Journal Papers

Shahid Mohammad Ganie, Majid Bashir Malik, Mir Aadil, Ghazala Muteeb, Mohd Farhan, and Mohammad Aatif “Explainable AI Based Hybrid DRM-Net Transfer Learning Model for Breast Cancer Detection and Classification Using Ultrasound Images”, Scientific Reports, Nature, Volume 15, December 2025, [Indexed in SCIE] [IF 3.8, SJR 0.874, Q1], (DOI: <Link>).

Shahid Mohammad Ganie and Majid Bashir Malik, “Lifestyle-Based Fuzzy-Enhanced ANN Model for Early Prediction of Type 2 Diabetes and Personalized Management in the North Indian Population”, diagnostics, MDPI, [Indexed in SCIE] [IF 3.3, SJR 0.773, Q1], Volume 15, December 2025, (DOI: <Link>).

Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, and Zhongming Zhao, “Interpretable Ensemble Models for Obesity Prediction: Leveraging SHAP and LIME for Lifestyle Data Analysis”, Scientific Reports, Nature, Volume 15, October 2025, [Indexed in SCIE] [IF 3.8, SJR 0.874, Q1], (DOI: <Link>).

Shahid Mohammad Ganie, and Pijush Kanti Dutta Pramanik, “Interpretable Lung Cancer Risk Prediction Using Ensemble Learning and XAI Based on Lifestyle and Demographic Data”, Computational Biology and Chemistry, Elsevier, Volume 117, August 2025, [Indexed in SCIE] [IF 3.1, SJR 0.5, Q2], (DOI: <Link>).

Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, and Zhongming Zhao, “Enhanced and Interpretable Multi-Cancer Prediction Using a Stacking Ensemble Approach with SHAP Analysis”, Bioengineering, MDPI, Volume 12, April 2025, [Indexed in SCIE] [IF 3.8, SJR 0.63, Q2], (DOI: <Link>).

Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, and Zhongming Zhao, “Enhancing Heart Disease Prediction: Designing and Evaluating Stacking and Voting Models with Multiple Datasets”, Scientific Reports, Nature, Volume 15, April 2025, [Indexed in SCIE] [IF 3.8, SJR 0.874, Q1], (DOI: <Link>).

Shahid Mohammad Ganie, Bobba Bharath Reddy, Hemachandran K, and Manjeet Rege, “Ensemble Learning Techniques for Obesity Risk Prediction using Lifestyle Data”, Decision Analytics Journal, Elsevier, [Indexed in ESCI] [SJR 0.68, Q2], (DOI: <Link>).

Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, and Zhongming Zhao, “Improved Prediction of Liver Disease from Clinical Data through an Evaluation of Ensemble Learning Approaches”, BMC Medical Informatics and Decision Making, Springer Nature, [Indexed in SCIE/SSCI] [IF 3.5, SJR 0.94, Q1], (DOI:  <Link>).

Anindita Saha, Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Rakesh Kumar Yadav, and Saurav Mallick, “VER-Net: A Hybrid Transfer Learning Model for Lung Cancer Detection using CT Scan Images”, BMC Medical Imaging, Springer Nature, Volume 24, May 2024,[Indexed in SCIE] [IF 2.7, SJR 0.54, Q2], (DOI:  <Link>).

Shahid Mohammad Ganie, and Pijush Kanti Dutta Pramanik, “A comparative analysis of boosting algorithms for chronic liver disease prediction”, Healthcare Analytics, Elsevier, Volume 5, June 2024, [Indexed in ESCI] [SJR 0.60, Q2], (DOI: <Link>).

Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Saurav Mallik, and Zhongming Zhao, “Chronic Kidney Disease Prediction Using Boosting Techniques based on Clinical Parameters”, PLOS ONE, [Indexed in SCIE] [IF 3.752, SJR 0.85, Q1], (DOI: <Link>).

Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Majid Bashir Malik, Saurav Mallik, Hong Qin, “An Ensemble Learning Approach for Diabetes Prediction using Boosting Techniques”, Frontiers in Genetics, [Indexed in SCIE] [IF 3.7, SJR 1.00, Q2], (DOI: <Link>).

Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Majid Bashir Malik, and Anand Nayyar, “An Improved Ensemble Learning Approach for Heart Disease Prediction using Boosting Algorithms”, Computer Systems Science and Engineering, Tech Science Press, [Indexed in SCIE] [IF 4.397, SJR 0.32, Q3], (DOI: <Link>).

Hemachandran K, Areej Alasiry, Mehrez Marzougui, Shahid Mohammad Ganie, Anil Audumbar Pise, M. Turki-Hadj Alouane, Channabasava Chola, “Performance Analysis of Deep Learning Algorithms in Diagnosis of Malaria Disease”, Diagnostics, MDPI, 2023, 13(3), 534, [Indexed in SCIE] [IF 3.992, SJR 0.66, Q2], (DOI: <Link>).

Shahid Mohammad Ganie, Majid Bashir Malik, and Tasleem Arif, “Performance Analysis and Prediction of Type 2 Diabetes Mellitus based on lifestyle data using Machine Learning Approaches”, Journal of Diabetes & Metabolic Disorders, Springer, Vol.21, pp 339–352, 2022, [Indexed in ESCI], [IF 2.8, SJR 0.65, Q2], (DOI: <Link>).

Shahid Mohammad Ganie and Majid Bashir Malik, “Comparative Analysis of Various Supervised Machine Learning Algorithms for the Early Prediction of Type-II Diabetes Mellitus”, International Journal of Medical Engineering and Informatics, Inderscience, pp 473-483, Vol. 14, No. 6, 2022, [Indexed in ESCI] [IF 0.168, SJR 0.19, Q4], (DOI: <Link>).

Shahid Mohammad Ganie, Majid Bashir Malik, “An Ensemble Machine Learning Approach for Predicting Type-II Diabetes Mellitus based on Lifestyle Indicators”, Healthcare Analytics, Elsevier, Volume 2, November 2022, [Indexed in ESCI] [SJR 0.60, Q2], (DOI: <Link>).

Shahid Mohammad Ganie, Majid Bashir Malik, and Tasleem Arif, “Early Prediction of Diabetes Mellitus Using Various Artificial Intelligence Techniques: A Technological Review”, International Journal of Business Intelligence and Systems Engineering, Inderscience, pp 325-346, Vol. 1, No. 4, 2021, (DOI: <Link>).

Shahid Mohammad Ganie, Majid Bashir Malik, and Tasleem Arif, “Various Platforms and Machine Learning Techniques for Big Data Analytics: A Technological Survey”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology©, 2018 IJSRCSEIT | Volume 3 | Issue 6 | ISSN: 2456-3307 (DOI: <Link>).

Najmu Nissa, Sanjay Jamwal and Shahid Mohammad Ganie*, “Early Detection of Cardiovascular Disease using Machine Learning Techniques an Experimental Study”, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, Vol.9 Issue-3, September 2020, [Indexed in Scopus], (DOI: <Link>).

Najmu Nissa, Sanjay Jamwal and Shahid Mohammad Ganie*, “Heart Disease Prediction using Machine Learning Techniques”, Wesleyan Journal of Research, (WJR), Vol 13 No 67, ISSN: 0975-1386.