Authors - Sivakami B U, M. Suresh Abstract - As the conversation shifts toward Industry 5.0 (I5.0), how prepared are manufacturing Micro Small Medium Enterprises (MSMEs) to adopt the foundational technologies of Industry 4.0 (I4.0)? Despite MSMEs' economic importance, they persistently face challenges in adopting essential technologies and undergoing digital transformation due to financial, infrastructural, and work-force-related barriers. The extant literature provides limited understanding of their readiness to implement individual I4.0 technologies. To address this gap, this study aims to develop a Fuzzy Logic-based readiness assessment framework to evaluate adoption potential and identify weaker attributes. Using an ontological approach, the framework identifies key enablers, criteria, and attributes. Findings reveal an “average ready” status, with critical gaps in IoT, Robotics, Automation, and Digital Twin technologies. The framework provides MSMEs with a roadmap to assess their readiness and strategically plan their I4.0 transition.
Authors - Drisya Murali, Suresh M Abstract - This study aims to develop a novel assessment framework that measures the practice of organizations in the construction industry for Construction 4.0. The research helps to measure and improve construction organizations’ practice level for Construction 4.0 by identifying key enablers and criteria and addressing weaker attributes. This research seeks to use a fuzzy logic approach within a conceptual framework to evaluate the current practice level of these organizations. By assessing organizational members' perspectives regarding strategic management, resource allocation, technological readiness, and socio-economic considerations, the study aims to identify critical practice criteria and categorize them into four enablers with detailed attributes. The assessment was done at one location, suggesting further evaluation could help improve practice levels across the organization. The management of a case construction organization would use the study to enhance the practice level of their organization to improve the Construction 4.0 practice. To enhance the organization's practice for Construction 4.0, focus on improving identified weaker attributes based on the proposed assessment framework model. The ultimate goal is to provide recommendations for improving practice level, thereby maximizing the benefits of Construction 4.0 in the construction industry. This study is the first to propose such a framework, contributing valuable insights to the research on Construction 4.0 in this sector.
Authors - Suhas Bhise, Devesh Kulkarni, Mayur Gaikwad, Parth Nevase Abstract - Warehouses and storage facilities are highly vulnerable to fires due to the presence of flammable materials and high-density storage layouts. Traditional fire detection and suppression systems frequently encounter limitations in such environments, such as delayed response times and restricted access to fire sources. The study describes the design and development of an automatic fire-fighting robot specifically for warehouses and storage facilities. The proposed robotic system combines advanced sensors for smoke, heat, and flame detection with real-time navigation capabilities to autonomously locate fire sources. Some of them uses thermal imaging cameras, infrared sensors, and LiDAR technology to precisely map the environment, detect obstacles, and navigate narrow aisles. The robot has a multi-directional water or foam nozzle for efficient fire suppression, as well as a decision-making algorithm that prioritizes critical fire zones, allowing for a quick and effective response. Furthermore, the system supports remote monitoring and control via a user-friendly interface, allowing for human intervention as needed. Heat-resistant materials and a long-lasting power supply improve the robot's durability, allowing it to operate continuously in extreme conditions. Experimental results show that the robot is effective at detecting fires early, responding quickly, and minimizing damage. The study seeks to provide a dependable, cost-effective solution for improving fire safety in warehouses and storage facilities, thereby reducing risk to both property and human life.
Authors - V. K. Abhang, Y. A. Shinde, S. N. Shingote, Adik Somal, Aglawe Nikhil, Akolkar Vivek, Ghondage Pratik Abstract - This project explores how machine learning can help optimize hybrid renewable energy systems, with a special focus on managing net metering costs. By analyzing real-time data from renewable sources and consumer energy usage, the goal is to create a smart, efficient framework that improves energy reliability while keeping costs low. The idea is to strike a balance ensuring that energy production and consumption align seamlessly with changing demand and environmental conditions. To make this happen, we’re using the Open Energy Modelling Framework (OEMOF), which helps optimize how energy is distributed, stored, and interacted with the grid. With OEMOF, we can simulate energy flows, make better decisions about energy trading and self-consumption, and develop cost-effective net metering strategies. On top of that, advanced predictive models for weather and energy demand forecasting allow for proactive system adjustments, making sure the setup remains efficient and reliable.Beyond the technical side, this approach directly supports the global shift toward sustainable energy. By making hybrid renewable energy systems more cost-effective and scalable, it not only helps individuals and businesses save money but also contributes to reducing carbon footprints moving us one step closer to a greener future.
Tuesday August 25, 2026 9:30am - 11:30am IST Virtual Room EGOA, India
Authors - Lokesh Khedekar, Suvarna Pawar Abstract - In order to increase the accuracy of threat detection for malicious URLs, this paper proposes an improved feature extraction methodology. High false positive rates are a common consequence of traditional detection systems' restricted feature sets. The suggested method extracts a wide variety of lexical, host-based, content-based, and character-level n-gram features in order to solve this. While host-based qualities offer contextual information like domain age and DNS validity, lexical features record structural irregularities. While n-gram features identify obfuscation through frequent character sequences, content-based features—such as login indicators, file extensions, and JavaScript references—reflect behavioral patterns. According to correlation analysis, the majority of traits are still weakly associated, providing a variety of complementary signals, even while some are strongly related. By properly recognizing benign URLs with 100% confidence, the suggested model showed dependable real-time prediction and obtained a high accuracy of 98.50% in detecting dangerous URLs.
Authors - Shreya Pulluri, Megha Mohan, Madduri Aditya Vardhan Reddy, Chitresh Simhadri, Geetha M Abstract - Bias in race and gender classification models creates strong ethical dilemmas, where disparities typically exist among groups of different demographics. In this research, a Bias-Aware Grad-CAM method is proposed that embeds interpretability-driven bias reduction within training. A ResNet model was trained for gender and race classification using the UTKFace dataset, which shows preliminary errors in minority group predictions. The Bias-Aware GradCAM framework proposed produces heatmaps to detect model attention areas, calculating Intersection over Union (IoU) between the heatmaps and pre-defined face bounding boxes. A reweighting process in the loss function, based on IoU scores, is introduced to retrain the model. Results show enhanced classification performance by aligning model attention with appropriate facial areas better. This strategy emphasizes the promise of explainability-guided methods to real-time bias reduction in face recognition models.
Authors - Swatee Nikam, Nilima Kulkarni, Amrita Manjrekar Abstract - With increasing data and its computation over clouds through various mediums it has become difficult to preserve privacy and then manipulate data according to the requirements. The third-party service providers have increased to do this manipulation over the bigdata which is collected over period of time. But again, the privacy is breached when the third party is involved with data of individuals and thus mechanism of homomorphic encryption (HE) is introduced which allows computation to be done on encrypted data. The initial work on homomorphic encryption were impractical but recent work is established with efficiency over practical application latest being embedded in Microsoft Edge browser. This paper gives a brief introduction to homomorphic encryption and its categorization over libraries created over recent years. Practical implementation HE is programmed over C++ language, with recent development the trial implementation is in progress over python language also. The libraries include such as Helib, SEAL, Blyss, concrete, etc. HE is developed as future enterprise for security and privacy guard up with application slated as geofencing with keeping secrecy of location or latigo polls for scheduling the meetings. The paper will discuss some other application with reference to new developed libraries under HE.
Authors - Mrunal Vibhute, Anjali Naik Abstract - In an era where misinformation spreads rapidly, the need for reliable and interpretable fake news detection systems is critical. This study evaluates deep learning models—Fully Connected Neural Networks (FCNN), Convolutional Neural Networks (CNN), Graph Convolutional Networks (GCN), Long Short-Term Memory (LSTM) networks, and FastText—for fake news classification. To ensure interpretability, Local Interpretable Model-agnostic Explanations (LIME) are applied to FCNN, CNN, and GCN, generating human-understandable explanations for predictions. While LSTM and FastText are included for performance comparison, they are excluded from interpretability analysis due to technical constraints. The models are trained using different input representations: TF-IDF for FCNN and CNN, and graph structures for GCN. This paper analyzes trade-offs between accuracy and explainability, offering insights into the effectiveness of deep learning models for fake news detection and contributing to the development of more transparent AI solutions.
Authors - Manan Parekh, Anmol Aafre, Parth Patel, Vaidehi Lad, Nirali Nanavati Abstract - The human eye is highly sensitive and vulnerable to various diseases that need timely attention; otherwise, they may lead to vision impairment. According to WHO (World Health Organization), most of these cases could be avoided through regular examinations. Therefore, early detection of eye disease has become a necessity. In this manuscript we propose a novel approach for prediction and classification of ocular diseases like age-related macular degeneration, cataract, diabetic retinopathy, glaucoma, hypertensive retinopathy, myopia, and normal using fundus images collected from multiple public sources. Our study comprises the study of multiple contemporary deep learning models, including EfficientNet-B4, ConvNeXt, DenseNet-201, and ViT-16B. Our proposed solution involves the implementation of the ConvNeXt model on our dataset, which achieves an accuracy of 94.14%. Our purpose behind this study is not only to develop an effective classification model but also to ensure its visual - understanding how and why the model is making certain decisions. To achieve this, we implemented the Gradient-Weighted Class Activation Mapping (GradCAM) to provide an explainable AI (XAI) based solution to help researchers and practitioners in the field.
Tuesday August 25, 2026 9:30am - 11:30am IST Virtual Room EGOA, India
Authors - Vinodh Kumar Minchula, Supraja Reddy A, Sharathchand Kodam Abstract - This paper investigates the characteristics of 5G NR-V2X channels and explores the benefits of relay-assisted communication for ensuring reliable and robust connectivity in dynamic vehicular environments. A detailed analysis of mmWave Base Station (BS) deployments utilizing MIMO antenna arrays of varying dimensions is conducted for the 5G NR n258 frequency band at 26 GHz. To evaluate system performance, a simulation framework is developed based on a Hyderabad Open Road Roundabout (ORR) scenario, aligned with 3GPP specifications, capturing vehicle mobility patterns and beam steering mechanisms to identify the optimal beam. To address the limitations of the NLoS, relay nodes are introduced to assist NLoS users, leading to measurable improvements in link quality and throughput. Experimental findings show that relay-assisted links yield up to a 4.1% increase in bit rate over direct NLoS connections. These results confirm the efficacy of relay-based architectures in enhancing signal robustness and extending mmWave communication range in V2X environments. The proposed framework provides a scalable and resilient solution for enabling high-performance vehicular networks in urban deployments. These findings clearly demonstrate that relay-assisted communication significantly enhances signal robustness and extends the effective range of mmWave links, thereby facilitating improved reliability and efficiency in next-generation vehicular communication networks.
