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

Published by: vikaspatanker

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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Published by: vikaspatanker

Authors: Nitin, Ishan Rana, Dr. Neha Gupta

Abstract: Food spoilage is a big concern affecting health, safety, and economy worldwide. This paper presents the design and implementation of a food spoilage detection system using an Arduino Uno microcontroller and MQ-135 gas sensor. The system detects gases such as ammonia, carbon dioxide, and volatile organic compounds (VOCs) released during food decomposition. Gas sensors provide a non-destructive and efficient way to monitor food quality by detecting chemical changes in the surrounding environment [1]. The MQ-135 sensor is mainly used due to its sensitivity to harmful gases associated with spoilage [2]. When the gas concentration exceeds a predefined threshold, the system alerts the user through an LED indicator in Arduino Board. The proposed system is cost-effective, portable, and easy to implement. It can be used in households, food storage facilities, and small-scale industries to ensure food safety and reduce wastage.

DOI: https://doi.org/10.5281/zenodo.20196415

Published by: vikaspatanker

Authors: Tejaswini Bagade, Preeti Wagh, Ms. Neeta Takawale

Abstract: This project focuses on the design and development of a Real-Time Traffic Flow Forecasting and Management System using machine learning and deep learning techniques. The system aims to predict traffic conditions accurately by analyzing real-time and historical traffic data collected from sensors, CCTV cameras, and GPS devices. Data preprocessing techniques are applied to remove noise and handle missing values for improved prediction accuracy. Advanced models such as LSTM, GRU, and CNN–LSTM are implemented to forecast traffic flow and support intelligent traffic management decisions. The proposed system helps reduce traffic congestion, improve road safety, optimize signal control, and enhance transportation efficiency through real-time monitoring and adaptive management strategies.

Published by: vikaspatanker

Authors: Mani G

Abstract: The fast growth and acceptance of cloud computing technology have completely changed the IT infrastructure of organizations, but along with that transformation, there have been several emerging security concerns. These security concerns have become hard to detect using conventional security approaches, due to the complexity and the evolution of new cyber attacks. In this paper, a complete deep learning cybersecurity framework will be proposed, to detect any threats in real-time within cloud computing environments. The cybersecurity framework consists of several deep learning models. They include the TCN with an autoencoder to detect anomalies at 99% accuracy with a false positive rate of 2.2% based on CSE-CIC-IDS2018 dataset, a transformer with CNN to detect network intrusions with 99.12% accuracy, and a federated learning method for detecting attacks in distributed environment without violating any user’s privacy at 98.3% accuracy in 300 communication rounds.

DOI: https://doi.org/10.5281/zenodo.20085675

Published by: vikaspatanker