ARCHIVES

1. K. Velu, N. Jaisankar, “Design of a CNN-Swin Transformer Model for Alzheimer’s Disease Prediction Using MRI Images”, IEEE Access,
vol.13, pp. 149409-149429, August 2025. (references)
2. Ioan-Alexandru herdea, Divya Tiwari, John Oyekan, and Ashutosh Tiwari, “Data Augmentation Framework for Improved Classification in
object Detectors”, IEEE Access, vol. 13, pp. 28476-2849, 14 Feb 2025.
3. Illakiya Thayumanasamy, Karthik Ramamurthy, “Performance Analysis of Machine Learning and Deep Learning Models for Classificationof Alzheimer’s Disease from Brain MRI”, IIETA, pp. 1961-1970, Dec 2022. [Online]. Available:
https://www.iieta.org/journals/ts/paper/10.18280/ts.390608.
4. Ahmeed Suliman Farhan, Muhammad Khalid, and Umar Manzoor,”Combined Oriented Data Augmentation Method for Brain MRI
Images”,IEEE Access, vol. 13, pp. 9981-9994, 7 Jan 2025.
5. Shunbao Li, Kang Liu, “An Interpretable Deep Learning Approach for Alzheimer’s Disease Diagnosis Using Gene Expression Data”,
IEEE Transactions on computation biology and Bioinformatics, vol. 22, No. 4, pp. 1727-1739, August 2025.
6. Osamah Q. Aldhafer, Hussain A. Alsadiq, and Mohammad Alshaboti,”Adaptive Data Augmentation Techniques for software
Requirements Classification using Deep Learning”, IEEE Acess, vol. 14, pp. 43841-43854, 12 Mar 2026.
7. Jiayu Ye, Dan Pan, An Zeng, Yiqun Zhang, Qiuping Chen and Yang Liu, “MssNet: An Efficient Spatial Attention Model for Early
Recognition of Alzheimer’s Disease”, IEEE Transactions on Emerging Topics in Computation Intelligence, vol. 9, No. 2, pp. 1454-1468, April
2025.
8. Samina Akram, Muhammad Amjad Iqbal, Muhammad Rashid, Muhammad Shahid Bhatti, Benish Fida, “A Hybrid Deep Learning Framework
for Early-Stage Alzheimer’s Disease Classification From Neuro-Imaging Biomarkers”, IEEE Access, vol. 13, pp. 101662-101680, June 2025.
9. Duy-Phuong Dao, Hyung-Jeong Yang, Jahae Kim and Ngoc-Huynh Ho, “Longitudinal Alzheimer’s Disease Progression Prediction
With Modality Uncertainty and Optimization of Information Flow”, IEEE Journal of Biomedical and Health Informatics, vol. 29, No. 1, pp.
259-272, January 2025.
10. Jiaqiang Li, Peng Yang, Junlong Qu, Bao Yang, Zhenghua Guan, Xuegang Song, Xiaohua Xiao, Tianfu Wang, Baiying Lei*, “Dual Attention
Graph Convolutional Network Fusing Imaging and Genetic Data for Early Alzheimer's Disease Diagnosis”, Orlando, FL, USA, December
2024.
11. Deepak Ranoji Naik , Amit Savyanavar, “Comparative Analysis of Deep Learning Techniques in Alzheimer’s Disease Diagnosis: Trends,
Challenges, and Future Directions”,Article, February 2026. Unpublished.
Available:https://www.sciencedirect.com/science/article/pii/S2215016126000610?via%3Dihub
12. Rakeshwari K, Barath kumar S, Sanjaykumar S, Ravibala K, Vasanthakumar S, “Predictive Modeling for Early Parkinson’s Disease
Diagnosis using AI”, 3rd International Conference on Inventive Computing and Informatics (ICICI-2025), pp. 1305-1311, July 2025.
13. Saravanan Srinivasan, Parthasarathy Ramadass, Sandeep Kumar Mathivanan, Karthikeyan Paneer Selvam, Basu Dev Shivahare and Mohd
Asif Shah, “Detection of Parkinson disease using multiclass machine learning approach”, Article, Jun 2024. Available: Detection of
Parkinson disease using multiclass machine learning approach - PMC
14. Ze Liu, Yutomg Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang Stephen Lin Baining Guo, “Swin Transformer: Hierarchical Vision
Transformer using Shifted Windows”, International Conference on Computer Vision (ICCV), Feb 2022.
15. Martin Arjovsky, Soumith Chintala and Leon Bottou, “Wasserstein GAN”, Dec 2017. Available: [1701.07875] Wasserstein GAN
16. Battula Srinivasa Rao, Mudiyala Aparna, “A Review on Alzheimer’s Disease Through Analysis of MRI Images Using Deep
Learning Techniques”, IEEE Access, July 2023.
17. Yiling Jiang, Zemgyi Yu, Xuanyu Yin, Haidong Guo, “Early Diagnosis and Progression of Alzheimer’s Disease Based on Long Short-
Term Memory Model ”, International Conference on Robotics, Intelligent Control and Aritifical Intelligence (RICAI), pp. 620-624, April
2024.
18. Mingxing Tan, Quoc V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks”, Sep 2020. Available:
[1905.11946] EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
19. Ghadah Naif Alwaid, Sidra Tahir, Mamoona Humayun and Walaa Gouda, “Improving Alzheimer’s Detection with Deep Learning and
Image Processing Techniques”, IEEE Access, vol. 12, pp. 153445-153456, 28 Oct 2024.
20. Shubhan Kadam, Jay Jani, Aniket Kudtarkar, Nikahat Mulla, “A Comparative Analysis of DCGAN and WGAN in Logo Generation”, 2024
3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), 02 July 2024.