1. Bukhari SNH, Masoodi F, Dar MA, et al. Prediction of erythemato-squamous diseases using machine learning. In: Applications of Machine Learning and Deep Learning on Biological Data. Masoodi F, Quasim M, Bukhari S, et al. Auerbach Publications 2023; 87.
2.
Ahmed A, Ahmad H, Khurshid M, et al. Classification of Skin Disease using Machine Learning. VFAST Trans Software Engineering 2023; 11: 109-22.
3.
Afrid V, Reddy BMK. An efficient feature reduction approach for dermatology disease detection utilizing neural network approach. Int J Computing Artif Intel 2021; 2: 6-11.
4.
Kaushik B, Vijayvargiya A, Uppal J, et al. Comparative analysis of machine learning approaches for classifying erythemato-squamous skin diseases. In: International Conference on Mining Intelligence and Knowledge Exploration 2023; 67-77. https://doi.org/10.1007/978-3-031-44084-7_7
5.
Ashvanth Louison A, Sujitha B. A review on melanoma skin cancer classification using deep learning techniques. Int J Curr Biomed Engineering 2025; 3: 26-4.
6.
Pandya DD, Degadwala S, Vyas D, et al. Advancing erythemato-squamous disease classification with multi-class machine learning. 2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). 2023; 542-547. https://doi.org/10.1109/I-SMAC58438.2023.10290599.
7.
Akarajarasroj T, Wattanapermpool O, Sapphaphab P, et al. Feature selection in the classification of erythemato-squamous diseases using machine learning models and principal component analysis. 2023 15th Biomedical Engineering International Conference (BMEiCON) 2023; 1-5. https://doi.org/10.1109/BMEiCON60347.2023.10322034.
8.
Singh SK, Sinha A, Yadav S. Performance analysis of machine learning algorithms for erythemato-squamous diseases classification. 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) 2022; 1-6. https://doi.org/10.1109/ICDCECE53908.2022.9793000.
9.
Bozok MN, Çalhan A. Diagnosis of erythemato-squamous skin diseases with machine learning algorithms. J Clin Exp Dermatol Res 2022; 13: 615.
10.
Sharma S, Sharma V. Comparison of machine learning techniques in the diagnosis of erythematous squamous disease. J Sci Res Technol 2023; 1: 1-9. https://doi.org/10.5281/zenodo.8218863.
11.
Surendiran R, Thangamani M, Narmatha C, et al. Effective autism spectrum disorder prediction to improve the clinical traits using machine learning techniques. Int J Engin Trends Technol 2022: 70: 434-59.
12.
Thanjaivadivel T, Jeeva S, Ahilan A. Real time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM. Computer Networks 2019; 151: 191-200.
13.
Prasanth A, Muthukumaran N. Primary open-angle glaucoma severity prediction using deep learning technique. Int J Curr Biomed Engineering 2023; 1: 30-7.
14.
Nithya A, Appathurai A, Venkatadri N, et al. Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images. Measurement 2020; 149: 106952.
15.
Shastri S, Kour P, Kumar S, et al. GBoost: a novel Grading-AdaBoost ensemble approach for automatic identification of erythemato-squamous disease. Int J Inf Technol 2021; 13: 959-71.
16.
Elsayad AM, Nassef AM, Al-Dhaifallah M. Bayesian optimization of multiclass SVM for efficient diagnosis of erythemato-squamous diseases. Biomed Signal Process Control 2022; 71: 103223.
17.
Alotaibi AS. Hybrid model based on ReliefF algorithm and K-nearest neighbour for erythemato-squamous diseases forecasting. Arabian J Sci Eng 2022; 47: 1299-307.
18.
Putatunda S. A hybrid deep learning approach for diagnosis of the erythemato-squamous disease. 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) 2020; 1-6. https://doi.org/10.1109/CONECCT50063.2020.9198447.
19.
Deva RS. EPFSESD: An Enhanced Pipeline Feature Selection Algorithm for Erythemato-Squamous Disease Detection. Des Eng 2021; 2373-88.
20.
Igodan EC, Obe OO, Thompson AFB, et al. Prediction of erythemato squamous-disease using ensemble learning framework. In: Explainable Artificial Intelligence in Medical Decision Support Systems. Imoize AL, Hemanth J, Do DT, Sur SN (eds.). 2022; 197-228.
21.
Badrinath N, Gopinath G, Ravichandran KS, et al. Classification and prediction of erythemato-squamous diseases through tensor-based learning. Proc Natl Acad Sci India A Phys Sci 2020; 90: 327-35.
22.
Chen HC, Widodo AM, Wisnujati A, et al. AlexNet convolutional neural network for disease detection and classification of tomato leaf. Electron 2022; 11: 951.
23.
Verma R, Singh V. Leaf disease identification using DenseNet. In: International conference on artificial intelligence and speech technology 2021; 500-11. https://doi.org/10.1007/978-3-030-95711-7_42.
24.
Mahmoud S, Gaber M, Farouk G, et al. Heart disease prediction using modified version of LeNet-5 model. Int J Intell Syst Appl 2022; 14: 1-12.
25.
Dakshina DS, Della Reasa Valiaveetil, Bindhu A. Alzheimer disease detection via deep learning-based shuffle network. Int J Current Biomed Engineering 2023; 1: 9-15.
26.
Rajkumar R, Gopalakrishnan S, Praveena K, et al. Darknet-53 convolutional neural network-based image processing for breast cancer detection. Mesopotam J Artif Intel Healthcare 2024; 2024: 59-68.
27.
Abedini Z, Jamzad M. Weight-based colour constancy using contrast stretching. IET Image Processing 2021; 15: 2424-40.
28.
Somal S. Image enhancement using local and global histogram equalization technique and their comparison. In: First International Conference on Sustainable Technologies for Computational Intelligence: Proceedings of ICTSCI 2019 2020; 739-753.
29.
Tesfai H, Saleh H, Al-Qutayri M, et al. Lightweight shufflenet based cnn for arrhythmia classification. IEEE Access 2022; 10: 111842-54.
30.
Trojovský P, Dehghani M. A new bio-inspired metaheuristic algorithm for solving optimization problems based on walruses behavior. Sci Rep 2023; 13: 8775.