Authors - Navneet S Patil, Shashidhar Kumbar, Sakshi Bhantanur, Arjav Jain, Satish Chikkamath, Sujata Kotabagi Abstract - Human movement prediction is a key machine learning domain whose purpose is to predict future movement from previous motion patterns and context information, with usage in autonomous vehicles, virtual reality, video games, and health care. In this study, the goal is to apply Convolutional Neural Networks (CNNs) for predicting human movement from the UCF50 dataset, whose collection contains action videos with a wide variety of actions. CNNs excel at discovering spatial and temporal patterns from video data and, thus, can be used in understanding motion complexities. In this work, a CNN-based approach is developed using a CNN architecture to assess motion dynamics and make accurate forecasts about future moves. By systematically preprocessing the dataset and optimizing the model’s architecture, the study achieved an accuracy of 99.09demonstrating the reliability and efficiency of CNNs in motion prediction tasks. Furthermore, the paper discusses existing methodologies in human motion prediction, comparing their performance and highlighting the advantages of CNNbased models in processing visual data. The results here bring out the potential of CNNs for real-world applications and set the foundation for future advancements in human activity recognition. The current study adds insight into machine learning methods and how they can be used to enhance motion prediction, with implications toward innovations in those fields that rely on precise modeling of human activities
Authors - V.Sudeep, V.Nishant, MM.Mohamed jasir Faiez, T.Monish, Yuvaraj kumar.GP, Akhil K J, Praveen.K Abstract - Docker containers are central to modern software development and deployment due to their portability, efficiency, and scalability. By isolating applications and dependencies, they provide a lightweight alternative to virtual machines, enabling consistent environments across platforms. However, Docker containers pose security challenges, including shared kernel risks, vulnerabilities in container images, and misconfigurations, which can lead to breaches.This paper examines security concerns in Docker containers and proposes a framework to identify and address vulnerabilities. The framework helps detect issues like outdated components and misconfigurations, offering insights to enhance security. Through practical use cases, it highlights its effectiveness in closing security gaps and equipping developers with tools to protect containers. The study emphasizes the need for proactive security measures and continuous vigilance in securing containerized systems.
Authors - Chaithra S Raju, Nimisha S, Arathi A N Abstract - The Digital payment landscape in India has seen rapid progress, driven by technological advancements, government initiatives and increased smartphone penetration. Digital wallets are becoming a payment method as they are convenient, secure and seamlessly integrate with financial services. Although Generation Z known for a Digital-first approach, inconsistency in the adoption of Digital wallets can be observed among this segment. This research will look at the reasons why Generation Z may adopt or not adopt Digital wallets, namely perceived ease of use, perceived usefulness and perceived security.A cross-sectional survey was undertaken for the 220 Gen Z respondents using a structured questionnaire. The statistical analysis was done by using SPSS analysis of variance to examine the impact of these factors on adoption behaviour. . The findings highlight that while convenience and utility drive adoption, security concerns remain a critical barrier.This study provides valuable insights for fintech companies, policymakers, and firms looking to bolster the digital payment infrastructure and build trust in Digital wallet services. Overcoming security concerns and improving the user experience can accelerate the transition towards a cashless economy.
Authors - S.Asha, Siddharth M Nair Abstract - According to a study, one out of every 20 people above the age of 65 are suffering from Alzheimer's. People with such neurological conditions have poor navigation skills and often wander around without having knowledge of where and what they are doing. In such situations, tracking them down is extremely important as it is life threatening to themselves and the people around them. It is also important to monitor elderly individuals' vitals like heart rate and steps along with detecting an impact (fall) so that necessary actions can be taken. Other than the strong personal motivation the current market needs a product through which people suffering from such neurological conditions can be supported. But not many are present in the current market and the ones that are, require the patient to wear some dedicated device like a neck ring or other uncomfortable devices. Often, people, especially elderly individuals lose their lives because 'it was too late'. There is a major requirement in today's market for a system which would send alerts and concerned individuals in case of any abnormality in detected data so that it would not be 'too late' to act. The sensors that are incorporated within the Apple Watch provide an ocean of valuable data which can be harnessed by caretakers and other concerned individuals. Now-a-days, people are too involved and busy with their work to stay at home and be there for elderly individuals at all times. Through this data, people can take care of their loved ones even when they are not around. By receiving timely notifications in case of any emergencies, the world would become a safer, more reliable place for all elderly individuals, especially those who suffer from Alzheimer’s and other neurological conditions.
Authors - Anitha D, Swetanshu Agrawal, Samudra Banerjee Abstract - Particularly affecting patient response to alkylating treatment, the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a well-established prognostic and predictive biomarker in gliomas. Conventional evaluation techniques are prone to limits including sampling mistakes and intratumoral heterogeneity and call for invasive tissue biopsies. In this work, we present a non-invasive, deep learning-based system for multi-modal magnetic resonance imaging (MRI) based MGMT promoter methylation prediction. The method combines improved preprocessing, automated tumor segmentation, and a customized EfficientNet-based classification architecture with structural MRI sequences including T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and FLAIR imaging. Our model achieves strong performance, high accuracy and generalizability in methylation status prediction. Comparative study including current literature shows either better or equivalent prediction performance, so highlighting the clinical possibilities of this technique. The suggested pipeline advances the function of virtual biopsy in neuro-oncology by providing a scalable, dependable, radiation-free substitute for MGMT methylation testing, therefore enabling individualized therapy planning.
Authors - Richa Goenka, Meenu Chawla, Namita Tiwari Abstract - In recent years, phishing attacks have emerged as a substantial hazard, endangering online businesses and security by exploiting users to divulge sensitive financial information through fraudulent websites. Despite various proposed methods, accurately distinguishing between legitimate and fraudulent sites in real-time remains challenging. This paper provides a new approach to identifying phishing URLs by employing a feature selection approach that integrates Genetic Algorithm and Particle Swarm optimization. This system optimizes feature selection through population initialisation, fitness evaluation, GA operations, and PSO integration, dynamically balancing exploration and exploitation. The objective is to identify significant features for supervised machine learning techniques, enabling precise phishing URL detection. For classification, multiple machine learning classifiers are employed among which XGBoost provided the best results. Experimental results using the hybrid feature selection prove that the machine learning classifier works much better than the prevailing feature selection approaches. This comprehensive approach provides a reliable method for detecting phishing URLs, improving internet security, and reducing the threats associated with phishing attacks.
Authors - Soumitra De, Jaydev Mishra Abstract - In this paper, a new method is focused to handle indeterminacy part of an imprecise data using neutrosophic set to generate proper constructive message. This method is capable to handle imprecise part of a neutrosophic data. Earlier no uncertain data set was handled this indeterminacy part of any uncertain data. We have drawn an output using this new method of any patient related data set that has suffering from disease. Vague logic is unable to process indeterminacy part. So only neutrosophic set is handled indeterminacy part of a imprecise data to outcome.
Authors - D.K. Chaturvedi, Nisha Verma Abstract - The technological intervention in our day-to-day life, impacted our social, physical, psychological and spiritual domains. The shoes are not untouchable from the latest innovations. The footwear is an essential wear in present time. The technology is completely changed the footwear industry and the customer flavour. Now the customer is looking for customized, smart footwear, which is environment friendly. The present footwear is using polymer soles (i.e. PVC, PU, EVA or Rubber), chemical based adhesives and animal leather upper material, which are not eco-friendly. A lot of research is going on to make sustainable and eco-friendly shoes with different biodegradable materials. The footwear industry is embracing both smartness and sustainability, blending technological innovation with eco-conscious practices. The smart footwear uses many types of sensors/IoTs to include different features of smartness. This paper discusses some innovations in footwear technology, important issues, challenges and their remedies related to design and development of smart sustainable footwear.
Authors - Kashinadh.S, Dhanush Devaraj, Yedukrishnan VS Abstract - Food safety and nutritional transparency are essential for public health, particularly as diet-related illnesses like obesity and diabetes rise. The Food Safety and Standards Authority of India (FSSAI) introduced a menu labeling policy in 2020, requiring restaurant chains to display calorie counts and nutritional information.The consumer awareness on the menu labelling is poor, and compliance is still low among restaurants. FSSAI Food Safety Connect app, which is designed to help with grievance redressal was having some negetive shades because of the complaints registered and reviews posted . This study employs stakeholder interviews, compliance audits, and sentiment analysis to evaluate how effective the policy is. The findings indicate that the compliance is lacking because of financial barriers and enforcement is also lacking. This study recommends implementing chatbot-driven grievance resolution, using QR codes for digital menus, and leveraging AI for compliance tracking as strategies to boost adherence. These solutions leads to the Sustainable Development Goals (SDGs 3 and 12) by enabling customers to make informed decisions while also promoting food safety. Menu labeling will become a more effective public health tool if we can improve digital enforcement in food industry.
Authors - Vanishree Pabalkar, Anuja Bokhare, Reena (Mahapatra) Lenka, Jaya Chitranshi Abstract - [1] Crime is one of the most worrying and widespread issues of our society. Criminal deterrence is essential for people safety. The overall crime inference when assessed, does help to keep a record of crime and assist in avoiding adversities. The aim of the study is to examine patterns in data acquired over time. Criminal violations offend humanity, and it should be prosecuted as soon as possible. Criminology is the scientific method of understanding crime and the motives behind the act. Criminology is an interdisciplinary area which gathers data and conducts further study into such offenses. While there is such a large amount of data on criminal activities, identifying and preventing crimes is one of the most difficult tasks. It is imperative to develop approaches and procedures for predicting future crimes and taking appropriate preventative steps. Cluster analysis includes breaking down huge data to minute groups with similar or identical characteristics. We can evaluate and assess methods, structures, layouts and interactions that are present in the data using visualization tools, so as to help uncover interesting areas and acceptable parameters for future analysis.
