Authors - Sehaj Preet Kaur, Rahul Chaudhary, Rashmy Moray, Shikha Jain, Sridevi Chennamsetti, Harsha Thorve Abstract - This study aims to investigate the factors affecting the usage intent of Non-Fungible Tokens (NFTs) and its adoption among Gen Z and Millennials. Purposive sampling technique was used Using structured questionnaire, primary data was gathered and statistical tool SEM using SmartPLS was employed to assess the influence of factors under UTAUT2 model on usage intention. The findings reveal that users are inclined to adopt NFTs when perceived as easy-to-use and hassle-free. Likelihood of adopting is further facilitated with some conditions like adequate resources and support. Interestingly, habituality toward traditional digital assets had diverted effort from the steepness toward NFTs while hedonic motivation shows a lack of inclination for novelty over utility. Besides, performance expectancy and social influence play a big role, while the perceived cost acts as a showstopper. The study contributes to the existing body of knowledge and the stakeholder to estimates NFTs intent use.
Authors - Alka Beniwal, Trishna Paul, Mukesh Kumar Rohil Abstract - In the rapidly changing landscape of daily life, medical imaging stands out as a significant and novel component, significantly impacting healthcare practices. The efficiency of medical imaging processes is pivotal, and within this realm, accurate image registration emerges as a key contributor. Despite its recognized importance, existing pipelines lack a definitive and well-defined structure tailored to the specific requirements of medical imaging. This paper exclusively directs its focus toward addressing this gap by thoroughly examining and redefining the image registration pipeline within the context of medical imaging. The objective is to enhance the efficiency of medical imaging procedures by establishing a tailored and comprehensive pipeline that aligns seamlessly with the unique demands of this critical domain. We also analyzed the different application areas of image registration in medical imaging with their benefits, challenges, and future directions.
Authors - Shashwat Avhad, Nikhil Chavan, Lalit Patil Abstract - An extensive overview of the surface electromyography (sEMG) methods for signal processing and their application to prosthetic hand control is given in this paper's abstract. Techniques for analyzing muscle activity to enable natural and accurate movement of the hands in prosthetic have advanced immensely as a result of growing interest in sEMG-based devices. This article discusses various techniques, such as wavelet transformation, machine learning-based algorithms, and time- and frequency-domain approaches, for feature extraction and classification from sEMG data. It also looks at how deep learning models have recently been included, and how it has helped to increase the precision and stability of sEMG signal classification. In addition, hybrid models that combine traditional statistical techniques with neural networks are investigated for their potential to improve prosthetic control precision and adaptability. The study tackles typical real-time signal recognition problems, like noise reduction and multi-degree freedom movement control management. The review's conclusion highlights the need for more study on multi-modal systems that use machine learning and sophisticated signal processing in order to enhance the usability and reliability of prosthetic devices.
Authors - Pranav Indurkar, Mansi Dangade, Apoorva Kumar, Harsh Thakar Abstract - This paper explores the chip implementation of a low-power RSA encryption system, optimizing resource and Power usage while maintaining the security. The RSA Algorithm modeled using Verilog, implemented on the XILINX SPARTAN-7 FPGA (XC7S50-CSGA324-2) with a comparative analysis of 4- bit to 8-bit algorithmic parameters (such as p, q, e, d, M, C, n, phi_n). Two approaches are studied: one with uniform bit sizes of algorithmic parameters and another with smaller p & q bit sizes. Results show that the second approach yields better efficiency. Future work with CADENCE CIC tools will further optimize power consumption. This work offers insights into designing low-power RSA encryption chip for modern digital systems.
Authors - Aditi Vanikar, Rana Vanikar, Mihir Sardesai, Rupesh Jaiswal Abstract - Preserving the security and performance of a network in today's networked environment depends upon monitoring and analyzing network traffic. The paper discusses our system overview that analyzes live network traffic from a mirrored port by means of DPI. It includes the provision for gaining insight into the usage of networked applications by sorting packets into particular categories, such as HTTP, video streaming, or other protocols. The technology employs state-of-the-art machine-learning algorithms and categorization in order to identify and alert potentially damaging packets in a timely manner. Upon detecting any threats, the system alerts the user so that he or she can take appropriate preventive measures. The hybrid strategy ensures improved visibility and security of network environments through concurrent, real-time threat detection and traffic classification.
Authors - Ritika Patki, Srushti Jamewar, Tanika Mathur, Mridula Korde Abstract - High temperatures accelerate the spoilage of dairy products, particularly milk, posing significant challenges for food safety and waste management. This study presents a novel sensor-based detection system designed to monitor milk spoilage by measuring real-time changes in pH levels and carbon monoxide (CO) concentrations, which are key indicators of microbial activity and biochemical shifts in milk. The system utilizes an Arduino UNO microcontroller integrated with a pH sensor and an MQ-7 gas sensor to assess milk freshness through a combination of pH and CO data. Results are displayed on an LCD screen with intuitive indicators of freshness status—categorized as "fresh," "not so fresh," or "spoiled"—ensuring ease of interpretation for users. Experimental validation across various milk samples demonstrates the system’s effectiveness in early spoilage detection. Future developments may focus on non-invasive methods and IoT-based miniaturization, incorporating machine learning algorithms for residual life prediction through blockchain integration. This approach promises to reduce food wastage and enhance food security, offering an accessible and sustain-able solution for milk spoilage detection.
Authors - Rajlaxmi Sunil Sangve, Riya Jha, Bhagyashri Narale, Sakshi Hosamani Abstract - Kidney disease is an asymptomatic disease, which leads to severe complications or even mortality if not diagnosed early. Routine diagnostic methods, such as serum-based tests and biopsies, are either less effective in the early stages of the disease. This paper proposes an automatic detection of kidney disease using CNNs applied to medical imaging data. Our model is designed to analyze computed tomography (CT) images for the identification of kidney disease, classifying normal and tumors. The proposed CNN architecture leverages deep learning techniques to extract features from these images and classify them with high accuracy. This paper aims to build a system for detection of kidney disease using CNN, based on a public dataset sourced from Kaggle. The paper involves several key stages, initiated from raw data preprocessing and feature selection, followed by training and evaluating machine learning model using CNN. Our proposed model demonstrated superior performance in kidney disease detection, achieving an accuracy of 95%.
Authors - Aman Bhimrao Kamble, Shafi Pathan Abstract - Deep Q-Learning (DQL) has emerged as a promising method for enhancing Network Intrusion Detection Systems (NIDS) by enabling dynamic and adaptive detection of evolving network threats. This review examines the strengths, limitations, and potential enhancements of using DQL in NIDS. Even while DQL increases the accuracy of anomaly detection and manages massive amounts of network data, it has drawbacks such slow convergence, high processing costs, and vulnerability to adversarial attacks. This study proposes improvements to overcome these problems, including efficient reward systems, hybrid architectures that combine DQL with other machine learning models, and continuous learning to adapt to changing threats. Recommendations for further research to enhance DQL's efficacy in real-time intrusion detection are included in the study's conclusion.
Authors - Shannon D’Souza, Rashmy Moray, Sridevi Chenammasetti, Shikha Jain Abstract - This study explores factors affecting investors' intentions to utilize Peer-to-Peer (P2P) lending platforms using the DeLone and McLean Information Systems Success (ISS) model and the Technology Acceptance Model (TAM). Data was collected through an online survey, yielding 283 valid responses from 350 distributed questionnaires, using snowball and convenience sampling. Structural Equation Modelling (SEM) with SmartPLS validated relationships between system quality, information quality, service quality, user satisfaction, social influence, and continuous intention to use. Findings indicate that system quality and service quality have a significant positive predictive effect on user satisfaction, while social influence positively affects ongoing usage intentions. Improved system performance, information accuracy, and service responsiveness can foster investor trust, platform adoption, and retention, guiding stakeholders and regulators toward an environment of stable and successful P2P lending.
Authors - Kumbhar Sanjivanee Rajan, Kulkarni Prachi Prashant, Pawar Prachi Baghwan, Patara Diya Milan, Abira Banik Abstract - “Holistic Heal” is an app developed using Flutter, tailored to address the growing interest in alternative healthcare options such as Ayurveda, Yoga, and Naturopathy. The app simplifies the process of discovering and accessing these specialized hospitals and wellness centers, making holistic healthcare more accessible to users. Harnessing the capabilities of geolocation services enables users to find the nearest facilities, offering detailed information for each. The app also ensures a user-friendly experience. Built with a focus on promoting holistic well-being, “Holistic Heal” showcases the potential of technology to enhance traditional healing practices and empower individuals in their journey towards a healthier, more balanced lifestyle. Depending on their demands, the patient can search the hospital. Upon the patient’s request, this application offers the hospital and physician details that are currently accessible. A suggested application has been designed to find the closest hospital with the requested medical specialty. Hospitals’ nearest locations are identified using the Global Positioning System (GPS), which provides real-time geographic data by triangulating signals from satellites. This data, integrated into smartphones, is combined with Google Maps Application Programming Interfaces (APIs) to determine optimal routes from the user’s current location to hospitals, accounting for road networks, traffic, and travel modes. A patient can use this application to discover the closest hospital based on the availability of expert consultants. This application that was built is easy to use and effectively gives patients the necessary information.
Authors - Malay Shah, Sayal Goyal, Rashmi Rane, Ruhi Patankar, Sarika Bobde, Arnav Jain Abstract - This study investigates the impact of risk-taking on football match outcomes, focusing on player substitutions. The analysis reveals that risk-taking propensity peaks when a team is trailing by 2-3 goals and diminishes when leading by the same margin. Younger managers outperform middle-aged ones in risky decisions, while older managers excel in later substitutions. Additionally, a manager's tenure with the team increases the effectiveness of risk-taking, particularly in earlier substitutions and stronger teams. This study also emphasizes the importance of mental state in player performance, proposing a framework combining Match Score Analysis (Kaplan-Meier Fitter) and Score Analysis to evaluate players' mental stability and survival rates during the game. By integrating these models, teams can make better-informed decisions regarding substitutions, considering both past performance and mental health, ultimately enhancing match outcomes. This research underscores the synergistic potential of combining black-box causal machine learning with interpretable models, offering valuable insights for football management and beyond.
Authors - Sanjana, Sukanya Sharma, Dipty Tripathi Abstract - Handwritten digit recognition is a key application in image processing and pattern recognition, with wide usage in areas such as postal services, banking, and mobile applications. This research paper presents a performance comparison between traditional machine learning models and deep learning models for accurate handwritten digit classification. The study focuses on developing a mobile application using Flutter integrated with TensorFlow Lite and Firebase to deliver real-time predictions. The app performs preprocessing on input images and employs model inference for efficient and accurate digit recognition. The objective is to determine the most effective model in terms of speed and accuracy for on-device predictions, emphasizing usability and real-time response
Authors - Rupan Panja, Rajani K. Mudi, Nikhil R. Pal Abstract - The Support Vector Machine (SVM) is a popular classification algorithm, however, it suffers from the drawback that the classification time for an unknown data point is proportional to the number of support vectors (SVs). Thus, its application for real-time decision-making, especially for complex class boundaries becomes problematic / impossible. In this article, we propose an algorithm, Self Organizing Support Vector Machine (SO-SVM), which can decrease the number of SVs without compromising the accuracy. In this algorithm, we first cluster the data points using self-organizing map to find potential class boundary points, which are crucial for determining the separating hyperplane in an SVM. The separating hyperplane is then learned from the selected boundary points, leading to a reduction in the number of SVs. Learning the SVM also becomes efficient because of the reduced number of data points. The proposed algorithm has been tested on a number of benchmark datasets and found to decrease the number of SVs without deteriorating the classification accuracy. The SO-SVM algorithm thus can be an efficient alternative to the SVM algorithm and thus can be applied instead of normal SVM without making a noticeable compromise with the performance.
Authors - Nitesh Pradhan, Aryan Baghla Abstract - Electrocardiogram (ECG) signals are effective indicators for detecting obstructive sleep apnea (OSA) due to their ability to reflect physiological changes associated with apnea events. EfficientApneaNet is a deep learning-based model for the detection of OSA from a single-lead ECG. In general, traditional approaches are reliable but exhaustive and costly; therefore, ECG-based methods are being sought. Earlier machine-learning methods, including Support Vector Machines and Random Forests, are prone to real data noise. Based on these, EfficientApneaNet joins previous advances in deep learning for further improvement in accuracy and robustness. The proposed architecture is powered by three novelties, namely: the ENBlocks, inspired by EfficientNet, Squeeze-and-Excitation blocks, and attention. ENBlocks make use of depthwise separable convolutions that reduce the computational complexity and amplify the efficiency of spatial feature extraction. The Squeeze and Excitation blocks carry out channel recalibration to focus on relevant patterns, while the attention mechanism underlines the critical temporal events within the ECG sequences. On the Apnea-ECG dataset, EfficientApneaNet realizes state-of-the-art performance with 91.47% accuracy, 85.92% sensitivity, and 94.91% specificity outperforming those of leading existing CNN-LSTM hybrids. It adopts Adamax optimization for stability while implementing the technique of cosine annealing for LR scheduling. The residual connections avoid gradient vanishing and explosion. Through ablation studies, it is confirmed that SE blocks and the attention mechanism are both essential to achieving high sensitivity and high specificity. In this respect, EfficientApneaNet would be considered a significant improvement in OSA detection, as it has successfully handled spatial and temporal complexities in ECG data.
Authors - Arun Kumar S, Niveditha S, Vikas K B, Varshini C, Hemalatha H U Abstract - Improved patient outcomes and effective management of Chronic Kidney Disease (CKD) depend on early detection, making it a major worldwide health concern. This study presents a hybrid machine learning model that combines Random Forest and LightGBM classifiers in order to accurately predict CKD stages. The dataset from Kaggle was used to build the model, and SMOTE was used to handle class imbalances in addition to feature engineering and data preprocessing. With regard to test data, the model's accuracy was 97.7%. Enhancing its actual clinical utility, a web-based tool was also developed to enable real-time forecasts of CKD stage based on patient inputs. Using a mixed-method approach that included more than 1600 participants' medical examinations and surveys With the integration of real-time clinical data and strong predictive models among its potential enhancements, the proposed approach provides a robust and easily utilized tool for clinical applications.
Authors - Nilesh Korade, Swaraj Waykar, Archit Waghmode, Shweta Tate Abstract - Though solo travel can be an enriching and adventurous experience, staying alone (without social interactions) can sometimes enhance feelings of loneliness and this can be detrimental to a traveler's mental health. In this paper, we present an AI Travel Buddy. This intelligent system uses real-time facial expression recognition to determine the emotion of travelers at the moment and provides personalized conversational topics based on the detected emotion using the emotion-aware voice assistant. Using the latest CNN architecture for emotion detection, and Conversational model for dynamic answers, the AI Travel Buddy is a companion to travelers, delivering emotional support to users on solo trips. The paper describes the technologies used, the system architecture, and its methodology, showing substantial improvement in user engagement emotionally alongside providing overall higher travel satisfaction.
Authors - Sunil Sangve, Rutuparn Kakade, Saamya Gupta, Sahil Akalwadi, Soham Pawar Abstract - The paper introduces a novel and detailed approach for automated generation of fully customizable and user-centric food recipes. The system utilizes fine-tuned GPT2 and fine-tuned YOLOv9 to work efficiently. The user can input the available ingredients by uploading an image which is then processed by the fine-tuned YOLOv9 model to extract the ingredients present in the image. Alternatively, the user can also input ingredients manually. The user input also contains macro and micro nutrient levels, caloric values, cholesterol levels, taste and texture. Utilizing fine-tuned GPT2 LLM, the system then generates a unique recipe that follows the input. Web application developed by Next JS 14 with backend support from Flask, helps greatly in enhancing User experience. The system also utilizes Stable Diffusion to generate colorful recipe images for user reference of how the final recipe should look like.
Authors - Austine S Manuel, Alana Ance John, Hamna Rafeeq, Thalhah Anas, Manoj V. Thomas Abstract - Smart assistive technologies improve the independence and access to transportation of the vision-impaired people [1]. This paper presents AI-enhanced smart glasses designed to empower visually impaired by transforming their interaction with the environment. The system combines cutting-edge technologies to offer a comprehensive assistive solution. A path navigation algorithm, powered by YOLOv8, ensures safe mobility by detecting obstacles and guiding users with audio feedback. Integrated OCR capabilities enable real-time text reading, while an image captioning module provides detailed scene descriptions for enhanced situational awareness. The glasses incorporate a camera and an ultrasonic sensor, delivering robust performance in diverse scenarios, including detecting objects at close proximity where traditional algorithms fall short. With seamless audio output for user interaction, the proposed solution bridges the gap between technology and accessibility, promising independence and confidence for visually impaired individuals. This innovative fusion of AI and sensory inputs defines a new benchmark for assistive devices.
Authors - Rohith Aryan TG, Kashish Sharma, Sunil Kumar, Abhay Sharma Abstract - A Revolution in the Diagnosis and Treatment Landscape. This paper brings forward how AI technologies can be utilized in earlier detection of mental illnesses, and how robotics provide therapeutic support to patients. Examining the various models, results and challenges on these technologies is aimed at putting attention on their perceived benefits and limitations in augmenting the health care of the mind. Comparative studies that include traditional treatment modalities will also be featured, along with recommendations for future research directions.
Authors - Vinayak Kanhegaonkar, Ayush Mahant, Ali Sayyed, Abha Shah, Aparna Junnarkar Abstract - India’s farmers battle many challenges before they are able to sell their produce to the consumer, one such challenge is related to middlemen and market access. The current scenario is such that middlemen hold significant influence over the market. Middlemen are the intermediaries that farmers sell their produce to because of reasons such as lack of infrastructure, price fluctuations, limited storage facilities, lack of information and more. This causes farmers to lose much of their profit and overall reduces their income. Middlemen take substantial profits from their work; they buy for less from farmers and sell for more to consumers. Through our research we aim to create an app, KrishiMitra, for farmers to facilitate direct access to the market. KrishiMitra will be able to reflect real-time market prices, provide multilingual support, and have a bidding system. With this app we hope to provide farmers a platform to get fair prices, some bargaining power, proper and complete information about market prices and current trends. In the future, the application can be integrated with more services that address other challenges faced by farmers such as limited storage space for produce and climate change impacts on produce.
Authors - Madhuri Wakode, Geetanjali Kale Abstract - Deep learning has many applications in healthcare especially for disease prediction from complex images. One such application is to predict diseases from chest X-rays (CXR). These models need huge amounts of data available for training. A single healthcare facility may struggle to collect sufficient data to build robust and efficient models. The apparent solution is collaboration among multiple healthcare facilities to use their data to build efficient models together. However, facilities may not want to share the patient’s sensitive data with other facilities or with the central server. Federated Learning (FL) allows multiple parties to build models without sharing their data with each other. FL allows parties to train the model locally on their private data and only share the trained model parameters to the server. Server averages the model parameters sent by all the parties to build a robust global model. Server sends this updated model to each party who then again trains the model locally. This process continues till the model convergence. We propose using federated learning average on a large CXR dataset for multi-label classification. Our results show that federated learning achieves the accuracy of ~82% as compared to ~91% of that of traditional centralized training method. FL with more robust algorithms and larger datasets, can achieve performance comparable to centralized approach with an added advantage of collaborative learning with privacy preservation.
Authors - Hepin Gondaliya, Parth Monpara, Dhaval Shah Abstract - Multiplier is a crucial factor influencing processor performance, making its optimization vital for efficient computing. This paper introduces a multiplier design that achieves remarkable improvements, reducing resource utilization by more than half, logic delay to nearly a quarter and total power consumption to less than a quarter of compared to conventional radix-2 booth’s multiplied The proposed design was synthesized and implemented using Xilinx Vivado on a Zedboard FPGA, demonstrating its effectiveness and scalability for modern FPGA-based systems.. . .
Authors - R. R. Bhoge, Ranjit R. Keole, Pravin P. Karde Abstract - In the rapidly evolving landscape of network security, Intrusion Detection Systems (IDS) play an indispensable role in distinguishing normal network traffic from anomalies. While traditional machine learning models have achieved notable success, the introduction of quantum-assisted techniques opens new avenues for improving accuracy and reliability. This review delves into the application of quantum supervised learning for Intrusion Detection, emphasizing recent breakthroughs and comparing the advantages of quantum methods to classical counterparts. As digital networks become increasingly interconnected, the demand for robust Intrusion Detection Systems has grown exponentially. This paper investigates quantum-enhanced cybersecurity solutions, particularly the incorporation of quantum supervised learning, to improve the detection and classification of network intrusions. The review discusses the theoretical principles of quantum supervised learning, its unique attributes, and the advancement of quantum-enabled IDS. This paper provides a detailed overview of how quantum computing is revolutionizing machine learning for cybersecurity, with an emphasis on the enhanced capabilities of IDS through quantum-assisted methodologies.
Authors - Prasanna Tupe, Parikshit N. Mahalle Abstract - This study presents a machine learning-based method for using the K-Nearest Neighbors (KNN) algorithm to optimize swing trading strategies. The algorithm forecasts stock price fluctuations over the next seven days using historical stock market data and offers various levels of nuanced trading signals. By empowering traders to make more accurate and knowledgeable judgments, this method outperforms conventional binary buy-and-sell recommendations. The KNN technique was selected due to its instance-based learning, non-parametric nature, and simplicity, which make it interpretable and computationally efficient. The model's accuracy rate demonstrated how well it could forecast changes in stock prices. This study provides a workable approach for swing traders looking to optimize their profits while highlighting the important role that machine learning plays in tackling the difficulties associated with stock market prediction. This study lays the groundwork for using machine learning to enhance trading tactics and financial market decision-making.
Authors - Atharva Madhukar Nimbalkar, Madhukar Nimbalkar, Madhura Gondhalekar, Yashvendra Singh Dhandal, Parikshit Mahalle, Pankaj Chandre Abstract - The digital divide continues to pose significant challenges to delivering quality education, particularly in remote and underserved regions with limited or no internet connectivity. This paper explores the innovative integration of three advanced technological solutions within the Spacelink: Educational System, designed to bridge this gap. The system incorporates an AI-driven transcription and translation model using Whisper, a web-based Learning Management System (LMS), and a real-time communication platform utilizing WebRTC. These tools are tailored for offline functionality, operating effectively through LAN-based communication to ensure uninterrupted educational services. The AI transcription system enhances accessibility by converting spoken content into text and supporting multiple languages, addressing linguistic diversity and inclusivity. The LMS provides a robust offline platform for content delivery, course management, and student assessment, synchronizing data seamlessly when connectivity is restored. Meanwhile, the WebRTC-based communication tool facilitates real-time audio and video interactions optimized for low-bandwidth environments, promoting interactive learning experiences. Together, these technologies offer a holistic solution to overcome educational barriers, enabling equitable access to high-quality learning resources and fostering collaborative education in disconnected settings.
