International Journal on Advanced Computer Engineering and Communication Technology
International Journal on Advanced Computer Engineering and Communication Technology P-ISSN: 2278-5140 Frequency: Biannual Publisher Website: https://mriindia.com/ Editor in Chief: Dr. Parikshit Mahalle Article Processing Charges: 50 USD Indexing In: Google Scholar, SCILIT, BASE, Semantic Scholar, Scribd, ResearchGate
BlockMedLedger: Secure Patient Health Records Using Blockchain and IPFS
In this paper we present BlockMedLedger, a decentralized patient health record management system based on blockchain and IPFS. BlockMedLedger provides solutions to the challenges of healthcare data silos, security vulnerabilities and patient ownership of their own data. The patient centric model supports patients, medical data owners, to have complete control over their own medical data, while providing an efficient process to facilitate secure sharing of the medical data with care providers initiated through smart contracts and cryptographic access controls. The system uses an Ethereum compatible blockchain to support access control decision and IPFS for decentralized encrypted storage of encrypted medical records. The implementation demonstrates good security, efficient access, retrieval and sharing of encrypted health information for health care providers and patients while meeting requirements specified in HIPAA utilizing zero-knowledge proofs and patient consent control features.
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Energy Harvesting System from Speed Breaker for Sustainable Power Generation
Exploration of alternate and sustainable power generating systems has become necessary due to the rapid increase in global energy consumption, the depletion of conventional fossil fuel resources, and growing environmental concerns. Creating an energy harvesting system from road infrastructure—specifically, speed breakers—to produce clean, renewable electricity is one creative strategy. This method makes use of the kinetic energy generated by moving vehicles over speed limiters, which would otherwise be squandered as vibrations and heat. Through the use of electromechanical devices like generators, piezoelectric transducers, or hybrid electromagnetic-piezoelectric systems, the vertical motion of vehicles is converted into rotational or compressive mechanical energy, which is then converted into electrical energy. The collected energy can be effectively stored in supercapacitors or batteries, which can then be used to power traffic signals, street lights, security systems, or even be connected to the local grid for communal use. Modern human life depends on energy, yet traditional energy sources are running out quickly.
The rapid growth of Android applications has changed mobile technology by making it accessible, innovative, and convenient for billions of users around the world. However, this popularity has also led to an increase in malicious apps that threaten user privacy, exploit sensitive data, and pose serious security risks. Traditional detection methods, like signature- based antivirus solutions, often fail against zero-day malware, obfuscated code, or overly permissive applications that seem safe but misuse data. To tackle these issues, we present AndroidRiskCheck, a hybrid framework that combines static program analysis with machine learning (ML) to evaluate risks in Android applications. The system extracts permissions and metadata directly from APK files using a Python-based pipeline, removing Java dependencies and ensuring light cross-platform performance. A smart XML parsing mechanism is used to manage both human-readable and binary AndroidManifest files, ensuring reliability across different APK structures. The features extracted are converted into numerical vectors and classified with an ensemble of three ML models: Multinomial Naive Bayes, Logistic Regression, and Gradient Boosting. This categorizes applications as Safe, Risky, or Malicious. A risk scoring system, permission categorization, and compliance checks with GDPR, CCPA, and DPDP regula- tions further improve the system’s clarity and practical use. The tool includes a Flask-based web interface to provide an easy upload-and-analysis process, along with visualization dashboards for risk scores and tracker detection. Additionally, the system allows optional extensions such as dynamic analysis (using Frida and mitmproxy), threat intelligence integration (like the VirusTotal API), and blockchain-based report storage for auditability and integrity. By combining lightweight static analysis, strong ML-driven classification, and privacy compliance checks, AndroidRiskCheck offers an effective, accessible, and adaptable solution for evalu- ating Android security.
Blockchain and AI-Enabled Pharmaceutical Supply Chain Tracker
The pharmaceutical industry struggles with issues like counterfeit medicines, poor transparency, and inefficient inventory handling. This study intro- duces a new system that combines Blockchain, Smart Contracts, and Ar- tificial Intelligence (AI) to build a safer and more reliable supply chain. Blockchain records every step securely and makes transactions tamper- proof, while smart contracts automatically verify and manage supply chain activities. AI is used to forecast medicine shortages so that reorders can be placed before stock runs out. The system is developed using Python, SQLite, and JSON, and experimental results show better tracking, fewer manual processes, and smarter inventory control. By bringing these tech- nologies together, the proposed solution not only reduces the risk of counter- feit drugs but also increases the overall efficiency of pharmaceutical supply chains. This integrated approach highlights how emerging technologies can be combined to create practical solutions for real-world healthcare challenges. Beyond pharmaceuticals, the framework can also inspire applications in other supply chains where trust, transparency, and predictive management are critical. Importantly, the entire blockchain and smart contract logic are implemented directly in Python, without relying on Solidity, Ganache, or any external blockchain platforms, making the system lightweight, self- contained, and easier to deploy.
Behavior-Based Continuous User Authentication Detection System
Continuous authentication offers a vital layer of protection by monitoring user activity beyond simple passwords or fingerprints scans. In this work, we present a system that actively and unobtrusively analyzes how individuals type and use their mouse throughout active desktop sessions. By gathering and processing behavioral data, our approach creates unique user profiles and employes modern machine learning- specifically Random Forest and XGBoost- to distinguish legitimate users from imposters within seconds. Experiments show this technique achieves accuracy above 90% with minimal false alarms, making it a practical solution for preventing sessions hijacking and unauthorized access in both workplaces and at home.
DyslexiSolve: Mind Map-Based AI Learning Platform for Dyslexic Students
Dyslexia is not a reading or writing disability it can impact confidence, learning ability, and broader academic experience, especially in conventional and distance learning. DyslexiSolve responds to the issue by delivering a graphically rich, mind map style learning platform that structures lessons in interactive diagrams to facilitate an exploration of complex concepts and simplify understanding. Driven by AI-based personalization, the program tailors learning pathways to align with each student's advancement, strengths, and weaknesses for a unique experience. Access is built-in to the design, including text-to-speech functions, dyslexia-aided fonts, and dynamic layouts to mitigate hindrances to learning. This document highlights the conceptual framework of DyslexiSolve, reviews literature on personalized learning for dyslexic students, and highlights how mind map-based instruction can enhance comprehension, retention, and engagement. The intention is to make schooling more inclusive, adaptive, and empowering for neuro diverse learners, transforming learning from a source of struggle into a possibility for growth.
PolySub : AI-Powered Multilingual Subtitle and Dubbing with GenAI
Language barriers go beyond communication gaps; they can restrict access to knowledge, limit collaboration, and reduce the global reach of digital content. PolySub addresses this challenge by providing an AI-powered platform for multi- lingual subtitling and dubbing, enabling seamless cross-language video accessibility. The system integrates OpenAI Whisper for transcription, Meta NLLB for translation and Meta MMS (TTS) model for audio generation, producing SRT files, dubbed videos with subtitles and original videos with embedded subtitles as outputs. Designed for scale, PolySub supports more than 150 languages, ensuring accessibility for diverse audiences. Evalua- tion shows strong performance, achieving a BLEU score of 37.0 and a BERTScore of 85.8, reflecting accurate, fluent, and se- mantically consistent output. This paper presents the conceptual framework of PolySub, outlines its architecture, and highlights how automated multilingual pipelines can enhance accessibility, scalability, and global communication across education, media, and professional domains.
AI-Powered Career Navigator Smart Roadmaps and Alumni Networking
AI- powered Career Navigator help students to get a perfect roadmap and guidance .This paper is mainly focused on career guidance with the data prediction. This is important to students go get a good roadmap. The system will predict the data on the basis of its previous data .Machine learning is used to get the job prediction.
Secure and Assistive UPI Transaction System for Visually Impaired People
Android-based mobile application presents a digital payment solution specifically for visually impaired users. Conventional UPI apps pose a significant challenge due to their reliance on visual elements. This solution addresses by integrating assistive technology for more independent and secure transactions. The application features voice-guided navigation, which leads users through each step of the transaction via audio prompts. For secure access and quick payments, the solution has incorporated biometric authentication and a QR code scanner. This approach enhances the overall user experience while maintaining a high level of security, aligning with the principles of inclusive design in financial technology. Developed with Android Studio, the application utilizes key libraries such as ML Kit for QR code scanning and AndroidX Biometric for fingerprint authentication. The solution employed a modular development strategy, allowing each component to be independently tested for reliability and performance. Extensive unit, integration, and functionality tests demonstrate that the application meets the necessary usability and security standards for financial technology designed for users with visual impairments. This project contributes to accessible digital finance by enabling the visually impaired to perform UPI transactions autonomously. Future work involves adding support for regional languages and integrating AI-based voice recognition for more intuitive command input.
Smart Portable Ventilator with IOT Connectivity and DC Charging Support
The increasing demand for affordable, portable respiratory support equipment, particularly in emergency scenarios and resource-constrained environments, has highlighted the need for smart portable ventilators with IoT connection and DC charging support solutions. This article describes the design and development of a smart portable ventilator that is connected to the Internet of Things and supports vehicle DC charging in order to ensure continuous functioning in emergency situations. The device includes an automated Ambu-bag compression mechanism controlled by a computer and enhanced by sensors to monitor vital factors like as oxygen saturation (SpO₂), airflow, and pressure. Medical practitioners may remotely monitor patients and send out mobile notifications in the event of anomalies thanks to an Internet of Things module that sends real-time data to cloud platforms. The device may also find the closest hospitals and doctors in real time for emergency medical aid by integrating IoT connectivity with a search engine. By combining a conventional AC supply with vehicle-based DC charging, the ventilator's dual-source technology increases portability and reliability while ensuring uninterrupted operation during patient transportation or power outages. Thanks to an easy-to-use interface with safety alerts and configurable parameters, clinicians can respond swiftly and with exact control. The proposed technology demonstrates an effective, economical, and scalable approach for emergency respiratory support, with potential applications in disaster relief operations, mobile medical units, and rural healthcare facilities.
Proposed Model to Identify Drug Trafficking on Social Media
The rise of social media sites, such as Instagram, Telegram, and, has unwittingly provided hidden channels for clandestine sales of illicit drugs, posing daunting challenges to regulatory and law enforcement agencies. Dealers use encrypted messaging, obfuscated language, and temporary or anonymous accounts, which make traditional keyword- based detection methods relatively ineffective. To address these intricacies, the present research presents an advanced Drug Trafficking Detection System that combines machine learning approaches with robust metadata analytics to identify abnormal behaviors and identify dubious activity patterns on social networks. Built on the MERN stack—MongoDB for big data handling, Express.js and Node.js for secure backend services, and React.js for reactive user interfaces the system facilitates real-time data retrieval, abnormality identification, and interactive visualization. Initial assessments suggest that the framework demonstrates high effectiveness in precise identification of drug- related content and interactions. Future work may include the incorporation of deep learning frameworks for intricate content analysis, investigation of privacy- aware surveillance mechanisms, and utilization of federated learning paradigms to enable globally scalable, safe, and efficient surveillance of digital drug trafficking networks.
Integrated Solar-Powered Smart Aeration and Waste Management System for Sustainable Pond Ecosystem
Pond ecosystems in rural and aquaculture environments encounter persistent issues such as reduced water quality, low dissolved oxygen (DO) levels, and surface waste accumulation. Conventional diesel-powered aeration and manual cleaning methods remain expensive and environmentally harmful, creating the need for sustainable and intelligent alternatives. This study focuses on the concept of an Integrated Solar‑Powered Smart Aeration and Waste Management System with IoT‑Based Monitoring designed for sustainable pond ecosystems. The system incorporates a solar-driven paddlewheel aerator for improved oxygenation and an IoT‑enabled waste control unit for surface debris management. IoT sensors continuously monitor key water quality parameters such as DO, pH, temperature, and turbidity, transmitting real‑time data to a cloud‑based dashboard for control and analysis. The design approach includes both simulation and preliminary hardware studies to examine system behavior, energy performance, and data acquisition efficiency under various operating conditions. Current research and experimental trends reveal that synchronization between solar‑powered aeration and IoT‑based waste management remains limited, and energy optimization frameworks suitable for off‑grid rural ponds are underexplored. Addressing these research gaps is essential for developing cost‑effective, eco‑friendly, and self‑sustaining smart pond systems that support long‑term aquaculture and environmental resilience.
CogniLock : Bridging Cognitive Threat Insights and AI Sentinel Defence
In the rapidly evolving digital landscape, individuals and organizations face an increasing range of identity-based and cognitive cyber threats such as credential leaks, impersonation, and social media exploitation. Traditional cybersecurity solutions primarily focus on enterprise network defence, leaving a critical gap in personalized, intelligent protection mechanisms. This paper presents CogniLock, a SaaS-based cybersecurity framework that bridges cognitive threat intelligence with an AI-driven sentinel defines system. CogniLock operates through two primary phases: a Threat Intelligence Dashboard and a Digital Firewall. The dashboard aggregates and visualizes real-time malicious Indicators of Compromise (IOC) include IP addresses, URLs, domain names, and file hashes from reliable sources such as Alien Vault OTX, Abuse IPDB, and URL Haus. The Digital Firewall, on the other hand, offers proactive personal security modules such as email breach detection, password strength and exposure assessment, LinkedIn impersonation monitoring through cognitive similarity matching, and integrated reporting tools for social media abuse via official cyber complaint portals. By combining cognitive analytics with automated AI defences, CogniLock improves situational awareness and enables users to identify, address, and react to identity-threats cyber threats efficiently. The proposed framework contributes toward establishing an intelligent, adaptive, and user-centric model for modern digital protection.
A Secure Digital Marketplace for Second-Hand Goods: Improving Trust, Transparency, and Sustainability in E-Commerce
The sharp increase in electronic commerce resulted in the implementation of safe, transparent, and easy-to-access channels that facilitate the exchange of popular products [1]. The project presents "A Secure Digital Marketplace for Second-Hand Goods" as a web application that provides a platform for confidence building, facilitation ease, and efficiency in peer-to-peer exchanges in second-hand goods. The proposed system includes a role-based authorization system for customers and sellers, thus ensuring the protection of data, secure transaction, and authenticated interaction by the users. With existing technologies in the web, the site allows sellers to present second-hand goods online to more customers, and customers have real listings and simple buying experiences [2]. Moreover, advanced security technologies such as fraudulent detection technologies and authenticated user profiles build confidence and minimize the risks involved in the usual second-hand trading mechanisms. This work outlines the architecture of the system, the implementation method, and the possible socio-economic impacts, thus making the solution a scalable model for a secure and sustainable online store platform in the second-hand market.
Bridging Natural Language and Databases: Intelligent Conversational System for Automated SQL Query Generation Using Natural Language Processing
Relational databases are the foundation of modern data management systems, widely utilised across industries for storing, organising, and retrieving information. However, interacting with these databases typically requires proficiency in Structured Query Language (SQL), which limits accessibility for non-technical users. Addressing this challenge, this paper presents a multimodal AI-powered SQL chatbot that allows users to query relational databases using natural language, voice commands, and visual schema inputs. The proposed system employs a fine-tuned T5 transformer model to convert natural language statements into executable SQL queries. It further integrates Optical Character Recognition (OCR) for schema extraction and multilingual translation modules to support diverse user inputs. Unlike conventional Text-to-SQL systems, this approach emphasises user-centric design, providing both textual and graphical result visualisation for improved interpretability. Experimental evaluation was conducted using benchmark datasets such as Spider and WikiSQL, alongside custom domain-specific datasets covering HR, Sales, and Academic data. The model achieved competitive accuracy and low latency, averaging 1.5–2 seconds per query, while ablation studies confirmed the impact of OCR and multilingual modules on performance. Furthermore, a user study involving 20 participants demonstrated high usability and satisfaction ratings, validating the chatbot’s potential to make database querying more natural and inclusive. This research contributes toward the democratisation of data access by merging transformer-based NLP, multimodal interaction, and user-focused design into an integrated, practical solution.
Design and Implementation of Wearable IoT Smart Gear for Real-Time Notification Display
This paper presents the design, development, and evaluation of three innovative wearable systems engineered for seamless digital communication and notification management without dependency on smartphones. The work integrates an IoT Smart Gear, WiFi-native Smart Glasses, and a transparent holographic display system (EDITH). Unlike traditional Bluetooth-tethered commercial wearables, the proposed systems operate independently through direct WiFi and cloud connectivity, ensuring real-time operation, extended range, and full modular customization. Each system is developed using open-source hardware (ESP8266/NodeMCU) and 3D-printed enclosures for affordability and scalability. Field trials validate the robustness of these devices, demonstrating notification delivery success rates above 96%, latency between 145–425 ms, and multi-day battery performance. Results confirm that open and low-cost IoT-based architectures can rival or surpass traditional commercial wearables in functionality, cost efficiency, and independence.
This paper reviews the growing field of Machine Unlearning (MU), a discipline that supports modern privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), both of which provide users the right to have their data removed. Drawing on insights from recent research, the work presents an overview of MU principles, algorithms, and their implementation within artificial intelligence (AI) chatbot systems. The central problem addressed is how to efficiently remove the impact of specific data samples from trained models without retraining them entirely. Current literature presents two main categories: exact unlearning, which ensures complete data removal, and approximate unlearning, which offers faster and more practical performance. The techniques are broadly classified into data-based, model-based, and hybrid approaches. Federated Unlearning (FU), an extension of MU for distributed systems, introduces further complexities such as user participation and privacy leakage. Verification strategies, including adversarial evaluations like Membership Inference Attacks (MIA), are critical to validating that the system truly forgets deleted information. This paper concludes with recommendations for incorporating MU into conversational AI to promote transparency, privacy, and responsible data handling.
Ensemble Machine Learning for Fish Abundance Prediction: A Multi-Model Stacking Approach with Environmental and Fisheries Data
Effective fisheries management demands accurate forecasting of abundance to avoid excessive harvesting and ensuring marine biodiversity is maintained. Typical assessment techniques that mainly use previous catch data often have no predictive capacity to incorporate complicated, time-varying environmental factors. We tackle this problem with a new methodology that implements an ensemble Machine Learning technique. The use of a stacking ensemble is utilized, where four heterogeneous Level 0 base learners, Random Forest, XGBoost, K-Nearest Neighbors, and a Multi-Layer Perceptron, were classi- fied. A Ridge regression model is used as the Level 1 meta-learner to optimally combine the base learners’ predictions. The model was trained and validated using a dataset of 1000 observations collected through a timeframe of fifteen years (2010-2024) from the Manila Bay fisheries area. The features executed to forecast abundance include eleven environmental parameters including water temperature, pH, dissolved oxygen, and several nutrients combined with five important fisheries parameters including fish- ing area, gear type, and species. The combined features ensemble achieves excellent predictive performance with an R-squared of 0.9922, Mean Absolute Error of 37.35, and Root Mean Squared Error of 49.37. An ablation study demonstrated that combining all features significantly outperformed a strictly environment- only model, which returned an R-squared of negative 0.0045, and provided a more balanced prediction compared to the fisheries- only model (R-squared 0.9968). The resulting model offers ac- tionable harvest recommendations based on predicted abundance categories (Lean, Moderate, and Abundant), providing a robust tool for adaptive fisheries resource management.
LINZO: An AI-Powered Multilingual Communication System for Professional Meetings.
As the global workforce continues to evolve rapidly, being able to share and communicate with colleagues in meetings in multiple languages for cross-cultural cooperation and decision making has never been more critical. This paper illustrates three whole-languages approaches that leverage AI- powered technologies (i.e. speech translation, speech to text transcription, and real-time captioning) to dissolve language barriers in a professional workplace and beyond. This study reviews those systems, proposes a conceptual prototype, and illustrates the ways that AI will harness Automatic Speech Recognition (ASR), Machine Translation, and Large Language Models (LLMs) to foster inclusivity, reduce latency, and improve the user experience. Recently released literature between 2018-2025 illustrates how combining real-time translation and user-centered design yield markedly improved comprehensibility, engagement, and productivity, as well as positive subjective user experiences from multilingual meetings.
Image Authenticity Detection: A Dual-Domain Approach Using Vision Transformers with DCT-Based Frequency Analysis and Explainable AI
The fast growth of AI-generated images via Generative Adversarial Networks (GANs) and diffusion models has posed unprecedented challenges to digital media authentication. This paper introduces a new deep learning system that combines Vision Transformers (ViT) with Discrete Cosine Transform (DCT) frequency domain analysis for manipulated and AI-generated image detection. Our dual-domain method overcomes key shortcomings of existing spatial-only detection approaches by jointly analysing pixel-level inconsistencies and latent frequency-domain artifacts characteristic of generative models. We embed explainable AI mechanisms via attention visualization and frequency activation mapping to offer transparent, interpretable predictions vital for forensic purposes. Thorough assessment on the Digital Image Forensics Dataset with 30,000 images shows improved performance with 95.43% accuracy, 95.78% precision, 95.01% recall, and 95.39% F1-score. The explainability component increases trust in automated detection systems by highlighting manipulated regions and frequency irregularities. Our findings confirm the significance of multi-domain feature fusion and interpretability in the construction of reliable image authentication systems.
InterviewExceler.AI: An AI-Powered Platform For Mock Interviews, CV Analysis, and Real Time Behavioural Feedback
Preparing for real-world interviews often requires multiple disconnected tools for practice, CV editing, and performance feedback. To bridge this gap, we present InterviewExceler.AI, a full-stack platform that provides an integrated and realistic mock interview experience. The system employs transformer-based models (BERT, SBERT) for role-specific question generation and client-side facial landmark analysis using CNNs with TensorFlow to capture non-verbal behaviour. These inputs are combined with heuristic scoring and Gradient Boosted Trees (XGBoost) to generate structured and actionable feedback. The platform integrates an interactive Next.js frontend, serverless convex functions, and modular ML pipelines, ensuring scalability and adaptability across diverse interview formats. Designed with reproducibility and extensibility in mind, it provides architecture details, implementation guidelines, evaluation protocols, and open-source artifacts to enable further research and adoption. Evaluation highlights effectiveness in three areas: generating relevant interview questions, analysing multimodal responses, and offering personalized coaching recommendations that improve with repeated use. Beyond technical contributions, InterviewExceler.AI seeks to reduce bias, enhance accessibility to professional interview training, and lay the foundation for next-generation AI-driven career support systems.
Real-Time Fall Detection Using MLP, OpenCV, and IoT Integration: Development of the FallNex Smart Application
Falls among older adults pose a serious health risk, as delayed assistance can lead to severe injuries or fatal outcomes. To address this challenge, we propose FallNex, a real-time fall detection system that integrates a machine learning model, lightweight visual monitoring, and IoT-based hardware. A Multi-Layer Perceptron (MLP) model performs the primary fall prediction using motion data collected from wearable sensors. To minimize false alarms, a simple OpenCV-based monitoring module evaluates frame differences from a camera feed to verify whether significant activity occurred at the moment of a suspected fall. The proposed hardware unit communicates with the FallNex mobile application to deliver instant alerts, maintain fall logs, and provide post-fall guidance through an integrated chatbot. Experimental results demonstrate that FallNex achieves high accuracy with reduced false positives while maintaining real-time responsiveness, making it suitable for home-based and assisted-living environments.
Emotionally Intelligent AI Companion for Enhancing Human-AI Interaction through Text and Voice Based On Sentiment Analysis
This research presents a multimodal AI companion designed to support adolescent mental health by enabling empathetic interactions through both text and voice.¹ ² Voice inputs are transcribed using OpenAI’s Whisper API, which provides low word-error rates and robust performance across diverse speech conditions.³ ⁴ The transcribed or typed text is then processed by a fine-tuned DistilBERT model for real-time detection of 28 emotions based on the GoEmotions dataset, capturing polarity and nuanced affective states.⁵ ⁶ Meta’s Llama 3.0 generates context-aware responses, adapting tone using detected emotions and user history stored through LangChain and MongoDB for personalization.⁷ ⁸ A FastAPI-based implementation supports secure deployment and includes a dashboard for tracking emotional trends over time.⁹ The prototype demonstrates high accuracy in both transcription and emotion recognition, outperforming unimodal baselines and strengthening affective computing through integrated voice and text capabilities.¹⁰ Future work includes expanding multilingual support to increase accessibility.¹¹ ³
AI-Driven Secure Hybrid MANET for Tactical Communication in Infrastructure-Free Zones
In critical situations such as military missions, natural disasters, or rural operations, reliable communication can decide the difference between success and failure. However, most traditional communication systems depend on existing infrastructure such as mobile towers or internet connectivity, which may fail or be unavailable in such areas. This paper presents an AI-driven tactical wireless communication system that enables devices to communicate without any fixed infrastructure, forming a self-organizing Mobile Ad-Hoc Network (MANET). The system uses hybrid communication protocols—Wi-Fi Direct, Bluetooth, and LoRa—to achieve both short-range and long-range communication. To ensure the network remains stable and efficient, an AI-based Q-learning routing algorithm dynamically selects the best communication path between nodes. The prototype was implemented using ESP32 microcontrollers, LoRa SX1278 modules, and a custom Android application. Security is achieved through AES-256 encryption, SHA-256 hashing, and blockchain- inspired message verification. Experimental results show significant improvements in throughput, reliability, and recovery time compared to traditional routing methods. The system demonstrates a promising step toward secure, intelligent, and infrastructure-free communication for tactical and emergency use.
Review on Development of a Smart Electric Pole with Integrated Security and Communication
Urban areas face growing challenges in energy consumption, safety, and infrastructure management. Conventional street lighting systems rely on fixed schedules, often leading to unnecessary power wastage and high operational costs. This study proposes a Smart Electric Pole system integrating solar energy, IoT technology, Light Dependent Resistors (LDRs), motion sensors, and smart surveillance cameras to optimize energy use and enhance urban security. The system ensures automatic lighting control by activating lights only in low-light conditions or when motion is detected, thereby reducing energy wastage significantly. Smart cameras provide real-time monitoring of public spaces, while an emergency switch allows citizens to trigger alerts in critical situations, improving responsiveness. IoT connectivity with cloud storage enables remote access, data management, and continuous monitoring. By combining renewable energy, intelligent automation, and security features, the proposed system promotes sustainable urban development, cost efficiency, and public safety, contributing to the broader vision of smart cities.
CareTrek: An AI-Assisted Multilingual Navigation and Safety System for Elderly Users
Senior citizens often encounter mobility limitations, medical vulnerabilities, and communication difficulties that can delay timely assistance during emergencies. Most existing navigation and health applications are not tailored for elderly needs, lacking simplified interfaces, continuous vital-tracking, and seamless family alert mechanisms. To bridge this gap, this paper introduces CareTrek, an assistive mobile safety and health system developed using React Native, Expo, Supabase, TypeScript, BLE sensors, and real-time GPS tracking. The application integrates continuous location monitoring, SOS alerting, Bluetooth-based health data collection, and a secure family-linking framework into a unified, senior-centric ecosystem. Evaluation through controlled simulations demonstrates reliable background GPS tracking with a ±6–12 m variation, BLE data consistency of 92–95%, and rapid emergency alert propagation under three seconds. With additional privacy safeguards such as Supabase Row-Level Security and encrypted device storage, CareTrek illustrates how sensor-integrated, AI-assisted mobile platforms can meaningfully enhance the safety, independence, and connectedness of elderly users.
Design and Implementation of a Fleet Management System Using Blockchain
Efficient management of vehicle fleets is nial for modern transportation networks, but old-fashioned centralized systems often face issues like data changes, lack of transparency, and inefficient processes. This paper presents a blockchain-based Fleet Management System that combines Distributed Ledger Technology with IoT devices to ensure secure, transparent, and tamper-proof operations. The system uses a multi-layered design that includes client, application, blockchain, consensus, and database layers, making it flexible and easy to expand. Smart contracts help automate tasks like checking drivers, planning vehicle maintenance, and handling payments, reducing the need for manual work and increasing accuracy. Real-time vehicle tracking using IoT sensors and GPS improves monitoring, while blockchain's ability to keep data unchanged builds trust and ensures reliability among all users. A comparison shows this system offers better reliability, lower costs, and more trust than traditional methods. The results show that using blockchain for fleet management provides a sustainable, efficient, and future-ready solution for smart transportation systems.
Real-Time AI/ML-Based Phishing Detection and Prevention System
Phishing attacks, which use phony emails, websites, and messages to trick users into disclosing private information like passwords or bank account information, have grown to be a significant cyber security concern. Phishing attacks are fraudulent attempts to obtain sensitive data by posing as a reliable individual or business. These attacks are growing more frequent and sophisticated. Antiquated phishing detection techniques that rely on set rules or recognized patterns frequently fall behind new phishing techniques. The potential of artificial intelligence (AI) to enhance phishing detection systems is discussed in this paper. AI detects and halts phishing more rapidly and precisely by using methods like machine learning, natural language processing, and pattern recognition. AI can identify subtle phishing indicators that traditional systems might overlook by analyzing vast volumes of data. The article also covers a variety of AI approaches, including deep learning, ensemble approaches, and supervised and unsupervised learning. It examines how well these AI systems function in practical settings and how well they adapt to novel phishing techniques. The study concludes by discussing the difficulties and potential advancements that will be required to address the ever-evolving and evolving phishing threats.
LifeMap: AI-Powered Visual Roadmap Builder for Career Goals
This research paper presents LifeMap, an AI-powered visual career roadmap system. Its purpose is to assist individuals in planning their long-term professional goals, tracking their progress, and adjusting them as needed. While traditional task managers focus solely on short-term activities, LifeMap provides a structured visual roadmap that breaks down larger career goals into smaller, achievable steps. The system is grounded in goal-setting theory, self-regulated learning, and self-determination theory, fostering continuous and meaningful career development. LifeMap provides personalized and relevant suggestions using natural language input, embedding-based resource matching, reinforcement learning-based scheduling, and fairness-aware ranking. By analyzing a user's educational background, interests, and career aspirations, the system recommends relevant skills, certifications, internships, and opportunities. Additionally, the platform includes community features that promote collaboration, progress sharing, and motivation, such as groups, discussions, and challenges. The platform is developed using React.js, Node.js, Express.js, MongoDB, and OpenAI-based models. Overall, LifeMap offers a scalable and human-centered solution that makes career planning more intuitive and effective through visual roadmaps, intelligent AI guidance, and collaborative community support.
SickleCare+: An AI-Powered Blood Report Analysis System for Sickle Cell Disease Management
Sickle cell disease SCD) is a hereditary blood disorder requiring continuous monitoring through blood parameter analysis. This paper presents SickleCare+, an innovative AI- powered system that combines Optical Character Recognition OCR) technology with machine learning algorithms to analyze blood reports for sickle cell patients. The system addresses the critical gap between complex medical data interpretation and patient understanding by automatically extracting key parameters from uploaded blood test reports and providing AI- generated health insights and alerts. Our study verifies the integration of Tesseract OCR with advanced machine learning algorithms, where 95% accuracy in parameter extraction and 90% in abnormality detection is achieved. The system has been developed utilizing a MERN stack architecture along with MongoDB Atlas as the secured storage that is HIPAA-compliant and scalable. Clinical validation indicates enhanced early detection of possible complications, where notable health trend analysis and predictive alerts are offered by the system. This effort advances digital health innovation by bridging the gap in the availability of specialty care, most appropriate for low- resource environments where specialist reading may not be easily accessible.
Psybridge: AI-Augmented Mental Health Assessments for Personalized Therapy
The application of artificial intelligence (AI) in mental health care is demonstrating promising potential for early identification, personalized insights, and improved therapy outcomes. However, existing tools are hindered by language accessibility, the integration of multiple assessments, and interpretability. We conduct a literature-based analysis of existing approaches in personality classification and clinical assessment, identify key gaps, and propose a unified solution that integrates psychological understanding with technological precision. This study lays the groundwork for a flexible and inclusive system to aid therapists in clinical settings. This research emphasizes the integration of AI- based personality profiling and clinical assessment into one multilingual, privacy-driven system. By combining MBTI-based insights with standardized screeners such as PHQ-9 and GAD-7, PsyBridge offers a unified solution for early identification and personalized therapy recommendations. The proposed framework demonstrates the potential of NLP-powered mental health tools to enhance accessibility and clinical accuracy.
Choosing the right career is one of the most important decisions for students. Traditional career guidance systems are often static, based on outdated datasets, and fail to provide dynamic, personalized advice. This research proposes an AI-Based Career Guidance System that leverages Generative AI (Gemini API) to generate real-time recommendations tailored to a student’s academic performance and interests. Unlike conventional models, this system does not rely on pre-stored datasets but dynamically analyzes inputs to suggest relevant career paths. User profiles and AI-generated suggestions are stored securely in MongoDB Atlas, with JWT authentication ensuring safe access. The system is lightweight, scalable, and designed to be user-friendly, making it suitable for educational institutions and self-guided learners.
Zeniva: Holistic AI Wellness System for Personalized Family-Centric Healthcare
Zeniva is a health care platform which we have designed to bring together bio metrics, emotional intelligence, predictive analytics, and total wellbeing in to one place to present very personal and preventive care. We have put in place a continuous health tracking of physical and behavioral signs which include heart rate, blood pressure, oxygen saturation, glucose levels, and stress markers via IoT sensors and machine learning models. Also, we use advanced sentiment analysis which looks at facial expression, voice tone and user action to identify early physical and mental health issues which in turn triggers in time interventions like guided meditation and mindfulness support. Integrating Ayurvedic principles, Zeniva gives out dosha based diet, herb and lifestyle recommendations which in turn promote natural healing and mind body balance. We have a family centered design which includes multi user profiles, remote health monitoring, AI supported telemedicine, automated reports, doctor recommendations and emergency alerts for home and clinical settings. To protect sensitive info while at the same time enabling collaborative learning Zeniva uses encryption, blockchain for integrity and federated learning which in turn guarantees secure, accessible and trusted health care innovation.
RetinAI-DR Fusion: Dual Deep Learning Framework for Diabetic Retinopathy Detection
Diabetic retinopathy (DR) has become one of the leading global causes of vision loss among people living with diabetes. The difficulty lies in its silent progression—tiny changes in the retinal blood vessels often occur long before noticeable symptoms appear, making early detection challenging. Traditional screening approaches rely heavily on expert evaluation of retinal images, which can be time- consuming, costly, and limited in availability, especially in underserved or remote areas. Recent progress in artificial intelligence has begun to change this landscape. Advanced deep learning frameworks such as EfficientNet and ResNet can now analyze retinal fundus images with remarkable speed and precision. These models perform at near-expert accuracy, opening the door to faster, more accessible, and automated screening solutions that can reach larger populations. By increasing efficiency and consistency, AI-driven systems have the potential to improve early diagnosis rates and reduce avoidable blindness. This study explores how these intelligent technologies are revolutionizing diabetic retinopathy detection, emphasizing their potential benefits, current limitations, and the steps needed to successfully integrate them into everyday clinical practice.
Pharmaguard: AI-Driven Prediction of Drug-Drug Interactions and Side Effects using LLMs
Drug-Drug Interactions (DDIs) continue to pose sig- nificant challenges in pharmacological safety, particularly when multiple medicines are prescribed simultaneously. The growing scale of biomedical data has created new opportunities to identify such interactions computationally, yet existing approaches often lack interpretability and adaptability to unseen drug pairs. This study presents a customized hybrid framework that combines the biomedical understanding of BioBERT with the reasoning capabilities of GPT to predict and explain potential DDIs. The model was developed and evaluated using the Mendeley Medicine Information Dataset (MID), which provides both structured and descriptive clinical information. BioBERT was fine-tuned on this dataset to capture drug-specific contextual embeddings, while GPT was prompted to generate short, clinically meaningful explanations for each predicted interaction. The proposed system achieved improved accuracy across multiple evaluation metrics, with an observed ROC-AUC of 0.91 and precision-recall balance outperforming classical and transformer-based baselines. In ad- dition, the explanation component produced outputs that phar- macists rated as factually reliable and contextually useful. The findings suggest that integrating domain-tuned representation learning with generative reasoning can contribute meaningfully to safe prescription analysis and clinical decision support.
Smartclass: Transforming Learning through IoT
Managing classroom environments and maintaining accurate attendance remain ongoing challenges in educational institutions, often consuming valuable teaching time and affecting the overall learning experience. The SmartClass system is designed to address these issues by integrating IoT- based environmental monitoring with automated attendance technologies, creating a modern, efficient, and data-driven solution for classroom management. Instead of relying on manual checks or traditional roll calls, SmartClass continuously tracks temperature, humidity, and lighting levels using low-cost sensors connected to microcontrollers, ensuring that students learn in a comfortable and well-regulated environment. At the same time, attendance is recorded automatically through RFID or face recognition, reducing errors and giving teachers more time to focus on instruction. Real-time dashboards, instant alerts, and secure cloud storage enable teachers and administrators to visualize classroom conditions, analyze trends, and make informed decisions. By combining automation, real-time sensing, and intelligent analytics, SmartClass bridges the gap between traditional classroom practices and modern smart learning technologies—creating a more organized, transparent, and student-centered educational environment where both teaching and learning can thrive.
AI-Powered Assistive Navigation Systems for the Visually Impaired
Vision impairment affects over 2.2 billion people worldwide, posing significant challenges to independent mobility and daily life. This review synthesizes recent advancements in AI-driven assistive technologies, encompassing lightweight object detection models, large vision–language models (LVLMs), embedded platforms, sensor fusion techniques, and user-centered interaction designs. The surveyed literature highlights recurring challenges, including limited contextual reasoning in lightweight models, dependency on network connectivity, privacy concerns, and the absence of long-term real-world evaluations. Building on these insights, we propose a practical server–client architecture in which a compact Raspberry Pi–based wearable integrates a camera module and ultrasonic sensor for local data capture and minimal preprocessing. Data is streamed to a local edge server or laptop that performs object detection, multisensor fusion for robust proximity alerts, and optional LVLM-based scene description with text-to-speech output. This design optimizes portability and battery life on the wearable while enabling high-fidelity, low-latency inference on the server, with an on-device fallback detector ensuring fail-safe operation during connectivity loss. Expected outcomes include improved detection accuracy (mAP in the ~75–90% range on optimized datasets), multi-fold latency reduction through offloading, reduced false positives via ultrasonic–vision fusion, and increased user trust through multimodal feedback. The discussion addresses potential limitations such as network dependency, power constraints, environmental sensing errors, LVLM computational demands, and privacy risks, alongside proposed mitigation strategies and evaluation metrics. The review concludes with a roadmap toward scalable, privacy-aware, and user-centered assistive navigation systems for the visually impaired. This paper reviews recent advances in AI-powered assistive navigation systems for the visually impaired, focusing on wearable and mobile platforms using computer vision, deep learning, and sensor fusion. It identifies common challenges such as network dependency, hardware limitations, and environmental sensitivity while outlining future research directions toward efficient, user-centered, and scalable assistive systems.
Flood AI: Data-Driven Prediction with C4.5 Decision Trees
Flooding is one of the most severe natural disasters caused when heavy rain and swollen rivers destroy lives, homes, and the surrounding environment. It is also a major source of economic loss worldwide. The ability to predict floods in advance and respond on time plays a vital role in reducing the damage they cause. However, traditional forecasting methods often rely on complicated models and large resources, which are not always practical or accessible in vulnerable regions. In recent years, data-driven approaches such as machine learning have shown promising results in improving flood detection and prediction. This study explores the use of the C4.5 decision tree algorithm to predict floods in three regions of India. By analyzing environmental data such as rainfall, temperature, humidity, and river water levels, the model was able to make accurate predictions about flooding events. The findings show that data-based solutions can not only support existing forecasting systems but also provide quicker responses and better preparedness for communities at risk.
Claire.Ai - Hybrid NLP-Based Clause Recognition and Authentication System for insurance Policy Document Retrieval
CLAIRE.AI streamlines the process of extracting information from insurance documents by enabling users to ask questions in natural language. Since understanding policy details often requires domain expertise and can be challenging for policyholders, this system bridges that gap through an AI driven framework that interprets queries and delivers precise responses. The platform incorporates Google Gemini 2.5 Flash within a Retrieval-Augmented Generation (RAG) pipeline to manage various document types, including PDFs and Office files. It employs context-aware chunking, multilingual functionality, and vector-based retrieval to maintain accuracy and accessibility. Developed using FastAPI, the system supports asynchronous execution, token-based authentication, and input validation, ensuring both secure and efficient performance. Additionally, voice-input capability improves user interaction. Experimental results indicate that the solution provides faster and more reliable policy information compared to manual review. Users can easily clarify coverage, claims, and terms without requiring specialized knowledge.
Blockchain-based Academic Certificate Fraud Detection: A Comparative Review and Combined Framework
The rapid rise in forged academic and professional certificates has become a major challenge across higher education and employment sectors. Manual verification is slow, inconsistent, and vulnerable to manipulation, especially with modern digital editing tools. Over the past decade, researchers have proposed automated approaches combining image forensic techniques, machine learning, and blockchain to enhance accuracy, transparency, and tamper-proof validation. This review summarizes recent advancements in feature extraction methods such as GLCM, LBP, SIFT, and copy-move detection, as well as deep learning models including CNN, VGG-16, and ResNet for forgery identification. It also examines blockchain-based frameworks leveraging IPFS, QR codes, and decentralized ledger architecture for immutable credential verification. Strengths, limitations, datasets, and implementation challenges are critically evaluated. The review concludes by proposing an integrated framework that combines machine learning-based forgery detection with blockchain-supported authentication to achieve secure, scalable, and trustworthy certificate verification.
CardioLung: AI-Driven Heart and Lung Analysis
Early detection of cardiovascular and pulmonary diseases is essential for improving patient outcomes, but traditional methods like auscultation are often subjective and require significant resources. While deep learning has shown potential in analyzing audio, it usually lacks a comprehensive, patient-focused interpretation. In this paper, we introduce a new hybrid AI system that combines three different data sources: chest auscultation audio, analysis by specialized deep learning models, and symptoms reported by the user. Our approach starts by converting standard 5-second audio clips into 2D Mel spectrograms. These spectrograms are then analyzed by a 'Committee of Experts'—two separate Convolutional Neural Networks (CNNs) specialized in heart and lung sounds. We tested our method using a custom CNN and a pre-trained EfficientNetV2-B0 model with transfer learning. The EfficientNetV2-B0 model performed better, achieving 92.15% accuracy for heart sounds and 90.5% for lung sounds. The unique final step in our system uses an LLM-based synthesizer to combine the technical results from both specialists with the user's described symptoms. In a qualitative study with 15 participants, 93% found the report generated by the LLM much clearer and more useful than the raw technical data. This hybrid, multi-modal system offers a reliable, accurate, and easy-to-use framework for e-Health screening.
Enhanced AI-Based Image and Video Retrieval System Using CLIP and Hybrid Semantic Indexing
Finding pictures and movies fast and precisely has become more crucial due to the explosive growth of multimedia content. In order to improve the efficiency and semantic significance of that procedure, this research presents a sophisticated AI-powered retrieval system. The system supports both text-based and image-based searches by combining Facebook AI Similarity Search (FAISS) [10][11]with Contrastive Language–Image Pre-training (CLIP). It produces more accurate results by enabling configurable weighting and removing irrelevant or negatively associated suggestions. During video retrieval, the system extracts individual frames using FFmpeg and indexes them using FAISS for frame-level similarity matching. With Precision@5 of 92.8%, Recall@10 of 89.1%, and an average query time of just 0.45 seconds, the method achieves remarkable performance. Recent developments in multimodal video processing[25],[26] and CLIP optimization[24] are combined to increase efficiency even more. All things considered, our approach offers a scalable and useful foundation for high semantic comprehension in real-time multimedia retrieval.
Intelligent Waste Management System using Internet of Things (IoT)
Rapid urbanization and population growth have made municipal solid waste management a pressing challenge, especially in regions where infrastructure development lags behind demand. This paper introduces an Intelligent Waste Management System that integrates Internet of Things (IoT) technologies with mechanical automation. The system consists of a smart bin equipped with a rack-and- pinion mechanism for waste sealing, an Arduino-based controller for automation, and a Bluetooth-enabled Android application for remote monitoring and control. Its main features include contactless waste disposal, automated wrapping and ejection of filled cartons, and capacity alerts through a buzzer and mobile notifications. Unlike conventional smart bins that only measure waste levels, the proposed system combines physical automation with user-friendly mobile integration, offering a more reliable and efficient solution. The design contributes to the Sustainable Development Goals (SDGs)[1], particularly Goal 11 (Sustainable Cities and Communities) and Goal 12 (Responsible Consumption and Production), by promoting hygienic and environmentally responsible practices.
Smart Air Quality Forecasting and Health Advisory using Machine Learning and IOT
Due to rapid urbanization and industrialization, air pollution has recently emerged as a significant environmental and health concern globally. Bad air contributes enormously to respiratory, cardiovascular, and other chronic diseases. In this paper, we introduce a smart machine learning application system for Air Quality Index (AQI) forecast and individual health advices. With an historical AQI database and meteorological parameters, the system can forecast air quality for around the clock in a range of cities. It also comes with custom health risk scores, lifestyle advice, and emergency alerts, reducing exposure-related complications. Geo-fencing alerts, multi-city AQI visualization, and AI-based health advisory are some of the features included to make it complete and adaptive. With the help of predictive models, geospatial data, and intelligent advisory systems, this tool interfaces environmental monitoring with preventive healthcare.
IOT and ML Based Multitasking Drone System for Solar Panel Fault Detection
As per the increasing demand for solar energy has highlighted the need for efficient and automated maintenance solutions for large-scale solar farms, where manual inspection is time-consuming, labor-intensive, and prone to errors. To overcome limitations of traditional methods using fixed sensors and thermal cameras, this research proposes an IoT and ML-based drone system equipped with RGB and thermal cameras to autonomously survey solar panels and capture image data. The images are uploaded to cloud storage in real time and combined with an existing dataset to train a CNN model for accurate detection and classification of faults such as dust accumulation, cracks, hotspots, shading, and diode failure. A cloud-based model comparison identifies faults and provides their GPS location along with severity levels via a user-friendly dashboard, enabling timely maintenance and improved efficiency. This system reduces operational costs, enhances fault detection accuracy, supports predictive maintenance, and significantly improves the lifespan and performance of solar installations, with potential scalability to other sectors such as wind turbine inspection, agriculture, and infrastructure monitoring.
GENWEB : An AI-Powered Platform For Intuitive Prompt-To-Website Generation
In today’s digital-first world, a strong online presence has become essential for individuals, creators, and businesses alike. Yet, despite countless website-building platforms such as Wix, Squarespace, and WordPress, creating a unique and personalized website remains a challenge for many. These platforms, while simplifying web development, still confine users within rigid templates and limited customization options. For someone without coding knowledge, translating a creative vision into a functional, aesthetic website often demands either expensive professional help or frustrating hours of trial and error. This persistent gap between imagination and execution highlights a major barrier the knowledge dependency of web creation. To address this challenge, Gen-Web introduces a new paradigm of website generation powered by artificial intelligence and intent-based design. Instead of choosing from predefined templates, users simply describe their vision in natural language for example, “I want a modern, dark-themed portfolio with a hero section, about page, and project gallery.” Gen-Web interprets this request, understands the intent, and constructs a fully functional, responsive website in real-time. Built using Next.js, Tailwind CSS, and LLM-based code synthesis, the system merges human creativity with machine intelligence. At its core, Gen-Web employs transformer-based models that analyze prompts, identify relevant components, and generate clean, modular code. Through Chain-of-Thought prompting and structured backend orchestration, it ensures logical flow, design coherence, and precise alignment with user intent. The entire process, from prompt input to live rendering and iterative refinement, is intuitive, fast, and adaptive. By bridging the gap between creativity and technology, GenWeb democratizes digital creation. It empowers anyone regardless of technical background to build personalized websites effortlessly, transforming the process of web development from a technical skill into a creative expression.
An Iterative Comprehensive Evaluation of Large Language and Vision Models in Medical AI: Benchmarks, Adaptability, and Deployment Challenges
PRISMA principles provide a thorough analysis of current advances in large language models (LLMs) and multimodal transformers for medical applications. As LLMs like GPT-4, BioGPT, Med-PaLM, and hybrid frameworks like COMCARE enter clinical processes, thorough synthesis is essential to increase performance, methodological adaptability, and implementation practicality in many healthcare situations. Their creativity in medical report writing, decision support, and diagnosis is notable, but the literature has not established a cohesive taxonomy that evaluates these models by uniform metrics, domain-specific generalizability, and ethical acceptability. Over 40 studies examined radiology report production, clinical question responding, cognitive assessment, and causal reasoning. After testing vision-language transformer architectures like PEGASUS and ETB MII for automated imaging-based reporting, graph-based reasoning was used to evaluate drug safety and interpretability of knowledge- integrated models like KELLM. As needed, BLEU, ROUGE, F1 score, CIDEr, and qualitative evaluations were used. Domain- adapted and hybrid models improve diagnostic accuracy, task- specific explainability, and clinician workload differently. Model illusion, biases, hostile manipulation, and resource-intensive fine- tuning persist. The report recommends strong benchmarking, public evaluation standards, and ethical frameworks for LLMs in high-stakes medical applications. This study defines LLMs' therapeutic utility and recommends infrastructure, ethics, and technology for safe and successful integration. This effort prepares scalable, interpretable, and equitable medical AI systems.
Deep Learning Algorithm for Early Pancreatic Cancer Diagnosis
Pancreatic cancer is usually detected late, making treatment difficult and survival rates low. Early detection using biomarkers is important, but no reliable biomarker has yet reached clinical use due to challenges in sample collection and the highly variable nature of pancreatic tumors. To improve diagnosis, new machine learning and deep learning methods are proposed. The research focuses on segmenting and classifying MRI/CT images and improving classifier performance using medical data. The proposed deep-learning model (HdiGTF-SIRNN) significantly improves accuracy and precision compared to existing methods, and additional techniques (RIDT-GDLBC and DHEGQDRLCS) also show better diagnostic performance.
Feature Drift Aware Boosted Ensemble with Feature Set Management in Concept Drift Analysis for Academic Data
Predictive modelling in education has become a cornerstone of learning analytics, supporting student performance forecasting, early identification of at-risk learners, and adaptive curriculum design. However, these models often suffer degradation due to concept drift, the phenomenon where the joint distribution of features and outcomes evolves over time. In academic settings, drift arises from shifting curricula, new teaching modalities, grading policy changes, and evolving student behaviors, making drift adaptation a pressing research challenge. While much of the literature addresses global model retraining, recent advances highlight the importance of feature drift—distributional changes localized to specific input features or feature groups. Blind retraining can lead to unnecessary computational costs and “overfixing” of stable features, whereas feature-level management allows for more targeted, efficient adaptations. This paper reviews the past decade’s progress in drift detection and adaptation, emphasizing feature-wise strategies in supervised learning models. We then propose a novel framework, Feature Drift-Aware Boosted Ensemble for Education (FDABE-Edu), an ensemble-based architecture designed to detect, isolate, and adapt to drifting features in academic data streams. FDABE-Edu incorporates a continuous Feature Drift Analyzer, a Weighted Ensemble that prioritizes stable features while down-weighting volatile ones, and an Update Manager for iterative recalibration. The framework leverages statistical and performance feedback to dynamically adjust drift thresholds and retrain only where necessary. A pseudo-code implementation, system flowcharts, and architectural diagrams are provided to demonstrate feasibility. Comparative analysis situates FDABE-Edu alongside state-of-the-art drift handling algorithms, illustrating its novelty in academic contexts. Finally, we discuss emerging research avenues, including hierarchical drift detection, semi-supervised learning with delayed labels, fairness-aware adaptation, and causal inference integration. By bridging feature-level analysis with ensemble learning, FDABE-Edu advances the development of robust, adaptive, and transparent predictive systems for evolving educational environments.
Comparative Analysis of Deep Learning and Traditional Clustering Methods for Thermal Thyroid Image Segmentation: A Clinical Evaluation Study
Thermal imaging has emerged as a revolutionary non-invasive diagnostic modality for thyroid disorders, oﬀering clinicians the ability to visualize real- time temperature distributions without exposing patients to harmful radiation. The accuracy of thyroid region segmentation in thermal images remains a critical bottleneck for developing reliable automated diagnostic systems and enhancing clinical assessment capabilities. This comprehensive study presents a rigorous comparative analysis of three distinct segmentation methodologies for thermal thyroid imaging: a simplified U-Net deep learning architecture, K-Means clustering with adaptive parameters, and Fuzzy C-Means (FCM) clustering with enhanced robustness features. Our evaluation framework incorporates multiple performance metrics alongside clinically relevant parameters to provide a holistic assessment of each approach. This paper specifically highlights areas where MATLAB-generated results and code can be integrated to enhance the reproducibility and clarity of the presented methodologies and findings.
AI-Driven Real-Time Sign Language Recognition System
Communication is a fundamental human right essential for social inclusion. Individuals with hearing or speech impairments face barriers due to limited understanding of sign language. This project presents an AI-driven real-time sign language recognition system that translates gestures into readable text or speech. The system recognizes both single-hand and dual-hand gestures, including daily expressions and gesture-based computations. It utilizes both Artificial Intelligence (AI) and Computer Vision for accurate gesture interpretation. OpenCV for image preprocessing, and MediaPipe for hand landmark detection. A Flask-based web interface enables real-time interaction, while the gestures are recognized and converted into text. The system operates efficiently with high accuracy and can interpret words like ‘SUN’. This AI-based solution enhances assistive communication and promotes inclusivity in education, healthcare, and accessibility applications.
Early Alzheimer’s Screening with Affordable ML Approach
Detecting Alzheimer’s disease at an early stage is essential for timely intervention and improved outcomes. This study presents a stepwise, cost-effective diagnostic framework that integrates cognitive assessments, family history, and blood biomarkers. Individual risk is estimated using logistic regression, with advanced diagnostic tests recommended only for high-risk cases. Explainable AI techniques, such as SHAP, offer trans- parent and interpretable predictions for clinicians and patients. The platform generates personalized risk reports via an intuitive digital interface, enabling scalable early screening, reducing unnecessary invasive testing, and supporting continuous cognitive health monitoring. This paper aims to present and analyze a stepwise, low-cost screening framework for early Alzheimer’s disease detection based on logistic regression and explainable AI. The framework is evaluated in terms of risk stratification design, practical deployability, and potential to reduce dependence on invasive diagnostics.
A review of AI usage in Indian Start-ups
The embedding of Artificial Intelligence (AI) in Indian start-ups is a burgeoning trend that holds significant promise for economic growth and innovation. AI is being leveraged across various sectors to enhance efficiency, drive innovation, and overcome traditional challenges such as infrastructure limitations and bureaucratic hurdles. However, the adoption of AI also presents challenges, including ethical considerations and the need for strategic government intervention to ensure equitable access and development. The following sections explore the opportunities, challenges, and strategic implications of AI usage in Indian start-ups.
Early Detection and Predictive Analysis of Parkinson’s Disease: A Comprehensive Review of Machine Learning and Deep Learning Approaches
Background: Parkinson’s Disease (PD) is a chronic neurodegenerative condition that is represented by a wide array of motor and non-motor features. Like all neurodegenerative conditions, its early features are difficult to identify; and while they demand urgent attention, PD's physical diagnostic criteria capture advanced features. Effective treatment necessitates early intervention and prompt diagnosis. Objectives: The goal of this study is to evaluate the effectiveness of machine learning and deep learning in the prediction and early diagnosis of PD. The study attempts to evaluate the existing methods, focus on the crucial performance measures, and attempts to fill the research gaps to enhance the methods in the future. Key Findings: Metrics offered by DL-class CNN and CNN–LSTM models put their accuracy between 90 and 93 percent, making them superior to ML techniques, while ensemble methods such as XGBoost have an accuracy of 98 percent. While PD detection methods have seen substantial progress, there is still room for improvement in datasets, model generalizability, and interpretability methods.Future directions focus on the fusion of multi-modal data, explainable AI, and IoT-based real-time monitoring, for a more robust, interpretable, and clinically deployable PD detection system.
A Systematic Review of Facial Expression Recognition (FER)
In order to improve human personification among humans, robots, and sophisticated symbols, by utilizing advances in artificial intelligence, Facial Expression Recognition (FER) seeks to identify facial expressions from still images. New problems and methods that are not given much thought in that FER mindset are confronted as the FER section moves from controlled laboratory conditions to increasingly complicated real-world scenarios. Advanced techniques have been developed quickly. This paper offers a thorough analysis of image-based static FER (SFER) methods, looking at everything from challenge-centred order to model-arranged improvement. We start with a basic analysis of current surveys, an overview of common datasets and methodologies, and a workflow using FER in order to build a solid basis.
AuraSnap: A Hybrid Model for Face-Based Photo Sorting and Organization
Aurasnap is an AI-powered photo management system that automates sorting and delivering event photos using face recognition. It detects and categorizes faces from large photo collections, saving photographers significant time and effort. The system also supports automatic watermarking to protect brand identity and integrates with PyWhatKit for direct WhatsApp photo delivery. Built with customtkinter for an easy interface and powered by OpenCV and PyTorch/TensorFlow for recognition, it offers both technical efficiency and user-friendliness. Overall, Aurasnap enhances productivity and customer satisfaction for photography studios.
Convolutional Neural Network-Based Phytopathological Diagnostic Framework for Precise Plant Disease Identification and Classification
Plant leaf diseases have a major impact on agricultural productivity worldwide and threaten food security. To address this challenge, this paper presents a deep learning-driven technique for the automatic Identifying and categorizing plant leaf diseases using CNN-based models. The proposed model is trained on a diverse dataset comprising images of both healthy and diseased leaves. with Image normalization and augmentation strategies employed To optimize accuracy, robustness As well as generalization. The CNN automatically extracts discriminative features from input images, enabling accurate disease identification without the need for manual intervention. Results indicate that the proposed model successfully reaches a high level of accuracy is 98.71% and outperforms conventional approaches, while maintaining efficiency under varying environmental even under varying Under lighting and background noise variations.. Furthermore, The system is lightweight along with optimized for use On mobile and edge computing devices, enabling in real-time disease monitoring directly On site. This makes the approach practical and scalable,Enabling farmers to identify diseases promptly, thereby reducing crop losses. and promote more sustainable agricultural practices.
Intelligent Monitoring and Predictive Analytics for Cloud-Native Applications using Machine Learning
Cloud-native applications have become essential components of today’s digital infrastructure due to their flexibility, scalability, and efficiency. However, their highly distributed and dynamic environments create complex monitoring challenges that conventional reactive systems fail to address effectively. Such reactive systems identify problems only after they occur, causing downtime, performance degradation, and revenue loss. To overcome these issues, this paper presents an AI-driven proactive monitoring framework that combines automation and predictive analytics. The framework integrates Prometheus for collecting detailed performance metrics and Grafana for real-time visualization, while a predictive engine powered by Isolation Forest and LSTM models anticipates anomalies and system failures before they occur. On average, the system predicts potential disruptions 10–15 seconds before a critical threshold is reached. In addition, automated workflows built with n8n and Jenkins execute corrective actions such as service scaling and restarts, enabling a self-healing environment. This intelligent and proactive system significantly improves reliability, reduces downtime, and transitions cloud monitoring from reactive problem-solving to predictive maintenance.