International Journal of Engineering Research and Science & Technology
Editor- In-Chief: Dr. Kenji Suzuki Title proper: International Journal of Engineering Research and Science & Technology Subject: Engineering, Management, Science and Technology Publisher Name: Unicorn Publications ISSN: 2319-5991 Dates of publication: 2013 Frequency: Quarterly Language: English Country: India Medium: Online Indexed by: CROSSREF Title DOI of the Journal: http://doi.org/10.62643/issn.2319-5991 E- Mail: editor@ijerst.org Impact Factor: 9.625
Design and Implementation of an IoT-Based Smart Power Monitoring and Overload Protection System
The increasing demand for electrical energy and the rapid growth of electrical appliances have created a need for intelligent power monitoring and protection systems. Traditional energy meters mainly provide total energy consumption for billing purposes and lack real-time monitoring, remote accessibility, and automatic protection features. This paper presents the design and implementation of an IoTbased smart power monitoring and overload protection system that enables continuous monitoring of electrical parameters and enhances electrical safety. The proposed system uses an ESP32 microcontroller integrated with a PZEM-004T energy sensor to measure key electrical parameters such as voltage, current, power, and energy consumption in real time. The measured data is processed by the ESP32 and transmitted to a cloud platform using IoT technology, allowing users to monitor electrical parameters remotely through a web dashboard. An automatic overload protection mechanism is incorporated using a relay module that disconnects the electrical load when the current exceeds a predefined threshold, preventing equipment damage and electrical hazards. Additionally, the system provides real-time status updates through an LCD display and sends instant alert notifications to users during abnormal conditions. The developed system provides a cost-effective, reliable, and intelligent solution for monitoring and protecting electrical systems in residential, commercial, and industrial environments, while improving energy awareness and safety.
IoT, Smart Power Monitoring, Overload Protection, ESP32, PZEM-004T Energy Sensor, Cloud Monitoring, Energy Management System
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
A COGNITIVE OPTICAL ANOMALY RECOGNITION SYSTEM FOR RAILWAY INFRASTRUCTURE INTEGRITY MONITORING
Technological evolution is observed in railway track inspection methods due to the passage from traditional image processing methods to more innovative data-driven models, owing to the inflexibility of more traditional methods upon being exposed to real-world scenarios. Though its capability of handling uncertain inputs has been improved with the inclusion of fuzzy-logic principles in it, owing to its reliance on manually coded knowledge, it has become less relevant. The current paper discusses a new complete process for railway track inspection that combines the autonomous identification power of convolutional neural networks and the logical deduction power of fuzzy logic inference. This new process combines the autonomous identification power of CNNs with the logical deduction power of fuzzy logic inference.
Image Processing, Fuzzy Logic, Otsu Method, Rail Track Detection, Google API Method, Histogram Equalization
Rag Publications Assistant: An AI-Powered System for Research Paper Analysis
The swift growth of scholarly and scientific publications on digital platforms has made it extremely difficult for academicians, students, and researchers to quickly find reliable and pertinent information. Conventional keyword-based search engines frequently yield a lot of items, necessitating the time-consuming and ineffective human analysis of abstracts and full texts by users. In order to get beyond these restrictions, the RAG Publications Assistant is an AI-powered publication retrieval system that combines contemporary Natural Language Processing (NLP) models with Retrieval-Augmented Generation (RAG) approaches. The system creates precise, context-aware answers to user inquiries by retrieving pertinent research papers from a structured knowledge store. Factual accuracy and insightful summarization are guaranteed by the system's combination of generative AI models and document retrieval. By facilitating contextual comprehension, intelligent response generation, and semantic search, the suggested approach increases research productivity. Without requiring a lot of manual labor, it helps scholars find pertinent literature fast, comprehend difficult ideas, and obtain new insights. By offering precise, dependable, and effective access to scholarly knowledge, the RAG Publications Assistant serves as an example of how AI-driven retrieval systems can revolutionize academic research operations
RAG, NLP, Semantic Search, Research Paper Analysis, AI-powered Retrieval System, Vector Embeddings, LLMs, Document Retrieval, Knowledge Extraction, Academic Search Automation, Context-Aware Responses, Information Retrieval, Generative AI, Semantic Indexing, Research Assistance System
Hybrid Predictive Model-Integrated Advanced Cyber Security Threat Intelligence
Using a hybrid stacking model that combines Random Forest (RF), K-Nearest Neighbor (KNN), and Multilayer Perceptron (MLP) classifiers, this expanded study offers an improved cyber-attack detection framework. To achieve high resilience and accuracy across a variety of benchmark datasets, such as NSL-KDD, CICIDS2017, CICDDOS2019, and X-IIOTID, the stacked ensemble leverages the advantages of these basic models. A Flaskbased web interface is used to install the system, allowing users to interact with uploaded test datasets and forecast cyberattacks in real time. By emphasizing important feature contributions, Explainable AI (XAI) approaches promote interpretability and transparent, well-informed decision-making. Results from experiments show that the hybrid stacking framework is an effective scalable and intelligent solution for proactive cybersecurity defense, with up to 100% accuracy.
Cyber-attack prediction, machine learning, deep learning, hybrid stacked model, NSL-KDD, CICIDS2017, DNN, explainable AI
Ad Click Fraud Detection Using Machine Learning Algorithm
Online advertising platforms have a serious problem with ad click fraud as it creates fraudulent clicks that skew campaign results and raise losses. This paper introduces an attention-based Convolutional Neural Network (CNN) framework for real-time ad click fraud detection in order to overcome the shortcomings of traditional machine learning and deep learning models in identifying intricate contextual patterns. In order to improve prediction reliability, the suggested improvement incorporates an attention mechanism into the CNN architecture to highlight the most instructive characteristics and sequential relationships in clickstream data. Furthermore, real-time fraud prediction via an interactive interface is made possible via a web-based deployment that makes use of the Flask framework. The results of experimental assessment show that the attention-enhanced CNN outperforms baseline models in terms of accuracy and resilience, underscoring its usefulness for scalable and realistic ad click fraud detection in dynamic online advertising contexts.
d Click Fraud Detection, Attention Mechanism, Convolutional Neural Network (CNN), Deep Learning, Clickstream Analysis, Real-Time Prediction, Flask Deployment, Online Advertising Security, Fraud Analytics, Feature Learning
Automated Medicinal Plant Classification Using PSO-Optimized Machine Learning Techniques
Enhancing medicinal plant categorization by parameter adjustment of the Support Vector Machine (SVM) in a PSO-optimized cascaded network is the main goal of this work's expansion. To choose the most pertinent qualities, Particle Swarm Optimization (PSO) is used to enhance deep features that were retrieved using ResNet50. When compared to other techniques, the customized SVM model dramatically increases classification accuracy and decreases misclassification. According to experimental data, the improved SVM performs better than the others, achieving an accuracy of 99.75%. This method guarantees an automated medicinal plant identification system that is more dependable, effective, and scalable.
Medicinal Plant Classification, ResNet50, Particle Swarm Optimization (PSO), Support Vector Machine (SVM), Parameter Tuning, Feature Optimization, Machine Learning, Deep Learning, Image Classification, Cascaded Network
High-Accuracy Cloud Job Failure Prediction Using a Two-Layer Voting Architecture
Modern cloud computing systems require precise job failure prediction for system dependability, resource efficiency, and ongoing service delivery. This study adds CatBoost, a strong gradient boosting algorithm, to a multilayer voting-based prediction framework to improve accuracy and resilience. CatBoost handles categorical features and complicated data distributions in cloud workloads better than traditional models, and its symmetric tree structure and built-in overfitting control promote generalization across varied and dynamic contexts. The enhanced system trains and evaluates the model using the Google Cluster 2019 trace dataset, outperforming machine learning and ensemble methods. In addition to model improvement, a Flask-based web interface allows users to upload task data and instantaneously receive failure predictions and categorization results. This integration connects theoretical models to practice. Experimental findings reveal that the CatBoost-based extension outperforms competing framework classifiers and delivers dependable results even on unseen data. The suggested addition enhances the cloud job failure prediction system's accuracy, scalability, and usability, making it appropriate for cloud infrastructure management and decision-making.
cloud job failure prediction, multilayer voting classifier, ensemble learning, CatBoost, Random Forest, XGBoost, ANN.
AUTOMATED BLOOD CANCER CLASSIFICATION USING DEEP NEURAL NETWORKS
Improving survival rates for blood cancer, particularly leukemia, depends on early detection. Traditional diagnostic techniques depend on the labor-intensive and subjectively interpretable manual microscopic examination of peripheral blood smear pictures. Deep learning advances in recent years have made automatic detection more accurate and reliable. A labeled leukaemia dataset with four classes—Benign, Early, Pre (Pre-B), and Pro (Pro-B)— is used in this study to train and assess the model. Deep CNN designs like VGG16 and VGG19, which offer powerful feature extraction but are computationally demanding, were mostly used in earlier studies. MobileNetV2, a portable and effective deep learning model tailored for real-time diagnosis, is proposed in this work. For reliable classification, the suggested approach combines deep feature learning, image preprocessing, and augmentation. The results of the experiments show that the MobileNetV2 based model improves diagnosis speed while retaining excellent accuracy, which qualifies it for practical clinical implementation
Blood Cancer Detection,Leukemia Classification,Deep Learning, Convolutional Neural Networks (CNN).
EFFECTIVE MACHINE LEARNING FEATURE SELECTION FOR CARDIOVASCULAR DISEASE DETECTION
The cause of death for many people worldwide is cardiovascular disease (CVD). According to the World Health Organization (WHO), cardiovascular disease (CVD) claimed 19.1 million lives in 2022, accounting for 33% of all fatalities worldwide. With standard machine learning, ECG is frequently utilized for automated CVD diagnosis; however, selecting the appropriate characteristics can be challenging. A scalable ML-based architecture for early CVD detection that makes use of the optimal feature selection can address this issue. By promptly diagnosing and treating CVD, this design aims to transform healthcare and reduce the number of fatalities. ML classifiers are used to gather, store, and evaluate data in order to predict heart problems. After removing ECG characteristics, FCBF, MrMr, alleviation, and PSO-optimization choose the best features. The Random Forest and trait-trained Extra Tree classifiers have 100% accuracy. On both small and big datasets, we also evaluate the suggested approach against the best one. The suggested approach might enhance quality of life by altering the way patients are treated and significantly reducing the number of deaths from CVD. In summary, this design improves healthcare systems and offers a practical CVD solution
Disease detection, ECG feature extraction, optimal feature selection, Particle Swarm Optimization, FCBF, MRMR, Relief, Random Forest, Extra Tree Classifier, Gradient Boosting.
HYBRID ENSEMBLE VOTING MODEL FOR ENHANCED SLEEP DISORDER DIAGNOSIS
Since sleep-related problems have a substantial impact on people's health and quality of life, accurate classification of sleep disorders is crucial for efficient diagnosis and treatment. Even though individual deep learning and machine learning models have shown encouraging outcomes, model bias and overfitting frequently limit their performance. This study offers an ensemble learning-based extension for the categorization of sleep disorders that integrates predictions from several machine learning models in order to overcome these difficulties. To increase robustness and classification accuracy, the outputs of optimized base classifiers are aggregated using a voting classifier. Experiments on the Sleep Health and Lifestyle Dataset show that the suggested ensemble strategy outperforms individual models with an accuracy of 97.3%. Additionally, to improve system usability and real-time user engagement, a Flask-based web interface with secure user authentication is created. The suggested extension offers an automated framework for diagnosing sleep disorders that is dependable, accurate, and easy to use.
Sleep Disorder Classification, Ensemble Learning, Voting Classifier, Machine Learning, Artificial Neural Network, Flask Framework, Healthcare Analytics
Dr. M. CHAITANYA KISHORE REDDY MADDIREDDY
EMOTION-DRIVEN CYBERBULLYING DETECTION WITH LSTM–GRU ENSEMBLE AND SECURE FLASK-BASED DEPLOYMENT
Cyberbullying detection has evolved with the integration of contextual, emotional, and sentiment-based features; however, further improvements are required for real-time and highly accurate predictions. In this extension, we enhance the existing cyberbullying detection framework by incorporating advanced ensemble deep learning models such as LSTM and LSTM+GRU, which efficiently capture sequential patterns in user-generated text. These models significantly improve classification performance and achieve up to 99% accuracy, demonstrating their effectiveness in detecting complex cyberbullying behaviors. To support practical usability, a Flaskbased front-end system with secure authentication is developed, enabling users to interact with the detection system in a simple and secure manner. The extended system offers improved accuracy, stronger robustness, and enhanced accessibility, making it a highly suitable solution for real-world cyberbullying monitoring and intervention applications.
Cyberbullying Detection, Emotion Analysis, Sentiment Features, LSTM, GRU, Ensemble Deep Learning, BERT, Toxic Dataset, Twitter Dataset, Sequential Modeling, Flask Front-End, User Authentication, Real-Time Prediction, Natural Language Processing (NLP).
AI-Driven Risk Prediction and Allocation Optimization for Cost Transparency in Public Infrastructure Renovation Projects
Due to inappropriate risk distribution among stakeholders, public infrastructure rehabilitation projects typically encounter cost overruns, timetable delays, and financial misallocation. Conventional methods of risk assessment lack the predictive intelligence necessary for clear financial planning and instead rely on qualitative judgment. An artificial intelligence-driven approach for integrated risk prediction, allocation analysis, and cost transparency in public infrastructure rehabilitation projects is presented in this study. Financial risk parameters, such as project budget, timing, contract type, contingency allocation, and stakeholder risk sharing, are modeled using publicly accessible statistics about construction delays and cost overruns. Random Forest, XGBoost, and Logistic Regression are examples of machine learning models that are used to forecast the likelihood of cost overruns and delays. To measure the impact of inappropriate risk distribution on total project costs, a Risk Allocation Impact Index is created. Cross-validation experimental assessment shows lower error and better prediction accuracy when compared to baseline models. In order to improve transparency, maximize risk sharing between public and private players, and minimize unforeseen financial losses in remodeling projects, the suggested framework offers a data-driven decision support tool. From a procurement strategy standpoint, this optimization structure can guide contract selection decisions such as Guaranteed Maximum Price (GMP), unit-price, design-build, or construction management-at-risk models. Aligning allocation weights with stakeholder capability improves financial resilience and reduces downstream claims.
Artificial Intelligence, Risk Prediction, Risk Allocation, Cost Overrun, Public Infrastructure, Machine Learning, Financial Transparency
A CLOUD-BASED AUTOMATED SYSTEM FOR ANALYZING REALTIME AIRLINE PRICE TRENDS USING AI-POWERED PREDICTIVE MODELS
The aviation industry has been struggling to have flight fares forecasted fast and precisely as the industry evolves dynamically. Factors such as the changes in demand, fuel costs and route specifications influence prices. The new approach presented in this research involves an attempt to resolve this issue with the help of generative artificial intelligence (GAI) to predict airfares on the fly. It provides a different model incorporating generative models, deep learning, and historical prices so that future fare predictions become more accurate. The study applies a GAI within the context of a modern web engineering system. The aim of its primary objective is to derive knowledge based on the intricate imagery and connections of the history of airline data. Using this approach, the model will be able to identify the complex relationships and be flexible to dynamism in the market. It is based on deep neural networks to deal with a wide range of circumstances and extract valuable information to get a better understanding of the numerous variables that influence the cost of flights. The strategy is aimed at predicting future events with accuracy in real time and allowing prompt reaction to the market volatility and delivering value.
airline fare forecasting, flight price analytics, intelligent prediction models, neural network-based time series modelling, LSTM networks, generative artificial intelligence, real-time data integration, ensemble classification techniques, web-enabled forecasting framework, API-driven prediction system. that conditional generative models are more accurate when working with structured data.
FLIGHT BOOKING ASSISTANT USING GENERATIVE AI
The Next-Generation Flight Booking Assistant powered by Generative Artificial Intelligence (Gen AI) represents a transformative shift in how travelers search, compare, and book air travel. Traditional flight booking platforms rely heavily on rule-based systems and static filters that often fail to capture user intent, contextual preferences, and dynamic market conditions. This project proposes an intelligent, conversational, and adaptive flight booking assistant that leverages Generative Adversarial Networks (GANs) along with large language models to provide personalized, real-time, and user-centric booking experiences. The system is designed to understand natural language queries, generate optimized travel itineraries, predict fare trends, and simulate multiple booking scenarios to recommend the most costeffective and convenient options. By learning from historical booking data, seasonal demand patterns, and user behavior, the assistant continuously improves recommendation quality. The integration of GANs allows the system to generate synthetic but realistic travel data to enhance model robustness, address data sparsity, and improve prediction accuracy under uncertain or rapidly changing conditions. Overall, the proposed system aims to reduce user effort, increase booking efficiency, improve customer satisfaction, and support airlines and travel platforms with intelligent demand forecasting and decision support. This research highlights the role of Gen AI in building scalable, adaptive, and future-ready flight booking ecosystems.
Software Defined Networks (SDN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Learning (DL), One-Dimensional Convolutional Neural Networks (1D-CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Structured Deep Convolutional Neural Network (SDCNN).
Ad click fraud has become one of the most critical challenges in the digital advertising ecosystem, causing huge financial losses to advertisers and reducing trust in online marketing platforms. Fraudulent clicks are generated either manually or through automated bots with the intention of inflating advertisement revenue, exhausting competitor budgets, or manipulating analytics. Traditional rule-based systems are no longer sufficient to handle the scale and complexity of modern fraud patterns. Therefore, this research proposes a datadriven approach using machine learning and deep learning algorithms for detecting and analyzing ad click fraud.The study explores the performance of multiple algorithms such as Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), Random Forest, and a proposed XGBoost-based system. Realworld ad click datasets are preprocessed through cleaning, normalization, feature engineering, and feature selection. Models are trained and evaluated using standard metrics such as accuracy, precision, recall, F1-score, and AUC. The results show that ensemblebased methods, especially XGBoost, outperform traditional and deep learning models in terms of accuracy, interpretability, and computational efficiency. The research concludes that combining robust preprocessing with advanced machine learning models can significantly improve fraud detection and reduce financial losses in digital advertising.
ENHANCEMENT OF POWER QUALITY IN A PV-POWERED EV CHARGING STATION CONNECTED TO A THREE-PHASE GRID
The improvement of power quality in a solar photovoltaic (PV) electric vehicle (EV) charging station is the main goal of this work. Both standalone and grid-connected modes of operation are available for the system. The EV battery is directly charged by the PV array in standalone mode. Surplus PV power is fed into the utility grid while it is in grid-connected mode. By lowering harmonic currents and adjusting for reactive power, the charging station also improves grid performance. Harmonic current compensation, EV battery charging and discharging control, and simultaneous charging or discharging with harmonic compensation are among the several functions it offers. With synchronization control for smooth grid reconnection, the control approach maintains total harmonic distortion below 5% in accordance with IEEE-519 standards, ensuring proper operation even under unbalanced grid voltages
Solar Photovoltaic (PV) System, Electric Vehicle (EV) Charging Station, Power Quality Improvement, Harmonic Compensation, Reactive Power Compensation
THREE-LEG, STANDBY CONVERTER-BASED SINGLE-PHASE TRANSFORMERLESS UNIFIED POWER QUALITY CONDITIONER
This paper proposes a transformer less single-phase to single-phase unified power quality conditioner (UPQC) based on three-leg and standby converters. The system consists of a two-leg module series connected to a threeleg module; the control system offers shunt and series compensation and can be designed to operate with a high modulation index under rated conditions and voltage sag events; additionally, a low standby power consumption and a new decoupling method to compensate voltage swells using the two-leg module. In comparison to the conventional solution, the proposed converter shows lower semiconductor losses and reduced waveform harmonic distortion, while retaining the same or better operational features, such as the elimination of circulating currents and line-frequency transformers.
Unified power quality conditioner, power quality,standby converter, three-leg converter
DESIGN AND PERFORMANCE ANALYSIS OF ANFIS-CONTROLLED BIDIRECTIONAL DC–DC CONVERTER FOR EV CHARGING
The rapid growth of electric vehicles (EVs) has intensified the need for efficient and stable energy management systems capable of minimizing output fluctuations and improving overall performance. In EV applications, the energy storage unit—primarily composed of lithium-ion batteries and auxiliary storage elements such as supercapacitors—plays a critical role in ensuring reliable power delivery and regenerative energy utilization. However, conventional charging and control strategies often suffer from voltage instability and performance distortion under dynamic operating conditions. This paper presents a dynamically designed bidirectional DC–DC converter integrated with a hybrid energy storage system for enhanced EV charging and discharging operations. An Adaptive Neuro-Fuzzy Inference System (ANFIS)-based intelligent control strategy is proposed to regulate power flow between the battery and supercapacitor while maintaining optimal state-of-charge conditions. The controller employs rule-based dynamic constraint management to improve energy distribution and reduce transient fluctuations. Simulation results demonstrate that the proposed ANFIS-controlled system achieves improved voltage stability, reduced output distortion, and enhanced energy efficiency compared to conventional control approaches. The proposed framework contributes to improved endurance, reliability, and cost-effective operation of electric vehicle energy storage systems.
ANFIS Controller, Bi directional DC-DC Converter, Super Capacitor/ Auxiliary Battery
Smart IoT-Based Weather Monitoring Ecosystem with Cloud Integration and Telegram Alert System
Precision farming and urban planning are severely hampered by environmental volatility and a dearth of hyper-local atmospheric data. Large geographic areas are frequently covered by generalized data from conventional weather stations, which miss microclimatic changes. Additionally, entry-level systems usually fail to integrate air quality monitoring with basic weather data. This project uses an ESP32 microcontroller and a suite of high-fidelity sensors to create an Internet of Things-based Real-Time Weather and Air Quality Monitoring Station in order to overcome these constraints.A BME680 integrated environmental sensor, which provides high-accuracy readings of ambient temperature, relative humidity, and barometric pressure, is used in the suggested system to collect atmospheric data. A digital precipitation sensor detects rain in real time, while an analog MQ-series gas sensor measures air quality. The ESP32 processes the data to produce a localized Air Quality Index (AQI). The system uses a multi-channel output design. A 4-in-1 MAX7219 Dot Matrix display with a continuous, non-blocking ribbon-scrolling algorithm is used to achieve localized visualization. Concurrently, the Firebase Realtime Database, which powers a specialized web dashboard for remote data visualization, maintains cloud-based synchronization. The solution incorporates a Telegram Bot API to improve responsiveness, giving consumers on-demand status updates and immediate automatic alarms sent straight to their mobile devices. The created prototype shows how cloud-level data management, instant messaging protocols, and hardware-level sensor collecting may all work together seamlessly. The solution keeps the user interface fluid even when data is being transmitted to the web and Telegram servers simultaneously by separating the display logic from network delay. The outcome is an affordable, scalable, and dependable Internet of Things solution that offers useful environmental information, making it a useful instrument for automated emergency alerting systems, smart city infrastructure, and localized weather forecasts.
System-Level Design and Behavioural Implementation of a Second–Order CT SigmaDelta Modulator
This paper presents the system-level design and behavioural implementation of a second-order continuous-time (CT)sigma-delta modulator for high-resolution, low-frequency sensing applications. The modulator architecture is synthesized using the MATLAB Delta-Sigma Toolbox and implemented behaviorally in Cadence Virtuoso using Verilog-A models. A cascade of integrators feed-forward (CIFF) architecture with a 1-bit quantizer and an oversampling ratio (OSR) of 12 is employed to achieve high resolution while maintaining stability. The discrete-time noise transfer function (NTF) is transformed to its continuous-time equivalent using the impulseinvariant transformation with an NRZ feedback DAC pulse. Simulation results demonstrate practical second-order noise shaping. For a 900 mV, 1 Hz sinusoidal input, the modulator achieves an SNR of 86dB, corresponding to an effective number of bits (ENOB) of 14 bits. The influence of the integrator's finite DC gain and limited gainbandwidth product (GBW) on performance is also evaluated. The results validate the proposed modulator architecture for precision low-bandwidth sensing front-end applications.
Sigma Delta Modulator(SDM), Continuous Time Modulator, Noise shaping, CIFF Architecture, High Resolution Data conversion, Low Frequency Sensing
An NLP-Based Approach for Fake News Classification Using Supervised Machine Learning Algorithms
The rapid spread of misinformation through social media and online news platforms has made fake news detection an important research problem. This project proposes a Natural Language Processing (NLP) based approach to classify news articles as real or fake using supervised machine learning algorithms. The system performs text preprocessing and feature extraction using techniques such as TF-IDF to convert textual data into numerical representations. Several classification models including Logistic Regression, Support Vector Machine, Random Forest, and Decision Tree are used to train and evaluate the dataset. The performance of the models is measured using accuracy, precision, recall, and F1-score to ensure reliable prediction. The proposed system helps users identify misleading information and supports the prevention of misinformation spread in digital media.
Fake News Detection, Natural Language Processing (NLP), Machine Learning, TF-IDF, Logistic Regression, Support Vector Machine (SVM), Random Forest, Text Classification, Supervised Learning
A STUDY ON EMPLOYEE JOB SATISFACTION IN SRINIDHI MILK DIARY
This study focuses on employee job satisfaction at Srinidhi milk dairy. The main objective of the study is to examine the level of job satisfaction among employees and identify the key factors influencing their motivation and performance. Employee satisfaction is essential for improving productivity, reducing employee turnover, and maintaining a healthy work environment. The study is based on primary data collection through structured questionnaires distributed to employee of Srinidhi milk diary. Secondary data was gathered from company records, journals, and relevant literature. The analysis includes important factors such as salary and benefits, working conditions, management support, job security, promotion opportunities, and work-life balance. The findings of the study provide insights into employees’ perceptions regarding their jobs and workplace environment. Based on the results, suitable suggestions are offered to improve employee satisfaction and enhance overall organizational effectiveness.
Employee job satisfaction, Work environment, Employee Motivation, Job Security.
Physiological Signal-Based Pain Recognition Using a Hybrid CNN–BiLSTM–GRU Architecture
Automatic pain recognition using physiological signals plays a crucial role in modern healthcare systems. Traditional pain recognition methods rely heavily on handcrafted feature extraction, which requires medical expertise and limits scalability. To address these limitations, this paper presents an enhanced deep learning– based pain recognition framework that integrates feature extraction and classification into a unified architecture. The proposed extension incorporates a hybrid deep learning model combining Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU) networks to effectively capture both spatial and temporal characteristics of physiological signals. Additionally, an ensemble stacking strategy is applied to strengthen prediction stability and overall performance. Multi-level contextual information is leveraged to provide a comprehensive representation of painrelated patterns, enabling accurate discrimination between pain and painlessness. Experimental results demonstrate that the proposed Extension CNN + BiLSTM + Bi-GRU model achieves superior performance, attaining an accuracy of 98.5%, thereby outperforming conventional machine learning methods and single deep learning models. The enhanced framework reduces dependence on extensive medical expertise, improves classification reliability, and offers a practical, scalable solution for automatic pain recognition in healthcare applications.
ain Recognition, Physiological Signals, Multi-Level Context Information, Hybrid Deep Learning, CNN– BiLSTM–GRU, , Ensemble Learning, Automatic Feature Extraction, Healthcare Analytics
A Power Management and stability improvement for Grid- connected PV Integrated with Hybrid Energy Storage System
The increasing penetration of photovoltaic (PV) generation into power grids introduces challenges such as power intermittency, voltage fluctuations, and reduced system reliability. Hybrid Energy Storage Systems (HESS), combining batteries and supercapacitors, provide an effective solution by leveraging the high energy density of batteries and the fast dynamic response of super-capacitors. This paper proposes a fuzzy logic–based energy coordination strategy for a grid-integrated PV system equipped with battery–super-capacitor hybrid storage. The fuzzy controller dynamically allocates power between the battery and super-capacitor based on PV power variations, load demand, and stateof-charge (SOC) conditions. The proposed scheme enhances power smoothing, reduces battery stress, and improves grid power quality. MATLAB/Simulink-based simulation results demonstrate superior performance in terms of reduced power fluctuations, improved DC-link voltage regulation, and extended battery lifespan compared to conventional rule-based control strategies
Fuzzy logic control, hybrid energy storage system, battery–supercapacitor, photovoltaic systems, gridconnected inverters, power management
NEURAL NETWORK–DRIVEN EFFICIENCY OPTIMIZATION OF PMSM FOR ELECTRIC VEHICLE PROPULSION SYSTEMS
Permanent Magnet Synchronous Motors (PMSMs) are widely adopted in electric vehicle (EV) propulsion systems due to their high-power density, fast dynamic response, and superior efficiency. However, maintaining optimal efficiency over wide speed–torque operating ranges and varying driving conditions remains a significant challenge. Conventional control techniques such as proportional– integral (PI) controllers and fixedparameter field-oriented control (FOC) schemes fail to ensure optimal efficiency under dynamic load and speed variations. This paper proposes a Neural Network (NN)–driven efficiency optimization strategy for PMSM-based EV propulsion systems. The proposed NN controller adaptively optimizes control parameters to minimize motor losses and improve overall efficiency. The effectiveness of the proposed method is evaluated under various speed and torque conditions. Simulation results demonstrate improved efficiency, reduced losses, and enhanced dynamic performance compared to conventional control approaches, validating the suitability of the NN-based strategy for EV applications.
Permanent Magnet Synchronous Motor (PMSM), Electric Vehicles (EVs), Artificial Neural Network (ANN), efficiency optimization, loss minimization, intelligent motor control
A Hybrid Machine Learning Model for AccurateCardiovascular Disease Prediction
Cardiovascular diseases cause death in the world. High blood pressure, family history, stress, age, sex, BMI, cholesterol, and unhealthy lifestyle are the risk factors. In this case we introduce MaLCaDD that is a cardiovascular disease detection system, which is implemented on the machine learning base, applied feature selection, Lasso based, involves Logistic Regression, SVM, Random Forest, Decision Tree and KNN classifiers and solves the missing data and the imbalance amongst the classes. The relative analysis indicates that MaLCaDD is more accurate compared to other technologies of detecting cardiovascular disease at its earliest stage
Cardiovascular Disease (CVD); Machine Learning; MaLCaDD; Random Forest; SVM; Logistic Regression; KNN; Feature Selection; SMOTE; Classification
MENTAL HEALTH ASSISTANT: AI-BASED DEPRESSION ANALYSIS AND COMMUNICATION SYSTEM
Mental health disorders, particularly depression, have emerged as a significant global health concern affecting individuals across various age groups. Early identification and timely intervention are essential to mitigate the adverse psychological and social impacts associated with depression. However, social stigma and limited access to professional mental health services often delay diagnosis and treatment. This paper presents an AI-based Mental Health Assistant designed to analyse depressive symptoms and provide intelligent, interactive communication support in a confidential and accessible manner. The proposed system employs Natural Language Processing (NLP) and Machine Learning (ML) techniques to analyse user-generated textual input and identify emotional and behavioural patterns indicative of depressive tendencies. The architecture consists of data preprocessing, feature extraction, model training, sentiment classification, and a conversational interface module. The trained model evaluates user responses to estimate depression risk levels and generates contextaware supportive feedback, coping strategies, and recommendations for professional consultation when required. Experimental evaluation demonstrates that the system achieves reliable classification performance while maintaining empathetic and responsive communication. The developed solution provides a scalable, cost-effective, and easily deployable platform that can complement traditional counselling services. The proposed AI-driven framework contributes to early detection, enhanced accessibility, and continuous mental health support through intelligent digital assistance
Artificial Intelligence, Depression Detection, Machine Learning, Mental Health Assistant, Natural Language Processing, Sentiment Analysis
BLOCK CHAIN BASED CERTIFICATE VERIFICATION SYSTEM
The rapid digitization of academic records has increased the demand for secure and reliable certificate verification mechanisms. Traditional verification systems rely on centralized databases, which are susceptible to forgery, data manipulation, and administrative delays. To address these challenges, this study proposes a blockchain-based certificate verification system that ensures integrity, transparency, and decentralized validation of academic credentials. The system leverages smart contracts deployed on the Ethereum network to securely record certificate hashes, thereby preventing unauthorized modification or duplication of issued credentials. The proposed framework is implemented as a decentralized application (DApp) with a React-based frontend and Solidity-based backend smart contracts. The system operates using the MetaMask wallet integrated with a Hardhat local Ethereum network for transaction management and testing. It defines two primary roles: an Administrator responsible for issuing and revoking certificates, and a User who verifies credentials using a unique certificate identifier. A smart contract-based revocation and status tracking mechanism is incorporated to enable dynamic certificate lifecycle management. Experimental validation demonstrates that the system enhances trust, reduces verification time, and strengthens protection against certificate fraud while maintaining operational efficiency. Traditional certificate verification methods rely on manual processes, centralized databases, and third-party verification services, which are time-consuming, costly, and vulnerable to tampering.
Blockchain; Certificate Verification; Smart Contracts; Ethereum; Decentralized Application (DApp); Certificate Revocation; Credential Authentication; Hardhat Network; MetaMask Integration
INTELLIGENT BUG TRIAGING FOR OPEN-SOURCE SOFTWARE THROUGH MACHINE LEARNING TECHNIQUES
In large-scale software development, bug triaging the process of categorizing and allocating software issues to the right developers is an essential but difficult activity. Manual triaging takes a lot of time, is inconsistent, and is subject to human bias. As a result, it frequently causes delays in issue resolution and misallocates developer resources. This study investigates the use of machine learning to automate and enhance the accuracy and efficiency of bug triaging. We assess a number of machine learning models, such as Multinomial Naive Bayes and Random Forest. We demonstrate a robust end-toend pipeline for data preprocessing, augmentation, model training, and evaluation using multi-label classification techniques, along with high prediction performance.
Bug Triaging, Machine Learning, Open-Source Software, Issue Classification, Software Maintenance
Unified Object Detection, Segmentation, and Tracking Using Mask R-CNN on COCO Subset
Object detection, instance segmentation, and multi-object tracking are important in modern intelligent vision systems applied in autonomous tasks, transportation, as well as surveillance. Nevertheless, when working in dynamic and complex conditions, classical approaches often cannot reach a compromise between accuracy of the detection, speed of the segmentation, and the real-time. Tests were done using the COCO 2017 val2017 subset that contains a number of diverse object categories with bounding box and segmentation annotations to overcome these concerns. In order to generate a specific assessment data, certain classifications such as human, automobile, bicycle, dog and truck were filtered. Scaling, normalization and data augmentation are some of the popular preprocessing techniques employed in order to enhance model resilience. The models implemented are also the Mask R-CNN which is used in detection and segmentation alongside YOLOv5, YOLOv8, and YOLOv26 to detect and track identities in real- time. Performance is measured using metrics such as mAP, iou, Precision, Recall, MOTA, MOTP and FPS. The object tracking reliability and the better accuracy-speed trade-offs in real-world conditions are the objectives of the unified framework.
Object Detection,Instance Segmentation, Multi- Object Tracking, Mask R-CNN, YOLOv8, Deep SORT