IES International Journal of Multidisciplinary Engineering Research
type: channel
description: Welcome to the IES International Journal of Multidisciplinary Engineering Research (IESIJMER), where scientific research and innovation will meet in the future., IESIJMER a leading multidisciplinary platform, is committed to investigating the dynamic nexus of science and technology, creating an atmosphere that supports ground-breaking concepts and game-changing research. A Centre for Innovation IESIJMER acts as a hub for scholars, practitioners, and thought leaders in a world where technology innovation is changing industries and demarcating borders. In order to promote information sharing that spurs innovation and sustainable growth across various fields, we are dedicated to closing the gap between theory and practice. Our Objective At IESIJMER, we are strong proponents of teamwork and the ability of interdisciplinary research to tackle today's intricate problems. Our goal is to encourage and enable scientists to push the envelope of what is conceivable in order to make a significant, ethical, and sustainable contribution to the advancement of science and technology. What We Provide Cutting-Edge Research: Through original research papers, insightful reviews, and thought-provoking case studies, our journal covers the latest developments and trends in science and technology management. Global Reach, Local Impact: Because of its diverse and international audience, IESIJMER offers a platform for global perspectives while maintaining a focus on local problems and solutions. Open Access for everybody: We are committed to making research accessible to everybody. As an open-access journal, IESIJMER encourages transparency and information exchange by providing unrestricted access to all of our publications. Become a Part of Our Community Why IESIJMER is more than simply a journal; we are a vibrant community of innovators, educators, and leaders. We would be delighted to have you join us as we explore new frontiers in science and technology. Whether you are a researcher tryi…
publisher: IESCE Publication
language: en
publication name: IES International Journal of Multidisciplinary Engineering Research
items: Rubik’s Encryption of Whatsapp Messages
items: Chordnova: Real-Time Chord Detection Web App
items: Agriaerox: Drone Based Soil and Air Quality Tester
items: Next-Generation Traffic Control: Adaptive Timer and Emergency Vehicle Priority in Intelligent Traffic
items: AI Powered Face Recognition And Attendance System
items: Smart Medicine Dispensing Robot
items: GPS Geofencing based Automatic Speed Control System for School Zone Safety
items: Automatic Pothole Filler
items: Windomax: Advanced Wall Climbing and Window Glass Cleaning Robot
items: Document Transporting System Using Intelligent Robot
items: AI Visual & Reading Aid for the Blind
items: AI-Powered Water Quality Detection and Purification Recommendation System Using Refactor Index
items: Medical Tracker App and Wearable Health Band
items: Robertanet: Enhancedroberta Transformer Based Model for Cyber Bullying Detection with Glove Features
items: AI Platform for Missing Children and Human Trafficking Detection
items: AI-Powered Android Application for Fruit and Vegetable Quality Detection
items: Edge Artificial Intelligence for Internet of Things: A Systematic Review of Recent Advances
items: Foundations and Methodologies of Rule-Based Semantic Role Labelling: A Review
items: Compressive Sensing: A Survey on Theory, Algorithms and Applications
items: Real-Time Automatic Speech Recognition Using Deep Learning
items: Eye Disease Detection Enhancement Using a Multi-Stage Deep Learning Approach
items: Sentiment Analysis on Social Media Using Deep Learning Techniques
items: Diabetic Retinopathy Detection Using Machine Learning Techniques
items: Cloth Changing Person Re-Identification Based on Backtracking Mechanism
items: Revolutionizing Crypto Investments
items: AI-Powered Food Recognition and Nutritional Analysis via Deep Learning
items: AI Powered Voicemail Prioritisation System
items: Mental Health Detection System Using Multimodal AI Techniques
items: PROPCHAIN: A Blockchain-Based Real Estate Platform
items: Lung cancer detection using AI

Rubik’s Encryption of Whatsapp Messages
type: item
description: Privacy concerns are at an all-time high, as is the right to keep such conversations secure and confidential. Users routinely are met with claims of end-to-end encryption. However, control is almost always with large corporations who can access data at will. Therefore, we propose a new system to be used with a Rubik's Cube based encryption technique to WhatsApp Web. This system encrypts and decrypts on the user's device, allowing messages to be viewed only by the sender and designated receiver. Client-side control of encryption shifts the balance of authority and control of the keys to the user, preventing access and building trust with the messaging system. This encryption scheme is designed to apply to stored text messages, with the intent of adding to the security of multimedia messages exchanged through the system.
subject: Articles
  topic: section
subject: Client-Side Encryption
  topic: keywords
subject: Rubik’s Cube Algorithm
  topic: keywords
subject: Data Privacy
  topic: keywords
subject: End-to-End Security
  topic: keywords
subject: Whatsapp Web
  topic: keywords
subject: Cryptography
  topic: keywords
creator: Nijo Nelson
creator: Michelle Aijo
creator: Nikhil V S
creator: Sania Saji
creator: Shejina N M Shejina N M
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 233
ending page: 241

Chordnova: Real-Time Chord Detection Web App
type: item
description: ChordNova is a browser-based intelligent web application designed for real-time chord recognition using advanced audio signal processing and machine learning. It captures live or uploaded audio through the Web Audio API and applies Constant-Q Transform (CQT) and Short-Time Fourier Transform (STFT) techniques to extract harmonic features. These are analyzed using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to classify chords, keys, and scales with high accuracy. The system provides interactive chord diagrams for guitar and piano and integrates practice and visualization modes to support learners and educators. Built on TensorFlow.js and React.js, ChordNova enables instant, crossplatform chord detection without installation. It aims to enhance music education, facilitate self-learning, and bridge the gap between auditory recognition and instrumental performance.
subject: Articles
  topic: section
subject: Chord Detection
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Audio Signal Processing
  topic: keywords
subject: Music Information Retrieval
  topic: keywords
subject: Real-Time Web App
  topic: keywords
creator: Shalbin Benny
creator: Saurav Sudheer P M
creator: Sahal Ameen A A
creator: Minsha Badhar
creator: Anaswara Dinesh
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 242
ending page: 251

Agriaerox: Drone Based Soil and Air Quality Tester
type: item
description: Access to accurate soil and environmental data is essential for sustainable and data-driven agriculture. This project introduces AGRIAEROX, a drone-based soil testing and environmental monitoring system designed to automate field assessment and reduce the need for labor-intensive manual sampling. The system integrates an ESP32 microcontroller with a soil moisture sensor, a DHT11 module for temperature and humidity measurement, and an MQ135 sensor for detecting air quality indicators such as CO₂ and volatile compounds. Mounted on a lightweight UAV platform, the drone can navigate across large or difficult-to-reach areas, collecting data from multiple points with consistent precision. All sensor readings are transmitted wirelessly to a base station, where they are displayed in real time and logged for further analysis. This allows farmers and field managers to track spatial variations in soil and atmospheric conditions, detect potential issues early, and make informed decisions regarding irrigation, fertilization, and crop health management. Preliminary testing demonstrates stable communication, reliable sensor performance, and strong potential for large-scale deployment. Overall, AGRIAEROX represents a cost-effective, scalable, and efficient solution for advancing precision agriculture and environmental monitoring.
subject: Articles
  topic: section
subject: Drone-based soil testing, precision agriculture technology,
  topic: keywords
subject: automated field monitoring
  topic: keywords
subject: ESP32 microcontroller integration
  topic: keywords
subject: wireless data transmission
  topic: keywords
subject: soil moisture sensing
  topic: keywords
subject: temperature–humidity analysis
  topic: keywords
creator: Fenex K Wilson Kolenchery
creator: Fathima Alsana K V
creator: Sooraj R Nair Vadakoot
creator: Vinmaya P
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 223
ending page: 232

Next-Generation Traffic Control: Adaptive Timer and Emergency Vehicle Priority in Intelligent Traffic
type: item
description: Traffic congestion plagues modern urban areas, leading to extended travel times, elevated fuel use, and worsened pollution. Conventional traffic lights rely on rigid timing cycles that fail to adapt to fluctuating traffic volumes, resulting in poor flow efficiency. With urban expansion and rising vehicle numbers, especially at high-traffic intersections, there's a pressing need for intelligent, real-time signal adjustment systems. This project introduces an Intelligent Traffic Control System featuring adaptive signal control and emergency vehicle prioritization. It leverages microcontrollers like Arduino NANO and wireless modules such as NRF24L01 to gather live traffic data via sensors or inputs. The system dynamically extends green-light durations based on vehicle density in each direction. Upon detecting an emergency vehicle—via RFID, GPS, or wireless signals—it creates a clear green corridor for swift, unobstructed passage. This enhances road safety, minimizes delays, and boosts emergency response effectiveness. Encompasses conceptual planning, literature review, system architecture, circuit design, and preliminary hardware integration, laying a solid foundation for automation. This will focus on comprehensive testing and evaluation. Long-term, this embedded solution supports smart-city initiatives by optimizing traffic flow, cutting energy consumption, and accelerating emergency services.
subject: Articles
  topic: section
subject: Internet of Things (IoT)
  topic: keywords
subject: Intelligent Traffic Control
  topic: keywords
subject: Real-time adaptive
  topic: keywords
creator: K. Nazal
creator: Hridul. M. U
creator: Fathima Thasneem M S
creator: Thomas Jaison
creator: Aswani. C. R
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 216
ending page: 222

AI Powered Face Recognition And Attendance System
type: item
description: Artificial Intelligence (AI) has emerged as a key driver of innovation in automation, data analysis, and intelligent decision-making. One of its most impactful applications is face recognition, which enables reliable and contactless identity verification through advanced computer vision and deep learning techniques. Face recognition systems analyze distinctive facial attributes to accurately identify individuals, making them suitable for applications such as security monitoring, access control, healthcare systems, and institutional management. This project presents an AI-powered face recognition and attendance system designed to automate attendance recording using real-time video input. The system employs convolutional neural networks (CNNs) along with Python-based libraries such as OpenCV, Dlib, and DeepFace to achieve accurate and efficient facial recognition. By integrating software intelligence with low-cost hardware components including a Raspberry Pi, webcam, and LCD display, the proposed solution offers a compact, economical, and dependable attendance management system with minimal human intervention. The automated approach significantly reduces time consumption, minimizes manual errors, and enhances security. In addition to technical benefits, the system supports digital transformation by promoting contactless and hygienic operations, which are increasingly important in modern institutional environments. Owing to its modular and scalable design, the system can be extended to applications such as smart surveillance, access control, and workforce monitoring. Overall, the project demonstrates the practical effectiveness of AI-driven face recognition in developing secure, intelligent, and efficient automated solutions
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: Face recognition
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Computer Vision
  topic: keywords
subject: Convolutional Neural network
  topic: keywords
subject: Real-time Video processing
  topic: keywords
creator: Alen Jose V
creator: Anagha N S
creator: Nandana Biju
creator: Sarin K R
creator: Sreerag K R
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 209
ending page: 215

Smart Medicine Dispensing Robot
type: item
description: The Smart Medicine Dispensing Robot is proposed as an automated healthcare support system intended to assist patients in managing prescribed medications accurately and on time. Medication non-adherence is a common issue among elderly individuals and patients with chronic illnesses due to memory lapses, physical limitations, and insufficient supervision, which can result in skipped doses or improper intake. To overcome these challenges, the developed system delivers medicines automatically according to a predefined schedule, provides timely alerts, and ensures precise dosage delivery to the user, thereby enhancing treatment compliance and patient safety. The system combines embedded control, sensing technologies, and IoT-based communication to form an efficient and interactive medical assistant. An ESP32 microcontroller serves as the core processing unit, coordinating servo motors for controlled dispensing, IR sensors for obstacle detection, and a voice recognition module for user interaction. Additional features such as a display unit, audio notifications, and an integrated water dispensing mechanism improve usability, particularly for users with reduced mobility. The modular architecture and economical design allow the system to be deployed in both domestic and clinical settings, with provisions for remote supervision and future expansion. Phase-1 development focuses on circuit design, simulation, and hardware integration to verify system functionality through virtual testing. The obtained results indicate that the proposed model is feasible, cost-effective, and capable of automating medication delivery reliably. Further development will include real-time implementation, IoT integration, and clinical evaluation to validate performance under practical conditions. Overall, the Smart Medicine Dispensing Robot aims to support independent patient care by integrating automation with essential healthcare assistance.
subject: Articles
  topic: section
subject: Smart Medicine Dispenser
  topic: keywords
subject: Medication Adherence
  topic: keywords
subject: Healthcare Robotics
  topic: keywords
subject: ESP32Microcontroller
  topic: keywords
subject: Automated Drug Delivery
  topic: keywords
subject: IoT-based Healthcare
  topic: keywords
creator: Anjana V A
creator: Lithin Leo
creator: Munawwir Mamu P T
creator: Swarnima K Shai
creator: Ashly Antony
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
starting page: 158
ending page: 165

GPS Geofencing based Automatic Speed Control System for School Zone Safety
type: item
description: Speeding near school premises continues to be a major contributor to road accidents, even with the presence of conventional preventive measures such as signboards, speed bumps, and surveillance by traffic personnel. To address the shortcomings of these traditional systems, this paper proposes an automated speed regulation solution that leverages GPS-based geofencing to identify school zones and regulate vehicle speed without relying solely on driver awareness. The system incorporates an ESP32 microcontroller, GPS receiver, display indicators, and alert mechanisms to continuously track the vehicle’s location and determine when it enters a predefined geofenced region. Once inside a school zone, the controller activates visual and audible warnings and restricts vehicle speed through a dedicated control interface. The prototype was evaluated through simulation and preliminary testing, demonstrating accurate zone identification, minimal response delay, and a framework scalable to multiple sensitive locations. The proposed system offers a cost-efficient, low-power, and easily deployable solution that can enhance safety near educational institutions and can be further extended to hospitals, residential neighborhoods, and other restricted areas. The work aligns with the emerging objectives of intelligent transportation systems and smart mobility infrastructure.
subject: Articles
  topic: section
subject: GPS
  topic: keywords
subject: Geofencing
  topic: keywords
subject: School Zone Safety
  topic: keywords
subject: ESP32
  topic: keywords
subject: Speed Control
  topic: keywords
subject: Intelligent Transportation System
  topic: keywords
creator: Govind P S
creator: Selvin Xavier
creator: Swathy E S
creator: Ummer Sinan
creator: Anitha Mathew
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
starting page: 173
ending page: 180

Automatic Pothole Filler
type: item
description: Road surface deterioration, particularly potholes, poses a significant threat to road safety, vehicle maintenance, and transportation efficiency. Conventional repair methods are time-consuming, costly, and rely heavily on manual labor, leading to frequent traffic disruptions and inconsistent repair quality. The proposed Automatic Pothole Filler System aims to address these challenges by introducing an autonomous mechanism capable of detecting and repairing potholes with minimal human intervention. The system integrates sensors, microcontroller, and automation technologies to provide a fast, efficient, and cost-effective road maintenance solution. In this project, ultrasonic and camera-based sensors are employed to identify potholes by detecting irregularities in the road surface. The data is processed by a microcontroller (Arduino Uno or Raspberry Pi), which then activates a motorized filling mechanism to repair the pothole. The system is designed to be compact, mobile, and adaptable for various road environments. It leverages embedded system technology, motor control, and automation principles to enhance operational precision and reliability. Furthermore, the design ensures safety, energy efficiency, and environmental sustainability by reducing unnecessary material wastage and traffic congestion during repair operations. Phase 1 of this project focuses on the design, development, and simulation of the system, ensuring that all components function cohesively. The results and field implementation will be conducted in Phase 2, where the system’s performance will be validated through real-time testing. This work contributes to the advancement of smart infrastructure by aligning with the vision of smart cities enabling intelligent, automated, and sustainable road maintenance systems that improve public safety and transportation efficiency.
subject: Articles
  topic: section
subject: Automatic Pothole Filler System
  topic: keywords
subject: autonomous detection and repair
  topic: keywords
subject: microcontroller (Arduino or Raspberry Pi)
  topic: keywords
subject: motorized filling mechanism
  topic: keywords
subject: environmental sustainability
  topic: keywords
subject: compact and mobile design
  topic: keywords
creator: Abhinav Jinan
creator: Anagha E A
creator: Kevin C V
creator: Sajith K S
creator: Dr. John Chembukkavu
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
starting page: 166
ending page: 172

Windomax: Advanced Wall Climbing and Window Glass Cleaning Robot
type: item
description: This project presents the design and development of a lightweight wall-climbing cleaning robot that uses a propeller-based adhesion system to adhere to vertical surfaces and perform cleaning tasks autonomously. By generating thrust perpendicular to the wall through a high-speed BLDC motor and propeller, the robot produces sufficient normal force to create frictional adhesion capable of counteracting gravity. The system integrates locomotion, cleaning, and control mechanisms to maintain balance, adhesion, and cleaning efficiency throughout operation. The developed prototype demonstrates the feasibility of employing aerodynamic adhesion for wall-cleaning applications, achieving stable control and efficient power utilization. Overall, this work contributes to the advancement of service robotics, particularly in automated cleaning and inspection systems for vertical structures.
subject: Articles
  topic: section
subject: Cleaning
  topic: keywords
subject: Wall-climbing
  topic: keywords
subject: Robot
  topic: keywords
subject: Propeller
  topic: keywords
subject: Adhesion
  topic: keywords
subject: Aerodynamic
  topic: keywords
subject: Autonomy
  topic: keywords
subject: Locomotion
  topic: keywords
subject: Control
  topic: keywords
subject: Robotics
  topic: keywords
creator: Denson P D
creator: Amy Antony
creator: Mohammed Razal K
creator: Krishnadarshan M S
creator: Nivya K Venu
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
starting page: 203
ending page: 208

Document Transporting System Using Intelligent Robot
type: item
description: The Document Transporting System with Intelligent Robot is an autonomous indoor delivery platform designed to streamline the transfer of documents and stationery within institutional and office environments. The system is built around an ESP32 microcontroller, leveraging its dual-core processing capability, integrated Wi-Fi/Bluetooth modules, and low-power operation for real-time navigation, communication, and system control. Obstacle detection and avoidance are achieved using an array of HC-SR04 ultrasonic sensors, while differential drive locomotion is implemented through high-torque DC motors operated via an L298N motor driver, enabling precise maneuverability in narrow corridors. To ensure secure access, the robot incorporates a capacitive fingerprint sensor for biometric authentication and a solenoid-actuated locking mechanism to safeguard the document compartment. User interaction is facilitated through a voice-recognition module, an LCD/OLED display for status feedback, and GSM-based SMS notifications for delivery updates. A battery management subsystem, powered by a high-capacity rechargeable Li-ion pack with an onboard charging and protection circuit, supports long-duration operation and real-time battery-level monitoring. The integration of embedded control, sensor fusion, biometric security, and wireless communication enables fully automated document delivery with minimal human intervention. This system significantly reduces manual workload, enhances security and traceability, and demonstrates a scalable approach to smart-campus and smart-office automation.
subject: Articles
  topic: section
subject: ESP32 microcontroller
  topic: keywords
subject: Ultrasonic sensors
  topic: keywords
subject: Obstacle detection
  topic: keywords
subject: Fingerprint sensor
  topic: keywords
subject: Solenoid lock
  topic: keywords
subject: Smart campus
  topic: keywords
subject: Secure delivery system
  topic: keywords
creator: Akhil C S
creator: Anjaly M A
creator: Fida Abbas
creator: Jaseel Ali
creator: Anitha Mathew
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
starting page: 151
ending page: 157

AI Visual & Reading Aid for the Blind
type: item
description: Blindness greatly restricts a person’s ability to move independently, recognize their surroundings, and access written information, making routine activities difficult without external help. The AI-Based Visual Aid with Integrated Reading Assistant is a wearable assistive solution created to support fully blind individuals by merging intelligent navigation functions with real-time reading assistance. Developed using the power-efficient Raspberry Pi 4, the device combines a camera module and ultrasonic sensors to locate objects, estimate distance, and provide voice feedback without requiring user intervention. Using the TensorFlow SSD Lite-MobileNet deep-learning model, the system identifies objects in real time while estimating proximity through a fusion of camera-based interpretation and ultrasonic measurements. To further improve accessibility, the system includes a Tesseract-based OCR module capable of detecting text in captured images and converting it into synthesized speech. The lightweight, hands-free design can be mounted on standard eyeglass frames, allowing comfortable and portable usage. Performance evaluation with 60 fully blind participants in controlled environments showed noticeable improvements in navigation speed and confidence when compared with conventional white-cane use. Participants also highlighted strong obstacle-detection accuracy and reliable reading performance under proper lighting. The modular architecture enables future enhancements such as advanced OCR capabilities, expanded object datasets, slippery-surface alerts, stair recognition, and GPS-assisted guidance. Overall, the project contributes to the development of affordable, intelligent assistive tools that enhance autonomy, safety, and daily accessibility for individuals with visual impairments.
subject: Articles
  topic: section
subject: AI-based visual aid
  topic: keywords
subject: wearable navigation system
  topic: keywords
subject: object detection
  topic: keywords
subject: ultrasonic distance measurement
  topic: keywords
subject: Raspberry Pi 4
  topic: keywords
subject: SSD Lite-MobileNet
  topic: keywords
creator: Godwin C V
creator: Hiba Afzel
creator: Kirandas K
creator: Subeena K S
creator: Nivya K Venu
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-09-02
publication date: 2026-09-02
starting page: 144
ending page: 150

AI-Powered Water Quality Detection and Purification Recommendation System Using Refactor Index
type: item
description: The proposed project introduces a smart water quality detection and purification recommendation system powered by artificial intelligence. The system uses sensors such as pH, turbidity, temperature, and dissolved oxygen to collect water data in real time. An ESP32 microcontroller with Wi-Fi and Bluetooth processes the readings and calculates a Refactor Index (RI), a single score that classifies water as “Good,” “Moderate,” “Poor,” or “Unsafe.” Results are displayed on an OLED screen and shared with users through a mobile application. Based on the RI score, the system also suggests suitable purification methods such as boiling, filtration, or UV treatment. This solution is low-cost, portable, and eco-friendly, making it useful for households, rural communities, aquaculture, and agricultural applications. By integrating AI and IoT technologies, the project ensures real-time monitoring, reduces health risks, and promotes sustainable water management.
subject: Articles
  topic: section
subject: Water Quality Detection
  topic: keywords
subject: Refactor Index
  topic: keywords
subject: IoT Sensors
  topic: keywords
subject: ESP32 Microcontroller
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Real-Time Monitoring
  topic: keywords
subject: Environmental Sustainability
  topic: keywords
subject: Smart Water System
  topic: keywords
creator: Abin Santhosh
creator: Adhith Sunil
creator: Adhwaith TT
creator: Anitta Raphi E
creator: Meethu M B
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
starting page: 135
ending page: 143

Medical Tracker App and Wearable Health Band
type: item
description: This system integrates a mobile-based medical tracker application with a wearable health band to deliver a comprehensive and proactive healthcare management solution. The mobile application acts as a centralized platform for managing medication schedules, tracking medicine supplies, and issuing timely refill alerts to ensure consistent treatment adherence. It also includes a smart lookup feature that uses real-time data to locate nearby pharmacies with the required medicines available, enhancing convenience and accessibility. The wearable health band complements this by providing continuous monitoring of vital parameters such as heart rate and blood pressure, along with an automatic fall detection and alert mechanism to ensure immediate response during emergencies. Together, these components create a seamless ecosystem that connects medication management, real-time biometric monitoring, and location-based assistance. This integrated approach not only improves patient compliance and emergency readiness but also promotes independent and safer health management, particularly benefiting elderly individuals and patients with chronic conditions.
subject: Articles
  topic: section
subject: Mobile Medical Tracker
  topic: keywords
subject: Wearable Health Band
  topic: keywords
subject: Real-Time Monitoring
  topic: keywords
subject: Medication Management
  topic: keywords
creator: Fletteesh Thomas C
creator: Navafa P M
creator: Nihal Rahiman N P
creator: Shima K V
creator: Aysha
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
starting page: 125
ending page: 134

Robertanet: Enhancedroberta Transformer Based Model for Cyber Bullying Detection with Glove Features
type: item
description: Social media has become a vital platform for communication, but it also encourages harmful behaviours such as cyberbullying, trolling, and hate speech. Manual moderation is slow and expensive, making automatic detection essential. This work presents an enhanced RoBERTa transformer model combined with GloVe word embeddings to detect cyberbullying in tweets. The system is tested against various machine learning and deep learning models and achieves about 95% accuracy, along with high precision, recall, and F1 scores. Cross-validation further confirms its reliability. The results show that advanced transformer models supported by effective feature representation can provide a strong solution for detecting cyberbullying on social media. Beyond academic evaluation, such a system can be valuable in protecting vulnerable users, especially teenagers, from psychological harm. Moreover, the approach has potential to be adapted across multiple social platforms, making it a promising step towards safer digital communities.
subject: Articles
  topic: section
subject: Human Trafficking
  topic: keywords
subject: Missing Children
  topic: keywords
subject: Facial Recognition
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: VGG-Face
  topic: keywords
subject: KNN Classifier
  topic: keywords
subject: CCTV Surveillance
  topic: keywords
subject: NLP
  topic: keywords
subject: Real-Time Detection
  topic: keywords
subject: AI in Law Enforcement
  topic: keywords
creator: Aneena anto
creator: Ann Mariya Joju vengakkal
creator: Ansel shanavas
creator: Anu Krishna K
creator: Akhila V A
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
starting page: 116
ending page: 124

AI Platform for Missing Children and Human Trafficking Detection
type: item
description: Human trafficking and child abduction remain critical global challenges due to fragmented data systems and the lack of real-time monitoring tools. Traditional search processes involving manual record verification and public alerts are often time-consuming and inefficient. This project proposes an AI-driven platform for Missing Children and Human Trafficking Detection, integrating facial recognition, pattern analysis, and natural language processing (NLP) to enable proactive victim identification. The system unifies multiple data sources — including CCTV feeds, FIR records, travel logs, and social media intelligence — into a centralized web portal. Using a VGG-Face-based Convolutional Neural Network (CNN) for deep facial feature extraction and a K-Nearest Neighbor (KNN) classifier for efficient matching, the platform automatically identifies missing individuals or traffickers across surveillance networks. Once a match is found, the system sends real-time alerts to concerned authorities and families. The model demonstrates high accuracy even under variations in lighting, pose, and image quality. The proposed framework enhances the speed, accuracy, and scalability of investigations, offering an effective AI-assisted approach for law enforcement and social welfare agencies. Future extensions include mobile integration, predictive route analytics, and national-level deployment for cross-border trafficking prevention.
subject: Articles
  topic: section
subject: Human Trafficking
  topic: keywords
subject: Missing Children
  topic: keywords
subject: Facial Recognition
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: VGG-Face
  topic: keywords
subject: KNN Classifier
  topic: keywords
subject: CCTV Surveillance
  topic: keywords
subject: NLP
  topic: keywords
subject: Real-Time Detection
  topic: keywords
subject: AI in Law Enforcement
  topic: keywords
creator: Abdul Hadhi T A
creator: Abhijith V Nair
creator: Ambady Babu
creator: K G Ananthu Krishna
creator: Athira Bose
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
starting page: 106
ending page: 115

AI-Powered Android Application for Fruit and Vegetable Quality Detection
type: item
description: The proposed project introduces a mobile app that helps people check the quality of fruits (like apples, bananas, and oranges) using artificial intelligence. The app works on Android phones and uses a powerful deep learning model called EfficientNetB5. It looks at pictures of fruits and tells whether they are “Good”, “Bad”, or “Mixed” (a mix of both good and bad signs). Users can take a photo or upload one in the app, and it will quickly check the fruit’s freshness. This is useful for both customers and shopkeepers. It helps people choose better fruits and avoid waste. For shops and food companies, the app offers a cheap and smart way to sort out bad fruits, and even allows robotic help in the future. This app shows how smart technology can make daily life easier, help the environment by reducing food waste, and support better decisions when buying or selling fruits and vegetables.
subject: Articles
  topic: section
subject: Deep Learning
  topic: keywords
subject: EfficientNetB5
  topic: keywords
subject: TensorFlow Lite
  topic: keywords
subject: Fruit Quality Detection
  topic: keywords
subject: Image Classification
  topic: keywords
subject: AI in Agriculture
  topic: keywords
creator: Devika C S
creator: Roshni
creator: Joyna
creator: Teena
creator: Remya P C
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-05
publication date: 2026-02-05
starting page: 100
ending page: 105

Edge Artificial Intelligence for Internet of Things: A Systematic Review of Recent Advances
type: item
description: Edge Artificial Intelligence (Edge AI) has become a crucial component in the Internet of Things (IoT) ecosystem, enabling real-time decision-making, enhanced privacy, reduced latency, and efficient data processing at the network’s edge. This research paper provides a comprehensive review of ten significant publications from 2020 to 2024 focusing on architectures, frameworks, optimization strategies, security mechanisms, and performance improvements in Edge AI for IoT. The review highlights commonalities, trends, research gaps, and future directions. Despite rapid advancements, various challenges such as limited resources, interoperability issues, and cybersecurity threats remain. The paper concludes by identifying promising future research opportunities that can shape the next generation of intelligent IoT systems.
subject: Articles
  topic: section
subject: Edge AI
  topic: keywords
subject: IoT
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Optimization
  topic: keywords
creator: Priya P P
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 264
ending page: 270

Foundations and Methodologies of Rule-Based Semantic Role Labelling: A Review
type: item
description: Semantic Role Labeling (SRL) provides a structured semantic interpretation of natural language by identifying predicate–argument relationships that encode event-level information such as agentivity, affected entities, location, and temporality. As a foundational layer in the NLP pipeline, SRL leverages syntactic features from part-of-speech tagging, chunking, and dependency parsing to generate semantic representations that support downstream tasks including question answering, information extraction, and machine translation. This review presents a detailed technical analysis of traditional rule-based SRL, which operationalizes linguistic theory through deterministic mappings between syntactic configurations and semantic roles. The methodology encompasses sentence segmentation, tokenization, POS tagging, lemmatization, predicate detection, syntactic dependency analysis, rule application, and role validation. Semantic roles are assigned through handcrafted patterns derived from grammatical relations such as nsubj, obj, iobj, and specific prepositional structures. While rule-based SRL ensures interpretability, structural consistency, and independence from annotated corpora, its reliance on manually engineered rules restricts generalization to diverse or ambiguous linguistic inputs. This paper rigorously evaluates these mechanisms, highlighting the architectural characteristics, strengths, and inherent limitations that define rule-based SRL within modern computational linguistics.
subject: Articles
  topic: section
subject: POS Tagging
  topic: keywords
subject: Dependency Parser
  topic: keywords
creator: Jiji A J
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 271
ending page: 279

Compressive Sensing: A Survey on Theory, Algorithms and Applications
type: item
description: Compressive Sensing (CS) is an advanced signal acquisition paradigm that enables the reconstruction of sparse or compressible signals from far fewer samples than required by classical Nyquist–Shannon sampling theory. This paper provides a comprehensive overview of the fundamental principles, algorithmic developments, and practical applications of CS. It begins by examining the core concept of sparsity, which forms the theoretical foundation for efficient signal representation in suitable transform domains. The paper then discusses the role of sensing matrices and their influence on measurement stability, robustness, and feasibility in real-world systems. Various reconstruction algorithms including optimization-based, greedy, and iterative thresholding techniques are analysed with respect to their computational complexity and reconstruction performance. Additionally, the paper highlights factors affecting CS performance, such as sparsity level, noise tolerance, and algorithmic scalability. Beyond theoretical insights, this work explores major application areas where CS has demonstrated significant impact, including medical imaging, wireless communications, radar systems, remote sensing, and low-power Internet-of-Things (IoT) devices. Recent advancements integrating deep learning with CS frameworks are also reviewed, underscoring the growing shift toward data-driven reconstruction methods. Overall, this paper aims to provide a unified understanding of CS, emphasizing its relevance, challenges, and emerging research directions in modern signal processing.
subject: Articles
  topic: section
subject: Compressive Sensing
  topic: keywords
subject: Sparse Representation
  topic: keywords
subject: Reconstruction Algorithms
  topic: keywords
subject: Signal Processing
  topic: keywords
subject: Sensing Matrices
  topic: keywords
creator: Akhila Arjunan T
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 252
ending page: 263

Real-Time Automatic Speech Recognition Using Deep Learning
type: item
description: Real-time speech recognition has evolved dramatically with the introduction of deep learning architectures, enabling high accuracy, low latency, and robust performance across diverse acoustic conditions. This paper provides a comprehensive review and proposed framework using state-of-the-art models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Transformers, and end-to-end architectures like DeepSpeech and wav2vec 2.0. A complete system workflow, block diagrams, algorithmic steps, results, and conclusions are also presented. These models enable efficient parallelization, improved context modeling, and robust performance under real-world noise conditions, making them suitable for applications such as AI assistants, streaming transcription services, conversational AI, navigation systems, and edge-deployed embedded devices. Despite these advancements, achieving real-time performance remains challenging due to factors such as inference latency, memory footprint, streaming complexity, and the difficulty of processing long utterances in low-resource environments. This paper presents a comprehensive study of state-of-the-art deep learning architectures for real-time Automatic Speech Recognition (ASR), highlighting their design principles, computational characteristics, model variants, and deployment considerations. A detailed analysis of Conformer and RNN-T based streaming systems is provided, along with illustrations, data flow diagrams, and experimental insights. The paper also discusses ongoing challenges including multilingual adaptation, noise robustness, and on-device model optimization and outlines future research directions toward more efficient, scalable, and human-level real-time speech recognition systems.
subject: Articles
  topic: section
subject: Speech Recognition
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: LSTM
  topic: keywords
subject: RNN
  topic: keywords
subject: Transformer
  topic: keywords
subject: End-to-End Models
  topic: keywords
subject: Real-Time Processing
  topic: keywords
creator: Minu Mohan
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-05
publication date: 2026-02-05
starting page: 89
ending page: 99

Eye Disease Detection Enhancement Using a Multi-Stage Deep Learning Approach
type: item
description: Eye disease detection enhancement using a multi-stage deep learning approach introduces a lightweight and multi-stage deep learning framework for the automatic detection of eye diseases using retinal OCT and fundus images. The proposed model is designed to achieve a balance between accuracy and computational efficiency, making it ideal for real-world deployment, particularly in resource-limited clinical environments. The system begins with a robust preprocessing phase that normalizes and enhances the input images while addressing issues related to rotation, translation, and illumination variations. This step ensures that the model remains invariant to positional and intensity distortions, thereby improving its robustness and consistency across diverse imaging conditions. The architecture of the model is composed of three sequential stages that work together to deliver highly accurate diagnostic predictions. In the first stage, fine-grained spatial and textural features are extracted from retinal images, allowing the network to capture subtle structural variations that often indicate early disease progression. The second stage integrates a dual-branch structure that combines convolutional feature blocks with identity mapping branches. This design helps the network simultaneously learn both low-level detailed features and high-level semantic representations, enabling a deeper and more comprehensive understanding of retinal abnormalities. The extracted features from both branches are then fused to retain discriminative information and eliminate redundancy before being passed into the final stage. In the classification stage, the merged feature representations are processed through fully connected layers and a softmax-based output unit to generate precise disease predictions. This multi-stage feature integration improves the model’s discriminative power and enhances its overall accuracy compared to conventional deep learning approaches. Experimental evaluations conducte…
subject: Articles
  topic: section
subject: Eye disease
  topic: keywords
subject: classification
  topic: keywords
subject: deep learning
  topic: keywords
subject: CNN
  topic: keywords
subject: Multi stage
  topic: keywords
creator: Nowreen Mohamedmon
creator: Nibin P N
creator: Sahal M A
creator: Sreehari E
creator: Neethu Prabhakaran
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-05
publication date: 2026-02-05
starting page: 80
ending page: 88

Sentiment Analysis on Social Media Using Deep Learning Techniques
type: item
description: Social media platforms generate vast amounts of opinion-rich data that reflect public emotions and attitudes. The project explores sentiment analysis using deep learning models to classify emotions in user-generated text. The system achieves high accuracy in identifying emotional polarity, helping interpret public opinion effectively. The project uses YouTube Data API, social media posts to extract comments from video links posted on social media. Comments are cleaned and passed through a multilingual sentiment analysis model based on BERT to classify emotions. The model supports both English and regional language inputs, including code-mixed text. Visual results are displayed as pie charts along with a comment-wise sentiment summary. The tool enables real-time sentiment extraction from video discussions using deep learning.
subject: Articles
  topic: section
subject: Sentiment Analysis
  topic: keywords
subject: Social Media
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Emotion Classification
  topic: keywords
subject: YouTube Data API
  topic: keywords
subject: Real-Time Sentiment Extraction
  topic: keywords
subject: Public Opinion
  topic: keywords
creator: Alen Shaju
creator: Hamis Ahammed K
creator: Jismi Joy
creator: Vysagh A U
creator: Aysha
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-05
publication date: 2026-02-05
starting page: 70
ending page: 79

Diabetic Retinopathy Detection Using Machine Learning Techniques
type: item
description: Diabetic Retinopathy (DR) is an eye disease, occurs because of diabetes that makes lesions in the retina and affect vision. If it is not noticed early, it will be led to blindness. If DR could find out early and get treated, the vision loss can be avoided. Manual diagnosis process takes long time, more cost effective and it is more complicated. Therefore, Computer Aided Diagnosis Systems can be used to reduce cost, time, and other serious complications. Many studies are taking place in this area, by using the Traditional Machine Learning Methods like Support Vector Machine (SVM), Random Forests (RF), Logical Regression (LR) etc. The researchers are now a days focusing to keep the security and privacy and to increase the efficiency and accuracy, hence Federated Learning (FL) is a good method for this. FL ensures Patient’s data Security and Privacy and also ensure accuracy of the Result.
subject: Articles
  topic: section
subject: Deep Learning
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
subject: Diabetic Retinopathy
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Support Vector Machine
  topic: keywords
creator: Gaya Nair P
creator: Dr. Lanitha B
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-20
publication date: 2026-01-20
starting page: 1
ending page: 9

Cloth Changing Person Re-Identification Based on Backtracking Mechanism
type: item
description: Person Re-Identification (Re-ID) plays a vital role in intelligent surveillance systems, enabling the tracking of individuals across different cameras. However, most existing systems rely heavily on clothing-based features, leading to performance failures when a person changes attire. To overcome this limitation, this paper presents a novel approach Cloth-Changing Person Re-Identification based on the Backtracking Mechanism. The system combines identity and body-shape features to achieve clothing-invariant recognition. A dual-branch architecture utilizing ResNet50 for appearance-based feature extraction and HRNet for body-shape modeling is implemented. The proposed feature infiltration and clothes suppression loss functions enhance discrimination while minimizing clothing bias. The approach achieves improved recognition accuracy under varying apparel conditions, making it practical for long-term surveillance and public safety. This integrated framework strengthens intelligent monitoring systems, ensuring reliable identification despite clothing variations and contributing significantly to public safety and security advancements.
subject: Articles
  topic: section
subject: Person Re-identification
  topic: keywords
subject: Computer vision
  topic: keywords
subject: CCTV Camera Surveillance
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Security
  topic: keywords
subject: Fraud Prevention
  topic: keywords
subject: ResNet-50
  topic: keywords
creator: Alana K C
creator: Aleena K C
creator: Alenta Nixon
creator: Athul E R
creator: Aasha V C
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
starting page: 181
ending page: 190

Revolutionizing Crypto Investments
type: item
description: Managing crypto investments for retail investors is often hindered by high volatility, poor timing (buying at peaks and selling at lows), and the inherent risks of centralized platforms. This project introduces a decentralized, automated SIP model for crypto investments, offering a non-custodial and multi-asset investment protocol to limit these challenges. The system automates crypto investing like a Systematic Investment Plan (SIP). All SIP rules (amount, frequency, maturity) are enforced automatically by smart contracts, ensuring trustless and transparent execution. Users maintain full custody of their funds in non-custodial wallets like MetaMask, and investments are made directly using stablecoins (USDT/USDC) into crypto pools (BTC, ETH, SOL, BNB). The purchased assets are stored in a smart contract vault until maturity, promoting structured long-term investing and verifiable on-chain transparency. By leveraging smart contracts and dynamic frequency validation, the system provides a consistent, reliable, and non-custodial solution for long-term wealth building in the decentralized Web3 space.
subject: Articles
  topic: section
subject: Decentralized SIP
  topic: keywords
subject: Crypto Investments
  topic: keywords
subject: Smart Contracts
  topic: keywords
subject: Non-Custodial
  topic: keywords
subject: Volatility
  topic: keywords
subject: Web3
  topic: keywords
subject: Token Swaps
  topic: keywords
subject: Portfolio Tracking
  topic: keywords
creator: Devika TD
creator: Sangeeth Karunakaran
creator: Basudev Balachandran
creator: Shinas S
creator: Dr S Brilly Sangeetha
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-01-25
publication date: 2025-01-25
starting page: 35
ending page: 47

AI-Powered Food Recognition and Nutritional Analysis via Deep Learning
type: item
description: People today are becoming more health-conscious and want to keep track of what they eat. However, many struggle because they don’t have the right tools to identify or analyze their food accurately. Manual tracking can be time-consuming and unreliable, making it hard to maintain a healthy diet. To solve this, we have proposed a new system using artificial intelligence and image recognition technology. This system will make food identification faster and more accurate. With AI-based food recognition, users can simply take a picture of their meal and get instant details such as food type and nutritional information. This helps people monitor their eating habits and make better health choices. Unfortunately, many users and organizations are still unaware of how this technology works or how it can benefit them in daily life.
subject: Articles
  topic: section
subject: Food Recognition
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Image Processing
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Nutrition Analysis
  topic: keywords
subject: Health Monitoring
  topic: keywords
subject: Computer Vision
  topic: keywords
creator: Annsil Tomy A
creator: Amith Gigy
creator: Alenstil T S
creator: Vinu Jose T
creator: Remya P C
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-20
publication date: 2026-01-20
starting page: 24
ending page: 34

AI Powered Voicemail Prioritisation System
type: item
description: Managing voicemails in critical sectors such as healthcare, finance, and customer service is often hindered by delays, inefficiencies, and missed urgent messages. This project introduces an AI-powered voicemail system designed to intelligently capture, analyze, and prioritize voice messages, ensuring that high-urgency cases are addressed immediately. Using Twilio for voicemail capture and Google SpeechRecognition for transcription, the system combines text and audio analysis to assess both semantic content and vocal tone. BERT-based text classification evaluates urgency and routes messages to the appropriate department, while OpenSMILE and Random Forest models analyze acoustic features to detect stress, pitch, and speed, indicating urgency levels. PII is automatically redacted using SpaCy to maintain privacy and data security. By merging speech processing, NLP, and machine learning, the system offers a scalable, reliable, and efficient solution for modern communication management.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Voicemail Prioritization
  topic: keywords
subject: Speech Recognition
  topic: keywords
subject: Urgency Detection
  topic: keywords
subject: Privacy Protection
  topic: keywords
creator: Aflah Ali
creator: Ajul N D
creator: Anfil Hassan
creator: Sona Santhosh
creator: Athira EP
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-20
publication date: 2026-01-20
starting page: 10
ending page: 23

Mental Health Detection System Using Multimodal AI Techniques
type: item
description: Mental health problems such as depression, anxiety, and stress are increasing worldwide and often remain unnoticed because people hesitate to seek help, fear judgment, or lack proper support. Many individuals and healthcare providers want accurate assessments, but they often struggle to get real-time, objective information. To solve this, we have proposed a new system using Multimodal AI technology. This system uses text, audio, and video inputs to detect emotional states in real time. Text input is analyzed using NLP and sentiment analysis, audio input is processed through speech-to-text and tone-based feature extraction using Librosa, and video input is analysed using DeepFace and OpenCV for facial emotion recognition. The system combines these results to classify the user’s mental state as mild, neutral, or severe, and provides instant personalized suggestions through a secure web interface. AI is a powerful tool for mental health because it supports early detection, real-time monitoring, and personalized feedback, helping users manage their well-being more effectively.
subject: Articles
  topic: section
subject: Mental Health Detection
  topic: keywords
subject: Multimodal AI
  topic: keywords
subject: Emotion Recognition
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
subject: Real-Time Monitoring
  topic: keywords
subject: Text Analysis
  topic: keywords
subject: Audio-Visual Analysis
  topic: keywords
creator: Abhijith P M
creator: Akshay K L
creator: Alan Anil
creator: Anaswara C V
creator: Dr.Sugan J
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
starting page: 191
ending page: 202

PROPCHAIN: A Blockchain-Based Real Estate Platform
type: item
description: The real estate sector faces numerous challenges, including lack of transparency, fraudulent transactions, and delays in property registration. Traditional land record systems are often paper-based and centralized, making them vulnerable to tampering and data loss. To address these issues, a blockchain- based real estate management system has been proposed. This system ensures secure and tamper-proof property records by leveraging the decentralized nature of blockchain technology. Smart contracts are used to automate property transactions, reducing the need for intermediaries and minimizing human error. Every transaction is stored permanently on the blockchain, ensuring transparency, traceability, and trust among all parties involved. By eliminating middlemen and enhancing security, the proposed system creates a more efficient and reliable platform for property buying, selling, and ownership transfer.
subject: Articles
  topic: section
subject: Blockchain Technology
  topic: keywords
subject: Smart Contracts
  topic: keywords
subject: Transparency
  topic: keywords
subject: Fraud Prevention
  topic: keywords
subject: Real Estate
  topic: keywords
subject: Decentralized System
  topic: keywords
creator: Abhijith N S
creator: Abilash P S
creator: Aiswarya P S
creator: Anitta Mariya Shajan
creator: Anaswara Dinesh
rights: Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-30
publication date: 2026-01-30
starting page: 58
ending page: 69

Lung cancer detection using AI
type: item
description: Disease detection by artificial intelligence introduces the importance of AI ,convolutional neural networks(CNNs) in medical image analysis for diseases especially lung cancer.Early disease detection,accuracy and consistency,time efficiency,scalability,improving healthcare outcomes are the primary need of the project.Developing a machine learning model specifically by CNNs to detect and classify lung cancer from medical images.Medical images,preprocessing techniques,feature extraction,machine learning model are methods are used here.Use of Convolutional Neural Networks,image processing techniques etc innovative approaches in this project.The key outcomes of the project is high accuracy,automated diagnosis,improved healthcare outcomes.This project "Disease Detection By Artificial Intelligence" demonstrates potential of machine learning in automating disease detection using medical images by achieving an accuracy of 94.297%
subject: Articles
  topic: section
subject: CNN
  topic: keywords
subject: Classification
  topic: keywords
subject: Images
  topic: keywords
subject: attention
  topic: keywords
subject: analysis
  topic: keywords
creator: Nowreen Mohamedmon
creator: Sahal M A
creator: Nibin P N
creator: Sreehari E
creator: Athira Bose
rights: Copyright (c) 2025 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-05-22
publication date: 2025-05-22
starting page: 332
ending page: 338