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.

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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.

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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.

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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.

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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.

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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.

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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.

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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

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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.

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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.

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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

 Abstract

Cardiovascular diseases (CVDs), especially myocardial infarctions (heart attacks), represent a leading cause of death globally. Traditional diagnostic approaches such as ECGs, biomarkers, and clinical assessments often fall short due to delayed response and limited sensitivity. The integration of Artificial Intelligence (AI) and Machine Learning (ML) introduces novel possibilities for early prediction, personalized diagnostics, and continuous patient monitoring. This paper presents a comprehensive review of AI's role in cardiovascular risk assessment by highlighting the limitations of conventional techniques, the structure of AI architectures, their clinical advantages, key case studies, and future potential involving hybrid and federated learning systems. Furthermore, it emphasizes data privacy, ethical concerns, and regulatory preparedness to ensure real-world deployment and trust in AI-driven systems

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence (AI), Machine Learning (ML), Myocardial Infarction, Cardiovascular Disease (CVD), ECG, Risk Stratification, Predictive Modeling, Explainable AI (XAI)

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Non playable character (NPC) is the core of an immersive and realistic game. As gaming turns into a huge industry, traditional script following NPCs need to be replaced with more dynamic characterization. This can be done through integrating an AI to the NPCs to make the game more immersive and enhance the realism of the game. Taking advantage of AI, an NPC can be given a framework which uses reinforcement learning and conversational AI within a simulation environment. This allows the NPC to engage in natural conversations with the player, learn from their past interactions and dynamically adapt their behavior with respect to the player. Using reinforcement learning the NPC are able to enhance their decision making based on their previous interactions with the player. Conversational AI makes the dialogue have more depth and context aware of the in-game environment. Testing the NPC in stimulated environment, the results demonstrate a more realistic and self-aware NPC.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Reinforcement learning, Conversational AI, Simulation, Dynamic-Decision making, Animation, Open-world exploration, virtualization

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In this Generation of emails, messages spam continues to pose several challenges to email ecosystem. Spam detection in emails have been a concern because the user security depends on the classification of emails as spam or ham. The existing methods for spam detection lack in precision and is a time consuming process. This paper provides a spam detecting model that accounts for the dynamic nature of spam mails and learning based clustering techniques for classifying spam and ham messages. The model contains various Machine Learning (ML) algorithms used for detection and classification of spam emails. The model is integrated with Artificial Intelligence (AI) for automatic detection of spam or ham messages, which is most advanced form of detecting spam compared to other methods. The model present a novel approach to detect spam using Random forest (RM) classifier which is further enhanced by the designed methodology. The model claims the effective methodology with robust and interpretable features for detecting the spam messages.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Deep Learning . Email Spam Detection . Machine Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The growth of deepfake technology has raised great concerns about privacy, misinformation and cybersecurity. Advanced AI can make it difficult to say real and false media, as deeper and more visual content can change. In this paper we will explore current methods to recognize and prevent deepfakes and check how well they work and have limitations. It also explains how deeplearning to create faces changes, focusing on Stylegan and how it is used to edit, restore and change faces in different styles.We also look into the famous deepfake tool deepfacelab and sketch it to work with high resolution facial films. Apart from Visual Deepakes, we look at FluentLip, the latest audio conditioned LipenSthesis model that improves synthesis language synchronization and smoothness. Finally, let's look at recent advances in speech production. We present an approach to using emotions to create more natural and controllable facial expressions. Regarding existing procedures, limitations, and trends, this review suggests more efficient identification measures, the ethical design of AI, and better public education to combat the growing threat of deepfakes.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Deep learning, deepfakes, face generation, deepfake detection, face-swapping, StyleGAN, AI ethics, audio-driven synthesis, talking face generation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Plastic Waste Management and Reward System is designed to promote responsible plastic disposal by leveraging technology to incentivise users for recycling efforts. The system integrates Flask for backend processing, AngularJS, HTML, CSS, and JavaScript for a dynamic frontend, and ImageDB for cloud-based image storage. MySQL is used for secure transaction and user data management, ensuring reliability and efficiency. The platform employs AI-powered image recognition models to classify plastic waste accu-rately, allowing users to earn rewards based on proper disposal. Rigorous testing methodologies ensure performance, security, and scalability. Future enhancements, including blockchain-based rewards, AI-driven classification improvements, and IoT-enabled smart bins, will further optimize waste tracking and management. This project presents an innovative approach to tackling plastic waste pollution by merging technology with sustainability.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Plastic Waste Management, Reward System, Recycling Incentives, Flask Backend, AngularJS Frontend, ImageDB Cloud Storage, MySQL Database, AI-powered Image Recognition, Waste Classification, Secure Transactions, Performance Testing, Security Testing, Scalability, Blockchain Rewards, IoT Smart Bins, Sustainability, Waste Tracking, Environmental Technology

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Agriculture forms the core of the Indian economy, but farmers still grapple with inefficient crop planning, unpredictable weather, volatile market prices, and limited access to real-time farm insights. 'Raitha Bandhava - Farmer's Companion' is an end-to-end Farming Management System that leverages the latest technologies like artificial intelligence (AI), machine learning (ML), and application programming interfaces (APIs) to empower farmers with data-driven insights. The platform enhances productivity through AI-based crop guidance, weather forecasting, and smart market trends analysis. With API integration, it connects the farmer with the government and private farm database to give insights regarding policies, subsidies, and trends within the market. Further, the platform offers AI-based disease detection, virtual input/output market for produce, and supply chain optimization features. This article discusses the architecture, implementation, and impacts of the system and how it addresses existing technological gaps in agricultural solutions. Pilot implementations initially have reported 20% increased yield and 15% reduction in input costs and affirmed the efficiency of the system. It also discusses digital literacy and connectivity challenges and proposes remedies such as offline capabilities and language capability.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Smart Farming, Crop Planning, Market Price Analysis, Weather Forecasting, Supply Chain Management, Digital Agriculture, Farmer Marketplace, AI-Based Disease Detection, Agricultural Technolo

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Lung cancer remains a major global health concern, with early diagnosis playing a crucial role in enhancing patient survival rates. According to the Global Cancer Observatory (GLOBOCAN 2024), lung cancer remains the most common cause of cancer-related deaths worldwide, accounting for over 2.4 million new cases and 1.8 million deaths annually. The application of Artificial Intelligence (AI) in medical imaging has opened new avenues for improving lung cancer detection. This review examines the role of AI, particularly deep learning algorithms, in analysing medical images such as CT scans, X-rays, and MRIs for lung cancer diagnosis and prognosis. Various AI-based techniques, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and meta-heuristic approaches like the Crow Search Algorithm (CSA), have shown substantial progress in identifying and categorizing lung nodules as benign or malignant. Pre-processing steps such as image segmentation, edge enhancement, and resampling contribute to improving image clarity, thereby enhancing the accuracy of AI-driven diagnostic models. Despite these advancements, challenges such as data imbalance, model interpretability, and generalization persist. This paper also explores the potential of Computer-Aided Diagnosis (CAD) systems in complementing AI methodologies for more precise and reliable clinical applications. Additionally, the study reviews the limitations of conventional histopathological diagnostic techniques and the potential of molecular biomarkers in refining lung cancer classification. The growing use of AI in healthcare is paving the way for personalized treatment strategies, yet the necessity for diverse and extensive datasets remains critical for improving model reliability. Through this review, we aim to provide a structured overview of AI-driven medical imaging advancements in lung cancer detection, offering insights to guide future research and development.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Deep Learning, Convolutional Neural Networks (CNNs), Transfer Learning, Lung Cancer Detection, Lung Nodule Classification, Computer-Aided Diagnosis (CAD), Machine Learning (ML), Artificial Intelligence (AI), Feature Extraction, Deep Neural Networks (DNNs), ResNet, GoogleNet, MobileNetV2, VGG16, InceptionV3, Support Vector Machines (SVM), Random Forest (RF), Optimization Algorithms, Segmentation Techniques, Generative Adversarial Networks (GANs), Conditional Tabular Generative Adversarial Networ

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Artificial intelligence (AI) is playing a critical role in wildlife conservation by enabling species monitoring, poaching prevention, and habitat restoration efforts [1] . Due to habitat loss, poaching, and climate change, wildlife conservation is a major concern. Habitat assessment and resource conservation involve AI-powered image analysis, which aids in assessing forest health, detecting deforestation, and identifying areas in need of restoration [2] . The AI-Powered Wildlife Conservation System improves animal conservation and monitoring by utilizing artificial intelligence. With the help of sophisticated image recognition algorithms, users can submit photos or scan animals in real time. Additionally, it offers vital conservation status data, showing, based on international databases, if an animal is vulnerable or endangered. The technology also uses the Google Maps API to find local physicians and animal rescue facilities, guaranteeing prompt assistance for wildlife that is hurt or in danger. This project intends to assist wildlife researchers, conservationists, and the general public in preserving biodiversity by fusing AI with geolocation services.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence (AI), Wildlife Conservation, Animal Identification, Endangered Species Protection, Rescue and Rehabilitation, Conservation Technology, Smart Conservation System, Google Map API, Image Recognition, Environmental Sustainability

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Steganography based on web technologies has improved in recent years from simple HTML and CSS manipulation to advanced techniques using artificial intelligence and advanced web APIs with cross-platform implementations. This review article analyses the progress, trends now, issues, and ways forward in web-based steganography. Classical steganography conceals data in images or sound, while web-based steganography extends this to web technologies. We review recent papers on methods such as HTML, CSS, JavaScript, HTTP headers, and web storage. Our research shows an increasing trend in web-based steganography applications of deep learning, especially those that utilize browser-native functionalities. Important research shortcomings are cross-browser compatibility, the absence of standardized metrics for evaluation, and few studies on steganalysis specific to the web. This review will be an asset to information security, data hiding, and web technology researchers and practitioners.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Web-Based Steganography, HTML Data Hiding, Web Page Steganography, Browser-Based Information Hiding, Network Security, Web Technology Encryption, Data Protection Strategies.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Research often lacks an incentive mechanism, and traditional Academic Publishing lacks transparent and equitable mechanism to reward the researchers and creates hindrances instead; this paper introduces the platform "Raktbeej", a blockchain-based platform that is inspired by the retroactive public goods funding which solves this problem by allowing authors to define royalty distribution percentages for cited works. Using smart contracts, Raktbeej automates the distribution of royalties to the cited authors whenever a donation is made to an author. We evaluate the potential of the platform to transform academic publishing for the better.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blockchain, Academic Publishing, Decentralised Science, Smart Contracts, Citations, Ethereum, Retroactive Public Goods Funding.

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Creative Commons Attribution 4.0 and The Open Definition