Volume 13 | Issue 7 |

 Abstract

Workplace safety, particularly in construction and industrial places, is a critical concern due to the high risks faced by the workers in the hazardous zones. To address these challenges, our study introduces a computer vision based system for virtual border identification and safety monitoring. Using real-time object detection algorithm YOLOv5, the system tracks human movements and monitors proximity to danger zones by overlaying virtual boundaries on live video streams. It instantly detects safety violations, triggering visual and audible alarms while notifying supervisors, and significantly reducing the chance of any accidents.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Virtual Safety Zones, Computer Vision, YOLOv5, Real-Time Monitoring.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Agriculture is the backbone of food security and economic stability, but crop diseases significantly impact yield and quality. Traditional disease detection methods often require expert intervention, making them time-consuming and inaccessible to many farmers. To address this challenge, this research proposes an Augmented Reality (AR)-Driven Disease Detection System for smarter farming. The system integrates computer vision, deep learning, and AR technology to provide real-time disease identification and treatment recommendations. The proposed framework utilizes image processing and convolutional neural networks (CNNs) to detect plant diseases from images captured by AR-enabled devices such as smartphones. The processed results are overlaid on the plant using AR visualization, allowing farmers to recognize affected areas and access treatment solutions instantly. Additionally, the system provides real-time recommendations, preventive measures, and expert consultation options, ensuring a proactive approach to disease management. By leveraging machine learning, real-time data processing, and AR-based visualization, this solution enhances precision farming, reduces dependency on chemical treatments, and improves crop health monitoring efficiency. Farmers receive immediate, visual feedback on their crops, highlighting potential disease symptoms through AR interfaces. Furthermore, the system facilitates teleconsulting features, enabling farmers to seek expert advice remotely. This approach not only reduces crop losses but also promotes sustainable agricultural practices by minimizing excessive chemical use and optimizing disease management strategies. Implementing this AI-driven smart farming technology aims to empower farmers, enhance decision-making, and contribute to a more efficient and resilient agricultural sector.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Augmented Reality (AR), Disease Detection, Smart Farming, Precision Agriculture, Deep Learning, Computer Vision, Convolutional Neural Networks (CNNs), Real-time Monitoring, Image Processing, Sustainable Agriculture, AI- driven Farming, Smart Crop Management

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Attendance management is a fundamental task in educational institutions and workplaces, but traditional methods, such as roll-calling or card- swapping, are prone to inefficiencies, inaccuracies, and time wastage. Keeping track of attendance the traditional way can often lead to mistakes and even manipulation. To solve this, our paper introduces a smarter approach--a Face Recognition-Based Attendance System. By using advanced computer vision and machine learning, this system makes attendance tracking more accurate, efficient, and hassle-free. The system utilizes OpenCV and dlib for detecting real-time face and recognition, enabling a seamless and contactless way to mark attendance. Our system uses the ResNet50 model to capture unique facial features, creating 128-dimensional embeddings to accurately match individuals with stored records. Attendance is securely logged in an SQLite database, and a simple Flask-based web interface makes it easy to access records anytime. By automating the process, this system removes the hassle of manual entry, reduces errors, and provides a reliable solution for schools, colleges, and workplaces. Additionally, the integration of Tkinter for the face registration interface ensures ease of use, while SQLite offers a reliable storage system. By implementing this solution, administrative workload is significantly reduced, accuracy is enhanced, and attendance management becomes more streamlined and efficient.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Face Recognition, Smart Attendance System, Computer Vision, OpenCV, dlib, Face Detection , Flask Web Application, Tkinter GUI, Automation, Attendance Management System, Scalable Solution.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Human-machine collaboration presents persistent challenges in aligning human intent with machine comprehension and execution. Large Language Models (LLMs) offer promising solutions by leveraging advanced natural language processing capabilities to bridge this gap. This paper surveys a novel framework that integrates LLMs into a vehicle "Co-Pilot," enabling autonomous systems to interpret and execute driving tasks based on human commands and contextual information. The proposed framework incorporates a robust interaction workflow and a memory mechanism to systematically organize and retrieve task-relevant data. By dynamically selecting appropriate controllers and planning optimal trajectories, the Co-Pilot adapts its operations to fulfill user-defined goals while maintaining safety and efficiency. Simulation experiments demonstrate the framework's ability to understand natural language instructions, plan actions, and execute driving tasks effectively, highlighting both its practical viability and limitations. Furthermore, the study emphasizes the importance of real-time adaptability in addressing complex driving scenarios and explores the concept of human-machine hybrid intelligence. This work illustrates the potential of LLMs to revolutionize autonomous driving by enabling more intuitive and effective human-machine collaboration.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Human-machine collaboration, Large Language Models (LLMs), Vehicle Co-Pilot, Human-machine interaction, Natural language understanding, Trajectory planning, Autonomous driving, Hybrid intelligence.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper presents a conceptual framework for a Women's Safety Protocol -- a proposed Python-based web application aimed at enhancing personal security through emergency alerts and real-time location tracking. By leveraging APIs such as Twilio and Geopy, the system is designed to discreetly notify emergency contacts when a user is in distress. Though still in the ideation stage, this proposal outlines the planned architecture, potential use of wearable integration, and future AI enhancements for emotional state detection. Future enhancement could involve AI-driven emotion detection using wearable devices. The app utilizes Geopy to track the user's location with up to 200-meter accuracy, ensuring swift response capabilities. In the event of an emergency, it notifies multiple trusted contacts simultaneously and requests the user to verify their safety using a secure passcode. If the passcode is not entered, the system escalates the alert automatically. Through the combination of real-time tracking, automated alerts, and a discreet, intuitive interface, the Women's Safety Protocol offers a reliable and effective solution for women to access help promptly--enhancing personal safety and enabling rapid assistance from friends, family, or authorities.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

In emergencies, real-time alerts and quick-response protocols, powered by the IoT, ensure women's safety by tracking location and enhancing security.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

To predict the price of second-hand cars, we have implemented three approaches namely, XGBoost, Random Forest, and Decision Tree Regressors. The Kaggle sourced dataset is cleaned, feature selected and treated for outliers in order to improve the accuracy. To evaluate the performance of the model and for knowing which factors in the price affect, we use the R2 score and Metrics evaluation of the Scikit learn module available in Python. Therefore, XGBoost is likely to outperform the rest because it is more efficient. Thus, we build a simple to use web application where Users may input Automobile details and get the same time price estimates. Our project serves to help buyers and sellers use the data to accurately predict a second-hand car price.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Random Forest, Decision Tree, XGBoost Regressor, Machine Learning, Car Price Prediction.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Accurate age detection has emerged as a useful tool in a constantly growing digital environment, spanning sectors including identity verification, wellness, healthcare, and personalized services. Conventional age estimation techniques use static inputs, but new opportunities for data-driven, real-time estimation have been made possible by developments in machine learning and predictive analytics. By utilizing deep learning models with real-time processing, we aim to create a robust system that adapts to diverse datasets and varying environmental conditions. The proposed framework employs optimization techniques to enhance efficiency of the model, ensuring seamless integration into cloud-based platforms for scalable deployment. The proposed study also indulges in deployment of the age detection model as a Software-as-a-Service for various security-based applications, such as security in net-banking. Beyond technical implementation, this study addresses the ethical considerations surrounding age detection, emphasizing fairness, transparency, and data privacy. The results of this research contribute to the growing field of machine learning-driven security applications, providing more insights, and secure identity verification. Through this work, ChronoDetect aims to demonstrate how AI-driven age estimation can be both efficient and ethically responsible, paving the way for broader applications in digital security domains.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine Learning, Predictive Analytics, Age Detection, Cloud Computing, Software-as-a-Service (SaaS), Net-Banking, Optimization Techniques, Real-time Processing, OpenCV, TensorFlow.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The main aim of this project is to develop and deploy an Expense Tracker application that enhances the management of finances through the utilization of advanced technologies. The application incorporates Optical Character Recognition to scan and extract information from invoices and receipts, along with a Large Language Model that classifies expenses into pre-defined categories. Using Flask for the frontend and Python for the backend, the project promises a smooth and friendly user interface. The OCR component employs sophisticated text recognition techniques to pull out transaction information from receipts, while the LLM categorizes transactions into typical expense categories such as food, utilities, and entertainment. Additionally, the app shows these categorized expenditures through interactive graphs and detailed transaction lists on the dashboard, offering users insightful information about their expenditures. This project showcases the successful integration of OCR and LLM technologies in actual applications, underlining the potential for automation of everyday financial work. The system seeks to enhance financial literacy and allow users to make knowledgeable choices, hence encouraging efficiency as well as access in managing expenses.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Automated Expense Tracking, Optical Character Recognition (OCR), Machine Learning Algorithms, Financial Management, Expense Categorisation, Tesseract OCR, Spending Insights, Neural Networks (CNN, LSTM), Real- Time Expense Monitoring, Data Extraction

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This solution introduces a web-based report generation tool that streamlines academic writing by integrating advanced formatting and data management features. The user- friendly interface supports autoformatting, including standardized font sizes, justified content, and 1.5-line spacing, while offering seamless data input and session management with auto-save functionality. AWS integration ensures secure storage and processing, and the tool includes robust data management capabilities such as historical access to previously generated documents. This comprehensive solution aims to enhance student productivity and reduce stress associated with academic report creation by providing an efficient, reliable, and userfriendly platform that adheres to academic standards.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Report Generation Tool, Web-Based Application, AWS Integration, Academic Support, Data Management, User-Friendly Interface, Document, Google AI Studio APIs

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The growing popularity of cryptocurrency has increased the need for accessible and efficient Bitcoin mining platforms. Traditional mining methods often involve high power consumption, costly hardware, and complex setups, making them unsuitable for individual users. This paper introduces cryptoMiner Pro, a lightweight Bitcoin mining solution that eliminates the need for specialized equipment. The platform emphasizes ease of use and energy efficiency, offering a clean and intuitive interface tailored for both novice and experienced users. Robust security measures are incorporated to safeguard user data and ensure secure transactions. Additionally, an integrated administrative module supports effective oversight of users and transactions. By simplifying the mining process, cryptoMiner Pro aims to democratize Bitcoin mining and make it more inclusive for a broader audience.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Accessibility, Bitcoin Mining, Lightweight Mining Platform, Secure Transactions, Transaction Management, User Interface

  License

Creative Commons Attribution 4.0 and The Open Definition