Volume 14 | Issue 6

 Abstract

Melancholy is a common mental disorder that has a significant impact on people's wellbeing and frequently results in serious emotional, cognitive, and physical deficits. More precise and scalable solutions are required since traditionaldiagnostic techniques, which rely on self-reported questionnaires and clinician interviews, are subject to subjectivity and prejudice. This work suggests a sophisticated machine learning-based method for using facial expression analysis to identify and categorize depression. By automatically evaluating and classifying depression severity, the process seeks to enhance the early detection of depressive disorders by utilizing cutting-edge deep learning algorithms. The study combines a number ofmachine learning models, such as EfficientNet, Vision Transformers, Visual Geometry Group (VGG16), and Residual Network (ResNet50). The goal of the suggested method is to automatically evaluate and categorize depression severity with more precision and dependability.

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Depression detection, facial expression analysis, deep learning, emotion recognition.

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This paper proposes a robust framework for detecting and classifying road surface defects--specifically cracks and potholes - using machine learning algorithms trained on annotated image datasets. High-resolution images of various road conditions are processed and fed into a CNN model, which learns visual features to differentiate between defect types and severities. Traditional inspection methods are labor-intensive, time-consuming, and subject to human error. With the emergence of computer vision and deep learning, particularly convolutional neural networks (CNNs), automated road surface analysis has become a practical solution. The rapid growth of urban infrastructure has made the maintenance of road surfaces a critical issue for city planners and civil engineers. Road cracks and potholes significantly contribute to traffic accidents and long-term infrastructure degradation. The system integrates pre-processing steps like image enhancement, edge detection, and data augmentation to improve detection accuracy under varied lighting and environmental conditions. The trained model achieves high precision in identifying surface anomalies, outperforming conventional techniques. Evaluation metrics such as accuracy, recall, and F1-score are used to validate performance. The proposed method offers scalable deployment options in real-time road surveillance systems through drones or vehicle-mounted cameras. Furthermore, the model supports predictive maintenance planning by pinpointing early-stage defects. This initiative reduces human effort, increases monitoring efficiency, and ultimately enhances road safety. The system's adaptability across diverse geographical terrains further highlights its practicality. With the integration of GPS and cloud storage, defect locations can be mapped and archived for future assessments. This AI-driven approach has the potential to revolutionize road maintenance and traffic safety management globally.

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 Keywords

Computer Vision, Pavement Crack Detection, Pothole Identification and Segmentation, Deep Learning, Convolutional Neural Network (CNN), Image Classification, Surface Defect Detection, Edge Detection, Automated Inspection, Road Maintenance, , Predictive Maintenance, Real-Time Detection.

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This paper presents a comprehensive review of deep learning-based approaches for real-time recognition of continuous sign language. The primary objective is to analyze and compare various techniques used in gesture recognition systems that translate sign language into text and speech, thereby enabling effective communication for deaf and hard-of-hearing individuals. The study focuses on computer vision-based models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Media Pipe-based hand tracking systems.

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Sign Language Recognition, Deep Learning, CNN, LSTM, Media Pipe, Computer Vision, Real-Time Systems, Assistive Technology

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Pneumonia is a serious respiratory disease that affects millions of people worldwide and can be life-threatening if not diagnosed and treated early. Traditional diagnosis of pneumonia is primarily based on clinical examination and interpretation of chest X-ray images by radiologists. However, this manual process can be time-consuming, subjective, and prone to human error, especially in regions with limited access to expert healthcare professionals. To address these challenges, the application of Deep Learning, particularly Convolutional Neural Networks (CNNs), has emerged as an efficient and reliable approach for automated pneumonia detection. This project focuses on developing a computer-aided diagnostic system that utilizes CNN models to classify chest X-ray images as pneumonia-infected or normal. CNNs are a class of deep neural networks specifically designed for image processing tasks, capable of automatically extracting relevant features such as edges, textures, and patterns from medical images. In this study, a large dataset of labeled chest X-ray images is used to train the model, enabling it to learn distinguishing characteristics of pneumonia. The proposed system involves several stages, including image preprocessing, data augmentation, model training, validation, and testing. Preprocessing techniques such as resizing, normalization, and noise reduction are applied to improve image quality and enhance model performance. Data augmentation methods like rotation, flipping, and zooming are used to increase dataset diversity and prevent overfitting. The CNN architecture typically consists of multiple convolutional layers, pooling layers, and fully connected layers that work together to extract features and perform classification. The trained model is evaluated using performance metrics such as accuracy, precision, recall, and F1-score to ensure reliability and effectiveness. Experimental results demonstrate that CNN-based models can achieve high accuracy in detecting pneumonia from chest X-ray images, often outperforming traditional machine learning methods. This automated system can assist radiologists in making faster and more accurate diagnoses, thereby improving patient outcomes..

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 Keywords

Pneumonia Detection, Convolutional Neural Network (CNN), Chest X-ray Imaging, Deep Learning, Medical Image Processing, Image Classification etc.

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This paper presents a multimodal AI-based arthritis detection system integrating five independent diagnostic modules: a Vision Module (knee X-ray classification, spine MRI degeneration analysis, and synovial fluid microscopy), a Lab Report Module, a Wearable Sensor Module, a Genomics Module, and a Voice Emotion Detection Module. The Vision Module employs DenseNet121 trained on 5,778 knee X-ray images, achieving 82.40% classification accuracy across five Kellgren-Lawrence (KL) grades. Spine MRI degeneration is assessed via signal intensity analysis, while synovial fluid inflammation is detected using ResNet50 with K-Means clustering. The Lab Report Module applies Random Forest classification on clinical biomarkers. The Wearable Module employs a neural network on MotionSense sensor data for risk stratification. The Genomics Module classifies arthritis-associated SNP markers, and the Voice Module detects pain and fatigue using MFCC-based feature extraction. All modules produce structured outputs integrated into an automated PDF medical report generator, providing a scalable, interpretable, and accessible clinical decision support system.

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Arthritis detection, deep learning, DenseNet121, multimodal AI, Kellgren-Lawrence grading, ResNet50, MFCC, genomics, wearable sensors, clinical decision support

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 Abstract

Predicting the stock market has always been tricky, given how random, complicated, and unstable prices can be. Classification-based methods--those that forecast whether prices will go up or down--have become popular, mostly because they fit real trading strategies well. This paper takes a close look at classification techniques used in stock market prediction, from old-school machine learning models to ensemble methods and deep learning. We dig into how these approaches work, what they do well, where they fall short, and how suitable they are for analyzing financial time-series data. On top of that, we highlight major research gaps when it comes to temporal stability, feature interaction, and robustness, and point out promising directions for future studies.

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Stock Market Prediction, Classification Models, Machine Learning, Deep Learning, Financial Time-Series.

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The proposed system is based on e-certificate system in India's educational framework that leverages blockchain technology to address the widespread issue of certificate forgery. The inherent qualities of blockchain, including its immutability and transparency, create a solid foundation for enhancing the security and reliability of educational certifications. The operational framework is built around creating and storing an electronic file containing essential academic information in a dedicated database. Concurrently, the system generates a unique hash value for this electronic file, which serves as an exclusive identifier. This hash is securely embedded within a blockchain block, benefiting from the technology's resistance to tampering. To enable verification processes, both an inquiry string code and a QR code are linked to each certificate, encapsulating necessary information for authenticity verification. Users can initiate validation by scanning the QR code with a mobile device or entering the inquiry string on a specific website. The hash stored in the blockchain is then checked to confirm that the certificate has not been altered. This proposed solution significantly enhances the credibility of traditional paper certificates by implementing a dependable, transparent, and tamper-resistant verification process.

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Blockchain, Digital Certificate, Hashing, E-Certificate, Certificate Verification, Transparency, QR Code, Secure Storage, Credential Authentication

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 Abstract

Ransomware has emerged as one of the most dam- aging cyber threats, targeting personal, enterprise, and critical infrastructure systems by encrypting data and demanding ran- som payments. Signature-based antivirus solutions fail to detect modern ransomware variants due to polymorphism, encryption, and obfuscation techniques. To address these limitations, this paper proposes a hybrid stacked machine learning framework for ransomware detection and classification. The proposed system integrates static and dynamic analysis to extract discriminative features such as Portable Executable headers, entropy values, API calls, file system operations, registry modifications, and network behavior. Feature selection is performed using an Extra Tree Classifier to reduce dimensionality and improve learning efficiency. A stacked ensemble architecture combining Extra Tree Classifier as the base learner and Logistic Regression as the meta learner is employed to enhance classification performance while maintaining interpretability and low computational overhead. The system is deployed using a Flask-based web interface for real-time file analysis. Experimental results demonstrate that the proposed approach achieves 98.2% accuracy with 1.2% false positive rate, outperforming traditional machine learning models and providing competitive performance compared to deep learning approaches with significantly lower computational cost, making it suitable for practical ransomware detection.

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 Keywords

Ransomware Detection, Malware Classification, Hybrid Machine Learning, Stacked Ensemble, Extra Tree Classi- fier, Logistic Regression, Cybersecurity, Static Analysis, Dynamic Analysis

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 Abstract

Efficient garbage separation is greatly essential in modern-day waste management systems and has direct implications for the efficiency of recycling, environmental sustainability, and public health. The classical process of manual garbage separation is cumbersome and prone to errors, as well as exposed to harmful materials. With the ever-increasing rate of urbanization and the consequent rise in consumer waste, there is an immense need for intelligent and automated solutions to effectively classify the garbage in a precise and minimally supervised manner. This paper describes Automated Garbage Classification based on Machine Learning (ML) and Deep Learning (DL) techniques to classify images of garbage into pre-defined classes of plastic, paper, metal, battery, and general trash. The project employs multiple techniques of classification like K-Nearest Neighbours (KNN), Support Vector Machine (SVM), and Random Forest Classification, along with a Convolutional Neural Network (CNN) to analyze and compare performance of each technique. Additionally, image processing techniques of resizing, normalizing images, and data augmentation are performed. Results reveal that the CNN approach outperforms classical machine learning techniques in terms of accuracy and reliability, especially in adverse conditions of light and background noise. The system is web-developed and based on Python Flask, enabling users to classify images for real-time results and confidence scores.

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 Keywords

Waste Segregation, Garbage Classification, Convolutional Neural Network (CNN), Machine Learning, Sustainability, Deep Learning, Image Classification.

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 Abstract

CE-39 Feature-Model Synergy in Cry-Based Detection of Neonatal Asphyxia Using Hybrid Cepstral and Neural Approaches

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CE-39 Feature-Model Synergy in Cry-Based Detection of Neonatal Asphyxia Using Hybrid Cepstral and Neural Approaches

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 Abstract

The alarming escalation of crimes against women worldwide necessitates urgent technological interventions capable of providing real-time safety mechanisms. This paper presents EmpowHer (also referred to as SafeHer), a production-grade women safety mobile application built on React Native with TypeScript. We analyse the system from a Senior Research Developer perspective, formally specifying both functional and non-functional requirements, proposing a layered microservices-oriented architecture, and justifying the complete technology stack. The design encompasses an Emergency SOS subsystem with sub-2-second trigger latency, continuous GPS tracking at five-second intervals, AES-256 encrypted local storage, trusted-contact management, offline SMS fallback, automated audio/video evidence recording, and an AI-driven risk-detection module.

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Women Safety Application, React Native, Emergency SOS, GPS Tracking, AES-256 Encryption, Microservices Architecture, Real-Time Location Sharing, Mobile Security, Firebase Cloud Messaging, AI Risk Detection.

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 Abstract

Now a day, World Wide Web has become a popular medium to search information, business, trading and so on. Various organizations and companies are also employing the web in order to introduce their products or services around the world. Therefore E-commerce or electronic commerce is formed. E-commerce is any type of business or commercial transaction that involves the transfer of information across the internet. In this situation a huge amount of information is generated and stored in the web services. This information overhead leads to difficulty in finding relevant and useful knowledge, therefore web mining is used as a tool to discover and extract the knowledge from the web. Besides, the security issues are the most precious problems in every electronic commercial process. This massive increase in the uptake of e-commerce has led to a new generation of associated security threats. In this paper we use techniques for security purposes, in detecting, preventing and predicting cyber-attacks on virtual space

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Ecommerce, Cybercrime, threats, security ,attacks.

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 Abstract

This study presents a scalable and efficient hybrid recommendation framework designed to address the challenges of modern e-commerce systems, including data sparsity, cold-start problems, and real-time processing requirements. The proposed system integrates multiple recommendation strategies, namely content-based filtering, collaborative filtering, and deep learning-based approaches, to leverage their complementary strengths. User interaction data, such as clicks, views, and ratings, is collected and processed through a structured pipeline involving data storage, preprocessing, and feature engineering. The framework employs content-based techniques for feature extraction, collaborative filtering for user-item similarity modeling, and advanced methods such as matrix factorization and neural networks, including recurrent and transformer-based architectures, to capture complex interaction patterns. A hybrid filtering mechanism combines these approaches to generate highly personalized and context-aware recommendations. The system is further evaluated using standard performance metrics such as precision, recall, and RMSE, along with A/B testing to validate its effectiveness in real-world scenarios. Additionally, the proposed architecture supports scalable deployment through API-based services and enables real-time recommendation generation. A continuous feedback loop is incorporated to refine model performance based on user interactions. Experimental observations indicate that the hybrid approach improves recommendation accuracy, diversity, and robustness compared to traditional baseline models. Overall, the framework enhances user engagement and satisfaction while providing a practical solution for large-scale recommendation systems in competitive e-commerce environments.

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Hybrid Recommendation, Collaborative Filtering, Content-Based Filtering, Deep Learning, Neural Collaborative Filtering, Matrix Factorization, E-commerce

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 Abstract

Exoplanet research has grown into one of the most compelling frontiers in modern astrophysics reshaping what we know about worlds beyond our own solar system. These are planets orbiting stars other than the Sun and detecting them is rarely straightforward, astronomers typically rely on indirect methods, with transit photometry being among the most productive [1]. Space missions like the Kepler Space Telescope and TESS have generated enormous quantities of observational data, collectively responsible for confirming thousands of exoplanet candidates [3][4].Even so, a large share of that data has historically required manual review, which is both slow and inconsistent in practice. Classical detection tools like the Box Least Squares algorithm do a reasonable job of flagging periodic dips in light curves, but they struggle with noise and frequently flag non-exoplanets as real candidates. This paper introduces a hybrid detection framework that draws on traditional algorithms alongside machine learning and deep learning methods. Combining Random Forest classifiers, convolutional neural networks and oversampling via SMOTE, the system targets three key improvements: fewer false positives, stronger detection accuracy and the ability to scale across much larger observational archives.

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exoplanet detection, machine learning, deep learning, light curve analysis, space exploration, transit photometry.

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Precision agriculture requires real-time monitoring and data-driven decision-making to optimize crop yield and resource utilization. This paper proposes a smart soil analysis and crop prediction system that integrates IoT sensors, a Raspberry Pi microcontroller, and machine learning algorithms. The system continuously monitors soil moisture, temperature, and pH, stores data in a cloud database, and predicts the most suitable crops using trained ML models. A relay-controlled water pump enables automated irrigation, improving water-use efficiency and reducing manual intervention. Experimental results demonstrate a crop prediction accuracy of 94% compared to conventional methods. The proposed solution offers a scalable, real-time, and sustainable framework for enhancing agricultural productivity and efficient resource management.

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IoT, Smart Agriculture, Crop Prediction, Soil Monitoring, Machine Learning, Raspberry Pi, Automated Irrigation, Precision Farming.

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Programming languages are generally designed using English-based syntax, which can make learning difficult for individuals who are more comfortable with regional languages. This paper presents Bhaashankit, a Marathi-based programming interpreter that allows users to write and execute programs using familiar linguistic constructs.

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Marathi Programming, Interpreter Design, Regional Language Computing, Lexer, Parser, Abstract Syntax Tree, Programming Education

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This research presents an interactive platform for AI-driven artwork generation, transforming written text into expressive, high-quality visuals for users of all artistic skill levels. The system integrates a pretrained Stable Diffusion model with a robust MERN stack ( MongoDB,Express.js, React.js, Node.js) web application. This architecture provides an accessible digital canvas that instantly turns ideas into visuals, making it a powerful tool for rapid ideation, storytelling, and accelerating creative workflows for professional designers and artists. Key features include support for high-resolution output and a categorized style selection, such as Meme, 3D Cartoon, 3D illustration, Sketch, Ghibli, Abstract and Logo generation enabling precise artistic control. Ultimately, the system enhances productivity while preserving the uniqueness of each user's vision, offering a vital bridge between imagination and innovation.

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Style Selection, Stable Diffusion, Generative AI, MERN stack, Artistic skills, high quality visuals

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Renewable Energy, Machine Learning, Energy Prediction, Optimization, Sustainability, Smart Grid

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Customer segmentation is a cornerstone of data-driven marketing, personalization, and decision-making. With the rapid growth of digital platforms, organizations now handle large-scale, high-dimensional customer data, making traditional segmentation techniques insufficient. This analytical study examines major clustering algorithms used for customer segmentation, evaluates their suitability for large datasets, and compares their performance in terms of scalability, accuracy, interpretability, and computational complexity. The proposed framework leverages Mini-Batch K-Means, a batch-based learning algorithm that processes data incrementally in small subsets, significantly reducing memory usage and improving execution speed. Experimental results demonstrate that the proposed scalable clustering framework achieves faster convergence and better resource utilization compared to conventional techniques, proving suitable for real-world big data applications.

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Customer Segmentation, Clustering Algorithms, Mini-Batch K-Means, K-Means, DBSCAN, Scalable Clustering, Big Data, Unsupervised Learning

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Air Quality Index (AQI), Air Pollution, Regression Techniques, Machine Learning, PM2.5, PM10, Support Vector Regression, Random Forest Regression, Deep Learning, LSTM

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Creative Commons Attribution 4.0 and The Open Definition