IES International Journal of Multidisciplinary Engineering Research
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 trying to publish your findings, an academic eager to stay up to date on the latest developments, or a practitioner looking for practical insights, IESIJMER is your gateway to a multitude of options. Examine the Future with IESIJMER The future is emerging at the intersection of science and technology. By working together, we can build a future where creativity is unhindered and science leads the way.
AI-Driven Internet of Things: Architecture, Use Cases, and Issues
The Internet of Things (IoT) has revolutionized the interaction between physical devices and digital systems by enabling seamless real-time data acquisition, communication, and control. Through the deployment of interconnected sensors, actuators, and embedded systems, IoT facilitates continuous monitoring and intelligent automation across diverse domains. However, the rapid growth in the number of IoT devices has resulted in the generation of massive volumes of heterogeneous data, posing significant challenges related to data processing, scalability, latency, and efficient resource management. Traditional IoT architectures often struggle to handle these challenges due to limited computational intelligence and centralized data processing mechanisms. Artificial Intelligence (AI) significantly enhances IoT capabilities by enabling systems to become self-intelligent, adaptive, and autonomous. The integration of AI with IoT—commonly referred to as AI-enabled IoT or AIoT—allows devices and systems to analyze data, learn from patterns, predict future events, and make informed decisions with minimal human intervention. AI techniques such as machine learning (ML), deep learning (DL), and reinforcement learning empower IoT systems to perform advanced tasks including anomaly detection, predictive maintenance, pattern recognition, and real- time optimization. This paper presents an in-depth analysis of AI-enabled IoT systems with respect to their architectural frameworks, major enabling technologies, application domains, and associated challenges. It explores how intelligent data processing can be distributed across cloud, fog, and edge layers to improve system scalability, reduce latency, and enhance privacy. The integration of machine learning and deep learning models with IoT data streams is discussed in detail, highlighting their role in improving decision accuracy and operational efficiency. Furthermore, the concept of edge intelligence is examined, where AI algorithms are deployed closer to data sources to enable faster responses, reduced network congestion, and energy-efficient operation. In addition, this paper addresses key challenges faced by AI-enabled IoT systems, including data security and privacy, interoperability, model complexity, energy constraints, and ethical considerations. By analyzing current trends and future research directions, this study demonstrates how the convergence of AI and IoT technologies can lead to highly efficient, autonomous, and intelligent systems capable of transforming industries such as healthcare, smart cities, industrial automation, transportation, and agriculture.
Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
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.
Chordnova: Real-Time Chord Detection Web App
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.
Agriaerox: Drone Based Soil and Air Quality Tester
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.
Drone-based soil testing, precision agriculture technology,
Next-Generation Traffic Control: Adaptive Timer and Emergency Vehicle Priority in Intelligent Traffic
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.
AI Powered Face Recognition And Attendance System
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
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.
GPS Geofencing based Automatic Speed Control System for School Zone Safety
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.
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.
microcontroller (Arduino or Raspberry Pi)
With the rapid growth of the global elderly population, there is an increasing demand for smart assistive technologies that enhance quality of life. This project proposes a WiFi-enabled talking robot designed to provide companionship, automated reminders, and emergency assistance for elderly individuals. The robot uses speech recognition and synthesis for natural interaction and incorporates AI-based sentiment analysis to respond empathetically and support emotional well-being. WIFI connectivity enables remote monitoring and communication by caregivers through mobile or web applications. The system provides reminders for medications and daily activities and includes fall detection to alert caregivers during emergencies. A simple voice interface ensures ease of use, while smart home integration and entertainment features further enhance comfort and engagement. By integrating AI, IoT, and speech technologies, the proposed system promotes independent living, safety, and emotional support for elderly users, representing an effective and modern assistive solution.
Windomax: Advanced Wall Climbing and Window Glass Cleaning Robot
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.
An Enhanced Mac Scheme for 802.11 with Constant Time Contention using Sequencing Technique
Wireless networks offer a vast variety of uses by both business and home users. Now, the industry accepts a handful of different wireless technologies. Each wireless technology is defined by a standard that describes unique functions at both the Physical and the Data Link layers of the OSI Model. These standards differ in their specified signaling methods, geographic ranges, and frequency usages, among other things. Stations using the IEEE 802.11 Standard contend for the channel using the Distributed Coordination Function (DCF) because it’s performance degrades especially with the large number of stations. IEEE 802.11 standard specifies a MAC scheme called CONTI that resolve the contention (collision) in constant number of time slots. The main aim is to have only one station remaining in contention that will transmit the data. CONTI shows better performance when compared to other scheme like DCF based on the parameters like throughput, collision rate, delay etc. The performance can be further improved by introducing a sequencing technique via VBA algorithm. Simulation is done using NS2 (version 2.34).
Experimental Study on The Extraction of Heavy Metals from Landfill Liner by Electrokinetic Method
Municipal solid waste dumps must be used for the proper disposal of household biodegradable waste. Landfills usually construct liner systems to prevent leachate from contaminating groundwater. Compacted clay is frequently the most basic of the several types of liner systems. Clay's limited permeability is the main argument in favour of utilizing it as a liner. Even though the compacted clay liners are kept for their designated usage, misuse could make leachate (which is a water like substance formed by mixing up of the percolated water and the water from degraded wastes) flow through them more intense. Leachate frequently contains heavy metals including lead, mercury, and cadmium in addition to toxicants, which can eventually seep into the groundwater and soil. Because they harm ecosystems and contaminate drinking water supplies, these toxic metals can pose a serious threat to human health and the environment. Thus, as a cost-effective alternative to conventional landfill liners, this study investigates the feasibility of using locally accessible bentonite and hydrated lime- stabilized clay. The study will examine the clay's geotechnical properties, including compressibility, shear strength, and hydraulic conductivity, to determine whether it is suitable for use as landfill liners. Reducing the heavy metal contamination of the clay liner is the aim of this effort. To avoid contaminating landfill liners, heavy metals will be reduced electrokinetically. This project report gives a summary of the work completed, the methodology, the test setup, and the current state of the project.
Comparison of Machine Learning Algorithms in Predicting the Cutting Force for Thermal Assisted Machining of Ti6Al4V
This paper is focused to predict cutting force of the titanium alloy (Ti–6Al–4V) due to the effect of feed rate, speed and temperature during the machining operation. The design of experiments was used to generate various combinations of feed rate, speed and temperature. Finite element modeling (FEM) using Abaqus used for simulate the machining parameters. The obtained results from FEM model and the experimental work are used to train the model using machine learning algorithm. The trained model is allowed to predict cutting force for various combinations of feed rate, speed and temperature. To verify the accuracy, regression analysis has been adopted in the paper to develop a second prediction model for cutting force.
Document Transporting System Using Intelligent Robot
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.
Signal Processing Techniques in 5G Technology
The advent of 5G technology has brought forth a paradigm shift in wireless communication, promising unprecedented data rates, ultra-low latency, and massive connectivity. At the heart of 5G lies advanced signal processing techniques, which play a pivotal role in realizing its ambitious goals. This paper presents a comprehensive review of the key signal processing techniques employed in 5G networks. Beginning with an overview of the fundamental principles of 5G communication systems, encompassing Massive Multiple-Input Multiple-Output (Massive MIMO), 3D beamforming and diversity, and Orbital Angular Momentum (OAM)-based multiplexing. The discussion delves into the intricacies of these techniques, highlighting their role in enhancing spectral efficiency, capacity, and reliability in next generation wireless systems. Techniques emerging technologies in 5G, such as millimeter-wave communications, cloud radio access networks (C-RAN), full-duplex mode operation, and digital radio-frequency processing. The discussion of these emerging techniques offers insights into the evolving landscape of 5G signal processing and its potential implications for future network architectures and applications. Moreover, this review discusses the challenges and research directions in signal processing for future 5G enhancements, including machine learning-based approaches and cooperative signal processing strategies. By synthesizing insights from recent research advancements and standardization efforts, this review provides a comprehensive understanding of the signal processing landscape in 5G technology, paving the way for future innovations and deployments in next-generation wireless networks.
With the growing need for sustainable and efficient farming practices, mechanization and renewable energy integration have become essential in modern agriculture. This paper presents a solar-powered seed spraying machine, designed to enhance the seed sowing process by automating seed distribution while eliminating reliance on non-renewable energy sources. The machine utilizes solar panels to generate electricity, which powers a motorized seed spraying mechanism, ensuring dispersal of seeds across the field. By replacing traditional manual sowing methods, this innovation aims to reduce labor costs, improve sowing accuracy, and increase overall productivity. The machine operates on a straightforward seed spraying mechanism, where seeds are released from a storage unit and dispersed over the soil. The solar energy system ensures uninterrupted operation during daylight hours, making the machine particularly beneficial for farmers in remote or off-grid areas. The compact and lightweight design allows for easy maneuverability, making it adaptable to different terrains and farming scales. Additionally, the system minimizes seed wastage and promotes optimal germination by maintaining a consistent seed distribution rate. By integrating renewable energy with agricultural automation, this paper offers a cost-effective, eco-friendly, and efficient alternative to traditional seed sowing techniques. It supports sustainable farming practices, reduces dependence on fossil fuels, and enhances productivity, making it a valuable tool for modern farmers looking to adopt advanced yet affordable agricultural technologies.
Blind individuals face significant challenges in navigating their surroundings independently. Our Advanced Blind Assistance System aims to enhance their mobility and confidence by integrating AI-driven technologies for real-time assistance. This system is equipped with text-to-speech functionality, allowing it to read printed text and convert it into audio output via a speaker. Additionally, it utilizes object detection to recognize day-to-day objects, informing the user about their surroundings. A key feature of our system is face recognition, enabling blind individuals to identify friends and loved ones, enhancing social interactions. Furthermore, an emergency alert button is integrated to provide safety assurance. When activated, it notifies the user’s close contacts via a dedicated mobile application, sharing the exact real-time location of the blind person (available in the upgraded version). By combining AI, IoT, and voice assistance, our project empowers visually impaired individuals to perform daily tasks with greater ease, fostering independence and improving their quality of life.
AI-based Advanced Blind Assistance System that integrates computer vision
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.
AI-Powered Water Quality Detection and Purification Recommendation System Using Refactor Index
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.
Medical Tracker App and Wearable Health Band
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.
Robertanet: Enhancedroberta Transformer Based Model for Cyber Bullying Detection with Glove Features
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.
AI Platform for Missing Children and Human Trafficking Detection
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.
AI-Powered Android Application for Fruit and Vegetable Quality Detection
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.
Leveraging the AWS Marketplace for Accelerated Life Sciences
The life sciences industry is increasingly adopting cloud-based digital infrastructures to meet demands for faster drug discovery, scalable bioinformatics, and compliant data management. AWS Marketplace has emerged as a pivotal ecosystem that provides on-demand access to validated software, datasets, machine learning models, and workflow solutions tailored for scientific research. This paper analyzes how AWS Marketplace accelerates life sciences operations through rapid deployment, reduced infrastructure overhead, and enhanced reproducibility. The study further evaluates architectural considerations, leading use cases, and strategic recommendations for organizations aiming to modernize their R&D environments. The life sciences domain—including pharmaceuticals, biotechnology, genomics, public health, and clinical research—is undergoing an unprecedented transformation driven by computational science, AI, and cloud platforms. Traditional research workflows often face challenges such as limited computational power, siloed datasets, slow software procurement cycles, and complex regulatory constraints. AWS Marketplace provides a curated digital catalog of third-party software, data assets, and machine learning tools that can be deployed instantly within secure AWS environments. Its consumption-based licensing model, automated deployment, and interoperability with AWS services allow researchers and institutions to accelerate scientific discovery and reduce operational bottlenecks.
Edge Artificial Intelligence for Internet of Things: A Systematic Review of Recent Advances
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.
Foundations and Methodologies of Rule-Based Semantic Role Labelling: A Review
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.
Compressive Sensing: A Survey on Theory, Algorithms and Applications
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.
Real-Time Automatic Speech Recognition Using Deep Learning
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.
Eye Disease Detection Enhancement Using a Multi-Stage Deep Learning Approach
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 conducted on three benchmark datasets OCT 2017, Dataset-101, and Retinal OCT C8 demonstrated that the proposed model consistently outperforms existing architectures in terms of accuracy, precision, and recall. The results confirm that the combination of spatial, frequency, and contextual features contributes to better generalization across different types of retinal disorders. Moreover, the lightweight nature of the network significantly reduces computational load and memory usage without compromising performance. This efficiency makes the system particularly suitable for clinical environments with limited computational infrastructure, such as portable diagnostic devices or remote screening setups. Overall, the proposed multi-stage deep learning framework represents a powerful and efficient solution for automated eye disease detection. It enhances diagnostic reliability, supports early disease identification, and provides a scalable pathway toward integrating artificial intelligence into ophthalmic care and decision-support systems.