International Journal of Data Science and IoT Management System
Editor- In-Chief: Dr. Jorge K Title proper: International Journal of Data Science and IoT Management System (IJDIM) Subject: Engineering, Computer Science, IOT, Management, Science and Technology Publisher Name: IJDIM Publications ISSN: 3068-272X 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.3068-272X E-mail: editor@ijdim.com Impact Factor : 6.125
Smart Parking Using QR Code is an intelligent parking management system designed to simplify and automate the process of vehicle parking and space management. The system uses QR code technology to provide users with a convenient method for parking registration, slot allocation, and entry and exit verification. Users can scan a QR code to access parking information, check available slots, reserve a suitable parking space, and receive parking details digitally. The system maintains parking slot information and updates availability based on vehicle entry and exit activities. Administrators can monitor parking spaces, manage vehicle records, and track parking transactions through a centralized interface. QR-based verification helps reduce manual record keeping, minimize entry and exit delays, and improve parking security. The proposed system can also support digital payment integration and automated parking notifications. The Smart Parking Using QR Code system aims to provide a cost-effective, user-friendly, and efficient solution for managing parking facilities in shopping malls, offices, educational institutions, hospitals, and other high-traffic locations. By integrating QR code technology with digital parking management, the system improves space utilization, reduces congestion, and enhances the overall parking experience.
B. ABHISHEK1 , D. PRANATHI2 , K. CHANDU SAI3 , D. SRAVANI4 , P.SRI LAKSHMI5
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0
SIGNSENSE: AI-POWERED HAND GESTURE-BASED LANGUAGE INTERPRETER
SignSense: AI Hand Gesture-Based Language Interpreter is an intelligent communication system designed to translate hand gestures into meaningful language using Artificial Intelligence and Computer Vision techniques. The system captures hand movements through a camera and processes the visual input to recognize predefined gestures in real time. A gesture recognition model identifies the corresponding signs and converts them into text or speech, helping bridge the communication gap between sign-language users and people who may not understand sign language. The application is developed using Node.js for backend services and React for an interactive user interface, while SQL is used to store user information, gesture data, and recognition records. The proposed system aims to provide a fast, accessible, and user-friendly solution for real-time gesture-based communication. By integrating AI, computer vision, web technologies, and database management, SignSense can support more inclusive communication in educational, social, and professional environments.
Artificial Intelligence, Computer Vision, Hand Gesture Recognition, Sign Language, Deep Learning, Human– Computer Interaction, Real-Time Communication, React, Node.js, SQL Database.
AGATHAMUDI SAI TANUJA1 , PUDI TOYA JAKSHI2 , SAKALA SAI VENKATA3 , MANASWINI4 , BETHA MOHAN SAI5 , CHANDRA SEKHAR AKULA6
Generative AI-Driven Synthetic Image Generation for Horticultural Seed Bag Detection and Classification
Horticultural seed bags are widely used in agricultural production, storage, transportation, and distribution, making their accurate identification and classification important for effective inventory management and quality-control activities. Conventional image-based approaches often depend on limited collections of real images, which may not adequately represent variations in orientation, lighting, background, packaging appearance, scale, occlusion, and camera viewpoint. This study proposes a Generative AI-driven approach for synthetic image generation and automated detection and classification of horticultural seed bags. The proposed framework employs Generative AI techniques, including GAN-based and diffusionbased models, to learn visual characteristics from available seed-bag images and generate realistic synthetic samples with diverse visual conditions. The generated images are subjected to quality verification based on realism, diversity, visual consistency, and preservation of important seed-bag characteristics. Selected synthetic images are then integrated with the original dataset to create an enhanced image collection for detection and classification. The approach is designed to increase the visual diversity of the available dataset while reducing dependence on extensive manual image collection. The generated outputs are further analyzed according to their usefulness for seed-bag identification and classification. The proposed framework demonstrates the potential of Generative AI for developing flexible and scalable solutions for horticultural image analysis, with applications in agricultural storage, packaging verification, inventory management, and automated quality-control operations.
Generative AI, Synthetic Image Generation, Horticultural Seed Bags, Seed Bag Detection, Image Classification, GAN, Diffusion Models, Agricultural Automation
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Real-Time Flood Monitoring & Alert System Using Remote Sensing
Floods remain one of the most frequent and destructive natural hazards, causing severe loss of life, infrastructure damage, and economic disruption across the globe. Conventional monitoring approaches that depend on river gauges, rainfall stations, and manual field surveys suffer from restricted spatial coverage, delayed analysis, and heavy reliance on human intervention, which often results in late detection and slow emergency response. This work presents a real-time flood monitoring and alert framework that fuses satellite/aerial remote-sensing imagery with a SegNet-based convolutional neural network to perform pixel-level semantic segmentation of flooded and non-flooded regions. The segmentation output is combined with auxiliary environmental parameters, namely rainfall intensity and river water level, to compute a quantitative flood-coverage percentage and to classify the overall risk into Safe, Moderate, or Very High categories. The framework is delivered through a Flask-based web application that allows users to upload imagery, view segmentation overlays and heat-maps, receive real-time alerts, and download automatically generated PDF reports. Experimental evaluation on annotated flood-imagery datasets shows a mean pixel accuracy of 95.2%, a mean Intersection-over-Union of 91.7%, and a Dice coefficient of 92.5%, together with a mean per-image inference latency below two seconds. These results indicate that the proposed system offers faster, more accurate, and more scalable flood detection than traditional ground-based techniques, making it a practical tool for disaster-management authorities.
Real-time flood monitoring, remote sensing, deep learning, SegNet, semantic segmentation, disaster management.
Cross-Domain Service Assurance for Private 5G Networks Using Streaming Telemetry and Dependency-Aware Event Correlation
Private 5G networks are increasingly being adopted in industries such as manufacturing, healthcare, logistics, and smart campuses, where reliable and uninterrupted connectivity is essential. However, ensuring service quality across radio access, transport, edge, and core network domains is challenging because of the complexity of distributed network infrastructures and the large volume of operational data. This paper presents a cross-domain service assurance framework that combines streaming telemetry with dependencyaware event correlation to provide real-time network observability and efficient fault management. Streaming telemetry continuously collects performance metrics from multiple network components, while dependency graphs represent the relationships between network services and infrastructure resources. The event correlation engine analyzes alarms across different domains to identify root causes, eliminate redundant alerts, and accelerate fault localization. The proposed framework improves service reliability, reduces Mean Time to Repair (MTTR), minimizes alarm flooding, and enhances overall operational efficiency. Experimental evaluation demonstrates faster fault detection, improved resource utilization, and better Quality of Service (QoS), making the proposed approach suitable for modern private 5G enterprise networks.
Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-nd/4.0
OPEN RAN-BASED ON-DEMAND 6G AGRICULTURAL NETWORKS: ADAPTIVE EDGE INFRASTRUCTURE FOR AUTONOMOUS FARMING SYSTEMS
The rapid advancement of Open Radio Access Network (Open RAN) technologies has created new opportunities for deploying flexible, scalable, and cost-effective wireless infrastructures in agricultural environments. This paper proposes an Open RAN-based on-demand 6G agricultural network that integrates Private 5G connectivity, edge computing, artificial intelligence, and emerging THz communication concepts to support autonomous farming systems. Unlike conventional agricultural communication architectures, the proposed framework dynamically allocates network resources according to field conditions, crop monitoring requirements, unmanned aerial vehicle operations, and autonomous agricultural machinery. Open RAN enables multi-vendor interoperability and programmable radio resource management, while edge computing facilitates lowlatency processing of sensor data and AI inference close to the farming environment. Private 5G enhances secure and reliable communication among IoT devices, drones, robotic equipment, and cloud platforms. Future THz-enabled links further improve bandwidth availability for high-resolution imaging and large-scale environmental sensing. The proposed architecture improves network scalability, spectrum utilization, operational flexibility, and autonomous decision-making while reducing deployment costs and communication latency. Simulation-based performance evaluation demonstrates improvements in network throughput, energy efficiency, task completion latency, and resource utilization compared with conventional agricultural wireless architectures. The proposed framework provides a practical pathway toward intelligent, sustainable, and highly adaptive 6Genabled precision agriculture ecosystems.
Knowledge-Graph-Assisted Fault Localization for Cloud-Native Open RAN and Edge Infrastructure
Cloud-native Open Radio Access Network (Open RAN) and edge computing infrastructures have become essential for supporting scalable and intelligent 5G services. However, the distributed nature of these environments makes fault localization and root-cause identification increasingly complex due to the large volume of telemetry data, dynamic resource allocation, and interdependent network components. This paper proposes a Knowledge-Graph-Assisted Fault Localization framework that integrates cloudnative observability with knowledge graph modeling to improve fault diagnosis in Open RAN and edge infrastructures. The framework constructs a knowledge graph representing relationships among network functions, services, containers, and edge nodes, while continuously collecting logs, metrics, and events from the infrastructure. Graph-based reasoning is applied to correlate anomalies and identify the underlying root cause of failures with high accuracy. Experimental evaluation demonstrates that the proposed approach reduces fault localization time, improves diagnosis accuracy, minimizes service disruptions, and enhances overall network reliability compared with conventional monitoring techniques. The framework provides an efficient and scalable solution for intelligent fault management in next-generation cloud-native Open RAN environments.
Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-nd/4.0
AI-NATIVE WIRELESS DIGITAL TWINS FOR PRECISION AGRICULTURE: EDGE INTELLIGENCE AND PREDICTIVE FARM OPERATIONS IN 6G ECOSYSTEMS
The rapid evolution of sixth-generation (6G) wireless networks is enabling intelligent, autonomous, and data-driven agricultural systems through the integration of artificial intelligence (AI), edge computing, and wireless digital twin technologies. This paper proposes an AI-native wireless digital twin framework for precision agriculture that combines Internet of Things (IoT) sensors, unmanned aerial vehicles (UAVs), weather stations, and Enterprise Private 5G/6G communication infrastructure to create a real-time virtual representation of agricultural fields. The proposed framework employs edge intelligence to process heterogeneous sensor data with minimal latency, enabling continuous monitoring of crop health, soil moisture, environmental conditions, and equipment status. Predictive AI models analyze the synchronized digital twin to forecast crop growth, irrigation requirements, disease outbreaks, and yield performance while supporting timely farm management decisions. Unlike conventional cloudcentric agricultural systems, the proposed architecture reduces communication delays, improves resource utilization, enhances network reliability, and supports scalable autonomous farming operations. Furthermore, the integration of AI-native networking and edge-based analytics strengthens sustainability by optimizing water consumption, fertilizer application, and energy usage. The framework also provides a foundation for future intelligent agricultural ecosystems by enabling real-time decision support, predictive farm operations, and adaptive wireless resource management. The proposed approach demonstrates the potential of AI-native wireless digital twins to transform precision agriculture into a resilient, efficient, and sustainable component of nextgeneration 6G ecosystems.
GITOPS-BASED CONTINUOUS DELIVERY OF CLOUDNATIVE 5G NETWORK FUNCTIONS WITH AUTOMATED CONFIGURATION VALIDATION
GitOps has emerged as a reliable approach for managing cloud-native applications through declarative configuration and version-controlled workflows. This paper presents a GitOps-based continuous delivery framework for Cloud-Native Network Functions (CNFs) in 5G environments, enabling automated deployment, configuration management, and lifecycle operations. The proposed approach integrates CI/CD pipelines with Kubernetes orchestration to ensure consistent and repeatable releases across distributed telecom infrastructures. Automated configuration validation mechanisms are incorporated to detect policy violations, misconfigurations, and deployment inconsistencies before production rollout. By leveraging infrastructure-as-code principles, the framework enhances operational efficiency, reduces manual intervention, and improves service reliability. Experimental evaluation demonstrates faster deployment cycles, improved configuration accuracy, and reduced downtime compared with traditional deployment methods. The study highlights how GitOps-driven telecom DevOps practices can accelerate 5G service delivery while maintaining configuration assurance, scalability, and compliance requirements.
AN OPTIMIZED YOLOV10 FRAMEWORK FOR DUAL DETECTION OF SAFETY HELMETS AND LICENSE PLATES
This research uses the most recent YOLOv10 object detection architecture to demonstrate a sophisticated computer vision system for real-time safety helmet detection and license plate recognition. The main goal is to improve vehicle monitoring and workplace safety by automatically recognising people who are not wearing safety helmets in industrial zones and recording license plates for regulatory and surveillance purposes. Efficient multi-object identification in difficult situations is made possible by YOLOv10, which is renowned for its exceptional speed and accuracy. To ensure reliable performance, the system is trained using annotated datasets that include a variety of helmet types and car plates under various circumstances. Construction workers' risk of suffering head injuries in highaltitude falls can be significantly decreased by donning safety helmets. This study suggests an enhanced safety helmet detection method based on YOLOv10 to solve the low detection accuracy of current algorithms for small objects and complicated settings in different situations.
SMART EDUCATIONAL APP SELECTION THROUGH INTELLIGENT RECOMMENDATIONS
Personalised recommendations are essential for improving students' educational experiences in the quickly changing digital age. The growing use of mobile devices has made it simpler to gather app usage data, which may be used to offer customised recommendations for educational apps. The goal of this study is to recommend appropriate apps for college students based on their app usage habits, with an emphasis on Undergraduate (UG), Postgraduate (PG), and Graduate levels. In order to extract pertinent characteristics from app descriptions and student interaction data, the dataset is pre-processed using Natural Language Processing (NLP) techniques such as text cleaning, tokenisation, and feature extraction. Students' tastes are precisely categorised by the algorithm, which then suggests apps that meet their academic demands.
SWIPEHIRE: A DUAL-MODE AI-DRIVEN JOB MATCHING PLATFORM FOR CANDIDATES AND RECRUITERS
The rapid growth of the digital job market has made traditional hiring platforms increasingly inefficient, with candidates spending excessive time browsing irrelevant listings and recruiters manually screening large volumes of unsuitable applications. In this work, we present SwipeHire, a dual-mode AI-supported hiring platform that reimagines job discovery and recruitment through a swipe-based interaction model inspired by modern consumer applications, combined with a transparent, rule-based skill-matching algorithm. The system supports two distinct user roles — candidates and recruiters — each guided through a tailored onboarding and profile-setup flow. Candidates browse job postings as swipeable cards annotated with an AI-generated match percentage derived from skill-overlap scoring, while recruiters review AI-ranked candidate profiles based on a composite fit score incorporating skill alignment, resume completeness, and relevant experience. A mutual right-swipe from both parties triggers an automatic match, unlocking a real-time chat channel built on WebSocket technology that supports text messaging, file sharing, and recruiter-initiated interview requests. The platform further provides candidates with a growth analytics dashboard visualizing application trends and in-demand skills, and recruiters with a recruiting analytics dashboard tracking shortlist rates and job performance. Administrative controls for user management and company verification, together with JWT-based authentication, password hashing, and rate-limiting, ensure the platform is both usable and secure. Implemented using the MERN stack (MongoDB, Express.js, React.js, Node.js) with Socket.IO for real-time communication, SwipeHire demonstrates that an explainable, algorithm-driven matching approach can deliver a fast, engaging, and trustworthy hiring experience for both job seekers and employers.
MUSIC EMOTION RECOGNITION USING DEEP NEURAL NETWORKS AND LIBROSA
In order to arrange, search, and suggest music on contemporary platforms, the categorization of musical emotions is crucial. The deep emotional content included in music may not be fully captured by traditional models, which frequently rely on raw audio or textual data. In order to effectively classify musical emotions, we suggest a Convolutional Neural Network (CNN)-based model in conjunction with Librosa for feature extraction. Mel-frequency cepstral coefficients (MFCCs), chroma characteristics, spectral contrast, and tonette representations are among the significant audio features extracted from music signals using Librosa in the suggested method. By preserving timbral, harmonic, and rhythmic elements important to emotion identification, these aspects offer a condensed and useful representation of the audio. From these collected features, hierarchical patterns are subsequently learned using the CNN model. While pooling layers highlight prominent emotional patterns and minimize dimensionality, convolutional layers automatically identify local correlations in the acoustic information. By removing the need for manually created feature combinations, this deep learning approach enables the model to successfully generalize across a variety of music samples. The suggested approach is able to capture intricate emotional links in music by fusing CNNs' patternlearning capabilities with Librosa feature extraction. This method provides a reliable and scalable solution for automated music emotion classification, supporting real-world platform applications including playlist creation, music analytics, and music recommendation.
EARLY LUNG CANCER PREDICTION USING CTGAN AND TREE-BASED MACHINE LEARNING TECHNIQUES
One of the main causes of cancer-related fatalities globally is still lung cancer, and increasing survival rates depends on early identification. In this work, a machine learning-based method for predicting lung cancer utilising clinical and lifestyle data from surveys is presented. The Synthetic Minority Over-sampling Technique (SMOTE) was used to address the dataset's class imbalance and guarantee equitable representation of both classes. Logistic regression was used as the meta-learner in a stacked ensemble model that combined CatBoost, XGBoost, LightGBM, AdaBoost, and Random Forest. The suggested methodology outperformed individual classifiers and came close to state-of-the-art performance documented in the literature, with an accuracy of 96.9% and a ROC AUC of 0.99. A Flask-based web application that offers an intuitive interface for prediction, visualisation, and outcome interpretation was also developed. Modules for user registration, input-based cancer risk prediction, and graphical display of test data results are all included in the system. The findings show that ensemble learning provides a reliable and user-friendly method for lung cancer prediction when combined with efficient preprocessing and web deployment.
An Intelligent Deep Learning Framework for Industrial Machine Sound Anomaly Detection Using YAMNet Embeddings and Bidirectional GRU Networks
Maintaining operational effectiveness, avoiding expensive equipment failures, and guaranteeing worker safety all depend on the ability to identify irregularities in industrial machine noises. However, because industrial surroundings are diverse and unpredictable, this task is difficult. incorporating shifting operational circumstances and background noise. In order to capture the crucial acoustic features of machinery sounds, this study offers a thorough method that makes use of sophisticated feature extraction techniques. Effective anomaly detection tactics are investigated using a variety of machine learning and deep learning techniques Several datasets are used in the study to assess the suggested techniques in various experimental settings. Extensive testing results show the approach's efficacy in real-world industrial settings and highlight its potential to improve sound analysis and predictive maintenance. By offering a dependable way to identify anomalies in machine operations, this work advances the capabilities of industrial monitoring systems.
AN INTELLIGENT FRAMEWORK FOR AERIAL IMAGE DETECTION USING U-NET AND YOLOV10
We provide a unique method that combines a U-Net architecture with YOLOv10 to handle the difficulties of few-shot aerial picture semantic segmentation, where unseen-category items in query aerial images must be identified using only a few annotated support images. Typically, few-shot segmentation involves segmenting query images pixel-bypixel using category prototypes that are extracted from support samples. However, objects in aerial photos frequently have irregular spatial distributions and arbitrary orientations, which results in notable differences in their properties. Low confidence scores and incorrect classification of same-category items with varying orientations are common outcomes of conventional approaches that do not take orientation changes into account. In order to get over these restrictions, our method combines YOLOv10 for quick and accurate object identification with U-Net for precise semantic segmentation, allowing for reliable item localisation even in challenging aerial scenarios. Because of YOLOv10's sophisticated detection capabilities, the system can recognise objects in a variety of sizes and orientations, producing trustworthy bounding boxes that direct the U-Net segmentation procedure. Combining these two architectures improves the network's ability to recognise rotated and dispersed aerial objects and reduces oscillation in confidence scores by reliably segmenting same-category objects independent of rotation or size. This U-Net + YOLOv10 framework successfully bridges the gap between effective object recognition and high-fidelity semantic segmentation in difficult aerial imagery by providing a scalable, rotation-robust few-shot aerial image segmentation solution.
A MACHINE LEARNING FRAMEWORK FOR REGIONAL LIFE EXPECTANCY PREDICTION
Life expectancy is a crucial indicator of a population's overall health and standard of living. This work offers an AI-driven approach for accurately predicting life expectancy using state-of-the-art machine learning algorithms. In particular, the CatBoost approach—which is renowned for its superior performance on structured data and categorical variables—was used to build a robust prediction model. The model was trained using a big dataset of health, demographic, and socioeconomic data from multiple countries. Performance evaluation results were positive, with a Mean Squared Error (MSE) of 0.18, Mean Absolute Error (MAE) of 1.15, testing R2 score of 93%, and training R2 score of 95%. These metrics confirm the model's excellent generalisation to new data and strong prediction power. By identifying key factors that affect life expectancy, this AI-powered approach can support health policy planning, improve resource allocation, and promote preventative healthcare initiatives across communities. The project shows how machine learning can be effectively used to provide insightful information on longevity prediction and public health. Keywords: Machine Learning, CatBoost, Life Expectancy Prediction, Socioeconomic Factors, HealthCare data and Regression Models.
LOW-LATENCY ASIC ARCHITECTURE FOR RECURSIVE KARATSUBA MULTIPLICATION EMPLOYING AN EFFICIENT SQRT CARRY SELECT ADDER
Computation intensive applications such as DSP, image processing, floating point processors and communication technologies today require efficient binary multiplication which usually is the most power and time-consuming block. This paper proposes an efficient design for unsigned binary multiplication to reduce delay. A 16×16-bit multiplier has been designed which is based on Vedic Karatsuba algorithm using reversible logic. It is optimized using adaptive and recursive approach combined with square-root- carry-select-adder. The designs have been coded in Verilog, synthesized in Xilinx ISE. Index Terms—Square Root Carry Select Adder, reversible logic, Karatsuba Multiplier, Recursive Adaptive Karatsuba Algorithm
Impact of Digital Payment Systems on Consumer Spending Behavior: An Empirical Study
The rapid growth of digital payment systems has transformed the way consumers conduct financial transactions. The increasing adoption of Unified Payments Interface (UPI), mobile wallets, internet banking, debit cards, and credit cards has significantly influenced consumer spending patterns. This study aims to examine the impact of digital payment systems on consumer spending behavior, including convenience, purchasing frequency, impulsive buying, and financial management. The research adopts a descriptive research design based on secondary data collected from published journals, government reports, books, and reliable online sources. The findings indicate that digital payment systems encourage faster transactions, enhance convenience, improve financial inclusion, and increase online shopping activities. However, concerns regarding cybersecurity, privacy, and overspending remain significant challenges. The study concludes that digital payment systems have positively influenced consumer spending behavior while emphasizing the need for consumer awareness regarding responsible financial management and digital security.
Digital Payments, UPI, Consumer Spending Behaviour, Cashless Economy, Financial Technology, E-commerce
Cyber Crime in Indian Banking Sector Challenges and Prospects
The rapid expansion of digital banking services in India has significantly transformed financial transactions by enabling convenient, real time, and remote access to banking facilities. However, this technological advancement has simultaneously increased exposure to cyber crimes such as phishing, malware attacks, ransomware incidents, insider threats, distributed denial of service attacks, and large scale data breaches. Financial institutions face increasing operational risks due to sophisticated attack vectors that exploit system vulnerabilities, human error, and weak security configurations. The growing dependency on online banking platforms, mobile applications, and digital payment systems necessitates robust detection and prevention mechanisms capable of identifying fraudulent patterns and mitigating financial losses. The challenges include evolving threat landscapes, high transaction volumes, and the need for real time monitoring, which traditional manual and rule based approaches struggle to manage effectively. This project presents an intelligent analytical framework designed to study cyber crime patterns within the Indian banking sector and provide predictive insights regarding fraudulent and malicious activities. The system performs structured data analysis, automated preprocessing, model training, and predictive evaluation to identify potential cyber crime categories based on transaction related parameters. By leveraging analytical modeling techniques, the proposed system enhances decision making, reduces dependency on manual intervention, and supports secure financial operations. The outcomes demonstrate improved detection capability, structured administrative control, and user level fraud prediction functionality, thereby contributing to safer digital banking ecosystems and strengthening resilience against cyber threats. Keywords: Cyber Crime, Banking Security, Fraud Detection, Predictive Analytics.
Project Monitoring System Using The Django Framework
The Project Monitoring System using the Django framework is a web-based application designed to streamline the process of monitoring and managing projects. This system allows project managers and team members to track project progress, monitor timelines, allocate resources efficiently, and ensure that project objectives are met. By leveraging Django’s scalability and security features, the system provides real-time updates, automatic reporting, and an intuitive interface. The proposed system aims to enhance project transparency, improve collaboration, and reduce delays by offering a centralized platform to oversee all aspects of project execution, ensuring timely completion and optimal resource utilization. Keywords: Project Monitoring System, Django Framework, Web-Based Application, Project Management, Real-Time Monitoring, Resource Allocation, Task Management, Progress Tracking, Collaboration, Automated Reporting, Database Management, Python, MySQL, Workflow Management, Software Engineering.
SymptoTrackAI Hybrid RAG Chatbot for Symptom Monitoring
The rapid advancement of digital technologies in the healthcare domain has significantly transformed the way individuals access medical information and monitor their health conditions. In recent years, the growing demand for accessible and continuous health monitoring solutions has highlighted the importance of intelligent digital systems capable of assisting individuals in understanding and interpreting symptoms effectively. Traditional health information systems and symptom checking methods often rely on static databases or rule-driven decision processes that lack contextual awareness and adaptability. These approaches frequently struggle to interpret natural language symptom descriptions provided by users, leading to inaccurate suggestions and limited user trust. The increasing reliance on digital platforms for preliminary health guidance has therefore created a strong need for intelligent systems capable of interpreting symptoms more effectively while ensuring reliable and meaningful outputs for users seeking early health insights. The proposed system introduces an intelligent symptom monitoring approach that utilizes advanced semantic analysis techniques to interpret userprovided symptom descriptions and identify relevant health conditions based on similarity patterns within medical data sources. The system focuses on analyzing textual symptom descriptions entered by users in natural language and comparing them with previously stored medical information to identify the most relevant matches. Through an efficient similarity analysis process, the system provides meaningful health-related guidance while maintaining high responsiveness and accuracy. The proposed approach improves the reliability and contextual relevance of symptom interpretation while supporting efficient health monitoring assistance. The system demonstrates how intelligent information retrieval techniques can enhance digital healthcare solutions by improving response relevance, reducing misleading outputs, and enabling scalable symptom monitoring capabilities. Keywords: Symptom Monitoring, Semantic Retrieval, Health Informatics, Artificial Intelligence.
1Boggavarapu Pavankalyan, 2Mr.T.Ravi Kumar
Digitized And Decentralized Blockchain-Based Social Media Platform
The "Digitized and Decentralized Blockchain-Based Social Media Platform" is a system that integrates Ethereum blockchain to enhance the privacy, security, and control of user data in social media. Users can register, log in, update their profiles, post content, interact with other users' posts by liking, disliking, or commenting, and view all posts made by others. All these activities are securely stored on the Ethereum blockchain, ensuring decentralization and transparency. This system aims to address common issues of centralized social media platforms such as data privacy concerns and censorship. Keywords: Blockchain, Decentralized Social Media, Ethereum, Smart Contracts, Distributed Ledger Technology (DLT), Data Privacy, Data Security, Decentralized Applications (dApps), Content Integrity, User Authentication, Digital Identity, Peer-to-Peer Networks, Transparency, Immutable Storage, Cybersecurity.
Mango Leaf Detection Using Transfer Learning
Mango cultivation plays a pivotal role in the agricultural economy of many countries, especially in tropical regions. However, the productivity and quality of mangoes are significantly affected by various leaf diseases, which often go undetected until they cause substantial damage. Traditional methods of disease identification are time-consuming and require expert knowledge, making them less feasible for timely intervention. The integration of deep learning techniques, particularly transfer learning, offers a promising solution for the early and accurate detection of mango leaf diseases. This project leverages the capabilities of transfer learning by utilizing pre-trained convolutional neural networks, specifically MobileNetV2, to identify and classify mango leaf diseases. By employing image preprocessing techniques and data augmentation, the model is trained to recognize patterns associated with different diseases. The system provides users with an intuitive interface to upload leaf images and receive instant diagnostic feedback, thereby facilitating prompt and informed decisionmaking in disease management. Keywords: Mango Leaf Disease, Transfer Learning, MobileNetV2, Deep Learning, Image Classification.
Hostel Management System using Django Framework
Hostel Management System is a system for managing the various activities in the hostel. It is used for managing the hostel information.It manages the student information, room information, room allocation details,It is also used to generate reports of student details, It keeps track of the number of student in the room and availability of the room,and it manages the student mess fee details and hostel annual fee details. Keywords: Hostel Management System, Student Information Management, Room Allocation, Hostel Administration, Fee Management, Django Framework, Python, MySQL Database, Web-Based Application, Administrative Automation.
The Role of Artificial Intelligence in Financial Services: Transforming Banking, Investment, and Asset Management
Background: The financial services industry has experienced a significant impact from artificial intelligence (AI) technology, influencing various areas such as banking, investment management, risk management, and regulatory compliance. Although the use of AI has increased, there is little empirical evidence that examines the effects of implementing AI on organizational performance. The study focuses on the impact of the use of AI on financial performance in banking, investment and asset management institutions. A quantitative research design was applied and the data were collected from the financial institutions which operate in different regions in the year 2020-2024. This study investigates how the intensity of AI adoption, investment in AI technologies, data quality, maturity of regulatory technology (RegTech) and legacy infrastructure impact organizational performance. Several statistical methods including descriptive statistics, correlation analysis and regression modelling, panel-data estimation were used to test the proposed hypotheses. The results suggest that the use of AI has a positive relationship with financial performance, operational efficiency, and fraud detection. Data quality and RegTech maturity, combined with the advantages of implementation, enhance the advantages of AI, and legacy infrastructure hinder performance outcomes. The results show that companies with higher levels of AI application achieve better returns and benefits than other companies with varying stages of AI application. The findings contribute to the existing body of knowledge on applying AI in the financial sector and provide practical insights for managers, regulators, and policymakers to maximize the value of their AI investment effectively.
Integrating Cybersecurity Awareness into Human Resource Management: A Framework for Organizational Resilience
In the age of the digital revolution, human resource technology (HRTech) has emerged as a critical enabler for organizations to automate hiring, onboarding, payroll, employee management, performance management, and workforce analytics. However, there are significant cybersecurity risks associated with the growing application of cloud computing, digital platforms, and data-driven decisionmaking in HRM. This research paper examines the cybersecurity awareness policy in human resources (HR) management, its effectiveness, issues, and solutions. This paper introduces a hybrid deep learning method to improve cybersecurity awareness in human resource management using the Cybersecurity Risks Dataset. The proposed Hybrid GRU+BiLSTM model effectively captures long- and short-term dependencies in sequential data and outperforms traditional models such as RNN, SVM and KNN. The results show the best classification performance, with 97% accuracy (acc), 97% precision (pre), 98% recall (rec), and 98% F1-score (F1), indicating high reliability in identifying cybersecurity risk. The findings highlight the models potential for supporting proactive decision-making and bolstering organizational resilience by augmenting threat identification and awareness in HR systems. Keywords—Human Resource Management (HRM), Cybersecurity Workforce, Digital Transformation, Workforce Development, Strategic Recruitment.
CLEAN BOT – An Intelligent LLM-Powered Data Quality Agent
The increasing volume of structured and unstructured digital data has created significant challenges in maintaining data quality across modern information systems. Conventional data cleaning approaches primarily rely on predefined rules and manual intervention, making them less effective in handling semantic inconsistencies, missing values, duplicate records, and heterogeneous data formats. This project presents Clean Bot, an intelligent data quality agent that utilizes locally deployed Large Language Models (LLMs) through Ollama to automate data cleaning and structuring tasks. The system integrates a Streamlit-based interface with a modular architecture to support data preprocessing, anomaly detection, validation, transformation, and structured output generation. By applying prompt-based reasoning, the proposed approach identifies inconsistencies, removes irrelevant information, standardizes data, validates fields, and converts processed information into structured JSON format. The modular design enables efficient handling of multiple data sources while supporting scalable and maintainable data quality management. Experimental implementation demonstrates that the proposed system provides an effective solution for improving data consistency, accuracy, and organization, making it suitable for intelligent data processing in real-world applications. Keywords— Data Quality Management, Data Cleaning, Large Language Models (LLMs), Ollama, Mistral, LLaMA, Streamlit, Intelligent Data Quality Agent, Prompt Engineering, Anomaly Detection, Data Validation, Data Transformation, Information Extraction, Structured JSON Output, Unstructured Data Processing.
The increasing adoption of cryptocurrencies has introduced new challenges in analyzing highly volatile financial markets where prices fluctuate continuously and traditional analytical methods often fail to provide timely insights. This work presents a comprehensive cryptocurrency market analysis system that combines data analytics, machine learning, and web technologies to support informed investment decisions. Historical market data of major digital assets, including Bitcoin, Ethereum, and Solana, are processed through data preprocessing, feature engineering, and normalization techniques to identify meaningful market trends and price movement patterns. A supervised learning model is employed to estimate short-term market behavior, while statistical indicators are utilized to evaluate momentum, volatility, and overall market conditions. To enhance user accessibility, the analytical framework is integrated into a fullstack web application developed using FastAPI and React, enabling interactive visualization of market information and real-time analytical updates. Furthermore, an artificial intelligence module generates descriptive market interpretations that assist users in understanding complex financial indicators without requiring extensive technical expertise. Experimental observations demonstrate that the proposed framework provides reliable market analysis, effective visualization, and consistent predictive performance under varying market conditions. The proposed system offers an integrated environment for cryptocurrency monitoring and decision support by combining predictive analytics with explainable AI-based insights, making it suitable for investors, researchers, and financial analysts. Keywords— Cryptocurrency Market Analysis, Machine Learning, Predictive Analytics, Feature Engineering, FastAPI, React, Real-Time Visualization, Large Language Models, Financial Forecasting.
DESIGN, MODELING, AND THERMO-MECHANICAL ANALYSIS OF AN AUTOMOTIVE DRUM BRAKE USING FUNCTIONALLY GRADED MATERIALS
The drum brake is a critical component of an automotive braking system that converts the kinetic energy of a moving vehicle into thermal energy through friction during braking. Continuous braking operations generate high temperatures and significant mechanical stresses, which may lead to thermal distortion, wear, brake fade, reduced braking efficiency, and structural failure. Therefore, selecting suitable materials for drum brake components is essential to improve thermal resistance, structural strength, durability, and service life. Functionally Graded Materials (FGMs) have emerged as advanced engineering materials because their material properties gradually vary throughout the thickness, thereby reducing thermal stresses, improving heat distribution, and enhancing mechanical performance compared with conventional homogeneous materials. This project presents the Design, Modeling, and Thermo-Mechanical Analysis of an Automotive Drum Brake Using Functionally Graded Materials by developing a three-dimensional CAD model of a drum brake and evaluating its thermal and structural behavior using ANSYS Workbench. The brake drum is modeled using CAD software and imported into ANSYS for finite element analysis. Appropriate thermal boundary conditions, braking loads, and material properties of functionally graded materials are applied to simulate actual braking conditions. The analysis focuses on evaluating important performance parameters including temperature distribution, heat flux, equivalent (Von Mises) stress, total deformation, thermal strain, directional deformation, and factor of safety. The simulation results demonstrate that the use of Functionally Graded Materials significantly reduces peak thermal stress, improves heat dissipation, minimizes deformation, and enhances the structural integrity of the drum brake under severe braking conditions. The proposed methodology provides an effective approach for designing lightweight, thermally stable, and durable automotive braking components while reducing prototype development cost, experimental testing, and product development time
E. VAISHNAVI, Ms.A.SANDHYA, CH. LEELA VARDHAN, G. ARUN KUMAR, B. NARESH