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
The AI Medical Voice Agent is an intelligent, voice-enabled healthcare assistant designed to provide real-time medical guidance through natural speech interaction. The system allows patients to describe symptoms verbally and receive instant, AI-driven responses that simulate a virtual doctor. It integrates Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-toSpeech (TTS) technologies to ensure smooth and humanlike communication. The agent analyzes user inputs, identifies key medical symptoms, and generates appropriate preliminary advice or recommendations. This solution aims to reduce waiting time, improve accessibility to basic healthcare information, and support early medical awareness. The system is especially useful in remote and underserved areas where access to doctors is limited. By automating initial consultations, it helps healthcare professionals save time and focus on critical cases. The proposed AI Medical Voice Agent is scalable, user-friendly, and cost-effective. Overall, it demonstrates how artificial intelligence can enhance digital healthcare services through voice-based interaction.
1 Dr. D. Nagesh Babu, 2Kondeti Jai Surya ,3Kota Dinesh Venkat,4Macha G R P Himavantha Reddy
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
IMAGE ENCRYPTION AND DECRYPTION USING CHAOTICDNA ALGORITHM
Image communication is widely used in personal, medical, military, and professional applications. With the rapid increase in digital image transmission over open networks, ensuring image security has become a critical challenge. Unauthorized access, hacking, and data manipulation can lead to serious information loss and privacy issues. The Image Encryption and Decryption project using a Chaotic DNA Algorithm aims to provide strong protection for digital images. It combines chaotic maps and DNA computing techniques to perform secure pixel scrambling and encoding. The encrypted image can be safely transmitted and accurately recovered only with the correct secret keys, ensuring confidentiality, integrity, and reliable image security. Keywords: Image Encryption, Image Decryption, Chaotic Algorithm, DNA Computing, Image Security, Information Protection
1 Dr. G. Prasuna, 2Kondru Anikshema, 3Mathi Mohana Krishna Siva SaiRam, 4Kavuri Efefra
THREATSENTINELGPT: REAL-TIME AI FRAMEWORK FOR DETECTING SECURITY THREATS AND GENERATING AUTOMATED ALERTS IN CYBERSECURITY AND PHYSICAL SURVEILLANCE SYSTEMS
ThreatSentinelGPT is an intelligent realtime AI-based security framework designed to detect, analyze, and respond to security threats in both cybersecurity and physical surveillance environments. The system integrates machine learning, deep learning, and Generative Pre-trained Transformer (GPT) models to monitor network traffic, system logs, CCTV video streams, and IoT sensor data. By combining anomaly detection, computer vision, and natural language processing, the framework identifies suspicious activities and generates automated alerts with contextual explanations. The system ensures scalability, low latency, and reliability through real-time processing and role-based access control. ThreatSentinelGPT enhances situational awareness, minimizes false positives, and reduces response time, making it suitable for smart cities, enterprises, and critical infrastructure protection.
,1 Mr. N. Lakshmi Narayan, 2Pittu Bindu Siva Madhavi, 3Pallam Yestheru Rani, 4Pallapu Prasanth
Real Time Pothole Detection in Video Streams Using AI-Driven Deep Learning Techniques
Road potholes are a major cause of traffic accidents, vehicle damage, and increased maintenance costs, while traditional manual inspection methods are slow and inefficient. To address this problem, this project proposes a Real-Time Pothole Detection System in Video Streams Using AI-Driven Deep Learning Techniques. The system employs a YOLOv3-Tiny deep learning model, based on convolutional neural networks, for fast and accurate pothole detection. The trained model is integrated into a Flask-based web application using OpenCV’s Deep Neural Network (DNN) module. Live video input is captured from a mobile camera stream and processed frame by frame to identify potholes, which are displayed with bounding boxes and confidence scores along with an alert mechanism which produces beep sound when the pothole is detected. This system provides road safety and reduces the need for manual inspection.
1Dr. P. V. S. Sarma, 2Kurmala Kavya, 3Mogili RaviRaj, 4Maraka SadaSivaRao
Sentiment Analysis on Google Play Store Data using NLP
Every day, thousands of users review mobile apps on the Google Play Store. User contentment, app ratings, and feature expectations are just a few of the important details included in these reviews. However, manually analysing this vast amount of unstructured material is challenging and time-consuming. The project suggests a sentiment-analysis system that uses Natural Language Processing (NLP) to automatically determine if user evaluations are favourable, negative, or neutral in order to address this problem. The procedure begins with gathering app reviews from publicly available datasets, after which the data is cleaned by eliminating special symbols, stop words, emojis, and undesired characters. NLP methods including tokenization, stemming, and word embeddings are used to process the cleaned text. A machine-learning model such as Random Forest, Decision tree or multilayer perceptron models are trained to classify the sentiment of each review.
1Mrs. D. Madhuri, 2Pachipulusu Sai Kavya Sri, 3Kathi Pujith, 4Neelam Tarak
Air Quality Prediction Using Machine Learning Algorithms for Environmental Monitoring and Public Safety
This project focuses on developing an Air Quality Prediction System using Machine Learning techniques to analyze and forecast air pollution levels. With rapid industrialization and urbanization, air pollution has become a major environmental and public health concern. Harmful pollutants such as Sulphur Dioxide (SO₂), Nitrogen Dioxide (NO₂), Respirable Suspended Particulate Matter (RSPM), Suspended Particulate Matter (SPM), and PM2.5 significantly affect human health and environmental sustainability. Therefore, predicting air quality in advance is essential for effective environmental monitoring and public safety. The proposed system utilizes machine learning algorithms such as Linear Regression, Decision Tree, Random Forest, Gradient Boosting, and Support Vector Machine to analyze historical air quality data and predict pollutant concentrations. KEYWORDS: Machine Learning, Linear Regression, Decision Tree, Random Forest, Gradient Boosting, and Support Vector Machine
1Dr. A. Tirupatiah,2Kolla Mohana Pravallika,3Modukuri Ram Gopal,4Nandyala Sravani
DETECTING HARMFUL WEB LINKS USING INTELLIGENT MACHINE LEARNING MODELS
The rapid expansion of the internet has led to an increase in malicious web links. Traditional blacklist-based detection methods are often insufficient, as attackers continuously generate new, previously unseen harmful URLs. This project presents an intelligent machine learning–based system for detecting harmful web links with high accuracy and real time efficiency. Various machine learning algorithms—including Logistic Regression, Random Forest—are trained and evaluated to determine the most effective approach. Overall, the project demonstrates the potential of intelligent ML-based models to strengthen web security and safeguard digital environments.
1Dr. M. Ratna Raju, 2Middiboina Nandini Kumari, 3Pinjala Venkata Siva Sai, 4Kolukula Naga Sateesh
EMPLOYEE ATTRITION PREDICTION USING HR ANALYTICS
Employee Attrition Prediction using HR Analytics is a data-driven approach to identify employees who are likely to leave an organization. Employee attrition causes financial loss, reduced productivity, and operational instability. The objective of this project is to predict employee attrition using supervised machine learning techniques by analyzing historical HR data. The system considers multiple factors such as job satisfaction, salary, experience, working hours, performance rating, and work-life balance. Data preprocessing and feature selection are applied to improve prediction accuracy. Machine learning models such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine are used for prediction. The system provides accurate results and actionable insights to HR managers, helping organizations reduce attrition and improve employee retention strategies.
1Dr. G. Prasuna, 2Kunchala Revathi, 3Papadesu Homa Indu, 4Nandamudi Jagadheesh
Subtrans AI: Intelligent AI System For Automatic Multilingual Subtitle Translation, Synchronization And Video Accessibility Enhancement
An intelligent system titled Subtrans AI: Intelligent AI System for Automatic Multilingual Subtitle Translation, Synchronization, and Video Accessibility Enhancement is developed to enhance the accessibility and comprehension of video content for diverse audiences. Traditional subtitle generation methods often involve manual effort and inaccuracies in translation and timing, reducing their effectiveness in multilingual environments. This project addresses these challenges by leveraging artificial intelligence techniques, including speechto-text processing, natural language processing, and machine translation, to automatically generate and translate subtitles across multiple languages. The system also ensures precise synchronization between audio and text, improving the overall viewing experience. Built using a Python-based backend along with HTML, JavaScript, and Tailwind CSS, the system ensures efficient performance and seamless interaction. The proposed solution supports applications in education, entertainment, and digital media platforms, enabling improved accessibility and broader content reach.
1 Mr. N. Lakshmi Narayana, 2 Kotha Venkata Lakshmi Sahithi, 3 Najana Varalakshmi, 4 Miriyala Prudhvi Raj
Identification Of Malicious URL’s Using Machine Learning for Proactive Cyber Threat Prevention
The rapid growth of internet usage has significantly increased cyber threats, especially through malicious URLs. These URLs are commonly used in phishing, malware distribution, and fraudulent activities. Traditional blacklist-based detection methods fail to identify newly generated malicious links. This project proposes a machine learning-based approach to identify malicious URLs proactively. The system extracts meaningful features from URLs and applies machine learning models to classify them as safe or malicious. Advanced algorithms such as XGBoost improve detection accuracy by learning complex URL patterns. Additionally, the system provides an evidence summary to explain why a URL is classified as malicious. This approach enhances user awareness and strengthens cybersecurity defences. Keywords: Malicious URLs, Machine Learning, Cyber Security, XG-Boost, Phishing Detection ,Feature Extraction, URL Classification
1Dr. A. Tirupatiah, 2Kasukurthi Divyajyothi, 3Gandikota Pavani, 4Borugadda Arunkumar
Predicting Student Performance Using Educational Data
Predicting student academic performance is an important task for educational institutions to enhance learning outcomes and provide timely support to students. Traditional evaluation methods mainly rely on examinations and manual observation, which often fail to identify students at risk in the early stages. This paper presents a machine learning-based system that analyzes educational data such as attendance, study hours, previous exam marks, and assignment scores to predict student performance. The system applies data preprocessing and classification techniques to generate accurate predictions. A web-based interface is developed using Flask to allow users to input student data and obtain results in real time. The proposed model helps educators identify students who may require additional academic support. By utilizing data-driven insights, the system improves decision-making and enhances academic planning. Overall, the approach contributes to efficient performance monitoring and supports the development of intelligent educational systems.
1Mr. T. Sesha Sai,2Gavini Hari Vikas,3Charan Kakumanu,4Gollapudi Kesava Naga Gupta
Artificial Intelligence Approaches for Cryptocurrency Money Laundering Detection on Blockchain Platforms
The rapid growth of blockchain technology and cryptocurrencies has transformed digital financial transactions, offering enhanced security, transparency, and decentralization. However, these advantages have also created new opportunities for financial crimes such as money laundering, fraud, and illicit fund transfers. Detecting suspicious activities within blockchain networks is challenging due to the pseudonymous nature of cryptocurrency transactions and the large volume of transactional data. This study explores the application of machine learning techniques for identifying money laundering activities in blockchain-based financial systems. The proposed approach utilizes transaction-related attributes, including wallet addresses, transaction values, and transaction frequencies, to classify transactions as legal or illegal. Advanced machine learning models such as Random Forest, AdaBoost, and XGBoost are employed to improve detection accuracy and analyze complex transaction patterns. The framework incorporates data preprocessing, normalization, model training, and transaction analysis to enhance predictive performance. Experimental findings indicate that machine learning models can effectively identify suspicious cryptocurrency transactions with high accuracy, precision, and recall. The study demonstrates the potential of artificial intelligencedriven analytics in strengthening anti-money laundering mechanisms, improving regulatory compliance, and supporting financial investigators in tracking illicit activities across blockchain networks. The proposed framework contributes to the development of secure and transparent cryptocurrency ecosystems while addressing emerging challenges in digital financial crime detection.
DESIGN AND IMPLEMENTATION OF RADIX-8 UNSIGNED BIT-PAIR RECODING MULTIPLIER FOR IEEE 754 FP16 MANTISSA ARITHMETIC IN AI ACCELERATORS
The rapid expansion of Artificial Intelligence (AI) and Deep Neural Network (DNN) applications has significantly increased the demand for high-performance arithmetic units optimized for reduced precision formats such as IEEE 754 half-precision (FP16). In FP16 computation, the mantissa multiplication stage dominates the power, delay, and area characteristics of the floating-point unit. Conventional Booth-based multipliers, including Radix-4 and Radix-8 architectures, introduce unnecessary hardware overhead due to sign extension (SE) and negative encoding (NE), which are redundant for unsigned mantissa operations. This paper proposes a Radix-8 Unsigned Bit-Pair Recoding (BPR) multiplier core specifically optimized for 11×11 FP16 mantissa multiplication. The architecture eliminates sign-extension logic and two’s complement correction terms by employing non-overlapping 4-bit recoding. A Predefined Logic Unit (PLU) generates 1X, 2X, and 3X multiples in parallel using a delayoptimized Carry Select Adder (CSLA). The merged partial product reduction technique compresses the multiplier height from n to n/4, resulting in only three partial product rows for a 12-bit operand. A Binary-to-Excess-1 Converter (BEC)- based Square-Root Carry Select Adder (SQRT-CSLA) is implemented for high-speed final summation. Compared to conventional 8×8 and 16×16 multiplier cores, the proposed 12×12 architecture achieves optimal precision without truncation error while significantly reducing hardware slack, and switching activity. The design is implemented using synthesizable Verilog HDL and verified through exhaustive simulation. Results demonstrate improved area efficiency. making the architecture highly suitable for AI accelerator data paths and embedded floatingpoint units.
DESIGN AND IMPLEMENTATION OF A RECONFIGURABLE 64-BIT VEDIC MULTIPLY-ANDACCUMULATE UNIT WITH DYNAMIC PRECISION SCALING FOR DSP AND AI APPLICATIONS
The exponential growth of Artificial Intelligence (AI), Deep Learning, and high-throughput Digital Signal Processing (DSP) applications has significantly increased the demand for highperformance and energy-efficient arithmetic hardware. Multiply-andAccumulate (MAC) units represent the fundamental computational building blocks in these systems, accounting for the majority of execution time in convolution, matrix multiplication, and filtering operations. However, conventional fixed-precision 64-bit MAC architectures suffer from underutilization, excessive power consumption, and long carry propagation delays, especially when executing lower-precision workloads such as 16-bit or 8-bit inference tasks. This paper presents the design and implementation of a Reconfigurable 64-bit Vedic Multiply-and-Accumulate (RMAC) unit based on the Urdhva Tiryagbhyam sutra of Vedic mathematics. The proposed architecture employs a hierarchical bottom-up design methodology, constructing the 64×64 multiplier from optimized 2×2 Vedic primitives scaled recursively to 4×4, 8×8, 16×16, and 32×32 blocks. A novel dynamic reconfiguration mechanism utilizing a 2-bit mode-select control and segmented carry-break logic enables the hardware to operate in three distinct precision modes: 1×64-bit, 2×32-bit parallel, and 4×16-bit parallel configurations. The architecture integrates a segmented combinational adder and a 136-bit accumulator with guard bits to prevent overflow during iterative accumulation cycles. By physically isolating carry propagation across predefined bit boundaries, the design ensures complete arithmetic independence in SIMD modes while preserving full-precision operation when required. The RMAC is modeled using synthesizable Verilog HDL and verified through comprehensive simulation across all operational modes. Compared to conventional static Booth-based MAC architectures, the proposed design demonstrates improved energy proportionality, enhanced hardware utilization, and reduced critical path delay due to the parallel nature of Vedic multiplication. The proposed architecture is highly suitable for modern AI accelerators, reconfigurable DSP cores, and energy-aware System-on-Chip (SoC) platforms.
ANALYZING MUSIC STREAMING TRENDS USING SPOTIFY DATA
This project Analyzing Music Streaming Trends Using Spotify Data aims to develop a data-driven system that analyses and predicts music popularity based on audio features collected from Spotify. The objective is to design and implement an interactive platform that processes song attributes such as danceability, energy, loudness, tempo and valence to identify trends and popularity patterns in music streaming. The system provides insights through visual analytics and popularity prediction to help understand listener preferences and evolving music trends. This platform ensures efficient data handling and accurate analysis using machine learning techniques. Key Words: MongoDB, Spotify Data, Music Trends Analysis, Popularity Prediction, Machine Learning
1Dr.P. Harini, 2Pikki Durga Prasad, 3Perugu Koti Sai Prakash, 4Chokka Raju
Automated Generative AI Text Summarization Engine for Efficient Content Extraction, Document Analysis, and Knowledge Discovery
The rapid growth of digital information has made it difficult for users to read and understand large volumes of textual content. This project focuses on developing a Smart Text Summarization system using the PEGASUS generative AI model to automatically generate concise summaries from lengthy text. The main objective of the system is to help users quickly grasp important information without reading the entire document. The application allows users to enter text or upload documents such as PDF and DOCX files for summarization. The PEGASUS transformer model is used to perform abstractive summarization by understanding the context and meaning of the text. The system is developed using Python and Flask for backend processing and HTML, CSS, and JavaScript for creating an interactive web interface. Additional libraries such as PyPDF2 and python-docx are used to extract text from documents. ReportLab is used to generate downloadable PDF summaries.
1Dr. P. Harini,2Nasaka Likhitha,3Palepu Sai Madhuvallika,4Nidamanuri Bharath Kumar
Analysing consumer sentiments in product reviews using full stack web development
Analysing consumer sentiment in product reviews is an effective approach to understand customer opinions in modern ecommerce platforms. With the rapid increase in online shopping, massive volumes of textual reviews are generated, making manual analysis inefficient and impractical. This paper presents a fullstack web-based sentiment analysis system that automatically classifies product reviews into positive, negative, and neutral sentiments using Natural Language Processing (NLP) and Machine Learning techniques. The proposed system performs text preprocessing, TF-IDF feature extraction, and sentiment classification using supervised learning algorithms such as Logistic Regression. The system is implemented using Python, Django, and Scikit-learn, providing real-time sentiment analysis, visualization, and user-friendly interaction for effective decision-making.
1Dr. C. Hari Kishan, 2U. Nanda Kumar, 3K. Phani Chaitanya, 4J. Ratnakumar, 5N. Srinivas
AI-POWERED RESUME SCREENING AND CANDIDATE RANKING SYSTEM
Recruitment processes in organizations often involve manual resume screening, which is time-consuming, inefficient, and susceptible to human bias. With the rapid growth of job applications, traditional screening methods fail to scale effectively. This paper presents an AI Powered Resume Screening and Candidate Ranking System developed using full stack web technologies. The proposed system automatically analyzes resumes and matches them with job descriptions using Natural Language Processing (NLP) techniques such as TF-IDF vectorization and cosine similarity. The system ranks candidates based on relevance scores, assisting recruiters in selecting suitable candidates efficiently. The application is developed using Python, Flask, HTML, Tailwind CSS, and a cloud-based database, providing a secure and user-friendly interface. This system reduces manual effort, improves screening accuracy, and enables faster and unbiased recruitment decisions
1 Prasad .V, 2G.Subhash Babu, 3P.Devanand, 4G.Prateet Sai, ⁵B.Venkaiah Naidu
Dynamic Stock Market Insights: Real-Time Trend Analysis with Python and Power BI
Stock market data changes rapidly and contains complex patterns, which makes manual analysis difficult, especially for beginners. Although stock price information is easily available, understanding historical trends and market behavior requires analytical support. This project, Dynamic Stock Market Insights: Real-Time Trend Analysis using Python and Power BI, focuses on analyzing historical stock market data to classify price trends. The dataset consists of Open, Close, High, and Low prices collected from historical records. These raw values are processed into meaningful features such as price change and volatility to represent market movement. Machine learning classification techniques are applied to learn patterns from past data and predict stock trends as increase, decrease, or stable. The system does not predict exact future prices but provides trend-based insights. A user-friendly web application is developed to allow users to input values and obtain predictions instantly. Data visualization techniques and Power BI charts are used to present results clearly. This project serves as an educational and analytical tool to understand stock market trends using data analytics and machine learning.
1Dr. P. Harini,2Gollaprolu Pardha Saradhi,3Darla Sri Sai Durga,4Addanki Manikanta
Securing Banking Transactions with Block chain A Token less Approach
The rapid expansion of digital banking has increased the need for stronger protection of financial transactions. Most traditional banking systems depend on centralized databases and token-based authentication methods, which are often exposed to cyberattacks, fraud, and data manipulation. This paper introduces a token-less banking transaction system that uses blockchain technology to strengthen security, transparency, and reliability. In the proposed system, banking transactions are stored as hash-linked blocks, making the records tamper-proof and immutable. Each transaction is encrypted, timestamped, and verified through cryptographic hashing without relying on tokens, wallets, or mining processes. The system includes multiple roles such as Admin, Agent, User, Auditor, and Attacker to reflect real banking operations and possible security threats. Any unauthorized change in transaction data immediately leads to a hash mismatch, allowing fraud to be quickly identified.
1Dr. P. Harini,2Gokaraju Vamsi Krishna,3Annamneedi Hemanth,4Cheedarla Koushik
AI-Powered Citizen Evidence Verification & Civic Complaint Management
The AI-Powered Citizen Evidence Verification & Civic Complaint Management System improves digital grievance redressal by integrating Artificial Intelligence with full-stack web technologies. It allows citizens to submit complaints with multiple images, geolocation data, and multilingual descriptions while ensuring authenticity through deepfake detection and image validation. NLP techniques are used for translation and urgency analysis to enable automated prioritization. The system enhances transparency, security, and efficiency in modern smart governance. Keywords: Artificial Intelligence (AI), Civic Complaint Management, Deepfake Detection, Natural Language Processing (NLP), Multilingual Processing, Image Verification, Smart Governance, Geolocation Validation, Role-Based Access Control (RBAC), Full-Stack Web Development.
1Dr. P. Harini,2Gattupalli Hema Venkata Naga Manideep,3Baig Gouse Baig, 4Guntur Jaya Naga Lakshmi Siva Durga
A THREE-STAGE AMPLIFIER FOR POWERING BIG CAPACITIVE LOADS USING BUFFERED ASYMMETRIC DUAL PATH WITH CASCODE MILLER COMPENSATION
This paper presents a new frequency compensation approach for threestage amplifiers driving a pF-to-nF capacitive load. Thanks to the cascode Miller compensation, the non-dominant complex pole frequency is extended effectively, and the physical size of the compensation capacitors is also reduced. A local Q-factor control (LQC) loop is introduced to alter the Q-factor adaptively when loading capacitance CL varies significantly. This LQC loop decides how much damping current should be injected into the corresponding parasitic node to control the Q-factor of the complex-pole pair, which affects the frequency peak at the gain plot and the settling time of the proposed amplifier in the closed-loop step response. Additionally, a left-half-plane (LHP) zero is created to increase the phase margin and a feed-forward transconductance stage is paralleled to improve the slew rate (SR). Simulated in 0.13-µm CMOS technology, the amplifier is verified to handle a 4-pF-to-1.5-nF (375× drivability) capacitive load with at least 0.88-MHz gainbandwidth (GBW) product and 42.3◦ phase margin (PM), while consuming 24.0-µW quiescent power at 1.0-V nominal supply voltage.
1 Suman Kumar Yerra Venkata, 2 Dr.J.Amarendra, 3 A.Sandeep Kumar Reddy
Mind Ease: A Web-Based AI Chatbot for Personalized Mental Health Support Using NLP and Sentiment Analysis
The prevalence of mental health issues among people of all ages continues to increase, and timely mental health assistance remains challenging due to various factors such as social stigma, time constraints, and a shortage of mental health resources. This article talks about Mind Ease, a web-based chatbot that uses NLP and mood analysis to help people with their mental health in a personalized way. The proposed system enables realtime text-based conversations. The chatbot can pick up on emotions and respond to them in a supportive manner. To enhance the accuracy of mood recognition a LSTM is utilized. This model is good at capturing contextual information and linguistic dependencies in user inputs. It provides individual feedback, self-care advise and coping strategies based on the emotions that it identifies to enhance emotional health. Mind Ease also has a mood-tracing screen that provides weekly and monthly analytical charts that demonstrate user’s emotional patterns. This will allow them to be more aware of themselves and monitor their progress. A crisis detection system is also integrated for identifying sensitive and high-risk talks and providing the appropriate type of support. Besides, user authentication and interaction logs can provide a basic degree of user personalization, improving the overall user experience. The proposed system highlights the potential of AI-powered conversational systems to deliver 24/7, easily accessible, and emotionally intelligent mental health assistance via a web-based interactive environment.
Advancing Phishing Detection with Voting Classifier, Hybrid Deep Models, and Flask-Based Real-Time Prediction Interface
Phishing attacks remain a significant cybersecurity threat, deceiving users into revealing sensitive information through fraudulent websites. This study proposes a machine learning–based phishing detection framework enhanced with advanced feature selection, deep learning, and explainable intelligence. Multiple feature importance techniques, including mutual information, chi-square analysis, and permutation importance, are applied across Feedforward Neural Networks, Deep Neural Networks, TabNet, and Wide and Deep models to identify influential phishing indicators. To address class imbalance and improve robustness, SMOTEENN resampling is combined with an ensemble Voting Classifier integrating Random Forest and Bagging with Decision Trees. Experiments conducted on two public phishing datasets demonstrate superior performance, where the Voting Classifier achieved 98.7% accuracy on the phishing websites dataset and 98.5% accuracy on the web page phishing dataset, outperforming individual deep learning models. Explainable Artificial Intelligence techniques such as LIME and SHAP are incorporated to interpret predictions and highlight feature contributions, ensuring transparency and trust. For real-world deployment, the framework is implemented using the Flask platform, offering an interactive web interface with secure user signup and signin using SQLite. Users submit URLs for analysis, and the system provides real-time predictions as “Phishing website” or “Non Phishing website,” supporting reliable and interpretable phishing detection for online security.
Systematic Assessment of Cyber Physical Security of Energy Management System for Connected and Automated Electric Vehicles
The rapid advancement of Connected and Automated Electric Vehicles (CAEVs) has increased reliance on Energy Management Systems (EMS), making them vulnerable to cyber-physical security threats that can compromise vehicle performance, safety, and energy efficiency. This paper presents a systematic assessment framework for evaluating the cyber-physical security of EMS in CAEVs. The proposed approach integrates multiple vehicle sensors and communication components to identify potential vulnerabilities within the control architecture. Various cyber-physical attack scenarios, including false data injection, sensor spoofing, and communication manipulation attacks, are modeled to analyze their impact on system operation. An EMS based on Model Predictive Control (MPC) is implemented to optimize energy utilization while maintaining vehicle performance under normal and attack conditions. Extensive simulations are conducted to collect operational data related to energy consumption, control actions, and vehicle dynamics. The collected data are analyzed using security-oriented performance metrics that evaluate system resilience, stability, comfort, and energy efficiency. The results provide a comprehensive understanding of how cyber-attacks influence EMS behavior and overall vehicle operation. The proposed assessment methodology enables the identification of critical vulnerabilities and supports the development of effective detection, mitigation, and recovery mechanisms, thereby enhancing the security, reliability, and sustainability of future connected and automated electric transportation systems.
Shaik Sameer Ahmed,Subramanian K.M,Imtiyaz Khan
A SMART THREE-PORT RECONFIGURATION CONVERTER USING FUZZY LOGIC FOR EMERGENCY ENERGY STORAGE VEHICLES
The rapid growth of electric vehicles (EVs) and renewable energy systems has increased the demand for efficient power conversion technologies capable of managing multiple energy sources and storage devices. Conventional converters suffer from limited flexibility, reduced efficiency, and poor dynamic performance under varying operating conditions. This paper proposes a Smart Three-Port Reconfiguration Converter (STPRC) integrated with a Fuzzy Logic Controller (FLC) for Emergency Energy Storage Vehicles (EESVs). The proposed converter consists of three power ports interfacing a battery energy storage system, supercapacitor bank, and DC load/renewable source. The reconfigurable architecture enables bidirectional power flow and seamless energy management during emergency situations such as grid outages, disaster relief operations, and mobile power support applications. The FLC continuously monitors battery stateof-charge (SOC), load demand, and source availability to determine optimal powersharing decisions. MATLAB/Simulink simulations demonstrate that the proposed system achieves faster dynamic response, reduced voltage fluctuations, improved converter efficiency, and enhanced battery lifespan compared with conventional PIcontrolled converters. Results indicate a reduction in settling time by 35%, voltage deviation by 42%, and energy utilization improvement by 18%, making the proposed system suitable for emergency energy storage vehicle applications.
1Mr. KANTE VENKATADURGAPRASAD , 2PATI MANIKANTA,
ANN BASED THREE-PHASE ISOLATED MULTILEVEL ACDC CONVERTER FOR DUAL ELECTRIC VEHICLE BATTERY CHARGING
The rapid growth of electric vehicles (EVs) has intensified the need for efficient, reliable, and intelligent charging infrastructures capable of simultaneously charging multiple vehicles. This paper presents an Artificial Neural Network (ANN)-based control strategy for a threephase isolated multilevel AC–DC converter designed for dual electric vehicle battery charging applications. The proposed topology employs a multilevel converter integrated with a high-frequency isolation transformer to achieve galvanic isolation, enhanced power quality, and reduced voltage stress across switching devices. The ANN controller is implemented to regulate the DC output voltage and charging current under varying grid conditions and battery state-of-charge levels. The intelligent controller provides superior dynamic response, reduced steady-state error, and improved robustness compared with conventional proportional–integral (PI) controllers. Furthermore, the dual-output architecture enables independent charging of two EV batteries with effective power sharing and minimal circulating current. Simulation studies carried out in MATLAB/Simulink demonstrate that the proposed ANNcontrolled system achieves low total harmonic distortion (THD), near-unity power factor, fast transient performance, and improved charging efficiency. The results validate the suitability of the proposed converter for future smart EV charging stations.
1Mr. P. V. V. RAMANA, 2CHALLA PURNA MANI TEJASWI
A MODULAR BUCK-BOOST MULTIPORT BIDIRECTIONAL CONVERTER WITH FUZZY LOGIC CONTROL FOR ENERGY MANAGEMENT IN HEVS
Hybrid Electric Vehicles (HEVs) require efficient energy management systems capable of coordinating power flow among multiple energy sources while maintaining high efficiency and operational reliability. This paper proposes a modular buck-boost multiport bidirectional DC–DC converter integrated with a Fuzzy Logic Controller (FLC) for intelligent energy management in HEV applications. The proposed converter enables bidirectional power transfer among the battery pack, supercapacitor bank, and traction motor through a compact and scalable architecture. The modular configuration reduces component stress, improves fault tolerance, and facilitates system expansion for different vehicle power ratings. A fuzzy logic-based energy management strategy is developed to dynamically allocate power according to vehicle operating conditions, battery state-of-charge (SOC), supercapacitor voltage, and load demand. The proposed controller effectively mitigates battery stress by assigning transient power requirements to the supercapacitor while maintaining optimal battery utilization. MATLAB/Simulink simulation studies validate the performance of the proposed system under acceleration, cruising, regenerative braking, and sudden load variations. Results demonstrate enhanced powersharing capability, improved energy efficiency, reduced battery current fluctuations, and superior dynamic response compared with conventional rulebased control methods. Therefore, the proposed converter and control strategy provide a promising solution for nextgeneration HEV energy management systems.
ENERGY MANAGEMENT AND POWER QUALITY IMPROVEMENT IN SPLIT SOURCE INVERTER-BASED PV SYSTEMS USING ADAPTIVE FUZZY SLIDING MODE CONTROL
The increasing penetration of photovoltaic (PV) systems into modern power networks necessitates the development of advanced control strategies that ensure efficient energy management and enhanced power quality. This paper proposes an Adaptive Fuzzy Sliding Mode Control (AFSMC)-based energy management approach for a Split Source Inverter (SSI)-based PV system. The split source inverter combines the functionalities of a conventional voltage source inverter and a DC–DC boost converter, thereby reducing component count and improving conversion efficiency. However, maintaining stable operation under fluctuating solar irradiance, load variations, and grid disturbances remains a significant challenge. The proposed AFSMC strategy integrates the robustness of Sliding Mode Control (SMC) with the adaptive reasoning capability of Fuzzy Logic Control (FLC) to regulate the DC-link voltage, improve power flow management, and mitigate power quality issues. The controller dynamically adjusts the switching control law to minimize tracking errors and suppress chattering effects associated with traditional SMC methods. MATLAB/Simulink simulations demonstrate that the proposed controller achieves superior dynamic performance, reduced total harmonic distortion (THD), improved power factor, enhanced voltage regulation, and efficient utilization of PVgenerated power compared with conventional PI and standalone SMC approaches. The results validate the effectiveness of the AFSMC strategy in enabling reliable and high-quality operation of SSI-based PV systems.
1Mr. KANTE VENKATADURGAPRASAD , 2BODDAPATI LAKSHMI TANMAI
A HYBRID ANFIS-SMC CONTROL STRATEGY FOR SOLAR-POWERED OFF-GRID ELECTRIC VEHICLE
The increasing adoption of electric vehicles (EVs) and the growing demand for sustainable transportation have encouraged the development of renewable energy-based charging infrastructures. Solar-powered off-grid EV charging systems provide an environmentally friendly solution for remote areas and regions with limited grid accessibility. However, the intermittent nature of photovoltaic (PV) generation and the nonlinear characteristics of battery charging necessitate advanced control techniques to ensure efficient energy utilization and stable system operation. This paper proposes a Hybrid Adaptive Neuro-Fuzzy Inference System–Sliding Mode Control (ANFIS-SMC) strategy for a solar-powered off-grid EV charging system. The proposed system integrates a PV array, a battery energy storage system (BESS), a bidirectional DC–DC converter, and an EV charging interface. The ANFIS controller performs intelligent energy management and reference generation, while the Sliding Mode Controller provides robust tracking and fast dynamic response under varying environmental and load conditions. The hybrid approach combines the adaptive learning capability of ANFIS with the robustness of SMC to improve voltage regulation, reduce charging fluctuations, and enhance overall system efficiency. MATLAB/Simulink simulations validate the proposed controller under different irradiance levels, battery states of charge, and EV charging scenarios. The results demonstrate superior performance in terms of settling time, charging stability, energy utilization efficiency, and robustness compared with conventional PI and standalone fuzzy control methods.
1Mr. KANTE VENKATADURGAPRASAD , 2ARAVELLI SRIKISHORE,