Authors - K.Surya Teja, Immanuel Anupalli, P.Sudheer Abstract - Maximum power point tracking (MPPT) is a vital module of photovoltaic (PV) systems. Traditional maximum power MPPT techniques struggle in a complex and ever-changing scenarios, and the solar system's output characteristic curve shows multi-peak phenomena owing to dissimilarities in temperature and light concentration. This paper proposes an adaptive hybrid RIME optimization technique which enhances the exploratory capabilities of the method during the initialization phase by integrating tent mapping. The goal is to improve feature selection tasks and MPPT for PV systems under partial shading condition. It uses piecewise mapping to optimize the algorithm's parameters and attack a fair steadiness amongst global exploration and local exploitation. The search method is dynamically adjusted with an adaptive inertia weight introduced, which further increases convergence speed, search efficiency and algorithm's adaptability. In order to reduce computational costs and increase classification accuracy, the hybrid method employs natural-inspired metaheuristics for feature selection, resulting in optimal subsets. When it comes to tracking speed, precision, and stability in the PV MPPT environment, the method beats PSO-BOA, conventional RIME, IRIME and HRIME approaches.
Authors - Gauree Prabhakar Sayam, Supriya Narad Abstract - Chronic non-communicable diseases like diabetes, heart disease, and obesity continue to increase globally, comprising 74% of all deaths, even as noted by the World Health Organization in the 2025 progress monitor on non-communicable diseases. This work describes the design and deployment of Health Risk Advisor, an AI (artificial intelligence) web application powered by machine learning that predicts early risks and provides personalized recommendations on disease prevention. The integration of ensemble models such as Random Forest and XG-Boost into a rule-based advisory engine allows the application to achieve more than 90% accuracy in making risk classifications, addressing access barriers to healthcare in underserved regions, such as rural India. From architecture and design, healthcare applications and benefits, to ethical AI challenges and considerations, this work discusses every aspect of the new technology using diverse sets of datasets that inform practices as well as recommend ethical AI. Evaluations showed reductions of the burden from NCDs between 20-30% by engaging the application in a preventive healthcare intervention, which is aligned to global health equity goals.
Authors - Saurabh Nimje, Reena Satpute, Utkarsha Pacharaney, Anup Bhitre Abstract - Breast cancer is considered as one of the top causes of mortality on women across the world making early and accurate diagnosis a key element in addressing patient outcomes. The work introduces artificial breast instances of cancer detection techniques in ultrasound imaging by means of Contrast Limited Adaptive Histogram Equalization (CLAHE) and ensemble deep learning framework. Data used was a balanced data set comprising of 200 ultrasound images that are made to be benign, malignant, and normal. The CLAHE preprocessing was quite useful in terms of image quality as it provided edge and local contrast enhancement and profited letting the lesions be seen more effectively. A number of the convolutional neural network (CNN) architectures were tuned collectively in an ensemble arrangement with soft voting and weighted averaging, and this produced an improved classification performance. The proposed model returned an accuracy of 93.7%, sensitivity of 92.5%, specificity of 94.5% and AUC of 0.97 even better than the baseline general CNN models and the single CNN models with CLAHE. The findings are indicative of the fact that CLAHE-enhanced ensemble learning is a robust, reproducible, and promising tool in breast cancer detection within ultrasound imaging that holds a great promise in clinical.
Authors - Naga Sujitha Vummaneni, Srilakshmi Bharadwaj, Himani Varshney Abstract - The global healthcare landscape is currently undergoing a radical transformation, driven by the dual catalysts of the post-pandemic necessity for remote care and the rapid proliferation of digital infrastructure in developing economies. This research paper presents a comprehensive study on the design, development, and strategic positioning of a desktop-based "Healthcare Management System with Telemedicine." Developed using the Java ecosystem—specifically Java Swing for the graphical user interface (GUI) and Java Database Connectivity (JDBC) for persistence—the system integrates third-party WebRTC services via Jitsi Meet to facilitate real-time virtual consultations. Unlike purely administrative Hospital Management Systems (HMS), this solution integrates clinical workflows with administrative tasks, offering a unified platform for patient authentication, appointment scheduling, and remote video consultation. This report goes beyond technical implementation to provide an exhaustive analysis of the Indian digital health market, projected to reach USD 106.97 billion by 2033. It critically evaluates market leaders such as Practo, Zocdoc, and Teladoc to identify structural gaps in service delivery, particularly regarding cost-barriers and infrastructure dependency in Tier-2 and Tier-3 cities. By adopting the Prototyping Model of software engineering, the research iteratively addresses requirements for security, usability, and legacy hardware compatibility. The findings suggest that while cloud-native SaaS models dominate the current market, lightweight Java-based desktop solutions offer distinct advantages in data sovereignty, offline capability, and operational stability for resource-constrained healthcare settings. The paper concludes with a roadmap for integrating Artificial Intelligence (AI) for predictive diagnostics and expanding into mobile ecosystems, positioning the developed system as a viable component of the emerging Global Initiative on Digital Health (GIDH).
Authors - Nevil Dhinoja, Shubh Patel, Binal Kaka Abstract - Gradient conflicts, computational complexity, and optimization instability are some of the issues with model-agnostic meta-learning, or MAML. We introduce a methodical methodology that integrates three improvement techniques: meta-level regularization, adaptive optimization management, and taskaware gradient. By combining three complimentary mechanisms—task-aware gradient modulation, meta-level regularization, and adaptive optimization management—this work suggests an organized design framework to increase the stability and robustness of MAML-based optimization. The paradigm provides a solid basis for the methodical creation of more reliable and scalable meta-learning systems, even while empirical evaluation is saved for later research.
Authors - Liyan Grace Shaji, Lakshmi K.S, Shazil Mohammad Iqbal, Don Basil Saj, Tom Thomas Abstract - Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects skills related to social interaction and communication. Of late, this is estimated to be prevalent in 1 among 100 children, across the world. Unfortunately, our present, diagnostic methods, like ADI-R and ADOS, rely on questionnaires, which render them to be time-consuming, expensive, and skill-dependent. Hence, to address these challenges, FaceIt is developed, which is a Deep Learning-based diagnostic tool that integrates real-time image capture and classification for rapid and accessible ASD screening. The tool efficiently processes facial images captured or uploaded by users, by performing preprocessing steps like cropping and alignment. A Convolutional Neural Network (CNN) extracts facial features to detect ASD, while a Bayesian CNN captures uncertainty in predictions. Its user-friendly interface allows self administration, devoid of professional supervision. The faster and more accessible preliminary screening even facilitates timely follow-up diagnostics if needed, thus making this an optimum solution for widespread use.
Authors - Nabeela Kausar, Ramiza Ashraf, Naila Ashraf, Romana Ali Abstract - The conventional way of preparing an advertisement is an elaborate process incorporating human subjectivity and human resources heavily dependent on creativity. Making advertisements by human effort can be regarded as an inefficient utilization of capital for small to medium-scale businesses due to increased cost of production. Even in current advancements in the development of generative techniques including LLM-based strategies for Advertisement Generation with Prompts, creating apt prompts for the depiction of products requires human expertise, making them less accessible. In order to overcome the challenges presented by the current models, we introduce a fast, affordable, and scalable platform for the automation of advertisement generation for products leveraging the capabilities of pre-trained diffusion models. The proposed system requires no training or fine-tuning since everything is performed at the inference level. The AI-aware system for designing assists in the identification of color schemes and attributes from the images of the products, whereas the descriptions and categories of the items help identify the theme and pattern recommendations for advertisements. These recommendations are channeled through a pre-trained Stable diffusion model guided by the LLaMA language model.
Authors - Naga Sujitha Vummaneni, Ishan Kumar, Adarsh Mittal Abstract - Digital evidence is now central to cyber investigations, legal trials, and organizational audits. However, traditional evidence management systems rely heavily on centralized storage, making them vulnerable to unauthorized modifications, insider attacks, and in complete audit trails. This research introduces a Blockchain-Based Evidence Management System designed to secure digital evidence through immutability, transparent verification, and tamper proof storage of evidence hashes. The proposed solution integrates Java FX as a user-friendly interface, MongoDB for storing meta data, SHA-256 for generating unique evidence fingerprints, and the Polygon Mumbai blockchain for permanent registration of hash values. Users can upload evidence, verify its authenticity, and review all actions through a detailed activity log. Experimental results show that blockchain-backed verification reliably identifies tampered evidence and significantly strengthens the chain of custody. The system offers an efficient, scalable, and secure enhancement to traditional evidence-handling methods.
Authors - Naga Sujitha Vummaneni, Adarsh Mittal, Ishan Kumar Abstract - The rapid growth of digital platforms has transformed the way individuals buy and sell goods. However, college students still largely depend on informal and unorganized methods for peer-to-peer trading. This paper presents UNIBID, a Java-based online marketplace designed specifically for college students to enable secure, reliable, and efficient product trading within the campus community. The system allows users to register, authenticate, list products, browse products, search and filter items, and perform secure purchase transactions. The backend is implemented using Java [1], while database operations are handled using a reliable database management system. The proposed system eliminates the drawbacks of manual trading such as lack of trust, delay in communication, and absence of product verification. Experimental results show that UNIBID significantly improves transaction speed, transparency, and user convenience compared to traditional methods. The system is scalable, secure, and suitable for deployment in real academic environments.
Authors - Nishu, Kajal, Pavitra Jangir, Ayush Kumar Gupta, Kiran Dikshit, Ajay, Vishal Shrivastava, Akhil Pandey, Ram Babu Buri, Harveer Choudhary Abstract - Despite the availability of digital voting systems, prior studies continue to identify gaps such as weak or voter authentication, security vulnerabilities and insufficient fraud prevention mechanisms. This paper presents BotoSafe, a secure and user-centered electronic voting (e-voting) platform developed for student government elections within educational institutions. The system implements multifactor authentication (MFA) using one-time password (OTP) verification and facial recognition with an anti-spoofing mechanism. To ensure the confidentiality and integrity of the voting process we employ the Advanced Encryption Standard in Galois/Counter Mode (AES-GCM). A developmental research design with a quantitative approach was used for the system development and evaluation. A mock election involving 84 students from Western Mindanao State University–Pagadian Campus was conducted, followed by a post-assessment survey. Results from the System Usability Scale (SUS) yielded a score of 72.08, indicating acceptable usability. User responses further showed that the system is easy to use, safe, and trustworthy for student elections. These findings indicate that BotoSafe is a viable e-voting solution for student government elections and may be further enhanced in future studies.
