Mitigating Pancreatic Cancer through Data-Driven AI Techniques, Holistic Health Record, iHELP and Integrative Systems

[1] Thomas G. Cotter & Mary Rinella, Nonalcoholic fatty liver disease 2020: The state of the disease. Gastroenterology. 158(7). 1851-1864. 2020. [2] Alessandro Mantovani, Alessandro Csermely, Graziana Petracca, Giorgia Beatrice, Kathleen ECorey, Tracey GSimon, Christopher DByrne, BCh & Giovanni Targher, Non- alcoholic fatty liver disease and risk of fatal and non-fatal cardiovascular events: An updated systematic review and meta-analysis. The Lancet Gastroenterology & Hepatology. 6(11). 903-913. 2021. [3] Cyrielle Caussy, Adrien Aubin & Rohit Loomba, The relationship between type 2 diabetes, NAFLD, and Cardiovascular risk. Current Diabetes Reports. 15. 2021. [4] Yuan-Xing Liua, Xi Liu, Chao Cen, Xin Li, Ji-Min Liu, Zhao-Yan Ming, Song-Feng Yu, Xiao-Feng Tang, Lin Zhou, Jun Yu, Ke-Jie Huang & Shu-Sen Zheng, Comparison and development of advanced machine learning tools to predict nonalcoholic fatty liver disease: An extended study. Hepatobiliary & Pancreatic Diseases International. 20(5). 409-415. [5] Natarajan Balasubramanian, Yang Ye & Mingtao Xu, Substituting human decision-making with machine learning: Implications for organizational learning. Academy of Management Review. 47(3). 2022. [6] R. Biswas, Outlining big data analytics in health sector with special reference to Covid-19. Wireless Personal Communications, volume. 124. 2097–2108. 2022. [7] Mumtaz Karatas, Leven Eriskin, Muhammet Deveci, Dragan Pamucar & Harish Garg, Big data for healthcare industry 4.0: Applications, challenges and future perspectives. Expert Systems with Applications. 200. 2022. [8] Sergio J. Sanabria, Amir M. Pirmoazen, Jeremy Dahl, Aya Kamaya & Ahmed El Kaffas, Comparative study of raw ultrasound data representations in deep learning to classify hepatic steatosis. Ultrasound in Medicine & Biology. 48(10). 2060-2078. 2022. [9] Ahmed Gaber, Hassan A. Youness, Alaa Hamdy, Hammam M. Abdelaal & Ammar M. Hassan, Automatic classification of fatty liver disease based on supervised learning and genetic algorithm. Appl. Sci. 12(1). 521. 2022. [10] Daniele Pastori, Arianna Pani, Arianna Di Rocco, Danilo Menichelli, Gianluca Gazzaniga, Alessio Farcomeni, Laura D’Erasmo, Francesco Angelico, Maria Del Ben & Francesco Baratta, Statin liver safety in non-alcoholic fatty liver disease: A systematic review and metanalysis. British Journal of Clinical Pharmacology. 88(2). 441-451. 2022. [11] Christopher D. Byrne, Banting memorial lecture 2022: ‘Type 2 diabetes and nonalcoholic fatty liver disease: Partners in crime’. Diabetic Medicine. 39(10). e14912. 2022. [12] Huapeng Lin, Terry Cheuk-Fung Yip, Xinrong Zhang, Guanlin Li, Yee-Kit Tse, Vicki Wing-Ki Hui, Lilian Yan Liang, Jimmy Che-To Lai, Stephen Lam Chan, & HenryLik- Yuen Chan, Age and the relative importance of liver-related deaths in nonalcoholic fatty liver disease. Hepatology. AASLD. 2022. [13] Nabil Noureddin, Mazen Noureddin, Amandeep Singh & Naim Alkhouri. Progression of nonalcoholic fatty liver disease-associated fibrosis in a large cohort of patients with type 2 diabetes. Digestive Diseases and Sciences. 67. 1379–1388. 2022. [14] H. GM, M. K. Gourisaria, S. S. Rautaray, and M. Pandey, UBMTR: Unsupervised Boltzmann machine-based time-aware recommendation system. Journal of King Saud University-Computer and Information Sciences, 2021. [15] M. A. Taufiq, N. Hameed, A. Anjum, & F. Hameed, m-Skin Doctor: A mobile-enabled system for early melanoma skin cancer detection using a support vector machine. eHealth 360° Springer, Cham, 468- 475, 2019 [16] S. Chandra, M. K. Gourisaria, H. GM, S.S. Rautaray, M. Pandey & S. N. Mohanty, Semantic analysis of sentiments through web-mined Twitter corpus. In Proceedings of CEUR Workshop Proceedings, vol. 2786, 122-135, 2021. [17] Ghulab Nabi Ahmad, Hira Fatima; ShafiUllah; Abdelaziz Salah Saidi; Imdadullah Faculty of Engineering & Technology, Electrical Engineering Section, University Polytechnic, Aligarh Muslim University, Aligarh, India, Efficient Medical Diagnosis of Human Heart Diseases Using Machine Learning Techniques With and Without GridSearch. IEEE Access. 10. 80151 – 80173. 2022. [18] H. GM, M. K. Gourisaria, M. Pandey and S. S. Rautaray, A comprehensive survey and analysis of generative models in machine learning. Computer Science Review, 38, 100285, [19] S. Suganyadevi, V. Seethalakshmi & K. Balasamy, A review on deep learning in medical image analysis. International Journal of Multimedia Information Retrieval, 11. 19–38. [20] G. Jee, H. GM, & M. K. Gourisaria, Juxtaposing inference capabilities of deep neural models over posteroanterior chest radiographs facilitating COVID-19 detection. Journal of Interdisciplinary Mathematics, 1-27. 2021. [21] Khalil Boukthir, Abdulrahman M. Qahtani, Omar Almutiry, Habib Dhahri & Adel M. Alimi, Reduced annotation based on deep active learning for Arabic text detection in natural scene images. Pattern Recognition Letters. 157. 42-48. 2022. [22] A. Sahu, H. GM & M. K. Gourisaria, A dual approach for credit card fraud detection using neural network and data mining techniques, 17th India Council International Conference (INDICON) IEEE, New Delhi, India. 1-7. 2020. [23] K. Prakash, & S. Saradha, A hybrid approach for prediction and stage wise classification of liver failure. 7th International Conference on Communication and Electronics Systems (ICCES). 2022. [24] Naoshi Nishida, Makoto Yamakawa, Tsuyoshi Shiina & Masatoshi Kudo. Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technology. Hepatology International. 13. 416–421. 2019. [25] An Tang, Bogdan Dzyubak, Meng Yin, Alexandra Schlein, Walter C. Henderson, Jonathan C. Hooker, Timoteo I. Delgado, Michael S. Middleton, Lin Zheng, Tanya Wolfson, Anthony Gamst, Rohit Loomba, Richard L. Ehman & Claude B. Sirlin. MR elastography in nonalcoholic fatty liver disease: Inter-center and inter-analysis-method measurement reproducibility and accuracy at 3T. European Radiology. 32. 2937–2948. [26] HuiChe, Lloyd G. Brown, David J. Foran, John L. Nosher & Ilker Hacihaliloglu, Liver disease classification from ultrasound using multi-scale CNN. International Journal of Computer Assisted Radiology and Surgery. 16. 1537–1548. 2021. [27] K. Prakash & S. Saradha. A deep learning approach for classification and prediction of cirrhosis liver: Non alcoholic fatty liver disease (NAFLD). 6th International Conference on Trends in Electronics and Informatics (ICOEI). 2022. [28] Ramya Manikyam, J. Todd McDonald, William R. Mahoney, Todd R. Andel & Samuel H. Russ. 2016. Comparing the effectiveness of commercial obfuscators against MATE attacks. In Proceedings of the 6th Workshop on Software Security, Protection, and Reverse Engineering (SSPREW’16). [29] R. Manikyam, Program protection using software based hardware abstraction. Ph.D. dissertation, University of South Alabama. 2019. [30] P. Kanagala & R. Jayaraman, FAA-cloud approach to minimize computation overhead using fuzzy-based crypto security, Soft Computing, 1-11. [31] P. Kanagala & R. Jayaraman, Effective encryption approach to improving the secure cloud framework through fuzzy-based encrypted cryptography, Soft Computing, 1-10. [32] R. Pulimamidi & P. Ravichandran, Connected health: Revolutionizing patient care through artificial intelligence innovations. Tuijin Jishu/Journal of Propulsion Technology 44 (3), 3940-3947. [33] R. Pulimamidi & P. Ravichandran, Enhancing healthcare delivery: AI applications in remote patient monitoring. Tuijin Jishu/Journal of Propulsion Technology 44 (3), 3948- [34] Naga Simhadri Apparao Polireddi & K. Chaitanya, Web accessibility evaluation of private and government websites for people with disabilities through fuzzy classifier in the USA. Soft