International Journal of AI Electrical Civil and Mechanical engineering
An Intelligent Machine Learning Framework for Adaptive Cyber Threat Detection Across Multi-Cloud Computing Environments
The rapid expansion of cloud computing has encouraged enterprises to adopt multi-cloud architectures to improve service availability, workload distribution, scalability, and business continuity. Despite these advantages, managing security across multiple cloud providers remains a significant challenge because each platform generates diverse security logs, network events, and access records. The fragmented nature of these environments makes it difficult for conventional security solutions to correlate security incidents and detect sophisticated cyberattacks in a timely manner. Consequently, there is a growing need for intelligent security frameworks capable of providing unified threat analysis across heterogeneous cloud infrastructures.This study proposes an adaptive artificial intelligence-based threat detection framework that enhances cybersecurity in multi-cloud environments through automated data analysis and intelligent decision-making. The proposed framework aggregates security-related information from multiple cloud resources, preprocesses the collected data, extracts discriminative features, and applies supervised machine learning techniques to classify benign and malicious activities. By learning behavioral characteristics from historical security events, the system effectively identifies anomalous network traffic, unauthorized authentication attempts, suspicious user behavior, privilege misuse, and other potential security threats. Unlike conventional rule-based approaches, the proposed framework continuously improves its detection capability by recognizing evolving attack patterns and reducing dependence on manually defined security rules.The effectiveness of the proposed framework is evaluated using standard machine learning performance metrics, including accuracy, precision, recall, F1- score, and false-positive rate. Experimental analysis demonstrates that intelligent learning models can significantly improve threat detection capability while minimizing false alarms and maintaining consistent performance in dynamic cloud environments. The proposed framework offers a scalable, efficient, and proactive security solution that strengthens cyber resilience, enables early threat identification, and supports secure service delivery across distributed multi-cloud computing platforms.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Joint Security–Performance Intelligence through Dual-Objective Learning for Autonomous Next-Generation Networks
The rapid evolution of 6G communication networks has introduced unprecedented challenges in ensuring secure, adaptive, and high-performance network operations. Traditional intrusion detection and network monitoring techniques, which primarily rely on static rules and manual analysis, are increasingly ineffective against sophisticated cyberattacks and dynamic traffic patterns. An intelligent Machine Learning (ML)-based framework is developed to perform dual-purpose analysis by simultaneously classifying network attacks and predicting network throughput using Classification and Regression Tree (CART)- based learning. The framework integrates Support Vector Machine (SVM), k-Nearest Neighbors (KNN), and a Tree-based Adaptive Optimization (TAO) ensemble model to improve detection capability and predictive accuracy. Data preprocessing techniques, including Label Encoding, feature standardization, and Synthetic Minority Over-sampling Technique (SMOTE), are employed to enhance data quality, reduce class imbalance, and improve model generalization. Experimental evaluation demonstrates that the TAO ensemble model outperforms conventional ML algorithms by achieving higher attack classification accuracy and more reliable throughput prediction across diverse network conditions. A Flask-based web application enables real-time traffic analysis, throughput prediction, and interactive visualization, facilitating practical deployment in intelligent network management systems. The proposed framework provides a scalable, adaptive, and computationally efficient solution for strengthening cybersecurity while optimizing communication performance, making it suitable for secure and intelligent resource management in future 6G network environments.
Secure Online Pharmacy Billing with Prescription Upload and Encrypted Invoice Management
The rapid growth of online healthcare services has accelerated the digital transformation of pharmacy billing and prescription management systems. Modern online pharmacies process large volumes of sensitive medical prescriptions, patient records, and financial invoices, making secure data management and controlled access essential. Ensuring the confidentiality, integrity, and availability of healthcare information is critical for maintaining patient trust and meeting regulatory requirements. However, many conventional pharmacy billing systems still rely on manual procedures or partially digital platforms where prescriptions and invoices are stored without adequate security. These systems often lack role-based access control, encrypted file storage, centralised data management, and secure authentication, making them vulnerable to unauthorised access, data tampering, billing delays, and human errors. To overcome these limitations, the proposed secure web-based pharmacy billing system integrates modern web technologies with cryptographic security techniques. The system is developed using the Flask framework to manage request routing, session handling, and role-based authorisation. Tiny Database (TinyDB) is used to store user information, product records, prescriptions, invoices, orders, and encrypted file metadata in JSON format. Sensitive documents, including prescriptions and invoices, are secured using Alleged Rivest Cipher (ARC2) encryption in Cipher Block Chaining (CBC) mode, while user passwords are protected using Secure Hash Algorithm (SHA-256) hashing. The proposed system enhances data security, minimizes manual effort, improves operational reliability, and provides secure, efficient, and role-based pharmacy billing and document management.
AI-Powered Brand Identity Modeling through Intelligent Name Generation and Market Evaluation
Establishing a strong and distinctive brand identity is a key factor in gaining a competitive edge, but creating original, meaningful, and market-relevant brand names is often a complex and time-consuming task. Conventional approaches primarily depend on human imagination, brainstorming sessions, and rule-based strategies, which can be subjective, inconsistent, and limited in their ability to recognize complex semantic relationships or adapt to evolving market trends. To address these challenges, this work introduces an AI-powered framework that combines natural language processing (NLP) with machine learning to automate the generation and assessment of brand names. The proposed methodology starts with comprehensive text preprocessing, including data cleaning, tokenization, lemmatization, and stop-word elimination to produce highquality textual inputs. It then employs transformer-based language models, such as DistilRoBERTa, to generate contextual embeddings that effectively capture semantic and linguistic information from words and phrases. For classification, several machine learning techniques, including Logistic Regression (LR), Random Forest Classifier (RFC), and Support Vector Machine (SVM), are trained using balanced datasets, where the Synthetic Minority Over-sampling Technique (SMOTE) is utilized to mitigate class imbalance. The developed system supports both individual and bulk brand name analysis, providing outputs such as predicted brand categories, relevance scores, and evaluation metrics through an intuitive web-based interface. By integrating advanced NLP capabilities with reliable machine learning algorithms, the proposed solution enhances scalability, efficiency, and prediction accuracy while minimizing manual effort, reducing bias, and accelerating the creation of effective, distinctive, and market-oriented brand names.
A Context-Aware ELECTRA-Based Dual-Output Framework for Intelligent Mobile Review Title and Rating Prediction
The increasing popularity of mobile applications has resulted in a substantial growth in usergenerated reviews and ratings, making them valuable resources for evaluating customer satisfaction and application quality. These reviews contain important information regarding user experiences, feature requests, software performance, and overall product perception. However, manually analysing large volumes of textual feedback is labour-intensive, inconsistent, and incapable of efficiently processing continuously growing datasets. Furthermore, conventional review analysis methods often struggle to capture contextual semantics and handle imbalanced rating distributions, leading to reduced prediction accuracy. To overcome these challenges, this research proposes an intelligent dual-target classification framework for mobile user reviews and ratings. The proposed system initially performs comprehensive Natural Language Processing (NLP) preprocessing and Exploratory Data Analysis (EDA) to improve data quality and examine the characteristics of the review dataset. Subsequently, Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA) is employed to generate contextual feature representations that preserve semantic relationships within user reviews. To alleviate class imbalance, the K-Means Synthetic Minority Over-Sampling Technique (K-Means SMOTE) is utilized to generate representative samples for minority classes, thereby improving model generalization. The extracted features are then processed using an Extra Trees Classifier (ETC) to simultaneously predict review titles and corresponding user ratings. Comparative analysis with Adaptive Boosting Classifier (ABC) and Tao Tree Classifier (TTC) demonstrates that the proposed framework achieves superior predictive performance. The developed system provides an efficient and scalable solution for automated mobile review analysis, enabling organizations to understand customer opinions, monitor application quality, and support data-driven product improvement strategies.
Adaptive Latent Representation Learning with Consensus Decision Intelligence for Trusted IoT Device Identity Verification
The continuous growth of the Internet of Things (IoT) has led to the deployment of a vast ecosystem of interconnected devices across domains such as healthcare, smart cities, industrial automation, and intelligent infrastructure. While this connectivity offers significant operational benefits, it also introduces serious cybersecurity risks, including unauthorized access, data tampering, malicious attacks, and network service interruptions. Conventional security solutions, such as Access Control Lists (ACLs), signature-based detection techniques, and ruledriven mechanisms, are increasingly inadequate because of their fixed configurations, dependence on continuous manual updates, and inability to effectively recognize newly emerging threats. To overcome these limitations, this research proposes an intelligent IoT security framework based on a Deep Autoencoder (DAE) that performs both device classification and data integrity verification. The DAE learns compact and informative feature representations from IoT network traffic, enabling accurate differentiation between legitimate and malicious device activities. By utilizing labeled datasets during training, the model captures complex communication patterns and detects anomalous behaviour with high precision. In addition, an integrated authentication module validates the authenticity and integrity of device communications before allowing access to the network, thereby strengthening overall system trust and security. Experimental evaluation demonstrates that the proposed framework achieves an accuracy of 97.8%, surpassing conventional machine learning techniques such as K-Nearest Neighbors (KNN) and Logistic Regression Classifier (LRC). Furthermore, a hybrid model referred to as DAE-BFL (BFL), which combines the Deep Autoencoder with Random Forest Classifier (RFC) and Logistic Regression Classifier (LRC), further improves classification reliability and robustness. The overall framework encompasses data preprocessing, model training, validation, and real-time performance assessment, providing a scalable, adaptive, and efficient solution for securing next-generation IoT networks.
Secure Digital Court Hearing Scheduler Supporting Role Based Access and Case Update
The rapid growth in legal cases and the increasing workload of judicial institutions have created a significant demand for efficient, secure, and centralized digital court management systems. Many courts continue to rely on manual record-keeping, paper-based documentation, email communication, and disconnected software tools to manage case records, hearing schedules, and legal correspondence. These conventional approaches often result in scheduling conflicts, misplaced documents, delayed communication, limited transparency, and inefficient coordination among courts, lawyers, and clients. Furthermore, sensitive legal documents are frequently stored or transmitted without adequate security mechanisms, making them vulnerable to unauthorized access, data tampering, and confidentiality breaches. These challenges highlight the need for a secure and integrated digital solution for modern judicial administration. The proposed Digital Court Hearing Scheduling Platform provides a centralized web-based system developed using Flask with TinyDB for structured data management. The platform implements RoleBased Access Control (RBAC) to provide courts, lawyers, and clients with secure, rolespecific access to case records, hearing schedules, and legal documents. To protect confidential information, legal documents and messages are encrypted using the Affine Cipher before storage and sharing, ensuring secure communication and controlled access. The system also supports hearing scheduling, appointment management, secure file sharing, and real-time coordination through a unified interface. The proposed platform improves judicial efficiency by reducing manual workload, strengthening document security, enhancing transparency, minimizing scheduling conflicts, and enabling faster case management, thereby providing a reliable, scalable, and secure digital solution for modern court administration.
Freelance Marketplace Platform with Profile Verification Bidding and Dispute Resolution
The rapid growth of digital freelancing has transformed the way clients and skilled professionals collaborate across geographical boundaries, creating new opportunities for remote employment and global service delivery. However, existing freelance marketplaces continue to face several challenges, including fake user accounts, unverified freelancer profiles, fraudulent job postings, non-transparent bidding processes, and inefficient job allocation mechanisms. These issues reduce platform reliability, increase the risk of scams, and weaken trust between clients and freelancers. Additionally, the absence of structured verification, secure communication, and effective dispute handling often leads to conflicts, delayed project completion, and poor user experience. These limitations highlight the need for a secure and transparent freelancing platform that ensures authenticated participation and efficient project management. The proposed Freelance Marketplace Platform with Profile Verification, Bidding, and Dispute Resolution (FMP-PVBDR) introduces a centralized webbased solution that implements Role-Based Access Control (RBAC) to support administrators, clients, and freelancers. Newly registered users undergo administrative verification before accessing platform services, ensuring profile authenticity and minimizing fraudulent activities. Clients can post project requirements with budget details, while freelancers submit competitive bids based on pricing and estimated completion time. Developed using React JS for the frontend, Django REST Framework for backend API development, and MySQL for data management, the platform provides secure communication, structured bidding, and efficient project coordination. Its modular architecture supports future enhancements, including online payment integration, dispute resolution, freelancer ratings, and performance evaluation, thereby improving transparency, operational efficiency, scalability, and user confidence in digital freelancing ecosystems.
Real-Time Disaster Resource Mapping With SOS Coordination And Volunteer Integration
Natural and human-induced disasters such as floods, earthquakes, fires, and industrial accidents have become increasingly frequent, posing significant challenges to emergency response, resource allocation, and public safety. Conventional disaster management systems primarily rely on manual communication, local authorities, and public announcements to disseminate emergency information. These approaches often suffer from delayed communication, limited accessibility, fragmented coordination, and the absence of a centralized information repository, particularly during large-scale emergencies. As a result, affected individuals frequently encounter difficulties in locating verified shelters, safe locations, and emergency resources, leading to panic, confusion, and delayed rescue operations. To address these challenges, this paper presents Disaster Resource Mapping with SOS (Save Our Souls) Coordination and Volunteer Integration, a centralized web-based platform designed to improve disaster preparedness and emergency response. The proposed system enables users to access verified safe locations with complete address and contact information while facilitating efficient coordination between administrators, volunteers, and affected communities. The administrator module manages user approvals, volunteer verification, and disaster resource information, whereas the user module allows registered users to maintain their profiles, send SOS alerts, and locate nearby safe places during emergencies. The platform is developed using React.js for the frontend, Django Representational State Transfer (REST) Framework for backend Application Programming Interface (API) development, and My Structured Query Language (MySQL) for secure data storage and management. The centralized architecture ensures reliable information dissemination, enhances resource accessibility, and supports effective volunteer coordination. Experimental implementation demonstrates that the proposed system significantly improves disaster information management, accelerates emergency response, and strengthens community resilience, thereby contributing to safer and more efficient disaster management practices.
Secure Online Patient Appointment Booking and Encrypted Medical History Management
The rapid digital transformation of healthcare services has created a strong demand for secure, efficient, and user-friendly systems to manage patient appointments and sensitive medical information. Historically, healthcare institutions relied on manual registers and isolated digital tools for appointment scheduling and record maintenance, which often resulted in inefficiencies, data inconsistency, and limited accessibility. The problem is further intensified by the increasing volume of patients, the need for timely medical services, and strict requirements for data confidentiality. Traditional systems lack centralized control, real-time updates, strong authentication mechanisms, and adequate protection for sensitive medical data, making them prone to errors, delays, and security vulnerabilities. These limitations highlight the need for a robust digital solution that can streamline scheduling workflows while ensuring secure handling of patient information. To address these challenges, this research proposes a secure online patient appointment booking and encrypted medical history management system built using a webbased architecture. This research integrates structured backend processing with Flask, secure data persistence using SQL Alchemy (SQLA), Object Relational Mapper (ORM), and encrypted user authentication through bcrypt hashing (BCH). Role-based access control (RBAC) ensures that patients and administrators access only authorized functionalities, while automated workflows reduce manual intervention and scheduling conflicts. The system also provides dynamic dashboards for improved visibility and decision-making. The significance of this research lies in its ability to enhance operational efficiency, improve data security, and support scalable healthcare service delivery. By combining secure authentication, structured data management, and intuitive user interaction, the system demonstrates a reliable and performance-optimized approach suitable for modern healthcare environments. The proposed solution not only overcomes the limitations of traditional appointment systems but also establishes a strong foundation for future digital healthcare applications.
Role-Based Waste Pickup Scheduling Platform with Resident Notification Optimization System Civic Services
Rapid urbanization and continuous population growth have significantly increased the complexity of Municipal Solid Waste (MSW) management in residential communities. Conventional waste collection systems primarily rely on fixed collection schedules and manual planning, making them inefficient in handling the varying waste generation patterns of modern urban environments. These traditional approaches often result in delayed waste collection, overflowing garbage bins, inefficient route planning, excessive fuel consumption, increased operational costs, environmental pollution, and potential public health risks. Furthermore, the absence of realtime coordination among residents, sanitation workers, and municipal authorities limits effective communication, making it difficult to respond promptly to collection requests, service complaints, and emergency situations. To address these challenges, the proposed Resident Clean Scheduler Organized Waste Collection System (RCS-OWCS) introduces a centralized web-based platform that automates and optimizes residential waste management operations. The system enables residents to schedule waste pickups, report sanitationrelated issues, and monitor collection status in real time. Sanitation workers receive optimized collection schedules and intelligent route guidance to improve operational efficiency, while municipal administrators can supervise collection activities, allocate resources, monitor workforce performance, and generate analytical reports for data-driven decision-making. By integrating automated scheduling, centralized data management, and real-time communication, the proposed system enhances service transparency, minimizes operational expenses, improves resource utilization, and reduces environmental impact. The platform supports sustainable Smart City (SC) development by promoting efficient waste collection, cleaner residential environments, improved civic participation, and technology-driven municipal service management.
CogniRetail: A Contextual Semantic Inference Architecture for MultiResolution Consumer Opinion Intelligence
The rapid expansion of e-commerce platforms has resulted in an unprecedented increase in online product reviews, making sentiment analysis an essential tool for understanding customer opinions and supporting business decision-making. These reviews contain valuable information regarding customer experiences, product quality, and purchasing preferences. However, manually analyzing large volumes of textual feedback is timeconsuming, inconsistent, and incapable of handling continuously growing datasets. In addition, conventional sentiment classification methods often struggle to capture contextual semantics and perform effectively when sentiment classes are unevenly distributed. To address these challenges, this research proposes an intelligent sentiment classification framework that integrates advanced semantic representation with hybrid deep learning techniques. Initially, customer reviews undergo comprehensive preprocessing and Exploratory Data Analysis (EDA) to improve data quality and examine the underlying characteristics of the dataset. Subsequently, Sentence Bidirectional Encoder Representations from Transformers (SBERT) generates contextual embeddings that preserve semantic relationships within the review text. To alleviate class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is employed to generate representative samples for minority sentiment categories, thereby improving model generalization. The extracted features are processed using a Deep Neural Network (DNN)-based feature selection mechanism combined with a Boosted Rules Classifier (BRC) to classify customer opinions into Negative, Neutral, and Positive sentiment classes. For performance comparison, the proposed framework is evaluated against Random Forest Classifier (RFC), Light Gradient Boosting Machine (LGBM), and Extreme Gradient Boosting (XGBoost). Experimental results demonstrate that the proposed model consistently achieves higher classification accuracy and reduced prediction bias, providing a scalable and reliable solution for customer sentiment analysis, product improvement, marketing optimization, and customer relationship management.
Attention-Guided Trivariate Sequential Intelligence for Academic Performance Inference and Student Stress Profiling
The increasing adoption of data-driven technologies in education has created a growing demand for intelligent systems capable of accurately analyzing student performance and behavioral patterns. Educational datasets contain valuable information, including study habits, attendance, engagement, and assessment records, which can be utilized to predict academic outcomes and identify factors affecting student success. Conventional Machine Learning (ML) models, including Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GB), Decision Tree (DT), Logistic Regression (LR), and Linear Regression (LinR), provide baseline predictive performance but often struggle to model complex nonlinear relationships, exhibit limited generalization, and lack interpretability. To address these challenges, this study proposes BARM, a hybrid framework that integrates Bidirectional Long Short-Term Memory (BiLSTM) with an Attention mechanism and rule-based models, namely the Optimal Decision Rule List Classifier (ODRLC) and Optimal Decision Rule List Regressor (ODRLR). The proposed framework simultaneously performs two classification tasks, FinalGrade and StressLevel, along with ExamScore regression through a dynamic model selection strategy that identifies the best-performing model based on evaluation metrics. A Flask-based web application enables real-time prediction, batch processing, and Exploratory Data Analysis (EDA) visualization. Experimental results demonstrate that BARM improves prediction accuracy, reliability, scalability, and interpretability, making it well suited for practical educational analytics and academic decision support.
Deep Multi Target Classification Framework for Water Quality Monitoring and Pollution Severity Level Assessment
Water quality evaluation is essential for protecting ecosystems and safeguarding human health. Conventional monitoring techniques primarily rely on manual sample collection followed by laboratory-based chemical and biological testing. Although these methods provide reliable results, they require considerable time, labor, and resources, making them unsuitable for continuous or real-time monitoring and delaying critical environmental decisions. This study introduces an intelligent and automated framework for water quality analysis and pollution prediction by leveraging advanced Machine Learning (ML) algorithms. The proposed solution is developed as a Flaskbased web application that integrates Classification and Regression Tree (CART) techniques for both pollution category identification and Water Quality Index (WQI) estimation. The implemented models include Linear Logistic Regression (LLR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), and Auto-Interpretable TaoTree (AITT). Among the classification models, AITT delivers the highest prediction accuracy for pollution level identification, while its regression variant achieves the best R² score for WQI estimation, demonstrating strong predictive capability. The application accepts 16 water quality parameters as user input and instantly generates pollution classifications along with WQI predictions. In addition, the platform provides Exploratory Data Analysis (EDA) visualizations, comparative model evaluation, and model retraining functionality, enabling environmental professionals to analyze trends and update predictive models using newly available datasets. By integrating CART-based machine learning workflows with the Flask framework, the system streamlines data preprocessing, model development, prediction, and visualization, minimizing manual intervention while improving accuracy and consistency. Built using scikit-learn, imodels, and pandas, the proposed framework offers a scalable, interpretable, and dependable approach to water quality assessment, supporting effective environmental monitoring and informed decision-making.
Secure Disaster Relief Donation Platform With Beneficiary Distribution Monitoring
Effective Disaster Relief Management (DRM) requires timely coordination, secure communication, and transparent management of donations and resource distribution among government authorities, donors, volunteers, and beneficiaries. Conventional disaster relief systems primarily depend on manual documentation, disconnected digital platforms, and informal communication channels, resulting in delayed response times, poor coordination, limited accountability, and inefficient resource utilization. The absence of centralized monitoring and secure information management often leads to duplication of efforts, inaccurate record maintenance, and difficulties in tracking donations and beneficiary requests. Additionally, inadequate authentication and security mechanisms expose sensitive disaster-related information to unauthorized access, reducing stakeholder trust and operational reliability. To address these challenges, this research introduces a secure web-based platform developed using the Flask framework. The system implements role-based access control for government authorities, collectors, and receivers, enabling secure management of disaster events, donation campaigns, inventory, aid requests, and resource distribution through a unified interface. TinyDB is utilized as a lightweight NoSQL database for storing user records, donations, campaigns, requests, and distribution details. To ensure confidentiality, the platform incorporates Vigenère cipher encryption for secure messaging and file sharing, with user-provided encryption keys protecting sensitive information during communication. Flask supports modular routing, session management, and secure authentication throughout the application. The proposed system enhances transparency, improves coordination, strengthens data security, automates disaster relief workflows, and provides a scalable digital platform that supports efficient, accountable, and wellorganized disaster response and donation management.
Secure Online Tender Document Submission With Bid Evaluation And Audit Monitoring
With the rapid growth of digital procurement platforms, organizations increasingly rely on online tendering systems to manage vendor selection, contract bidding, and document exchange. While digital tendering improves accessibility and operational efficiency, it also introduces serious security challenges related to data confidentiality, bid integrity, and unauthorized access. Sensitive bid documents, pricing information, and communication between buyers and sellers are often exposed to risks such as data leakage, tampering, and impersonation when handled through conventional web portals or email-based systems. In traditional tender management systems, bid submissions and documents are usually stored in plain format or protected only by basic login mechanisms. Such systems lack strong encryption, audit trails, and controlled access, making them vulnerable to cyberattacks, insider threats, and disputes regarding bid authenticity. The absence of traceability and real-time monitoring further reduces trust among participants and weakens the transparency of the procurement process. To overcome these limitations, this research integrates a web-based platform built using Flask with a Camellia with Advanced Encryption Standard (AES) Cryptographic framework to protect all sensitive data including bids, documents, and messages. Role-based access control (RBAC) ensures that administrators, buyers, and sellers can only perform authorized operations. Encrypted storage and secure file handling prevent unauthorized viewing or modification of tender data, an audit logging mechanism records every critical action, providing traceability and accountability throughout the tender lifecycle. The significance of this system lies in its ability to combine security, transparency, and usability within a single platform. By ensuring encrypted data handling, controlled access, and comprehensive audit monitoring, the proposed system enhances trust between all stakeholders and provides a reliable foundation for modern digital procurement and e-tendering environments.
Smart Municipality Workforce – Response System
Running a municipality on paper registers and phone-call complaints sounds old-fashioned, but it is still how a lot of local bodies work, and it causes real problems: workers mark attendance for each other, complaints get lost between departments, and nobody can really say whether a reported issue was fixed or just forgotten. In this paper we describe a Smart Municipal Workforce and Public Service Management System that we built to close these gaps in one place instead of patching them one at a time. The platform verifies worker attendance using a live face match against a registered profile combined with the device's location, lets citizens file complaints that are automatically routed to the least-busy supervisor, and turns those complaints into worker tasks that cannot be closed without a beforeand-after photo. It also allows citizens or staff to report surplus food which can then be collected by NGOs, and gives administrators a single dashboard to see attendance, tasks, complaints and food collection activity together. We implemented it in Flask, SQLAlchemy, OpenCV and ONNX Runtime on a SQLite backend and tested it with five different user roles. The results show that once attendance, complaints, and tasks are linked with one workflow it is much easier to trace a citizen’s complaint all the way through to a verified resolution, something that the single-purpose systems we studied were not really designed to do. Index Terms - Attendance verification, complaint-totask workflow, face embedding, municipal governance, role-based access control
UNIFIED FINE-GRAINED ACCESS CONTROL FOR PERSONAL HEALTH RECORDS IN CLOUD COMPUTING
Attribute-based encryption has been a promising encryption technology to secure personal health records (PHRs) sharing in cloud computing. PHRs consist of the patient data often collected from various sources including hospitals and general practice centres. Different patients' access policies have a common access sub-policy. In this paper, we propose a novel attribute-based encryption scheme for finegrained and flexible access control to PHRs data in cloud computing. The scheme generates shared information by the common access subpolicy, which is based on different patients' access policies. Then, the scheme combines the encryption of PHRs from different patients. Therefore, both time consumption of encryption and decryption can be reduced. Medical staff require varying levels of access to PHRs. The proposed scheme can also support multi-privilege access control so that medical staff can access the required level of information while maximizing patient privacy. Through implementation and simulation, we demonstrate that the proposed scheme is efficient in terms of time. Moreover, we prove the security of the proposed scheme based on security of the ciphertext-policy attribute-based encryption scheme.
THALLAPALLI ROHITH VARMA, Mrs. G. PRIYANKA
ADVANCED HARMONIC MITIGATION IN GRID-INTEGRATED SOLAR PV SYSTEMS USING FRACTIONAL ORDER NOTCH FILTER AND FLC
The increasing penetration of grid-connected solar photovoltaic (PV) systems has introduced several power quality challenges in modern electrical power systems, including harmonic distortion, voltage fluctuations, reactive power imbalance, and poor power factor. Conventional notch filters and classical control techniques often exhibit limitations in dynamic performance and disturbance rejection under varying operating conditions. This paper proposes a Fractional Order Notch Filter (FONF) integrated with a Fuzzy Logic Controller (FLC) for enhancing the power quality performance of grid-connected solar PV systems. The proposed controller employs fractional calculus-based filtering to achieve superior harmonic attenuation and frequency selectivity, while the fuzzy logic controller provides adaptive control under uncertain environmental and load conditions. The grid-connected PV system consists of a photovoltaic array, DC-DC boost converter with maximum power point tracking (MPPT), voltage source inverter (VSI), and the proposed FONF-FLC control scheme. The performance of the proposed system is evaluated under various operating scenarios including irradiance variations, nonlinear loads, and grid disturbances. Simulation results demonstrate significant improvements in total harmonic distortion (THD), power factor correction, reactive power compensation, and grid current quality compared to conventional integer-order notch filter approaches. The proposed methodology satisfies IEEE-519 harmonic standards and provides enhanced stability and robustness for renewable energy integration.
K.E Ch Vidhya Sagar, Chennaiahgari Sai Surender Goud
ASSESSMENT OF ELECTRIC VEHICLE CHARGING STATION EFFECTS ON DISTRIBUTION SYSTEM POWER QUALITY
The rapid adoption of electric vehicles (EVs) has significantly increased the demand for charging infrastructure worldwide. Although Electric Vehicle Charging Stations (EVCS) contribute to environmental sustainability and reduced greenhouse gas emissions, their largescale integration into power distribution networks introduces several power quality challenges. This paper presents a comprehensive analysis of the impact of EV charging stations on power quality parameters such as voltage fluctuations, harmonic distortion, power factor deterioration, voltage sag, voltage swell, and system imbalance. Different types of EV chargers, including slow charging, fast charging, and ultra-fast charging systems, are analyzed to assess their effects on electrical distribution systems. Simulation studies are carried out under various loading conditions to evaluate Total Harmonic Distortion (THD), voltage regulation, and reactive power requirements. The study further investigates mitigation techniques such as active power filters, smart charging strategies, renewable energy integration, and energy storage systems to improve overall power quality performance. The results demonstrate that proper control and management strategies can significantly reduce the adverse effects of EV charging stations on modern power systems.
Optimal Dispatch Control of Hybrid AC Microgrid Using Reinforcement Learning over rule-based approach for Enhanced Renewable Energy Utilization
The current power systems need intelligent control methods which help them operate their microgrids safely and efficiently with minimal need for traditional diesel generators. This research paper describes the development and execution of an intelligent energy management system (EMS) which operates a hybrid AC microgrid that combines solar photovoltaic (PV) and wind energy conversion system (WECS) and battery energy storage system (BESS) and diesel generator. The primary objective of the proposed system is to reduce diesel generator operating hours by prioritizing renewable energy utilization and optimal battery scheduling while maintaining continuous load supply. A reinforcement learning (RL)–based supervisory control strategy is developed and integrated into the microgrid framework using MATLAB/Simulink. The RL controller establishes optimal operational procedures which include battery charging and discharging and renewable energy dispatch prioritization and DG activation based on system conditions which include load demand and renewable power availability and battery state of charge (SOC). The proposed controller is compared with a conventional rule-based energy management strategy to evaluate improvements in system performance. The simulation results show that the RL-based EMS system performs better than the rule-based system because it increases renewable energy usage while decreasing diesel generator usage during testing with different irradiance and wind speed and load demand conditions. The results also confirm enhanced battery utilization efficiency and stable power balance across the common AC bus while ensuring uninterrupted load supply throughout the operating period. The intelligent control system design establishes an effective system for control operations.
ELECTRIC VEHICLE-TO-VEHICLE (V2V) ENERGY TRANSFER USING MOTOR INVERTER AND WINDING
Among the most effective methods for lowering automobile emissions is the electrification of automobiles. People are hesitant to promote electric cars (EVs) due to concerns about their range. By addressing the "last mile" issue, energy transfer between EVs helps reduce range anxiety in an emergency. Nevertheless, optimising efficiency is not taken into account by the current vehicleto-vehicle (V2V) energy-sharing technique, which relies on motor inverters and motor windings. A novel V2V architecture is suggested to solve this problem by utilising a control mechanism involving soft-switching and a decreased switching frequency. When transferring power from a voltage source to a motor with an inductor winding and core made of silicon steel, a reduced switching frequency can significantly cut down on core loss. Simulation results at 150 kW and laboratory scale-down power tests at 1.5 kW prove the efficacy of the suggested strategy. This research presents a novel vehicle-to-vehicle (V2V) energy transfer technique that utilises the inverters and windings of two electric vehicles to address these issues. The suggested technique makes advantage of three-phase interleaved control, which enables the effective and smooth sharing of power between two vehicles. The control system modifies the duty cycle and switching frequency in response to the needed power level and the voltages of the batteries. The system becomes adaptable and can handle various charging situations because of this. The concept was validated through the use of 150 kW simulation testing and 1.5 kW laboratory studies. The outcomes demonstrate that the system is functioning as intended, achieving extremely high efficiency while maintaining a motor torque close to zero. About 99.47% efficiency was the highest value measured. In conclusion, this study lays forth a straightforward, inexpensive, and very effective method of V2V charging that makes use of already electric vehicle parts. Reduced energy loss and improved reliability are the results of the suggested method's use of soft-switching control and low switching frequency operation. This method can alleviate concerns about EV range and make them more useful in times of urgency when charging is not an option.
Performance Comparison of Conventional VSI and H5 Transformerless Inverters in a Utility-Scale Grid-Connected Solar PV System
Transformerless grid connected photovoltaic (PV) inverters are very popular because they are highly efficient, they occupy less space and they are relatively cheaper. Nevertheless, they have serious limitations of common-mode (CM) voltage variations, leakage current, and deterioration of power quality of the grid. In order to solve these problems, more complex inverter topologies with no transformer like the H5 inverter have been introduced. However, there is a lack of extensive comparative studies of the medium-power PV systems operating on the same operating conditions.The paper includes a comparative performance study of two inverter designs, a traditional three-phase two-level voltage source inverter (VSI) and an H5 transformerless inverter in a 175 kW grid-connected PV system. A hybrid modelling and control system is created with irradiance and temperature variations that are realistic. The inverter performances are critically checked according to switching features, common-mode voltage, ground leakage current, grid current quality, harmonic spectrum, and instantaneous active power transfer.Simulation results in time and frequency domains have shown the conventional VSI to have large changes in CM voltages and large leakage current. Conversely, the H5 topology is an excellent system to suppress leakage current through the decoupling of the PV array during the freewheeling periods and enhances the overall quality of the power. Moreover, the H5 inverter injects a sinusoidal grid current of high quality with under IEEE519 harmonic levels and performs better than the usual VSI in the medium-power transformerless PV applications.
Suresh Reddy1 , Immanuel Anupalli2 , P.Sudheer3
Model-Based vs Data-Driven Battery State Estimation in PV Systems under Different Operating Conditions
The precise determination of battery state of charge (SOC), state of health (SOH) and state of power (SOP) is required to ensure the safe and reliable operation of photovoltaic (PV)-battery energy storage systems that act under extremely dynamic charging and discharging conditions. In the given paper, a narrow comparative study of three classical estimation methods, including open-circuit-voltage (OCV)-based estimation, linear Kalman filter (KF) and extended Kalman filter (EKF) will be conducted and evaluated with the help of a common 24-hour ultra-challenging PV and load current profile as the reflection of a realistic microgrid functioning. The profile consists of high-frequency bidirectional variations on current, half-cycle reversible charge, constant power discharge substrate, and effects of temperature variation.All estimators are checked by means of a high-fidelity electro-thermal battery model which includes capacity degradation and inner resistance increasing with aging and temperature variations. The accuracy of the SOC tracking, SOH estimation error, and SOP prediction potentialities of every method are evaluated.The findings indicate that the OCV-based approach has serious limitations when subjected to dynamic loading, which results in almost linear SOC estimates and excessively conservative SOP predictions because of the effect of polarization of voltages. The KF enhances the performance of SOC tracking over that of OCV estimation but has observable deviations at elevated SOC and towards endof-discharge due to the linearization assumptions of the model. The EKF also improves the accuracy of the estimation by considering the battery nonlinearity which results in better SOC tracking and enhanced SOP prediction at the cost of higher estimation sensitivity to fast changing parameters.The paper emphasizes that though the OCV tools are only applicable when the system is in a steady-state, model-based filtering techniques, especially the EKF, offer reliable real-time predictive estimation of a PV-battery system under extreme operating conditions, and therefore are better placed when it comes to the practical use of energy management.
P.Srikanth1 , Immanuel Anupalli2 , P.Sudheer3
A Hybrid RIME Optimization Framework for Enhanced Feature Selection in Photovoltaic MPPT
Maximum power point tracking (MPPT) is an essential box of the photovoltaic (PV) systems. The most common maximum power MPPT methods do not work well in a highly varied and dynamic situation, and phenomenologies of multi-peaks is observed in the curve of the output characteristic of the solar system due to the dissimilarities of temperature and light concentration. The paper suggests a combination of RIME optimization method that improves exploratory nature of the method in the initialisation stage, which involves combining tent mapping. It is aimed at enhancing the feature selection activities and MPPT of PV systems under the partial shading condition. It employs the piecewise mapping to optimize parameters in the algorithm and assault a reasonable stability between global exploration and local exploitation. The search strategy is dynamically modified and an adaptive inertia weight is added which further enhances the speed of convergence, effectiveness of the search as well as flexibility of the algorithm. To help decrease the cost of computations and maximize classification probability, the hybrid approach uses natural-inspired metaheuristics when performing feature selection which produce optimal subsets. In terms of monitoring speed, accuracy and stability in the PV MPPT environment, the technique is superior to PSO-BOA, traditional RIME, IRIME and EHRIME technique.
K Surya Teja1 , Immanuel Anupalli2 , P.Sudheer3
Intelligent Fault Diagnosis of Solar PV Systems Using Machine Learning Techniques
Solar photovoltaic (PV) systems need effective fault detection system in order to sustain effective energy generation under different environmental and operating conditions. The proposed study is a data-driven study on fault categorization using machine learning methodologies of detecting faults in PV systems under nonlinear and noisy conditions. Electrical data of PV panels such as current voltage (I V ) and power voltage (P V ) of the panels were measured in three operating conditions: Healthy, Shading, and Open-Circuit. Important electrical characteristics that included open-circuit voltage, shortcircuit current, maximum power point voltage and current, fill factor, and some statistical indicators were determined to describe the behavior of the system.Naive Bayes and k-Nearest Neighbors (KNN) are two supervised learning algorithms that were used to identify faults. All of these models were optimized with the help of hyperparameter tuning and k-fold cross-validation to provide accurate performance. Comparative analysis revealed that the KNN classifier had greater detection capacity along with an accuracy of 95.47, whereas the Naive Bayes classifier had stable and consistent performance and an accuracy of 94.13, which proved to be robust in the nonlinearity and noise in measurements.On the whole, the findings suggest that Naïve Bayes and KNN models can be utilized in real-time detection of PV faults with a help of the easily measurable electrical parameters. The suggested solution is applicable in the practical monitoring of PV systems and it can endorses proactive maintenance measures, enhance reliability in the systems, and minimize energy waste.
G Venkata Suresh Reddy1 , Immanuel Anupalli2 , Dr.P.Sudheer3
A NOVEL MODULAR MULTIPORT BIDIRECTIONAL BUCKBOOST DC–DC CONVERTER FOR HYBRID ELECTRIC VEHICLE APPLICATIONS
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 stateof-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 power-sharing 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.
1Mr.K. GIRINATH BABU, 2DR.Y.V. BALARAMA KRISHNA RAO, 3VULLI ABHINAYA, 4KOULAS VIVEK CHANDRA, 5KUKUDALA BHARATH SIMHA REDDY
A HIGH-GAIN FIVE-LEVEL INVERTER TOPOLOGY FOR PHOTOVOLTAIC APPLICATIONS UNDER WIDE INPUT VOLTAGE VARIATIONS
Photovoltaic (PV) energy systems are increasingly being integrated into modern power grids due to their environmental benefits and sustainable energy generation capabilities. However, the intermittent nature of solar irradiation causes significant variations in PV output voltage, which adversely affects the performance of conventional inverter systems. This paper presents a novel high-boost five-level inverter topology designed to operate efficiently under a wide range of input voltage variations. The proposed inverter integrates a high-gain DC–DC boosting stage with a five-level multilevel inverter structure, reducing the requirement for bulky transformers and improving overall power conversion efficiency. An Artificial Neural Network (ANN)-based control strategy is employed to regulate the voltage gain and maintain stable output voltage under varying solar conditions. The multilevel output waveform significantly reduces harmonic distortion while enhancing power quality. MATLAB/Simulink simulations are carried out under different irradiance conditions to validate the effectiveness of the proposed system. The results demonstrate superior voltage boosting capability, reduced Total Harmonic Distortion (THD), improved efficiency, and enhanced dynamic response compared with conventional two-level and three-level inverter systems.
1Mr.D. NAVEEN KUMAR, 2Mrs.CH.LAKSHMI 3NEELAMOLLA MADHURI , 4KUNDELLA VIJAY KUMAR, 5THAKKALA THARUN REDDY
BIDIRECTIONAL ELECTRIC VEHICLE-TO-VEHICLE POWER TRANSFER THROUGH INTEGRATED MOTOR DRIVE SYSTEM
Electric vehicle-to-vehicle (V2V) charging is a recent approach for sharing energy among electric vehicles (EVs). Existing V2V approaches with an off-board powersharing interface add extra space and cost for EV users. Furthermore, V2V power transfer using on-board type-2 chargers reported in the literature is not efficient due to redundant conversion stages. This article proposes a new method for V2V power transfer by directly connecting the two EV batteries together for sharing energy through the type-2 ac charger input ports and switches. The active rectifiers of onboard type-2 chargers are not used for rectification during V2V charging, instead only a few switches are used as interfaces to connect the two EV batteries together, to avoid redundant power conversion and associated losses which effectively improve the overall V2V efficiency. The possible V2V charging scenarios of the proposed V2V approach are vali dated using a MATLAB/Simulink simulation study. Furthermore, a scaled experimental prototype is developed to validate the proposed V2V method practically.
1DR.K.S.C.G MAURYAN2 .MR. K. RAJASEKHA, 3MADHANAGULA BALRAJ, 4NANDIPATI SHIVA KUMAR, 5VALLAMULA SHIVA
SMART POWER TRACKING AND CONTROLLING SYSTEM USING GSM TECHNOLOGY
This project offers a strategy that uses GSM technology to assist consumers in effectively managing their electricity usage. Users may turn off devices or check their solution's SMS-based remote control and monitoring capabilities. Additionally, the gadget records energy consumption over time, giving customers insights into areas where they can reduce their energy and financial expenditures. The integration of GSM technology ensures the system's accessibility and usefulness. Ultimately, this approach reduces energy waste in residential and commercial environments, encourages energy conservation, and lowers electricity expenses. Smart Power Tracking and Controlling using GSM Technology enables remote monitoring and management of electrical devices. The system uses a microcontroller, GSM module, and sensors to track power usage and control devices via SMS. It provides real-time alerts for power issues, tracks usage patterns, and allows for automated control. This solution is ideal for home automation, industrial monitoring, and energy management, promoting efficient energy use and reduced wastage. The system provides real-time alerts for power outages, overloads, or unusual usage patterns, enabling prompt action. It also logs usage data for analysis and optimization. Ideal for home automation, industrial monitoring, and energy management, this solution promotes efficient energy use, reduces wastage, and supports predictive maintenance. Its scalability and customization options make it suitable for various applications, from residential to commercial settings. It integrates a microcontroller, GSM module, and sensors to track power usage, detect anomalies, and control devices via SMS. The system provides real-time alerts for power outages, overloads, or unusual usage patterns, enabling prompt action to prevent damage or reduce wastage. It also logs usage data for analysis and optimization, helping users identify areas for improvement. With features like automated control, scheduled switching, and predictive maintenance alerts, this solution is ideal for home automation, industrial monitoring, and energy management applications. Its scalability and customization options make it suitable for various settings, from residential to commercial and industrial environments, promoting efficient energy use and reduced operational costs.
Ch. Laxmi1 , Mr.D.S. Veeranna2 Paidipalli Rushmitha3 , Mudavath Sagar4 , Mohammad Amzath5