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All Journal International Journal of Advances in Artificial Intelligence and Machine Learning
Sirisati, Ranga Swamy
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Computer Science & IT

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Hepatitis Disease Prediction Using Convolutional Neural Network Algorithm in Machine Learning Technology Sirisati, Ranga Swamy; Jayasri, B.; Avanthi, A.; Ramyasri, A.; Sowmya, K.
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 2 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i2.419

Abstract

Background of Study: Hepatitis is a significant viral infection causing liver inflammation, potentially leading to hepatocyte death and impaired liver function. Types B (HBV) and C (HCV) can cause chronic hepatitis, cirrhosis, and cancer. Globally, around 257 million people are infected with HBV and 71 million with HCV. Early detection of chronic Hepatitis B is crucial for effective management.Aims and Scope of Paper: This study aims to predict hepatitis progression in patients from their medical histories. It seeks to enhance prediction accuracy by addressing challenges like noise and inefficiency caused by similar aspect values and distributions within datasets.Methods: Machine learning, a branch of AI, is employed for chronic disease prediction. The study primarily utilizes the K-Nearest Neighbour (KNN) algorithm to predict and eliminate redundant data and noise. Other models evaluated include Logistic Regression, Random Forest, and Convolutional Neural Networks (CNN), with SMOTE used for dataset balancing.Result: KNN achieved 0.970 accuracy, Logistic Regression 0.966, and Random Forest 0.95. The CNN model demonstrated exceptional performance, reaching 1.0 accuracy with perfect precision, recall, and F1-score for Hepatitis A and B.Conclusion: While KNN performed well among traditional methods, deep learning models like CNN show superior accuracy and generalizability, offering a robust framework for hepatitis prediction.
Diabetic Retinopathy Prediction Using Deep Learning: Insights From CNN Sirisati, Ranga Swamy; Navyasri, V.; Swathi, T.; Akhila, M.; Astried, Astried
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 3 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i3.420

Abstract

Background of study: Diabetic Retinopathy (DR) is a severe microvascular complication of diabetes mellitus that can lead to vision loss if not detected early. With over 93 million individuals affected globally, the need for accurate and efficient diagnostic systems has become urgent. Traditional screening methods depend on manual interpretation of fundus images by ophthalmologists, which is time-consuming and prone to subjectivity.Aims: This research seeks to create and assess a deep learning diagnostic model designed to reliably identify the severity levels of diabetic retinopathy using retinal fundus images. The research also explores model interpretability using Shapley Additive Explanations (SHAP) to increase transparency in AI-assisted medical diagnosis.Methods: Convolutional Neural Networks (CNNs) were implemented using transfer learning with pretrained architectures such as ResNet50 and InceptionV3. The EyePACS dataset, containing images categorized into five DR severity levels, was used for model training. Preprocessing techniques, including contrast enhancement, histogram equalization, and data augmentation, improved image quality and model generalization. The models were optimized with the Adam and assessed through accuracy, precision, recall, F1-score, and AUC. Additionally, SHAP analysis was employed to interpret and illustrate the model’s predictions.Results: The proposed CNN-based model achieved 98.5% accuracy, with a sensitivity and specificity of 0.99, demonstrating strong performance across multiple DR stages. Comparison with existing studies revealed a notable improvement in diagnostic accuracy. SHAP visualizations confirmed that critical retinal features such as microaneurysms, hemorrhages, and cotton-wool spots were key predictors influencing model decisions.Conclusion: The findings validate the efficacy of deep learning, particularly CNNs, in enhancing early detection and classification of diabetic retinopathy. The integration of SHAP interpretability bridges the gap between AI predictions and clinical trust, making this approach a promising tool for large-scale automated DR screening and supporting ophthalmologists in timely diagnosis and treatment.
Plant Disease Detection Using Image Processing and Machine Learning Sirisati, Ranga Swamy; Sravya, J.; Sruthi, D.; Nandhu, A.; Sree, R. Navya; Irawati, Dyah Ayu
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 3 No. 1 (2026): Forthcoming Issue (March)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v3i1.433

Abstract

Background: Plant diseases continue to threaten agricultural productivity worldwide, causing significant reductions in crop yield and quality. Traditional visual inspection by farmers or experts is often slow, subjective, and unreliable, especially across large plantation areas. With the increasing availability of digital imaging technologies, automated detection through image processing and machine learning presents a promising alternative.Aims: This study aims to develop an enhanced plant disease detection framework using image processing combined with machine learning algorithms, particularly Support Vector Machine (SVM) and Convolutional Neural Networks (CNN).Methods: A dataset of 54,306 leaf images from the PlantVillage collection was used to train and test the models. Preprocessing steps included resizing, noise removal, background segmentation, and feature extraction. CNNs were trained for end-to-end classification, while SVM operated on manually extracted features. A 10-fold cross-validation procedure was employed to ensure robustness. Fine-tuning strategies and comparative experiments were implemented to evaluate performance consistency across dataset variants.Result: The system demonstrated strong capability in early disease detection, achieving 97% accuracy for healthy leaves and moderate performance (56%) for certain diseased classes due to visual similarity and image noise. Background segmentation improved focus on disease features, while grayscale images reduced reliance on color cues but lowered classification accuracy.Conclusion: The findings confirm that machine learning, particularly CNN-based models, can significantly enhance plant disease diagnosis and support timely agricultural decision-making. Future improvements will explore advanced deep learning architectures, expanded datasets, multimodal imaging, and IoT integration for real-time field deployment.
Co-Authors Akhila, M. Astried, Astried Avanthi, A. Dyah Ayu Irawati, Dyah Ayu Jayasri, B. Nandhu, A. Navyasri, V. Ramyasri, A. Sowmya, K. Sravya, J. Sree, R. Navya Sruthi, D. Swathi, T.