Volume 13 | Issue 9 |

 Abstract

In modern urban environments, the enforcement of traffic regulations remains a significant challenge due to increasing vehicle density and limited enforcement personnel. This project presents a scalable, AI-driven traffic violation monitoring system that automates the detection, documentation, and notification of traffic offenses such as helmet non-compliance and stop-line crossing. The system leverages a YOLO-based object detection model for real-time analysis of CCTV feeds, coupled with image processing techniques for accurate violation identification. An integrated OCR-based ANPR module extracts license plate numbers from captured frames, enabling automated challan generation and offender identification. The backend, powered by a Flask server, manages violation records, initiates Stripe-based payment processing, and renders geo-visualizations through Google Maps API. To enhance field responsiveness, a GPS-enabled Android application tracks traffic officers and dispatches Firebase Cloud Messaging (FCM) alerts to nearby personnel upon violation detection. Additional features include WhatsApp notifications and Google Text-to-Speech (TTS) announcements to expand alert reach and improve engagement. Tested in simulated environments mimicking urban intersections, the system demonstrated high accuracy in de- tection and efficient communication workflows. With support for automated alerts, digital payments, and real-time enforcement coordination, the proposed architecture offers a comprehensive and responsive solution for modern traffic law enforcement systems.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Traffic Monitoring, YOLO, Helmet Violation, Stop-Line Detection, Firebase, Stripe, Android App, Smart City

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Optimizing power consumption in a variety of applications, including HVAC, smart homes, and industrial systems, is imperative due to the growing demand for energy- efficient solutions. Significant energy waste from unnecessary motor operation frequently raises operating expenses and has an adverse effect on the environment. In order to overcome this difficulty, the project creates an intelligent energy management module that uses the current room temperature to dynamically regulate motor speed. The system makes sure motors only run when necessary, lowering power consumption and preserving ideal environmental conditions by using a sensor to continuously monitor the surrounding temperature and a microcontroller to proportionately adjust motor speed. By preventing abrupt oscillations, this proportional, smooth control enhances motor longevity and stability. The system is made to integrate easily with IoT platforms, allowing for improved automation and remote monitoring. Efficiency will be further increased by upcoming developments like cloud analytics and predictive control based on machine learning. By automating motor control and reducing wasteful energy use in temperature- sensitive applications, this project advances sustainable energy management.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Smart Energy Management, Internet of Things, Real-time Monitoring, Adaptive Fan Control, Human Presence Detection, Wireless Sensor Network, Energy Efficiency, Automation Voltage.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Finding a compatible roommate, especially for university newcomers, is a persistent challenge. Current platforms rely on static profiles, limiting their ability to capture user preferences. This paper proposes a groundbreaking web application, the first to leverage Large Language Models (LLMs) for roommate matching. LLMs surpass traditional methods by enabling natural language dialogues. Users can explore potential matches based on lifestyle, communication styles, and cultural backgrounds through free-flowing conversation. This paper explores the theoretical foundation of the LLM- powered system, details its integration, and discusses the potential to revolutionize roommate matching, particularly for immigrant students.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Roommate Matching, Large Language Models (LLMs), Natural Language Processing (NLP), Conversational Search, University Students, Immigrant Students, Personalized Matching

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Continuous monitoring of psychiatric and elderly patients is essential for ensuring safety and timely intervention in critical healthcare environments. Many elderly patients may be unable to express pain whenever care is actually crucial. Traditional wearable-based systems often fall short due to discomfort, limited compliance, and inability to detect abnormal disturbances among patients in real-time. The World Health Organization (WHO) recommends a doctor to population ratio of 1:1000; however, India currently averages only 1 doctor per 1500 people. To address these limitations and bridge the gap in scenarios lacking human supervision, this paper proposes a non- intrusive, vision-based framework for real-time abnormal activity detection in elderly people and for psychiatric patient monitoring using a deep learning framework integrated with alert system. The system combines YOLOv8 and DeepSORT for identification and tracking the patient. For behavioral analysis, two deep learning models were compared: a shallow 3D Convolutional Neural Network (3D CNN) and a hybrid MobileNetV2 model combined with GRU (Gated Recurrent Unit). Experimental results demonstrate that the MobileNetV2-GRU model outperforms the shallow 3D CNN in fall detection and for violence detection, the MobileNetV2-LSTM model is employed due to its superior ability to capture temporal features. Upon detecting abnormality in patients, the system triggers real-time alerts to caregivers via the Twilio API. Future work will focus on improving the robustness of the identity tracking module and extending the framework to multi- subject environments.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Continuous monitoring of psychiatric and elderly patients is essential for ensuring safety and timely intervention in critical healthcare environments. Many elderly patients may be unable to express pain whenever care is actually crucial. Traditional wearable-based systems often fall short due to discomfort, limited compliance, and inability to detect abnormal disturbances among patients in real-time. The World Health Organization (WHO) recommends a doctor to population ratio of 1:1000; however,

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Efficient food identification systems face challenges with the wide range of food categories and the high computational demands associated with extremely complex dishes. There is a need for a real-time solution that not only detects the food but also identifies its possible allergens and nutritional content, enabling efficient identification before distribution to those in need. Such a solution should ensure safe, accurate, and effective food identification in advance. This helps not only to reduce food waste but also to combat hunger. The proposed system's performance is evaluated using a confusion matrix, which helps assess model accuracy by displaying correctly and incorrectly classified data points and highlighting misclassification patterns. In our work, the ResNet50 model shows 85-90% accuracy with a loss of 0.3 on simple food images, but accuracy decreases with more complex food items. In contrast, InceptionV3, benefiting from multi-scale processing, achieves 88-92% accuracy with a loss of 0.25, demonstrating higher precision and recall with visually complex dishes. When combining ResNet50 and InceptionV3 through a stacking ensemble, performance significantly increases, reaching an overall accuracy of 93%, showing a substantial enhancement in classification accuracy across diverse food images.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

ResNet50, Inception V3, Allergen Detection, Food Image Classification, Stacking Ensemble, Food Detection, Nutritional Analysis.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper presents EduVidGuard, an innovative AI-based solution for validating the authenticity and educational relevance of video content on e-learning platforms. With the increasing prevalence of user-generated content, ensuring quality and topic alignment has become a significant challenge. EduVidGuard integrates advanced AI technologies that analyze both audio and visual aspects of videos to determine if they meet educational, accuracy, and contextual standards. It employs OpenAI Whisper for transcription and Tesseract OCR for visual code extraction, enabling an intelligent content review pipeline. This paper explores the architecture, functionality, evaluation, and potential implications of EduVidGuard in enhancing digital education integrity.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

EduVidGuard: An AI Video Validator for Education Platforms

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Modern DevOps pipelines prioritize speed, effi- ciency, and automation but often overlook the cognitive state of the human operators managing them. Prolonged deployment ses- sions and critical incident handling can lead to stress, fatigue, and distraction, increasing the likelihood of human-induced errors. This paper proposes an AI-Augmented DevOps framework that integrates real-time cognitive monitoring into CI/CD workflows. The system employs standard webcams for emotion recognition (DeepFace), eye aspect ratio-based fatigue detection (MediaPipe), and computer vision (OpenCV) to assess operator state. Based on detected cognitive strain, the framework can pause ongoing deployments or issue rest alerts via seamless integration with GitHub Actions. A Streamlit-based dashboard provides real- time visualization of cognitive metrics and operational status. Experimental evaluation in a simulated CI/CD environment demonstrated ?90% emotion detection accuracy, ?95% fatigue detection accuracy, and sub-2-second trigger latency, showing the potential of cognitive-aware DevOps systems in reducing operational risk and enhancing developer well-being.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

DevOps, Cognitive Monitoring, AI-Augmented Automation, Emotion Detection, Fatigue Detection, Computer Vision, CI/CD

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Pneumonia is an inflammatory condition of the lung primarily affecting the small air sacs known as alveoli caused by microorganism infection which can be viral or bacterial. It remains a critical public health concern worldwide, particularly in low resource settings, affecting severely specific age groups such as newborns & infants under 2 years old and adults above 65, due to their weak immune system. While Chest radiograph imaging is the most well-known screening approach used for detecting pneumonia in the early stages, its blurry and low illumination nature may call forth human error in manual diagnosis. Hence, the contribution of this work is the development of a two-stage pneumonia detection Expert System fusing the capabilities of both ensemble convolutional networks and the Transformer mechanism. In the first stage, a binary classification ensemble model is employed to detect whether a given chest X-ray indicates pneumonia or not. Upon a positive detection, the second stage activates a multi-class classification ensemble model that further categorizes the pneumonia into viral or bacterial, thus providing a finer level of diagnostic detail. The ensemble learning extracts strong features from the raw input X-ray images in two different scenarios: ensemble A (i.e., DenseNet201, Xception and InceptionResNet V2) and ensemble B (i.e., DenseNet201, Xception and VGG-16). The proposed ensemble deep learning model recorded 95.95% classification performance in terms of overall accuracy and F1-score for the binary classification task, while it achieved 88.57% for multi-classification task. To ensure that the diagnosis is not only automated but also interpretable to end-users, including healthcare professionals, the model is fed to an expert system where users can upload X-ray images, get a classification result and see the highlighted region which supports the diagnosis through Local Interpretable Model-Agnostic Explanations (LIME), a black box testing strategy. The proposed framework could provide promising and encouraging explainable identification performance compared to the individual or existing ensemble models building trust of users and healthcare professionals on the result.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Pneumonia, Chest X-Ray, Deep Learning, Explainable AI, Ensemble Model, LIME

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Blackhole attacks in Wireless Sensor Networks (WSNs) pose significant threats to data integrity and network reliability. These attacks involve malicious nodes diverting or dropping data packets, disrupting communication, and compromising network integrity. Existing loopholes in wireless sensor network defences include vulnerabilities in encryption protocols and limited scalability of anomaly detection algorithms. By leveraging advanced cryptographic techniques and anomaly detection algorithms like Quantum cryptography, it aims to identify and mitigate the impact of malicious nodes within the network. Additionally, anomaly detection algorithms continuously monitor network behavior to detect deviations indicative of blackhole attacks. Through simulations and experimental factors, a defense mechanism to establish secure communication channels between sensor nodes and base stations, safeguarding data transmission against interception and manipulation is designed. This enhances the security posture of WSNs, resulting in the effectiveness of the approach in mitigating the impact of blackhole attacks.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

WSN, Security, Black Hole, vulnerabilities, Quantum Cryptography

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The protection and mental health of women and children continue to be pressing societal concerns, but while immense problems remain in the form of restricted access to mental healthcare, unawareness of legal rights, and inadequate provisions for emergency support mechanisms, immense problems still abound. The StrongHer project meets the above challenges through the creation of a secure, accessible, and integrated online platform. The platform provides virtual counselling services, mental health screenings using GAD-7 and PHQ-9 scales, gamified learning exercises that enhance legal awareness, and instant emergency response using IoT-based wearable devices. Sensitive medical records are stored securely using blockchain technology (Ethereum and IPFS) to maintain data privacy and integrity. Developed using ASP.NET Core MVC, Microsoft SQL Server, NodeMCU (IoT module), and blockchain integrations, StrongHer integrates the latest technologies to establish a digital safe space. The system empowers users by boosting their mental resilience, giving them timely support, encouraging legal literacy, and providing real-time emergency communication features. By combining counselling, legal aid, education, and emergency response within one environment, StrongHer is a complete solution to tackle the complex issues of vulnerable groups, thus making a significant contribution towards the safety and well-being of society.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Women and Child Safety, Mental Health Assessment, Blockchain- based Medical Record Storage, Emergency SOS Alert System, IoT-based Safety Devices, Virtual Counselling.

  License

Creative Commons Attribution 4.0 and The Open Definition