International Journal of Engineering and Science Research
Memory Management Unit (Mmu) Using Verilog Hdl
The Memory Management Unit (MMU) is a crucial component in modern computer systems that is responsible for translating virtual addresses generated by the CPU into physical addresses used by the main memory. This project presents the design and simulation of a simplified Memory Management Unit using Verilog Hardware Description Language (HDL). The primary objective of this work is to demonstrate the process of virtual to physical address translation using key components such as the Translation Lookaside Buffer (TLB) and Page Table. In this design, the TLB is used as a high-speed cache to store recent address mappings, thereby reducing memory access time. When a virtual address is generated, the MMU first checks the TLB for a match. If a match is found (TLB hit), the corresponding physical address is obtained directly. If no match is found (TLB miss), the system accesses the page table to retrieve the required physical page number and updates the TLB for future use. The entire system is modeled and simulated using Verilog HDL, and the results are verified through waveform analysis using simulation tools. The output waveforms clearly demonstrate the behavior of TLB hit and miss conditions along with correct address translation. This project effectively illustrates the fundamental working of memory management in a simplified manner and provides a strong foundation for understanding real-world MMU architectures.
Memory Management Unit (MMU), Virtual Address Translation, Physical Address Mapping, Translation Lookaside Buffer (TLB), Page Table, Verilog HDL, Hardware Design, Digital System Simulation, Computer Architecture, Memory Management.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
Performance Benchmarking of a Novel CNN–BiLSTM–Attention Hybrid Model for Short-Term Spatiotemporal Wind Speed Forecasting Using Indian SCADA Data
Short-term wind speed forecasting is fundamental to the reliable integration of variable renewable energy into modern power systems. Although numerous machine learning and deep learning models have been proposed, comprehensive benchmarking of hybrid attention-based architectures against both classical and modern baselines on real Indian SCADA data accompanied by thorough residual diagnostics and statistical validation remains scarce. This paper reports a rigorous performance benchmarking of a CNN–BiLSTM–Attention hybrid model against five established baselines: Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Support Vector Machines (SVM), hybrid ARIMA–ANN, and standalone CNN–BiLSTM. Using a cleaned 8,760-hourly SCADA dataset from an operational onshore Indian wind turbine, the proposed hybrid achieved MAE = 0.824 m/s, RMSE = 1.146 m/s, MAPE = 13.7%, and R² = 0.924 on the held-out test set, corresponding to RMSE reductions of 22.4%, 18.7%, 23.8%, 24.0%, and 11.2% versus ANN, LSTM, SVM, ARIMA–ANN, and CNN–BiLSTM respectively. Residual diagnostics Q-Q plots, Ljung–Box, Shapiro–Wilk, and Breusch–Pagan tests confirm white-noise behaviour with no systematic autocorrelation or heteroskedasticity. The Diebold–Mariano test verifies that observed improvements are statistically significant (p < 0.001 against all baselines). Wind-regime stratification further reveals that the hybrid's advantages are most pronounced in high-wind and transitional conditions. The findings establish the CNN–BiLSTM–Attention architecture as the most accurate and statistically robust candidate for operational deployment in Indian wind corridors.
Framework for Sustainable Digital Operations in Smart Educational Institutions through IoT and Data Analytics
Digital technologies are growing at such a rapid pace that the operational paradigms of educational institutions across the world are changing. This review paper aims at a meta-analysis of the data/information available in the literature on the evolution of sustainable digital operations strategy frameworks for smart educational institutions. Since its inception, this study combines empirical evidence from the past three decades of academic inquiry examining the role of similar converging elements of Internet of Things (IoT) enablement, data analytics, and process-excellence paradigms affecting institutional governance, resource optimization and pedagogical delivery. The paper draws from a collection of 30 peer-reviewed references from the broader fields of engineering, education management, and information systems; critically assessing common frameworks in the fields while highlighting cross-cutting thematic gaps such as limited scalability mechanisms, poor sustainability integration, and suboptimal stakeholder-centric design. The framework proposed integrates five interdependent pillars smart infrastructure, real time data governance, predictive analytics, green operations, and adaptive process management thereby providing an holistic approach to next generation educational digitization. Results show that IoT-enabled operational intelligence in institutions leads to quantifiable performance improvements in the areas of resource efficiency (34%) and student engagement (27%). The paper concludes by outlining a future research agenda for practitioners, policymakers, and technology architects striving for scalable, sustainable, and equitable smart educational ecosystems. (≈250 words)
Smart educational institutions; Internet of Things (IoT); data analytics; digital operations strategy; sustainable framework; process excellence; educational technology management
Copyright (c) 2022 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
Combining Social Media Sentiment And High-Dimensional Indicators For Bitcoin Price Range Prediction Using Lstm
The rapid rise of cryptocurrencies, particularly Bitcoin, has transformed global financial systems and attracted significant interest from investors, researchers, and financial institutions. However, Bitcoin markets are highly volatile and influenced by multiple factors such as technical indicators, investor psychology, global events, and social media discussions. Traditional forecasting models mainly rely on historical price trends and technical indicators, often failing to capture the emotional and behavioral aspects that strongly affect cryptocurrency prices. This article proposes a hybrid framework that combines Natural Language Processing (NLP) techniques with Long Short-Term Memory (LSTM) neural networks to predict the next-day Bitcoin price range. The proposed system integrates high-dimensional technical indicators with sentiment features extracted from Twitter posts. Historical Bitcoin market data collected over six years is combined with millions of cryptocurrency-related tweets to create a comprehensive prediction dataset. NLP methods are applied to preprocess and analyze textual content, generating sentiment scores that represent investor emotions and market reactions. These sentiment features are merged with technical indicators such as moving averages, Relative Strength Index (RSI), volatility measures, and trading volume. The LSTM network is selected because of its ability to learn sequential and temporal dependencies from time-series data. Unlike traditional machine learning methods, LSTM can retain long-term patterns and identify nonlinear relationships between sentiment changes and market movements. Experimental results demonstrate that integrating sentiment analysis significantly improves prediction accuracy and model robustness. Sensitivity analysis further confirms the importance of sentiment-driven information in cryptocurrency forecasting. The proposed framework contributes to modern financial analytics by bridging quantitative market analysis with qualitative behavioral insights from social media. The study highlights the growing role of artificial intelligence and deep learning in cryptocurrency prediction systems and demonstrates how sentiment-aware forecasting models can support better trading decisions, risk management, and investment planning.
Bitcoin, Cryptocurrency Prediction, LSTM, NLP, Sentiment Analysis, Twitter Data, Deep Learning, Technical Indicators.
Talha Quaiser, Mohd Reyan Ali, Mohammad Abdul muqeet
Enhancing Agricultural Decision-Making with Combined Language Models
Agricultural advisory systems play a crucial role in supporting farmers by providing timely and accurate solutions to crop-related problems. However, building reliable question-answering systems for agriculture is challenging due to the diverse, domain-specific, and often noisy nature of farmer queries. In this project, a retrieval-based agricultural question-answering framework is proposed to deliver accurate and trustworthy responses by leveraging historical expert-curated data. The system utilizes a pretrained sentence embedding model to convert farmer queries into dense vector representations, followed by similarity-based retrieval using FAISS to identify the most relevant past questions and their corresponding expert answers. Unlike generative models, which often produce vague or hallucinated responses, the proposed approach ensures factual correctness by reusing validated advisory content. Experiments conducted on a large farmer query dataset demonstrate that the system achieves a training accuracy of 100% and a test accuracy of 95.9%, with qualitative evaluation confirming high relevance and agricultural validity of the retrieved responses. The results indicate that retrieval-based question answering is a practical, efficient, and reliable solution for real-world agricultural advisory applications.
Agricultural Advisory System, Retrieval-Based Question Answering, Sentence Embeddings, FAISS, Natural Language Processing (NLP), Information Retrieval, Semantic Similarity, Farmer Query Analysis, Expert Advisory Retrieval, Dense Vector Representation, Pretrained Language Models, Agricultural Informatics.
Sarfaraz khan1,Syed asadullah2,Abdul ahmed khan
Detecting The Small Object Recognition By Drone Images Using Yolo
The accurate detection and classification of dental anomalies from radiographic images play a crucial role in modern dentistry for effective diagnosis and treatment planning. However, manual interpretation of Orthopantomogram (OPG) X-rays is often time-consuming, prone to human error, and requires expert knowledge. This work proposes an automated deep learning-based framework for dental anomaly detection and tooth type classification using a Transformer- enhanced YOLOv10 model optimized with hyperparameter tuning. The system is trained on a labeled dental OPG dataset containing six classes, including four tooth types (molar, premolar, canine, incisor) and two anomalies (impacted and capped teeth). To ensure practical usability, a Flask-based web application has been developed, providing a user-friendly interface with modules for image upload, real-time prediction, results visualization, and performance analytics through interactive charts. Experimental results demonstrate that the proposed model achieves high detection accuracy and robustness in identifying dental structures and anomalies. The integration of advanced deep learning techniques with a lightweight web application offers a cost-effective and efficient solution, supporting dentists in clinical decision-making and enhancing patient care.
Dental Anomaly Detection, Orthopantomogram (OPG), YOLOv10, Transformer Networks, Deep Learning, Tooth Classification, Medical Image Analysis, Hyperparameter Optimization, Computer Vision, Dental Radiography, Automated Diagnosis, Flask Web Application, Object Detection, Clinical Decision Support System, Artificial Intelligence in Dentistry.
Abdullah Mohammed Sarwar, Tabrez Bin Sayeed
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Machine Learning Based Method for Insurance Fraud Detection on Class Imbalance Datasets with Missing Values
Insurance fraud has become one of the fastest-growing financial crimes in the global insurance industry. Fraudulent activities negatively impact insurance companies by increasing operational losses, damaging customer trust, and raising insurance premiums for honest policyholders. Traditional fraud detection systems rely mainly on manual investigations and predefined business rules. These systems are no longer sufficient because fraudsters continuously develop more sophisticated methods to bypass verification mechanisms. Machine learning has emerged as an effective solution for identifying fraudulent insurance claims automatically by analyzing hidden patterns within historical data. However, insurance datasets usually suffer from two major problems: class imbalance and missing values. Fraudulent claims represent only a very small portion of the total claims, making prediction difficult for conventional machine learning algorithms. In addition, incomplete customer records and missing claim information reduce the reliability of predictive systems. This paper presents a detailed machine learning-based framework for insurance fraud detection. The proposed system integrates preprocessing techniques, missing value handling, Synthetic Minority Oversampling Technique (SMOTE), feature engineering, and advanced classification algorithms such as Logistic Regression, Decision Tree, Random Forest, and XGBoost. Experimental results demonstrate that ensemble learning algorithms significantly improve fraud detection accuracy, precision, recall, and F1-score compared to traditional approaches.
Insurance Fraud Detection, Machine Learning, Class Imbalance, SMOTE, Missing Values, Random Forest, XGBoost, Gradient Boosting, Logistic Regression, Anomaly Detection.
Mohammed Zohaib Hussain, Syed Saqlain Ahmed, Mohammed Abdul Raqeeb Haqqani
Combining Social Media Sentiment And High-Dimensional Indicators For Bitcoin Price Range Prediction With Cart
In this study, I propose a method for forecasting the next-day Bitcoin price range by integrating Natural Language Processing (NLP) techniques with a Long Short-Term Memory (LSTM) network. The model combines high-dimensional technical indicators with sentiment features extracted from Twitter data through advanced NLP methods to capture complex market behavior and investor sentiment. By performing sequential analysis on both numerical financial indicators and textual sentiment information, the LSTM model effectively learns temporal dependencies and hidden patterns that influence Bitcoin price movements. The experiments are conducted using six years of historical Bitcoin market data along with millions of relevant Twitter posts collected from cryptocurrency-related discussions. Various preprocessing and feature extraction techniques are applied to improve data quality and enhance the learning performance of the model. The proposed hybrid framework demonstrates the effectiveness of combining deep learning architectures with sentiment analysis to improve forecasting accuracy, robustness, and interpretability in highly volatile cryptocurrency markets. Furthermore, sensitivity analysis is employed to optimize the contribution of sentiment features and evaluate their impact on prediction performance. The results indicate that sentiment-driven insights significantly enhance forecasting capability when integrated with technical indicators. This research provides a novel and dynamic approach for cryptocurrency market prediction and offers valuable insights for investors, analysts, and financial researchers seeking more reliable Bitcoin price forecasting models.
Bitcoin price prediction, Long Short-Term Memory (LSTM), Natural Language Processing (NLP), sentiment analysis, Twitter data, technical indicators, cryptocurrency forecasting, deep learning, financial prediction, volatile markets.
M.A Raoof Khan, Mohd Aman, Syed Amaan Mohiuddin
Deep Traffic-VTS: An Enhanced Framework for Real-Time Vehicle Detection and Traffic Prediction
Urban traffic congestion remains one of the most pressing challenges in modern metropolitan areas, resulting in increased travel times, fuel wastage, environmental degradation, and reduced transportation efficiency. Rapid urbanization and the growing number of vehicles on roads have made traditional traffic management systems inadequate for handling dynamic traffic conditions effectively. This paper presents DeepTraffic-VTS, an intelligent hybrid traffic management system that combines Long Short-Term Memory (LSTM) networks for time-series traffic forecasting with YOLOv8 (You Only Look Once) for real-time vehicle detection and classification. The proposed system analyzes historical traffic parameters, including vehicle count, average vehicle speed, road occupancy, and congestion levels, to predict future traffic conditions with improved accuracy. Simultaneously, the YOLOv8-based computer vision module processes live traffic video feeds to detect and classify multiple vehicle categories such as two-wheelers, cars, buses, trucks, and emergency vehicles in real time. By integrating predictive traffic analysis with real-time situational awareness, the system generates adaptive vehicle-type recommendations and optimized traffic guidance to improve navigation efficiency and reduce congestion. In addition, the proposed framework supports intelligent traffic control mechanisms such as emergency vehicle prioritization, dynamic route optimization, and congestion-aware transportation planning. The hybrid integration of deep learning and computer vision enables proactive traffic management rather than reactive congestion handling. Experimental analysis demonstrates that DeepTraffic-VTS can significantly enhance urban mobility, reduce fuel consumption, minimize travel delays, and contribute toward the development of sustainable smart city transportation systems.
Intelligent Traffic Management, Deep Learning, Long Short-Term Memory (LSTM), YOLOv8, Vehicle Detection, Traffic Forecasting, Smart Transportation, Congestion Prediction, Computer Vision, Adaptive Navigation System, Real-Time Traffic Analysis, Intelligent Transportation Systems (ITS).
Syed Abdul Hannan, Mohammed Ahmed Khan, Mohammed Azeem Uddin
IoT-Based Alcohol Detection and Engine Control System
Most of these days, we hear lot of accidents due to drunken driving. Drunken drivers will not be in stable condition and so the rash driving is the inconvenience for other road users and also question of life and death for the drunken driver and for others. This project is being designed by the modeling software like Catia V5; its analysis is being done by using Ansys Workbench, and later then fabricated. In this project, we are developing an Auto Lock System. Alcohol sensor will be attached with our microcontroller. While alcohol wafers smelled by the sensor. Sensor sends the input voltage to microcontroller. If there are any traces of Alcohol above the set limit, then the system will lock the Engine at the same time will automatically give a buzzer We can avoid accidents by checking the driving people on the roads. So that we can avoid accidents.
Drunken Driving Detection, Alcohol Sensor, Auto Lock System, Microcontroller, Engine Locking System, Road Safety, Accident Prevention, Embedded System, CATIA V5, ANSYS Workbench, Buzzer Alert.
Syed Ali Saif Khundmiri, Syed Sufiyan Ali, Muzamil Ahmed Khan
Dynamic Ransomware Detection Using Time-Based API Call Analysis
The increasing growth of cyber threats has made malware detection an important challenge in cybersecurity. This project proposes a machine learning-based multi-class malware detection system capable of identifying malware types such as ransomware, adware, keyloggers, rootkits, and botnets. The system uses preprocessed and normalized feature-based data to train models including Random Forest, XGBoost, and LSTM. Comparative analysis shows that XGBoost provides the highest classification accuracy and reliability. A Flask-based web application is developed to provide real-time malware prediction along with preventive measures. The proposed system demonstrates the effectiveness of machine learning, especially ensemble techniques, in enhancing malware detection and cybersecurity protection.
Malware Detection, Machine Learning, XGBoost, Random Forest, LSTM, Cybersecurity, Multi-Class Classification, Flask Web Application.
Heart Disease Detection Using Feature Extraction And Artificial Neural Networks
Heart disease is one of the major causes of death worldwide, and early diagnosis is essential for reducing mortality rates. This paper presents an efficient heart disease detection system using feature extraction techniques and Artificial Neural Networks (ANN). The proposed system collects physiological parameters such as heart rate and body temperature through sensors and processes the data using embedded systems. The extracted features from the medical data are analyzed using ANN algorithms to identify patterns associated with heart disease. Feature extraction improves the performance and accuracy of the system by selecting the most relevant information from the collected data. The integration of IoT technology enables real-time monitoring and remote access to patient health information. Experimental results demonstrate that the proposed system achieves an accuracy greater than 85%, which is higher than many traditional machine learning approaches. Therefore, the system can be effectively used for early heart disease diagnosis and continuous health monitoring.
Heart Disease Detection, Artificial Neural Network, Feature Extraction, IoT, Embedded System
Ayesha Saleh Baghabra, Manne Deepika, Buyankar Eshwar
Enhanced Yolov11m For Real-Time Multi-Scale Traffic Detection Under Haze Conditions
Traffic object detection is a fundamental component of intelligent transportation systems, autonomous vehicles, and smart surveillance applications. However, environmental conditions such as haze and fog significantly reduce image visibility, weaken object boundaries, and introduce background noise, thereby decreasing the performance of traditional object detection algorithms. Lightweight object detectors are suitable for real-time deployment on edge devices, but they often struggle to maintain detection accuracy under adverse weather conditions due to limited contextual understanding and insufficient multi-scale feature representation. This project proposes an enhanced YOLOv11m framework for real-time multi-scale traffic detection under haze conditions. The proposed model introduces three major architectural improvements: Attention-Gate Convolution (AGConv), Multi-Dilation Sharing Convolution (MDSC), and Cross-Channel Feature Fusion Module (CCFM). AGConv improves spatial attention and suppresses irrelevant background information, MDSC enhances receptive field diversity for better multi-scale feature extraction, and CCFM dynamically recalibrates channel-wise feature importance for efficient feature fusion. The model is trained and evaluated on traffic datasets containing foggy and hazy scenes using standard performance metrics including mean Average Precision (mAP), Intersection over Union (IoU), and Frames Per Second (FPS). Experimental results demonstrate that the proposed YOLOv11m achieves improved detection accuracy compared with YOLOv11n while maintaining lightweight architecture and real-time processing capability. The system achieves approximately 376 FPS with only 2.6 million parameters while improving mAP@0.5 and mAP@0.5:0.95 under challenging weather conditions. The proposed framework provides an efficient and practical solution for intelligent transportation systems operating in degraded visual environments.
YOLOv11m, Intelligent Transportation Systems, Traffic Object Detection, Haze Detection, Multi-Scale Feature Extraction, Deep Learning, Computer Vision, AGConv, MDSC, CCFM, Real-Time Detection, Autonomous Driving.
Munaf Yezdani, Abid Sohail Bin Mazi, Abdul Rafe Obaid
Defect Detection in Solar Cell Images Using Deep Learning
This research study introduces a unique method that makes use of a wide range of deep learning (DL) techniques for automated flaw identification in solar cell images. The research paper investigates how well 24 distinct convolutional neural network (CNN) architectures — ResNet, DenseNet, VGG, Inception, MobileNet, Xception, SqueezeNet, and AlexNet — classify solar cells into defected and non-defective categories. The study performs a thorough assessment of a wide variety of models, concentrating on high-performance architectures and lightweight models suitable for resource-constrained contexts. A balanced and well-curated dataset of 3,102 images of solar cells with a range of common faults was used. MobileNetV2 and Xception demonstrated excellent performance in defect identification, with accuracy rates of 99.95% and 99.29% respectively, with minimal validation losses. This study demonstrates the potential of efficient models such as MobileNetV2 for real-world use in solar energy generation.
computer vision, defect detection, deep learning, image classification, photovoltaics.
Saifullah khan, Syed Sohail Ali, Ksk Kashyap
Fake Face Detection Based On Videos Using OpenCV And Neural Network Architecture
The rapid development of the Internet has enabled the widespread distribution of manipulated facial images, particularly Deepfakes, which are increasingly difficult to detect using conventional methods. While current approaches focus on spatial domain features or complex network architectures, they often lack robustness against sophisticated forgery techniques. To address this, we propose a MobileNetV2-based Deepfake detection framework that leverages efficient convolutional feature extraction for accurate classification of real and fake facial images. The framework begins with OpenCV-based preprocessing, including face detection, alignment, and normalization, to ensure consistent input quality and enhance the discriminative features for detection. MobileNetV2, a lightweight yet powerful convolutional neural network, is employed to automatically learn hierarchical spatial features from the preprocessed facial images, eliminating the need for handcrafted features. By combining OpenCV preprocessing with MobileNetV2, the proposed system effectively captures subtle visual artifacts and texture inconsistencies introduced by Deepfake manipulation. This approach enables robust and scalable detection, generalizing well across diverse datasets and real-world scenarios, providing a practical solution for automated Deepfake detection in security, media verification, and social media monitoring applications.
Deepfake Detection, MobileNetV2, OpenCV, Computer Vision, Deep Learning, Neural Network Architecture.
Mohd Faizan Ali, Mohd Shoaib, Mohd Imamuddin Naveed
Assessing The Psychological Impact Of Internet Blackouts: A Stress Analysis Case Study
Internet connectivity plays a vital role in communication, education, business, and access to essential services in today’s digital world. When internet services are suspended during politically sensitive situations, it can significantly influence people’s mental well-being. Such disruptions may lead to increased stress, anxiety, frustration, and emotional instability. This research analyzes the psychological effects of the internet shutdown that occurred during the Bangladesh Quota Movement in July 2024. A total of 2,085 individuals participated in the survey, sharing their experiences related to academic activities, workplace responsibilities, and social interactions during the shutdown period. The collected textual responses were analyzed using Natural Language Processing (NLP) methods to identify patterns related to emotional and behavioral changes. To classify stress levels, a Multi-Layer Perceptron (MLP) model was implemented. The study highlights how sudden digital disconnection can impact mental health and emphasizes the importance of stable internet access in modern society.
Internet Blackout, Mental Health, Stress Analysis, NLP, MLP, Bangladesh Quota Movement.
Affan Khaleel Khan, Syed Ibrahim, Mohammed Abdul Mujahed
Integrated Emotion And Sentiment Analysis Using Multi-Modal Data
In the digital era, the rapid growth of online reviews has significantly influenced consumer behavior and public opinion. These text-based sentiments play a crucial role in shaping decisions and perceptions. However, analyzing sentiments and emotions in written reviews presents unique challenges due to the complexity of human language, contextual variations, and the presence of sarcasm or ambiguity, particularly in low-resource languages like English. This study introduces a comprehensive framework for text-based sentiment analysis and emotion detection tailored to English reviews. The framework focuses solely on linguistic features—such as lexical patterns, syntactic structures, and semantic relationships—to capture the underlying emotions and opinions expressed in text. By leveraging advanced natural language processing and deep learning techniques, the system enhances the accuracy and depth of sentiment interpretation. A dedicated dataset of English text reviews has been developed to support this research, providing a valuable resource for future studies. The proposed approach is validated through a detailed case study, demonstrating its effectiveness and practical applicability in real-world scenarios.The integrated framework performs both emotion classification and sentiment analysis simultaneously through multi-task learning. By leveraging complementary information from multiple sources, the system significantly improves accuracy, contextual understanding, robustness against noisy data, and adaptability to complex human emotions. Experimental results demonstrate that the proposed multi-modal approach outperforms traditional single-modal systems in terms of prediction accuracy and real-world applicability.This work can be applied in various domains including social media monitoring, customer feedback analysis, healthcare, education, virtual assistants, mental health assessment, and intelligent human–computer interaction systems. The project highlights the importance of multi-modal AI systems in building more empathetic, context-aware, and intelligent applications for future technologies.
Sentiment Analysis, Emotion Detection, Natural Language Processing (NLP), Deep Learning, Text Mining, English Reviews, Multi-Task Learning, Opinion Mining, Semantic Analysis, Lexical Features, Syntactic Analysis, Emotion Classification, Context-Aware Computing, Human–Computer Interaction, Social Media Analytics, Artificial Intelligence, Machine Learning, Text Classification, Customer Feedback Analysis, Intelligent Systems.
Mohammed Omer, Shamoon Taib Khan, Abdul Bur
Changes in Butterfly Fauna at Different Altitudes in Central Jharkhand
This study investigates the changes in butterfly fauna across different altitudinal levels in Central Jharkhand. Butterflies, being sensitive bioindicators, provide valuable insights into the health and diversity of ecosystems. The research aimed to document the variations in species composition, abundance, and diversity of butterflies at varying altitudes, ranging from lowland forests to high-altitude regions. Field surveys were conducted over a year, covering three distinct altitudinal zones. Standardized methods, including transect walks and visual counts, were employed to record butterfly species and their abundance. The results revealed significant variations in butterfly diversity and composition across the different altitudes. Lowland areas exhibited higher species richness, with a dominance of generalist species, while mid-altitudinal regions showed a mix of both generalist and specialist species. High-altitude zones, characterized by harsher climatic conditions, had lower species diversity, with the presence of altitude-specialist species. Environmental factors such as temperature, humidity, and vegetation type were found to influence butterfly distribution and diversity. This study highlights the importance of altitudinal gradients in shaping butterfly communities and underscores the need for conservation efforts in diverse habitats.
Butterfly diversity, Altitudinal gradients, Ecosystem health, Species composition, Conservation strategies.
Copyright (c) 2023 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
Impact of Content, Teachers and Academic Environment on Students’ Academic performance
The study investigates the impact of content, teachers, and the academic environment on students' academic performance, aiming to understand the multifaceted factors influencing educational outcomes. This research employs a mixed-methods approach, combining quantitative data from student performance metrics and qualitative insights from surveys and interviews with students and educators. The analysis focuses on three key components: the relevance and clarity of educational content, the effectiveness and engagement of teachers, and the overall academic environment, including facilities and support systems. Results indicate that high-quality content, delivered in an engaging and comprehensible manner, significantly enhances student performance. Furthermore, teachers' expertise, teaching methods, and their ability to create an inclusive and motivating classroom atmosphere play a crucial role in student achievement. The academic environment, characterized by adequate resources, a supportive learning culture, and positive peer interactions, also contributes substantially to academic success. The study underscores the interconnectedness of these factors, suggesting that improvements in content delivery, teacher training, and academic infrastructure are essential for optimizing student performance.
Academic Performance, Educational Content, Teacher Effectiveness, Academic Environment, Student Achievement.
Smart Human Action Monitoring Using RGB And Motion Signals
Human action recognition is a vital component of smart monitoring systems, with applications in healthcare, surveillance, and intelligent indoor environments. This project presents a smart human action monitoring system that relies exclusively on RGB images to detect and classify human activities in real time. The system processes image frames to extract essential features such as body posture, joint positions, and movement patterns. These features are then analyzed to recognize common human actions, including sitting, walking, running, drinking, and other daily activities. By leveraging advanced image processing and deep learning techniques, the system achieves high accuracy, robustness to varying lighting conditions, and efficiency suitable for real-time deployment. Experimental results demonstrate the system’s ability to monitor human activity reliably, providing a practical solution for indoor action recognition and smart environment applications.
Human Action Recognition, RGB Image Processing, Smart Monitoring System, Deep Learning, Real-Time Activity Detection, Human Activity Classification, Computer Vision, Indoor Surveillance, Image-Based Recognition, Motion Analysis, Pose Estimation, Intelligent Monitoring Systems, Activity Recognition, Artificial Intelligence.
Shahbaz Ahmed, Asim Ahmed Khan, Syed Maqdoom Masroor Abrar
Fort2bck: Hybrid Cryptographic Validation For Robust Healthcare Data Protection
Fort2BCK is a robust security framework designed to address major vulnerabilities in healthcare blockchain systems, including data tampering, unauthorized access, and limitations in consensus protocols. The framework introduces a dual-layer verification mechanism that strengthens existing consensus algorithms using advanced cryptographic techniques such as RSA, ECDSA, and Zero-Knowledge Proofs (ZKPs). This additional security layer improves authentication, auditability, and resistance against malicious activities within system.
Healthcare Blockchain, Fort2BCK, Cryptographic Validation, RSA, ECDSA, Zero-Knowledge Proofs (ZKP), Data Security, Consensus Protocols, Blockchain Security, Healthcare Data Protection.
Enhanced YOLOv11m for Real-Time Multi-Scale Traffic Detection under Haze Conditions
In intelligent transportation systems, accurate traffic object detection plays a vital role in improving road safety, traffic monitoring, and autonomous driving applications. However, detecting vehicles, pedestrians, and other road objects under hazy weather conditions remains a challenging task due to reduced visibility, low contrast, blurred object boundaries, and background noise. Existing lightweight object detection models such as YOLOv11n provide real-time performance but often suffer from reduced detection accuracy in adverse weather environments, especially when dealing with multi-scale objects and complex traffic scenes. To address these limitations, this project proposes an enhanced object detection model named Proposed v11m, developed based on the YOLOv11n framework. The proposed system introduces several architectural improvements to enhance feature extraction and multi-scale representation while maintaining lightweight computational complexity. Firstly, an Attention-Gate Convolution (AGConv) module is integrated into the backbone network to improve contextual awareness and suppress irrelevant background information. Secondly, a Multi-Dilation Sharing Convolution (MDSC) module is incorporated to capture features at multiple receptive fields, improving sensitivity toward objects of varying scales. Additionally, a Cross-Channel Feature Fusion Module (CCFM) is designed within the neck network to adaptively recalibrate channel-wise feature importance and strengthen feature fusion. The proposed model is trained and evaluated using traffic datasets containing haze-affected road scenes. Experimental results demonstrate that Proposed v11m achieves improved detection accuracy compared to the existing YOLOv11n model, with higher mAP values while preserving real-time inference speed. The model achieves efficient performance with only 2.6 million parameters and high frame processing capability, making it suitable for deployment on resource-constrained edge devices and embedded intelligent traffic systems. Overall, the Proposed v11m model provides an effective and robust solution for real-time traffic object detection in challenging hazy environments, contributing toward safer and more reliable intelligent transportation systems.
Traffic Object Detection, YOLOv11m, Hazy Weather Detection, Intelligent Transportation Systems, Multi-Scale Feature Extraction, Attention-Gate Convolution, Cross-Channel Feature Fusion, Real-Time Object Detection.
Mohammed Ali Rizwan, Khan Javvad Ahmed, Mohammed Mustafa Uddin
Dynamic Ransomware Detection Using Time-Based Api Call Analysis
Dynamic Ransomware Detection Using Time-Based API Call Analysis is a cybersecurity-based project developed to detect ransomware attacks in real time. The project monitors API calls generated by applications and analyses their timing behavior to identify malicious activities. Machine learning techniques are used to classify whether the behaviour is normal or malicious. Dynamic Ransomware Detection Using Time-Based API Call Analysis is a cybersecurity- based project developed to detect ransomware attacks in real time. Ransomware is a type of malicious software that encrypts user files and demands payment for recovery. Traditional antivirus systems often fail to detect newly emerging ransomware variants. This project focuses on monitoring API calls generated by applications and analysing their timing behavior. By studying the sequence and time intervals of API calls, the system can identify suspicious activities related to ransomware attacks. Machine learning techniques are used to classify whether the behavior is normal or malicious. The proposed system helps in early ransomware detection before severe damage occurs, thereby improving system security and reducing data loss.
Ransomware Detection, API Call Analysis, Dynamic Malware Analysis, Cybersecurity, Machine Learning, Time-Based Analysis, Malicious Behaviour Detection, Real-Time Threat Detection, Data Security, Malware Classification, System Monitoring, Behavioral Analysis.
Mohammed Riyaz, Syed Mukeet Uddin, Syed Mohammed Ali
GPS Threat Detection Using CG-Trained ANN Framework
The widespread dependence on Global Navigation Satellite Systems (GNSS), particularly GPS, has significantly increased vulnerabilities to intentional threats such as spoofing and jamming attacks. While various machine learning techniques—including Convolutional Neural Networks (CNNs), ensemble methods, and deep learning architectures—have demonstrated promising results in threat detection, the exploration of advanced optimization algorithms for training Artificial Neural Networks (ANNs) remains limited. This paper provides a comprehensive review of GNSS security threats and existing machine learning-based detection approaches. It proposes a novel research framework that employs the Conjugate Gradient (CG) optimization algorithm for efficient ANN training. Furthermore, it details the integration of CG-trained ANN (CG-ANN) models to achieve accurate, low-latency, and real-time detection of spoofing and jamming in GPS signals, potentially enhancing GNSS resilience in critical applications.
GNSS, GPS spoofing, jamming, artificial neural networks, conjugate gradient optimization, machine learning.
Mr. MohdTekBurhanuddin, Mr. Syed Abdul Baqi,
Secure And Transparent E‑Voting System Using Blockchain, Smart Contracts, Differential Privacy, And Self‑Sovereign Identity
Electronic voting systems have become increasingly significant in modern democratic environments due to the growing demand for transparency, scalability, and digital accessibility. Traditional voting systems suffer from challenges such as centralized control, vote tampering, lack of transparency, operational delays, and high management costs. Although blockchain technology has emerged as a revolutionary solution for secure digital transactions, many existing blockchain-based voting systems still fail to achieve strong voter privacy, efficient scalability, and regulatory compliance. This paper presents BP‑Vot‑Lite, a secure and transparent blockchain-enabled electronic voting framework integrating smart contracts, differential privacy, and Self‑Sovereign Identity (SSI). The framework ensures voter anonymity, immutable vote storage, transparent result verification, and decentralized identity management. Differential privacy mechanisms are employed to prevent reconstruction attacks while maintaining statistical accuracy in election outcomes. Experimental analysis demonstrates improved latency, enhanced privacy preservation, and reliable performance in distributed blockchain environments. The proposed architecture contributes toward developing secure, transparent, scalable, and privacy-preserving digital election systems suitable for modern e-governance applications.
Blockchain, Electronic Voting, Smart Contracts, Differential Privacy, Self‑Sovereign Identity, Hyperledger Besu, Privacy Preservation, Secure Voting, Decentralized Systems.
Mohammed Museb Ahmed, Mohammed Abdullah Shareef, Md Abubakar Siddique
Under Water Net: Efficient Visual Detection of Marine Garbage for Eco Monitoring
Marine pollution poses a severe threat to the sustainability of aquatic ecosystems and the blue economy.Effective detection and classification of underwater debris are crucial for enabling timely interventions and supporting marine conservation efforts. In this project, we present an advanced underwater garbage detection system based on YOLOv10n, a cutting-edge, lightweight object detection model optimized for resource-constrained IoT and underwater robotic platforms. Building on the challenges identified in traditional detection models—such as high computational costs and deployment complexity—we replace older backbones like CSPDarknet with the more efficient YOLOv10n architecture. YOLOv10n is designed with an emphasis on speed, low parameter count, and high accuracy, making it ideal for real-time underwater applications. Our system achieves robust debris detection with high precision, while significantly reducing memory and processing requirements, thereby facilitating deployment on embedded and mobile devices. This project demonstrates the feasibility and effectiveness of using YOLOv10n for scalable and eco-friendly marine monitoring solutions, providing a practical approach to combat marine pollution through intelligent automation.
Marine Pollution, Underwater Garbage Detection, YOLOv10n, Object Detection, Deep Learning, IoT, Edge Devices, Marine Monitoring
Sadath Fatima,Syeda Iqra Kaukab, Hufaida Umama
Machine Learning Enhanced By Sentiment Analysis For Cyberbullying Detection Using Nlp And Lstm
This research presents an automated deep learning-based methodology for detecting cyberbullying in social media text, emphasizing the practical application of the LSTM architecture combined with Natural Language Processing (NLP). Safe digital communication relies heavily on the quality and efficiency of content moderation, making accurate cyberbullying detection critical for minimizing emotional harm and improving online experiences. Traditional manual moderation methods are time-consuming, inconsistent, and prone to human error, limiting scalability in large platforms. By leveraging memory cells and gating mechanisms of LSTM, the proposed system effectively extracts complex sequential features from textual data. This enables precise differentiation between offensive and non-offensive text while maintaining computational efficiency, allowing the model to function effectively even in environments with dynamic slang and linguistic nuances. A balanced and well-curated dataset of social media text was used to train and validate the LSTM-based model. The dataset includes diverse abusive types, capturing variations in harassment, insults, and threats. The model undergoes rigorous preprocessing, including tokenization, stop word removal, and lemmatization, to improve generalization and reduce overfitting. Experimental results demonstrate that the LSTM model achieves high classification accuracy while maintaining a lightweight footprint suitable for deployment in digital scenarios. Performance metrics confirm the model's reliability in identifying offensive text, highlighting its potential to enhance automated quality control in social media moderation.
Deep Learning, Cyberbullying Detection, LSTM Architecture, Text Classification, Natural Language Processing, Sentiment Analysis, Automated Moderation.
Syed Sufiyan Uddin, Syed Shahriyar Ali, Safwan Ahmed
Crack Vision: Sophisticated Concrete Crack Identification Through Transfer Learning and Deep Learning
Crack Vision is a deep learning-powered application designed to detect and classify cracks in concrete surfaces with exceptional accuracy. Unlike traditional manual inspection methods, which are time-consuming and prone to human error, Crack Vision leverages state-of-the-art convolutional neural networks and transfer learning techniques—enhanced with alternative architectures such as EfficientNetB3—to deliver reliable, real-time predictions. The system has been trained on the METU concrete crack dataset, containing balanced sets of cracked and non-cracked surface images, enabling robust binary classification. Integrated into a user-friendly Flask web interface, Crack Vision allows users to easily upload images, preview them, and instantly receive classification results along with confidence scores. Additional visualization tools, including accuracy/loss charts and confusion matrices, provide transparency into model performance. This scalable solution offers a practical tool for engineers, inspectors, and infrastructure maintenance teams, enabling faster, more consistent assessments and contributing to improved structural safety and long-term durability in civil engineering projects.
Deep learning, Concrete crack detection, Convolutional neural networks, Transfer learning, Binary classification, Flask web interface, Structural safety, Infrastructure maintenance
Mohd Maaz Taqi , Mohd Abdul Mateen Khan , Mohammed Naveed
An Empirical Investigation into Secondary Students' Attitudes Toward ICT in Education in Alipurduar, West Bengal
Information and Communication Technology (ICT) has emerged as a transformative force in contemporary education, reshaping pedagogy and learning outcomes globally. This study empirically investigates the attitudes of secondary school students toward ICT in education in Alipurduar district, West Bengal, India a geographically and socioeconomically marginalized northern border district with a predominantly tribal and tea-garden community demography. The study adopted a descriptive survey design involving 240 students (Classes IX–X) selected through stratified random sampling from six secondary schools across government and private management categories. A validated 30-item five-point Likert-type attitude scale measuring cognitive, affective, and behavioural dimensions was administered during the 2020–21 academic session. The study hypothesized that gender and school management type do not significantly influence students' ICT attitudes. Results revealed that 76.67% of respondents held a favourable attitude toward ICT. A statistically significant difference was found between government and private school students (t=3.84, p<0.05), while gender differences were non-significant (t=1.42, p>0.05). Barriers including infrastructural deficiency and limited internet access were prominently self-reported. The study concludes that equitable ICT policy and targeted investment are critical for educationally underserved districts.
ICT attitudes, secondary students, digital divide, West Bengal, educational technology
Copyright (c) 2021 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
Assessment of Alternative Fuel Performance: Emission and Efficiency Analysis of Diesel-Tyre Pyrolysis Oil Blends
As we all know the due to the scarcity of conventional fuels and the Crude oil, the price of the conventional fuel is going up day by day and there Will no more conventional fuel in future and it is also Increasing the environmental pollution by the usage Of crude oil .And by using biofuels also we are consuming the natural products Therefore in the investigation the oil taken will pyrolysis oil which will be obtained by the Pyrolysis of the waste of automobile tire , and by this the disposal of waste rubber are Becoming the threat to the environment will also be Solved. By this investigation we can not only solve the shortage of crude oil but also can reuse the waste automobile tires, in the initial stage the test will be conducted on four Strokes single cylinder diesel engine by using diesel. Experimental investigation will be carried out on the same engine by changing the crank angle and by using the tire pyrolysis oil blended with Diesel in Different proportions such as T10, T20 and T30, to Find out the performance parameters, by changing the inlet pressures as 200 bar, 220 bar and 240 at different load condition for each inlet pressure 6, 9 & 12, with crank angle 23deg standard. After performing the experiment the performance characteristics would be determined.
Tire Pyrolysis Oil, Diesel Engine, Alternative Fuel, Waste Tire Recycling, Biofuel Blends, Compression Ignition Engine, Fuel Injection Pressure, Crank Angle, Engine Performance, Emission Characteristics, Waste Rubber Management, Pyrolysis Process, Sustainable Energy, CI Engine, Fuel Blending, Renewable Energy, Thermal Efficiency, Brake Specific Fuel Consumption, Environmental Pollution Reduction, Waste-to-Energy Technology
Md Amer Ullah Baig, Syed Zubair, Mohammed Khaja Asad Uddin, Mohammed Ameerouddin