International Journal of Engineering and Science Research
Convolutional Neural Network for Binary Brain Tumor Classification Using MRI Scans
Brain tumor detection and classification play a critical role in early diagnosis and treatment planning. In this project, we implemented a deep learning-based Convolutional Neural Network (CNN) model to classify MRI brain images into tumor and non-tumor categories. The model was trained on a pre-processed MRI dataset using image normalization and augmentation techniques to improve generalization. Our CNN architecture, consisting of convolutional, pooling, and fully connected layers, achieved a training accuracy of approximately 97–98% and a test accuracy of 92–94%.This work was inspired by the base paper “Multimodal Ensemble Fusion Deep Learning Using Histopathological Images and Clinical Data for Glioma Subtype Classification”, which employed an advanced ensemble fusion approach combining CNNs, Transformers, and clinical data to achieve a classification accuracy of 93.6% with an AUC of 0.967. While our implementation focuses solely on MRI image classification with a single CNN model, the results demonstrate comparable accuracy levels. Unlike the base paper, our model does not incorporate multimodal data fusion or ensemble strategies, making it computationally simpler and more lightweight, while still achieving reliable performance for binary brain tumor detection. The findings indicate that deep learning models can effectively support medical imaging tasks, with scope for future enhancement through multimodal integration, ensemble learning, and advanced evaluation metrics such as precision, recall, and F1-score.
Brain tumour detection, Convolutional Neural Network (CNN), Deep learning, Binary classification Image preprocessing, Clinical data integration, Precision, recall, F1-score.
Mohammed Ahmeduddin Taha, Mohammed Zaid, Ibrahim Khan
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
Heart Failure Patient Survival Prediction Using Machine Learning
This research presents Heart Guard AI, a machine learning-based clinical decision support system for predicting heart failure patient survival outcomes. The system employs an XG Boost classifier trained on 5,000 clinical records with 12 physiological features. SMOTE is applied to address class imbalance, and Select K Best with Chi-square test performs feature selection. Benchmarked against five algorithms, the system achieves 99.70% accuracy and AUC-ROC of 0.9998. A Flask web application provides real-time High Risk / Low Risk predictions with factor-level clinical analysis.
Heart Failure Prediction, XG Boost, SMOTE, Machine Learning, Clinical Decision Support, Select K Best, Flask Deployment, Survival Prediction.
The widespread deployment of FASTag for electronic toll collection across India has significantly improved traffic efficiency and reduced manual intervention. However, this rapid digitization has also introduced new vulnerabilities, including unauthorized transactions, duplicate tag usage, and GPS spoofing attacks. Conventional rule-based fraud detection systems are increasingly ineffective in identifying such evolving and sophisticated fraud patterns. To overcome these limitations, this study proposes a machine learning-driven fraud detection framework that utilizes historical transaction data, behavioral analytics, and anomaly detection techniques to identify fraudulent FASTag activities in real time.The proposed system integrates supervised learning models, particularly Random Forest, for accurate fraud classification, alongside unsupervised methods such as Isolation Forest and Autoencoders to detect anomalous transaction patterns. Key attributes considered in the analysis include transaction timestamp, vehicle classification, toll plaza location, and user behavior trends. Furthermore, the framework incorporates real-time data processing through streaming analytics to enable immediate detection and response to suspicious activities.Experimental evaluation indicates that the proposed approach significantly enhances detection accuracy while reducing false positives compared to traditional rule-based methods. By continuously adapting to emerging fraud patterns, the system ensures scalability, robustness, and improved financial security. This research contributes to the development of a secure, intelligent, and efficient digital toll collection ecosystem, making it highly suitable for large-scale deployment in modern transportation infrastructures.
FASTag fraud detection, machine learning, anomaly detection, Random Forest, Isolation Forest, Autoencoder, real-time analytics, intelligent transportation systems
Thirumani Nikhil, Anany Singh, K Deeksha,Manoj Reddy
Real-Time Hand Gesture Controlled UAV Using Computer Vision Techniques.
Unmanned Aerial Vehicles (UAVs) are widely deployed in applications such as surveillance, inspection, and environmental monitoring. Conventional UAV control methods depend on handheld controllers or mobile devices, which may be impractical in situations requiring hands-free or rapid response interaction. This study proposes a real-time, vision-driven hand gesture control system that enables intuitive and touchless UAV operation. The system captures live video using a camera and processes it through a computer vision pipeline to detect and track hand movements. Using landmark-based hand modelling , key features are extracted and analyzed to identify predefined gestures based on spatial relationships among hand joints. The implementation leverages Python along with OpenCV and MediaPipe Hands for efficient gesture recognition. Recognized gestures are mapped to specific flight commands and transmitted to a DJI Tello drone via its Software Development Kit (SDK), allowing real-time execution of actions such as takeoff, landing, directional navigation, and altitude adjustment. To enhance operational safety, the system integrates mechanisms such as gesture validation delays, emergency stop controls, and automatic landing triggered by low battery levels or communication disruptions.Experimental evaluation demonstrates that the system achieves reliable gesture recognition with minimal latency, resulting in smooth and stable drone maneuvering. The proposed framework eliminates the need for traditional input devices and offers a practical solution for natural human–drone interaction. This approach is particularly beneficial for applications in search and rescue, surveillance, and advanced human–machine interfaces, demonstrating its potential as a scalable and effective UAV control paradigm.
Hand Gesture Recognition, UAV Control, Computer Vision, Human–Drone Interaction, MediaPipe Hands, OpenCV, Real-Time Systems, DJI Tello, Contactless Control, Intelligent Interfaces
Thakur Tarun Singh, Sanjay Kumar Anugula, Merugu Swapnil Reddy, Kundarapu Dikshitha
Multipurpose Locker Security System Using Image Processing and IOT
Security is becoming an important issue everywhere nowadays. Every person wants his house, factory, bank etc. to be secured.The Implementation of a digital door locker security system using image processing and IOT platform.User face is registered for the accessing the door on all times. If the user face is matched it asks for OTP (One Time Password), which is used as dual step verification.If the locker is accessed by any intruder or an unregistered person, the Pi cam captures the image and stores it in the folder for future reference to the owner.And if the person is wearing mask or doesn't want to go through facial recognition process, they can access the locker remotely through registered mobile. In order to overcome the above problems. Various types of feature sets can be used, such as: Two step verification (Face detection algorithm, and random password generation OTP) to access the locker. IOT based MQTT protocol for saving the data (name, date & time who and when the locker was accessed) for future reference.Owner can give the access of the locker to the people he wanted and can be removed simultaneously.Images are saved from the live feed of the person whose data is not saved in the system. To Study Algorithm used for face recognition, which is Haar Cascade Algorithm, also known as Voila-Jones Algorithm.To design a digital door locker security system using image processing and IOT platform.Implementation of MQTT protocol on the security system and generation of OTP.
Digital Door Locker, Image Processing, Internet of Things (IoT), Face Recognition, Haar Cascade Algorithm, Viola-Jones Algorithm, One-Time Password (OTP), Two-Step Verification, MQTT Protocol, Raspberry Pi Camera, Smart Security System, Facial Authentication, Remote Access Control, Intruder Detection, Dual Authentication, Smart Locker System, Access Monitoring, Real-Time Surveillance, Secure Authentication, IoT-Based Security System.
Mohammed Mubasheer Ahmed , Shaik Azeem Pasha , Shaik Farook
A Novel Approach of Progress Study of Track Annealing In Solid State Nuclear Track Detectors : Fundamentals of Track Annealing Re-examined
Impact of temperature on track registration in Solid State Nuclear Track Detectors has remained a key area of research and interest since the advent of insulting solids as charged particle detectors. Phenomenon of track annealing has throughout occupied a centre stage in all SSNTD research. In the present work, an approach has been developed to express track diameter after post irradiation annealing in terms of pre-annealed track diameter which also makes possible to identify and use an exclusive parameter that can most accurately describe annealing induced repair of damage quantitatively,also it evolves a new approach to study the progress/kinetics of annealing phenomenon.
Track Annealing, Track Etch Rate (VT), Bulk Etch Rate (VB), Etch Rate Ratio (V), Track Diameter (D).
IoT Based Real-Time Animal Detection System For Smart Agricultural Crop Protection
Human–wildlife conflict has intensified in recent years due to deforestation, agricultural expansion, and habitat fragmentation, forcing wild animals to enter human settlements and farmlands. This study proposes a cost-effective and automated animal intrusion detection system that leverages an ESP32-CAM module integrated with Internet of Things (IoT) technology and deep learning algorithms.The system continuously captures real-time images and processes them using a trained object detection model to identify the presence of wild animals. Upon detection, instant alerts are transmitted to users such as farmers or forest authorities via mobile applications, enabling timely preventive action. Additionally, a local buzzer mechanism is activated to deter animals and provide immediate on-site warning.The ESP32-CAM functions as an edge device for image acquisition, while cloud-based platforms facilitate communication and notification delivery. The proposed solution minimizes human intervention, reduces crop damage, and enhances safety in rural and forest-adjacent areas. Its affordability, low power consumption, and ease of deployment make it particularly suitable for remote regions.
IoT, ESP32-CAM, Deep Learning, YOLOv5, Animal Intrusion Detection, Real-Time Alert System
G.SaiGanesh, P.Ushasri, S.Vishal, Y.Srinija
A Comprehensive Benchmark Dataset for Traffic Accident Detection Using YOLOv8
The automatic detection of traffic accidents has become an essential focus area in computer vision, propelled by the rapid advancements in autonomous and intelligent transportation systems (ITS). To achieve reliable and real-time detection of accident scenarios, YOLOv8, the latest evolution in the YOLO family, offers a powerful and efficient framework for object detection in complex and dynamic traffic environments. Unlike traditional approaches that struggle with occlusions, varying lighting conditions, and high-speed vehicle motion, YOLOv8 provides superior detection accuracy through its optimized architecture, featuring re-parameterized convolutional layers, decoupled detection heads, and advanced feature fusion mechanisms. These enhancements enable the model to precisely identify accident-related events, such as collisions, overturned vehicles, or lane departures, from surveillance video streams.
Traffic Accident Detection, YOLOv8, Computer Vision, Deep Learning, Object Detection, Road Safety, Real-Time Monitoring.
Zeba Tabassum, Mohd Taha Ullah, Syed Zahed
Advancing Kidney Tumor Detection in CT Scans with a Hybrid Computational Framework
Kidney diseases such as cysts, tumors, and stones are life-threatening conditions that require early and accurate detection for effective treatment. Traditional diagnostic methods using CT scans often depend on manual interpretation by radiologists, which can be time-consuming and prone to error. To address this challenge, we propose a deep learning–based automated system for multi-class kidney disease classification using CT images. The system utilizes EfficientNetV2B0, a state-of-the-art convolutional neural network, to extract deep features from CT scans. A custom classification head with Global Average Pooling, Dropout, and Dense layers is employed to classify images into four categories: Normal, Cyst, Tumor, and Stone. Data augmentation and class weighting are applied to handle dataset imbalance and improve generalization. The model achieves high accuracy and robustness, outperforming conventional CNN approaches. Furthermore, the trained model is integrated into a Flask web application, providing a user-friendly interface with functionality for image upload, real-time prediction with confidence scores, and visualization of training results through charts. This approach demonstrates the potential of advanced deep learning models combined with web deployment to support radiologists in fast, reliable, and scalable kidney disease diagnosis.
Kidney Disease, Deep Learning, CT Imaging, EfficientNetV2B0, CNN, Medical Diagnosis, Image Classification, Data Augmentation, Web Application
Phish detect pro: A Servlet-Based Smart Wallet Simulation And Approval Phishing Detection Framework Using Intent Validation
This paper presents PhishDetectPro, a servlet-based smart wallet simulation and approval phishing detection framework using intent validation. Approval phishing represents one of the most deceptive forms of blockchain fraud, wherein users are manipulated into granting unlimited token access to malicious smart contracts without their conscious awareness. The proposed framework employs a Smart Intent Validation and Risk Detection (SIVRD) algorithm that performs pre-execution analysis of wallet approval transactions, evaluating parameters such as spender identity, approval amount, interaction history, and behavioral patterns to compute a real-time risk score. The system simulates the complete lifecycle of an approval phishing attack through four integrated modules: Smart Wallet Simulation, Approval Phishing Detection, Token Drain Simulation, and Blockchain Ledger & Block Creation. Built on Java EE (Servlet-JSP), Apache Tomcat, and MySQL, the framework provides a controlled, educational, and research-ready environment to study, demonstrate, and mitigate approval-based blockchain scams. Experimental results demonstrate effective detection of malicious approval patterns with clear user-facing alerts, blockchain-backed transaction traceability, and transparent visual analytics via a JSP dashboard. The paper also presents a comparative literature analysis and recommends enhanced smart wallet security controls inspired by EIP-4337 account abstraction.
Approval Phishing, Blockchain Security, Smart Contract, ERC-20, SIVRD, Token Drain, Intent Validation, DeFi Fraud, Wallet Security, Java Servlet
Ismail Uddin Syed , Mohammed Saif Ibrahim, Mohammed Abdul Ahad
Convolutional Neural Network For Binary Brain Tumor Classification Using MRI Scans
Brain tumors are one of the most critical health conditions that require early and accurate diagnosis. This paper presents a Convolutional Neural Network (CNN)-based approach for binary classification of brain tumors using MRI images. The proposed system processes MRI scans through preprocessing and data augmentation techniques to improve model performance. The CNN model extracts important features and classifies images into tumor and non-tumor categories. The model achieves high accuracy with reduced computational complexity compared to existing multimodal approaches. This system helps in reducing manual effort and supports medical professionals in early diagnosis.
CNN, Brain Tumor, MRI, Deep Learning, Image Classification
Intelligent Network Traffic Anomaly Detection Using ML Algorithms
This project presents an intelligent network traffic anomaly detection system using advanced machine learning techniques to enhance cybersecurity. With the rapid growth of internet usage and increasing cyber threats, traditional rule-based intrusion detection systems have become ineffective in identifying evolving and unknown attacks. To address this challenge, a data-driven approach is proposed using the CatBoost algorithm, a state-of-the-art gradient boosting technique known for its high accuracy and efficient handling of categorical and numerical data. The system is trained and evaluated on the KDD Cup 1999 dataset, which contains labeled instances of normal and malicious network traffic, including attacks such as Denial-of-Service (DoS), Probe, Remote-to-Local (R2L), and User-to-Root (U2R). The methodology involves data preprocessing, feature selection, and model training to improve performance and reduce computational complexity. Comparative analysis with traditional algorithms such as Decision Trees and Random Forest demonstrates that the proposed model achieves superior accuracy exceeding 99%, along with better generalization and reduced overfitting. To ensure practical usability, the system is deployed as a Flask-based web application that supports user authentication, real-time anomaly prediction, and visualization of performance metrics such as confusion matrix and feature importance. This integration enables seamless interaction and real-world applicability for network administrators and cybersecurity professionals. Overall, the proposed system provides a scalable, efficient, and highly accurate solution for network intrusion detection, contributing to improved protection of digital infrastructure and offering a foundation for future enhancements such as real-time monitoring and multi-class attack classification. Additionally, the system enhances decision-making by providing interpretable insights into key network features contributing to anomalies. Its adaptable architecture allows easy integration with real-time monitoring systems and future expansion into advanced intrusion prevention mechanisms
Mohammed Haris Uddin, Khaja Rahber Uddin , Syed Safwan Waseem
Automated Detection and Classification Of Tooth Types And Dental Anomalies In Panoramic Radiographs
This paper proposes an automated deep learning framework for detecting and classifying dental anomalies from Orthopantomogram (OPG) X-rays using a Transformer-enhanced YOLOv10 model. The system simultaneously identifies four tooth types (incisors, canines, premolars, molars) and two distinct anomaly categories (caries and periapical lesions) in a single inference pass. A Flask-based web application is integrated for real-time prediction and visualization, enabling immediate clinical deployment without specialized infrastructure. The model achieves an overall mAP@0.5 of 92.0%, outperforming baseline YOLO variants by up to 7.8 percentage points. The system supports clinical decision-making in dentistry by reducing manual analysis errors, improving diagnostic throughput, and providing accessible AI-assisted dental screening in resource-constrained environments.
Dental anomaly detection, YOLOv10, Panoramic radiography, OPG X-ray, Deep learning, Transformer, Object detection, Flask, Computer-aided diagnosis, Caries, Periapical lesion.
Machine Learning Approach For Predicting Parkinson’s Disease At Early Stages
Parkinson’s Disease (PD) is a chronic neurodegenerative disorder that affects motor control, coordination, and speech. Early detection is essential to slow disease progression and improve life quality. One of the earliest and most prevalent symptoms is dysphonia—altered voice characteristics in terms of pitch, loudness, and quality. In this paper, we propose a predictive framework using the K-Nearest Neighbors (KNN) algorithm for early diagnosis of PD through speech signal analysis. The methodology applies feature extraction, normalization, and dimensionality reduction on vocal parameters such as jitter, shimmer, and harmonic-to-noise ratio. The optimized model identifies PD patients effectively by comparing vocal features with those of healthy controls. The experimental results show that KNN, when properly tuned, offers high accuracy and interpretability for clinical applications.
Parkinson’s Disease, K-Nearest Neighbors, Speech Analysis, Machine Learning, Dysphonia, Early Diagnosis.
Mohammed Nawaz Khan, Md Adnan Khan, Mohammed Affan Suleman
Quantum Secret Sharing Protocol Security In Cloud Environment
Quantum Secret Sharing (QSS) is a cryptographic primitive that distributes a secret among multiple parties using quantum information, ensuring only authorized subsets can reconstruct it. Existing QSS approaches rely on conventional network topologies and maximally entangled states, often requiring full participation and lacking efficiency in redundancy, authentication, and privacy. This work proposes a QSS framework on a distributed quantum network using a threshold-based (t, n) scheme, enabling flexible secret reconstruction. A custom weighting system and quantum variant of Dijkstra’s algorithm dynamically select optimal players, improving performance. Additionally, CRYSTALS-Kyber post-quantum cryptographic primitives are integrated for secure authentication and identity obfuscation. Security analysis and simulations show that the proposed protocol outperforms traditional QSS models, offering stronger resilience against classical and quantum adversarial threats.
Quantum Secret Sharing (QSS), Threshold Scheme (t, n), Distributed Quantum Networks, Quantum Cryptography, Entanglement, Quantum Communication, Dijkstra’s Algorithm (Quantum Variant), Post-Quantum Cryptography, CRYSTALS-Kyber, Authentication, Privacy Preservation, Secure Multiparty Computation, Quantum Security, Network Optimization.
Mohammed Sulaiman Ahmed Khan, Syed Khaja Faizuddin, Mohammed Amanullah
Enhanced Stock Price Prediction Across Global Markets Through Data-Driven Modeling
The stock market is highly volatile and influenced by numerous factors operating simultaneously, making accurate prediction a challenging task. Over the years, several studies have attempted to forecast stock prices using statistical, machine learning, and deep learning approaches. However, these models often fail to capture the combined effect of external factors such as public sentiment, which has a significant impact on market movement. In this work, we propose a hybrid algorithm that integrates Twitter sentiment analysis with a Long Short-Term Memory (LSTM) network to predict the next day’s closing stock price. Sentiment analysis is performed using part-of-speech (POS) tagging to evaluate public opinion extracted from Twitter data, while LSTM is employed to model the temporal dependencies in historical stock prices. By combining sentiment-driven signals with sequential deep learning, our model provides a more reliable prediction of stock price movements compared to conventional approaches. Experimental results demonstrate that incorporating sentiment into LSTM improves accuracy and offers a clearer insight into the future direction of stock prices.
Stock Price Prediction, Sentiment Analysis, Twitter Data, Long Short-Term Memory (LSTM), Deep Learning, Time Series Forecasting, Natural Language Processing (NLP)
Syed Shah Mohiuddin Zohaib, Mohd Saif Ur Rahman Khan, Mohd Faisal Ahmed
KEGAT: A Knowledge-Enhanced Graph-Aware Transformer for Detecting AI-Generated Fake News
With the continuous evolution of advanced large language models like GPT, the proliferation of AI-generated fake news presents growing challenges to information dissemination. Traditional text classification methods struggle to detect such content due to their limited capacity to distinguish between authentic and fabricated news. To address this issue, this study introduces an MLP (Multi-Layer Perceptron) Classifier integrated with Natural Language Processing (NLP) techniques for detecting AI-generated fake news. Textual data is preprocessed through tokenization, stop-word removal, and vectorization to extract meaningful features, which are then used as inputs to the MLP network. The classifier leverages multiple hidden layers and nonlinear activation functions to capture complex linguistic patterns that characterize fabricated news. A new dataset, generated using GPT-4 and covering 42 news categories, was developed to train and evaluate the system. Experimental results demonstrate that the proposed MLP model achieves reliable accuracy and strong F1 scores, surpassing traditional machine learning approaches. These findings highlight the potential of MLP-based architectures in enhancing fake news detection and safeguarding online information integrity.
Fake News, MLP, NLP, GPT, Machine Learning.
Syed Ali Asrar,Syed Akmal Uddin, Mohammed Ali Hussain
Machine Learning–Based Obesity Detection Using Feature-Optimized Xgboost And Comprehensive Evaluation
Obesity has emerged as a major global health concern due to its strong association with chronic diseases such as diabetes, hypertension, cardiovascular disorders, and metabolic complications. Early and accurate prediction of obesity risk is essential for guiding preventive interventions. This project presents a robust machine learning framework for classifying obesity levels using an enhanced XGBoost model trained on a structured lifestyle and physical-condition dataset. The dataset undergoes systematic pre-processing that includes label encoding, normalization, and stratified train-test splitting to ensure reliable learning. The XGBoost classifier is chosen for its superior ability to capture complex feature interactions, handle mixed data types, and reduce over fitting. Comprehensive evaluation metrics such as accuracy, confusion matrix, and classification report demonstrate that the model achieves high predictive performance. Important visualizations, including correlation heatmaps and feature-importance plots, provide deeper insights into the factors influencing obesity outcomes. The final trained model and processed datasets are saved for deployment and future research. This study highlights the effectiveness of gradient-boosting approaches in health-risk prediction and contributes to the development of intelligent decision-support systems in healthcare.
Mohammed Saifullah, Mohammed Abdul Aziz, Khaja Asif Uddin
Multiclass Mental Illness Prediction Using Lstm And Natural Language Processing
Mental health disorders have become a significant global concern, affecting millions of individuals worldwide. With the increasing usage of social media platforms, users often express their emotions, thoughts, and psychological conditions through textual content. Detecting mental illness from such data is a complex task due to informal language, emotional depth, and metaphorical expressions. This research presents a hybrid deep learning framework for multiclass mental illness prediction using Natural Language Processing (NLP). The system integrates domain-specific transformer models such as MentalBERT and MelBERT with Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks. MentalBERT captures contextual mental health-related features, while MelBERT identifies metaphorical language patterns. The model processes social media text data and classifies it into multiple mental health conditions such as depression, anxiety, PTSD, and bipolar disorder. Experimental results show improved accuracy and performance compared to traditional machine learning methods. This work contributes to early detection and mental health awareness through intelligent text analysis systems.
Mental Health, NLP, Deep Learning, MentalBERT, MelBERT, CNN, BiLSTM, Multiclass Classification
Mohammed Salah Afzal, Syed Idrees Hussain, Mohammed Abdul Muzammil
Predictive Modeling For Early Lung Cancer Detection Using Ensemble Machine Learning
Lung cancer remains one of the leading causes of cancer-related deaths worldwide, accounting for approximately 1.8 million deaths annually. Early detection is critical for improving patient survival rates and enabling timely therapeutic interventions. This paper presents an intelligent machine learning-based prediction system for early lung cancer detection using survey-based clinical and lifestyle data. The proposed system employs a stacked ensemble learning model that integrates five powerful base classifiers, namely CatBoost, XGBoost, LightGBM, AdaBoost, and Random Forest, with Logistic Regression as the meta-learner that produces the final binary prediction. To address the inherent class imbalance in the dataset (270 cancer vs. 39 non-cancer cases), the Synthetic Minority Over-sampling Technique (SMOTE) was applied, ensuring a balanced training distribution. The proposed stacked ensemble model achieved an accuracy of 96.9%, precision of 96.98%, recall of 96.82%, F1-score of 96.90%, and an ROC-AUC score of 0.99, outperforming all individual classifiers and demonstrating state-of-the-art performance. Additionally, a Flask-based web application was implemented, providing a user-friendly interface for real-time prediction, data visualization, and result interpretation. The system is modular, scalable, and clinically accessible. The proposed system leverages advanced ensemble learning techniques to improve prediction reliability and reduce model variance compared to single classifiers. A balanced dataset is achieved using SMOTE, which enhances the model’s ability to correctly classify minority (non-cancer) cases and reduces bias. The system ensures high sensitivity (recall), which is critical in medical diagnosis to minimize false negatives and avoid missed cancer cases. Feature importance analysis highlights key contributing factors such as smoking habits, anxiety, fatigue, and respiratory symptoms, improving interpretability. The model demonstrates robust generalization capability, validated using cross-validation techniques to ensure consistent performance across unseen data. A modular architecture is designed, allowing easy scalability and integration with other healthcare systems or datasets in the future.
Lung Cancer Detection, Ensemble Machine Learning, SMOTE, Stacked Classifier, CatBoost, XGBoost, LightGBM, Flask Web Application, Predictive Modeling, Healthcare AI.
Nashad Noor Yussuf Dinni, Affan Bin Hassan, Asim Bin Awad Mahfooz
A Dual-Stage Framework For Cavity Detection In Nuclear Materials Using Image Super-Resolution (ISR) And Yolov10s
Radiation-induced cavities and voids within structural materials present a significant challenge for ensuring the long-term safety and efficiency of nuclear reactors. Accurate detection and quantification of these cavities are critical for understanding irradiation damage mechanisms and predicting material degradation. Traditional object detection models such as YOLOv8 and Faster R-CNN have shown reasonable performance in cavity detection; however, their bounding-box-based outputs often fail to precisely localize small or irregularly shaped cavities, particularly under degraded imaging conditions. To address these limitations, this paper introduces CavityNet, a novel two-stage deep learning framework that combines Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) with a Swin Transformer-based UNet (Swin-UNet) architecture for improved cavity detection in irradiated microstructures. In the first stage, ESRGAN enhances underfocused or overfocused scanning transmission electron microscopy (S/TEM) images by reconstructing fine textures and high-frequency cavity boundaries. In the second stage, these super-resolved images are passed through a Swin-UNet model, which integrates hierarchical self-attention mechanisms with a UNet-style encoder-decoder pipeline to achieve pixel-level segmentation of cavities. This approach enables both global contextual understanding and local feature refinement, improving detection accuracy in complex microstructural environments. The model is trained and evaluated using publicly available datasets from the Canadian Nuclear Laboratory (CNL) and Nuclear Oriented Materials & Examination (NOME), achieving an F1-score improvement of 31% on underfocused images and a precision improvement of 161.3% with ISR augmentation. The proposed CavityNet framework represents a significant step forward in automated microstructural analysis, offering enhanced performance, robustness to imaging conditions, and potential for real-time material degradation monitoring in nuclear environments.
Cavity Detection; ESRGAN; Image Super-Resolution; Swin-UNet; Nuclear Materials; S/TEM; Deep Learning; Irradiated Materials; CavityNet; Object Detection; Microstructural Analysis; YOLOv8.
M.A Noman, Arsalan Ahmed Qureshi, Abdul Hannan
Advanced Surveillance With Yolov12: Fusion- Based Detection Of Threatening Objects
The rapid evolution of intelligent surveillance systems has accelerated the adoption of deep learning-based object detection models for enhanced situational awareness, automated threat recognition, and proactive security monitoring. This study presents an Advanced Surveillance Framework utilizing YOLOv12 for fusion-based detection of threatening objects such as firearms, knives, explosives, unattended suspicious baggage, and other hazardous items in complex real-world environments. The proposed system integrates multi-sensor data fusion, combining RGB visual imagery and infrared sensing to improve detection performance under low-light conditions, partial occlusions, crowded scenes, and adverse environmental situations. The proposed framework leverages the advanced capabilities of YOLOv12, including optimized detection heads, transformer-based attention modules, adaptive anchor strategies, and enhanced feature aggregation mechanisms, to achieve superior speed–accuracy trade-offs compared with conventional object detection models. A multimodal feature fusion module is incorporated to exploit complementary spatial and thermal information, improving robustness and minimizing false positives and false negatives in threat detection. Furthermore, an intelligent threat prioritization mechanism is introduced for real-time classification and alert generation based on threat severity levels, enabling rapid response in critical surveillance scenarios. The system is implemented using Python-based deep learning frameworks and designed for scalable deployment in smart surveillance infrastructures. Experimental analysis demonstrates that the proposed fusion-based YOLOv12 model significantly outperforms traditional single-sensor and conventional YOLO-based approaches in terms of precision, recall, mean Average Precision (mAP), inference speed, and detection robustness, making it a powerful and reliable solution for modern surveillance applications in public safety, transportation hubs, border security, defense monitoring, and smart city security networks. The proposed framework contributes toward next-generation intelligent surveillance by combining real-time deep learning detection, multimodal sensor fusion, and adaptive threat intelligence into a unified security architecture capable of supporting autonomous and large-scale surveillance ecosystems.
YOLOv12, Threat Detection, Intelligent Surveillance, Object Detection, Sensor Fusion, Multimodal Fusion, Deep Learning, Computer Vision, Real-Time Detection, Public Safety, Smart Surveillance, Weapon Detection, Infrared Imaging, Security Monitoring, Artificial Intelligence.
Mohd Ali Abbas Khan, Mohd Omer Ahmed, Mohammed Mujeeb Uddin
AI-Powered Smart Surveillance Camera System
The widespread deployment of surveillance cameras in both public and private domains has intensified the need for intelligent monitoring solutions that can surpass the limitations of human-based observation. Continuous manual supervision of multiple video streams often leads to fatigue, reduced attention, and delayed reactions, increasing the likelihood of overlooked critical events.To overcome these challenges, this paper presents an AI-driven smart surveillance system that integrates real-time video processing with deep learning techniques for automated monitoring. The proposed system utilizes advanced object detection models such as YOLOv5 and YOLOv8 to achieve fast and accurate identification of humans and relevant objects within video frames. In addition, centroid-based tracking is employed to maintain consistent identification of individuals across consecutive frames.The system is capable of recognizing potentially suspicious behaviors, including unauthorized intrusion, prolonged loitering, abnormal crowd gathering, and unattended objects. Detected events are systematically recorded using a lightweight SQLite database, ensuring efficient data storage and retrieval. A web-based interface developed using Flask enables live video streaming with detection annotations and real-time event updates for users.By reducing reliance on manual monitoring and incorporating intelligent filtering mechanisms, the system minimizes false alarms while improving overall surveillance effectiveness. Furthermore, its scalable architecture and compatibility with IoT frameworks make it suitable for deployment in smart cities, industrial environments, educational campuses, and high-security zones.
Artificial Intelligence, Smart Surveillance, Object Detection, YOLOv5, YOLOv8, Deep Learning, Video Analytics, Centroid Tracking, Activity Recognition, IoT Integration, Real-Time Monitoring
Obtain Patient Health Data Priority Classification In A Remote E-Healthcare System While Maintaining Privacy
Wireless Body Area Networks (WBANs) have emerged as a key technology for enabling continuous, real-time healthcare monitoring. Despite their potential, significant concerns remain regarding the protection of sensitive patient information and the confidentiality of diagnostic models used by healthcare providers. Although several privacy-preserving approaches have been introduced, many suffer from limitations in computational efficiency and classification accuracy.To address these challenges, this paper presents an efficient privacy-preserving priority classification scheme (PPC) designed for remote e-healthcare environments. The proposed approach enables the WBAN gateway to classify encrypted patient data based on priority levels without requiring interaction with external entities. This non-interactive mechanism reduces processing delays and allows timely forwarding of medical data according to its urgency.Security analysis demonstrates that the proposed scheme safeguards both patient privacy and the confidentiality of medical decision models while performing accurate classification. Furthermore, experimental evaluation, conducted using an Android-based application and Java server implementations, shows that the PPC scheme achieves low computational overhead and reduced communication costs, making it suitable for real-time healthcare applications.
WBAN, E-Healthcare, Privacy Preservation, Priority Classification, Encrypted Data, Data Security, Low Latency, Efficiency
A. Kavya Sri, B. Tarun Yadav, K. Laxmi Prasanna, T. Anji
Smart Village Governance System – Fund Transparency And Issue Reporting System
Rural governance in many developing regions continues to rely heavily on manual processes, which often result in inefficiencies, delays, and limited transparency. This paper presents the design and implementation of a Smart Village Governance System, a web-based platform aimed at improving accountability, transparency, and citizen engagement in village administration. The system enables real-time access to government fund allocation details, expenditure records, and remaining balances, ensuring financial clarity for residents. Additionally, it provides a structured mechanism for villagers to report local issues such as water supply disruptions, electricity failures, road damage, and sanitation concerns. The platform allows users to track complaint status, while administrators can manage and resolve issues efficiently through a centralized interface. Built using modern web technologies including React.js, Node.js, and MySQL, the system ensures scalability, performance, and data reliability. The proposed solution demonstrates how digital transformation can significantly improve governance practices and strengthen communication between authorities and rural communities.
Smart Governance, Rural Development, Fund Transparency, Issue Reporting System, E-Governance, Web Application, Village Administration, Digital Transformation
B. Navakanth Raju, K. Sai Meghana, M. Akanksha, Vaishnav Mathrudas
Fair grade AI: An Intelligent System For Transparent And Unbiased Exam Evaluation
Fair and consistent evaluation of descriptive and handwritten examination answers remains a significant challenge in modern education due to subjectivity, evaluator bias, and time constraints. This paper presents FairGrade AI, an intelligent exam evaluation system designed to automate and standardize the grading process while maintaining transparency and interpretability. The proposed system integrates Optical Character Recognition (OCR) to digitize handwritten responses, followed by a Retrieval-Augmented Generation (RAG) framework that retrieves relevant evaluation criteria and domain-specific reference material. A large language model (LLM), guided through structured prompt engineering, evaluates student answers strictly based on the retrieved context to ensure criteria-aligned scoring. The system generates not only marks but also detailed explanations, identified gaps, and actionable feedback, thereby enhancing both assessment quality and learning outcomes. The architecture is implemented using a modular full-stack approach with React for the frontend, FastAPI for the backend, MongoDB for data management, and a vector database for semantic retrieval, with scalable deployment supported by cloud services. Experimental evaluation demonstrates improved consistency and reduced grading time compared to traditional manual methods, while maintaining close alignment with human assessment standards.The proposed framework demonstrates how the integration of OCR, retrieval-augmented generation, and large language models can enable a more transparent, scalable, and consistent approach to automated exam evaluation in educational settings.
Automated Exam Evaluation, Optical Character Recognition (OCR), Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Criteria-Based Grading, Explainable AI, FastAPI, React JS, MongoDB, Vector Database, Semantic Retrieval, Prompt Engineering, Educational Technology, Bias Reduction, Scalable AI Systems
IoT Based Railway Track Crack Detection System
Railway Safety is a critical concern as track defects, particularly cracks, frequently cause derailments and catastrophic accidents. This project introduces IoT- based railway track detection and alerting system to address this issue. The proposed system employs Ultrasonic sensors, a microcontroller and a GPS module to identify structural anomalies in real time. Upon detecting a crack, the system halts its operation and transmits the precise GPS location to railway authorities using IoT protocols, enabling timely interventions and reducing the risk of accidents .The system operates autonomously, significantly minimizing the need for manual inspections while providing continuous monitoring over extensive railway networks. By leveraging IoT, the system ensures efficient, cost- effective operations, enhancing both the safety and reliability of railway transportation. The implementation of this solution has the potential to reduce maintenance costs, prevent derailments, and improve overall infrastructure safety. The IoT-based system significantly reduces the need for manual inspection, lowers operational costs, and enhances the overall reliability of railway infrastructure .This technology not only improves detection accuracy but also enables predictive maintenance by identifying faults at an early stage, thereby preventing derailments and major accidents. The IoT-based system significantly reduces the need for manual inspection, lowers operational and maintenance costs, and enhances the overall reliability and safety of railway infrastructure. By leveraging automation, cloud analytics, and real-time monitoring, the proposed system offers a smart, scalable and cost-effective approach to modern railway safety management.
Mohd Roman Ali, Abdul Sarfaraz Qureshi, Md Ghouse
Lingua Hands: AI Sign Language Translator with Reverse Mode1
LinguaHands is an AI-powered sign language translator designed to bridge the communication gap between hearing-impaired individuals and the general public using multimodal inputs such as video and gesture recognition. The system integrates advanced technologies including MediaPipe for hand landmark detection, a deep learning model for gesture classification, and text-to-speech systems for generating human-like voice responses. Users can perform sign language gestures in front of a camera, and the system analyzes these gestures to deliver real-time, context-aware text and audio translations. The system also supports a reverse mode where spoken or typed text is converted into animated sign language output. Built with a simple and interactive interface, LinguaHands aims to improve accessibility and inclusivity for the hearing-impaired community, especially in public and educational environments. The system serves as an efficient, scalable solution for real-time sign language interpretation and awareness.
A Comprehensive Review Of Emerging Technologies In Solar Powered Electric Vehicles
The overuse of petroleum products, high dependency on limited fossil fuels, and the growing pollution due to burning of fuels petroleum, diesel, and other gases, demands the need for sustainable renewable alternatives. The Solar Freeways and solar powered vehicles offer the best solution in automobile industry. This initiative includes embedding solar panels in roadways to generate power for solar powered Electric Vehicles (EVs) and charging it wirelessly. This overcomes the negative effects of pollution caused due to burning of petroleum and other products of fossil fuels and facilitates in saving the limited fossil resources for future, besides it also supports smart city initiatives. The solar powered approach promotes national development, enabling Electric Vehicles (EVs) like electric cars, electric buses and other transportation modes to rely on cleaner electricity rather than fossil fuels. This review paper briefly explores various charging methods and charging systems for renewable-energy–driven Electric Vehicles (EVs). A micro-scale prototype demonstrates a wireless, inductive charging mechanism for moving Electric Vehicles (EVs), enhanced by optimized pre-installed solar panels. Vehicle-mounted coils enable charge transfer to a moving Electric Vehicles (EVs) without stopping the electric car. The complete system of charging comprises of solar panels, a battery, transformers, regulators, copper coils, AC/DC converters, a microcontroller, and an LCD display, illustrating wireless, road-integrated charging. The concept shows how solar-powered wireless charging could be embedded in roads to support Electric Vehicles (EVs) operation continuously, reducing downtime and fuel costs while promoting sustainability. Additional plans include integrating AI assistance and IoT connectivity to create a more intelligent, internet-enabled charging solution.
Mohammed Sohail Mutahhar, Mohd Abdul Samad Khan , Mahfooz Abdullah
Advanced Electronic Voting Machine Using Fingerprint Sensor And Arduino
The rapid growth of electronic system has increased the need for secure, transparent, and reliable voting systems. This paper presents the design and implementation of an advanced Electronic Voting Machine (EVM) integrated with a fingerprint sensor and Arduino microcontroller to enhance and improve election security and eliminate wrong voting practices. The proposed (suggested) system uses biometric authentication to uniquely identify voters, assuring that only authorized individuals are allowed to cast a vote. The fingerprint sensor captures and verifies voter fingerprints against a pre-stored database, preventing false representation and multiple voting procedures. The Arduino platform acts as the central control unit, managing voter verification, vote recording, and system operations efficiently with minimal hardware complexity and low power consuming. Also, making the system suitable for large-scale deployment in both urban and rural areas. The projected EVM decreases human interference, and minimizes operational faults, also improves voter sureness in the election procedure. The results prove that the fingerprint-based voting system offers the high reliability, fast verification, and strong safety, and making it up-to-date self-governing elections. This project plays a vibrant role in refining the election process by minimizing human mistakes, and giving quick and precise vote counts. The system certifies that each voter can give or cast only one vote and all that data is safely recorded or stored for final process or we can say results process. Identity verification is secure and distinctive thus which may implement in mechanical device to realize high secure election. The Electronic Voting Machine using Arduino Uno demonstrates how embedded systems can contribute efficiency and transparent elections in both small-scale and large-scale applications and develop secure voting system.
Zainab Ria Rasheed, Fariha Fatima, Syeda Haleema Sadia