Artificial Intelligence Revolutionizing Cancer Care (eBook, PDF)
Precision Diagnosis and Patient-Centric Healthcare
Redaktion: Kumar Swarnkar, Suman; Devarajan, Harshitha Raghavan; Chhabra, Gurpreet Singh; Guru, Abhishek
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Artificial Intelligence Revolutionizing Cancer Care (eBook, PDF)
Precision Diagnosis and Patient-Centric Healthcare
Redaktion: Kumar Swarnkar, Suman; Devarajan, Harshitha Raghavan; Chhabra, Gurpreet Singh; Guru, Abhishek
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This book delves into the transformative power of AI, offering a comprehensive exploration of its role in enhancing cancer diagnosis, treatment, and patient management.
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This book delves into the transformative power of AI, offering a comprehensive exploration of its role in enhancing cancer diagnosis, treatment, and patient management.
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Produktdetails
- Produktdetails
- Verlag: Taylor & Francis
- Seitenzahl: 280
- Erscheinungstermin: 25. Februar 2025
- Englisch
- ISBN-13: 9781040271230
- Artikelnr.: 72642474
- Verlag: Taylor & Francis
- Seitenzahl: 280
- Erscheinungstermin: 25. Februar 2025
- Englisch
- ISBN-13: 9781040271230
- Artikelnr.: 72642474
- Herstellerkennzeichnung Die Herstellerinformationen sind derzeit nicht verfügbar.
Suman Kumar Swanrkar received a Ph.D. (CSE) degree in 2021 from Kalinga University, Nayar Raipur. He received M.Tech. (CSE) degree in 2015 from the Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India. He has 2+ year of experience in IT industry as Software Engineer and 6+ year of experience in Educational Institutes as Assistant Professor. Currently associated with Shri Shankaracharya Institute of Professional Management and Technology, raipur as Assistant Professor in Computer Science & Engineering Department. He Has Guided 5+ MTech Scholars and some of undergoing. He has Published and grant Indian/Australian patent, some are waiting for grant. He has authored and co-authored of more than 15 journal articles including WOS & Scopus papers Presented research paper in 3 international conferences. He has Contributed to book chapter, published by publications of international repute. He has lifetime Membership of IEEE, IAENG, ASR, IFERP, ICSES, Internet Society, UACEE, IAOP, IAOIP, EAI, CSTA. He has Successfully completed many FDP, Training, webinar & Workshop and Completed the 2-Weeks comprehensive online Patent Information Course. Proficiency in handling the Teaching, Research as well as administrative activities. He has contributed massive literature in the fields of Intelligent Data Analysis, Nature-Inspired Computing, Machine Learning and Soft Computing. Abhishek Guru received a Ph.D. (CSE) degree in 2021 from Kalinga University, Naya Raipur. He received his MSc. (CS) degree in 2012 from Makhanlal Chaturvedi Rashtriya Patrakarita Vishwavidyalaya, Bhopal, India. He has 3 Months of experience in IT industry as a Software Engineer and 10.1 years of experience in educational institutes as an Assistant Professor. Currently associated with KL Deemed to Be University, Green Fields, Vaddeswaram, India as Assistant Professor in Computer Science & Engineering Department. He has published and granted Indian/Australian patents, some are waiting for grants. He has authored and co-authored of more than 10 journal articles including WOS & Scopus papers Presented research papers in 2 international conferences. He has contributed to book chapters, published by reputed international publishers and has lifetime Membership of IAENG, ASR, IFERP, ICSES, Internet Society, UACEE, IAOIP, EAI, CSTA. He has successfully completed many FDP, Training, webinar & workshops. and also Completed the 2-Weeks comprehensive online Patent Information Course. Proficiency in handling Teaching, Research as well as administrative activities. He has contributed massive literature in the fields of Network Security, Cyber Security, Cryptography, and IoT. Gurpreet Singh Chhabra (Ph.D. CSE) has more than 15 years of teaching experience and is currently working as an Assistant Professor in the Computer Science & Engineering department at GITAM School of Technology, GITAM Deemed to be University, Visakhapatnam. He has research interests in Deep Learning, Machine Learning, Data Science, and Fog Computing. He is a life member of the ISTE (Indian Society for Technical Education) and IAENG (International Association of Engineers). Also, He has credit for many national and international papers, patents, books, and book chapters. His qualifications are fortified with a great deal of creativity and problem-solving skills. Harshitha Raghavan Devarajan is an AI researcher with an unwavering passion for revolutionizing the healthcare industry through the potential of advanced technology. Graduating from New York University, he has consistently sought out opportunities to engage in meaningful research and contribute to technological advancements. His journey embodies the synergy between a deep-rooted commitment to pushing the boundaries of innovation and a profound understanding of the transformative power of artificial intelligence. With "Reimagining Healthcare: AI's Journey to Transforming an Industry," he invites the readers to explore his experiences and insights as he strives to reshape healthcare through the lens of cutting-edge AI, aiming to make a lasting impact on countless lives.
1. K-Means Clustering for Knowledge Discovery in Big Data Cancer Research.
2. Applying Reinforcement Learning to Optimize Cancer Treatment Protocols
in Machine Learning Frameworks 3. Extraction of Real-Time Data of Breast
Cancer Patients and Implementation with ML Techniques. 4. Decoding Images
Convolutional Neural Networks in Oncological Medical Imaging. 5.
Uncovering Insights in Cancer Research with Centroid-Based Clustering on
Big Data. 6. The Role of Machine Learning in Remote Cancer Management: A
Systematic Review. 7. Revolutionizing Cancer Drug Discovery Deep Learning
Neural Networks for Accelerated Development. 8. Empowering Patients
Enhancing Engagement And Self-Care In Cancer Treatment With Bayesian
Networks. 9. Enhancing Cancer Detection and Classification with Ensemble
Machine Learning Approaches. 10. Ethics, Regulation, and Machine Learning
Navigating Oncological AI Deployment with Decision Trees. 11. A
Comprehensive Review of Big Data Integration and K-Means Clustering in
Cancer Research. 12. Applications of Generative Adversarial Networks (GANs)
in Healthcare. 13. Performance Analysis of Stochastic Gradient Descent and
Adaptive Moment Estimation Optimization Algorithms for Convolutional Neural
Networks. 14. Enhancing Oncology with Predictive Analytics for Cancer
Diagnosis and Treatment with Random Forests. 15. Automated Diagnosis of
Brain Tumors from MRI Scans Using U-Net Segmentation.
2. Applying Reinforcement Learning to Optimize Cancer Treatment Protocols
in Machine Learning Frameworks 3. Extraction of Real-Time Data of Breast
Cancer Patients and Implementation with ML Techniques. 4. Decoding Images
Convolutional Neural Networks in Oncological Medical Imaging. 5.
Uncovering Insights in Cancer Research with Centroid-Based Clustering on
Big Data. 6. The Role of Machine Learning in Remote Cancer Management: A
Systematic Review. 7. Revolutionizing Cancer Drug Discovery Deep Learning
Neural Networks for Accelerated Development. 8. Empowering Patients
Enhancing Engagement And Self-Care In Cancer Treatment With Bayesian
Networks. 9. Enhancing Cancer Detection and Classification with Ensemble
Machine Learning Approaches. 10. Ethics, Regulation, and Machine Learning
Navigating Oncological AI Deployment with Decision Trees. 11. A
Comprehensive Review of Big Data Integration and K-Means Clustering in
Cancer Research. 12. Applications of Generative Adversarial Networks (GANs)
in Healthcare. 13. Performance Analysis of Stochastic Gradient Descent and
Adaptive Moment Estimation Optimization Algorithms for Convolutional Neural
Networks. 14. Enhancing Oncology with Predictive Analytics for Cancer
Diagnosis and Treatment with Random Forests. 15. Automated Diagnosis of
Brain Tumors from MRI Scans Using U-Net Segmentation.
1. K-Means Clustering for Knowledge Discovery in Big Data Cancer Research.
2. Applying Reinforcement Learning to Optimize Cancer Treatment Protocols
in Machine Learning Frameworks 3. Extraction of Real-Time Data of Breast
Cancer Patients and Implementation with ML Techniques. 4. Decoding Images
Convolutional Neural Networks in Oncological Medical Imaging. 5.
Uncovering Insights in Cancer Research with Centroid-Based Clustering on
Big Data. 6. The Role of Machine Learning in Remote Cancer Management: A
Systematic Review. 7. Revolutionizing Cancer Drug Discovery Deep Learning
Neural Networks for Accelerated Development. 8. Empowering Patients
Enhancing Engagement And Self-Care In Cancer Treatment With Bayesian
Networks. 9. Enhancing Cancer Detection and Classification with Ensemble
Machine Learning Approaches. 10. Ethics, Regulation, and Machine Learning
Navigating Oncological AI Deployment with Decision Trees. 11. A
Comprehensive Review of Big Data Integration and K-Means Clustering in
Cancer Research. 12. Applications of Generative Adversarial Networks (GANs)
in Healthcare. 13. Performance Analysis of Stochastic Gradient Descent and
Adaptive Moment Estimation Optimization Algorithms for Convolutional Neural
Networks. 14. Enhancing Oncology with Predictive Analytics for Cancer
Diagnosis and Treatment with Random Forests. 15. Automated Diagnosis of
Brain Tumors from MRI Scans Using U-Net Segmentation.
2. Applying Reinforcement Learning to Optimize Cancer Treatment Protocols
in Machine Learning Frameworks 3. Extraction of Real-Time Data of Breast
Cancer Patients and Implementation with ML Techniques. 4. Decoding Images
Convolutional Neural Networks in Oncological Medical Imaging. 5.
Uncovering Insights in Cancer Research with Centroid-Based Clustering on
Big Data. 6. The Role of Machine Learning in Remote Cancer Management: A
Systematic Review. 7. Revolutionizing Cancer Drug Discovery Deep Learning
Neural Networks for Accelerated Development. 8. Empowering Patients
Enhancing Engagement And Self-Care In Cancer Treatment With Bayesian
Networks. 9. Enhancing Cancer Detection and Classification with Ensemble
Machine Learning Approaches. 10. Ethics, Regulation, and Machine Learning
Navigating Oncological AI Deployment with Decision Trees. 11. A
Comprehensive Review of Big Data Integration and K-Means Clustering in
Cancer Research. 12. Applications of Generative Adversarial Networks (GANs)
in Healthcare. 13. Performance Analysis of Stochastic Gradient Descent and
Adaptive Moment Estimation Optimization Algorithms for Convolutional Neural
Networks. 14. Enhancing Oncology with Predictive Analytics for Cancer
Diagnosis and Treatment with Random Forests. 15. Automated Diagnosis of
Brain Tumors from MRI Scans Using U-Net Segmentation.