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.
