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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Lentera Perawat Rechtsnormen Journal of Law International Journal of Educational Insights and Innovations (IJEDINS) International Journal of Technology and Modeling Journal of Social Entrepreneurship and Creative Technology Journal of Multidisciplinary Sustainability Asean
Patel, Priya
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health 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.
Resilience among nurses working in maternity wards in Bangladesh: A qualitative study Sharma, Aarav; Patel, Priya; Mehta, Rohan
Lentera Perawat Vol. 6 No. 4 (2025): October - Desember
Publisher : STIKes Al-Ma'arif Baturaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52235/lp.v6i4.590

Abstract

Background: Nurses working in maternity wards in Bangladesh face complex clinical and emotional challenges due to maternal complications, high patient loads, and limited resources. Resilience has been recognized as a critical factor in helping nurses cope with stress, adapt to demanding environments, and sustain compassionate care. Objective: This study aimed to explore and describe the experiences of resilience among nurses working in maternity wards in Bangladesh. Methods: A qualitative interpretive descriptive design was employed, guided by Thorne’s framework. Purposive sampling recruited eight registered nurses from maternity units, including labor and delivery, postnatal, and maternal high-dependency wards, in tertiary hospitals. Semi-structured individual interviews were conducted between October 2020 and January 2021, lasting 30–90 minutes, and were audio recorded. Data were analyzed inductively to identify themes reflecting resilience experiences. Trustworthiness was ensured using COREQ guidelines. Results: Three overarching themes emerged: (1) The transition period, reflecting anxiety and lack of preparedness during the early stages of maternity nursing; (2) Gaining trust of mothers, families, and colleagues, highlighting the challenges of acceptance, communication, and professional recognition; and (3) Having a positive mindset, emphasizing psychological resilience, optimism, and self-care practices that enabled nurses to cope with workplace stress. Conclusion: Resilience among maternity nurses in Bangladesh is developed through a dynamic interplay of personal adaptation, social support, and psychological coping strategies. Strengthening structured mentorship, fostering supportive workplace cultures, and integrating resilience training into professional development are crucial to enhance maternal care quality and reduce burnout among maternity nurses.
Scaling Social Impact: A Longitudinal Analysis of Sustainable Business Models for Waste Bank Social Enterprises in Urban Indonesia Yuliastuti, Hilda; Patel, Priya; Green, David
Journal of Social Entrepreneurship and Creative Technology Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jseact.v2i5.2662

Abstract

Waste Bank Social Enterprises (WBSEs) are crucial for addressing urban waste challenges in Indonesia but struggle with scalability and financial sustainability. Many fail to move beyond micro-scale operations, limiting their social impact. This study identifies the business model characteristics that enable WBSEs to sustainably scale. It analyzes the evolutionary process of their business models over time. A 36-month longitudinal, mixed-methods study was conducted on twelve urban Indonesian WBSEs. We combined quantitative performance metrics with 72 semi-structured interviews to analyze their scaling trajectories. Findings reveal a stark divergence. Most WBSEs stagnated, trapped by a precarious aggregation-only model. The Rapidly Scaling enterprises were universally differentiated by a strategic pivot: adopting value-adding processing. This transformation allowed them to exit the low-value commodity trap and secure stable, high-value industrial contracts. Sustainable scaling is contingent upon a fundamental business model transformation from a passive collector to an active producer. This evolution from a community project to a market-integrated social enterprise is essential for financial resilience and amplifying social impact.
Community-Based Coral Reef Conservation In The Kepulauan Seribu Prihadi, Donny Juliandri; Gupta, Meera; Patel, Priya
Journal of Multidisciplinary Sustainability Asean Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijmsa.v2i3.2593

Abstract

Background. The coral reef ecosystems of Kepulauan Seribu National Park, crucial for marine biodiversity and local livelihoods, face severe anthropogenic pressures from coastal development and tourism. Community-Based Conservation (CBC) has emerged as a vital strategy to counteract this degradation, yet its specific impacts in this high-pressure marine area require rigorous evaluation. Purpose. This research evaluates the ecological efficacy and socioeconomic contributions of established CBC programs in Kepulauan Seribu. The study investigates the key factors influencing successful community participation and the resulting impacts on coral reef health. Methods. A mixed-methods approach was employed, integrating quantitative ecological surveys with qualitative socioeconomic analysis. Ecological data were collected via Line Intercept Transects (LIT) at multiple CBC-managed sites and non-CBC (control) sites. Socioeconomic data were gathered through semi-structured interviews (n=120) with community members, program managers, and local stakeholders. Results. The findings indicate that CBC-managed reefs exhibited significantly higher live coral cover and greater reef fish biomass compared to control sites. Socioeconomic analysis revealed enhanced community stewardship, supplementary income streams, and increased environmental awareness. However, institutional challenges, particularly funding instability and policy conflicts with tourism development, were identified as persistent obstacles. Conclusion. Community-based conservation demonstrates tangible success in mitigating reef degradation in Kepulauan Seribu. This study concludes that while CBC is effective, its long-term sustainability hinges on strengthening institutional support, resolving resource-use conflicts, and ensuring equitable benefit sharing for participating communities.
Co-Authors Donny Juliandri Prihadi Green, David Gupta, Meera Iyer, Ananya Joshi, Nikhil Kumar, Rohan Mehta, Rahul Mehta, Rohan Parikh, Vruti Patel, Hemant Rathod, Ajaysinh Sharma, Aarav Sharma, Arjun Sharmar, Rahul Sureja, Heli Sureja, Nitesh Yuliastuti, Hilda