Volume 13 | Issue 7 |

 Abstract

This paper presents a novel image enhancement technique that integrates Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT) with cubic spline interpolation and Contrast Limited Adaptive Histogram Equalization (CLAHE). The method decomposes low-resolution images into frequency sub-bands, processes these sub-bands to estimate high-frequency details, and reconstructs enhanced high-resolution outputs. By addressing challenges such as edge blurring and loss of fine details, the algorithm offers significant improvements over traditional methods. Experimental evaluations on the Kaggle Super-Resolution Dataset demonstrate enhanced Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), particularly for medical and satellite imaging applications. The approach's adaptability and efficiency make it a promising solution for diverse imaging needs.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Image enhancement, wavelet transform, cubic spline interpolation, CLAHE, super-resolution, SWT, DWT, thresholding

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The proposed project is a cab fare comparison prototype designed to help users estimate and compare cab service prices effectively. Instead of relying on direct API integrations from platforms like Ola, Uber, and Rapido, this prototype uses custom-developed APIs based on publicly available pricing information and predefined parameters. Users can register, authenticate, and manage their profiles, while the platform utilizes location-based inputs to assist with fare estimation. The system also offers trip history, user reviews, and a fare analytics page to help both passengers and drivers.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cab Fare, Custom APIs, Prototype, Location-based Estimation, Analytics, Django, React.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A restaurant's ability to take orders for food is essential. This is something that waiters do for customers when they dine at restaurants. Typical restaurant ordering procedures may lead to a number of problems. Server and client misunderstandings during order taking are the root cause of all problems. A short wait for the server to come and take the order is also required of the customer. The current setup is somewhat antiquated, using paper and printed menus to keep track of customer orders. Consequently, a real-time ordering system developed to manage the ordering process for restaurants is the Food Ordering System using QR Code technology. Therefore, the QR Code meal ordering system is a remedy for that problem. Smartphones serve as the foundation of the system since they are now indispensable in modern culture. The restaurant will include a QR code on the menu that customers must scan. Using this method, the buyer may also be sure they got what they requested. Additionally, the restaurant staff has access to the order list and may review the menu.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

QR Code, Food, Restaurant

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Educational institutions face significant challenges in maintaining effective communication between parents and mentors, which directly impacts student performance and engagement. ConnectEd is a web-based application designed to bridge this communication gap by providing a structured and efficient platform for tracking student progress, facilitating real-time interactions, and ensuring transparency between parents and mentors. Technologically, ConnectEd is built using ReactJS, HTML, CSS, and JavaScript for an interactive and responsive frontend, while the backend is powered by Node.js and MongoDB, ensuring scalable and secure data management. The system also incorporates Pinata for decentralized storage where necessary, reinforcing data integrity. Multi-language support is integrated to eliminate communication barriers, making the platform accessible to a diverse user base.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Educational Communication, Parent-Mentor Interaction, Student Performance Monitoring, Web-Based Educational Platform, Academic Progress Tracking, Attendance Management, Timetable Scheduling, Secure User Authentication, Data Privacy, ReactJS Frontend, Node.js Backend, MongoDB Database, Multilingual Support, Admin User Dashboard.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The distribution of humanitarian aid is susceptible to fraud, inefficiencies, and a lack of transparency. This paper presents an AI-integrated blockchain framework to detect fraud and ensure secure aid distribution. The system incorporates machine learning (ML) to detect anomalies in aid transactions and Hyperledger Fabric to maintain immutable, decentralized transaction records. Zero-Knowledge Proofs (ZKPs) facilitate privacy-preserving beneficiary verification, ensuring safe and transparent transactions. The proposed system increases trust, accountability, and efficiency in aid distribution. Experimental findings illustrate enhanced fraud detection accuracy and real-time transaction verification.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blockchain Technology, AI-Based Fraud Detection, Hyperledger Fabric, Zero Knowledge Proofs, Humanitarian Aid, Cryptographic Security

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid advancements in artificial intelligence (AI) and embedded systems have paved the way for innovative healthcare solutions, especially for chronic disease management. This paper presents the design and implementation of Smart Nurse, an AI-powered robotic healthcare assistant for cancer patients. The system integrates real-time patient monitoring, emergency alert mechanisms, and medication dispensing functionalities using Raspberry Pi. Key features include a fall detection system based on YOLO and sensor data fusion, an emotion detection model combining facial expressions (ResNet-50), voice tone (MFCCs), and sentiment analysis (BERT), and an AI-driven chatbot utilizing GPT-based natural language understanding with OpenAI Whisper for speech recognition. The robot navigates autonomously using SLAM-based AI navigation with ESP32CAM and ultrasonic sensors. It communicates via a hybrid MQTT and API-based system to synchronize with a Django backend and a locally hosted database. Emergency situations trigger a loud SOS alarm and Twilio SMS alerts to caretakers. The hardware framework includes a battery-powered structure with servo-controlled gravity-based medication dispensing. The system is designed to function without cloud dependency, ensuring affordability and privacy. This paper details the system architecture, hardware-software co-design, and the implementation methodologies for AI models, real-time communication, and embedded robotics. The experimental results indicate that the system enhances patient safety, ensures timely medication, and provides emotional support, making it a promising solution for remote healthcare assistance.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

healthcare robotics, artificial intelligence, cancer care, patient monitoring, emotion detection, fall detection, medication management, embedded systems, YOLO, BERT, ResNet50, SLAM.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project introduces a blockchain-based e-vault system that ensures secure, transparent, and tamper-proof storage and verification of digital certificates. By utilizing the immutable and decentralized nature of blockchain technology, the system effectively eliminates risks associated with certificate fraud and unauthorized alterations. It features two types of users: Admin/Authorized Users, who can upload certificates with recipient details, and Normal Users, who are permitted to verify them. Upon successful upload, certificates are stored on the blockchain and recipients are notified via email with the certificate ID and related information. Users can access all their certificates through email-based login, with an optional Merge Account feature to combine multiple accounts for unified access. Additional functionalities include a Portfolio Page for resume generation, a dynamic pricing model to support institutional sustainability, a user guidance feature for easier navigation, and a live chatbot for real-time assistance. This system not only secures digital credentials but also empowers users and organizations with tools for professional development and efficient certificate management.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blockchain, Digital Certificates, E-Vault, Tamper-Proof Storage, Authentication, Certificate Verification, Immutable Ledger, Credential Management, Email-Based Access, Resume Generation, Portfolio Page, Real-Time Support.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Face Recognition Attendance Management System is an innovative solution developed to eliminate the need for manual roll calls. This system provides a quick and accurate replacement of traditional attendance methods by applying computer vision techniques such as Convolutional Neural Networks (CNN) and Haar Cascade. The system captures images, detects faces, and matches them with pre-stored images for automated attendance recording. Future enhancements include cloud integration and mobile app support for real-time monitoring

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Face Recognition, Attendance Management, HOG, CNN, OpenCV, Machine Learning, Deep Learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The health of grape plants is crucial for ensuring high-quality vineyard yields and maintaining the economic sustainability of viticulture. Effective disease detection is a pivotal aspect of modern agricultural management, as diseases such as black measles, leaf blight, and black rot can significantly impact crop production. This paper discusses research on some advanced methods in the field of grape plant disease detection by incorporating machine learning algorithms and image processing techniques. In this paper, the use of spectral imaging, neural networks, and field-based monitoring systems for early, precise, and cost-effective diagnosis of diseases is discussed and the user interface is in the regional language Kannada for better usability of farmer. By addressing the limitations of traditional manual inspection methods, this research aims to highlight innovative approaches that enhance efficiency and reduce the environmental impact of disease management practices. The findings underscore the potential of precision agriculture in revolutionizing disease control strategies in viticulture.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Grape Plant Disease Classification, Image Processing, Deep Learning, Feature Extraction, CNN

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Precision agriculture is an emerging area that applies modern information technology and machine learning to create news ways of identifying and diagnosing plant diseases to promote sustainable farming practices. This paper aims to review the application of machine learning and deep learning techniques in plant disease detection and classification in precision agriculture. It also proposes a different approach in classifying relevant literature which is based on the employed methodology - classification or object detection, and reviews the literature on datasets available for plant disease detection and classification. This work comprises a comprehensive analysis within the scope of object detection and classification of plant diseases utilising the PlantDoc dataset. The conclusion reached in this research is that YOLOV5 is the best object detection algorithm and that ResNet50 and MobileNetv2 models are the best image classifier models relative to the time cost of training the models and the accuracy of produced images.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Classification,deeplearning,disease detection,machine learning,object detection,precision agriculture

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Creative Commons Attribution 4.0 and The Open Definition