International Journal of Multidisciplinary Engineering In Current Research
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Machine Learning for Personalized Learning Systems
Machine Learning (ML) integration into the sphere of educational technologies has transformed the delivery, pace, and evaluation of the content to a single learner. This paper explores how ML-driven personalized learning systems (PLS) can be used to improve student engagement, performance prediction, and adaptive content recommendation. The two main goals are to compare the relative accuracy of supervised ML algorithms employed in PLS and to analyze quantifiable effects on the performance of learners. The study is based on a secondary quantitative methodology to synthesise empirical data regarding 18 peer-reviewed studies on the topic (2020-2025) on the topics of ASSISTments, OULAD, Coursera, and Udemy and controlled experiments on Indian and global student groups. The hypothesis was that the ensemble and deep learning models are more accurate and personalized compared to classical algorithms. Findings attest to the consistent 86 to 96 percent accuracy of Random Forest, SVM (hyperparameter tuning) and Deep Knowledge Tracing (DKT) variants, and post-test improvements of 15.8 to 24% and engagement improvements of 13 to 20 percent of AI-personalized cohorts compared to control groups. These gains are discussed in the context of Indian K-12 and highereducation, covering the areas of scalability, equity, and pedagogical alignment. The conclusion of the paper is that ML-based PLS provide statistically significant learning advantages but should be trained by teachers and ethically secured to be deployed sustainably.
Copyright (c) 2026 Author https://creativecommons.org/licenses/by-nc-nd/4.0
Hybrid Deep Learning Models With Attention Mechanisms For Short-Term Wind Speed Forecasting: A Comprehensive Review And Benchmarking Of CNN–Bilstm–Attention On Real SCADA Data
Wind speed forecasting is a critical enabler of reliable renewable energy integration, particularly in countries such as India where installed wind capacity exceeds 40 GW and continues to grow under ambitious 2030 renewable targets. While hybrid deep learning architectures combining convolutional, recurrent, and attention components have shown promise on synthetic and benchmark data, real-world validation on operational SCADA data remains essential for translating such models into deployable forecasting tools. This paper presents a comprehensive review of hybrid deep learning models with attention mechanisms for short-term wind speed forecasting and reports a full benchmarking study of the CNN–BiLSTM–Attention architecture on real SCADA-recorded data collected from an onshore Indian wind turbine over the calendar year 2018. The dataset comprises 52,592 ten-minute observations resampled to 8,760 hourly samples, with chronological 80/20 train-test splitting. Six forecasting models ARIMA, SVR, Random Forest, XG Boost, LSTM, and CNN–BiLSTM–Attention were compared using MAE, RMSE, MAPE, and R². The hybrid model achieved the best real-SCADA performance with MAE = 0.824 m/s, RMSE = 1.146 m/s, MAPE = 13.7%, and R² = 0.924, outperforming all baselines. Residual diagnostics using the Ljung–Box, Shapiro–Wilk, and Breusch–Pagan tests confirmed white-noise error behaviour, while the Diebold–Mariano test verified statistical significance of the observed gains. These results establish operational viability of the hybrid architecture for Indian wind corridors and provide empirical benchmarks for grid integration and regulatory-compliance applications.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
The Indian telecommunications industry has experienced rapid transformation over the last decade due to technological innovation, digitalization, and increasing internet penetration. Among the major contributors to this transformation is Reliance Jio, which revolutionized the telecom sector with affordable data services, nationwide 4G connectivity, and digital ecosystem expansion. This paper examines the role of Jio as a mobile service provider in India, its business model, market performance, technological advancements, customer services, competitive strategies, and future prospects. The study highlights how Jio transformed the Indian telecom landscape through disruptive pricing strategies and digital inclusion initiatives. The paper also discusses challenges faced by telecom providers and evaluates the future opportunities for Jio in the era of 5G technology and digital transformation.
Impact Of Mobile Advertisements On Customer Perception And Brand Loyalty
Mobile advertising has emerged as one of the most influential digital marketing tools due to the rapid growth of smartphone usage and mobile internet penetration. Businesses increasingly utilize mobile advertisements through applications, social media platforms, websites, SMS marketing, and video content to attract consumers and strengthen brand relationships. This paper examines the impact of mobile advertisements on customer perception and brand loyalty. It explores how factors such as personalization, trust, entertainment, informativeness, and irritation influence consumer attitudes toward mobile advertisements. The study further analyzes the relationship between customer perception and brand loyalty in the digital era. Findings from recent literature suggest that well-designed and personalized mobile advertisements positively affect customer engagement, purchase intention, and long-term loyalty, whereas intrusive and irrelevant advertisements negatively influence brand perception. The paper concludes that organizations should adopt customer-centric mobile advertising strategies to enhance brand trust and loyalty.
A Study on Consumer Preferences, Satisfaction, and Switching Behavior in the Indian Cellular Services Market
The Indian cellular services market has emerged as one of the largest and most competitive telecommunications sectors in the world. Rapid technological advancements, affordable tariffs, mobile number portability, and the expansion of digital services have significantly transformed consumer behavior in the telecom industry. This study examines consumer preferences, customer satisfaction, and switching behavior among users of cellular services in India. The research explores the factors influencing customer choice of telecom operators, determinants of customer satisfaction, and reasons behind customer switching intentions. The study identifies network quality, pricing, internet speed, customer support, value-added services, and brand reputation as major determinants affecting customer decisions. Furthermore, the study highlights that customer dissatisfaction, attractive competitor offers, and poor service quality are the primary causes of switching behavior. The paper also discusses the implications for telecom operators in improving customer retention strategies and sustaining competitive advantage in the dynamic Indian telecom market.
Hybrid Deep Learning Models With Attention Mechanisms For Short-Term Wind Speed Forecasting: A Comprehensive Review And Benchmarking Of CNN–Bilstm–Attention Using Synthetic SCADA-Style Data For Robust Preliminary Validation
The integration of wind energy into modern power systems demands forecasting frameworks capable of handling the non-linear, non-stationary, and chaotic behaviour of wind dynamics. While machine learning and deep learning approaches have individually demonstrated substantial gains over classical statistical methods, hybrid architectures coupling convolutional feature extraction with bidirectional recurrent memory and attention mechanisms have recently emerged as the most promising paradigm for short-term wind speed prediction. This paper presents a comprehensive review of hybrid deep learning models with attention mechanisms and introduces a preliminary benchmarking of a CNN–BiLSTM–Attention architecture using a synthetic SCADA-style hourly dataset. The synthetic dataset was generated to replicate the statistical properties of real onshore wind turbine telemetry, including diurnal cycles, seasonal variability, and stochastic fluctuations characteristic of Indian wind corridors. Six forecasting models were benchmarked: ARIMA, Support Vector Regression (SVR), Random Forest, XG Boost, Long Short-Term Memory (LSTM), and the proposed CNN–BiLSTM–Attention hybrid. Using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R²) as evaluation metrics, the hybrid model achieved the lowest errors (MAE = 0.83 m/s, RMSE = 1.15 m/s, MAPE = 13.8%, R² = 0.923) and demonstrated robust behaviour across low-, medium-, and high-wind regimes. The controlled synthetic environment provides a reproducible baseline for assessing architectural stability before deployment on field data. Findings confirm that the CNN–BiLSTM–Attention architecture delivers consistent improvements over both classical and standalone deep learning baselines, and establish a structured protocol for preliminary validation of hybrid forecasting models prior to real-SCADA deployment.
Real-Time Safety Monitoring System In Coal Mines Using IOT
Coal mining is one of the most hazardous industrial activities due to the presence of toxic gases, fire accidents, and poor environmental conditions inside underground mines. This paper presents a real-time safety monitoring system for coal mines using Internet of Things (IoT) technology. The proposed system continuously monitors critical environmental parameters such as methane gas, carbon monoxide (CO), smoke, fire, temperature, and humidity using different sensors including MQ2, MQ7, MQ135, flame sensor, and temperature sensors. The collected data is processed using a Raspberry Pi Pico W microcontroller and transmitted wirelessly through IoT technology for remote monitoring. An OLED display is used for local monitoring, while alert notifications are sent to authorized personnel through Telegram notifications whenever abnormal conditions are detected. The system improves miner safety by enabling early detection of hazardous conditions and providing immediate alerts for preventive actions. Experimental results demonstrate that the proposed system efficiently detects unsafe conditions and provides reliable real-time monitoring suitable for underground coal mine applications.
Internet of Things (IoT), Coal Mine Safety, Raspberry Pi Pico W, Gas Sensors, Real-Time Monitoring, Wireless Communication.
An Medical Data A User Authentication Of A Cloud Data
With the rapid adoption of cloud computing in healthcare, securing medical data stored and accessed through cloud platforms has become a critical priority. Unauthorized access to sensitive health records can lead to severe privacy violations and misuse of patient information. Therefore, robust user authentication mechanisms are essential to ensure that only legitimate users can access or modify medical data in cloud environments. This paper presents a secure and efficient user authentication scheme tailored for cloud-based medical systems, focusing on protecting data integrity and ensuring user privacy. The proposed scheme employs a multifactor authentication model that integrates otp verification, cryptographic credentials, and device-based factors to authenticate users accessing electronic health records (EHRs). To counter emerging threats, especially from quantum-capable adversaries, the protocol incorporates post-quantum cryptographic techniques, ensuring long-term security resilience. The protocol is formally verified using the ProVerif tool and evaluated against standard security criteria, demonstrating resistance to attacks such as replay, man-in-the-middle, insider threats, and stolen-verifier attacks. Furthermore, performance analysis confirms the protocol's low computational and communication overhead, making it suitable for real-time medical applications. The results highlight the protocol's ability to maintain a secure and scalable framework for confidential medical data access in cloud environments, balancing security, usability, and efficiency
Cloud Computing, Healthcare Security, User Authentication, Electronic Health Records (EHR), Multi-Factor Authentication, Post-Quantum Cryptography, Cloud-Based Medical Systems, ProVerif, Data Privacy, Secure Access Control.
Iot Based Automatic Breaking Control System For Ev Vehicle And Monitoring System
Electric vehicles (EV) are getting more and more popular in today’s society as a result of rising petrol prices. An IoT based automated breaking Control system for EV vehicles is proposed in this paper, together with a monitoring system. An EV’s battery monitoring and control system measures the battery’s voltage and temperature. Sensors, a microprocessor, a Wi-Fi module, and a battery make up this system. It is built using the affordable microcontroller. It has an ultrasonic sensor and works as an automated braking system. The controller receives the data sent by the ultrasonic sensor, which is used to identify obstacles, and uses them to regulate the brake mechanism. Voltage, temperature, and battery data are passed to the microcontroller, which subsequently sends them over Wi-Fi to the Blynk application. It is suggested that the parameters of the EV be monitored immediately using a Blynk app.
Electric Vehicle (EV), IoT, Automated Braking System, Ultrasonic Sensor, Battery Monitoring, Blynk Application, Obstacle Detection, Real-Time Monitoring, Embedded Systems, Smart Safety System.
A Lightweight Deep Learning Framework For Fingerprint Liveness Detection
Fingerprint Liveness Detection (FLD) is a critical component of biometric authentication systems that protects against presentation attacks using artificial fingerprints fabricated from materials such as silicone, gelatine, and latex. Existing methods based on Convolutional Neural Networks (CNNs) or multimodal biometric traits have shown promising performance; however, they often increase system complexity, computational cost, and hardware requirements. To overcome these limitations, this paper presents a lightweight deep learning framework for robust fingerprint liveness detection. The proposed system employs an efficient object detection model with an enhanced backbone and a decoupled detection head, enabling the extraction of fine ridge-level features such as pore distribution and distortions, along with global liveness cues including perspiration dynamics and texture irregularities. Unlike multimodal approaches that require auxiliary biometric data, the proposed framework operates solely on fingerprint images, ensuring hardware simplicity while maintaining high discriminative capability. The model is trained end-to-end on benchmark datasets using advanced regularization techniques and a cosine-annealed Adam optimizer to improve generalization and reduce overfitting. Experimental evaluations demonstrate that the proposed framework achieves superior spoof detection accuracy, strong resistance to novel attack materials, and fast inference speed compared to existing state-of-the-art approaches. With its lightweight architecture and adaptability, the system provides a practical and scalable solution for improving the reliability of biometric authentication in real-world environments.
Fingerprint Liveness Detection (FLD), Biometric Authentication, YOLOv8n, Deep Learning, Spoof Detection, Fingerprint Recognition, Lightweight Framework, Ridge-Level Features, Real-Time Processing, Biometric Security, Presentation Attack Detection (PAD), Feature Extraction, Cosine Annealing, Object Detection, Artificial Fingerprints.
Enhancing Credit Card Fraud Detection in Banking Using Neural Networks
Credit card fraud has become one of the most critical issues in modern banking systems due to the rapid increase in digital transactions and online payment platforms. Traditional fraud detection techniques often fail to identify sophisticated fraudulent activities because of highly imbalanced transaction data and evolving fraud patterns. This paper presents an intelligent fraud detection framework using the TabNet deep learning architecture for efficient classification of fraudulent and legitimate transactions. TabNet utilizes sequential attention mechanisms to identify the most important transaction features while maintaining interpretability and computational efficiency. The proposed system includes data preprocessing, class balancing, model training, performance evaluation, visualization, and deployment through a Flask-based web application. Experimental results demonstrate that the proposed TabNet model achieves high accuracy, precision, recall, and ROC-AUC performance while reducing false positives compared to traditional machine learning approaches. The system also supports real-time prediction and visualization for practical banking applications.
Credit Card Fraud Detection, Deep Learning, TabNet, Banking Security, Neural Networks, Flask, Financial Fraud
Enhancing Medicare Fraud Detection Through Machine Learning
Agriculture faces critical challenges such as labor shortages, water scarcity, and rising operational costs. To address these issues, this paper presents the design and implementation of a Smart Agricultural Robot Bulldog (SARDOG), an autonomous robotic system integrated with Internet of Things (IoT) technologies and advanced sensing mechanisms. The proposed system utilizes LiDARbased navigation, environmental sensors, and a robotic arm to perform multiple agricultural tasks such as soil analysis, crop monitoring, fruit picking, and automated irrigation. SARDOG aims to improve productivity, reduce manual labor, and enable precision farming through real-time data acquisition and intelligent decision-making.
Smart Agriculture, IoT, Robotics, Embedded Systems, Soil Monitoring, Autonomous Navigation
Medicare fraud is one of the major challenges faced by the healthcare industry, leading to significant financial losses and reduced trust in healthcare systems. Traditional fraud detection methods mainly rely on manual auditing and rule-based systems, which are often inefficient in detecting complex and evolving fraudulent activities. This project proposes an intelligent Medicare fraud detection system using machine learning techniques to improve the accuracy and efficiency of identifying fraudulent healthcare claims. The system analyzes historical medical claim data and applies various machine learning algorithms such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine to classify claims as fraudulent or legitimate. Data preprocessing, feature selection, and class balancing techniques are used to enhance model performance. The proposed model helps in detecting suspicious billing patterns, abnormal claim behavior, and provider fraud with reduced human intervention. Experimental results demonstrate that machine learning-based approaches provide higher accuracy, faster detection, and better scalability compared to traditional methods. The developed system can assist healthcare organizations and insurance providers in minimizing financial losses and improving the reliability of Medicare services.
Medicare Fraud Detection, Machine Learning, Healthcare Analytics, Fraud Prevention, Random Forest, Decision Tree, Healthcare Claims, Artificial Intelligence, Data Mining, Predictive Analytics.
Sentiment Analysis For Cyberbullying Detection Using NLP And LSTM
The phenomenon of cyberbullying has emerged as a critical challenge in the digital landscape, posing detrimental effects on individuals and broader societal well-being. A practical solution to this widespread issue involves the accurate identification of cyberbullying within social media platforms, which constitute a significant share of digital communication. While traditional approaches have primarily utilized machine learning algorithms and pre-trained language models, these often face challenges such as high computational complexity and limited adaptability to nuanced linguistic patterns. This paper proposes an advanced framework that leverages Natural Language Processing (NLP) techniques combined with Long Short-Term Memory (LSTM) networks to improve cyberbullying detection in online text. The framework applies refined text preprocessing steps—such as tokenization, stop word removal, stemming, and lemmatization—to ensure high-quality and noise-free input data. Sentiment features and contextual patterns are extracted using embedding methods to preserve semantic information. These processed inputs are then fed into an LSTM model, which effectively captures the sequential and temporal dependencies in textual data, making it well-suited for understanding the dynamic nature of cyberbullying language. Additionally, to address class imbalance in the multi-class setting, resampling techniques are employed, improving the model's robustness without inducing bias. The proposed system demonstrates that combining deep learning with comprehensive NLP enhances the accuracy and contextual understanding required for effective cyberbullying detection.
Cyberbullying Detection, Natural Language Processing (NLP), Long Short-Term Memory (LSTM), Deep Learning, Text Classification, Sentiment Analysis, Social Media Analytics, Text Preprocessing, Word Embeddings, Multi-Class Classification.
Detecting Ai-Generated Fake News Using MLP Classifier
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.
AI-generated fake news, Multi-Layer Perceptron (MLP), Natural Language Processing (NLP), GPT-4, Fake news detection, Text classification, Linguistic patterns, Semantic cues, Syntactic cues, Tokenization, Stop-word removal.
A Semantic Weight Adaptive Model Based on Visual Question Answering
This project presents a multilingual Visual Question Answering (VQA) web application developed using the Flask framework by integrating deep learning and Natural Language Processing (NLP) techniques. The system utilizes the transformer-based BLIP model, specifically the Salesforce/blip-vqa-base architecture, to answer user questions based on uploaded images and short-duration videos. The BLIP model combines a Vision Transformer (ViT) for extracting semantic visual features with a transformer-based text decoder for generating contextually accurate answers. Unlike traditional CNN-LSTM-based VQA systems, the proposed model performs joint multimodal learning, enabling improved understanding of both visual content and natural language queries. A key feature of the proposed system is its multilingual capability, allowing users to interact with the application in various Indian languages such as Hindi, Telugu, Tamil, and Kannada. To support multilingual communication, a translation module is integrated that converts user questions into English before processing them through the VQA model and subsequently translates the generated answers back into the user’s preferred language. Although the current implementation uses a simplified mock translation mechanism, the architecture is designed for future integration with advanced Neural Machine Translation (NMT) systems such as IndicTrans2. The application also supports short video inputs by extracting keyframes from uploaded videos and generating context-aware responses based on visual analysis. Beam search decoding is employed during answer generation to produce coherent, grammatically meaningful, and high-probability responses. In addition, the system incorporates secure file upload validation and real-time processing within a scalable web environment. The proposed multilingual VQA system demonstrates the effectiveness of transformer-based multimodal AI models in creating accessible, intelligent, and interactive applications. The system has potential applications in education, assistive technologies, healthcare support, surveillance, and smart human-computer interaction systems, while also providing a foundation for future advancements in multilingual and multimodal artificial intelligence research.
Flask, Multilingual Visual Question Answering (VQA), BLIP, Deep Learning, Natural Language Processing (NLP), Vision Transformer (ViT), Transformer Models, Video Question Answering, Image Understanding, Multilingual Translation, Web Application, Indian Languages, Real-Time AI Systems.
Optimizing Mobile App Recommendations Using Crowdsourced Educational Data
This paper presents a content-aware educational recommendation framework for mobile applications used by university students. The system addresses the discovery problem that emerges when learners must select among many similar apps for study planning, note sharing, research support, productivity, and collaboration. The proposed approach combines crowdsourced app metadata with student interaction traces and applies NLP-based preprocessing to transform text into structured features. A Random Forest classifier is trained to predict the most suitable academic cohort - Undergraduate, Postgraduate, or Graduate - and the resulting class probabilities are used to rank apps by relevance. The framework is designed to be lightweight, interpretable, and easy to deploy in a web or cloud environment. Compared with GRU-based sequence modeling and other classical baselines, the ensemble method offers stronger stability on sparse educational data, better resistance to overfitting, and easier interpretation through feature importance. The manuscript formalizes the project as a complete IEEE-style paper, including problem definition, methodology, architecture, implementation, evaluation, and future extensions.
educational recommendation systems, mobile learning, crowdsourced data, natural language processing, Random Forest, TF-IDF, interpretable machine learning.
Dual Detection Of License Plates And Helmets Using An Optimized Yolo And Neural
The rapid increase in road accidents caused by riders not wearing helmets and traffic rule violations has created the need for an intelligent automated monitoring system. This project, titled “Dual Detection of License Plates and Helmets Using an Optimized YOLO and Neural Network”, proposes a real-time traffic surveillance solution capable of simultaneously detecting motorcycle riders, identifying whether helmets are worn, and recognizing vehicle license plates. The proposed system utilizes the YOLO (You Only Look Once) object detection algorithm due to its high speed and accuracy in real-time image processing. The optimized YOLO model is trained to detect helmets, motorcycles, and number plates from surveillance camera footage. After detecting the vehicle, Optical Character Recognition (OCR) techniques are applied to extract the license plate number. A Neural Network-based classification model enhances the accuracy of helmet detection under varying lighting, weather, and traffic conditions. The system processes video frames continuously and identifies violations instantly. If a rider is detected without a helmet, the system captures the license plate and stores the violation details in the database for further action. Experimental results show high detection accuracy and fast processing speed, making the proposed system suitable for smart city traffic monitoring applications. The proposed framework reduces manual traffic monitoring efforts, improves road safety enforcement, and provides a scalable intelligent transportation solution.
YOLO Algorithm Helmet Detection, License Plate Recognition, Neural Network Object Detection ,OCR (Optical Character Recognition) ,Deep Learning ,Traffic Surveillance, Real-Time Monitoring ,Computer Vision
Blockchain-Assisted Privacy And Security Enhancement In Federated Learning
Federated Learning (FL) enables multiple clients to collaboratively train machine learning models without sharing raw data, thereby enhancing data privacy and security. However, traditional federated learning systems remain vulnerable to privacy leakage, poisoning attacks, and centralized server failures. To address these challenges, this project proposes a Blockchain-based Privacy-preserving and Secure Federated Learning (BPS-FL) framework. The proposed framework integrates threshold Paillier homomorphic encryption to achieve secure gradient aggregation while preserving client privacy. In addition, blockchain technology is incorporated to provide decentralized, transparent, and tamper-proof record management. A Byzantine-robust aggregation mechanism is also introduced to identify and mitigate malicious gradient updates without compromising data confidentiality. The proposed BPS-FL framework improves security, transparency, robustness, and trustworthiness in distributed learning environments while maintaining reliable model performance and scalability.
Federated Learning, Blockchain, Homomorphic Encryption, Privacy Preservation, Byzantine-robust Aggregation, Secure Aggregation, Machine Learning, Data Security, Distributed Systems.
A Machine Learning Framework for Monthly Crude Oil Price Prediction with Cat Boost
Crude oil is a globally significant energy resource whose price fluctuations have far-reaching economic and industrial impacts. Accurate forecasting of crude oil prices is crucial for strategic decision-making in sectors such as finance, energy, and transportation. This project presents a machine learning-based approach to predict monthly crude oil prices using historical market data and engineered time-series features. The model is developed using the CatBoost Regressor, a high-performance gradient boosting algorithm known for its efficiency, accuracy, and ability to handle complex non-linear data. The predictive features include: Lagged prices from previous months Rolling statistical indicators (mean and standard deviation) Temporal features such as month and year (encoded using sine and cosine transformations to preserve seasonality) Both percentage and absolute monthly price changes, The dataset spans over four decades (1983–2025), ensuring that the model captures long-term patterns and short-term fluctuations. Model performance is evaluated using RMSE, MAE, and MAPE metrics, demonstrating strong predictive accuracy and generalization capability.
Crude Oil Price Prediction, Machine Learning, CatBoost Regressor, Time Series Forecasting, Energy Economics, Predictive Analytics, Gradient Boosting, Historical Market Data, Feature Engineering, Rolling Statistics, Lag Features, Seasonal Analysis, RMSE, MAE, MAPE, Financial Forecasting, Oil Market Analysis, Data Science, Artificial Intelligence, Economic Prediction.
Cloud-Network-End Security Integration for Smart Wireless Environments
The rapid evolution of wireless communication infrastructure, cloud computing, and the Internet of Things (IoT) has fundamentally transformed modern information systems. Traditional wireless network security mechanisms, which focus primarily on end-to-end data transmission protection using cryptographic techniques, are no longer adequate for the complex demands of cloud-based collaborative services. The shift from data transmission to cloud-driven information services introduces new security vulnerabilities across cloud platforms, network layers, and end devices. To address this critical challenge, this paper proposes a Cloud-Network-End Collaborative Security Architecture that provides coordinated protection across all three layers. The proposed framework integrates Advanced Encryption Standard (AES-256) for data confidentiality, SHA-256 for integrity verification, role-based access control for authorization, and a centralized cloud server for scalable data management. The architecture is implemented using Java EE (JSP, Servlets), Apache Tomcat, and MySQL, and is evaluated through functional and non-functional testing. Results demonstrate improved data confidentiality, end-to-end integrity assurance, and efficient access control in heterogeneous network environments. The proposed system supports emerging applications including smart cities, healthcare, autonomous transportation, and industrial IoT, providing a trustworthy and scalable security foundation for next-generation wireless environments.
Cloud-Network-End Security, Wireless Network Security, AES Encryption, SHA-256, Cloud Computing, IoT Security, Collaborative Security Architecture, Data Integrity, Heterogeneous Networks.
Edge-Ready Road Damage Detection Using Enhanced Yolov10n With Hyperparameter Tuning
Road infrastructure plays a critical role in transportation systems, economic development, and public safety. However, road defects such as potholes, cracks, and surface wear can lead to traffic accidents, vehicle damage, and increased maintenance costs if not detected at an early stage. Traditional road inspection methods rely heavily on manual monitoring, which is time-consuming, labor-intensive, and often inefficient for large-scale road networks. Recent advancements in deep learning and computer vision have enabled automated road damage detection systems capable of identifying defects with high accuracy in real time. This paper presents a lightweight road damage detection framework using YOLOv10n with hyperparameter tuning for deployment on edge computing devices. The proposed system is designed to detect multiple types of road damage, including potholes, longitudinal cracks, transverse cracks, and surface deterioration, while maintaining low computational complexity. The methodology includes data collection, annotation, preprocessing, augmentation, model training, hyperparameter optimization, testing, and deployment on embedded edge devices such as NVIDIA Jetson Nano and NVIDIA AGX Orin. Experimental results demonstrate strong detection performance with Precision of 98.6%, Recall of 97.3%, F1-Score of 97.8%, and mAP@0.5 of 98.8%. Real-time deployment evaluation shows inference speeds of 7.5 FPS on Jetson Nano and 67 FPS on AGX Orin, confirming the effectiveness of the proposed lightweight architecture for smart transportation and intelligent road maintenance applications. The system offers an efficient, scalable, and practical solution for smart city infrastructure management.
Road Damage Detection, YOLOv10n, Hyperparameter Tuning, Deep Learning, Edge Computing, Smart Cities, Computer Vision, Pothole Detection, Intelligent Transportation Systems, Real-Time Detection.
Detecting The Small Object Recognition By Drone Images Using Yolo
UAV imagery has become an essential tool in applications such as traffic monitoring, disaster response, and airspace management, owing to its flexibility, portability, and low operational cost. However, object detection in UAV images poses significant challenges due to factors like small object sizes, complex and cluttered backgrounds, and high levels of noise. To overcome these challenges, this study proposes an advanced object detection approach based on YOLOv10, a state-of-the-art model known for its enhanced architectural efficiency and detection capabilities. The model is optimized for UAV aerial scenarios, with a particular focus on improving small object detection through refined feature extraction and enhanced spatial understanding. The proposed YOLOv10-based framework integrates adaptive feature enhancement and deep semantic learning to improve detection performance under challenging UAV imaging conditions. By leveraging modern advancements in convolutional attention mechanisms, multi-scale detection heads, and optimized backbone architectures, the system effectively captures fine-grained details while maintaining real-time processing capabilities. This approach enables robust object detection in complex UAV environments and demonstrates the potential of YOLOv10 as a powerful solution for aerial imagery analysis.
YOLOv10, UAV Imagery, Small Object Detection, Deep Learning, Drone Vision, Aerial Object Recognition, Convolutional Attention.
A Decentralized Approach to Certificate Authentication and Issuer Trust Using Blockchain
Verifying the authenticity of educational degree certificates is critical, especially during recruitment, where forged documents can cause significant disruptions and productivity losses. Traditional verification methods rely heavily on manual processes and centralized databases, making them vulnerable to delays, errors, and data tampering. These systems lack a unified, secure platform for seamless interaction between issuers, holders, and verifiers. To address these limitations, this paper proposes a decentralized, blockchain-based certificate verification and issuer validation system. Utilizing Ethereum, the solution stores certificate hashes on the blockchain, ensuring data immutability and tamper resistance. Each participant—issuer, holder, validator, and verifier—is represented as a peer node in the network. A hash-based search mechanism significantly reduces certificate lookup time, even when the certificate is not found. Experimental evaluation shows the system is cost-effective in terms of gas consumption and offers fast, reliable verification. This integrated approach ensures secure, transparent, and efficient certificate management and validation.
Safety This project centers on blockchain-based certificate authentication, emphasizing decentralization, tamper-proof digital credentials, and transparent issuer trust. It leverages smart contracts and cryptographic hashing to prevent forgery, ensure privacy, and enable peer-to-peer verification without reliance on centralized authorities. The focus is on building a resilient infrastructure that guarantees authenticity, minimizes verification time, and eradicates vulnerabilities in traditional certificate management systems.
Impact Of Tool Profile And Material On Meechanical And Microstructural Behaviour Of FSSW Joints
Friction Stir Spot Welding (FSSW) is an advanced solid-state joining process widely used for lightweight materials such as aluminum alloys and brass. In this study, similar and dissimilar joints of Aluminum 6061 and Brass IS319 were fabricated using FSSW with EN31 tools having taper square and diamond pin profiles. The weldments were produced using a vertical milling machine at a rotational speed of 900 rpm, feed rate of 25 mm/min, and depth of cut of 2.8 mm. Mechanical performance of the joints was evaluated through tensile testing using a Universal Testing Machine (UTM). Experimental results revealed that the taper square profile produced superior joint strength compared with the diamond profile for all material combinations. The highest ultimate tensile load of 16.92 kN was obtained for Brass–Brass joints fabricated using the taper square profile tool, while Aluminum–Aluminum joints achieved a maximum load of 3.82 kN. The study demonstrates that tool geometry significantly influences heat generation, material flow, and weld quality in FSSW.
Friction Stir Spot Welding, Aluminum Alloy 6061, Brass IS319, EN31 Tool, Tool Geometry, Tensile Strength.
Intelligent Sports Team Management Powered By Machine Learning
Sports like basketball and baseball have seen significant advancements through the effective use of sports analytics. In contrast, machine learning applications in football have largely concentrated on outcome prediction rather than player evaluation. This study aims to bridge that gap by presenting a descriptive analysis of football player performance using a football-specific dataset. Traditionally, player performance assessments rely on expert panels, though the criteria they use remain undisclosed. In this research, the Support Vector Classifier (SVC) algorithm is employed to analyze and classify player performance data, identifying key functional attributes relevant to different playing positions. By tuning kernel functions and hyperparameters, the model effectively highlights the most impactful performance metrics, offering objective insights that align with expert evaluations. The dataset used comprises detailed performance data from football matches, making the analysis specific and relevant to the sport. The application of SVC allowed the development of highly accurate classifications with minimal error, thus validating the algorithm’s effectiveness in rating prediction tasks. The results indicate that SVC can serve as a powerful tool in football analytics, enabling data-driven decision-making for coaches, analysts, and scouts. This approach not only enhances transparency in player assessment but also supports more strategic planning based on performance-driven evidence.
Football Analytics, Machine Learning, Support Vector Classifier (SVC), Player Performance Analysis, Sports Analytics, Performance Prediction, Football Dataset, Classification Algorithms, Data-Driven Decision Making, Artificial Intelligence in Sports, Player Evaluation, Predictive Analytics, Sports Data Mining, Hyperparameter Tuning, Football Performance Metrics
Deep Learning Advancements And Challeng In
With the exponential rise of video content on social media platforms, particularly YouTube, which handles over 500 hours of uploads every minute, efficient video indexing, retrieval, and summarization have become critical challenges. Traditional methods rely heavily on user-provided metadata such astitles, tags, and descriptions, which are often inaccurate or unrelated to the actual content. To overcome these limitations, recent advances in vision-language models, such as BLIP (Bootstrapping Language-Image Pretraining) transformers, have enabled more accurate and automated video understanding by jointly learning from visual and textual modalities. This paper presents a systematic review of deep learning-based video summarization approaches, with a particular emphasis on BLIP-based models and their potential to bridge the gap between raw video content and semantic interpretation. Out of more than 300 research studies, 44 were shortlisted using strict inclusion criteria, and their methodologies, applications, and datasets are critically analyzed. The review highlights how BLIP transformers enhance summarization performance by generating context-aware captions, enabling semantic indexing, and improving retrieval efficiency. The insights provided in this study offer valuable guidance for researchers and practitioners aiming to leverage deep learning and vision-language models for managing largescale video data in social networking platforms.
Video Summarization, Deep Learning, BLIP Transformer, Vision-Language Models, YouTube Video Analysis, Semantic Video Retrieval, Automated Video Indexing, Social Media Analytics, Content-Based Video Retrieval, Artificial Intelligence, Multimedia Processing, Video Captioning, Semantic Understanding, Large-Scale Video Data, Computer Vision
Enhancing Hospitality Management Through Ml-Based Cancellation Prediction
Hotel booking cancellations present a significant challenge for the hospitality industry, as they create uncertainty in room occupancy and disrupt operational planning. A high rate of cancellations can lead to revenue loss and inefficient resource utilization. This study focuses on predicting whether a customer will cancel a hotel booking using a machine learning approach. Specifically, a Multi-Layer Perceptron (MLP) Classifier, a type of deep learning model, is employed to capture complex patterns within hotel booking data. The model is trained and optimized by tuning key hyperparameters, including the number of hidden layers, number of neurons, activation functions, and learning rate, to enhance predictive accuracy. By accurately forecasting booking cancellations, the proposed system enables hotel managers to optimize room allocation, improve resource management, minimize financial losses, and support informed decision-making. Overall, this approach demonstrates the potential of machine learning techniques in improving efficiency and service quality within the hospitality sector.
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 global blue economy. The accumulation of underwater debris such as plastics, fishing nets, and industrial waste has significantly impacted marine biodiversity and environmental health. Effective detection and classification of such debris are essential for enabling timely intervention and supporting large-scale marine conservation efforts. In this work, we propose an advanced underwater garbage detection system based on YOLOv10n, a state-of-the-art lightweight object detection model optimized for resource-constrained environments such as IoT devices and underwater robotic platforms. Unlike traditional object detection models that rely on computationally intensive backbones such as CSPDarknet, the proposed system leverages the efficient architecture of YOLOv10n to reduce model complexity while maintaining high detection accuracy. The proposed model emphasizes low parameter count, reduced inference latency, and real-time processing capability, making it highly suitable for deployment on embedded systems. Experimental results demonstrate that the system achieves robust and precise detection of underwater debris under challenging conditions such as low visibility, color distortion, and dynamic backgrounds. Furthermore, the reduced memory footprint and computational requirements enable scalable deployment across multiple platforms, including Autonomous Underwater Vehicles (AUVs) and edge devices. This research highlights the feasibility of integrating lightweight deep learning models into marine monitoring systems and provides a practical, cost-effective, and eco-friendly solution to combat marine pollution through intelligent automation.
Integrating Quantum Vision Theory with Deep Learning for Enhanced Object Recognition Using Heavy QV-Xception
In this work, we extend the recently proposed Quantum Vision (QV) theory in deep learning for object recognition by integrating it with the Xception architecture, forming a novel Heavy QV-Xception model. The QV theory, inspired by the particle-wave duality in quantum physics, treats objects as information waves rather than static images, enabling deep neural networks to capture richer representations. Building on this concept, our Heavy QV-Xception model leverages a robust QV block to transform conventional images into wave-function representations and processes them through the depthwise separable convolutional layers of Xception for enhanced feature extraction. This hybrid approach benefits from both the quantum-inspired information representation and the efficient, high-performance architecture of Xception. Extensive experiments on multiple benchmark datasets demonstrate that the Heavy QV-Xception model consistently outperforms standard Xception and other conventional CNNs, highlighting the effectiveness of combining QV theory with advanced deep learning architectures for improved object recognition accuracy.