American Journal of AI Cyber Computing Management
• Editor- In-Chief: Dr. Mainso Jubarg • Title proper: American Journal of AI Cyber Computing Management (AJACCM) • Subject: AI ML, Data Science, Management, Cyber Security, Computer Science, IOT, Engineering All, Science and Technology • Publisher Name: AJACCM Publications • ISSN: 3069-0102 • Frequency: Quarterly • Language: English • Country: USA • Medium: Online • Indexed by: CROSSREF, MDPI Scilit, Google Scholar Ext • Title DOI of the Journal: http://doi.org/10.64751/issn.3069-0102
AI ENABLED GRIEVANCE REDRESSAL ANALYTICS PLATFORM
This paper Presents an AI-Enabled Grievance Redressal Analytics System Platform that automates complaint registration, classification, prioritization, and management using Artificial Intelligence (AI) and Machine Learning (ML). The proposed system is designed to improve the efficiency of traditional grievance redressal by automatically predicting the Issue Type and Priority Level from complaint descriptions submitted by citizens. Complaint text is preprocessed using Natural Language Processing (NLP) techniques and TF-IDF (Term Frequency–Inverse Document Frequency) for feature extraction. A trained Scikit-learn model classifies complaints and assigns priority levels such as High, Medium, or Low. The application is developed using Python, Flask, SQLite, HTML, CSS, and JavaScript, providing a simple and userfriendly interface for complaint submission, tracking, and administration. A centralized dashboard enables administrators to monitor complaints, update their status, and analyze grievance records. By reducing manual effort, improving classification accuracy, and ensuring faster complaint resolution, the proposed system enhances transparency, operational efficiency, accountability, and citizen satisfaction in municipal grievance management.
1Dr. G. Prasuna,2Valiveti Tejaswini, 3Pinna Triveni,4Gonaboyina Manikanta
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
ONLINE TESTING AND MONITORING QUALITY MEDICINES AND CONSUMBALES
The Online Testing and Monitoring of Quality Medicines and Consumables system is designed to help identify whether a medicine or consumable product is safe and suitable for use by using machine learning techniques. The system allows users to enter the details of a medicine, such as its composition, manufacturing information, and other quality-related parameters. Based on a trained dataset, the machine learning model predicts whether the medicine is consumable or nonconsumable. the application provides an easy-to-use online platform where users can quickly verify the quality status of medicines before use. It also helps reduce the risk of consuming low-quality or unsafe medicines by providing fast and accurate predictions. The system improves transparency, supports quality monitoring, and assists users in making informed decisions. This project demonstrates how machine learning and web technologies can be integrated to enhance medicine quality assessment and contribute to public health and patient safety.
1 Dr. Y. Chitti Babu,2 Pittu Krishaveni,3Somisetty Kalyani,4Syed Nagurvali
SENTIMENT ANALYSIS OF SOCIAL MEDIA PRESENCE
This paper presents a Sentiment Analysis of Social Media Presence system using Machine Learning (ML) and Natural Language Processing (NLP). The main objective of this work is to identify whether social media posts or comments express positive, negative, or neutral sentiments. With the rapid growth of social media platforms, people share a large number of opinions, reviews, and experiences every day. Analyzing this information manually is difficult, timeconsuming, and may produce inaccurate results. To overcome this problem, the proposed system automatically analyzes text using Machine Learning techniques. The input text is first cleaned and preprocessed using the NLTK library by removing unwanted words and symbols. Next, the TF-IDF Vectorizer converts the processed text into numerical features. The Logistic Regression algorithm is then used to classify the sentiment of the given text as positive, negative, or neutral. The system is developed using the Flask framework, which provides a simple and user-friendly interface and displays the predicted sentiment along with its confidence score. The proposed system provides a fast, accurate, and efficient solution for sentiment analysis. It can be applied in customer feedback analysis, product review analysis, brand reputation monitoring, and market research to help organizations make better decisions based on public opinion.
1Dr. Ratna Raju Mukiri,2Thuraka. Alekhya,3Seelam. Sirisha,4Sanaka. Naga Mounika
An Intelligent Learning Path Dashboard for Enhancing Skills Through Personalized Learning
This project presents SkillAura, an intelligent and gamified Learning Path Dashboard designed to enhance the technical and professional skills of students, graduates, and working professionals. The system addresses the common problem of unstructured online learning, where learners fall into “tutorial hell” due to the absence of a clear, sequential learning roadmap. SkillAura organizes technical domains into structured learning paths consisting of modules, quizzes, and practical projects, allowing users to track completion, earn experience points, and monitor their overall skill development through an interactive dashboard. The platform is built using a full-stack architecture with React.js for the frontend, Flask for the backend REST APIs, MongoDB Atlas as the cloud database, Tailwind CSS for styling, and JWT-based authentication for secure access. An Admin Command Center is also provided, functioning as a complete content management system for managing users, learning paths, quizzes, projects, and platform analytics. Traditional learning platforms rely on scattered courses and manual tracking, resulting in reduced motivation and inconsistent progress. SkillAura overcomes these limitations by combining personalized roadmaps, gamification, and progress analytics into a single, centralized, and learner-centric platform.
1 P. Sindhura, 2 P. Bala Subrahmanyam, 3 P. Ravi Sai Kumar, 4 Y. Manoj Kumar Reddy
AUTOMATED STUDENT ATTENDANCE MONITORING AND ANALYTICS SYSTEM FOR COLLEGES
This paper presents an Automated Student Attendance Monitoring and Analytics System for Colleges that integrates Artificial Intelligence based face recognition with modern web technologies to provide a centralized, web-based platform for recording, monitoring, and analyzing student attendance. The system enables faculty to mark attendance automatically through real-time face detection and recognition, while students and administrators access attendance history, subjectwise percentages, and institutional analytics through role-based dashboards. The platform further integrates classroom management, exam-eligibility verification, online proctoring, and automated Excel/PDF report generation within a single application built using Flask (Python), HTML/CSS/JavaScript, SQLite, and OpenCV-based ONNX recognition models. The proposed system addresses the limitations of traditional manual, register-based attendance methods by eliminating proxy attendance, reducing administrative workload, and enabling real-time, data-driven academic decision-making.in this many uses there with the project developed by the computer vision and more security options.
1Dr. M. Ratna Raju Mukiri, 2Mallavarapu Chamundeswari, 3Mohammad Thanveer Raza, 4Kaki Rajesh
AI Based Real-Time Crop Image Analytics for Crop Insurance-PMFBY
This project presents an AIbased real-time crop image analytics system designed to support crop insurance under the Pradhan Mantri Fasal Bima Yojana (PMFBY). The system enhances crop assessment by automatically analyzing images to detect crop health, diseases, and damage using artificial intelligence, computer vision, and deep learning techniques. Users can upload crop images through a web-based interface, and the system provides instant and accurate results, enabling quick evaluation of crop conditions. Traditional crop assessment methodsrely on manual inspection, which is time-consuming, labor-intensive, and prone to human errors, often leading to delays in insurance claim settlements and lack of transparency. These limitations highlight the need for an automated, efficient, and reliable solution that can perform real-time analysis with minimal human intervention.The proposed system addresses this need by integrating technologies such as Python, Flask, OpenCV, and deep learning models to deliver fast and consistent outcomes. It helps farmers, insurance agents, and authorities make better decisions by providing data-driven insights. By reducing human effort, minimizing errors, and accelerating the verification process, the system improves the overall efficiency and reliability of crop insurance assessment under PMFBY, making it a scalable
1Mr. N. Lakshmi Narayana, 2Masanam Vinay, 3Nuthalapati Sri Nandini, 4Kattupalli Naveen Kumar
Computer Vision-Runtime Remote Video Quality Assessment
This project presents a Runtime Remote Video Quality Assessment system based on Computer Vision techniques. The system evaluates the visual quality of uploaded videos by analyzing important quality parameters such as brightness, contrast, and blur. It provides a simple web-based platform that enables users to register, log in, upload videos, and receive instant quality assessment results. The proposed system ensures efficient and automated video quality evaluation, reducing the need for manual inspection. Traditional video quality assessment methods often rely on reference videos, specialized software, or human evaluation, making the rmtime-consuming, for realtime applications. These methods also lack a simple and accessible platform for remote video quality monitoring. There is a need for a lightweight, cost-effective, and automated video quality assessment system that can accurately evaluate video quality and provide immediate feedback to users. The proposed system addresses these challenges by integrating Computer Vision and Image Processing techniques to deliver fast, reliable, and user-friendly video quality analysis.
1Dr. P. V. S. Sarma, 2Karreddula Malleswari, 3 Jampani RamCharan, 4Shaik Hamiya
AI-Based Crop Recommendation for Farmers
Agriculture is one of the most important sectors that supports the economy and provides food for the growing population. However, many farmers face difficulties in selecting the right crop because soil conditions and weather factors vary from place to place. Choosing an unsuitable crop can reduce productivity and increase financial loss. This project presents an AIBased Crop Recommendation System for Farmers that uses Artificial Intelligence (AI) and Machine Learning (ML) to recommend the most suitable crop based on soil and environmental conditions. The system analyzes important factors such as nitrogen (N), phosphorus (P), potassium (K), temperature, humidity, soil pH, and rainfall to predict the best crop. Different machine learning algorithms, including Random Forest, Decision Tree, and KNearest Neighbors (KNN), are trained and compared to select the most accurate model. The selected model is integrated into a simple web application developed using Python and Flask, allowing users to enter input values and receive instant crop recommendations. The proposed system helps farmers make informed decisions, improve agricultural productivity, reduce the risk of incorrect crop selection, and encourage sustainable farming through accurate, reliable, and data-driven recommendations.
1Dr. A. Tirupathaiah, 2G. Ramanjana Devi,3D. Eswar Lakshmi Tirumala Sai, 4G. Murali Mohan
AI-DRIVEN SMART PARKING AVAILABILITY DETECTION PLATFORM
The AI-Driven Smart Parking Availability Detection Platform is designed to help drivers quickly identify available parking spaces using artificial intelligence and machine learning techniques. The system analyzes parking area data to determine whether parking slots are available or occupied, providing users with real-time parking availability information through a user-friendly web platform. Users can search for nearby parking locations, check the current status of parking spaces, and view availability before reaching their destination.The platform uses an AIbased detection model to monitor parking occupancy and improve the accuracy of parking space identification. By providing timely and reliable parking information, the system reduces the time spent searching for parking, minimizes traffic congestion, and decreases unnecessary fuel consumption. It also enhances user convenience by displaying parking status and allowing navigation to the selected parking location through integrated map services. The platform maintains user profiles and parking history, enabling a personalized experience while supporting efficient parking management. This project demonstrates how artificial intelligence, machine learning, and modern web technologies can be integrated to develop an intelligent parking solution.
1 Dr. A. Tirupathaiah, 2Bachula Syamala, 3Gara Leela Rajeswari, 4Ballipalli Venkata Sai Charan
Text to Video of Various PIB Press Releases Using Artificial Intelligence in English and Regional Languages
The project "Text to Video of Various PIB Press Releases Using Artificial Intelligence in English and Regional Languages" is an AI-based system that automatically converts Press Information Bureau (PIB) press releases into informative videos in both English and regional languages by integrating text, visuals, voice narration, and subtitles to make government information more engaging and accessible. Existing PIB press releases are primarily text-based, making them difficult for many users to read and understand, while manual video creation is time-consuming, costly, and requires technical expertise, resulting in delays in information dissemination. To overcome these challenges, the proposed system automates the text-to-video generation process using Artificial Intelligence by summarizing press releases, translating the content into regional languages, generating relevant visuals, producing natural voice narration, and creating synchronized videos with subtitles. The project is developed using Python and employs Natural Language Processing , Deep Learning, Text-to-Speech, Neural Machine Translation, and Generative Artificial Intelligence. It utilizes BART for text summarization, MarianMT for multilingual translation, CLIP and BLIP for image-text understanding, and Coqui TTS for speech synthesis. The proposed system enables faster dissemination of multilingual government information, improves accessibility, reduces manual effort, and enhances public awareness through accurate and engaging AI-generated videos.
1Mr. N. Lakshmi Narayana, 2G. Sai Sri Bhavya, 3Harshini Hechina, 4 Immadisetty Yugandhar Prasad
माध्यमिक स्तर के अध्ययनरत विद्यार्थियों के सामाजिक अध्ययन विषय की शैक्षिक उपलब्धि का क्षेत्र, विद्यालय के प्रकार एवं लिंग के आधार पर तुलनात्मक अध्ययन
प्रस्तुत अध्ययन का उद्देश्य माध्यमिक स्तर के विद्यार्थियों की सामाजिक अध्ययन विषय की शैक्षिक उपलब्धि का क्षेत्र (शहरी एवं ग्रामीण), विद्यालय के प्रकार (राजकीय एवं निजी) तथा लिंग (छात्र एवं छात्राएँ) के आधार पर तुलनात्मक अध्ययन करना था। प्रस्तुत अध्ययन में वर्णनात्मक सर्वेक्षण विधि का प्रयोग किया गया। अध्ययन हेतु जयपुर जिले के माध्यमिक विद्यालयों में अध्ययनरत 400 विद्यार्थियों का चयन यादृच्छिक प्रतिदर्श विधि द्वारा किया गया। इनमें 200 शहरी एवं 200 ग्रामीण क्षेत्र के विद्यार्थी सम्मिलित थे। प्रत्येक क्षेत्र से 100 राजकीय एवं 100 निजी विद्यालयों के विद्यार्थियों का चयन किया गया तथा प्रत्येक विद्यालय श्रेणी में 50 छात्र एवं 50 छात्राओं को सम्मिलित कर समान प्रतिनिधित्व सुनिश्चित किया गया। सामाजिक अध्ययन विषय की शैक्षिक उपलब्धि के मापन हेतु शोधकर्ता द्वारा निर्मित उपलब्धि परीक्षण का प्रयोग किया गया। प्राप्त आँकड़ों के विश्लेषण हेतु माध्य, मानक विचलन एवं t-परीक्षण का उपयोग किया गया। अध्ययन के निष्कर्षों से ज्ञात हुआ कि शहरी एवं ग्रामीण विद्यार्थियों की सामाजिक अध्ययन विषय की शैक्षिक उपलब्धि में सार्थक अंतर पाया गया, जिसमें शहरी विद्यार्थियों की उपलब्धि अपेक्षाकृत अधिक रही। इसी प्रकार शहरी एवं ग्रामीण दोनों क्षेत्रों में निजी विद्यालयों के विद्यार्थियों की शैक्षिक उपलब्धि राजकीय विद्यालयों की अपेक्षा अधिक पाई गई। लिंग के आधार पर किए गए विश्लेषण से यह स्पष्ट हुआ कि शहरी राजकीय, शहरी निजी तथा ग्रामीण निजी विद्यालयों में छात्राओं की शैक्षिक उपलब्धि छात्रों की अपेक्षा अधिक थी, जबकि ग्रामीण राजकीय विद्यालयों में छात्र एवं छात्राओं की शैक्षिक उपलब्धि में कोई सार्थक अंतर प्राप्त नहीं हुआ। अध्ययन से यह निष्कर्ष निकाला गया कि सामाजिक अध्ययन विषय की शैक्षिक उपलब्धि पर क्षेत्र, विद्यालय के प्रकार तथा कुछ परिस्थितियों में लिंग का महत्वपूर्ण प्रभाव परिलक्षित होता है। अतः सामाजिक अध्ययन विषय के शिक्षण को अधिक प्रभावी, गतिविधि-आधारित एवं विद्यार्थी-केंद्रित बनाया जाना चाहिए तथा विशेष रूप से ग्रामीण एवं राजकीय विद्यालयों में गुणवत्तापूर्ण शिक्षण संसाधनों एवं अधिगम अवसरों की उपलब्धता सुनिश्चित की जानी चाहिए।
सामाजिक अध्ययन, शैक्षिक उपलब्धि, माध्यमिक स्तर, राजकीय विद्यालय, निजी विद्यालय, शहरी क्षेत्र, ग्रामीण क्षेत्र, लिंग, तुलनात्मक अध्ययन।
माध्यमिक स्तर के विज्ञान विषय के विद्यार्थियों की अध्ययन आदतों पर ई-अधिगम उपागम की प्रभावशीलता का प्रयोगात्मक अध्ययन
वर्तमान डिजिटल युग में सूचना एवं संचार प्रौद्योगिकी ने शिक्षा के क्षेत्र में व्यापक परिवर्तन किए हैं। ई-अधिगम उपागम ने शिक्षण-अधिगम प्रक्रिया को अधिक प्रभावी, लचीला तथा विद्यार्थी-केंद्रित बनाया है। ई-अधिगम के माध्यम से विद्यार्थियों को स्व-गति से अध्ययन करने, डिजिटल संसाधनों का उपयोग करने, पुनरावृत्ति करने तथा विषयवस्तु को अपनी सुविधा के अनुसार समझने के अवसर प्राप्त होते हैं। इससे न केवल उनकी शैक्षिक उपलब्धि में सुधार होता है, बल्कि उनकी अध्ययन आदतों का भी सकारात्मक विकास होता है। अध्ययन आदतें विद्यार्थियों की नियमित अध्ययन प्रवृत्ति, समय प्रबंधन, नोट्स तैयार करने, पुनरावृत्ति, एकाग्रता तथा स्व-अध्ययन जैसी व्यवहारगत विशेषताओं को प्रभावित करती हैं, जो उनके समग्र शैक्षिक विकास में महत्वपूर्ण भूमिका निभाती हैं। प्रस्तुत अध्ययन का उद्देश्य माध्यमिक स्तर के विज्ञान विषय के विद्यार्थियों की अध्ययन आदतों पर ई-अधिगम उपागम की प्रभावशीलता का अध्ययन करना था। अध्ययन में प्रयोगात्मक शोध विधि का उपयोग किया गया। राजस्थान राज्य के भरतपुर जिले के माध्यमिक विद्यालयों में अध्ययनरत कक्षा 9 के 80 विद्यार्थियों का उद्देश्यपूर्ण न्यादर्शन विधि द्वारा चयन किया गया। चयनित विद्यार्थियों को समान रूप से प्रयोगात्मक एवं नियंत्रित समूहों में विभाजित किया गया। प्रयोगात्मक समूह को ई-अधिगम उपागम के माध्यम से तथा नियंत्रित समूह को पारंपरिक शिक्षण विधि द्वारा विज्ञान विषय का अध्यापन कराया गया। विद्यार्थियों की अध्ययन आदतों के मापन के लिए डॉ. बी. वी. पटेल द्वारा निर्मित अध्ययन आदत मापनी का प्रयोग किया गया। प्राप्त आँकड़ों का विश्लेषण मध्यमान, मानक विचलन तथा t-परीक्षण के माध्यम से किया गया। अध्ययन के निष्कर्षों से ज्ञात हुआ कि ई-अधिगम उपागम के माध्यम से शिक्षण प्राप्त करने वाले विद्यार्थियों की अध्ययन आदतों में पारंपरिक शिक्षण विधि से शिक्षण प्राप्त करने वाले विद्यार्थियों की अपेक्षा अधिक सकारात्मक परिवर्तन हुआ। परिणामों से यह स्पष्ट हुआ कि ई-अधिगम उपागम विद्यार्थियों में नियमित अध्ययन, समय प्रबंधन, स्व-अध्ययन, डिजिटल संसाधनों के प्रभावी उपयोग तथा पुनरावृत्ति जैसी अध्ययन आदतों के विकास में महत्वपूर्ण भूमिका निभाता है। अतः विद्यालयी विज्ञान शिक्षण में ई-अधिगम उपागम का समुचित उपयोग विद्यार्थियों की अध्ययन आदतों के विकास के लिए लाभकारी सिद्ध हो सकता है।
ई-अधिगम उपागम, अध्ययन आदतें, विज्ञान शिक्षण, माध्यमिक स्तर, प्रयोगात्मक अध्ययन, सूचना एवं संचार प्रौद्योगिकी।
AN INTELLIGENT CREDIT CARD FRAUD DETECTION SYSTEM BASED ON GEOSPATIAL FEATURES AND ENSEMBLE MACHINE LEARNING
As the global economy transitions rapidly towards digitization and cashless transactions, credit cards have become the primary medium of exchange. Consequently, the incidence of fraudulent activities has escalated exponentially, resulting in multi-billion dollar annual losses for financial institutions and consumers worldwide. Traditional static, batch-processing fraud detection systems struggle to adapt to the highly dynamic and contextdependent nature of modern transactional fraud. In this paper, we propose a comprehensive, real-time credit card fraud detection framework that integrates geospatial and occupational user behavior features with supervised machine learning models. We systematically evaluate six classification algorithms: Logistic Regression, K-Nearest Neighbors (KNN), Gaussian Naïve Bayes, Support Vector Machines (SVM), Decision Trees, and Random Forest Classifiers. The models are evaluated using actual transaction data containing geospatial coordinates and cardholder occupations. The empirical results demonstrate that the Random Forest and Decision Tree classifiers achieve a superior classification accuracy of up to 97.87%, with the Random Forest model achieving 100% recall on the test set. Furthermore, we deploy the predictive models within a real-time Django-based web application integrated with an SQLite database. This deployment enables instantaneous transaction evaluation, behavioral profiling, and administrative audit logging. The architecture successfully bridges the gap between theoretical machine learning models and practical, low-latency deployment in financial technologies.
Credit Card Fraud Detection, Machine Learning, Django Framework, Geospatial Analysis, Behavioral Profiling, Ensemble Classifiers, Real-time Web Systems.
A Vision Transformer-Based Multi-Camera Biomechanical Motion Analysis Framework for Early Sports Injury Risk Prediction in Elite Athletes
Sports injuries remain one of the most significant challenges affecting elite athletic performance, career longevity, and competitive success across professional sports. High-intensity training, repetitive biomechanical loading, improper movement mechanics, muscular imbalances, fatigue accumulation, and inadequate recovery substantially increase the probability of musculoskeletal injuries involving the anterior cruciate ligament (ACL), hamstrings, Achilles tendon, shoulder complex, and lower back. Traditional injury prevention strategies primarily depend on manual video analysis, wearable sensors, force plate assessments, and expert clinical evaluation. Although these approaches provide valuable biomechanical insights, they often require specialized laboratory equipment, extensive human expertise, time-consuming analysis, and subjective interpretation, limiting their scalability for continuous athlete monitoring during routine training sessions. Recent advances in computer vision, deep learning, and artificial intelligence provide new opportunities for automated markerless biomechanical analysis capable of continuously assessing movement quality and identifying injury risks before clinical symptoms appear. This paper proposes a Vision Transformer-Based Multi-Camera Biomechanical Motion Analysis Framework for early sports injury risk prediction in elite athletes. The proposed architecture integrates synchronized multi-camera video acquisition, markerless human pose estimation, three-dimensional skeletal reconstruction, Vision Transformer-based spatiotemporal feature extraction, biomechanical parameter analysis, temporal motion modeling, explainable artificial intelligence, and injury risk prediction into a unified intelligent framework. Multi-view synchronized cameras continuously capture athlete movements during sprinting, jumping, cutting, landing, throwing, and change-of-direction activities. Markerless pose estimation reconstructs accurate three-dimensional skeletal trajectories, while Vision Transformers learn global biomechanical relationships among body joints across temporal sequences. The framework further estimates joint angles, angular velocity, acceleration, ground contact characteristics, symmetry indices, kinetic chain coordination, and fatigue-related movement deviations to identify early biomechanical abnormalities associated with sports injuries. Experimental evaluation demonstrates that the proposed framework significantly improves injury prediction accuracy, movement classification performance, biomechanical interpretation, computational robustness, and early injury detection compared with conventional CNN-based motion analysis systems. The proposed architecture establishes an intelligent and scalable foundation for next-generation AI-assisted athlete monitoring and precision sports medicine.
N. Raghunadha Reddy,Buturi Grace Jemimah
A Neuromorphic Spiking Neural Network-Based Continual Learning Framework for Energy-Efficient Edge Intelligence in Autonomous UAV Swarm Navigation
The rapid advancement of autonomous Unmanned Aerial Vehicles (UAVs) has significantly transformed numerous civilian, industrial, and defense applications including precision agriculture, environmental monitoring, disaster management, surveillance, search and rescue operations, intelligent transportation, smart city monitoring, and military reconnaissance. Modern UAV swarm systems operate collaboratively to accomplish complex missions through distributed sensing, cooperative decision-making, adaptive path planning, and decentralized communication. Unlike single-drone systems, autonomous UAV swarms offer enhanced robustness, scalability, fault tolerance, and mission efficiency by enabling multiple UAVs to coordinate dynamically in uncertain environments. However, realizing real-time intelligence on resourceconstrained edge devices remains a significant challenge because conventional deep learning models demand substantial computational resources, memory capacity, and energy consumption, which exceed the limitations of lightweight onboard embedded processors. Furthermore, continuously changing operational environments require UAVs to learn new navigation behaviors without forgetting previously acquired knowledge, making continual learning an essential capability for long-term autonomous swarm intelligence. This paper proposes a Neuromorphic Spiking Neural Network-Based Continual Learning Framework for energy-efficient edge intelligence in autonomous UAV swarm navigation. The proposed framework integrates event-driven neuromorphic sensing, Spiking Neural Networks, Continual Learning, Graph-based Swarm Coordination, Reinforcement Learning, Neuromorphic Edge Computing, Explainable Artificial Intelligence, and adaptive knowledge consolidation into a unified autonomous navigation architecture. Event-based visual sensors continuously capture environmental changes and convert them into asynchronous spike streams processed by energy-efficient neuromorphic hardware. The continual learning module dynamically adapts navigation policies while preserving previously acquired swarm behaviors through synaptic consolidation and memory replay mechanisms. Experimental evaluation demonstrates that the proposed framework significantly improves navigation accuracy, continual learning capability, energy efficiency, obstacle avoidance performance, swarm coordination, computational scalability, and mission robustness compared with conventional deep learning-based UAV navigation systems. The proposed architecture establishes a scalable and intelligent foundation for future autonomous edge computing in collaborative UAV swarms.
Singareddy Hemasri,K.S. Raghavendra Reddy
Impact of Digital Leadership on Innovation Performance: Mediating Role of AI Capability and Moderating Role of Technological Turbulence
The changes brought about by digital transformation have shifted the landscape in which companies generate value and compete, making creativity and originality more important than ever before and making leadership skills more crucial to achieving organizational performance. This study examined how digital leadership affects innovation performance through the mediating role of AI capability and the moderating role of technological turbulence. Digital leadership is the strategic competency of leaders in digital adoption, data-driven decision-making, and the development of an innovation-oriented culture. AI capability refers to an organization's capacity to acquire, integrate, and leverage AI technologies to enhance organizational performance, while technological turbulence refers to the speed and unpredictability of technological changes within an organization's external environment. The findings revealed a significant positive impact of digital leadership on innovation performance, both directly and indirectly through AI capability. Furthermore, technological turbulence significantly moderated the relationship between digital leadership and innovation performance by strengthening this relationship under dynamic technological conditions. The study contributes to the existing body of knowledge on digital transformation and innovation by providing an integrated framework for understanding the interplay between digital leadership, AI capability, and technological turbulence in enhancing innovation performance.
VoiceSentry: Hybrid Neural Network Approach for Deep Fake Audio Recognition
The rapid rise of synthetic media has created serious concerns regarding the authenticity of audio content. Detecting deep fake audio has become increasingly important, especially in areas such as digital security and media verification. This project presents a webbased system that uses deep learning techniques to classify audio files as either real or fake. The model combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to capture both feature patterns and time-based dependencies in audio signals. The system allows users to upload datasets, process audio files, and train the model through a simple interface. During experimentation, the dataset is divided into training and testing portions to evaluate performance effectively. The trained model achieves an accuracy of around 95%, along with strong precision and recall values. Visual tools such as confusion matrix and training graphs are used to better understand the model’s behavior. In addition, users can upload new audio samples and get instant predictions. The system performs well in identifying manipulated audio, with only a small number of incorrect predictions. Overall, the project provides a practical and efficient solution for deep fake audio detection and can be useful in real-world applications. Keywords—Deep fake audio detection, audio classification, deep learning, convolutional neural network (CNN), long short-term memory (LSTM), hybrid model, audio preprocessing, feature extraction, training-testing split, confusion matrix, performance evaluation, accuracy, precision, recall, F1-score, real-time detection, web-based application, audio forensics, media verification, user interface
ParkSphere: An Advanced Digital Vehicle Parking Platform
The growing need for efficient parking management in urban areas has led to the adoption of smart, web-based solutions that automate daily operations. This project presents a parking management system developed using Python and MySQL, designed to support both administrators and users in handling parking activities more effectively. Administrators can log in to add and manage multiple parking locations, assign unique identifiers, and update slot availability and pricing details as required. On the user side, individuals can register, log in, and explore available parking areas along with real-time slot information. The booking process is straightforward, allowing users to select a location, choose a slot, and enter vehicle details. Once confirmed, the slot status changes to “Parked.” The system also records entry and exit times, which are used to automatically calculate parking charges. After completing payment, users can release the slot, making it available for others. In addition, the system maintains a record of previous bookings, including payment and status details. By combining real-time updates, automated billing, and structured management of multiple locations, the system reduces manual effort and improves overall efficiency, offering a practical solution for modern parking needs.
ActiLedger: Privacy-Enhanced Multimodal Human Activity Recognition Using Blockchain in Crowdsensing
The demand for secure and reliable data sharing has grown rapidly with the expansion of distributed systems and mobile sensing applications. This work presents a blockchain-based cloud framework that enables secure interaction between stakeholders and mobile devices. The system is designed with a simple web interface where users can register, log in, and access various features without difficulty. During registration, user information is stored in the blockchain along with transaction details such as block number, hash value, and timestamp, which helps maintain data integrity and traceability. After logging in, stakeholders can create sensing tasks using specific keywords. These tasks are recorded in the blockchain, allowing multiple mobile devices to participate and upload relevant sensed data. The uploaded data is stored securely in encrypted form within the cloud server. A keyword-based search mechanism is provided so that stakeholders can retrieve only the required data using valid inputs, while incorrect inputs return no results. Performance is analyzed using graphs that show block computation time and search efficiency with cache memory. Overall, the system offers a practical, secure, and scalable solution for decentralized data sharing.
Next-Generation Human Activity Classification Using Hybrid Models for Digital Healthcare
The need for accurate human activity recognition has increased with the rise of wearable devices and smart healthcare systems. This project focuses on developing a web-based application that can identify human activities using both machine learning and deep learning techniques. The system is designed in a simple way so that users can easily register, log in, and use different features without difficulty. After logging in, users can load the Kuhar dataset and view basic details such as total records, features, and activity labels. One important step in this work is feature selection, where Extreme Learning Machine (ELM) is used to reduce the large number of features into a smaller and more useful set. This helps in improving performance and reducing processing time. The system then applies different models along with the proposed ELM-GRU-AM model. From the results, it can be observed that the proposed model gives better accuracy compared to others, reaching around 96%. The system also shows performance using metrics like accuracy, precision, recall, and F1- score, along with confusion matrix graphs for better understanding. Users can also upload new test data and get predictions instantly. Overall, the system provides a simple and effective way to recognize human activities with good accuracy. Keywords—Human activity recognition, sensor data analysis, machine learning, deep learning, ELM-GRU-AM model, Extreme Learning Machine (ELM), feature selection, dimensionality reduction, GRU, attention mechanism, classification, Kuhar dataset, data preprocessing, training and testing, performance evaluation, accuracy, precision, recall, F1-score, confusion matrix, model comparison, real-time prediction, activity classification, web-based application, user interface
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SMART CYBERBULLYING DETECTION USING MACHINE LEARNING
Cyberbullying is a major problem on the internet that affects teenagers and adults and has led to serious consequences such as depression and suicide, making the regulation of content on social-media platforms a growing need. This study uses data from two different forms of cyberbullying—hate-speech tweets from Twitter and personal-attack comments from Wikipedia forums—to build a model for detecting cyberbullying in text data using Natural Language Processing (NLP) and machine learning. The problem is framed as a binary classification task, and three feature-extraction methods and four classifiers are studied to outline the best approach. A Support Vector Machine is used for Twitter hate speech and a Random Forest classifier for Wikipedia personal attacks. For the tweet data the model provides accuracies above 90%, and for the Wikipedia data it provides accuracies above 80%; the proposed Support Vector Machine approach reaches an accuracy of around 96% for detecting cyberbullying content, which is better than the existing systems. The system is implemented in Python with a Flask web framework. By going beyond simple pattern matching, the proposed approach provides more precise detection and can help protect users from social-media bullies and support the moderation of harmful content.
K.Govardhani Harshitha, Mehaboob Karishma
IOT CYBER ATTACK DETECTION USING MACHINE LEARNING AND DEEP LEARNING
The rapid proliferation of Internet of Things (IoT) devices has transformed various sectors, enhancing efficiency and connectivity. However, this expansion has also introduced significant vulnerabilities, making IoT systems attractive targets for cyberattacks. This paper presents a comprehensive approach to IoT cyber-attack detection, addressing the unique challenges posed by the diverse and often resource-constrained nature of IoT environments. By integrating machine learning and deep learning algorithms with anomaly detection techniques, the proposed system aims to identify and mitigate potential threats in real time, ensuring the integrity and security of IoT networks. The detection mechanism is designed to monitor network traffic and device behaviour continuously, learning normal operational patterns to effectively identify deviations indicative of cyber threats. The system was trained and evaluated on the CICAPT-IIoT dataset (over 4 GB, comprising 95% normal records and 5% attack records) using four algorithms: Random Forest, Support Vector Machine (SVM), Logistic Regression, and a 2D Convolutional Neural Network (CNN2D). Each algorithm was evaluated using accuracy, precision, recall, F-Score, confusion matrix, and AUC-ROC analysis. Results show that Logistic Regression and CNN2D achieved the highest performance, both exceeding 99% accuracy, followed by Random Forest (96%) and SVM (94%). These results confirm that machine learning and deep learning models are highly effective at distinguishing normal IoT traffic from cyber-attack traffic, enabling proactive detection, automated incident response, and an overall improvement in the security posture of IoT deployments.
CLOUD-CONNECTED DIGITAL TWIN ARCHITECTURE FOR INTELLIGENT INDUSTRIAL PROCESS MANAGEMENT
The increasing digitalization of industrial environments has accelerated the integration of Industrial Internet of Things devices, cloud computing, cyber-physical production systems, artificial intelligence, machine learning, advanced analytics, and digital twin technologies. Modern industrial processes generate continuous heterogeneous data from sensors, machine controllers, production equipment, enterprise applications, maintenance systems, quality platforms, and energy monitoring infrastructures. Conventional industrial process management systems frequently operate through fragmented data environments, isolated automation layers, static monitoring dashboards, and reactive decision procedures, limiting their ability to maintain synchronized operational intelligence across distributed production assets. Digital twins provide dynamic virtual representations of physical industrial entities, but standalone or locally constrained twins may lack scalable computation, long-term storage, fleet-level analytics, cross-site coordination, automated model lifecycle management, and enterprise integration. This paper proposes a CloudConnected Digital Twin Architecture for Intelligent Industrial Process Management that integrates physical industrial assets, heterogeneous sensing, edge preprocessing, secure communication, cloud-native data services, real-time digital twin synchronization, machine-learning analytics, process optimization, predictive maintenance, enterprise API integration, historical data modernization, model operations, sustainability-aware workload management, and closed-loop operational feedback. The proposed methodology establishes continuously updated virtual representations of industrial equipment and processes using vibration, temperature, pressure, acoustic, electrical, quality, production, maintenance, and contextual information. Edge resources perform initial validation, filtering, event detection, and latency-sensitive processing, while cloud services provide scalable historical analytics, multi-asset comparison, machine-learning model development, simulation, and long-term optimization. Digital twin state management combines current observations with operating context, maintenance history, process configuration, and predicted conditions to support intelligent process decisions. Secure API lifecycle management protects distributed service interactions, while communication integrity controls strengthen resistance to tampering and unauthorized manipulation. A comparative analytical evaluation indicates that the proposed architecture can reduce process deviation, improve anomaly detection, decrease decision latency, lower unplanned downtime, improve equipment availability, reduce unnecessary cloud data transmission, and increase energy efficiency compared with conventional monitoring and isolated digital twin architectures. The findings demonstrate that cloud-connected digital twins provide a scalable foundation for intelligent, secure, adaptive, and sustainable industrial process management.
DATA-DRIVEN DIGITAL TWIN FRAMEWORK FOR PREDICTIVE QUALITY CONTROL IN SMART MANUFACTURING SYSTEMS
The transformation of conventional production environments into interconnected smart manufacturing systems has created significant opportunities for predictive quality control through the integration of Industrial Internet of Things sensing, digital twin technology, machine learning, cloud-edge computing, and data-driven decision support. Traditional quality inspection frequently depends on end-of-line testing, periodic sampling, fixed process limits, and manual intervention, which can identify defects only after material, machine time, and production resources have already been consumed. This paper proposes a data-driven digital twin framework for predictive quality control in smart manufacturing systems. The proposed framework establishes a continuously synchronized relationship between physical production assets and their virtual representations by integrating real-time sensor measurements, machine operating conditions, process parameters, product-quality observations, maintenance history, and machine learning predictions. Heterogeneous data involving vibration, temperature, acoustic behavior, electrical current, spindle speed, feed rate, pressure, energy consumption, dimensional measurements, surface characteristics, and environmental conditions are acquired and transformed through a systematic pipeline containing edge validation, preprocessing, synchronization, feature extraction, multi-source data fusion, digital twin updating, predictive modeling, anomaly detection, and quality-risk assessment. Multiple machine learning models, including Random Forest, Support Vector Machine, Gradient Boosting, Artificial Neural Network, and Long Short-Term Memory networks, are incorporated to predict defect probability, identify process instability, classify product-quality states, and detect progressive deviations before final inspection. The architecture further integrates secure application programming interfaces, cloud-native analytical services, enterprise data modernization, energyaware workload scheduling, and model lifecycle management. A representative prototypeoriented evaluation indicates that the proposed framework can improve early defect detection, reduce false quality alarms, increase process visibility, lower scrap and rework, and support proactive adjustment compared with conventional threshold-based inspection. The study demonstrates that predictive quality control requires more than an isolated machine learning model; it requires continuous synchronization among physical production systems, digital twins, governed data pipelines, predictive analytics, and operational feedback. The proposed framework provides a scalable foundation for intelligent, adaptive, secure, and data-driven quality management in smart manufacturing
IOT-INTEGRATED DIGITAL TWINS FOR REAL-TIME MANUFACTURING PROCESS OPTIMIZATION AND FAULT DETECTION
The increasing complexity of modern manufacturing systems has created a strong requirement for intelligent architectures capable of continuously monitoring physical equipment, detecting emerging faults, predicting operational deviations, and optimizing production processes in real time. Conventional manufacturing monitoring systems frequently depend on isolated sensors, static process models, fixed alarm thresholds, periodic inspections, and fragmented analytical applications. Such approaches provide limited capability for interpreting dynamic interactions among machinery condition, process parameters, product quality, energy consumption, tool degradation, and production performance. This paper proposes an Internet of Things-integrated Digital Twin framework for real-time manufacturing process optimization and fault detection. The proposed methodology combines industrial sensing, edge intelligence, multisensor data fusion, secure IoT communication, continuously synchronized Digital Twins, predictive analytics, machine-learning-based fault detection, process optimization, API-driven interoperability, MLOps lifecycle management, and closed-loop decision support within a unified cyber-physical manufacturing environment. Real-time observations involving vibration, temperature, acoustic behavior, motor current, spindle speed, process force, energy consumption, tool condition, product quality, and machine availability are collected from physical manufacturing assets. Edge services perform data validation, filtering, event detection, and feature extraction before relevant information is transmitted to Digital Twin services. The virtual representations maintain continuously evolving machinery and process states and compare observed behavior with historical and expected operational patterns. Multi-sensor fusion improves fault identification by combining complementary evidence from mechanical, thermal, electrical, acoustic, and process sources. Predictive analytics estimates emerging degradation and quality risks, while the process optimization module evaluates feasible operational adjustments according to productivity, quality, reliability, energy efficiency, and equipment health. Secure API lifecycle mechanisms enable interoperability among machines, gateways, Digital Twin platforms, analytics services, maintenance applications, and enterprise systems. A representative evaluation demonstrates improvements in fault detection accuracy, anomaly response latency, process efficiency, defect reduction, predictive maintenance effectiveness, energy utilization, Digital Twin synchronization, and machinery availability compared with conventional threshold-based monitoring and isolated IoT analytics. The results indicate that IoT-integrated Digital Twins provide a scalable foundation for adaptive, resilient, secure, and sustainable manufacturing optimization.
CYBER-PHYSICAL DIGITAL TWIN ARCHITECTURE FOR ADAPTIVE PRODUCTION CONTROL IN INTELLIGENT FACTORIES
The rapid evolution of intelligent factories has created a strong requirement for production control architectures capable of responding dynamically to machine degradation, process disturbances, quality variation, changing workloads, material inconsistency, energy constraints, and unexpected equipment events. Conventional production control systems frequently depend on static schedules, isolated automation logic, predefined thresholds, and fragmented information flows that provide limited adaptability under continuously changing industrial conditions. Cyber-physical systems and digital twin technologies provide an opportunity to establish persistent interaction between physical manufacturing assets and their virtual representations, enabling real-time monitoring, predictive assessment, simulationassisted decision-making, and adaptive production coordination. This paper proposes a cyber-physical digital twin architecture for adaptive production control in intelligent factories. The proposed methodology integrates heterogeneous industrial assets, Industrial Internet of Things sensors, programmable controllers, edge gateways, secure communication services, standardized APIs, synchronized digital twin models, machine learning analytics, predictive maintenance intelligence, adaptive production decision mechanisms, and governed control feedback. The architecture continuously acquires vibration, temperature, machine state, tool condition, production rate, quality indicators, energy consumption, and operational context from physical manufacturing systems. Edge services validate and preprocess incoming observations before synchronizing corresponding digital twin states. Analytical services identify degradation patterns, predict equipment risk, evaluate process performance, and assess alternative production responses. An adaptive decision layer combines digital twin state, asset criticality, production priorities, machine availability, quality risk, and operational constraints before generating recommendations or authorized supervisory control actions. Secure API lifecycle management protects service communication, while production-oriented model operations support controlled deployment and monitoring of analytical models. Representative experimental analysis indicates that the proposed architecture can reduce production response latency, decrease unplanned downtime, improve throughput, increase schedule adherence, reduce defect rates, and strengthen digital twin synchronization compared with conventional static production control. The proposed architecture provides a scalable foundation for resilient, adaptive, secure, and intelligence-driven production management in next-generation smart factories.
DIGITAL TWIN-ENABLED SMART MANUFACTURING FRAMEWORK FOR REAL-TIME PROCESS MONITORING AND OPTIMIZATION
The rapid advancement of Industry 4.0 has transformed manufacturing environments into highly connected, automated, and data-intensive production ecosystems in which physical machinery, Industrial Internet of Things devices, cyber-physical systems, cloud platforms, artificial intelligence models, and enterprise applications continuously interact. Despite these developments, many industrial facilities continue to experience limitations associated with fragmented process visibility, delayed fault detection, inefficient parameter adjustment, unplanned equipment downtime, quality deviations, excessive energy consumption, and disconnected decision-making. Conventional monitoring systems primarily represent current or historical process states and frequently lack the capability to maintain continuously synchronized digital representations of physical production assets. This paper proposes a Digital Twin-Enabled Smart Manufacturing Framework for Real-Time Process Monitoring and Optimization. The framework integrates heterogeneous Industrial IoT sensing, edge computing, real-time data validation, multisource sensor fusion, digital twin synchronization, process state modeling, machine learning-based predictive analytics, anomaly detection, predictive maintenance, adaptive process optimization, secure API communication, enterprise data integration, cloud-native deployment, and continuous operational feedback. The proposed digital twin maintains a dynamic representation of machinery, production processes, equipment health, operating parameters, quality indicators, and resource conditions. Real-time observations involving vibration, temperature, pressure, current, voltage, speed, tool condition, production cycle information, and environmental variables are continuously synchronized with the digital environment. Predictive analytics identifies developing faults and quality risks, while the optimization layer recommends or applies controlled adjustments to process parameters according to production objectives and safety constraints. The conceptual evaluation compares the proposed framework with a conventional monitoring architecture and indicates improvements in process visibility, anomaly detection accuracy, early warning time, equipment availability, defect reduction, downtime control, decision latency, and resource utilization. The framework further incorporates secure service integration, production-oriented model lifecycle management, sustainable orchestration, and controlled modernization of legacy enterprise information. The study demonstrates that digital twins can evolve beyond visualization tools into continuously synchronized decision-support systems capable of connecting physical manufacturing operations with predictive intelligence and adaptive optimization.
ELEVATING PHISHING DETECTION PERFORMANCE WITH MACHINE LEARNING AND DEEP LEARNING-ENABLED FEATURE SELECTION
Phishing remains one of the most persistent and rapidly evolving cybersecurity threats, exploiting deceptive websites, malicious URLs, fraudulent messages, compromised domains, and social-engineering strategies to obtain sensitive information such as usernames, passwords, financial credentials, personal records, and authentication tokens. Conventional phishing detection mechanisms based on blacklists, manually defined rules, static signatures, and heuristic filters provide useful protection against previously identified attacks but often exhibit limited effectiveness against zero-day phishing websites, short-lived malicious domains, obfuscated URLs, and dynamically changing attack patterns. Furthermore, machine learning-based phishing detection models frequently process large and redundant feature spaces containing irrelevant, correlated, or noisy attributes, which may increase computational overhead and reduce generalization capability. This research proposes an intelligent phishing detection framework that integrates machine learning, deep learning-enabled feature selection, multi-source phishing feature extraction, hybrid classification, and real-time risk assessment. The proposed framework extracts URL lexical characteristics, domain and host-based properties, webpage content indicators, Hypertext Markup Language and JavaScript features, security certificate attributes, redirection behavior, and contextual metadata. A deep learningenabled feature selection module employs representation learning and importance estimation to identify the most discriminative phishing indicators while eliminating redundant and low-contribution attributes. The selected feature subset is subsequently evaluated using machine learning classifiers such as Random Forest, Support Vector Machine, XGBoost, and Logistic Regression, together with deep learning architectures including Multilayer Perceptron, Convolutional Neural Network, and Long Short-Term Memory networks. A hybrid decision engine combines model confidence, anomaly indicators, and contextual risk information to classify web resources as legitimate, suspicious, or phishing. The proposed architecture consists of five interconnected layers: Data Acquisition, Preprocessing and Feature Engineering, Deep Learning-Enabled Feature Selection and Intelligent Detection, Risk Assessment and Response, and Application/User layers. Illustrative conceptual evaluation demonstrates that the proposed hybrid framework can achieve higher detection accuracy, precision, recall, F1-score, and lower response latency than blacklist-based, conventional machine learning, and standalone deep learning approaches. The framework provides a scalable foundation for intelligent phishing protection across browsers, email gateways, enterprise networks, financial platforms, educational environments, and cloud-based security services.
IOT BASED GARBAGE LEVEL MONITOR FOR DUSTBINS
The rapid growth of urban populations, commercial activities, residential communities, educational institutions, healthcare facilities, and public infrastructure has significantly increased the quantity of solid waste generated every day. Conventional waste collection systems primarily depend on fixed collection schedules, manual inspection, and periodic transportation of garbage from dustbins to disposal or processing facilities. Such approaches frequently result in overflowing dustbins, unnecessary collection trips, unpleasant odors, environmental pollution, increased fuel consumption, inefficient utilization of municipal resources, and poor public hygiene. This research proposes an IoT Based Garbage Level Monitor for Dustbins that continuously measures the amount of waste accumulated inside a dustbin and transmits real-time status information to authorized users or waste-management personnel. The proposed system employs an ultrasonic sensor mounted near the upper section of the dustbin to measure the distance between the sensor and the surface of accumulated waste. An IoT-enabled microcontroller such as the ESP32 acquires the sensor readings, performs filtering and calibration, calculates the approximate fill percentage, and classifies the dustbin condition into predefined categories such as Empty, Low, Medium, High, and Full. When the garbage level exceeds a configurable threshold, the system automatically generates an alert and transmits the information through Wi-Fi using MQTT or HTTP(S) communication protocols. A cloud-supported monitoring platform stores timestamped garbage-level information, maintains historical records, manages alert priorities, and provides centralized visualization through mobile or web dashboards. The proposed architecture consists of five major layers: Sensor Data Acquisition, Edge Processing and Fill-Level Classification, IoT Communication, Cloud and Waste Analytics, and Application/User layers. The framework further incorporates duplicate-alert suppression, communication retry mechanisms, device identification, battery or connectivity status monitoring, and configurable collection thresholds. Prototype-oriented evaluation is defined using level detection accuracy, alert success rate, system reliability, data transmission success, and response latency. Illustrative conceptual results indicate that the proposed system can improve monitoring effectiveness and reduce response delays compared with manual inspection, fixed-schedule collection, and basic standalone monitoring. The framework is low-cost, scalable, and suitable for smart cities, residential communities, campuses, hospitals, railway stations, bus terminals, shopping complexes, industrial facilities, and public waste-management environments.