Authors - Ganesh Puri, Pratik Jadhav, Mayuri Gawande, Gadekar Gayatri, Tushar Badakh Abstract - Air quality is a key determinant of public health, increasingly impacted by natural events such as wildfires and volcanic eruptions, as well as human-induced sources like vehicular and industrial emissions. This research focuses on predicting air pollution levels using two advanced models: Long Short-Term Memory (LSTM) networks and Auto-Regressive Integrated Moving Average (ARIMA). A dataset is developed by combining precise ground station data from multiple ground stations. It also features real-time inputs from low-cost ESP32-powered IoT sensors. The sensor fusion approach enhances both spatial coverage and data granularity, offering a more comprehensive representation of environmental conditions. The ThingSpeak IoT platform is employed for live data collection, visualization, and remote monitoring. LSTM networks are selected for their capability to model complex, long-term dependencies in time-series data, while ARIMA provides a robust statistical baseline for comparison. Experimental results demonstrate that models trained on the fused dataset significantly outperform those using individual data sources, resulting in more accurate and reliable air quality forecasts. This study presents a scalable and cost-effective framework for air quality monitoring, supporting timely, data-driven interventions by environmental agencies and contributing to improved public health management in both urban and rural areas.
Authors - Dasari Keerthi Sai Naga Sudha, Navaneeth Rajamohan, Chakravaram Hari Priya, Mandapati Bindu Sree, Beena B.M. Abstract - The present project illustrates a comprehensive crop health monitoring and recommendation system using environmental condition data in the context of optimizing agricultural practices. Crop health monitoring utilizes AWS SageMaker Studio in training and deploying a machine learning model to classify crops as either healthy or unhealthy based on environmental inputs like temperature, humidity, rainfall, N, P, K and Ph. A Flask application, developed in SageMaker Studio, is designed as the interface for a real-time crop health prediction tool, giving farmers actionable inputs for timely intervention. Finally, a crop recommendation system, using AWS SageMaker, analyzes the environmental dataset to suggest the most suitable crop for a given region. These systems combined create sustainable farming practices and contribute positively to the improvement of agriculture productivity.
Authors - Narayan Irkal, Sanjana Ambore, Praveen Bachalapur, Nihar Kulkarni, Mohammed Azharud-din, Suneeta V Budihal Abstract - This paper presents a simulation-based study of standalone 5G networks focusing on such key performance metrics as latency, throughput, and packet loss. Three network configurations-Small Cells, Massive MIMO, and Beamforming-are considered for evaluating their impact on network performance. Three types of traffic-Voice, Video, and IoT, with different data requirements-are also considered in this study. The simulation models the real-world scenario using a 3.5 GHz carrier frequency and 100 MHz bandwidth with different modulation schemes (QPSK, 16QAM, 64QAM) and coding rates (0.5, 0.7, 0.9). The performance metrics are calculated through a combination of signal-tonoise ratio, resource block allocation, and transport block size. The results are visualized by line and bar plots, emphasizing the efficiency and trade-offs of different configurations under diverse traffic scenarios. This work explains the main issues of making 5G networks more dependable and efficient based on the upsurge in demand of high-speed-low-latency in modern applications.
Authors - Zeeshan Mirji, Muzammil Kharadi, Prajwal Shiggavi, Abdul Razzak R Yergatti, Mohammed Azharuddin Adhoni, Suneeta V Budihal Abstract - Software-defined networks (SDNs) enable flexibility by decoupling the control and data planes, but their complex, dynamic structures challenge traditional optimization methods like rule-based algorithms and Queuing Theory (QT). To address this, we propose a framework using graph neural networks (GNNs) and deep reinforcement learning (DRL). GNNs model network components as nodes and connections as edges, learning efficient representations to improve metrics like delay, jitter, load balancing, and scalability. Our approach delivers scalable, real-time SDN optimization, significantly outperforming QT in simulations, paving the way for advanced data-driven network control.
Authors - Iliyas Kinnal, Yashwant Danaraddi, Amogh Gujamagadi, Aditya Deshpande, Mohammed Azharuddin, Sunita V Budihal Abstract - Analyzing the performance of the communication system in terms of real-time applications can be judged by network performance analysis. Wireless networks consist of transmission power, which determines all the key performance metrics, such as throughput, delay, packet loss, and energy efficiency. With an increase in transmission power, it can cover greater signal strength and reduce packet error but will lead to increased amount of interference, energy consumption, and potential network congestion. The NS3 network simulator provides a robust platform for modeling and analyzing the impact of transmission power on wireless networks. It will allow researches to simulate real-life scenarios that alter transmission power levels and hence the effects it will have on the performance of the network under greatly differing conditions. Critical parameters such as SNR, RSSI, and link reliability can effectively be measured through NS3, thus providing the optimum network configuration insights.
Authors - Soni R. Ragho, Rohan R. Swami, Tanuja S. Gaikwad, Aaditi P. Narke, Shubham M. Atak Abstract - India's agricultural sector is a vital pillar of its economy, providing livelihoods to millions of people. However, plant diseases position a major threat to crop productivity, leading to significant financial losses. The unpredictable nature of climate change has further exacerbated the spread of these diseases, highlighting the need for early and accurate detection to prevent large-scale crop damage. Traditional disease identification methods, which rely on human observation, are often ineffective, subjective, and prone to misdiagnosis. Incorrect assessments may result in the misuse of pesticides, causing economic burdens and environmental harm. Consequently, the development of an advanced and reliable plant disease detection system is essential for promoting sustainable farming practices. The rise of artificial intelligence and image processing has introduced innovative techniques for detecting plant diseases. Deep convolutional neural networks (CNNs) have demonstrated exceptional efficiency in identifying and classifying plant diseases with high accuracy. These models utilize sophisticated machine learning techniques to analyze high-resolution leaf images, ensuring fast and precise disease detection. This study aims to design an advanced CNN- based model to improve the accuracy and effectiveness of plant disease identification, providing farmers with actionable insights for better disease control. By harnessing AI-driven technologies, this research seeks to reduce agricultural losses, enhance crop yields, and contribute to the long-term sustainability of India’s agricultural sector. Additionally, integrating such intelligent systems can optimize resource management and reduce reliance on harmful chemical treatments.
Authors - Prerna Agrawal, Savita Gandhi Abstract - Pneumonia is a predominant cause of illness and death globally, particularly affecting vulnerable groups such as children, the elderly, and immunocompromised individuals. Timely and precise diagnosis is essential for effective therapy; however, conventional chest X-ray (CXR) evaluation by radiologists is prone to human error and constrained availability, especially in resource-limited environments. Recent breakthroughs in deep learning have facilitated automated and highly precise medical picture analysis, presenting a possible alternative for pneumonia identification. About 473,780 cases of pneumonia were reported in India in 2022–2023 and throughout this time, pneumonia was caused by 11,497 baby fatalities that aged between 1 to 12 months and 4,571 pediatric pneumonia-related deaths have been reported that aged between 1 to 5 years. In 2024 the annual incidence rate of community acquired Pneumonia in India is estimated to be between 5 and 11 per 1,000 people. This research proposes a system named PneumoSense, an intelligent automatic pneumonia diagnosis system employing DenseNet121, a deep learning model recognized for its efficacy in medical imaging applications. PneumoSense is an automated, intuitive interface that allows users to upload an x-ray image for analysis to ascertain the presence of pneumonia. Upon detection, the algorithm generates a prediction score and advises medical consultation. The model underwent comprehensive testing against various lung illnesses, such as COVID-19 pneumonia, fibrosis, and effusion, confirming its reliability in practical applications. Experimental findings indicate that DenseNet121 surpasses other deep learning algorithms, including CNN, ResNet50, and NASNet, attaining superior recall of 0.9795 and AUC score of 0.9808. The workflow of PneumoSense is also discussed in the paper. PneumoSense diminishes diagnostic inaccuracies, improves accessibility, and provides a feasible AI-driven substitute for manual diagnosis. This study underscores the revolutionary capacity of deep learning in medical diagnostics, facilitating early pneumonia detection, enhancing patient outcomes, and alleviating the workload on radiologists.
Authors - Vani K S, Aditya Andotra, Tanmay Sinha, Kaatyaini Jaiswal, Roshan Kumar Sahu Abstract - It’s very important for understanding and responding to customer emotions in real time to have an effective and satisfiable customer service. This study introduces an automated Sentiment Analysis System for Helpdesk Calls, leveraging Long Short-Term Memory (LSTM) networks and advanced Natural Language Processing (NLP) techniques to enhance service efficiency. Traditional sentiment analysis methods mostly fail to capture the nuances of spoken language, which reminds the need for a more robust approach. The proposed system processes helpdesk calls recordings, applying speech normalization, noise reduction, and feature extraction using Mel-Frequency Cepstral Coefficients (MFCCs) before classification. By integrating machine learning and deep learning models, the system provides real-time sentiment insights, allowing operators to prioritize and address calls based on emotional tone. Performance evaluation using accuracy, precision, recall, and F1-score ensures continuous model refinement. This scalable solution and methodology reduce manual effort, enhances customer interactions, and fosters improved satisfaction and loyalty, representing a significant advancement in automated customer service technology.
Authors - Pavan.B.Shivalli, Mahamadshiraj.B, Nitin.P.Savvase, Samit.Patil, Sarvesh.R.Karkannavar, Mohammed Azharuddin, Suneeta.V.Budhihal Abstract - A simulation model for inter-vehicle communication is presented in this research with the goal of improving traffic control and road safety during collisions. The model mimics data transfers between vehicles using OMNET++ and SUMO, allowing for the real-time identification and notification of traffic accidents. The framework makes use of "VEINS" to seamlessly integrate traffic and network modeling, enabling efficient data packet transfers for both routine and urgent messages. Dedicated Short-Range Communication (DSRC) with a 300-meter range and optimized power transfer at 9mW, which increases energy efficiency, are two important aspects. The framework achieves high-speed data transfer by using the UDP protocol, which is necessary for prompt response in accident situations. The findings show that vehicles can communicate reliably with one another, which could speed up emergency responses and lessen traffic interruptions caused by accidents.The model can be modified to accommodate different traffic situations and allows for the addition of more safety beacons. This model may be expanded in the future to support practical Intelligent Transportation Systems (ITS) applications.
Authors - BG.Shresta, P.Hari Sankar, Pinninti Anju Chowdary, Beena B.M Abstract - This project is to utilize cloud computing in deploying an ML-enabled Flask application for the prediction of water quality. The model, trained on historical datasets of water quality, is integrated into a user-friendly web interface constructed using Flask. Specifically, the main objective is to explore the deployment and hosting of the applications across the AWS cloud platform to ensure scalability, reliability, and efficiency. Other deployment methods scrutinized include AWS Elastic Beanstalk, AWS Lambda, and EC2. For deploying, Elastic Beanstalk was adopted as the principal deployment because it can natively host the application and handle scalability while providing end-to-end management of infrastructure. However, issues arose with regards to Lambda’s resource limitation in handling gigantic ML models, while configuration problems of EC2 were inevitable in scaling. The project itself reflects strengths and limitations of these services and points towards very robust infrastructure in resource-intensive ML applications. It’s applicable not only in the scalable solution of real-time water quality prediction but also comparative with studies on deploying Flask applications within AWS, with insights to optimize performance and future applications based on cost-effectiveness.
