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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Rechtsnormen Journal of Law Journal of Technology Informatics and Engineering International Journal of Educational Insights and Innovations (IJEDINS) International Journal of Technology and Modeling
Patel, Priya
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Other

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Explainable Artificial Intelligence based Deep Learning for Retinal Disease Detection Sureja, Nitesh; Parikh, Vruti; Rathod, Ajaysinh; Patel, Priya; Patel, Hemant; Sureja, Heli
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.717

Abstract

This research focuses on the automated identification of retinal diseases. To address this challenge, an artificial intelligence-based approach developed utilizing five deep learning models namely Xception, InceptionV4, EfficientNet-B4, SqueezeNet, and ResNet-264. The model leverages transfer learning to enhance its performance. It is trained on a dataset of optical coherence tomography (OCT) images to classify retinal conditions into four categories: (1) diabetic macular edema, (2) choroidal neovascularization, (3) drusen, and (4) normal. The training dataset, sourced from publicly available repositories, comprises 1,08,312 OCT retinal images covering all four categories. The proposed models achieved good results. InceptionV4 outperformed other models across multiple metrics, achieving the highest accuracy (99.50%), precision (100%), recall (100%), AUC (100%), and F1 score (100%). It surpassed SqueezeNet (accuracy: 98.00%, precision: 98.00%, recall: 98.00%), EfficientNet-B4 (accuracy: 98.50%, precision: 98.50%, recall: 98.50%), Xception (accuracy: 78.25%, precision: 80.36%, recall: 77.75%, F1 score: 99.50%), and ResNet-264 (accuracy: 87.75%, precision: 87.94%, recall: 87.50%, F1 score: 87.98%). The results highlight the effectiveness of deep learning models combined with transfer learning in achieving accurate and efficient retinal disease detection. Future research could focus on expanding the dataset and exploring hybrid architectures to enhance classification accuracy and improve generalization across various retinal conditions
The Role of Electronic Evidence in the Civil Case Evidence Process Joshi, Nikhil; Kumar, Rohan; Patel, Priya
Rechtsnormen: Journal of Law Vol. 3 No. 1 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/rjl.v3i1.2090

Abstract

Background: The increasing reliance on digital technology has significantly impacted legal proceedings, especially in civil cases, where electronic evidence plays an increasingly central role. With the proliferation of digital communications, social media, and electronic transactions, the types of evidence available have expanded, leading to new challenges for legal professionals in managing and evaluating such evidence. Electronic evidence is now considered a crucial aspect of civil litigation, yet questions remain regarding its admissibility, reliability, and role in the broader evidence process. Objective: This study aims to explore the role of electronic evidence in the civil case evidence process, focusing on its impact on case outcomes, the challenges associated with its handling, and its integration into traditional legal frameworks. Method: A qualitative research design was employed, utilizing case studies, legal documents, and expert interviews. The data was analyzed to examine the practical applications of electronic evidence in civil cases and the associated legal and procedural challenges. Results: The findings reveal that while electronic evidence is essential in modern civil litigation, it often faces challenges in terms of authentication, privacy concerns, and its acceptance in court. The study also found that the increasing complexity of digital evidence requires enhanced legal procedures for handling and presenting such evidence effectively. Conclusion: Electronic evidence plays a pivotal role in civil litigation, but its integration into the evidence process requires further legal refinement to address emerging challenges effectively.
Exploring the Integration of Augmented Reality in Programming Education to Enhance Student Engagement in High Schools Sharmar, Rahul; Patel, Priya
International Journal of Educational Insights and Innovations Vol. 1 No. 2 (2024): December 2024 - International Journal of Educational Insights and Innovations (
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

The integration of Augmented Reality (AR) in programming education has emerged as a promising approach to enhance student engagement and learning outcomes, particularly in high school settings. This study investigates the effectiveness of AR-based learning tools in improving students' understanding of programming concepts and fostering critical thinking skills. Using a mixed-methods approach, the research involved 60 high school students divided into an experimental group (using AR tools) and a control group (following traditional methods). Data was collected through surveys, classroom observations, performance assessments, and qualitative interviews. Results indicate that the experimental group demonstrated significantly higher levels of engagement, with 85% reporting increased interest in programming compared to 60% in the control group. Additionally, the experimental group outperformed the control group in both theoretical knowledge and practical application, scoring 25% higher on quizzes and completing tasks 20% faster. Qualitative feedback highlighted the immersive and interactive nature of AR as key factors in enhancing learning experiences. However, challenges such as technical difficulties and the need for teacher training were also identified. The findings suggest that AR can effectively bridge the gap between theoretical knowledge and practical application, making it a valuable tool for programming education. This study provides insights for educators and policymakers on integrating AR into curricula and highlights the importance of addressing technical and training barriers to ensure successful implementation.
Advancing Medical Diagnostics with Deep Learning: A Novel Approach to Disease Detection and Prediction Patel, Priya; Sharma, Arjun; Mehta, Rahul; Iyer, Ananya
International Journal of Technology and Modeling Vol. 2 No. 2 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i2.109

Abstract

Deep learning has revolutionized various fields, including medical diagnostics, by enabling more accurate and efficient disease detection and prediction. This paper explores the latest advancements in deep learning applications for medical diagnostics, emphasizing how convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models enhance diagnostic accuracy. The study discusses the integration of deep learning with medical imaging, electronic health records (EHRs), and genomic data to improve early disease detection and personalized treatment strategies. Additionally, ethical considerations, challenges, and future directions in deep learning-based diagnostics are analyzed. The findings highlight the potential of deep learning to transform healthcare by reducing diagnostic errors, optimizing treatment plans, and improving patient outcomes.
Offline Counterfactual Evaluation for Advertising and Recommendation Slot Policies: A Reproducible Study on the Open Bandit Dataset (Small) Mu, Jinyi; Ye, Tong; Patel, Priya
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.500

Abstract

Offline or counterfactual evaluation is a critical capability for iterating advertising and recommender ranking strategies when online A/B testing is slow, expensive, or risky. Off-policy evaluation (OPE) estimates the expected reward of a candidate policy using logged interaction data from a different behavior policy. Still, it can suffer from high variance under poor overlap and can be misleading when the operational objective is choosing among candidate policies rather than minimizing point-estimation bias alone. This paper presents a fully reproducible empirical study of IPS, self-normalized IPS (SNIPS), doubly robust (DR), and Switch-DR estimators on the Open Bandit Dataset (OBD) small release. Using the Men and Women campaigns (10,000 logged item-impressions per campaign and behavior policy) collected by uniform random and Bernoulli Thompson Sampling (BTS), we construct a held-out oracle for stationary slot-wise policies from the random-traffic split and evaluate both value estimation and policy-ranking consistency on random-logged and BTS-logged test sets. Across 1,000 nonparametric bootstrap replications, IPS and SNIPS are accurate on randomly logged data, whereas BTS-logged data exhibit extreme importance weights and very small effective sample sizes (ESS), making IPS-based ranking unreliable under weak support. Switch-DR is most useful in moderate-overlap regimes, where it truncates high-variance corrections. Still, it introduces bias that depends on the switching threshold and must therefore be stress-tested rather than treated as a universally superior estimator. Finally, we provide a structured reporting template—based on oracle decomposition, overlap diagnostics, and estimator components—for explaining why a policy appears better and how reliable that conclusion is.
Co-Authors Iyer, Ananya Joshi, Nikhil Kumar, Rohan Mehta, Rahul Mu, Jinyi Parikh, Vruti Patel, Hemant Rathod, Ajaysinh Sharma, Arjun Sharmar, Rahul Sureja, Heli Sureja, Nitesh Ye, Tong