International Journal of Engineering and Science Research
Development of Smart Sensor-Based Robotic System for Automated Tasks
The rapid advancement in robotics and embedded systems has enabled the development of intelligent systems capable of performing tasks with minimal human intervention. This project focuses on the design and development of a smart sensor-based robotic system for automated tasks. The proposed system integrates multiple sensors, a microcontroller, and actuators to create an efficient and responsive robotic platform. The robot utilizes sensors such as ultrasonic sensors for obstacle detection, infrared sensors for path tracking, and temperature or gas sensors for environmental monitoring. These sensors continuously collect real-time data from the surroundings, which is processed by the microcontroller to make intelligent decisions. Based on the sensor inputs, the robot can navigate autonomously, avoid obstacles, and perform specific predefined tasks such as object detection, monitoring, and transportation. The system is designed to improve efficiency, accuracy, and safety in various applications, including industrial automation, smart surveillance, and environmental monitoring. The use of automation reduces human effort and minimizes errors in repetitive or hazardous tasks. Additionally, the robotic system is cost-effective and can be easily expanded by integrating advanced technologies such as wireless communication and artificial intelligence.
Smart Robotic System, Embedded Systems, Sensor-Based Robot, Microcontroller, Ultrasonic Sensor, Infrared Sensor, Autonomous Navigation, Obstacle Detection, Industrial Automation, Environmental Monitoring
Structural Behavior Of Reinforced Concrete Shear Walls With Openings: A Review
Reinforced concrete (RC) shear walls are primary lateral-load resisting elements in medium- and high-rise buildings, yet their performance is frequently interrupted by architectural and service openings required for doors, windows, ducts and corridors. This review research paper synthesizes past experimental, analytical and numerical work on RC shear walls with different opening configurations, with emphasis on how opening size, shape, position, number and reinforcement detailing influence strength, stiffness, ductility, energy dissipation and failure mode. The reviewed literature indicates that openings reduce the effective wall web area and disturb diagonal compression fields, causing stress concentration at corners and premature cracking when boundary elements, coupling beams and diagonal reinforcement are insufficient [1]. Centrally placed small openings can often be accommodated with moderate reductions in capacity, whereas large, eccentric, staggered or multiple openings may transform a solid wall into a coupled wall system governed by pier flexure, spandrel shear and localized crushing. Meta-analysis of reported trends shows that opening area ratio and shear-span ratio are the most consistent predictors of degradation, while detailing around openings controls post-peak stability. Modern finite-element, strut-and-tie and performance-based design studies improve prediction, but uncertainties remain for irregular openings, low-aspect-ratio walls, high axial load, cyclic degradation and post-installed penetrations. The paper concludes that safe design requires configuration-specific assessment rather than simple gross-area reduction, and it recommends integrated experimental databases, validated nonlinear models and practical detailing rules for resilient walls with openings. It also emphasizes practical evaluation of existing buildings where new openings are added after construction without original seismic design intent [2]
Reinforced concrete shear wall; opening configuration; seismic performance; stiffness degradation; ductility; finite-element modelling; meta-analysis
Energy And Spectrally Efficient Signalling For Next Generation Iot
The rapid expansion of Internet of Things (IoT) applications has created a demand for communication systems that are both energy-efficient and spectrally efficient. Narrowband IoT (NB-IoT), operating within a limited bandwidth of 180 kHz, faces challenges in achieving high data rates while maintaining low power consumption. This project proposes an energy- and spectrally efficient signalling technique based on Hilbert Transform-assisted Fast Orthogonal Frequency Division Multiplexing (HT-FOFDM) combined with Pulse Amplitude Modulation (PAM). The proposed method aims to enhance spectral efficiency by compressing subcarrier spacing while maintaining energy efficiency suitable for low-power IoT devices. The system is designed and evaluated under Additive White Gaussian Noise (AWGN) channel conditions, and its performance is compared with the conventional Single Carrier Frequency Division Multiple Access (SC-FDMA) scheme. Simulation results demonstrate that the proposed HT-FOFDM system achieves higher spectral efficiency, improved Bit Error Rate (BER) performance, and reduced power consumption within the same bandwidth constraints. The proposed approach provides a promising solution for next-generation IoT communication systems and can be further extended to support massive connectivity in future wireless networks.
Internet of Things (IoT), Narrowband Internet of Things (NB-IoT), Hilbert Transform, Fast Orthogonal Frequency Division Multiplexing (FOFDM), HT-FOFDM, Pulse Amplitude Modulation (PAM), Spectral Efficiency, Energy Efficiency, Bit Error Rate (BER), Additive White Gaussian Noise (AWGN), Single Carrier Frequency Division Multiple Access (SC-FDMA), Wireless Communications
Investigation on paddy crop disease detection and classification using SVM and CNN Algorithms
Rice cultivation plays a fundamental role in ensuring food security and sustaining agricultural economies, particularly in developing countries. One of the major factors affecting rice productivity is the occurrence of leaf diseases, which significantly reduce crop yield when not identified at an early stage. Conventional disease diagnosis relies on visual inspection by farmers or agricultural experts, making the process time-consuming, subjective, and often inaccurate. Although deep learning techniques have shown promising results in automated disease recognition, many standalone convolutional neural network (CNN) models experience performance degradation when dealing with visually similar disease categories and complex field conditions. To address these challenges, this paper presents PaddyCare AI, an intelligent paddy leaf disease diagnosis system based on a hybrid deep learning architecture. The proposed framework employs the pre-trained MobileNetV2 network to perform efficient feature extraction while utilizing a Support Vector Machine (SVM) classifier to improve decision boundaries and classification performance. The model was trained and evaluated using a balanced dataset comprising five classes: Bacterial Blight, Blast, Brown Spot, Tungro, and Healthy leaves. Experimental results demonstrate that the proposed MobileNetV2–SVM hybrid framework achieves superior classification accuracy compared with conventional CNN-based classifiers while maintaining computational efficiency suitable for practical deployment. To enhance accessibility, the trained model is integrated into a web-based application that enables farmers to upload leaf images and receive immediate disease diagnosis along with preventive recommendations. The proposed system facilitates timely intervention, minimizes crop losses, improves agricultural productivity, and promotes precision farming through artificial intelligence.
Paddy Leaf Disease Detection, Deep Learning, MobileNetV2, Support Vector Machine, Computer Vision, Precision Agriculture, Artificial Intelligence, Image Classification, Smart Farming, Plant Disease Diagnosis
Computer Vision-Based Signal Processing For Gesture- Controlled Air Canvas
Human–Computer Interaction (HCI) is rapidly evolving towards touchless and intuitive systems. This project presents a Computer Vision-Based Signal Processing approach for a Gesture-Controlled Air Canvas, enabling users to interact with a computer using hand gestures without physical input devices. The system uses a webcam to capture real-time video input and processes it using computer vision techniques. MediaPipe is employed for accurate hand detection and tracking of 21 hand landmarks, while OpenCV is used for image processing and visualization. Signal processing techniques such as noise reduction and smoothing are applied to improve the stability and accuracy of hand movement tracking. Based on detected gestures, the system allows users to draw, erase, and select colors on a virtual canvas. The fingertip position is tracked continuously to create smooth and natural drawing strokes in real time. This project provides a low-cost, efficient, and user-friendly solution for touchless interaction. It has potential applications in smart classrooms, virtual presentations, accessibility systems, and human-computer interaction interfaces.
Human–Computer Interaction (HCI), Computer Vision, Gesture Recognition, Air Canvas, Signal Processing, MediaPipe, OpenCV, Hand Landmark Detection, Touchless Interaction, Real-Time Image Processing
Shaik Saniya, T srujana, Sravani varikuppala
Intrusion Detection System Using Regulated Patrolling Robot
Robotics and the Internet of Things (IoT) are rapidly transforming the field of remote monitoring and automation. This project presents the design and implementation of a Wi-Fi-controlled surveillance robot vehicle capable of real time video streaming and remote navigation through a web-based interface. The primary objective of the system is to develop a low-cost, portable, and efficient robotic platform that can be utilized for surveillance in environments where human access is difficult, hazardous, or impractical. The surveillance robot is built around the ESP32-CAM module, which integrates a microcontroller, Wi-Fi connectivity, and an onboard camera into a compact platform. An L298N motor driver is employed to control two BO motors, enabling the robot to move in forward, backward, left, and right directions. The entire system is powered by a rechargeable battery, making the robot fully wireless, portable, and suitable for remote operations. The ESP32-CAM functions as a web server, providing live video streaming to a smartphone or computer over a Wi Fi network. Users can access a web-based control interface to monitor real-time video and transmit navigation commands to the robot. This functionality enables the robot to operate as a mobile surveillance unit for monitoring both indoor and outdoor environments remotely. The proposed system offers an effective and economical solution for applications such as security surveillance, military monitoring, disaster rescue operations, industrial inspection, and home automation. The project demonstrates the successful integration of IoT and robotics technologies to develop a smart, scalable, and cost-effective surveillance robot vehicle.
Internet of Things (IoT), ESP32-CAM, Surveillance Robot, Wi-Fi Control, Real-Time Video Streaming, Robotics, Remote Monitoring, L298N Motor Driver, Web-Based Interface, Wireless Robot Vehicle
Gondhi Pranathi, Vemla Vaishnavi, Tanneru Srivani
Predictive Analysis of Gold Prices Using Machine Learning Approaches
Gold is one of the most important financial assets, and accurately predicting its price is a challenging task due to the influence of various economic and market-related factors. This study proposes an intelligent gold price prediction system that combines machine learning and deep learning techniques to improve forecasting performance. The proposed framework integrates structured financial data, including historical gold prices, inflation rates, interest rates, and exchange rates, with unstructured information such as financial news and social media sentiment to better capture market trends and investor behavior. Among the models evaluated, the Long Short-Term Memory (LSTM) model achieved the best performance, with an accuracy of 94.2%, a Root Mean Square Error (RMSE) of 2.31, and a Mean Absolute Error (MAE) of 1.87, outperforming traditional machine learning models such as Linear Regression and Support Vector Machine (SVM). The proposed approach also incorporates sentiment analysis and Explainable Artificial Intelligence (XAI), enabling not only more accurate predictions but also greater transparency in understanding the factors influencing the model's decisions. The results demonstrate that the proposed framework is an effective, reliable, and scalable solution for real-time gold price forecasting and can support investors, analysts, and financial institutions in making informed decisions.
Gold Price Prediction, Machine Learning, Deep Learning, LSTM, Natural Language Processing (NLP), Sentiment Analysis, Time Series Forecasting, Explainable AI (XAI), Economic Indicators
Shaik Abdul Aziz, Mohammed Abdul Razzak, Mohammed Safwan
An Ensemble Deep Learning Model for Vehicular Engine Health Prediction
Predictive maintenance is increasingly vital in the automotive sector to ensure engine reliability, safety, and cost efficiency. Modern engines generate complex, multivariate sensor data, making early detection of failures challenging. This study presents a comprehensive vehicular engine health monitoring framework using an ensemble of machine learning models, including Logistic Regression, Support Vector Machines, Random Forest, XGBoost, and deep learning approaches. The system incorporates rigorous preprocessing steps—duplicate removal, median-based imputation, feature scaling, and Synthetic Minority Over-sampling Technique (SMOTE) - to handle noisy and imbalanced datasets. Models were evaluated using metrics such as accuracy, precision, RMSE, MAE, confusion matrix, and AUC. Among all configurations, the Random Forest Classifier achieved the best performance with a balanced accuracy of 71%, demonstrating effective prediction of engine health under varying operating conditions. The framework was deployed via a Flask-based web application, enabling real time predictions and early warnings of potential engine faults. This approach provides a scalable, practical solution for reducing maintenance costs and improving vehicular safety.
Predictive Maintenance, Deep Learning, Ensemble Model, Vehicular Engine Health, Random Forest Classifier, Anomaly Detection, SMOTE, Real-Time Monitoring
Mohammed Osman Abdul Ghani, Md Ghouse, Shaik Ahsan Uddin
Machine Learning Approaches For Real-Time Carbon Emission Prediction And Mitigation
The project, “CO2 Emission Rating by Vehicles Using Data Science,” is a data-driven initiative aimed at assessing and rating the carbon dioxide (CO2) emissions of new light-duty vehicles available for retail sale in Canada in 2022. Leveraging the power of Python programming and employing sophisticated machine learning models, like the Random Forest Classifier and the Decision Tree Classifier, this project offers a comprehensive analysis of vehicle emissions. The dataset utilized for this project contains crucial information, including fuel consumption ratings, CO2 emissions in grams per kilometer, CO2 ratings on a scale from 1 (worst) to 10 (best), and smog ratings on a scale from 1 (worst) to 10 (best). These data elements provide a holistic perspective on the environmental performance of various vehicle models, allowing consumers and policymakers to make informed choices. We are planning to employ the Random Forest Classifier, a powerful ensemble learning algorithm, and the Decision Tree Classifier to build predictive models to achieve better training and testing accuracy by combining these advanced algorithms and a rich dataset, this project aims to contribute to sustainable transportation solutions and empowers consumers to make environmentally conscious decisions when purchasing vehicles. The CO2 Emission Rating system developed here serves as a valuable tool for evaluating the environmental impact of different vehicle models, helping reduce carbon emissions and mitigate climate change. The developed linear regression model achieved an accuracy of 99.447%, demonstrating its robustness in predicting vehicle CO₂ emissions and supporting reliable real-time mitigation strategies.
Carbon Emission Rating, Machine Learning Models, LSTM, Fuel Consumption, Decision Tree Classifier, Random Forest Classifier, Linear Regression Accuracy, Deep Learning, Ensemble Methods, Data Science For Sustainability
Sadiya Fatima, Mohammed Mubashir Ahmed, Salwa Baig
Enhancing Thyroid Disease Prediction: Integrating Machine Learning Models for Improved Diagnostic Accuracy and Interpretability
Thyroid disorders represent a substantial global public health burden, frequently remaining undetected until significant clinical complications arise. This paper presents a comprehensive machine learning framework for early and accurate thyroid disease prediction, systematically integrating four established classification algorithms: Logistic Regression, Random Forest, Gradient Boosting, and XGBoost. The proposed system addresses the critical challenge of class imbalance through the Synthetic Minority Over-sampling Technique (SMOTE), and employs rigorous preprocessing including missing value imputation, categorical encoding, and recursive feature elimination. Feature interpretability is operationalized via SHAP (SHapley Additive exPlanations) values, enabling clinical transparency and physician trust. Experimental evaluation on the UCI Machine Learning Repository thyroid dataset demonstrates that the ensemble-based XGBoost model achieves 98.6% accuracy, 97.8% precision, 98.1% recall, and a macro F1-score of 97.9%, outperforming all individual classifiers evaluated. The framework further incorporates a concept drift monitoring module to maintain adaptive model performance in dynamic clinical environments. This integrated solution provides a robust, interpretable, and clinically deployable tool for thyroid disease screening and diagnosis support.
Thyroid Disease Prediction, Machine Learning, XGBoost, Random Forest, Gradient Boosting, Logistic Regression, SMOTE, SHAP, Explainable AI, Feature Engineering, Ensemble Learning, UCI Repository, Clinical Decision Support, Concept Drift, Interpretability
Danish Arfan Khan, Ghulam Mohammed Khan Asim, Mohammed Abdul Noman
Road Accident Analysis and Accident Severity Prediction
Due to the exponentially increasing number of vehicles on the road, the number of accidents occurring on a daily basis is also increasing at an alarming rate. With the high number of traffic incidents and deaths these days, the ability to forecast the number of traffic accidents over a given time is important for the transportation department to make scientific decisions. In this scenario, it will be good to analyze the occurrence of accidents so that this can be further used to help us in coming up with techniques to reduce them. Even though uncertainty is a characteristic trait of majority of the accidents, over a period of time, there is a level of regularity that is perceived on observing the accidents occurring in a particular area. This regularity can be made use of in making well informed predictions on accident occurrences in an area and developing accident prediction models. In this Work, we have studied the inter relationships between road accidents, condition of a road and the role of environmental factors in the occurrence of an accident. We have made use of data mining techniques in developing an accident prediction model using Apriori algorithm and Support Vector Machines. Bangalore road accident datasets for the years 2014 to 2017 available in the internet have been made use for this study. The results from this study can be advantageously used by several stakeholders including and not limited to the government public work departments, contractors and other automobile industries in better designing roads and vehicles based on the estimates obtained.
Road Accident Prediction, Data MIning, Apriori Algorithm, Support Vector Machines, Bangalore Accident Dataset, Traffic forecasting
Mohammed Salman Ateef, Mohd Abdul Danish, Mohd Abdul Jameel
Innovative Flood and Landslide Prediction using Machine Learning
Natural disasters such as floods and landslides pose serious threats to human life, infrastructure, and the environment, particularly in regions with complex geographical conditions and vulnerable populations. This study presents a machine learning–based framework for the prediction of floods and landslides using historical weather data, rainfall records, soil characteristics, and other environmental parameters. Several machine learning algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting, and XGBoost, were developed and evaluated to identify the most effective prediction models. Performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics. Among the tested models, Random Forest achieved the best performance for flood prediction, while Gradient Boosting provided superior results for landslide prediction, with both models attaining an accuracy of nearly 97% on the test dataset. Exploratory Data Analysis (EDA) was conducted to analyze data distribution, identify feature correlations, and detect outliers, thereby improving model efficiency and reliability. The proposed system was implemented as a Flask-based web application that enables real-time prediction, visualization, and model comparison. The results demonstrate the effectiveness of machine learning techniques in supporting disaster prediction and early warning systems.
Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting, Exploratory Data Analysis (EDA)
Shaik Irfan, Shaik Junaid Ahmed, Syed Abul Numan
A Survey on the State-of-the-art Multi-Task Learning Models: Techniques, Open Problems and Future Directions
Multi-Task Learning (MTL) is a type of machine learning technique where a model is trained to perform multiple tasks simultaneously. In deep learning, MTL refers to training a neural network to perform multiple tasks by sharing some of the network’s layers and parameters across tasks. In MTL, the goal is to improve the generalization performance of the model by leveraging the information shared across tasks. By sharing some of the network’s parameters, the model can learn a more efficient and compact representation of the data, which can be beneficial when the tasks are related or have some commonalities. MTL can be useful in many applications such as natural language processing, computer vision, and healthcare, where multiple tasks are related or have some commonalities. Despite the recent progress in deep learning, most approaches still go for a silo-like solution, focusing on learning each task in isolation: training a separate neural network for each individual task. Many real-world problems, however, call for a multi-modal approach and, therefore, for multi-tasking models. Multi-task learning (MTL) aims to leverage useful information across tasks to improve the generalization capability of a model. Hence, a detailed survey on the summarization on MTL model will be carried out in this abstract. In this survey, we provide a well rounded view on state-of-the-art MTL techniques within the context of deep learning. The contributions of the MTL model concern the following. First the MTL from a network architecture point-of-view will be considered. The comprehensive overview as well as examine the pros and cons of the recent familiar MTL techniques will be included. A variety of optimization schemes to undertake the joint learning of many tasks will be examined. The qualitative constituents of the works, their commonalities and the dissimilarities will be explored. Finally, a huge investigational assessment over various databases will be provided to examine the advantages and disadvantages of different models, including both architectural as well as optimization strategies.
Artificial Intelligence, Robotics, Cloud comput-ing, resource allocation, optimization
Swapnali Sunil Gawali, Dr. D. B. Kshirsagar
Exploring Deep Learning and Machine Learning Approaches for Brain Hemorrhage Detection
Brain hemorrhage is a critical medical condition that requires rapid and accurate diagnosis to minimize mortality and long-term neurological damage. Conventional diagnostic approaches rely on manual interpretation of CT scan images by radiologists, which can be time-consuming and may introduce variability due to human factors and limited availability of specialists. To address these challenges, this work presents a deep learning-based framework for automated brain hemorrhage detection from CT images. The proposed approach follows a structured pipeline that includes preprocessing, data augmentation, feature extraction, and classification. A custom Convolutional Neural Network (CNN) and a transfer learning-based InceptionV3 model are developed and evaluated on a dataset of 2,501 CT images. The dataset consists of two classes (normal and hemorrhage) and is divided using a stratified approach to ensure balanced training. Experimental results indicate that while the custom CNN achieves strong performance, the InceptionV3 model demonstrates superior accuracy, robustness, and generalization capability. The best performing model is further deployed as a Flask-based web application, enabling real-time predictions from uploaded CT images. These findings highlight the effectiveness of deep learning techniques as reliable decision-support tools for early diagnosis in clinical environments
Brain hemorrhage detection, deep learning, convolutional neural network, InceptionV3, transfer learning, CT scan, medical image classification, computer-aided diagnosis
Pakalapati Abhinav Varma, Mohd Irfan Uddin, Simeen Fatima Jameel
Blockchain Integrated AI for Secure Data Access and Sharing
The rapid expansion of digital ecosystems across healthcare, finance, and critical infrastructure has made secure data access and sharing a paramount challenge of the decade. Centralised access control systems are vulnerable to single points of failure, insider threats, and lack tamper-proof audit mechanisms. This paper presents a Blockchain-Integrated AI (BAIA) framework that orchestrates permissioned Hyperledger Fabric smart contracts with a transformer-based behavioural anomaly detection engine for real-time threat identification and adaptive permission management. Each access request is evaluated against a continuously updated AI risk model trained on 18 months of enterprise access logs comprising 4.2 million transactions across healthcare, financial, supply chain, and IoT domains. Smart contracts encode three tiers of access policy—static role-based rules, dynamic risk-adjusted escalations, and emergency lockdown—enforced deterministically on every network node. Experiments on a held-out three-month test set demonstrate that BAIA achieves 97.3% anomaly detection accuracy with a false positive rate of 0.8%, processes access requests at 310 ms median latency, and reduces unauthorised access incidents by 89.4% compared to rule-based baselines. A federated training protocol allows participating organisations to improve the shared AI model without exposing raw access logs. The immutable ledger provides cryptographically verifiable audit trails for every access event, combining the auditability of blockchain with the adaptability of machine learning.
Blockchain, Smart Contracts, Artificial Intelligence, Secure Data Access, Hyperledger Fabric, Anomaly Detection, Federated Learning, Data Integrity, Cybersecurity, Access Control
Anwar Shareef, Mohammed Abdul Bilal, Maohammed Naseer Uddin
Steganography Toolkit For Secure Image And Audio Messaging
With the rapid growth of digital communication and internet-based systems, ensuring the security and confidentiality of sensitive information has become a critical challenge. Traditional security methods such as cryptography protect the content of data but do not conceal its existence, making it vulnerable to detection and potential attacks. To address this limitation, steganography provides an effective solution by hiding secret information within digital media files such as images and audio, thereby enhancing the overall security of communication. This paper presents a Steganography Toolkit for Secure Image and Audio Messaging, which enables users to securely embed confidential messages into multimedia files. The proposed system utilizes the Least Significant Bit (LSB) technique for data embedding, ensuring minimal distortion in the carrier media. Additionally, the system incorporates password-based encryption to protect the hidden message and SHA-256 checksum verification to maintain data integrity and detect any unauthorized modifications. The toolkit is implemented as a web-based application using Python and Flask, providing a user-friendly interface for uploading carrier files, embedding secret messages, and generating stego files. Experimental results demonstrate that the proposed system maintains high media quality while achieving efficient data hiding. Performance evaluation using metrics such as Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) confirms that the embedding process introduces negligible distortion. Overall, the system offers a secure, efficient, and practical solution for covert communication and data protection in modern digital environments.
Steganography, Information Hiding, LSB Technique, Image Steganography, Audio Steganography, Secure Communication, Flask Framework
Mohd Fasi, Mohammed Mubasheeruddin Naser, Mr. Mohammed Mateen Ahmmed
The Fair Hire AI System is designed to support fair, transparent, and efficient recruitment by leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques. Traditional hiring processes are often influenced by human bias and time-consuming manual screening, which can impact the fairness and accuracy of candidate selection. The proposed system addresses these challenges by evaluating applicants solely on the basis of their skills, qualifications, and professional experience while eliminating sensitive personal information such as name, gender, and location to ensure an unbiased assessment process. Candidates can upload their resumes, which are analyzed using Natural Language Processing (NLP) techniques to extract relevant information and compare it with job requirements. Based on this analysis, the system generates candidate rankings and presents recruiters with a structured and user-friendly view of potential applicants through an intuitive interface. Additionally, the system ensures secure handling of candidate data and facilitates efficient recruitment management. By reducing manual effort, improving selection accuracy, and promoting fairness, transparency, and inclusivity, the Fair Hire AI System contributes to a more effective and equitable hiring process.
Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Fair Hiring, Recruitment System, Resume Screening, Candidate Ranking, Bias Reduction, Talent Acquisition, Data Security
The Web-Based Homemade Food Ordering System is a full-stack web application designed to bridge the gap between homemaker food providers and local consumers seeking fresh, hygienic, and affordable homemade meals. The platform enables homemakers to register, manage profiles, and list food items, while users can conveniently browse, search, and filter available food based on categories such as vegetarian, non-vegetarian, tiffins, and snacks. The system integrates location-based services (LBS) to display nearby food providers, improving accessibility and enhancing user experience. It also supports pre-ordering and subscription-based meal planning, allowing users to place advance orders for daily, weekly, or monthly food requirements. The application is developed using React.js for the frontend and Node.js with Express.js for backend services, along with a MySQL database for efficient data storage and management. The system follows a scalable client server architecture to ensure performance, reliability, and maintainability. The primary objective of this project is to empower home-based food entrepreneurs, especially homemakers, by providing them with a digital platform to sell homemade food, while simultaneously offering users a convenient, cost-effective, and healthy alternative to restaurant-based fast food services.
Homemade Food System, Web Application, React.js, Node.js, Express.js, MySQL, Location-Based Services, Food Ordering Platform, Subscription System, Pre-order System, Homemaker Empowerment, Online Food Delivery
Blockchain-Based Supply Chain Transparency For Agricultural Produce
Agricultural supply chains face significant challenges related to transparency, traceability, data security, and stakeholder trust. Traditional supply chain systems often rely on centralized databases and manual record-keeping processes, which can lead to information asymmetry, food fraud, inefficient logistics, and reduced profitability for farmers. To address these limitations, this paper presents a Blockchain-Based Supply Chain Transparency System for Agricultural Produce that leverages decentralized ledger technology to ensure secure, transparent, and tamper-resistant management of supply chain information. The proposed system records and tracks agricultural products throughout their lifecycle, including cultivation, harvesting, processing, transportation, storage, and retail distribution. By utilizing blockchain technology, every transaction is permanently recorded, time-stamped, and verified, ensuring data integrity and preventing unauthorized modifications. The framework incorporates Hyperledger Fabric, MongoDB, QR code technology, and smart contracts to facilitate secure data sharing and automated transaction processing among farmers, distributors, retailers, and consumers. Each product batch is assigned a unique QR code linked to its blockchain record, enabling consumers to instantly access verified information regarding product origin, quality, freshness, and supply chain history. Smart contracts further enhance efficiency by automating business processes and ensuring transparent and fair transactions between stakeholders. The proposed system improves product traceability, reduces dependency on intermediaries, minimizes fraud, and strengthens consumer confidence in agricultural products. Furthermore, the solution promotes sustainable agricultural practices, operational efficiency, and accountability across the supply chain ecosystem. Future enhancements may include integration with Internet of Things (IoT) devices for real-time environmental monitoring and mobile-based platforms for improved accessibility and stakeholder engagement. The proposed framework demonstrates the potential of blockchain technology in transforming conventional agricultural supply chains into secure, transparent, and trustworthy systems.
Blockchain Technology, Agricultural Supply Chain, Supply Chain Transparency, Traceability, Hyperledger Fabric, Smart Contracts, QR Code, Food Safety, Decentralized Ledger, Data Integrity, Sustainable Agriculture
C Bavyasri, P Harini, P Jashmary, U Madhavi
Sign language serves as a vital communication medium for individuals with hearing and speech impairments. However, communication between sign language users and non-users remains a major challenge. This project proposes a real-time sign language detection system that utilizes deep learning and transformer-based object detection techniques to recognize hand gestures and convert them into meaningful text. The system captures live video through a webcam and performs hand detection and preprocessing to extract the region of interest. Advanced deep learning models, such as Convolutional Neural Networks (CNNs) and Detection Transformers (DETR), are employed to accurately classify hand gestures. Each video frame is processed in real time, and the corresponding sign is predicted and displayed as text on the screen. To improve recognition accuracy, preprocessing techniques including image normalization, resizing, and noise reduction are applied. The model is trained using labeled datasets containing various hand gestures, ensuring robust and reliable performance under different conditions. The proposed system provides efficient, fast, and accurate gesture recognition suitable for real-world applications. This project helps bridge the communication gap between sign language users and the wider community by enabling seamless interaction. Furthermore, the system can be extended to support sentence formation, speech conversion, and integration with assistive technologies for enhanced accessibility.
Sign Language Detection, Deep Learning, Convolutional Neural Network (CNN), Detection Transformer (DETR), Hand Gesture Recognition, Computer Vision, Real-Time Detection, Image Processing, Assistive Technology, Text Conversion
Y Jasmitha, P Manasa, K Himaja, K Pavani
The rapid growth of the fashion and textile industry has increased the demand for intelligent systems that assist users in making informed clothing decisions. This project proposes FabriFit, an AI-based mobile application that automatically identifies fabric types from uploaded cloth images and helps users select cost-effective clothing options. The system utilizes Artificial Intelligence (AI), Machine Learning (ML), and Convolutional Neural Networks (CNNs) to analyze fabric textures and classify them into categories such as cotton, silk, wool, rayon, and other textile materials. AI-driven image analysis provides an efficient method for recognizing textile patterns and attributes, enabling automated fabric identification while reducing the need for manual inspection. In addition to fabric classification, the application estimates the required cloth length for stitching using user body measurements such as height and weight. Based on fabric costs and tailoring charges, the system performs a comparative analysis between ready-made garments and custom-stitched clothing, allowing users to identify the most economical option. By integrating fabric recognition, cost estimation, image classification, and a user friendly mobile interface, FabriFit functions as an intelligent fashion assistant. The proposed system enhances customer decision-making, minimizes unnecessary expenditure, and promotes smarter clothing purchases in the modern digital fashion ecosystem.
Artificial Intelligence (AI), Machine Learning (ML), Convolutional Neural Networks (CNN), Fabric Identification, Image Classification, Textile Analysis, Cost Estimation, Custom Stitching, Ready-Made Garments, Mobile Application, Fashion Technology
Blockchain Based Fake Product Detection System
The Blockchain-Based Fake Product Detection System is designed to address the increasing problem of counterfeit products across various industries, including electronics, pharmaceuticals, and fashion. Fake products not only result in significant financial losses for companies but also pose serious safety risks to consumers. Traditional product verification methods, such as barcodes and manual inspection, are often unreliable and can be easily duplicated or manipulated. To overcome these challenges, the proposed system utilizes blockchain technology, which provides a secure, decentralized, and tamper-resistant mechanism for data storage. In this system, product information is stored on the blockchain, ensuring that once the data is recorded, it cannot be altered or deleted. Each product is assigned a unique identification number or Quick Response (QR) code that serves as its digital identity. The system consists of two primary users: the administrator and the customer. The administrator registers product information on the blockchain, while customers verify product authenticity by entering or scanning the product identification number. The system then validates the information against blockchain records and displays whether the product is genuine or counterfeit. This verification process is fast, reliable, and eliminates the need for third-party authentication. Overall, the proposed system improves security, transparency, and trust in the product verification process. By leveraging blockchain technology, it ensures data integrity and prevents duplication of product information. Furthermore, the system can be expanded and integrated into real-world supply chains to effectively reduce counterfeit products and enhance consumer confidence.
Blockchain, Counterfeit Product Detection, Product Authentication, QR Code, Supply Chain Management, Decentralized System, Data Integrity, Smart Contracts, Product Verification, Consumer Trust
Intelligent Multi-Model Transport Booking System
Modern transportation systems, including buses, trains, and airlines, typically operate through separate booking platforms, creating challenges for travelers seeking efficient journey planning and ticket reservations. Passengers are often required to search multiple systems to compare schedules, fares, routes, and seat availability, resulting in increased planning time and inconvenience. The absence of a unified platform can lead to inefficient route selection, higher travel expenses, and underutilization of available transportation services. To address these limitations, this paper proposes an Intelligent Multimodal Transport Booking System that integrates bus, rail, and air transportation services within a single platform. The system utilizes real-time data and user preferences to provide optimized travel recommendations, seamless route planning, and efficient ticket booking. By enhancing accessibility, reducing travel complexity, and improving decision-making, the proposed solution aims to deliver a more convenient and cost-effective travel experience for users.
Multimodal Transportation, Intelligent Booking System, Journey Planning, Route Optimization, Ticket Reservation, Real-Time Data Integration, Smart Transportation
Design and Simulation of Two-Floor Elevator Controller FSM Using VHDL
An elevator controller is a crucial digital control system used to ensure safe and efficient movement of elevators between floors. This project presents the design and implementation of an elevator controller using a Finite State Machine (FSM) approach. In this method, the elevator operation is divided into well-defined states such as idle, moving up, moving down, door open, and door close. State transitions occur based on floor requests and sensor inputs. The primary objective of this project is to develop a reliable control system that accurately responds to user floor selections, manages motor direction, and ensures proper door operation. The FSM approach simplifies the overall design by clearly defining system states and transitions, making the controller easy to design, analyze, and implement. The system can be implemented using hardware description languages such as Verilog or VHDL and validated through simulation tools. This project enhances understanding of sequential logic design and digital control systems. It is highly applicable in embedded systems and automation environments where structured state based decision-making is required. The proposed design provides efficient performance, reduced complexity, and scalability for multi-floor elevator systems.
Elevator controller, Finite State Machine (FSM), digital control system, sequential logic, Verilog, VHDL, embedded systems, automation, state transition, motor control
Salveru Pragathi, Rajaram Varshini, Ravvala Ramya
Design and Analysis of a Compact Slot Antenna for 5G Sub-6 GHz Applications
The rapid advancement of fifth-generation (5G) wireless communication technology has created a growing demand for compact, efficient, and high-performance antennas capable of operating in the Sub-6 GHz frequency spectrum. This project presents the design and analysis of a compact slot antenna optimized for 5G applications, particularly targeting the 3.5 GHz frequency band. The proposed antenna consists of a rectangular slot etched on a metallic ground plane and is excited through a microstrip feed line to achieve effective impedance matching and enhanced radiation performance. The antenna is fabricated on an FR-4 (or Rogers) substrate, providing a cost effective solution while maintaining reliable electrical characteristics. The antenna design is modeled and analyzed using electromagnetic simulation software to evaluate critical performance parameters, including return loss, Voltage Standing Wave Ratio (VSWR), bandwidth, gain, and radiation pattern. Simulation results indicate that the antenna achieves a return loss below –10 dB, maintains a VSWR of less than 2, and offers sufficient bandwidth with satisfactory gain for wireless communication applications. The radiation pattern remains stable across the operating frequency range, ensuring dependable signal transmission and reception. Owing to its compact dimensions, simple structure, and efficient performance, the proposed antenna is highly suitable for integration into modern wireless devices such as 5G smartphones, Internet of Things (IoT) modules, wearable devices, and portable communication systems. Furthermore, the antenna design provides opportunities for future enhancements, including bandwidth improvement through advanced slot configurations, the addition of parasitic elements, and multi-band operation to support Wi-Fi, LTE, and upcoming wireless communication standards. Overall, the proposed compact slot antenna offers a practical, scalable, and cost-effective solution for Sub-6 GHz 5G wireless communication systems. It satisfies current performance requirements while providing a strong foundation for future developments in antenna engineering and next-generation communication technologies.
5G Communication, Sub-6 GHz Antenna, Slot Antenna, Microstrip Feed, Return Loss, VSWR, Bandwidth Enhancement, Radiation Pattern, Wireless Communication, IoT Devices, Compact Antenna Design, Electromagnetic Simulation
The Personalized Real-Time Update System is designed to deliver relevant and timely information to users by analyzing their preferences, interests, and behavioral patterns. Traditional notification systems often generate excessive and irrelevant updates, leading to information overload and reduced user engagement. To address these challenges, the proposed system utilizes real-time data processing and personalization techniques to provide customized content updates that match individual user needs. The system continuously collects and processes live data from multiple sources, enabling instant notification delivery based on user-defined preferences and interaction history. Modern technologies such as Node.js, cloud based databases, and real-time communication frameworks are integrated to ensure high scalability, low latency, reliability, and efficient performance. Features including user profile management, content filtering, preference analysis, and push notification services contribute to an enhanced user experience by delivering meaningful and context-aware updates. By combining real-time computing with intelligent personalization mechanisms, the proposed system improves information accessibility, increases user engagement, and minimizes unnecessary notifications. The solution demonstrates how advanced real-time technologies can be leveraged to build responsive, efficient, and user centric information delivery platforms.
Personalized Real-Time Updates, Real-Time Data Processing, Push Notifications, User Preference Analysis, Content Filtering, Node.js, Cloud Database, User Engagement, Information Delivery System, Personalized Notifications
The Abnormal Fetal Detection System is designed to assist healthcare professionals in the early detection of fetal abnormalities using ultrasound imaging and advanced deep learning techniques. Early identification of fetal health issues is essential for ensuring better maternal and neonatal outcomes. The proposed system employs a Convolutional Neural Network (CNN) model to analyze ultrasound images and accurately classify fetal conditions as either normal or abnormal. The system provides a user-friendly interface through which doctors can upload ultrasound scans, enter patient information, and obtain prediction results. It performs image preprocessing, feature extraction, and automated classification to generate diagnostic reports that support clinical decision-making. Additionally, the application ensures secure storage and management of patient records, promoting efficient healthcare services and data accessibility. By reducing manual effort and enhancing diagnostic accuracy, the system contributes to improved prenatal care, timely medical intervention, and increased awareness of fetal health risks. The integration of artificial intelligence in fetal monitoring offers a reliable and efficient solution for supporting obstetric diagnosis and healthcare management.
Abnormal Fetal Detection, Ultrasound Images, Deep Learning, Convolutional Neural Network (CNN), Medical Image Processing, Prenatal Care, Fetal Health Monitoring, Artificial Intelligence, Image Classification, Healthcare Management
J Akhila, K Akshitha, P Deekshitha, N Hema Sri
Finding suitable hostel accommodation is a major challenge for students relocating to cities for higher education. Information regarding hostel availability, rental fees, facilities, safety, and location is often scattered across multiple sources, making the search process time-consuming and unreliable. Traditional methods require students to visit hostels physically, make numerous inquiries, and depend on outdated information, leading to inconvenience and uncertainty. NexGen Hostels is a web-based Hostel Management System developed to address these challenges through a centralized digital platform. The system enables hostel owners to efficiently manage property listings, update room availability, and maintain hostel information in real time. Students can search, filter, compare, and select hostels based on criteria such as budget, location, amenities, and occupancy. The platform enhances transparency, accessibility, and user convenience by providing accurate information, streamlined communication, and quick decision-making support. By integrating modern web technologies, database management, and online accommodation services, NexGen Hostels offers an efficient, reliable, and user-friendly solution for hostel discovery and management.
Hostel Management System, Student Accommodation, Web-Based Platform, Hostel Search, Room Availability, Digital Hostel Booking, Database Management, Real-Time Updates, Online Accommodation Services, User-Friendly Interface
G. Shirisha, K. Shirisha, E. Sowmya Sree
Construction sites are among the most hazardous workplaces, where non-compliance with Personal Protective Equipment (PPE) regulations can result in severe injuries and fatalities. Traditional safety monitoring methods primarily depend on manual supervision, which is labor-intensive, prone to human error, and ineffective for large scale construction environments. This paper presents an AI-based PPE Detection and Worker Safety Management System that automates safety compliance monitoring using advanced computer vision techniques. The proposed system employs the YOLOv8 object detection model to identify workers and detect PPE components such as helmets, safety vests, and masks in real time. In addition to PPE detection, the framework incorporates worker registration, administrative approval, unique worker identification, violation tracking, and automated policy enforcement. An intelligent chatbot interface enables supervisors to access real-time information regarding worker attendance, PPE compliance, and safety violations through natural language queries. The system maintains comprehensive safety records and identifies repeated violations, thereby supporting proactive decision making and enhanced workplace safety. By integrating artificial intelligence, worker management, and intelligent analytics into a unified platform, the proposed solution improves monitoring efficiency, strengthens regulatory compliance, and contributes to a safer construction environment.
Personal Protective Equipment (PPE), Construction Safety, YOLOv8, Computer Vision, Worker Tracking, Violation Detection, Artificial Intelligence, Safety Compliance Monitoring, Chatbot, Occupational Safety
The project titled "Digital Detox" aims to utilize machine learning algorithms to predict the levels of mobile addiction among individuals. Mobile addiction is a growing concern in modern society, with adverse effects on mental health and productivity. This project focuses on using various predictive models to classify the addiction level into categories such as low, moderate, and high. The proposed method includes a comprehensive data analysis pipeline that begins with data preprocessing, including encoding categorical variables and scaling features. The datasets is then split into training and testing subsets. Multiple machine learning algorithms, including Decision Tree Classifier, Random Forest and Cat boost, are employed to train models on the dataset. The performance of these models is evaluated using key metrics such as accuracy, precision, recall, and F-score. The results are visualized through confusion matrices and classification reports. By leveraging these predictive models, the project aims to provide valuable insights into the factors contributing to mobile addiction, enabling targeted interventions and awareness campaigns.
Machine Learning, Predictive models, Online Learning, Mobile Addiction, Classification, Cat boost, Decision Tree, Random Forest, Accuracy, Precision, Recall, F score, Confusion matrix, Data Preprocessing