Open Access International Journal of Science and Engineering
Researcher Connect Innovation and Impact Private Limited
A Brief Survey on Machine Learning Techniques for Blood Glucose Prediction
Prognosis of blood glucose level is one of the significant roles in the effective treatment of diabetes which is a chronic metabolic disease that affects a good number of people in the world. Machine learning methods were of great interest in the recent as it is able to detect and predict the glucose level effectively based on the large range of features such as historical records, physiological indicators, and lifestyle data. It is a survey study that provides a detailed insight into the current investigation on blood glucose level forecast using machine learning methods. And the necessity of the accuracy of the prediction of blood glucose level in the diabetes care and the restrictions of the current methods. Roughly half of the machine learning methods applied to this field, similar support vector machines, random forests, regression models, decision trees and neural networks. Deep learning models are remembered with specific emphasis due to promising applications in the ability to learn time dependence and the ability to follow complex patterns in blood glucose records. Recurrent neural networks (RNNs), convolutional neural networks (CNNs), and long short-term memory (LSTM) networks are particular examples of deep learning models. The questionnaire proceeds with the recapping of the current issues as well as unresolved research inquiries in the field of machine learning-based blood glucose level detection and prediction.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-sa/4.0
IoT Based Multi-Parameter Environmental Monitoring System for Smart Cities
In this paper, a cost-effective Internet of Things system for real-time environmental monitoring in smart cities is proposed, which overcomes the limitations that exist in the traditional centralized system. The proposed system is designed to measure the Air Quality Index, Total Dissolved Solids of water, and soil moisture using a BME680 sensor, a TDS sensor, and capacitive sensors, respectively, connected to an ESP32 microcontroller for processing and transmission of data over Wi-Fi to a web interface. This allows public access without the need for any special software and captures the variations in the smart city environment, including the AQI peaks due to traffic. The proposed open-standard system (Wi-Fi, HTTP, JSON) is scalable for smart city implementation and can be extended in the future to include machine learning algorithms for AQI forecasting. Performance analysis of the proposed system using simulated data from Katraj, Pune, shows its sensitivity to both short-term and long-term variations.
Missing Person Detection in Railway Stations Using AI-Based YOLOv8 and Clothing Attribute Recognition
Identifying missing persons in crowded public environments such as railway stations remains a difficult task for law enforcement agencies due to heavy passenger flow, frequent visual obstructions, and the limitations of manual CCTV monitoring. To address these challenges, this work presents an automated missing person detection approach based on artificial intelligence (AI) techniques, combining the YOLOv8 deep learning model with clothing attribute recognition [1]. The system detects individuals from surveillance video streams and extracts distinguishing clothing attributes, such as garment type and color, which assist in identification when facial information is partially visible or unavailable. The proposed system architecture consists of video preprocessing, YOLOv8-based person detection, clothing attribute analysis, and an alert generation mechanism. Experimental evaluation conducted on railway-like surveillance data achieves a mean Average Precision (mAP@0.5) of 94.4% while maintaining efficient processing suitable for real-time monitoring. The results demonstrate that incorporating clothing-based cues enhances identification reliability in crowded scenes and supports practical deployment in large-scale public surveillance infrastructures.
A Responsible and Risk-Aware Artifical Intelligence Framework for Multimodal Sleep Cycle Monitoring Using Wearable and Contactless Physiological Sensing
The use of artificial intelligence has made persistent and tailored monitoring of physiological functions possible, though with the problem of reliability and safety in deployment . The aspect of sleep monitoring is the most sensitive since it is closely linked with cardiovascular, metabolic, and cognitive well-being. Most current automated systems are based on a small number of sensing modalities and non-transparent learning models which limit robustness and interpretability in real-word settings. This paper introduces a multimodal artificial intelligence sleep monitoring system that combines heterogenous physiological and behavioral indicators of sleep cycles in a late-fusion learning system. The framework maintains modality- specific representations and enables adaptive temporal integration to enhance resilience in unconstrained conditions. In order to raise its reliability, the system integrates uncertainty-aware inference and intrinsic explainability, enabling predictive confidence estimation and features contributions supplied by individual modalities. Instead of creating an autonomous diagnostic system, the offered method acts under the version of the decision-support tool because it reveals interpretable outputs and highlights the predictions of low level of confidence to be reviewed by experts. Experimental findings show better performance compared to unimodal baselines especially in physiologically ambiguous sleep states, while maintaining transparency and reliability. This framework adds a deployable and interpretable solution of intelligent sleep monitoring solution to healthcare.
AI-Driven Crop Disease Prediction and Management System: Enhancing Agricultural Sustainability Through Intelligent Analysis
Agriculture, the pillar of global food security, is increasingly under threat from crop diseases, resulting in tremendous economic and productivity losses. Conventional disease detection mechanisms are generally slow, labor- intensive, and lacking the precision necessary for timely intervention. This paper discusses the revolutionary potential of AI-based crop disease prediction and management systems, with a focus on their implementation in contemporary agricultural practices. Leveraging deep learning architectures like MobileNetV2, the systems allow accurate image-based disease detection, complemented by environmental factors like temperature, humidity, and weather to improve predictive accuracy. Innovations like real-time alerts, multilingual interfaces, and adaptive treatment advice for crops like cotton, sugarcane, and soybean are discussed. We also cover existing architectures, data-driven approaches, and deployment issues to estimate scalability across various agricultural ecosystems. The paper further discusses integrating a subscription-based agronomist service and an AI-based e-marketplace to improve accessibility and sustainability. By bridging technological advancements with pragmatic agricultural applications, this study highlights the potential of AI to improve crop resilience and empower farmers with smart, data-driven disease management solutions.
Early Detection & Prediction of Environment changes from Satellite Images using Deep Learning
Results presented in the proposed paper make a case for accurate environmental monitoring and early detection of ecological changes. From the studies done, we established that reliable deep learning-based satellite image assessment methods are the future of environmental science. Due to the high accuracy for land-use and land-cover assessment that comes with these innovations, they are bound to exponentially improve the rate of detection in deforestation, urban sprawl, and the depletion of water bodies while at the same time reducing the influence of manual interpretation errors. The work fortifies good sustainable development practices through automated, data-driven environmental assessment and informs useful insights into long-term ecological management.
A Hybrid Deep Learning Technique for Compressed Video Enhancement
In recent years, the high-quality video is highly demanded. But at the same time, video quality is degraded due to factors such as compression, noise, blur and various environmental conditions, which give rise to the need for using the various video enhancement techniques These techniques aim to recover the quality of degraded videos and therefore enhancing the visual quality of the videos by addressing tasks such as denoising, super-resolution, coloration , stabilization de-flickering, and reducing compression artifacts using traditional methods as well as modern methods ,which will be discussed in the paper later. Despite using notable advancements, several issues remain unresolved, and overcoming them is vital to achieving robust, efficient, and widely applicable solutions. Traditional video enhancement methods suffer from many limitations like not balancing computational efficiency and high-quality output, which give rise to degraded performance in real-time scenarios. Over the past few years, deep learning methods have brought substantial advancements in the enhancement of compressed video, prominently surpassing the traditional techniques in terms of enhancing the visual quality by reducing the compression effects manifolds. In this paper, an extensive overview of the video enhancement techniques is presented and a Hybrid Deep Learning Enhancement Technique for Compressed Video is proposed.
LLM-Assisted Cyber Threat Analysis Using Static Features and Malware Pattern Mining: A Comprehensive Review and Future Directions
Cybersecurity is one of the most significant global challenges due to rapid growth of ransomware and polymorphic malware. Traditional detection methods, such as signature-based and static analysis techniques, are effective only for known threats and often fail against zero-day attacks and obfuscated malware. The paper focuses on behavior-based malware detection, which enables real time analysis and improved detection of unknown malware. The proposed system employs a hybrid approach that combines dynamic behavioral indicators with AI-driven prompt engineering which used LLM and ML hybrid model. Experiments were conducted using dataset including [Malware Analysis Datasets: API Call sequences, Malware Analytics Dataset Leveraging Cuckoo Sandbox, Malware detection model, CICIDS-2017], and performance evaluated using standard metrics. The results demonstrate that the proposed approach achieves more accuracy, outperforming traditional models, highlighting its effectiveness for early-stage malware detection.
Monocam Drone Swarm Guard: A Real-Time Collision Detection and Avoidance System
Another technology which is becoming more popular in our skies is the unmanned aerial vehicles (UAVs or drones) particularly when they are operated in swarms. With the increased use of drones’ concern over mid-air collisions has also become a natural issue particularly where visibility is low or when the drones are operating within a very close radius. This paper presents Monocam Drone Swarm Guard- a system that enables drones to navigate a shared airspace with one camera safely. Rather than implementing an intricate sensing system we decided to operate the system on a normal laptop with live video and discovered that it was able to follow through with drones even under vegetation and motion blur conditions. Our system can detect potential collisions before they happen by applying deep learning (YOLOv8) to detect and using a combination of a Kalman and Extended Kalman Filters to track and predict drone paths. Based on our experiment the findings indicate that the approach is effective in realtime tracking and ensuring safety of drone swarms when their operation occurs in adverse and realistic environments.
Attention-Enhanced Mobile-Net for Multi-Class Brain Tumor Classification Using MRI Images
Effective treatment planning for brain tumors relies on an early and accurate diagnosis. Manual MRI analysis involves a lot of work and is prone to inconsistent diagnoses. Using a Multi-Head Attention-enhanced MobileNet architecture, this paper presents a challenging in terms of technology automated classification system. The suggested model performs better in four classes: glioma, meningioma, pituitary, and non-tumor. It does this by utilizing depth-wise separable convolutions for computational efficiency and attention mechanisms for global feature dependency. A peak test accuracy of 96.3% can be seen in the experimental results, providing significant quantitative improvements over ordinary CNN and VGG19-based approaches.
AgriTalk: A Trustworthy and Risk-Aware Bilingual AI Ecosystem for Multi-Crop Farm Management
Systemic inefficiencies exist throughout the Indian agricultural industry, especially within the multi-crop regions of Maharashtra, due largely to their reliance on analog (manual, notebook-based), record keeping systems. These analog systems result in poor data accuracy, slow response time for decision- making, and no real-time visibility into farm profitability. This research study describes AgriTalk as a bilingual (English and Marathi) voice-assisted farm management ecosystem that uses large language models and retrievable augmented generation technology to professionalize smallholder farming operations. Furthermore, AgriTalk makes it easier for smallholder farmers to transition to digital technology with the provision of automatic labor tracking and granularity of plot management, along with providing complete traceability of value- added products such as raisins. Additionally, AgriTalk implements India’s 7 Sutras of Trust and the Digital Personal Data Protection Act (2023) to illustrate how AI-based, risk-aware architectures can offset the harmful effects of linguistic bias and model hallucination. Overall, preliminary research indicates that there are no longer inaccuracies in automated wage calculations as relate to piecework records; instead, there is now complete accuracy in computing automated wages and an elimination of administrative costs from maintaining manual records in comparison to manual record-keeping systems.
Sharebite: Smart Food Donation and Waste Management System
Two of the biggest issues faced by the world today, in terms of scale, are hunger and food wastage, particularly in developing nations such as India. Every day, millions of pounds of edible food go to waste from people’s homes, restaurants, hotels, and social gatherings, while at the same time, people struggle to get a decent amount of food for themselves and their families. This is due to a lack of coordination between food donors, volunteers, and NGOs who know where to find hungry people, a lack of awareness among people on how to donate food, and a lack of a formal structure for distributing excess food from one person/place to another. This project aims to create ShareBite, a smart food donation and wastage management system.It connects individuals who want to donate food with those who are willing to accept food donations; provides information to facilitate donation processes (including how and when to donate); and integrates modern web technology into the donation system to improve transparency, accountability, and efficiency in redistributing surplus food. Additionally, it provides a way for donors and recipients of food donations to communicate through an SMS-based system without requiring a smartphone. The system promotes environmental sustainability in the disposal of expired food through composting. The proposed system makes an impact on social welfare, sustainability, and the effective utilization of surplus food, with future scope for intelligent prediction models and large-scale deployment.
Performance Analysis of Object DetectionAlgorithms Using Standard Evaluation Metrics
Object Detection is an integral and fundamental part of Computer Vision that helps in identifying and locating objects within images or video streams. It enables systems to focus on and learn from visual data effectively. Unlike image classification, which assigns a single label to an entire image, object detection provides both the class of objects and their spatial positions using bounding boxes and confidence scores. It combines the capabilities of image classification and object localization, allowing the detection of multiple objects simultaneously. It determines the class, type, and exact location of objects within an image. These capabilities make object detection highly effective and essential for numerous day-to-day and real-world applications such as surveillance, autonomous vehicles, traffic monitoring, healthcare imaging, and automated inspection systems. The output of an object detection model typically includes labeled bounding boxes, which indicate how precisely the objects are detected. These boxes accurately enclose objects of interest, including humans, animals, vehicles, and other entities. Designing, developing, and deploying models that ensure accuracy and computational efficiency in object detection remains a key research focus, as performance directly impacts reliability in practical applications. This work aims to study, evaluate, and prepare a dataset to analyze various object detection techniques. The evaluation is based on their ability to accurately classify and identify objects using standard performance metrics such as precision, F1-score, mAP (mean Average Precision), and inference time. The goal is to identify the most suitable approach for day-to-day, real-time, and real-world applications.
Principal Component Analysis-Based Embedding Analysis for Inland Water Detection
This work presents a Principal Component Analysis of 64-dimensional AlphaEarth geospatial embeddings for inland water detection, emphasizing interpretability and efficiency in AIdriven environmental monitoring. Normalized embeddings are projected into a two-dimensional space to examine the separability of water and non-water pixels. The projection reveals that water pixels form a compact, well-separated cluster distinct from non-water samples, demonstrating that the pretrained embedding space inherently encodes robust water-discriminative structure along its leading components. The first two principal components account for 60–70% of the total variance while preserving clear class separation, facilitating effective dimensionality reduction. These results highlight the advantages of linear, interpretable techniques like Principal Component Analysis over nonlinear alternatives, promoting transparency and reducing risks associated with black-box models in critical applications. By enabling lightweight linear classifiers, the approach supports resourceefficient, scalable, and trustworthy water detection systems suitable for deployment in regulated, risk-aware AI frameworks for global water security and environmental management.
Automated Software Vulnerability Detection using Large Language Models: A Comparative Study from Cloud to Edge
Recent developments in software systems have escalated the quantity of security vulnerabilities. Traditional Static Application Security Testing (SAST) tools rely only on method matching which can lead to a high number of false positives, as they are limited and cannot assess complex and context dependent vulnerabilities. With this limitation there was a need to investigate more advanced techniques for performing semantic code analysis. This study provides a detailed comparison of Large Language Models (LLMs) for automatically detecting vulnerabilities for both cloud and edge deployment environments. A multistage experimental pipeline was developed to evaluate the accuracy of a privacy preserving edge deployment using the Meta Llama 3 (8B) model running on consumer grade hardware (NVIDIA RTX 3060) against using Google Gemini 2.5 Flash for cloud-based inference. The methodology was developed using a hybrid dataset combining 2,000 annotated samples from both Microsoft’s CodeXGLUE benchmark and publicly available web vulnerability sources for standard vulnerability categories including SQL Injection, XSS, Buffer Overflows and Memory Leaks. Three prompting strategies (Zero-Shot, Role Based and Few-Shot prompting) were evaluated in this study. The results of the experiments show that the Few-Shot model trained locally achieved a recall of 96.0%, precision of 76.5% and an F1-score of 85.2%. In addition, this optimized local model outperformed the cloud baseline in overall accuracy (83.56% vs. 81.0%) while still preserving data privacy on-premise.
Interpretable Machine Learning Approach for Real-Time Diabetes Risk Prediction
Diabetes mellitus is one of the most prevalent chronic metabolic diseases and represents a major global public-health challenge in the 21st century. It is characterized by persistently elevated blood glucose levels caused by insufficient insulin production, insulin resistance, or both mechanisms. Type 2 Diabetes Mellitus (T2DM) accounts for most cases and is strongly associated with lifestyle factors such as obesity, sedentary behaviour, unhealthy dietary habits, and genetic predisposition [1]. Long-term uncontrolled diabetes can lead to severe complications including cardiovascular diseases, neuropathy, retinopathy, kidney failure, and reduced life expectancy [2]. Traditional diagnostic methods such as fasting plasma glucose tests, oral glucose tolerance tests, and glycated haemoglobin (HbA1c) assessments are clinically reliable but often require laboratory infrastructure and repeated testing [3]. With the rapid growth of healthcare datasets and electronic health records, machine learning techniques have emerged as effective tools for early disease prediction and healthcare analytics [7], [8]. This paper proposes a machine learning-based diabetes prediction system using a Decision Tree classifier. The model analyses several clinical parameters including glucose level, body mass index (BMI), lipid profile indicators, kidney function markers, and age-related attributes to predict diabetes risk. Data preprocessing techniques such as normalization, missing value handling, and class imbalance correction are applied to improve model performance. The trained model is integrated into a Flask-based web application capable of classifying individuals into non-diabetic, pre- diabetic, and diabetic categories. The system demonstrates the potential of interpretable machine learning models for early risk detection and decision support in modern healthcare systems.
Design Optimization Algorithm: A Novel Method for Design Optimization of PMSM
The performance evaluation of the Permanent Magnet Synchronous Motors (PMSMs) is done with varying operational conditions such as power, voltage & speed, different rotor and stator diameters, magnetic materials. This analysis is accomplished by the MATLAB-based algorithms. This investigation evaluates and graphically represents the crucial performance metrics: magnetic flux, cogging torque and torque. Through variable critical considerations and relating magnetic resources like Ferrite, SmCo, Alnico and NdFeB understandings are graphically presented as torque characteristics, magnetic flux behavior and effects of cogging. The outcomes are useful for enhancing PMSM design in industrial usance requiring smooth operation, high efficiency and high performance.
Voice Assistance using ESP32 & Conversational AI
Recent days Conversational AI is becoming more promising technology and it will speedily interact with Human machine. An advanced conversational AI model is developed by OpenAI using the ESP32 microcontroller and ChatGPT, to create a powerful voice assistant is main aim of building this project. The suggested technology seeks to revolutionize user engagement in intelligent settings by offering voice-activated, user-friendly access to data and services. The ESP32 microcontroller is the voice assistant's physical core. It has strong connectivity and can be used in many different ways. The ESP32 improves the assistant's usefulness beyond local processing by making it easy to connect to online sites [9]. The voice assistant work to become better capacity to understand and react to user inquiries in conversational English blending with ChatGPT, a state-of-the-art natural language processing and generation model. Configuring the ESP32 for audio input and output, putting advanced speech recognition algorithms into practice to record user commands, integrating ChatGPT for natural language processing capabilities, and creating an easy-to-use interface are all crucial steps in the development process. Moreover, during the development process, strict considerations are made about resource optimization, privacy, and security. This new voice assistant has a lot of features. It can give you personalised news, information, and weather updates as they happens by controlling a variety of smart home appliances, such as home theater systems, lights, and thermostats. Helping users keep track of their schedules by giving them personalised calendar events, alerts, and reminders, as well as strong web search features that make it easy to find useful information.
Design and Implementation of an Artificial Intelligence Powered Personal Finance Coach for Enhancing Financial Literacy in Students
Picking up cash basics matters for young people, still they often face difficulty. Usually resources can be limited, making it difficult to stay on top of spending. Managing small budgets often feels stressful. Watching every dollar takes effort when bills pile up out. Beginning the saving becomes easier. A handy tool helps sort spending into categories while keeping an eye on resources as it moves through the budget instantly. Created actually around lessons, game features, and a sense well of improvement in a safe practice system handles payment practice while using a one time code. This setup includes an actually verification code. Reviewing results, while trying to close the gap between what we think we know how things like work in practice real usually financial skills. Smart system shows up in talks about money skills. Data based system plays a role too. These ideas sit together when discussing how people handle financial analysis of a future based, digital finance.
Vehicle-to-Grid (V2G) Systems in Smart Grids: A Review and Case Study on AI-Based Energy Management and Grid Stability
Electric vehicles (EVs) are considered as one the most important part and parcel of future smart energy systems due to the smart electrification of transportation, and Vehicle-to-Grid (V2G) technology that leads to a viable way in improving grid flexibility, control peak loads and integrate renewable energy sources. The focus majorly on bidirectional power- electronic topologies, communication frameworks, control techniques, and AI-enabled optimization for uncertainty management and predictive operation helps to evaluate the current level of V2G research. The research addresses key deployment obstacles, such as worries about battery deterioration, immature infrastructure, unclear business models, cybersecurity vulnerabilities, and new power- quality issues including harmonics and supraharmonics, through a thorough synthesis of previous studies. A concentrated case study of India's electric bus (E-bus) transition highlights depot-level medium-voltage V2G (MV2G) as a currently underutilized system-level asset and demonstrates how fragmented planning and unidirectional grid readiness increase operational stress. Studies indicate that intelligent energy management, battery-health-aware operation, secure authentication frameworks, standardized communication protocols, and supportive regulatory procedures must all be coordinated for V2G implementation to be successful. The research indicates that in order to achieve scalable, economically viable, cyber-secure, and power-quality- resilient smart grid ecosystems, V2G must be positioned as a proactive design component rather than a post-deployment supplement.
An Automated Attendance System Using Enhanced Face Detection and Recognition
Attendance management is a critical administrative function in educational institutions; however, conventional methods such as manual roll calls are time-consuming, error-prone, and susceptible to proxy attendance. This study proposes an automated attendance system based on enhanced face detection and recognition techniques, offering a contactless and efficient solution. The system utilizes a webcam for real-time image acquisition and incorporates adaptive preprocessing methods, including contrast stretching and brightness normalization, to ensure robustness under varying illumination conditions. Face detection is performed using multiple Haar Cascade classifiers, while recognition is achieved through a hybrid template matching approach with adaptive thresholding. A human-in-the-loop verification mechanism is introduced to minimize false recognition, and attendance is recorded in an Excel format with duplicate prevention. Experimental evaluation demonstrates reliable real-time performance without relying on computationally intensive deep learning models. Without using computationally demanding deep learning models, experimental evaluation shows dependable real-time performance. The system is appropriate for real-world classroom and small-scale institutional applications because it delivers consistent accuracy and low processing latency under a variety of environmental conditions. Additionally, its lightweight architecture makes it simple to deploy on common computing equipment.
AI-Powered Real-Time Violence Detection in CCTV Feed
We can observe that it is an ever-increasing problem of violence in the streets, in transport hubs, and other large areas of people, a tendency that has been developing over the past few years. We currently have predominantly conventional CCTV surveillance that is primarily reactive, and we rely heavily on human intervention to monitor numerous video feeds. In this manner, we find the method to be quite a burden on operators, resulting in response lag, and we are not getting early indications of violent action, which subsequently diminishes its value in prevention. In this regard, we present a practical, scalable, proactive AI-based system for real-time violence detection using CCTV video. A lightweight computer vision and deep learning framework is proposed for real-time violence detection using OpenCV for video capture and preprocessing. Mediapipe Pose extracts 2D/3D skeletal keypoints, which are processed by a TensorFlow-based BiLSTM model to learn spatiotemporal patterns of violent actions. Trained on benchmark datasets and real surveillance data, the system achieves 93.7% accuracy in classifying aggressive behaviors. A pose aggregation and sliding-window voting mechanism improves robustness in crowded and occluded scenes, reducing false positives below 4%. The system also triggers real-time SMS alerts via Twilio with contextual metadata for rapid response and intervention. There are extensive testing of edge devices and the cloud platform, with end-to-end alert latency below 1.3 seconds, including with high-resolution inputs, and it can be scaled linearly to multiple camera streams. The Framework encourages the deployment of privacy-aware systems by being privacy-conscious (using only anonymized pose representations) and by promoting on-device inference. The suggested solution demonstrates that it is possible to achieve accurate, low-latency, cost-effective violence detection with minimal hardware requirements and open-source software, which is why it can be used in schools, transportation hubs, commercial areas, and other public venues.
Full Stack Doctor Appointment Booking System
Now a days, due to digital transformation of healthcare services has significantly changed so the way of work has changed. Previously it was depended on manual system just like telephone calls physical visits or registers management. Cause of it, public was facing difficulty to book appointment and it waiting time was so longer. Even sometime patient records are also losses. Doctors were not able to manage time too. in this situation, patients load was increased and it was difficult to manage the crowd of patients. The Full Stack Doctor Appointment Booking System provides automated process like booking appointment, accepting appointment by doctor, searching doctor by patient and manage all records about doctors and patients by Admin team. Patient can check history and rating of doctors. It helps to manage the time for both doctors and patient. Patient can select better doctors based on his/her rating and history. As we know that internet is available every corner of the world so patient can book appointment from anywhere and based on appointment date they will have to go hospital. Overall, the Full Stack Doctor Appointment Booking System more reliable and scalable solution for current healthcare management system.
A Systematic Review of the Tripartite Crisis in Modern Web Browsers: Resource Inefficiency, Privacy Failures, and Automation Gap
Intelligent automation at low levels, privacy, and resource efficiency are the three major deficiencies in web browsers, which impact billions of users. This review, which examined 48 research papers, shows that modern browsers (such as Chrome, Firefox, Safari and Edge) favour security over efficiency and innovation. We observe that the multi-process architecture uses 234-287% more memory than required, browser fingerprinting uniquely identifies 99.24% of users, and AI-driven automation completes 14% of tasks. We present IntelliBrowse, a tri-layered architecture that adapts memory consumption, improves privacy and incorporates AI automation. It seeks to optimise memory usage by 40-60%, lower the entropy of fingerprinting, and enhance automation. This review suggests that such shortcomings can only be removed by significant architectural changes.
Hybrid AI Models for Enhanced Harmonic Source Localization in Renewable-Dominated Microgrids
This paper proposes a hybrid artificial intelligence framework that combines Graph Neural Networks (GNNs) and Transformer architectures for harmonic source localization in renewable-dominated microgrids. The proposed method utilizes spatial information derived from network topology along with temporal harmonic measurements to improve detection accuracy. Simulation scenarios based on a modified IEEE 34-bus microgrid demonstrate the effectiveness of the proposed approach under different operating conditions including grid-connected and islanded modes.
Digital Content Provenance and Anti-Forgery System: A Dual-Layered Framework for Secure Image Verification
Advances in image editing tools and synthetic media generation have significantly increased the difficulty of distinguishing manipulated images from authentic visual content, thereby challenging the reliability of digital evidence. This work presents a Digital Content Provenance and Anti-Forgery System based on a dual-layer authentication strategy. The initial layer ex-amines provenance-related information through systematic analysis of image metadata to identify indications of post-processing and structural inconsistencies. The secondary layer applies visual forensic analysis based on compression residual patterns and supervised learning to identify manipulation artifacts that are not detectable through metadata inspection alone. The proposed approach is evaluated using a publicly available image tampering dataset and demonstrates effective discrimination between authentic and manipulated images, accompanied by statistically supported confidence measures. The results indicate that the integrated provenance and forensic framework support reliable image authentication in digital forensics and content verification scenarios.
Pneumatic systems have become integral in modern automation, industrial processes, and control systems, serving as reliable alternatives to electrical and hydraulic systems in various applications. This paper provides an extensive exploration into the theory and practical aspects of pneumatics, including their physical principles, components, operational mechanisms, circuit design, logical control, and safety protocols. Drawing from technical training literature, the discussion bridges foundational knowledge with industrial relevance, presenting a thorough resource for academic and research pursuits in automation engineering.
Face Recognition Smart Attendance System
Facial recognition has emerged as a key area of research in recent years because of its vital role in biometric authentication for numerous applica-tions, such as access control and attendance management systems. Effective at-tendance management is essential for organizations, but traditional methods can be challenging and time-consuming. Speech recognition, RFID, biometrics, and eye tracking are just a few of the many automatic identification techniques. Face recognition is one of the most widely used biometric methods for identifi-cation verification. This research presents a deep learning-based facial recogni-tion attendance system based on convolutional neural networks. It employs transfer learning using three pre-trained convolutional neural networks that are further trained on the dataset. These networks demonstrated remarkable per-formance, achieving high prediction accuracy while maintaining a reasonable training time.
Automated Skin Lesion Border Detection Techniques – A Key Component in Melanoma Screening
Accurate identification of lesion boundaries in dermoscopic images plays a critical role in automated melanoma screening, as the effectiveness of subsequent feature analysis and diagnostic decisions depends heavily on segmentation performance. This review presents a comprehensive overview of skin lesion border-detection methodologies, highlighting their evolution from classical image-processing techniques and hybrid approaches to advanced deep-learning–based segmentation models. In addition, it discusses commonly used public datasets, standard performance metrics, and typical segmentation challenges encountered in real-world clinical images. The paper further identifies existing limitations in current research and outlines promising future directions aimed at improving robustness, generalization, and clinical applicability. Special attention is given to dataset availability, fair benchmarking practices, and practical recommendations for developing reliable and clinically viable lesion segmentation systems.
Blockchain-Based Electronic Voting Systems
This research examines the feasibility of implementing blockchain-based electronic voting systems in India to enhance electoral transparency and integrity. The study employs a methodology that combines Distributed Permission Ledger Technology (DPLT) and the Solana blockchain, resulting in a multilayered system. The main findings highlight the effectiveness of blockchain technology in mitigating electoral fraud and manipulation when implemented with precision, underscoring the importance of meticulous design and execution. These findings contribute significantly to discussions surrounding the modernization of electoral processes in the digital age and support the hypothesis that blockchain can address vulnerabilities in traditional voting methods. Moreover, the study marks a significant step toward modernizing elections, preserving democratic principles, and reinforcing the role of technology in addressing persistent electoral challenges, ultimately enhancing accessibility, security, and transparency in elections and strengthening democracy in the digital era.
Review of Specialist Recommendation System Based on User-Reported Symptoms
Finding the right medical specialist according to symptoms is one of the main problems that healthcare patients face. This can lead to delayed diagnoses, increased healthcare costs, and compromised health outcomes. A variety of dig-ital solutions have been developed over the years, such as rule-based symptom checkers, machine learning models, and assistants driven by large language mod-els(LLMs). This paper reviews these approaches used within the healthcare sector for the identification of medical specialty. Machine learning models, large language models, and retrieval-augmented generation frameworks applied for patient interaction, symptom analysis, and specialist recommendation have been explored. In addition, the paper discusses challenges related to ethical considera-tions and data privacy. Finally, it outlines emerging research directions with the aim of improving the robustness, reliability, and ethical deployment of AI-driven healthcare assistants.
Full Stack WEB based IDE Vibe-Code Editor
Modern software development is witnessing a paradigm shift toward browser-based Integrated Development Environments (IDEs), yet current cloud-based solutions frequently struggle with high operational costs, network latency, and significant data privacy risks when integrating AI. This research presents the design and implementation of a high-performance, web-based AI IDE built on the Next.js 15 framework, designed to solve these challenges through a” local-first” architecture. The core execution engine leverages the WebContainers API, utilizing WebAssembly (Wasm) to run a full Node.js runtime entirely within the browser tab, thereby eliminating the need for remote virtual machines and reducing infrastructure overhead. A primary innovation of this system is the integration of local AI inference via Ollama, specifically utilizing the CodeLlama model to provide real-time, context-aware code completions and debugging assistance. By conducting all AI processing at local-host:11434, the IDE ensures that proprietary source code remains strictly on the user’s hardware, providing an” air-gapped” level of security. The user interface features the Monaco Editor for a professional coding experience, a custom virtual file tree for project management, and an Xterm.js-powered terminal for shell interaction. Supporting a variety of templates such as React and Express, and backed by MongoDB via Prisma ORM, this project demonstrates that local LLMs can effectively provide high-quality code generation on consumer hardware. Ultimately, this research offers a blueprint for decentralized, private, and zero-cost development environments, providing a scalable alternative to existing cloud-dependent IDEs.
OPENDOTS: A Survey and Problem Study on Intelligent IoT Data Visualization Platforms
OpenDots presents a survey-driven study of current challenges in real­time sensor data visualization and interpretation within Internet of Things (IoT) ecosystems. Existing IoT dashboards are widely used for data collection and plot­ting; however, they often suffer from fragmentation, limited customization, and a lack of meaningful interpretability beyond raw visualizations. As IoT deploy­ments scale, users are increasingly required to manually analyze large volumes of time-series data, leading to higher cognitive load and reduced decision effi­ciency. This paper analyzes commonly used IoT visualization platforms and identifies key gaps related to explainability, accessibility, and intelligent data in­teraction. Based on these observations, the paper outlines the problem space mo­tivating the need for unified, web-based platforms capable of supporting real­time data access, controlled sharing, and high-level analytical assistance. A brief conceptual overview of OpenDots is presented to illustrate a possible research direction toward addressing these challenges, without delving into system design or implementation details. The study aims to serve as a foundational reference for future research on intelligent, user-centric IoT data analytics platforms.
Design and Fabrication of Portable Tilling Machine
Agriculture remains a crucial sector in developing economies, where efficient soil preparation significantly affects crop yield and farmer productivity. Traditional manual tillage is labor-intensive, while conventional engine-powered tillers are costly and unsuitable for small farms. This project presents the design and fabri­cation of a portable, battery-powered electric tilling machine aimed at providing an affordable, lightweight, and eco-friendly alternative. The system replaces inter­nal combustion engines with a rechargeable DC motor and battery setup, offering reduced noise, zero emissions, and lower operating costs. The compact design allows easy maneuverability in small fields, gardens, and hilly terrains. Perfor­mance calculations, cost analysis, and working principles are presented to validate the feasibility of the machine. The proposed system improves tillage efficiency while reducing human drudgery and operational expenses.
AgroSmart: An Intelligent Agricultural Assistant for Farmers Using SaaS-Based AI/ML Architecture
The project AgroSmart (or Krushi Care Hub) is designed as a multimodal digital platform designed to help farmers relocate to the data-driven agriculture. The system integrates several computing tools into one interface, such as the leaf-disease detection module with images and the crop recommendation engine with an assessment of the soil properties and the climate conditions of a specific region. The primary contribution that is made in this work is the hybrid decision making framework of the platform which validates the predictions made by the algorithm by referring to real-time weather and already approved agricultural principles that guarantee field-coded advice. In order to improve transparency and confidence among users, the platform uses explainable AI methods to show the factors which are behind each recommendation including rainfall, temperature changes and nutrient availability. The combination of sophisticated analytics, real-time data and interpretable outputs will help AgroSmart to provide reliable and easily accessible decision-support technology to farmers interested in applying smart farming. In addition to the discipline, AgroSmart assists farmers with the business aspect of farming. It entails a marketplace where goods are sold to buyers bypassing the middle man and a price predictor which assists farmers to make the most when selling their goods. It can be the AI chatbot or the live weather notifications, but the platform is created to assist farmers in reducing their expenses, increasing the yield, and gaining more income by means of a friendly, user-friendly mobile platform.
Heart Disease Prediction using Machine Learning
Early detection and timely medical intervention significantly improve patient outcomes. However, traditional diagnostic methods can be time-consuming and rely heavily on expert interpretation. With advancements in Artificial Intelligence and the increasing availability of medical datasets, machine learning offers a reliable and efficient approach to heart-disease prediction. This project, Heart Disease Prediction Using Machine Learning, presents an end-to-end predictive system developed using machine learning algorithms. The objective is to analyse clinical parameters such as age, sex, chest pain type, cholesterol, resting blood pressure, maximum heart rate, exercise-induced angina, ST depression (oldpeak), and thalassemia to predict the likelihood of heart disease. The methodology includes data preprocessing, Exploratory Data Analysis (EDA), baseline model comparison, correlation study, hyperparameter tuning, and evaluation using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Among the models evaluated—Logistic Regression, K-Nearest Neighbours, and Random Forest—the optimized Logistic Regression model exhibited the best-balanced performance, achieving strong accuracy and interpretability. The system demonstrates the potential of machine learning in enhancing early detection, supporting clinical decision-making, and improving healthcare delivery.
Dynamic Multilingual Retrieval-Augmented Generation (RAG) System for Table-Heavy PDFs
Baseline Retrieval-Augmented Generation (RAG) systems show great promise in helping AI generate relevant and grounded responses while reducing hallucinations and lowering computational costs [1][2]. By merging information retrieval with large language models (LLMs), RAG frameworks let models access external knowledge sources, which improves factual accuracy and response reliability. However, baseline RAG systems mainly work with unstructured sequential text and struggle with structured data formats, especially tables [3][4]. Tabular data often has complex relationships between rows, columns, headers, and numerical values. Conventional text-based retrieval methods do not capture these relationships well. In real-world situations, many documents, like research papers, financial reports, and enterprise PDFs, mix unstructured text with structured elements such as tables, charts, and multi-level headers. Traditional RAG pipelines often fail to keep the two-dimensional structure of tables during preprocessing. This leads to fragmented or misleading retrieval results. Such structural issues can harm downstream generation, causing incomplete or inaccurate responses, especially when numerical or table-related information is involved. To tackle these challenges, this study investigates a better way to retrieve complex multi-format data in a format friendly to LLMs [5][6]. The suggested method emphasizes structure-aware preprocessing and semantic chunking strategies that maintain contextual and structural relationships in both text and tables. By creating semantically meaningful chunks with controlled overlap, the approach ensures that essential contextual information stays intact during retrieval. Additionally, the system supports table-heavy documents written in multiple Latin-based languages by using multilingual embedding models. This allows for both same-language and cross-language retrieval, enabling users to query documents in different languages without losing semantic accuracy. Overall, the proposed method improves the performance of RAG systems for complex, real-world document collections by enhancing retrieval accuracy, contextual grounding, and usability across various document formats.
Palm Print Recognition for Identity Verification Using Deep Learning Techniques
Biometric authentication methods are widely used today to improve security in digital systems. Among the available biometric traits, palm prints are considered reliable because the contain several unique features such as major palm lines, wrinkles, patterns, and texture details. These characteristics remain mostly unchanged over time and differ from person to person. This paper describes a palm print recognition system developed using deep learning and proposed system includes image pre-processing, feature learning and classification. Pre-processing steps such as resizing, normalization, and noise removal are applied to enhance the quality of palm images. Unlike traditional palm print recognition methods that depend manually on extracted features, the CNN model automatically learns important features directly from the images. The proposed system achieves more than 99% recognition accuracy and performs even in the variations in lighting conditions. The system can be effectively used in applications such as access control, digital banking and government-based identity verification systems.
Real-Time Violence Detection and Instant Alert System Using Deep Learning and Computer Vision
Traditional CCTV surveillance systems primarily record events, making it difficult for authorities to respond promptly to incidents of violence. This paper presents a real-time violence detection and instant alert system that leverages deep learning and computer vision techniques to identify violent activities such as fights, assaults, and aggressive behaviour from live video streams. The proposed system integrates convolutional neural networks (CNN) and long short-term memory (LSTM) models (or YOLO based architectures) to accurately detect violent actions in complex environments. Upon detecting suspicious activity, the system triggers automated alerts via SMS, email, or mobile application notifications to concerned authorities, along with the incident’s location and video snippet. This solution can be deployed in public spaces, educational institutions, workplaces, malls, airports, and smart city surveillance to enhance safety and enable rapid response during emergencies. By combining machine learning, real-time video processing, and alert mechanisms, the proposed framework offers a proactive and cost-effective approach to improving public security and reducing response time.