Authors - Shylaja P, Jayasudha J S Abstract - The Question Answering system (QA) is one of the popular and widely used ap-plications of NLP. It is an information retrieval system that attempts to find the correct answer for a question based on the given paragraph text. Transformers have been widely used for QA tasks, due to their contextual embedding, attention mechanism, and transfer learning for specialized tasks. Transformer-based models can be easily fine-tuned with large datasets. Such models provide state-of-the-art performance over large datasets for question-answering tasks. The proposed approach compares performance of transformer based model over a small sized dataset. We incorporated an answer formation layer along with transformers to comprehend contextual, syntactical, and semantic information from small-sized datasets. We developed a set of rules according to question categories to generate semantically and syntactically coherent option sentences based on the questions. These rules transformed option phrases into contextually appropriate sentences. We evaluated SBERT transformer models namely all-mpnet-base-v2, all-MiniLM-L6-v2, all-distilroberta-v1 over answer formatted data and it showed in-crease in accuracy. Answer formation rules over noun phrases of small-sized datasets can help transformers to learn contextual knowledge about the options in the QA sample, Addition of answer formation stage on samples of SciQ data resulted in a rise in accuracy from 86 % to 92 % when using all-MiniLM-L6-v2 model.
Authors - Asritha Paruchuri, Gudivada Krishna Prakash, Mulla Junaid Rahman, Lambu Damarukanath, Guttikonda Prashanti Abstract - The sharing of images in decentralized settings needs high assurances of secrecy, integrity and controlled access. The fast development of cloud-based services and online communication tools have multiplied the communication of sensitive images, and the traditional centralized storage and single-layer security systems are susceptible to cyber-attacks, unauthorized access, and data leakage. The presented paper outlines a safe and decentralized image-sharing system based on Advanced Encryption Standard (AES), the Secret Sharing scheme by Shamir, blockchain authentication, and decentralized storage with the Interplanetary File System (IPFS). First, the input image is encrypted with the help of AES to provide high cryptographic confidentiality. The ciphertext image is further split into shares with secret sharing scheme that avoids unauthorized disclosure and only allows the reconstruction of the encrypted image when the necessary number of valid shares is received. The encrypted shares that are generated are stored in a decentralized way using IPFS, which is highly available, fault tolerant, and does not have a single point of failure. Decentralized access control, participant authentication and integrity verification that is tamper-resistant are enforced using blockchain technology. In the reconstruction process, the encrypted image is reconstructed with the help of Lagrange interpolation and then decrypted with the help of AES, which guarantees safe and lossless recovery of the original im-age. The suggested framework offers multi-layer security, increases confidentiality and data integrity, removes centralized vulnerabilities, and is highly resistant to unauthorized access and data-alteration.
Authors - Mach Thai Loc, Nguyen Hong Phuc, Huu-Cuong Nguyen Abstract - With the development of e-commerce and global supply chains, there is a growing concern about fake or counterfeit products. Current methods for verifying product authenticity are often cumbersome ,time consuming, and vulnerable to tampering. In order to address these issues, for the purpose of this project, a QR code based "Fake Product Detection System" is introduced. In this system, the manufacturer creates an exclusive QR code for each product. The manufacturer then keeps the QR code in a database. If the QR code is scanned through the web-based application, the code is instantly verified. If the code is unique and has not been used be-fore, the product is genuine. But if the code is duplicated or used multiple times, the product is deemed counterfeit. The system is implemented using the Flask web development framework, SQLite database, and web interfaces using the HTML/CSS duo, which is lightweight and easy to use. Other notable features of the system are user authentication, history logging, suspicious image upload for the QR code, and detection of counterfeit items. Overall, this solution would offer a simple, economical, and efficient means to uncover Trojan products while fostering trust amongst consumers and aiding manufacturers to track counterfeit practices.
Authors - K. Thirupathi Reddy, K. Venkata Ajay Kumar, M. Kaveri Abstract - This study explores female creators’ subjective lived experiences navigating human–AI interaction (HAI) within generative design ecosystems. It examines how creators engage with intelligent systems during collaborative creation processes and how they negotiate creative agency between algorithmic outputs and personal meaning-making. Drawing on an Interpretative Phenomenological Analysis (IPA) approach, the study involves seven women who actively utilize Canva’s AI-enabled capabilities to produce professional digital content. Data were collected through in-depth semi-structured interviews and digital observation of design outputs distributed on Instagram. The findings indicate that participants interpret Canva AI as a collaborative creative partner that supports iterative dialogue, experimentation, and reflective decision-making. Rather than replacing human authorship, AI interaction functions as a mediated process in which creators provide prompts, reinterpret generated results, and refine instructions to align outcomes with their subjective intentions. This interaction fosters a sense of psychological safety, particularly among non-professional designers, enabling them to explore creative practices with greater confidence. Through this ongoing negotiation between human agency and algorithmic assistance, participants describe pathways toward professional identity formation and increased participation in contemporary digital creative cultures. Overall, the study highlights how intelligent design systems can shape meaning-making processes, reinforce creative self-efficacy, and support women’s evolving roles within AI-assisted visual communication practices.
Authors - Dudi Gnana Prasoona, Zeenathunnisa, Yamuna V, Pushyami B, Ramandeep Kaur, Navjot Kaur Abstract - In the global health sector, Diabetes is a major concern which needs accurate and effective models for early prediction. This work is quantitative re-search work. The dataset was collected from CDC Diabetes Health Indicators, and we used Light Gradient Boosting Machine (LightGBM) model for predicting diabetes. Since this research work is binary classification-based work, in our data preprocessing stage, we used Synthetic Minority Oversampling Technique (SMOTE) for controlling class imbalance and for feature selection we used Chi-square test to improve the model performance. The proposed LightGBM model showed its ability to recognize complex correlation between diabetes-related health indicators with the training accuracy of 92% and a ROC-AUC score of 0.97 on the test dataset. Overall, the findings highlight that predictive accuracy is significantly improved after applying both imbalance data controlling and most correlated feature selection techniques.
Authors - Lekshmipriya Vijayan, Bindu V R Abstract - In the present paper, a model on an EOQ policy for deteriorated inventory items with stock-sensitive demand pattern under inflation when the deterioration rate is considered to be a linear function of time. Partially backlogged shortages form is allowed to occur in this system. The required conditions are stated to validate the optimal solutions of the present model. Furthermore, the average cost function and decision variables such as shortages time-point and replenishment cycle have been computed with the help of a step-by-step solution procedure and Mathematica software 12.3. Finally, a numerical example as well as its post-optimal analysis for theoretical model is presented to illustrate the pro-posed work.
Authors - Windy Permata Suyono, Marsellisa Nindito, Dwi Handarini, Ratna Anggraini, Eka Septariana Puspa, Surya Anugrah, Sabo Hermawan, Rio Firnanda, Irima Rahmadani Abstract - Gas leaks pose a threat to safety because they can cause fires and damage to property, and they are sometimes fatal. Traditional detection methods are manually dependent or delayed in response, which means they are not always reliable and timely. This paper presents Safe- Gas system for gas leak detection, monitoring and automatic shut-off. The system uses low-cost gas sensor, flame sensors, load cell and an ESP32 microcontroller for local processing. The system is connected to the cloud via Firebase to send alerts, and it has a battery backup to keep it running when the internet or power goes off. The app supports both remote and autonomous valve shutoff. SafeGas is a name that stands for resilience and accuracy. The designers and developers of the device have tested it in the laboratory and in the field to ensure it meets the set standards. First, the system aims to reduce the number of false alarms. Second, it can operate without an internet connection. Third, it can take safety measures independently. The embedded system and cloud integration aspects of the project demonstrate how they can be combined to achieve the desired results.
Authors - Sachin Ratnaparkhi, Parikshit Mahalle, Pankaj Chandre Abstract - Spatial judgment, incorrect furniture size, and poor personalized decor advice are common issues in most interior design planning. The aim of this paper is to introduce an AI-powered Augmented Reality Interior Design Assistant that makes it possible for users to visualize furniture and decor in real spaces using accurate real-world measurements. Spatial mapping using SLAM based AR core plane detection and depth sensing allows for more accurate estimations in room sizes, identifies objects in the scene, and makes AI-driven suggestions on furniture size and styles. .A hybrid AI engine is built using K-nearest neighbours, collaborative filtering and feature extraction methods. The AR rendering process takes care of depth by modifying 3D assets to expected sizes to make sure everything is placed correctly. The AR application is based on Unity 3D with AR Foundation and ARCore, the backend services are provided by python(flask) connected through RESTful APIs, for user profile and catalog management Firebase/PostgreSQL is used. Scikit is used for building machine learning models which is supported with Numpy and Panda for data handling. The assistant will also provide design tips through a conversational AI feature that makes it accessible to everyone. Tests show a significant reduction in spatial errors, much faster design decisions, and better relevance of recommendations. These results indicate that real-scale visualization with AI suggestions tremendously increases design confidence and at the same time reduces the need for redesigns. This system connects AR visualization with AI interior support for a smooth and smart design experience.
Authors - Deepikaa R Ra, Sriram S, Sudhanthira G Abstract - Huntington’s disease is a devastating brain disorder. It gradually destroys nerve cells due to mutations in the HTT gene that disrupt gene functions. Years of research have not led to effective treatments that can slow or stop the disease. Clearly, we need faster ways to find new drugs. This paper introduced an AI-powered systems biology framework that examines both transcriptomic and clinical data to identify drugs that could be repurposed for Huntington’s disease. First, it uses ordinary least squares regression to remove any unusual variables followed by creating gene co-expression networks to closely examine the specific molecular disorder in the disease. Next, they conduct differential network analysis to identify pathways and transcriptional regulators that go awry and compare known drug effects with Huntington’s molecular signatures, rating each drug based on its ability to reverse those harmful gene changes. This helps them quickly focus on drugs that might actually be effective. The entire setup allows researchers to filter, rank, and test potential treatments efficiently, improving the process's reproducibility and reliance on real data.
Authors - Vani K S, Nanditha B, R Bharadhwaj, Rishika Ghai Abstract - Internet of Things (IoT) applications have experienced fast development resulting in massive interconnectivity of devices, and IoT networks have become susceptible to security risks of Sybil, flooding, and masquerading attacks. Conventional centralized security schemes lack flagella, lack the dynamism of trust evaluation, and are vulnerable to single-point failures, whereas the current blockchain-based systems impose too much extra computational and energy load to be applicable in resource-constrained IoT applications. These issues underscore the necessity to have a lightweight, decentralized, and trust-conscious security system that can be used to guarantee secure IoT communication in adversarial environments. The paper presents a lightweight framework of blockchain-based trust that can be exploited to provide security to IoT communication against network-level attacks. The suggested architecture combines a decentralized blockchain architecture and dynamic trust assessment operation to distinguish trustful nodes and isolate bad actors. It uses a trust-sensitive Proof-of-Work (PoW) architecture to verify block authenticity, in which a node trust score is calculated following communication behavior and history of interaction. Technique of order of preference similarity to Ideal solution (TOPSIS) is used to choose the high trust nodes to validate the transaction securely, which minimizes the amount of computation wasted and increases the network reliability.
