Category Archives: Uncategorized

Authors: Harsh Vardhan, Aanurag Gangwar

Abstract: Air pollution continues to be one of the most important environmental problems in fast-growing parts of the Indo- Gangetic Plains (IGP), such as Agra and Noida, characterized by declining air quality levels. The current paper offers a comparative analysis of the Air Quality Index (AQI) temporal and seasonal trends in the cities of Agra and Noida based on the daily data from the Central Pollution Control Board (CPCB) for the period of 2021–2025. The descriptive statistical analysis demonstrates that AQI is higher and more variable in Noida than Agra, which implies that Noida is under higher levels of pollution. The time-series analysis reveals considerable AQI dynamics in the two cities, including peaks of the parameter under discussion during winter months. The results of the monthly and seasonal analysis also suggest strong seasonality, according to which AQI scores peak during winter and post-monsoon months, while monsoon months are associated with improved air quality. In terms of AQI categories, Noida witnesses more days classified as “Poor,” “Very Poor,” and “Severe,” while Agra shows a larger number of “Moderate” and “Satisfactory” days. Finally, the autocorrelation analysis demonstrates a high level of AQI dependence on the time dimension in both cities. These differences have been observed due to variations in the sources of emission, population density, pollution transport dynamics, and weather conditions. In conclusion, the analysis shows that Noida is more heavily and variably polluted than Agra. This study offers valuable guidance for designing air quality management plans specific to regions and helps frame effective measures for pollution-prone urban areas.

DOI: https://zenodo.org/records/20095989

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Authors: Harsh Vardhan, Aanurag Gangwar

Abstract: Air pollution continues to be one of the most important environmental problems in fast-growing parts of the Indo- Gangetic Plains (IGP), such as Agra and Noida, characterized by declining air quality levels. The current paper offers a comparative analysis of the Air Quality Index (AQI) temporal and seasonal trends in the cities of Agra and Noida based on the daily data from the Central Pollution Control Board (CPCB) for the period of 2021–2025. The descriptive statistical analysis demonstrates that AQI is higher and more variable in Noida than Agra, which implies that Noida is under higher levels of pollution. The time-series analysis reveals considerable AQI dynamics in the two cities, including peaks of the parameter under discussion during winter months. The results of the monthly and seasonal analysis also suggest strong seasonality, according to which AQI scores peak during winter and post-monsoon months, while monsoon months are associated with improved air quality. In terms of AQI categories, Noida witnesses more days classified as “Poor,” “Very Poor,” and “Severe,” while Agra shows a larger number of “Moderate” and “Satisfactory” days. Finally, the autocorrelation analysis demonstrates a high level of AQI dependence on the time dimension in both cities. These differences have been observed due to variations in the sources of emission, population density, pollution transport dynamics, and weather conditions. In conclusion, the analysis shows that Noida is more heavily and variably polluted than Agra. This study offers valuable guidance for designing air quality management plans specific to regions and helps frame effective measures for pollution-prone urban areas.

DOI: http://doi.org/

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Authors: Hanathika T, Gayatri K

Abstract: Diabetic retinopathy (DR) is a major cause of avoidable vision loss worldwide, and deep learning approaches have shown promising results on large-scale retinal image datasets such as EyePACS. However, many existing works mainly emphasize overall accuracy or referable DR detection, while giving less importance to factors like model reliability, interpretability, and performance on noisy real-world data. To address these limitations, this study presents a Multi-Attention Residual Network (MARN) built upon EfficientNet-B0 for simultaneous DR grading and referable DR classification using a resized version of the EyePACS Kaggle dataset. The proposed architecture integrates a residual fully connected head with dropout regularization and is trained using class-balanced sampling along with cross-entropy loss. The model is evaluated on both a five-class DR grading task and a clinically significant binary classification task (referable DR ≥ moderate versus non-referable DR). Experimental results on a subset of 6,081 images show that MARN improves five-class validation accuracy from 0.4618 to 0.4881 and increases the macro-F1 score from 0.4905 to 0.5195 when compared to a strong EfficientNet-B0 baseline. For referable DR detection, the model achieves an accuracy of 0.780, with sensitivity of 0.776 and specificity of 0.783, demonstrating a slight improvement in specificity while preserving high sensitivity. Further analysis indicates notable performance gains in Severe and Proliferative DR categories, with ROC-AUC scores of 0.865 and 0.915, respectively. In addition, Grad-CAM visualizations highlight that the model focuses on clinically relevant lesion regions, while t-SNE representations show improved clustering of advanced DR features. Overall, the proposed MARN framework delivers consistent improvements in classification performance, effective identification of vision-threatening DR, and enhanced interpretability, making it a reliable and explainable tool for clinical decision support rather than a purely black-box model.

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Authors: Shubham Aher, Atharva Lambate

Abstract: Adaptive traffic signal control is an important requirement for reducing urban congestion and improving traffic flow in smart cities. Traditional fixed-time signal systems work on pre-defined schedules and cannot respond effectively to sudden changes in traffic demand, peak-hour congestion, road incidents, or uneven lane usage. This research paper presents an intelligent traffic signal control system that combines Reinforcement Learning (RL) with vehicle-mounted Internet of Things (IoT) sensors. In the proposed system, vehicles provide anonymized and aggregated traffic information such as position, speed, lane approach, queue formation, and movement direction. This information is collected by roadside aggregation units and used by reinforcement learning agents to dynamically select signal phases at intersections. The main objective of the system is to reduce average waiting time, queue length, unnecessary stops, vehicle idling, and unfair lane delays while maintaining data privacy. A multi-agent Advantage Actor-Critic based approach is considered for controlling multiple intersections, and other RL algorithms such as Q-learning, Deep Q-Network, and Proximal Policy Optimization may also be applied depending on the traffic environment. The system is evaluated through SUMO-based traffic simulation. The study shows that RL-based signal control can improve performance compared with fixed-time and threshold-based control methods, with preliminary simulation results indicating approximately 30% improvement in waiting time and queue length. The paper also discusses methodology, deployment process, scalability, communication challenges, privacy protection, limitations, and future scope of RL-IoT based intelligent traffic management.

DOI: https://doi.org/10.5281/zenodo.20095635

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Authors: Ayura Ajinath Athare, Prof.Sweety Wanave-

Abstract: Abstract- Artificial Intelligence (AI) is rapidly transforming the global pursuit of sustainability by enabling intelligent, data-driven decision-making across urban development, renewable energy, and environmental monitoring. This paper explores AI’s applications in building smart cities, optimizing renewable energy systems, and advancing climate change monitoring, with a focus on India’s smart grid journey. Smart city initiatives integrate AI for traffic management, waste handling, and energy distribution, creating resource-efficient ecosystems. Renewable energy systems benefit from AI’s predictive analytics in demand forecasting, renewable integration, and energy storage, particularly relevant for India’s National Smart Grid Mission. AI also plays a pivotal role in climate monitoring by processing satellite imagery, IoT sensor data, and big data models to predict weather patterns, detect environmental degradation, and enable disaster preparedness. However, ethical concerns such as bias, transparency, privacy, and equitable access must be addressed to ensure inclusive adoption. A literature review of over 20 scholarly works and policy frameworks highlights current advancements, gaps, and future opportunities. The proposed framework integrates technical, ethical, and governance considerations for sustainable AI. By combining AI innovation with ethical governance, nations can accelerate progress toward the United Nations Sustainable Development Goals (SDGs) while ensuring fairness and resilience.

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Authors: Ramse Dhananjay Devdas, Pawar Gorakhnath Vishwanath, Prof. N. K. Patil

Abstract: Phishing attacks and malicious QR codes constitute two of the most prevalent vectors of cybercrime, accounting for billions of dollars in financial losses annually. Existing defences rely on server-dependent machine learning pipelines or easily bypassed keyword heuristics that produce unacceptable false-positive rates on legitimate sites. This paper presents AIPhiShield, a browser-native cybersecurity tool that replaces heuristic match-ing with a Logistic Regression classifier trained on 20 structural URL features using Python and scikit-learn, then exported as a 2.5 KB JSON weight file and executed entirely within the browser via a custom JavaScript inference engine. No URL is transmitted to any external server for machine learning scor-ing, preserving user privacy. Detection is augmented by cross-referencing the OpenPhish live phishing feed and a curated 52-entry compound-phrase blacklist. The integrated system ad-ditionally provides QR image scanning, live webcam QR scan-ning, an LLM-powered cybersecurity chatbot routed through a Flask proxy that conceals the API key from frontend code, voice input, and geolocation-enriched scan history. The trained model achieves 100% accuracy, precision, recall, and F1-score on a stratified 72-sample test set, with zero false positives and zero false negatives. Feature importance analysis identifies HTTPS usage, high-risk top-level domain, and raw IP address as the three strongest predictors.

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Authors: Prof. K.S.Tamboli, Meher Karan Dnyandev, Gaiwad Nikhil Ganesh, Kate Dhruv Balsabheb

Abstract: The increasing demand for sustainable transportation and the need to reduce environmental pollution have accelerated the adoption of electric vehicles (EVs). However, the high cost of new EVs and the large number of existing internal combustion engine (ICE) vehicles present a significant challenge. Retrofitting of existing vehicles into electric vehicles has emerged as a practical and cost-effective solution to address this issue. This process involves replacing the conventional engine, fuel system, and exhaust components with an electric motor, battery pack, and motor controller. This paper focuses on the selection and integration of key components, particularly the electric motor and controller, which play a vital role in determining the performance, efficiency, and reliability of the converted vehicle. Various types of motors such as BLDC and induction motors are analyzed along with suitable controller strategies. The study also highlights design considerations, system integration challenges, and safety aspects involved in the for conversion process. Retrofitting not only reduces carbon emissions and fuel dependency but also extends the life of existing vehicles, making it an environmentally and economically viable solution. The proposed approach contributes to sustainable mobility while promoting innovation in electric vehicle technology.

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Authors: Aditi Nandiraju, Hunar D, Ashutosh,, Somraj, Janaki Kandasamy

Abstract: As critical infrastructure in aviation and healthcare becomes increasingly complex, traditional reactive strategies for maintenance and security are proving insufficient for handling dynamic real-world environments. This research examines the integration of AI-enabled predictive monitoring and security frameworks to create resilient, self-sustaining systems that can manage uncertainty with minimal human intervention. Central to this transition is the application of AI and machine learning models—such as XGBoost, CNNs, and LSTMs—to move from scheduled to proactive maintenance by accurately predicting the Remaining Useful Life (RUL) of aircraft engines and providing early warnings for cardiac events in healthcare. Simultaneously, the study prioritizes security by developing defense mechanisms against cyber-physical threats, including GPS spoofing, ADS-B vulnerabilities, and unauthorized network intrusions across both aviation and smart airport infrastructures. Despite these advancements, significant barriers remain, including high computational overhead, a lack of model interpretability (the "black box" problem), and a gap between simulation and real-world deployment. This work concludes that the future of dependable infrastructure lies in unified, lightweight, and explainable frameworks that allow systems to autonomously detect threats, recover from faults, and maintain themselves in unpredictable conditions.

DOI: https://doi.org/10.5281/zenodo.20093181

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Authors: Saanvi Anup K, Nandana D Nair, Shajahan Basheer, Suresha R

Abstract: In the digital age, social media platforms generate vast amounts of unstructured data that serve as a goldmine for businesses, marketers, and content creators. Identifying trending topics and understanding content engagement dynamics is critical for strategic decision- making. This report reviews 30 research papers focusing on social media analytics, ranging from big data architecture to advanced deep learning models. Based on this review, we propose a ‘Social Media Analyzer’ system designed to extract trending hashtags, perform sentiment analysis on user engagement, and provide actionable insights. We select "A Deep Learning Sentiment Analyser for Social Media Comments in Low-Resource Languages" (Paper #13) as our base paper for its robust handling of informal text. The proposed work integrates Topic Modelling (LDA) with a Hybrid Deep Learning Classifier to predict content virality and audience sentiment.

DOI: https://doi.org/10.5281/zenodo.20092205

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Authors: Sukanth Tumu, Balasubbareddy Mallala, Sudhakar Babu Thanikanti, U.Nikhil tej, B.Murali, N.Suresh

Abstract: The Real-Time Grid Monitoring System is an IoT-based project designed to continuously monitor and control electrical parameters in a power distribution setup. The system utilizes a NodeMCU (ESP8266) microcontroller for real-time data acquisition, processing, and wireless communication. A potentiometer is used to simulate and monitor voltage variations, while an LM324 operational amplifier serves as a crucial component for detecting short-circuit and open-circuit faults in the grid. In the event of such abnormalities, or when undervoltage conditions occur, a buzzer is activated to provide an immediate alert.The system incorporates two relays, enabling remote switching of connected loads through an IoT-based web interface, allowing users to manually control devices from anywhere using a smartphone or computer. Additionally, a 16×2 LCD display presents real-time voltage status, load condition, and fault information locally. This integration of hardware monitoring and IoT control ensures improved reliability, safety, and user convenience. The proposed system provides a cost-effective and scalable approach to enhance smart grid management, offering real-time visibility and quick response to faults. It demonstrates the potential of IoT in modern electrical systems by bridging automation, monitoring, and fault detection into a unified platform. Keywords: NodeMCU, RELAYS, BUZZER, LOADS, LCD, VOLTAGE MONITOR, LM324, OC, SC.

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