K. Suresh Kumar, PhD, is an Assistant Professor at the Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India. He has presented papers in various national and international conferences and journals, written many chapters, and has one copyright and one patent to his credit. His fields of interest include computer networks, data analytics, and natural language processing.S. Sundaresan, PhD, is an Assistant Professor at the Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India. He has published more than 13 book chapters, 17 articles in various conferences and journals, and has two patents. His areas of research focus on wireless communication and networks.R. Prithiviraj, PhD, is an Assistant Professor in the Department of Electronics and Communication Engineering at the SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India. He specializes in radiation-hardened design for high-frequency circuits, including PLLs and clock generators.T. Ananth Kumar, PhD, is an Associate Professor at the Dept. of Computer Science and Engineering, IFET College of Engineering, Villupuram, Tamil Nadu, India. He has presented papers various at national and international conferences and journals, published seven books and holds six patents. His fields of interest are networks on chips, computer architecture, and ASIC design.S. Balamurugan, PhD, is the Director of Research at iRCS, an Indian Technological Research and Consulting Firm. He has published 75 books, 300 papers in international journals and conferences and 300 patents. With 20 years of research on various cutting-edge technologies, he provides expert guidance in technology forecasting and decision making for leading companies.

• Series Preface xxiPreface xxiiiAcknowledgments xxviiPart I: Core Concepts and Evolution of Platform Engineering 11 Empowering Developer Productivity through Platform Engineering: A Transformative Approach to Scalable and Streamlined Software Development 3C. Saranya Jothi, E. Surya, M. Syed Rabiya, B. Lalitha and R. Roselinkiruba1.1 Introduction to Platform Engineering 41.2 Core Components of a Platform Engineering Strategy 61.3 Automation and Its Role in Enhancing Productivity 81.4 Impact on Developer Experience 111.5 Quantifying Developer Productivity through Platform Engineering 121.6 Future Trends and Opportunities in Platform Engineering 141.7 Conclusion 152 Best Practices for Building Scalable Software Platforms 19Swetha S. and Joe Prathap P. M.2.1 Introduction 192.2 Goals and Expectations 202.3 Basic Key Metrics 222.4 Cloud Computing as Infrastructure 242.5 Microservice Architecture 252.6 Choosing the Right Database Solution 262.7 Effective Scaling Methods 282.8 Conclusion 303 The Role of Platform Engineering in DevOps Transformation—The Future of Software Delivery: How Platform Engineering Transforms DevOps 33S. Sri Devi, T. Sangeetha and Olukayode A. Oki3.1 Introduction 343.2 Benefits of Platform Engineering 363.3 Role and Responsibilities of Platform Engineers 373.4 Tools/Technology in Platform Engineering 37Contents vii3.5 The Origin Story of DevOps 383.6 Extending Agile to the Full Life Cycle 393.7 Difference Between Software Engineer and DevOps Engineer 403.8 The Role of SDLC in DevOps 423.9 Top Programming and Scripting Languages for DevOps 443.10 Choosing the Optimal Operating System for DevOps: Linux, Windows, or MacOS 453.11 What is Command Line Interface? 463.12 What is a Shell? 473.13 Networking and Its Role in DevOps 483.14 Exploring Infrastructure as Code (IaC): Automation, Scalability, and Efficiency in DevOps 513.15 Conclusion 554 The Impact of Platform Engineering on Developer Productivity 59K. Dhivya and Matthew Olusegun Adigun4.1 Introduction 594.2 Key Principles and Components 654.3 Comparison with Platform Engineering, Traditional DevOps, and SRE 68viii Contents4.4 Platform Engineering Centralizes Tooling and Infrastructure 714.5 Developer Productivity: Key Metrics and Challenges 754.6 Common Productivity Bottlenecks in Software Development 784.7 Boosting Developer Productivity 824.8 Self-Service Developer Portals and Internal Platforms 834.9 Examples of Companies that Successfully Adopted Platform Engineering 874.10 Proposed Project: "Boosting Developer Productivity through Platform Engineering: A Practical Exploration with Internal Developer Platforms" 904.11 Conclusion 94Part II: Platform Engineering for Specific Technologies and Architectures 975 Platform Engineering for Cloud-Native Applications 99Amanpreet Singh, Rupinder Singh and Jaswinder Singh5.1 Introduction 1005.2 Cloud-Native Applications 1015.3 Literature Review 1035.4 Cloud-Native Application Advantages 1125.5 Tools and Technologies 1155.6 Areas of Challenge in Platform Engineering 1195.7 Challenges and Considerations 1215.8 Conclusion 1286 Platform Engineering for Cloud-Native Applications: Strategies for Scalable, Cost-Effective, and Automated Cloud Adoption 133P. Divya, R. Parthiban, S. Jayalakshmi and R. Rajmohan6.1 Introduction 1346.2 Core Principles of Platform Engineering 1346.3 Programming for Productivity and Networking 1376.4 Continuous Integration and Continuous Deployment (CI/CD) 138\6.5 Containerization and Orchestration 139x Contents6.6 Cloud-Native Platform Stack 1416.7 Protocols in Building and Managing Cloud-Native Platform 1456.8 Tools and Technology in Platform Engineering 1476.9 Challenges in Platform Engineering for Cloud-Native Applications 1516.10 Best Practices in Platform Engineering for Cloud-Native Applications 1546.11 Performance Analysis 1556.12 Future Trends 1576.13 Conclusion 1587 Optimization Techniques Hybridized into Deep Learning Models 161K. Pathmapriya and Joe Prathap P. M.7.1 Introduction 1627.2 Research Prospects 1637.3 Deep Learning Technique for Diagnosis of Syndrome 1687.4 Meta-Heuristics Algorihms in Medical Diagnosis 1707.5 Data Synthesis 1777.6 Deploying Healthcare Solutions 1797.7 Discussion and Result 1807.8 Challenges and Future Direction 1817.9 Conclusion and Future Research Ideas 1828 Machine Learning and Automation in Platform Engineering: Transforming Monitoring, Scaling, and Self-Healing 189R. Roselinkiruba, Vasumathy M., J. Jude Moses Anto Devakanth, C. Saranya Jothi, J. Kavitha and L. Sharmila8.1 Introduction 190Contents xi8.2 Proposed Methodology 1948.3 Case Study and Practical Examples 2018.4 Experimental Results and Analysis 2038.5 Conclusion and Future Work 2109 An Investigative Analysis on Security Challenges in Cloud Computing Models and Solutions 215N.A. Natraj, B. Sundaravadivazhagan, Giri. G. Hallur and Supriya Shrikant Laykar9.1 Introduction 2169.2 Literature Review 2209.3 Research Methodology 2249.4 Security Challenges and Solutions in Cloud Computing Models 2339.5 Quantitative Analysis and Findings 2449.6 Conclusion 25010 Applying Ensemble Deep Learning for Enhanced Security in Platform Engineering 255R. Saranya, S.S. Uma, T.S. Sivarani, Naveena A. Priyadharsini and Sunday Adeola Ajagbe10.1 Introduction 25610.2 Related Works 25810.3 Proposed Methodology 26110.4 Experimental Result and Discussion 27210.5 Conclusion and Future Work 282Part III: Application-Specific Platforms and Emerging Technologies 28711 Reinforcement Learning–Driven Secure and Energy-Efficient Transmission Framework for Scalable Platform Engineering 289Femila. L., S.P. Subotha, Lavanya Devi. N. and J. Arul King11.1 Overview 29011.2 Background 29411.3 Approach/Methodology 29811.4 Results and Discussion 30311.5 Conclusion 30612 Platform Engineering for Scalable AI Deployments in Healthcare: Enabling Automated Skin Blemish Detection 309P. Kalpana, T. Sangeetha, S. Siamala Devi and Morenikeji E. Coker12.1 Introduction 31012.2 Related Work 31312.3 Modules 31512.4 Methodology 31812.5 Experiments 32412.6 Performance Analysis 32812.7 Conclusion 33113 AI-Driven Platform Engineering for Skin Disease Diagnosis: A Comparative Study of DenseNet Architectures 333R. Karthick Manoj, S. Aasha Nandhini and M. Batumalay13.1 Introduction 33413.2 Literature Survey 33513.3 Methodology 33913.4 Result and Discussion 34313.5 Conclusion 35014 An Overview of Current Advances in Blockchain Technology, Platform Engineering, and DevOps and Their Implications 353Balaji Ganesh R., Deebalakshmi R. and R. Thilagavathy14.1 Introduction 35414.2 Literature Review 35514.3 Characteristic of Blockchain 35814.4 Types of Blockchain 36614.5 Blockchain Platforms 36814.6 Blockchain Products 37014.7 Limitations of the Block Chain 37314.8 Platform Engineering 37814.9 DevOps in the Blockchain Industry 38014.10 Results and Discussion 38214.11 Conclusion 38614.12 Future Work 38715 A Multifaceted Approach to Lung Cancer Detection and Segmentation: Platforms, Algorithms, and Emerging Technologies 391S.S. Uma, S.N. Sindhu Bairavi, R. Saranya, J. Assis Nevatha and Olusola Kunle Akinde15.1 Introduction 39215.2 Literature Survey 39415.3 Proposed System 39515.4 Result and Discussion 40615.5 Conclusion 41616 An AI-Augmented IoT System for Small-Scale Cold Chain Applications 419Divya James and T.K.S. Lakshmi Priya16.1 Introduction 42016.2 Cold Chains 42116.3 MSME's in Cold Chain 42416.4 Need for an Architecture 42616.5 Proposed Architecture 42816.6 Experimental Evaluations 43116.7 Application of AI in Cold Chain Systems 44116.8 Quantitative Analysis of AI for IoT-Enabled Cold Chain Management 44416.9 Conclusion 450Bibliography 45017 A Data-Driven Framework for Crop Price Prediction Using ML, Statistical, and Hybrid Ensemble Models 455Manimegalai R., Logendar G., Srirengapriya G. and Ayesha S.K.17.1 Introduction 45617.2 Literature Survey 45717.3 Methodologies 46017.4 Experimental Results 46617.5 Conclusions and Future Work 474References 475Index 477

K. Suresh Kumar, PhD, is an Assistant Professor at the Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India. He has presented papers in various national and international conferences and journals, written many chapters, and has one copyright and one patent to his credit. His fields of interest include computer networks, data analytics, and natural language processing.S. Sundaresan, PhD, is an Assistant Professor at the Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India. He has published more than 13 book chapters, 17 articles in various conferences and journals, and has two patents. His areas of research focus on wireless communication and networks.R. Prithiviraj, PhD, is an Assistant Professor in the Department of Electronics and Communication Engineering at the SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India. He specializes in radiation-hardened design for high-frequency circuits, including PLLs and clock generators.T. Ananth Kumar, PhD, is an Associate Professor at the Dept. of Computer Science and Engineering, IFET College of Engineering, Villupuram, Tamil Nadu, India. He has presented papers various at national and international conferences and journals, published seven books and holds six patents. His fields of interest are networks on chips, computer architecture, and ASIC design.S. Balamurugan, PhD, is the Director of Research at iRCS, an Indian Technological Research and Consulting Firm. He has published 75 books, 300 papers in international journals and conferences and 300 patents. With 20 years of research on various cutting-edge technologies, he provides expert guidance in technology forecasting and decision making for leading companies.

• Series Preface xxiPreface xxiiiAcknowledgments xxviiPart I: Core Concepts and Evolution of Platform Engineering 11 Empowering Developer Productivity through Platform Engineering: A Transformative Approach to Scalable and Streamlined Software Development 3C. Saranya Jothi, E. Surya, M. Syed Rabiya, B. Lalitha and R. Roselinkiruba1.1 Introduction to Platform Engineering 41.2 Core Components of a Platform Engineering Strategy 61.3 Automation and Its Role in Enhancing Productivity 81.4 Impact on Developer Experience 111.5 Quantifying Developer Productivity through Platform Engineering 121.6 Future Trends and Opportunities in Platform Engineering 141.7 Conclusion 152 Best Practices for Building Scalable Software Platforms 19Swetha S. and Joe Prathap P. M.2.1 Introduction 192.2 Goals and Expectations 202.3 Basic Key Metrics 222.4 Cloud Computing as Infrastructure 242.5 Microservice Architecture 252.6 Choosing the Right Database Solution 262.7 Effective Scaling Methods 282.8 Conclusion 303 The Role of Platform Engineering in DevOps Transformation—The Future of Software Delivery: How Platform Engineering Transforms DevOps 33S. Sri Devi, T. Sangeetha and Olukayode A. Oki3.1 Introduction 343.2 Benefits of Platform Engineering 363.3 Role and Responsibilities of Platform Engineers 373.4 Tools/Technology in Platform Engineering 37Contents vii3.5 The Origin Story of DevOps 383.6 Extending Agile to the Full Life Cycle 393.7 Difference Between Software Engineer and DevOps Engineer 403.8 The Role of SDLC in DevOps 423.9 Top Programming and Scripting Languages for DevOps 443.10 Choosing the Optimal Operating System for DevOps: Linux, Windows, or MacOS 453.11 What is Command Line Interface? 463.12 What is a Shell? 473.13 Networking and Its Role in DevOps 483.14 Exploring Infrastructure as Code (IaC): Automation, Scalability, and Efficiency in DevOps 513.15 Conclusion 554 The Impact of Platform Engineering on Developer Productivity 59K. Dhivya and Matthew Olusegun Adigun4.1 Introduction 594.2 Key Principles and Components 654.3 Comparison with Platform Engineering, Traditional DevOps, and SRE 68viii Contents4.4 Platform Engineering Centralizes Tooling and Infrastructure 714.5 Developer Productivity: Key Metrics and Challenges 754.6 Common Productivity Bottlenecks in Software Development 784.7 Boosting Developer Productivity 824.8 Self-Service Developer Portals and Internal Platforms 834.9 Examples of Companies that Successfully Adopted Platform Engineering 874.10 Proposed Project: "Boosting Developer Productivity through Platform Engineering: A Practical Exploration with Internal Developer Platforms" 904.11 Conclusion 94Part II: Platform Engineering for Specific Technologies and Architectures 975 Platform Engineering for Cloud-Native Applications 99Amanpreet Singh, Rupinder Singh and Jaswinder Singh5.1 Introduction 1005.2 Cloud-Native Applications 1015.3 Literature Review 1035.4 Cloud-Native Application Advantages 1125.5 Tools and Technologies 1155.6 Areas of Challenge in Platform Engineering 1195.7 Challenges and Considerations 1215.8 Conclusion 1286 Platform Engineering for Cloud-Native Applications: Strategies for Scalable, Cost-Effective, and Automated Cloud Adoption 133P. Divya, R. Parthiban, S. Jayalakshmi and R. Rajmohan6.1 Introduction 1346.2 Core Principles of Platform Engineering 1346.3 Programming for Productivity and Networking 1376.4 Continuous Integration and Continuous Deployment (CI/CD) 138\6.5 Containerization and Orchestration 139x Contents6.6 Cloud-Native Platform Stack 1416.7 Protocols in Building and Managing Cloud-Native Platform 1456.8 Tools and Technology in Platform Engineering 1476.9 Challenges in Platform Engineering for Cloud-Native Applications 1516.10 Best Practices in Platform Engineering for Cloud-Native Applications 1546.11 Performance Analysis 1556.12 Future Trends 1576.13 Conclusion 1587 Optimization Techniques Hybridized into Deep Learning Models 161K. Pathmapriya and Joe Prathap P. M.7.1 Introduction 1627.2 Research Prospects 1637.3 Deep Learning Technique for Diagnosis of Syndrome 1687.4 Meta-Heuristics Algorihms in Medical Diagnosis 1707.5 Data Synthesis 1777.6 Deploying Healthcare Solutions 1797.7 Discussion and Result 1807.8 Challenges and Future Direction 1817.9 Conclusion and Future Research Ideas 1828 Machine Learning and Automation in Platform Engineering: Transforming Monitoring, Scaling, and Self-Healing 189R. Roselinkiruba, Vasumathy M., J. Jude Moses Anto Devakanth, C. Saranya Jothi, J. Kavitha and L. Sharmila8.1 Introduction 190Contents xi8.2 Proposed Methodology 1948.3 Case Study and Practical Examples 2018.4 Experimental Results and Analysis 2038.5 Conclusion and Future Work 2109 An Investigative Analysis on Security Challenges in Cloud Computing Models and Solutions 215N.A. Natraj, B. Sundaravadivazhagan, Giri. G. Hallur and Supriya Shrikant Laykar9.1 Introduction 2169.2 Literature Review 2209.3 Research Methodology 2249.4 Security Challenges and Solutions in Cloud Computing Models 2339.5 Quantitative Analysis and Findings 2449.6 Conclusion 25010 Applying Ensemble Deep Learning for Enhanced Security in Platform Engineering 255R. Saranya, S.S. Uma, T.S. Sivarani, Naveena A. Priyadharsini and Sunday Adeola Ajagbe10.1 Introduction 25610.2 Related Works 25810.3 Proposed Methodology 26110.4 Experimental Result and Discussion 27210.5 Conclusion and Future Work 282Part III: Application-Specific Platforms and Emerging Technologies 28711 Reinforcement Learning–Driven Secure and Energy-Efficient Transmission Framework for Scalable Platform Engineering 289Femila. L., S.P. Subotha, Lavanya Devi. N. and J. Arul King11.1 Overview 29011.2 Background 29411.3 Approach/Methodology 29811.4 Results and Discussion 30311.5 Conclusion 30612 Platform Engineering for Scalable AI Deployments in Healthcare: Enabling Automated Skin Blemish Detection 309P. Kalpana, T. Sangeetha, S. Siamala Devi and Morenikeji E. Coker12.1 Introduction 31012.2 Related Work 31312.3 Modules 31512.4 Methodology 31812.5 Experiments 32412.6 Performance Analysis 32812.7 Conclusion 33113 AI-Driven Platform Engineering for Skin Disease Diagnosis: A Comparative Study of DenseNet Architectures 333R. Karthick Manoj, S. Aasha Nandhini and M. Batumalay13.1 Introduction 33413.2 Literature Survey 33513.3 Methodology 33913.4 Result and Discussion 34313.5 Conclusion 35014 An Overview of Current Advances in Blockchain Technology, Platform Engineering, and DevOps and Their Implications 353Balaji Ganesh R., Deebalakshmi R. and R. Thilagavathy14.1 Introduction 35414.2 Literature Review 35514.3 Characteristic of Blockchain 35814.4 Types of Blockchain 36614.5 Blockchain Platforms 36814.6 Blockchain Products 37014.7 Limitations of the Block Chain 37314.8 Platform Engineering 37814.9 DevOps in the Blockchain Industry 38014.10 Results and Discussion 38214.11 Conclusion 38614.12 Future Work 38715 A Multifaceted Approach to Lung Cancer Detection and Segmentation: Platforms, Algorithms, and Emerging Technologies 391S.S. Uma, S.N. Sindhu Bairavi, R. Saranya, J. Assis Nevatha and Olusola Kunle Akinde15.1 Introduction 39215.2 Literature Survey 39415.3 Proposed System 39515.4 Result and Discussion 40615.5 Conclusion 41616 An AI-Augmented IoT System for Small-Scale Cold Chain Applications 419Divya James and T.K.S. Lakshmi Priya16.1 Introduction 42016.2 Cold Chains 42116.3 MSME's in Cold Chain 42416.4 Need for an Architecture 42616.5 Proposed Architecture 42816.6 Experimental Evaluations 43116.7 Application of AI in Cold Chain Systems 44116.8 Quantitative Analysis of AI for IoT-Enabled Cold Chain Management 44416.9 Conclusion 450Bibliography 45017 A Data-Driven Framework for Crop Price Prediction Using ML, Statistical, and Hybrid Ensemble Models 455Manimegalai R., Logendar G., Srirengapriya G. and Ayesha S.K.17.1 Introduction 45617.2 Literature Survey 45717.3 Methodologies 46017.4 Experimental Results 46617.5 Conclusions and Future Work 474References 475Index 477

K. Suresh Kumar, PhD, is an Assistant Professor at the Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India. He has presented papers in various national and international conferences and journals, written many chapters, and has one copyright and one patent to his credit. His fields of interest include computer networks, data analytics, and natural language processing.S. Sundaresan, PhD, is an Assistant Professor at the Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India. He has published more than 13 book chapters, 17 articles in various conferences and journals, and has two patents. His areas of research focus on wireless communication and networks.R. Prithiviraj, PhD, is an Assistant Professor in the Department of Electronics and Communication Engineering at the SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India. He specializes in radiation-hardened design for high-frequency circuits, including PLLs and clock generators.T. Ananth Kumar, PhD, is an Associate Professor at the Dept. of Computer Science and Engineering, IFET College of Engineering, Villupuram, Tamil Nadu, India. He has presented papers various at national and international conferences and journals, published seven books and holds six patents. His fields of interest are networks on chips, computer architecture, and ASIC design.S. Balamurugan, PhD, is the Director of Research at iRCS, an Indian Technological Research and Consulting Firm. He has published 75 books, 300 papers in international journals and conferences and 300 patents. With 20 years of research on various cutting-edge technologies, he provides expert guidance in technology forecasting and decision making for leading companies.

• Series Preface xxiPreface xxiiiAcknowledgments xxviiPart I: Core Concepts and Evolution of Platform Engineering 11 Empowering Developer Productivity through Platform Engineering: A Transformative Approach to Scalable and Streamlined Software Development 3C. Saranya Jothi, E. Surya, M. Syed Rabiya, B. Lalitha and R. Roselinkiruba1.1 Introduction to Platform Engineering 41.2 Core Components of a Platform Engineering Strategy 61.3 Automation and Its Role in Enhancing Productivity 81.4 Impact on Developer Experience 111.5 Quantifying Developer Productivity through Platform Engineering 121.6 Future Trends and Opportunities in Platform Engineering 141.7 Conclusion 152 Best Practices for Building Scalable Software Platforms 19Swetha S. and Joe Prathap P. M.2.1 Introduction 192.2 Goals and Expectations 202.3 Basic Key Metrics 222.4 Cloud Computing as Infrastructure 242.5 Microservice Architecture 252.6 Choosing the Right Database Solution 262.7 Effective Scaling Methods 282.8 Conclusion 303 The Role of Platform Engineering in DevOps Transformation—The Future of Software Delivery: How Platform Engineering Transforms DevOps 33S. Sri Devi, T. Sangeetha and Olukayode A. Oki3.1 Introduction 343.2 Benefits of Platform Engineering 363.3 Role and Responsibilities of Platform Engineers 373.4 Tools/Technology in Platform Engineering 37Contents vii3.5 The Origin Story of DevOps 383.6 Extending Agile to the Full Life Cycle 393.7 Difference Between Software Engineer and DevOps Engineer 403.8 The Role of SDLC in DevOps 423.9 Top Programming and Scripting Languages for DevOps 443.10 Choosing the Optimal Operating System for DevOps: Linux, Windows, or MacOS 453.11 What is Command Line Interface? 463.12 What is a Shell? 473.13 Networking and Its Role in DevOps 483.14 Exploring Infrastructure as Code (IaC): Automation, Scalability, and Efficiency in DevOps 513.15 Conclusion 554 The Impact of Platform Engineering on Developer Productivity 59K. Dhivya and Matthew Olusegun Adigun4.1 Introduction 594.2 Key Principles and Components 654.3 Comparison with Platform Engineering, Traditional DevOps, and SRE 68viii Contents4.4 Platform Engineering Centralizes Tooling and Infrastructure 714.5 Developer Productivity: Key Metrics and Challenges 754.6 Common Productivity Bottlenecks in Software Development 784.7 Boosting Developer Productivity 824.8 Self-Service Developer Portals and Internal Platforms 834.9 Examples of Companies that Successfully Adopted Platform Engineering 874.10 Proposed Project: "Boosting Developer Productivity through Platform Engineering: A Practical Exploration with Internal Developer Platforms" 904.11 Conclusion 94Part II: Platform Engineering for Specific Technologies and Architectures 975 Platform Engineering for Cloud-Native Applications 99Amanpreet Singh, Rupinder Singh and Jaswinder Singh5.1 Introduction 1005.2 Cloud-Native Applications 1015.3 Literature Review 1035.4 Cloud-Native Application Advantages 1125.5 Tools and Technologies 1155.6 Areas of Challenge in Platform Engineering 1195.7 Challenges and Considerations 1215.8 Conclusion 1286 Platform Engineering for Cloud-Native Applications: Strategies for Scalable, Cost-Effective, and Automated Cloud Adoption 133P. Divya, R. Parthiban, S. Jayalakshmi and R. Rajmohan6.1 Introduction 1346.2 Core Principles of Platform Engineering 1346.3 Programming for Productivity and Networking 1376.4 Continuous Integration and Continuous Deployment (CI/CD) 138\6.5 Containerization and Orchestration 139x Contents6.6 Cloud-Native Platform Stack 1416.7 Protocols in Building and Managing Cloud-Native Platform 1456.8 Tools and Technology in Platform Engineering 1476.9 Challenges in Platform Engineering for Cloud-Native Applications 1516.10 Best Practices in Platform Engineering for Cloud-Native Applications 1546.11 Performance Analysis 1556.12 Future Trends 1576.13 Conclusion 1587 Optimization Techniques Hybridized into Deep Learning Models 161K. Pathmapriya and Joe Prathap P. M.7.1 Introduction 1627.2 Research Prospects 1637.3 Deep Learning Technique for Diagnosis of Syndrome 1687.4 Meta-Heuristics Algorihms in Medical Diagnosis 1707.5 Data Synthesis 1777.6 Deploying Healthcare Solutions 1797.7 Discussion and Result 1807.8 Challenges and Future Direction 1817.9 Conclusion and Future Research Ideas 1828 Machine Learning and Automation in Platform Engineering: Transforming Monitoring, Scaling, and Self-Healing 189R. Roselinkiruba, Vasumathy M., J. Jude Moses Anto Devakanth, C. Saranya Jothi, J. Kavitha and L. Sharmila8.1 Introduction 190Contents xi8.2 Proposed Methodology 1948.3 Case Study and Practical Examples 2018.4 Experimental Results and Analysis 2038.5 Conclusion and Future Work 2109 An Investigative Analysis on Security Challenges in Cloud Computing Models and Solutions 215N.A. Natraj, B. Sundaravadivazhagan, Giri. G. Hallur and Supriya Shrikant Laykar9.1 Introduction 2169.2 Literature Review 2209.3 Research Methodology 2249.4 Security Challenges and Solutions in Cloud Computing Models 2339.5 Quantitative Analysis and Findings 2449.6 Conclusion 25010 Applying Ensemble Deep Learning for Enhanced Security in Platform Engineering 255R. Saranya, S.S. Uma, T.S. Sivarani, Naveena A. Priyadharsini and Sunday Adeola Ajagbe10.1 Introduction 25610.2 Related Works 25810.3 Proposed Methodology 26110.4 Experimental Result and Discussion 27210.5 Conclusion and Future Work 282Part III: Application-Specific Platforms and Emerging Technologies 28711 Reinforcement Learning–Driven Secure and Energy-Efficient Transmission Framework for Scalable Platform Engineering 289Femila. L., S.P. Subotha, Lavanya Devi. N. and J. Arul King11.1 Overview 29011.2 Background 29411.3 Approach/Methodology 29811.4 Results and Discussion 30311.5 Conclusion 30612 Platform Engineering for Scalable AI Deployments in Healthcare: Enabling Automated Skin Blemish Detection 309P. Kalpana, T. Sangeetha, S. Siamala Devi and Morenikeji E. Coker12.1 Introduction 31012.2 Related Work 31312.3 Modules 31512.4 Methodology 31812.5 Experiments 32412.6 Performance Analysis 32812.7 Conclusion 33113 AI-Driven Platform Engineering for Skin Disease Diagnosis: A Comparative Study of DenseNet Architectures 333R. Karthick Manoj, S. Aasha Nandhini and M. Batumalay13.1 Introduction 33413.2 Literature Survey 33513.3 Methodology 33913.4 Result and Discussion 34313.5 Conclusion 35014 An Overview of Current Advances in Blockchain Technology, Platform Engineering, and DevOps and Their Implications 353Balaji Ganesh R., Deebalakshmi R. and R. Thilagavathy14.1 Introduction 35414.2 Literature Review 35514.3 Characteristic of Blockchain 35814.4 Types of Blockchain 36614.5 Blockchain Platforms 36814.6 Blockchain Products 37014.7 Limitations of the Block Chain 37314.8 Platform Engineering 37814.9 DevOps in the Blockchain Industry 38014.10 Results and Discussion 38214.11 Conclusion 38614.12 Future Work 38715 A Multifaceted Approach to Lung Cancer Detection and Segmentation: Platforms, Algorithms, and Emerging Technologies 391S.S. Uma, S.N. Sindhu Bairavi, R. Saranya, J. Assis Nevatha and Olusola Kunle Akinde15.1 Introduction 39215.2 Literature Survey 39415.3 Proposed System 39515.4 Result and Discussion 40615.5 Conclusion 41616 An AI-Augmented IoT System for Small-Scale Cold Chain Applications 419Divya James and T.K.S. Lakshmi Priya16.1 Introduction 42016.2 Cold Chains 42116.3 MSME's in Cold Chain 42416.4 Need for an Architecture 42616.5 Proposed Architecture 42816.6 Experimental Evaluations 43116.7 Application of AI in Cold Chain Systems 44116.8 Quantitative Analysis of AI for IoT-Enabled Cold Chain Management 44416.9 Conclusion 450Bibliography 45017 A Data-Driven Framework for Crop Price Prediction Using ML, Statistical, and Hybrid Ensemble Models 455Manimegalai R., Logendar G., Srirengapriya G. and Ayesha S.K.17.1 Introduction 45617.2 Literature Survey 45717.3 Methodologies 46017.4 Experimental Results 46617.5 Conclusions and Future Work 474References 475Index 477