Authors - Kashyap Patel, Urvashi Chaudhari, Chirag Patel, Nirav Bhatt Abstract - Automatic traffic surveillance has a hard time finding and classifying vehicles that are trying to get in the way. To keep an eye on things in real time, you need to be able to tell the difference between cars, trucks, buses, and other types of vehicles. Traffic management systems need to be able to accurately identify vehicles as the number of cars on the road grows. This paper examines various machine learning (ML) and deep learning (DL) techniques employed to identify and categorize vehicles in images and videos. The authors emphasize the significance of algorithms, such as CNNs, YOLO, and AdaBoost, in enhancing detection accuracy and efficiency. This paper examines various published re-search studies to discern methodologies, datasets, and future research directions in vehicle detection and classification, offering insights into the existing techno-logical landscape and its prospective developments.
Authors - Renukaradya V, Kumar P K Abstract - Ethylene and vinyl acetate or EVA is a co-polymer used as a substitute for a lot of materials. EVA is a versatile material and it has a lot of applications ranging from electronics, healthcare, footwear, building applications etc. It is mainly used in sport shoes due to its property to absorb shock impact and insulation properties. In addition, EVA is very cost-friendly, produces no odor, and light in weight material. But with overuse of it, the cellular structure chang-es and can affect the shoes' quality and insulation properties. In addition to the cellular structure, the air molecules present in it also collapse. This paper focus-es on the bonding properties of EVA at different temperatures and its dielectric properties under different operating and manufacturing conditions. The upper, bottom, and sides of EVA shoes are exposed to high voltage till the breakdown. The experimentation was done at Electrical HV laboratory on the university campus where a 100kV HVAC testing system is available. This paper presents the tabulated results on the dielectric strength of EVA shoes under varying operating conditions. Additionally, it examines the bonding properties of EVA shoes at different manufacturing temperatures, aiming to predict their lifespan, quality, and finish. The results of these studies are thoroughly discussed within the document.
Authors - Norrakith Srisumrith, Sunantha Sodsee Abstract - The critical need for transparent and trustworthy machine learning in cybersecurity operations drives the development of this integrated Explainable AI (XAI) framework. Our methodology addresses three fundamental challenges in deploying AI for threat detection: handling massive datasets through Strategic Sampling Methodology that preserves class distributions while enabling efficient model development; ensuring experimental rigor via Automated Data Leakage Prevention that systematically identifies and removes contaminated features; and providing operational transparency through Integrated XAI Implementation using SHAP analysis for model-agnostic interpretability across algorithms. Applied to the CIC-IDS2017 dataset, our approach maintains detection efficacy while reducing computational overhead and delivering actionable explanations for security analysts. The framework demonstrates that explainability, computational efficiency, and experimental integrity can be simultaneously achieved, providing a robust foundation for deploying trustworthy AI systems in security operations centers where decision transparency is paramount.
Authors - Amelia Santosh, Bhavika Pradeep, Dhanuvarsha S S, Harisurya Reddy S, Shruthi L Abstract - Real-time analysis, high accuracy, and robust privacy protection across several institutions are necessary for financial fraud detection. Restrictions on data sharing and non-IID transaction patterns cause traditional centralized models to fail. Graph Neural Networks (GNNs) for anomaly detection and a structured fraud reporting mechanism are integrated in this paper’s federated learning-based fraud detection framework. While GNNs capture intricate relationships between accounts, devices, and transactions, the system allows institutions to jointly train a global model without exchanging raw data. The feasibility of implementing collaborative fraud detection across financial institutions is demonstrated by the experimental results, which show improved fraud detection performance, enhanced recall on minority fraud cases, and effective privacy preservation.
Authors - Killol Pandya, Aneri Pandya, Trushit Upadhyaya, Upesh Patel, Poonam Thanki, Kanwarpreet Kaur Abstract - The proposed Multiple Input Multiple Output dual-port antenna radiates for Ultra Wide-Band (UWB) applications. The engineered structure exhibits between the 2.10 GHz to 9.5 GHz frequency. The structure consists dual radiating elements which are positioned at certain distance in order to minimize the effect of inter element interference. The radiator is planar and having triangular shape at the upper side to disturb the current path which eventually creates better radiation. A couple of up arrow shaped slots have been created to improve the current distribution. The microstrip feed line is utilized to excite the antenna structure. A partial ground plane with isolating technique was created to receive the UWB response. The middle layer between the radiators and the ground plane is having the FR4 material which is a cost effective for the bulk production. The physical antenna has been developed from the prototype and the results were measured. The simulated results are aligned with the measured results which shows the antenna potential. The primary diversity parameters such as Diversity Gain, Envelope Correlation Coefficient, Channel Capacity Loss and Mean effective gain were also measure and their simulated values fall under the expected span. The developed antenna is well suitable for UWB wireless applications.
Authors - Tiurida Lily Anita, Dino Gustaf Leonandri, Mohd. Nor Shahizan Ali Abstract - In this paper, we address the problem of rainy condition classification in order to allow autonomous systems to ensure safe operation in different weather conditions of rain, especially for drones. The earlier weather condition classification methods are inclined towards using big and computationally costly models and cannot thus be employed in real-time on resource-constrained platforms such as drones and edge devices. The motivation behind this work is to introduce a light-weight, efficient deep model which would be able to classify various rain conditions with low computational cost so that it may be deployed efficiently on low-resource devices. We present a novel CNN architecture and evaluate its performance on a collection of seven distinct rain conditions. The models are bench marked against some of the state-of-the-art pretrained models to demonstrate the compromise between efficiency and accuracy. Performance is evaluated using accuracy, inference time, and model size. The model has accuracy 95.93% with least model size 89.09 KB with inference time of 32.664 ms bridging the gap in lightweight and real-time classification.
Authors - Mouniesh V, Sona S, Mariya Ashile K, Karthick Panneerselvam Abstract - This paper presents the implementation experience of indigenously developed Data Concentrator Units (DCUs) for Advanced Metering Infrastructure (AMI) system. The DCU functions as the last-mile communication bridge between field devices and the Head-End System (HES), enabling efficient data collection, aggregation, and management. By performing local processing and aggregation, it reduces data traffic over the Wide Area Network (WAN), there-by improving communication efficiency, scalability, and reliability. The DCU firmware is designed for flexible communication and secure data handling, sup-porting pluggable WAN and Neighbourhood Area Network (NAN) communication modules compliant with proprietary BHARAT IoT standards, that can be upgraded or replaced without requiring complete system replacement or rede-sign. It also ensures robust data security through AES-GCM-GMAC encryption. The NAN module is implemented using LoRa technology, enhanced through the LoRaPro communication module, which increases payload capacity from the standard 256 bytes to 1 KB using an advanced packet stitching and slicing algorithm that ensures reliable reconstruction of larger messages. The paper discusses the major design and development challenges encountered and the methodologies adopted to address them.
Authors - Devika K S, Jiju K, Dinesh Kumar R, Ashish Murikingal, Anoop V G Abstract -This paper presents the implementation experience of indigenously developed Data Concentrator Units (DCUs) for Advanced Metering Infrastructure (AMI) system. The DCU functions as the last-mile communication bridge between field devices and the Head-End System (HES), enabling efficient data collection, aggregation, and management. By performing local processing and aggregation, it reduces data traffic over the Wide Area Network (WAN), there-by improving communication efficiency, scalability, and reliability. The DCU firmware is designed for flexible communication and secure data handling, sup-porting pluggable WAN and Neighbourhood Area Network (NAN) communication modules compliant with proprietary BHARAT IoT standards, that can be upgraded or replaced without requiring complete system replacement or rede-sign. It also ensures robust data security through AES-GCM-GMAC encryption. The NAN module is implemented using LoRa technology, enhanced through the LoRaPro communication module, which increases payload capacity from the standard 256 bytes to 1 KB using an advanced packet stitching and slicing algorithm that ensures reliable reconstruction of larger messages. The paper discusses the major design and development challenges encountered and the methodologies adopted to address them.
Authors - Gaurav Kulkarni, Maya Rathore Abstract -In digital world, cyber-attacks are becoming more sophisticated and popular. The conventional intrusion detection models are not adequate in challenging threat escapes. Importantly, the major reason for increasing demand in the networks, unauthorized access is increasing their interests in these areas. Various network environments and organizations are tackling numerous of attacks on their network at frequent times. Traditionally, various manual methods are used for intrusion detection such as packet and flow analysis, traffic log reviewers and monitoring the security. Nevertheless, the manual techniques for such type of the detections takes too much time and also the result obtained is not up to the mark, so due to this it is difficult to predict all types of attacks and intrusions for network security. To overcome these issues, several conventional researches have concentrated on intrusion detection models to offer effective security to the networks. Conversely, it results with accuracy and speed lacks. For enhancing the intrusion detection, research make use of a Deep Learning (DL) Unravelled Spatial Features in Multilayer Perceptron with Gradient Jacobian Matrix. Gaussian Activation is used to enhance the Intrusion detection system for an effective classification. In the proposed research work we are using the RT-IoT dataset and the final efficiency has been analyzed by using various parameters like overall correctness, actually correct, correctly identified by the model,and the balance between the both values of recall and precision (Harmonic Mean). Furthermore, the current work and the proposed model is developed to contribute to avoid the different cyber threats by timely identifying such type of intrusion in the networks.
Authors - Tiurida Lily Anita, Ali Faik, Muhammad Zilal Hamzah, Hainnuraqma Rahim Abstract - Web accessibility and usability are fundamental pillars for ensuring effective digital inclusion, especially in higher education institutions committed to equity in access to information. This study aimed to evaluate the usability and accessibility of the website of the Inclusion, Social Equity, and Gender Unit at the Technical University of Manabí, using the WCAG 2.0 guidelines. A mixed methodology with a qualitative and applied approach was employed. Initial results revealed a low level of compliance with accessibility standards, highlighting deficiencies in the principles of perceptibility and operability, such as the absence of alternative descriptions for images and insufficient contrast. After implementing improvements, the website achieved 76% compliance according to a manual review, with notable progress in responsive design and the incorporation of an accessibility toolbar. However, challenges remain regarding the principle of robustness, underscoring the importance of combining automated tools with thorough manual evaluations. Future work will adopt WCAG 2.1 guidelines and integrate advanced assistive technologies to overcome current limitations, promoting a more inclusive and accessible digital environment for all users.
