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
Gait Recognition System Biometric Identification in IoT-Enabled Smart Environments
The paper presents a comprehensive IoT-integrated gait recognition system for forensic identity verification in smart surveillance environments. The proposed system uses the OpenGait deep learning framework and two silhouette extraction methods, Robust Video Matting (RVM) and traditional Gaussian Mixture Model (GMM) background subtraction, to process video sequences into normalised silhouette datasets for recognition. The whole pipeline from video ingestion and preprocessing to feature extraction, similarity matching, and delivering the results is orchestrated by a Flask-based web application. The system has two modes, one is for offline forensics of archived videos, and the other is for real-time monitoring using a webcam with MJPEG streaming and ServerSent Events (SSE). Subject profiles are stored in a SQLite database backend with MD5 based de-duplication. Evaluation on controlled indoor datasets demonstrates high identification accuracy and robust performance with small variations in clothing and walking speed. The paper addresses major shortcomings of current gait recognition prototypes such as the lack of acceptable interfaces, integrated databases and live recognition support. Thus the system provides a deployable platform suitable for perimeter security, elder care, retail loss prevention and smart city forensics.
Gait Recognition, Open Gait, Silhouette extraction, Biometric forensic, Flask, Hashing, Smart Surveillance.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Heart Disease Prediction Using Deep Learning and Ensemble Machine Learning Models
Heart disease remains one of the leading causes of mortality worldwide, necessitating the development of accurate and reliable prediction systems. This study presents a comparative analysis of several ensemble machine-learning algorithms and a deep neural network (DNN) model for the prediction of heart disease using clinical data. The models evaluated include Random Forest, AdaBoost, Gradient Boosting, XGBoost, LightGBM, and a deep-learning model built using TensorFlow. The dataset comprises patient records with attributes such as age, cholesterol level, resting blood pressure, and exercise-induced angina. Performance was evaluated using standard metrics including accuracy, precision, recall, and F1- score. Among all the models, the Deep Neural Network achieved the highest performance with an accuracy of 98%, outperforming the best ensemble-based model, XGBoost, which achieved 97%. The results demonstrate that deep learning can offer superior predictive capability for cardiovascular risk detection, making it a viable choice for real-world clinical decision-support systems. The system is implemented in Python with a Django web framework and a relational database, integrating data preprocessing, model training, evaluation, and a web interface for prediction. By combining traditional ensemble techniques with a deep neural network, the proposed system enhances the reliability of medical prediction and supports early detection and prevention of heart disease
Legal and Structured Extraction of Location Data from Google Maps Using Python and the Google Places API
The exponential growth of location-based services has made geographic and business-related data crucial for applications including market analysis, travel planning, and service optimisation. Google Maps contains a vast repository of real-time, location-specific data such as business names, ratings, addresses, and user reviews. This project focuses on programmatically accessing such data using Python— not through web scraping, which violates Google’s Terms of Service, but by leveraging the official and authorised Google Places API. Through the API, structured data about places such as restaurants, hospitals, and tourist spots can be retrieved legally and efficiently using simple HTTP requests. The project integrates the API with Python to send queries, parse JSON responses, and extract relevant fields such as name, location, rating, and user reviews; the retrieved data can then be stored in files or databases and visualised for further analysis. By adhering to legal and ethical data-usage practices, the project highlights responsible data access while demonstrating practical skills in Python programming, RESTful APIs, and JSON handling. Pagination and error handling allow large datasets to be collected reliably, and alternative open data sources such as OpenStreetMap and Foursquare are discussed for free access to similar geographic datasets. Unit and functional testing confirmed correct behaviour with no defects, demonstrating a stable, scalable, and legally compliant alternative to traditional scraping.
Biyyala Bharathamma, Mr. Ch. Bhupathi Raju
The integrity and traceability of crime evidence play a vital role in ensuring fair and just legal proceedings. Traditional systems for managing evidence suffer from centralization, vulnerability to tampering, lack of transparency, and inefficient tracking mechanisms. To address these issues, this paper proposes a blockchain-based crime evidence system designed to ensure immutability, transparency, and enhanced security. The system leverages cryptographic hashing, symmetric encryption, and a blockchain structure to maintain an unalterable chain of custody. Features include secure user authentication, geolocation tracking, QR code-based verification, and comprehensive audit logging. Developed using technologies such as Python, Streamlit, SQLite, bcrypt, Fernet, and SHA-256, the system provides a robust platform for digital evidence handling. Functional and security testing confirms the effectiveness of core mechanisms including tamper detection and access control. While the system demonstrates promising capabilities, challenges related to scalability, storage, and legal compliance remain. Future enhancements include integration with enterprise blockchain platforms and adoption of advanced cryptographic protocols.
GORLI SATYA GANESHWAR, Mr. S. KESAVA RAO
A Document Retrieval System Using Retrieval-Augmented Generation
In the age of exponential information growth, effective document retrieval has become essential for knowledge-based systems. This project presents a Document Retrieval System using RetrievalAugmented Generation (RAG), an architecture that combines traditional retrieval mechanisms with the generative power of transformer-based language models. RAG enhances the retrieval process by dynamically integrating external documents into the response-generation pipeline, allowing the system to produce more accurate and contextually relevant answers. The system first retrieves relevant passages from a pre-indexed document corpus using vector-similarity search (FAISS), and the retrieved documents are then passed to a generative language model that synthesises the final output based on both the input query and the retrieved content. This hybrid approach ensures factual grounding while maintaining the flexibility of generative models, and it reduces hallucinations by conditioning generation on retrieved context. The solution is implemented in Python using LangChain, FAISS, and transformer-based language models, and is particularly effective for question answering, summarisation, and knowledge-base augmentation. Testing and evaluation across document processing, semantic retrieval, response generation, scalability, and reliability confirmed that the system retrieves relevant information accurately and generates context-aware responses. The approach demonstrates improved performance over traditional retrieval-only or generation-only models, offering a scalable and intelligent document-access system suitable for enterprise, academic, and personal use.
Rayudu Bharath Kumar, Mr. Ch. Bhupathiraju
DeepGuard: Enhancing Violence Detection in Smart Cities Through Deep Learning
DeepGuard is a deep-learning-based violence detection system designed to enhance public safety in smart-city surveillance environments. The system uses computer vision and a hybrid neural architecture—a Convolutional Neural Network (ResNet18) for spatial feature extraction combined with a Bi-directional Long Short-Term Memory (Bi-LSTM) network for temporal modelling—to analyse video footage from CCTV and uploaded sources. Frames are sampled at fixed intervals, resized, normalised, and converted to tensors before being passed through the hybrid model, which classifies the sequence as Violence or Non-Violence. When a violent activity is detected, the system triggers an immediate alert to designated personnel through an interactive Streamlit-based interface. The implementation is realised in Python using PyTorch, OpenCV, NumPy, and Pillow. By combining spatial and temporal cues, the system reduces dependence on continuous manual monitoring and provides a scalable, automated solution suitable for integration with existing smart-city surveillance infrastructure. Functional, integration, system, performance, and accuracy tests confirm the correctness of all modules and end-to-end behaviour of the prototype.
K. Mahesh Lakshmana Kumar, Mr. P. Satyanarayana
Cybersecurity: Data Protection Using Hybrid Encryption and Steganography
In the evolving landscape of digital communication and data storage, cybersecurity remains a paramount concern. This project proposes a hybrid security framework that combines hybrid encryption (AES + ECC) with image-based steganography to provide enhanced protection for user data stored on centralized or decentralized servers. The hybrid encryption approach ensures that data cannot be decrypted even if malicious actors obtain partial keys, as it uses both symmetric and asymmetric algorithms. Additionally, steganography conceals sensitive messages within image files, enabling covert data transmission while maintaining the appearance of innocuous media. To further ensure data integrity, the system generates a unique hash code for each uploaded file, allowing verification at any time. Access control is fortified through multi-factor authentication, combining traditional credentials with OTP-based email verification. Beyond security operations, the platform also includes user-education tools, providing learning materials and cybersecurity news updates. Developed using Python and MySQL, the application empowers users to encrypt files, hide data in images, retrieve decrypted files, and stay informed about modern threats—all through a secure and interactive web interface. By layering encryption, steganography, hashing, and multi-factor authentication, the proposed system provides a self-secured, tamper-resistant environment that addresses insider threats and the limitations of single-layer protection.
Cyberbullying Detection on Social Media Platforms Using Natural Language Processing and 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.
Detection of Cyber Attacks in Networks Using Machine Learning Techniques
Advances in computing and communication technologies have produced broad benefits for individuals, organisations, and governments, but they have also introduced serious problems such as protecting important data, securing storage platforms, and ensuring data availability. Cyber threats have reached a level that can threaten public and national security, so Intrusion Detection Systems (IDS) have been developed to defend against cyber attacks. In this work, machine-learning algorithms are used to detect attacks based on the modern CICIDS2017 dataset. A Support Vector Machine was used to detect port-scan attempts, achieving accuracy rates of 97.80% and 69.79% for the respective evaluations reported in the source. In addition to SVM, other algorithms—Random Forest, Convolutional Neural Network, and Artificial Neural Network—were evaluated, with reported accuracies of SVM 93.29%, CNN 63.52%, Random Forest 99.93%, and ANN 99.11%. The CICIDS2017 dataset covers benign traffic and common updated attacks such as DoS, DDoS, brute force, XSS, SQL injection, infiltration, port scan, and botnet, with more than 80 network-traffic features. The system is implemented in Python using scikit-learn and Keras with a desktop interface for loading data, training models, and comparing performance. The results identify which algorithm provides the best accuracy for detecting whether a cyber attack has occurred, supporting more reliable network defence.
Mantrabuddi Sanjay Bhargav, Mr. P. Satyanarayana
Customer Churn Prediction in the Telecom Industry Using Machine Learning
Customer churn means customers leaving a company or stopping the use of its services. This project focuses on identifying such customers at an early stage so that businesses can take necessary actions. It is very useful in industries like telecom, banking, and online platforms where customer retention is important. In this project, machine-learning techniques are used to analyse customer data such as usage details, billing information, and customer behaviour. First, the data is cleaned by handling missing values and converting it into a proper format; then, important features that influence customer decisions are selected. After preprocessing, the dataset is divided into training and testing data, and machine-learning algorithms such as Logistic Regression and Decision Tree are applied to build the prediction model. The model is trained using past customer data and learns patterns from it; once training is complete, it predicts whether a customer is likely to leave or continue using the service. The performance of the model is evaluated using accuracy and other metrics. This project helps companies understand customer behaviour and identify the main reasons for churn, so that businesses can take preventive steps such as providing better services, offering discounts, or improving customer support. The system was validated through ten functional and validation test cases that all passed. Overall, this system helps increase customer satisfaction, reduce customer loss, and improve business profit.
Pappala Shanmukha Rao, Mrs. T. Varalakshmi
Currency Recognition System Using Image Processing
This paper presents a currency recognition system developed using image processing and machine learning techniques to address the challenges faced by visually impaired individuals and in situations involving worn or damaged currency notes. The system captures images of currency notes and preprocesses them using techniques such as noise removal, grayscale conversion, and image enhancement. Feature extraction is performed using methods like Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and ORB. The core of the system is built using the EfficientNet-B0 deep learning architecture, implemented with PyTorch and TensorFlow, and integrated with a user-friendly interface using Streamlit. The system aims to provide real-time, accurate classification of currency denominations, supporting multiple currencies and varying note conditions. Testing confirms successful functionality across unit, integration, and acceptance phases. The system offers a scalable, cost-effective, and portable alternative to traditional hardware-based solutions.
REGADA PARADESINAIDU, Mrs. T. VARALAKSHMI
A Review of Modern Maintenance Techniques for Improving Mechanical System Reliability
The functional effectiveness of major components usually determines the operational reliability of large mechanical equipment. Therefore, to guarantee the dependability of mechanical equipment, timely maintenance is required before failure. This paper is a critical discussion of the reliable maintenance and care of mechanical systems to achieve the fullest life of equipment and efficiency. It discusses the shift in the past from reactive and preventative maintenance to smart, data-driven predictive and reliability-focused operations that Industry 4.0 technologies provide. The principles of reliability optimization design are discussed, which focus on combining the methods of probabilistic approach and dependability on a system level. Performance comparison is drawn between various maintenance strategies, and common mechanical failures of rotating machines, including bearing, gearbox, and misalignment faults, are discussed in terms of causes and consequences. Also, the paper addresses predictive maintenance and the significance of the Internet of Things (IoT)-based sensor technologies in real-time condition monitoring as part of lifecycle management. The literature review has indicated new trends related to machine learning (ML), deep reinforcement learning, and predictive maintenance optimization models. The paper ends with defining strategic research gaps and showing the necessity of a single, AI- and IoT-based maintenance system that will allow making the industry more reliable, cost-effective, and sustainable.
Mechanical System Reliability, Predictive Maintenance, Lifecycle Management, IoT Sensors, Equipment Lifespan, Fault Diagnosis
A Review of Scalable Big Data Processing in Cloud-Native Environments
The vast amount of data created by social media, Internet of Things (IoT) devices, e-commerce platforms, and enterprise applications has presented many difficulties in efficient storage, processing, and analysis of large data sets. The limitations faced by the traditional big data systems in solving today's data-intensive workloads include scalability, flexibility, and processing speed. Cloud-native technologies have proven to be effective solutions as they allow distributed computing, elastic scalability, the ability to deploy to containers and automated management of resources. In this paper, the scalable big data processing in cloudnative environment is reviewed. The study covers how big data architectures have changed over time, with a shift from Hadoopbased to microservices, containerization, orchestration and event-driven architectures. It discusses the key big data processing methods, such as batch, real-time and stream processing, and popular frameworks such as Apache Hadoop, Apache Spark, Apache Flink and Apache Kafka. Moreover, the paper examines the application of Docker, Kubernetes and serverless computing in scalable data processing. Lastly, the issues of security, interoperability, latency and resource optimization are discussed, along with future research directions on efficient cloud-native big data systems.
Big Data Processing, Cloud-Native Computing, Scalability, Apache Spark, Stream Processing, Big Data Frameworks
Leveraging PL/SQL for Cloud Data Solutions & Management
As organizations increasingly migrate to cloud-based architectures, the demand for scalable, efficient, and secure data management solutions grows. PL/SQL (Procedural Language/Structured Query Language), a powerful extension of SQL developed by Oracle, plays a crucial role in the management of relational databases in the cloud. This research explores how PL/SQL can be leveraged to enhance cloud data solutions, focusing on optimizing performance, ensuring security, and improving scalability within cloud environments. The paper examines key techniques for integrating PL/SQL with cloud platforms, discusses best practices for data processing, and highlights the unique challenges and benefits of running PL/SQL in the cloud. With real-world case studies and expert recommendations, this article provides a comprehensive guide to harnessing the power of PL/SQL in cloud-based data systems.
AI-BASED FARMER QUERY SUPPORT AND ADVISORY SYSTEM
The AI-Based Farmer Query Support and Advisory System is designed to assist farmers by providing real-time agricultural guidance through multi-modal inputs such as voice, text, and images. The system integrates advanced technologies including Speech-to-Text (STT), Natural Language Processing (NLP), and Convolutional Neural Networks (CNNs) to understand farmer queries and analyze crop conditions. It supports bilingual communication in Telugu and English, making it accessible to a wider range of users. The advisory engine processes user inputs and provides personalized recommendations on crop management, pest control, fertilizers, and preventive measures. The system also incorporates weather updates and market analysis to enhance decision-making. By combining artificialintelligencewithuser-friendly interaction.
Mrs. A. Rajini Devi 1 , T. Varsha 2 , M. Akshitha 3 , B. Gayatri Harshini 4, V. Renusri 5
SECURING DATA PRIVACY IN PERMISSIONED BLOCKCHAIN NETWORKS USING IDENTITY-BASED ENCRYPTION
Permissioned blockchain architectures provide a robust foundation for controlled data sharing among verified participants, yet ensuring payload confidentiality within such networks poses persistent challenges. This paper proposes a framework that integrates Identity-Based Encryption (IBE) with Ethereum smart contracts and the Inter Planetary File System (IPFS) to achieve fine-grained, identity-anchored access control. By using each user's unique identifier as the cryptographic public key, the system eliminates the certificate management overhead inherent in traditional Public Key Infrastructure while maintaining strong data privacy guarantees. A fully functional prototype was implemented in Python and Solidity, demonstrating that only explicitly authorized recipients can decrypt stored messages while non-authorized parties are systematically excluded. Evaluation results confirm system correctness, data integrity, and practical feasibility for enterprise non-transactional data sharing scenarios.
Mr. R Krishna Nayak1*, G. Sreeja2 , E. Shyamala3 , D. Sahithi4 , M. Sandhya5
Software Vulnerability Detection Tool Using Machine Learning Algorithms
With the rapid growth of software applications, security vulnerabilities have become a major concern in modern software development. Traditional vulnerability detection techniques rely heavily on manual code reviews and rule-based tools, which are often timeconsuming and unable to detect complex security flaws efficiently. To address thisissue, this research proposes a Software Vulnerability Detection Tool using Machine Learning algorithms that automatically identifies vulnerable and non-vulnerable code patterns from software datasets. The proposed system utilizes machine learning techniques to analyze code features and classify them based on the presence of vulnerabilities. An ensemble learning approach is implemented by combining multiple machine learning algorithms to improve the accuracy and reliability of vulnerability detection. The system allows users to upload datasets, train models, and visualize performance metrics such as accuracy, precision, recall, and F1-score through a web interface. Experimental results demonstrate that the ensemble classifier achieves high performance in detecting vulnerabilities, with improved prediction accuracy compared to individual algorithms. The developed tool provides an efficient and automated solution for identifying potential security weaknesses in software code, thereby assisting developers in improving software security and reducing the risk of cyber attacks. Software security has become a major concern due to the increasing number of cyber threats and vulnerabilities present in modern software applications. The experimental results demonstrate that the proposed approach provides efficient and reliable detection of software vulnerabilities, helping developers identify potential security risks early in the development process and improve overall software security.
Mr. U. Venkat Rao1 Assistant Professor, N.Manisha2 , K.Rishika3 , P.Aloukika4 , K.Divya5
Eduadapt: Anai-drivenadaptivelearningplatform for Personalized Academic Support
Eduadapt Is An Artificially Intelligent, Learner-Centric Educational System Conceived To Resolve The Shortcomings Inherent In Conventional E-Learning Environments That Deliverrigid, Non-Adaptive Content Uniformly To All Users. The Platform Concentrates On Academically Demanding Subjects, Particularly Formal Languages And Automata Theory (Flat), Design And Analysis Of Algorithms (Daa), And Quantitative Aptitude. Although Widely-Used Platforms Such As Coursera, Udemy, And Byju's Offer Structured Learning Materials, Theyremain Largely Static And Devoid Of Meaningful Personalization. Independently Operating Ai Utilities Such As Chatgpt, Scholarcy, And Quillbot Similarly Fall Short By Functioning Outside The Boundaries Of Structured Academic Curricula. Eduadapt Remedies These Deficiencies By Embedding Large Language Models (Llms) And Natural Language Processing (Nlp) Within A Cohesive,Curriculum-Integrated Educational Ecosystem. Its Feature Set Encompasses On-Demand Personalizedtutoring,Dynamicvisualrepresentationsofcomputationalabstractionsincludingdfaandnfastate Diagrams, AiAssisted Summarization Of Study Materials, And An Analytics-Driven Assessment Dashboard That Monitors Performance And Highlights Knowledge Gaps. By Unifying These Capabilities Within A Single Platform Aligned With Academic Syllabi, Eduadapt Fosters Improved Conceptual Retention, Accelerates Revision Workflows, And Delivers Tailored Academic Guidance. Rigorous Testing Across Functional, Performance, Boundary, And Adversarial Scenarios Validates The System's Operational Reliability And Accuracy, Underscoring Its Capacity To Measurably Enhance Student Learning Outcomes.
AUTOMATED SCOLIOSIS ASSESSMENT FROM SPINE X-RAY USING GENERATIVE ADVERSARIAL NETWORKS (GANs)
Scoliosis, a lateral or S-shaped curvature of the spine, needs to be diagnosed accurately and as early as possible for effective treatment. Conventional assessment relies on the manual measurement of cobb angle which is a labour-intensive process potentially subject to human error. We propose a novel two-phase Generative Adversarial Networks (GAN) based method for automatic scoliosis evaluation in this work. We propose the first stage of a GAN based model to specifically segment the spine, which is then followed by a second stage in which the main clinical features such as the cobb angles are extracted and analysed to evaluate how severe the curvature is. The model, which is trained on labelled spine X-ray datasets, shows high accuracy in segmentation and scoliosis. We present a model trained on labelled datasets of spine X-rays, that has high segmentation accuracy and scoliosis classification and reduces manual analysis. The experimental findings reaffirm the strength of this framework, and highlight its possibilities for integration into clinical routine in order to improve diagnostic accuracy and minimize the existence of unnecessary imaging
1Mr. S. Chandra Shekher, 2K. Reetwika, 3K. Sneha Latha, 4D. Krishna Sri, 5Ch. Laxmi Vaishnavi
ONLINE TESTING & MONITORING OF QUALITY OF MEDICINE AND CONSUMABLE
This Project aims to resolve The quality and safety of medicines and medical consumables are of utmost importance for the health of the public and the delivery of healthcare services. Traditional quality control procedures, such as laboratory testing, are time - consuming and may not detect the deterioration of products during storage and transportation. Thus timely deployment of the product may not be possible.This paper proposes an innovative method for the online testing and real-time monitoring of quality of medicines. It is performed through a secure online application. The application has an authentication mechanism, along with access control, which allows the efficient management of the inventory by authorized personnel, such as the administrator or the store manager. The Smart Medicine Storage, Quality Monitoring, and Expiry Alerting System using IoT is a system aimed at improving the safe storage of medicines. Medicines are very sensitive to environmental changes like temperature, humidity, and light.Also The Proposed system uses the IoT to monitor the storage environment of medicines using sensors. The collected data is processed by a microcontroller like Arduino or Node MCU and then sent to the cloud platforms like Think Speak or Blink for real-time monitoring and visualization. The collected dataset is then passed to the Machine Learning algorithm to perform the classification task to check whether the storage environment is safe or unsafe for the medicines. The users can access the system by using the mobile or web application to check the real-time data, the history of the medicines, and the alerts. The system also stores the essential information about the medicines,like manufacturing date and the expiry date, in the database.The system then sends notifications to the users about the expiry of the medicines and alerts about change in the environment, which can affect the medicines, to ensure proper storage of the medicines.This solution can be effectively implemented in hospitals, pharmacies, labs, and homes, thereby developing smart healthcare technology.
1Mrs. K. Sandhya Rani , 2 Ms. N. Laxmi , 3Ms. P. Siri , 4 Ms. G. Vasavi Amulya, 5Ms. CH. Vaishnavi
RACISM DETECTION IN TWEETS USING STACKED GCR-NN WITH SENTIMENT ANALYSIS
Racism detection on social media, particularly on Twitter, has become a critical challenge due to the volume of harmful and discriminatory content. This paper presents a system using Stacked GCR-NN (Gated Convolutional Recurrent Neural Networks) combined with Sentiment Analysis to classify tweets into three categories: Not Racist, Direct Racism, and Indirect Racism. Convolutional layers capture local linguistic patterns while recurrent layers model sequential dependencies. A sentiment analysis layer further enhances detection by identifying negative emotional tones. The system achieves strong performance in distinguishing both explicit and implicit racist content.
Mrs.K.Helini1 , M.Srijana2 , M.Sri Varsha3 , M.Sathwika4 , K.Manogna5
WEB CLOUD: WEB-BASED CLOUD STORAGE FOR SECURE DATA SHARING ACROSS PLATFORMS
The rapid adoption of cloud storage has created a growing need for secure, accessible, and cross-platform data sharing solutions. Web Cloud is a web-based cloud storage system designed to provide end-to-end secure data storage and seamless sharing across multiple devices and operating systems. By implementing client-side encryption, robust access control mechanisms, and zero-knowledge architecture, the system ensures that users retain full control over their data while enabling secure collaboration. Web Cloud supports major web browsers and mobile platforms, allowing users to upload, store, share, and access files securely without depending on third-party trust for data confidentiality. It integrates AES-256 encryption, role-based access control (RBAC), and secure sharing links with expiration and password protection. The system addresses key challenges such as data breaches, unauthorized access, and platform fragmentation in traditional cloud storage services. Experimental results demonstrate high security, usability, and performance, making Web Cloud an ideal solution for individuals, enterprises, and organizations requiring secure cross-platform data sharing.
TRUST-BASED PRIVACY PRESERVING PHOTO SHARING IN ONLINE SOCIAL NETWORKS
The explosive growth of Online Social Networks (OSNs) has made photo sharing a primary means of social interaction. However, sharing co-owned photos often leads to serious privacy violations because current OSN privacy policies are user-centric and fail to consider the privacy preferences of all individuals appearing in the photo. Trust-Based Privacy Preserving Photo Sharing in Online Social Networks is a novel framework that leverages trust relationships among users to dynamically control the visibility and anonymization of co-owned photos. The system computes trust scores based on social interactions, relationship strength, and user feedback. It then applies selective anonymization techniques such as face blurring, region masking, or pixelation on sensitive parts of the image according to the minimum trust threshold set by co-owners. By integrating trust evaluation with image processing and access control mechanisms, the framework effectively reduces privacy leakage while preserving the usability and visual quality of shared photos. The proposed system supports multi-user decision-making for coowned content and provides a scalable, user-friendly solution for secure photo sharing in platforms like Facebook, Instagram, and Twitter. Experimental results demonstrate significant reduction in privacy risk with minimal impact on photo utility and social engagement.
SpADe: MULTI-STAGE SPAM ACCOUNT DETECTION FOR ONLINE SOCIAL NETWORKS
The exponential growth of Online Social Networks (OSNs) has led to a massive increase in spam accounts that spread malicious content, phishing links, fake news, and unsolicited advertisements. Traditional spam detection methods often rely on single-stage classification using either profile features or content features, resulting in high computational cost and limited accuracy. SpADe (Spam Account Detection) is a novel multi-stage spam account detection framework with a reject option. It progressively analyzes accounts using increasingly complex and costly features across four stages: basic account characteristics, URL-based analysis, content and behavioral patterns, and advanced graph and temporal features. Early stages filter obvious spam accounts using lightweight features, while uncertain cases are forwarded to later stages, significantly reducing overall computation while maintaining high detection performance. The system was implemented and evaluated on real-world OSN datasets. Experimental results demonstrate superior performance compared to existing single-stage and multi-stage approaches, achieving high accuracy, precision, recall, and F1-score with lower processing time and cost. SpADe offers a scalable, efficient, and practical solution for social network platforms and security analysts to combat evolving spam threats.
SOCIAL Q&A: AN ONLINE SOCIAL NETWORK BASED QUESTION AND ANSWER SYSTEM
The exponential growth of online information has increased the demand for effective Question and Answer (Q&A) systems. Traditional Q&A platforms suffer from slow response times, low answer quality, and a high percentage of unanswered questions due to reliance on random browsing or altruism. SocialQ&A is an intelligent online social network-based Q&A system that leverages social relationships to improve answer quality and reduce waiting time. By utilizing common-interest and mutual-trust properties of social networks, the system actively forwards questions to friends or users who are most likely to provide relevant, highquality answers. SocialQ&A integrates user interest profiling, question categorization, and a question-user mapping algorithm based on interest similarity and social closeness. The system was evaluated through simulations and analysis of real-user behavior, demonstrating significant improvements in answer quality, response time, and user satisfaction compared to conventional Q&A platforms. SocialQ&A offers a scalable, community-driven solution ideal for educational institutions, enterprise knowledge sharing, and general social platforms.
SECURE AND EFFICIENT DATA DEDUPLICATION IN JOINT CLOUD STORAGE
The exponential growth of data in cloud storage has led to significant challenges in storage efficiency, bandwidth consumption, and management costs. Data deduplication is a proven technique that eliminates redundant copies of data, storing only a single instance while reducing storage space and upload bandwidth. However, in multi-cloud or Joint Cloud Storage environments (where multiple cloud providers collaborate to offer global services), achieving secure deduplication becomes complex due to privacy concerns, key management issues, and dynamic ownership changes. This project presents SED (Secure and Efficient Deduplication) — a novel scheme designed specifically for Joint Cloud Storage systems. It supports secure cross-user deduplication without relying on a fully trusted key server. The system employs convergent encryption combined with advanced ownership management and dynamic access control to ensure that identical data is stored only once while preserving data confidentiality. It handles dynamic operations such as user revocation, ownership updates, and efficient verification. By integrating hybrid cloud concepts (public cloud for storage and private cloud for ownership control), the proposed system resists collusion attacks, duplicate faking attacks, and side-channel leaks. Experimental evaluation shows substantial savings in storage space (up to 70–90% in redundant datasets) and reduced communication overhead compared to traditional schemes. The solution is scalable, secure, and ideal for enterprise Joint Cloud environments seeking optimized storage with strong privacy guarantees.
RISK ASSESSMENT IN SOCIAL NETWORKS BASED ON USER ANOMALOUS BEHAVIORS
Online Social Networks (OSNs) have become integral to modern communication, enabling users to connect, share information, and engage globally. However, the open nature of these platforms exposes them to various security and privacy risks from users exhibiting anomalous behaviors, such as fake accounts, compromised profiles, spam campaigns, coordinated misinformation, and cyberbullying. Risk Assessment in Social Networks Based on User Anomalous Behaviors is a machine learning-driven framework designed to detect and quantify risks associated with deviant user activities. The system analyzes user behavioral patterns (posting frequency, interaction graphs, content semantics, and temporal activity) to identify deviations from normal behavior. It employs unsupervised anomaly detection techniques, including clustering of similar users, behavioral modeling, and metrics like deviation scores to assign risk levels to individual users or groups. By grouping users with similar profiles (based on demographics, activity levels, and network structure) and building group-specific normal behavior models, the framework achieves higher accuracy than global models. Experiments on real-world datasets (e.g., Facebook-like graphs) demonstrate effective detection of diverse anomalous behaviors such as Sybil attacks, click-spam, and collusion networks while maintaining low false positives. This approach enhances platform security, reduces malicious activity, and supports proactive risk mitigation for OSN providers and users.
PRIVACY CHARACTERIZATION AND QUANTIFICATION IN DATA PUBLISHING AN INTELLIGENT FRAMEWORK FOR MEASURING PRIVACY LEAKAGE IN PPDP TECHNIQUES
The increasing interest in publishing large-scale individual datasets for medical research, market analysis, and economic studies has raised serious privacy concerns. While numerous Privacy-Preserving Data Publishing (PPDP) techniques such as k-anonymity, l-diversity, and t-closeness have been proposed, most lack a comprehensive and quantifiable privacy characterization model. This paper presents a novel multi-variable privacy characterization and quantification framework that models attributes as a multi-dimensional privacy risk space. The framework redefines prior and posterior adversarial beliefs and analyzes the sensitivity arising from attribute combinations. We demonstrate that privacy leakage cannot be accurately measured using a single metric and therefore propose two new quantification metrics: Distribution Leakage and Entropy Leakage. Using these metrics, we systematically evaluate well-known PPDP techniques. Experimental results show that existing schemes suffer from significant limitations in privacy protection. The proposed framework provides a solid foundation for designing, analyzing, and comparing future privacypreserving data publishing mechanisms, enabling better trade-offs between privacy and data utility.
OPTIMAL DRUG DOSAGE CONTROL STRATEGY OF IMMUNE SYSTEMS USING REINFORCEMENT LEARNING
The treatment of cancer and other immune-related disorders remains a major challenge in modern medicine due to the highly complex, nonlinear, and uncertain dynamics of tumorimmune interactions. Conventional chemotherapy and immunotherapy regimens often rely on fixed or standardized drug dosage schedules, which frequently result in either insufficient tumor suppression or severe toxicity to healthy tissues and the immune system itself. Such static approaches fail to adapt to individual patient responses, leading to suboptimal therapeutic outcomes and increased risk of side effects. This project presents an intelligent Reinforcement Learning (RL)-based optimal drug dosage control strategy for immune systems. The proposed framework models the tumorimmune-drug interactions as a dynamic control problem and employs a critic-only reinforcement learning architecture combined with a discounted non-quadratic performance index. This approach effectively transforms the robust tracking problem with input constraints and model uncertainties into an unconstrained optimal tracking control task. The RL agent learns to determine the optimal drug dosage in real time by observing the current state of tumor cell population, immune (effector) cell population, and drug concentration. The system aims to drive the tumor burden toward a desired low or zero level while maintaining immune cell counts within a safe and effective range, thereby achieving a superior balance between therapeutic efficacy and toxicity minimization. The framework was implemented and extensively evaluated using established mathematical models of tumor-immune dynamics under various uncertainty conditions and dosage constraints. Simulation results demonstrate that the proposed RL-based controller achieves effective tumor suppression with significantly lower cumulative drug usage compared to traditional fixed-dose strategies. It exhibits strong robustness to parameter variations and external disturbances while preserving healthy immune function. This work offers a promising foundation for developing adaptive, personalized, and intelligent drug dosing systems in precision oncology and immunotherapy.
CHILD’S PLAY WITH MACHINE LEARNING: A PLATFORM FOR YOUNG INNOVATORS TO CREATE AI SOLUTIONS
The rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies has created a growing need for early exposure and accessibility among young learners. Child’s Play with Machine Learning: A Platform for Young Innovators to Create AI Solutions is designed to bridge the gap between complex AI concepts and beginner-level understanding by providing an interactive, user-friendly environment tailored for students and young creators. The platform simplifies machine learning workflows through visual programming, pre-built models, and guided tutorials, enabling users to design, train, and deploy AI applications without requiring extensive coding knowledge. The system integrates drag-and-drop interfaces, real-time feedback mechanisms, and cloud-based deployment features to enhance learning outcomes and encourage creativity. It supports a variety of AI domains, including image recognition, speech processing, and predictive analytics, allowing users to experiment with real-world problem-solving scenarios. Additionally, the platform promotes collaborative learning by enabling users to share projects, participate in challenges, and receive mentorship from experienced developers and educators. By combining education with innovation, the platform aims to nurture critical thinking, problem-solving skills, and technological confidence among young innovators. It also aligns with modern educational goals by making AI learning accessible, engaging, and practical. Ultimately, this initiative contributes to building a future-ready generation capable of leveraging AI technologies for societal and technological advancement.
Machine Learning, Artificial Intelligence, Young Innovators, Educational Platform, Visual Programming, AI Applications, Interactive Learning, Cloud Deployment, Beginner-Friendly AI, STEM Education