyber Security and Cyber-Crime -- Chapter 7. Cybersecurity Affected Social Life Using Online Transactions in India -- Chapter 8. Cyber-Physical Systems for Enhancement in Security While Using Cyber -- Chapter 9. Security of Linear Regression Models -- Chapter 10. Optimizing Image Encryption Efficiency Through Advanced Deep Learning Architectures and Techniques -- Chapter 11. Machine Learning for Healthcare Fraud Detection: A Comprehensive Review Literature -- Chapter 12. Impact of the COVID-19 Pandemic on Healthcare Fraud: A Comprehensive Literature Review -- Compilation of References -- About the Contributors -- Index.
520 3 $a "In an era defined by rapid technological advancement and a pressing need for effective governance, the intersection of machine learning and cybersecurity has emerged as a pivotal area of exploration and innovation. E-governance serves as a vital framework for enhancing the delivery of public services, increasing governmental transparency, and fostering citizen engagement. However, as governments increasingly rely on digital infrastructures, they expose themselves to a myriad of cyber threats that can undermine public trust and security. The contemporary landscape of e-governance must not only adapt to the wave of new digital tools but also ensure the security and integrity of the data that underpins them. Leveraging Futuristic Machine Learning and Next-Generational Security for e-Governance brings together a comprehensive collection of insights and research from leading experts in the fields of artificial intelligence, cybersecurity, andpublic administration. The contributions to this volume encompass theoretical frameworks, case studies, and practical applications that showcase the transformative potential of integrating machine learning with next-generation security solutions. With this resource, researchers, practitioners, and academics can work toward a new age where e-governance thrives at the nexus of machine learning and cybersecurity."-- $c Provided by publisher.
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653 $a Blockchain.
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653 $a Exogenous Determinants.
653 $a Greentech Trends.
653 $a Machine Learning.
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653 $a Online Transactions.
653 $a Optimizing Image Encryption Efficiency.
653 $a Skill Development.
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653 $a Sustainable E-Governance.
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700 1 $a Ya'akub, Noor Inayah Binti. $3 974281
700 1 $a Kumar, Rajeev. $3 737242
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830 0 $a Advances in electronic government, digital divide, and regional development (AEGDDRD) book series. $3 995004
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