American Journal of AI Cyber Computing Management
• Editor- In-Chief: Dr. Mainso Jubarg • Title proper: American Journal of AI Cyber Computing Management (AJACCM) • Subject: AI ML, Data Science, Management, Cyber Security, Computer Science, IOT, Engineering All, Science and Technology • Publisher Name: AJACCM Publications • ISSN: 3069-0102 • Frequency: Quarterly • Language: English • Country: USA • Medium: Online • Indexed by: CROSSREF, MDPI Scilit, Google Scholar Ext • Title DOI of the Journal: http://doi.org/10.64751/issn.3069-0102
Machine Learning Based Cyber Attacks Detector in Software Difined Networks (Sdn) For Healthcare Systems
In 2026, the healthcare sector remains a primary target for sophisticated cyber threats, with ransomware and insider attacks surging by over 35% compared to previous years. Traditional static network architectures struggle to protect the burgeoning ecosystem of Internet of Medical Things (IoMT) devices and sensitive Electronic Health Records (EHR). This paper proposes a robust Machine Learning-based Cyber Attack Detector (MCAD) integrated within a SoftwareDefined Networking (SDN) framework tailored for modern healthcare infrastructures. By decoupling the control plane from the data plane, the SDN architecture provides a centralized, programmable vantage point that enables realtime traffic monitoring and rapid threat mitigation. Our method utilizes a Layer 3 (L3) learning switch application on an SDN controller (such as Ryu) to capture high-fidelity network features, which are then processed through a hybrid machine learning pipeline. The system employs advanced ensemble algorithms, including XGBoost, Random Forest, and CatBoost, to identify diverse attack vectors such as DDoS, probe scans, and unauthorized lateral movements. A specialized feature selection mechanism ensures high detection accuracy while maintaining the low latency required for critical medical environments. Experimental results on modern healthcare-specific datasets demonstrate an F1- score of 0.99 for normal traffic and a significant reduction in false alarm rates. Furthermore, the integration of Explainable AI (XAI) components allows network administrators to interpret the "why" behind flagged anomalies, fostering trust in automated security responses. This holistic approach addresses the "crown jewel" protection mandate of 2026, providing a scalable, proactive defense mechanism that ensures patient safety and regulatory compliance in increasingly complex digital health landscapes.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
दिव्यांग एवं सामान्य किशोर विद्यार्थियों की भावनात्मक परिपक्वता पर सोशल मीडिया के प्रभाव का तुलनात्मक अध्ययन
प्रस्तुत अध्ययन का उद्देश्य दिव्यांग एवं सामान्य किशोर विद्यार्थियों की भावनात्मक परिपक्वता पर सोशल मीडिया के प्रभाव का तुलनात्मक अध्ययन करना है। वर्तमान डिजिटल युग में सोशल मीडिया किशोरों के जीवन का एक महत्वपूर्ण अंग बन चुका है, जिसके कारण उनके भावनात्मक विकास पर इसके प्रभाव का अध्ययन अत्यंत आवश्यक हो जाता है। इस अध्ययन में सर्वेक्षण विधि का प्रयोग किया गया। शोध हेतु दिव्यांग एवं सामान्य किशोर विद्यार्थियों का चयन कर उनसे संबंधित आंकड़ों का विश्लेषण सहसंबंध विधि द्वारा किया गया। अध्ययन में सोशल मीडिया उपयोग एवं भावनात्मक परिपक्वता के मध्य संबंध का परीक्षण किया गया। अध्ययन के परिणामों से ज्ञात हुआ कि दिव्यांग विद्यार्थियों में सोशल मीडिया एवं भावनात्मक परिपक्वता के मध्य सहसंबंध गुणांक नगण्य एवं असार्थक है। वहीं सामान्य विद्यार्थियों में सहसंबंध नकारात्मक संबंध को दर्शाता है। विद्यालय के प्रकार के आधार पर भी समान प्रवृत्ति देखी गई, जहाँ दिव्यांग विद्यार्थियों में संबंध नगण्य रहा, जबकि सामान्य विद्यार्थियों में सोशल मीडिया उपयोग के साथ भावनात्मक परिपक्वता में कमी देखी गई। अतः यह निष्कर्ष निकाला जा सकता है कि सोशल मीडिया का प्रभाव सामान्य विद्यार्थियों की भावनात्मक परिपक्वता पर नकारात्मक रूप से पड़ता है, जबकि दिव्यांग विद्यार्थियों पर इसका प्रभाव नगण्य है। यह अध्ययन शिक्षकों एवं अभिभावकों के लिए उपयोगी सिद्ध हो सकता है।
सोशल मीडिया, भावनात्मक परिपक्वता, दिव्यांग विद्यार्थी, सामान्य विद्यार्थी, किशोरावस्था, सहसंबंध, विद्यालय प्रकार, मनोवैज्ञानिक विकास
NEUROMARKETING AND AI: PREDICTING CONSUMER EMOTIONS AND BRAND PREFERENCES USING MACHINE LEARNING
Artificial Intelligence (AI) and neuromarketing are transforming the way organizations understand consumer behavior in the digital marketplace. Traditional marketing approaches often fail to capture the subconscious emotional responses that strongly influence consumer decision-making and brand preference. This study explores the integration of neuromarketing techniques and machine learning algorithms to predict consumer emotions and identify brand preferences with greater accuracy and efficiency. The research examines how AI-driven analytical models can interpret emotional and behavioral data collected through consumer interactions, facial expressions, sentiment analysis, and cognitive responses. The study adopts a quantitative research approach by collecting data from consumers through structured questionnaires and digital behavioral observations. Machine learning techniques such as Random Forest, Support Vector Machine (SVM), and Neural Networks are applied to analyze emotional patterns and predict consumer preferences toward brands. The findings indicate that AI-based neuromarketing models significantly improve the prediction of consumer emotional engagement, purchase intention, and brand loyalty compared to conventional marketing analytics methods. The research further highlights the growing strategic importance of predictive analytics in developing personalized marketing campaigns, improving customer experience, and enhancing competitive advantage in dynamic business environments. The study contributes to the emerging literature on AI-enabled consumer analytics by providing a conceptual and empirical understanding of how machine learning can support emotionally intelligent marketing strategies. The results offer valuable implications for marketers, researchers, and organizations seeking to leverage advanced technologies for data-driven decision-making and customer-centric brand management.
Neuromarketing, Artificial Intelligence, Machine Learning, Consumer Emotions, Brand Preference, Predictive Analytics, Consumer Behavior, Digital Marketing Analytics
Phishing Detection System through Hybrid Machine learning Model
Phishing attacks, introduced around 1996, have become a major cybersecurity threat by using fake emails and websites to steal sensitive information. Despite many detection techniques, a complete solution is still lacking. This study applies machine learning to detect phishing by analyzing URL features from a dataset of over 11,000 websites. The URLs are converted into vector form for model training and testing. Multiple algorithms such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), Gradient Boosting (GBM), KNN, and SVC are evaluated. A hybrid model called LSD, combining LR, SVC, and DT with voting mechanisms, is proposed. Feature selection using the Canopy method and optimization through cross-validation and Grid Search enhance performance. Results show that the LSD model achieves higher accuracy and efficiency compared to other models in detecting phishing URLs
Mrs.Kousari Kumari¹, Akshaya Naidi², Talla Sneha³, Kuna Shivani4
The rapid growth of urbanization and population has led to a significant increase in solid waste generation, which poses serious challenges to effective waste management in smart cities. Traditional waste collection methods are inefficient, labor-intensive, and often result in overflowing dustbins, leading to environmental pollution, foul odors, and the spread of diseases. To address these challenges, this project proposes an IoTbased Smart Dustbin System using ESP32, ultrasonic sensors, GPS, and the Blynk cloud platform for realtime monitoring and alert generation. The collected data is analyzed and displayed on a web or mobile application dashboard that can be accessed by municipal authorities or waste management staff. When the waste level in a bin reaches a predefined threshold, an automatic alert or notification is sent to the waste collection team. This enables the authorities to schedule waste collection only when necessary, thereby reducing unnecessary trips and optimizing resource utilization.Additionally, GPS tracking can be integrated with waste collection vehicles to monitor their location and plan the most efficient routes for waste pickup
Mrs K.Sandhya Rani1, M.Kavya 2, S.Likhitha3, D.Srija4,T.Meghana5
AI -BASED INTERNSHIP RECOMMENDATION ENGINE FOR PM INTERNSHIP SCHEME
This paper presents an AI-based internship Recommendation System developed for the PM Internship Scheme to simplify internship management and improve student career guidance. The system is a wed-based platform built using python and the Django framework, enabling student to search, apply, and receive internship recommendations based on their skills and performance.it integrates as online test module with automates evaluation to assess student knowledge accurately and efficiently. A machine learning component is used to predict suitable job roles based on academic data and test results, helping students make informed career decisions. The system also provide a centralized database for managing internship details, ensuring decision-making, the proposed system improves the overall efficiency and effectiveness for internship allocation
1Mrs. B. Archana, 2R. Sindhu, 3K. Nikhitha, 4N. Swapna, 5 M. Madhuri
Disaster Preparedness and Response Education System for Schools and Colleges
This research presents a sophisticated AI-driven digital platform designed to overcome the inherent limitations of traditional disaster preparedness training- specifically its lack of interactivity, personalisation, and scalability. By leveraging advanced Natural Language Processing (NLP) techniques, including transformer-based and extractive summarization, the system intelligently synthesis diverse data sources such as PDFs, text files and YouTube transcripts into cohesive, drill-specific summaries. These summaries serves as the foundation for an automated assessment engine that generates unified quizzes, which administrators can further customise with manual questions to ensure comprehensive evaluation. To maximize user engagement and learning retention, the platform features a centralized dashboard where learners can access resources, track performance, and earn badges based on predefined achievement thresholds. A critical component of the system is the “Disaster Assistant”, an intelligent conversational agent that provides real- time, content-aware responses to queries regarding cyclone preparedness, emergency response, and risk mitigation. Built with a modular SQLite3 database schema for secure administrative oversight and performance analytics, the platform demonstrates that the integration of AI-driven summarization, automated scoring and realtime assistance significantly strengthens disaster readiness and fosters a more adaptive, accessible educational experience.
1 Mrs. Aileni Eenaja, 2 A. Ria, 3 Ch. Varshini, 4 Ch. Vardhini, 5 G. Akhila, 6 P. Sreeja
Digi Kisan: AWeb-Based Agricultural E-Auction Platform for Transparent Crop Trading
Agriculture is vital to the livelihoods of millions of people, especially in developing countries. However, traditional agricultural marketing often relies on middlemen, which lowers profits for farmers and makes pricing unclear. Farmers often struggle to find fair buyers and obtain competitive prices for their crops. To tackle these problems, this paper introduces DigiKisan, a web-based agricultural e-auction platform that directly connects farmers and buyers through an online bidding system. The system is built using the Flask web framework, Python for the backend, and SQLite as the database. The user interface is created with HTML, CSS, and JavaScript to ensure an interactive web experience. DigiKisan has three types of users: administrators, farmers, and buyers. Farmers can list crops for auction, buyers can submit competitive bids in real time, and administrators can oversee and manage the system. The platform automatically checks bids and closes auctions based on time limits while generating digital contracts for completed transactions. DigiKisan enhances market transparency, removes middlemen, and allows secure crop trading using digital technology. This system shows how web applications can modernize agricultural markets and help farmers achieve fair market prices.
Agricultural marketplace, online auction system, web application, digital agriculture, smart farming.
¹ R. Krishna Naik , 2 Ali Soujanya, 3 Christina Joseph, 4 Tejaswi Reddy Karna, 5Madakam Divya
Customerchurnisamajorconcernfororganizationsinindustriessuchastelecom,banking,ecommerce, OTT platforms. Churn occurs when customers discontinue a service or shift to competitors, resulting in revenue loss and higher marketing expenses. Traditional methods for handling churn rely on rule-based analysisandmanualmonitoring,whichareoftenreactive,lessaccurate,andcostly.Thisprojectproposes a machine learning-based churn prediction system that analyzes customer behavior, service usage, and demographic information to identify individuals who are likely to leave. By detecting churn risks in advance, the system enables companies to design personalized retention strategies, reduce unnecessary promotional costs, and improve customer satisfaction. The proposed solution not only improves the accuracy of churn prediction but also enables organizations to take proactive measures, reduce and enhance customer satisfaction. By offering insights into the underlying reasons for churn, it strengthens customer loyalty, minimizes revenue loss, and ensures long-term sustainable growth.
Mrs.K.Helini1 , T.Sathvika 2 , G.ShivaJoshna 3 , D.VijayaLaxmi4 , A.Sruthi 5
The AlgoVis – Data Structures Visualizer is a web-based application designed to help students understand data structures and algorithms through visual representation. Many students find it difficult to learn algorithms by reading code alone. This project solves that problem by displaying algorithms in an interactive and step-by-step animated format.The system allows users to select an algorithm, provide input data such as arrays or graphs, and observe how the algorithm processes the data visually. Algorithms like sorting, searching, and graph traversal are shown using animations that clearly explain comparisons ,swaps, and transitions. The application also provides code snippets alongside the visualization to help users connect theoretical concepts with practical implementation. This project is developed using HTML, CSS, and JavaScript, focusing on DOM manipulation and animation techniques. AlgoVis improves conceptual understanding, enhances problem-solving skills, and makes learning algorithms more engaging and effective. It is especially useful for undergraduate students studying Data Structures and Algorithms.
1Mrs. K. Sandhya Rani, 2A. Kavya, 3B. Ahalya, 4D. Nandini, 5Amala. Ksheersagar
ROBUST OBJECT-BASED VIDEO FORGERY DETECTION USING DEEP CONVOLUTIONAL NEURAL NETWORKS
The rapid advancement of video editing technologies has made it increasingly easy to manipulate video content, leading to the rise of sophisticated object-based forgeries that can mislead viewers and compromise the authenticity of digital media. Detecting such forgeries has become a critical challenge in fields such as digital forensics, media verification, and security. This paper presents a robust approach for object-based video forgery detection using Deep Convolutional Neural Networks (CNNs), designed to identify subtle spatial and temporal inconsistencies introduced during manipulation. The proposed method leverages a deep CNN architecture to automatically extract discriminative features from video frames, capturing both visual artifacts and motion irregularities associated with tampered regions. The system incorporates frame-level analysis combined with temporal consistency checks to improve detection accuracy. Advanced preprocessing techniques, including frame extraction, noise filtering, and normalization, are applied to enhance model performance. The model is trained on diverse datasets containing both authentic and forged videos, enabling it to generalize effectively across different types of manipulations. Experimental results demonstrate that the proposed approach achieves high detection accuracy and robustness against complex forgery techniques such as object insertion, removal, and replacement. Compared to traditional methods, the deep learning-based model shows superior performance in terms of precision, recall, and processing efficiency. Overall, this work provides an effective and scalable solution for detecting object-based video forgeries, contributing to improved digital content authenticity and trust in multimedia systems.
Deep Learning, Convolutional Neural Networks, Video Forgery Detection, Object-Based Manipulation, Digital Forensics, Computer Vision, DeepFake Detection, Multimedia Security
The Mini Web Firewall Application is a lightweight and efficient security framework designed to protect web applications from common cyber threats by monitoring, analyzing, and filtering HTTP requests. With the rapid growth of webbased services, applications are increasingly vulnerable to attacks such as SQL Injection, Cross- Site Scripting (XSS), and brute-force attempts, which can lead to unauthorized access, data breaches, and system failures. The proposed system introduces a rule-based firewall layer that acts as an intermediary between the client and server, ensuring that only legitimate and safe requests are processed. The firewall inspects incoming requests, extracts critical parameters, and evaluates them against predefined security rules using patternmatching techniques such as Regular Expressions. Suspicious or malicious requests are immediately blocked, while valid requests are forwarded to the server for processing. Additionally, the system maintains logs of all suspicious activities, including IP addresses, timestamps, and attack types, enabling administrators to analyze patterns and enhance security measures. The application is implemented using Python and the Flask framework, making it cost-effective, scalable, and easy to deploy. The modular design improves maintainability and allows future integration of advanced technologies such as machine learning for intelligent threat detection. Overall, the system enhances web application security, ensures reliable performance, and provides a practical solution for protecting small-scale applications from evolving cyber threats.
1Mrs.G. SWETHA, 2T. SUMANA SRI, 3U. RAKESH, 4N. PREETHAM
The rapid growth of digital communication and cloud-based storage systems has significantly increased the demand for secure file sharing mechanisms. Traditional file sharing systems rely heavily on password-based authentication, which is vulnerable to cyber threats such as phishing, bruteforce attacks, and credential theft. To address these limitations, this project proposes a Secure File Sharing System with Biometric Encryption that integrates strong cryptographic techniques with biometric authentication. The system uses Advanced Encryption Standard (AES-256) to encrypt files before storage, ensuring confidentiality and protection against unauthorized access. Additionally, biometric authentication methods such as facial recognition are implemented using computer vision techniques to verify user identity. Biometric data is processed and stored securely using hashing algorithms like SHA-256, preventing misuse of sensitive personal information. The system follows a layered architecture consisting of presentation, application, and data layers to ensure scalability, maintainability, and security. Only authenticated users can decrypt and access files, thereby maintaining data integrity and authenticity. The proposed system is suitable for applications in educational institutions, corporate environments, healthcare systems, and cloud storage platforms where data security is critical. By combining biometric verification with encryption, the system significantly enhances security compared to traditional methods and reduces the risks associated with unauthorized access and data breaches.
1Mr.R. BHARATH, 2A. MAANVI BHARADWAJ, 3V. VIJAY KRISHNA, 4M. RAKESH REDDY
The Smart Study Planner is an intelligent and adaptive system designed to enhance students’ academic performance through efficient time management and personalized study scheduling. Traditional study planning methods often fail to consider individual learning pace, subject priorities, and dynamic schedules, leading to inefficiency and increased stress. This system addresses these limitations by utilizing data-driven techniques and adaptive algorithms to generate customized study plans based on user inputs such as subjects, deadlines, priorities, and available time. The planner incorporates features such as automated reminders, progress tracking, and dynamic schedule adjustment to ensure consistent learning and balanced subject coverage. By continuously analyzing user performance and study behavior, the system updates schedules in real time, promoting productivity and reducing last-minute pressure. The application is designed with a user-friendly interface and integrates modern web technologies to ensure accessibility and ease of use. Furthermore, the system supports scalability and can be enhanced with advanced machine learning models for predictive planning and performance optimization. The Smart Study Planner not only helps students organize their study routines but also fosters discipline, improves focus, and enhances learning outcomes. Overall, it serves as a comprehensive solution for modern education challenges by combining automation, personalization, and intelligent decision-making to create an effective study environment.
1Mrs.A. JYOSHNA, 2Y. SHIVA KISHORE, 3M. BHAVITHA, 4S. CHARAN
The rapid evolution of digital learning platforms has transformed education, yet a significant gap persists between passive learning and true conceptual understanding. The proposed system, Concept to Clarity Learning (CCL), addresses this issue by introducing an AI-driven, interactive learning environment that emphasizes active recall and self-explanation. Unlike traditional systems that focus on content delivery and memorization, CCL encourages learners to articulate concepts in their own words and evaluates their understanding using an intelligent AI Coaching Engine. The system integrates learning and testing modes, enabling users to first grasp foundational knowledge and then validate their comprehension through descriptive or objective assessments. By leveraging advanced natural language processing techniques, the system identifies conceptual gaps, logical inconsistencies, and missing information in user responses. A unique feature of the system is the generation of a “Clarity Score,” which quantitatively represents the learner’s depth of understanding while providing structured, Socratic feedback to guide improvement. The architecture follows a modern four-tier design, incorporating a React-based frontend, Node.js backend, Supabase database, and external AI services for validation and content processing. This design ensures scalability, responsiveness, and real-time feedback. Ultimately, CCL transforms traditional study methods into an engaging, feedback-driven learning process, improving knowledge retention, exam performance, and practical application skills.
1Mrs.B. NIRUPAMA, 2U. HRUSHNA, 3M. RAMAKRISHNA, 4V.SAI DHARAHAS
Fake news detection has emerged as a critical research area due to the rapid spread of misinformation across digital platforms and social media. This project presents an intelligent system that leverages machine learning and Natural Language Processing (NLP) techniques to automatically classify news articles as real or fake. The system processes textual data through multiple stages including data collection, preprocessing, feature extraction, model training, and classification. Text preprocessing techniques such as tokenization, stop-word removal, stemming, and normalization are applied to improve data quality. Feature extraction is performed using TF-IDF vectorization to convert textual content into numerical representations suitable for machine learning models. Algorithms such as Naive Bayes, Logistic Regression, and Support Vector Machines (SVM) are implemented to achieve accurate classification. The system is trained on labeled datasets and evaluated using performance metrics such as accuracy, precision, recall, and F1-score. The proposed approach reduces dependency on manual fact-checking and enhances real-time detection capability. Additionally, the system is scalable and can be integrated into web platforms to assist users in verifying information authenticity. This solution contributes to minimizing misinformation, improving public awareness, and promoting trustworthy information dissemination. Overall, the project demonstrates an efficient, reliable, and automated framework for fake news detection in modern digital environments.
1Mrs.L. SHIRISHA, 2T. HEMANTH, 3S. SHRAVIKA, 4M. LAXMAN
Artificial Intelligence (AI) has significantly transformed creative domains, including music composition, by enabling automated generation of musical content. This project focuses on developing an intelligent music generation system that leverages machine learning and deep learning techniques to compose original and high-quality music. The system utilizes models such as Recurrent Neural Networks (RNN), Long Short- Term Memory (LSTM), and transformer architectures to learn complex musical patterns including melody, rhythm, harmony, and pitch from large datasets of MIDI and audio files. By analyzing these patterns, the model is capable of generating new musical sequences that closely resemble human-created compositions. The system accepts user inputs such as genre, mood, tempo, or textual prompts and produces customized music outputs, thereby enhancing personalization and usability. The architecture includes key stages such as data collection, preprocessing, model training, and music generation, ensuring a structured and efficient workflow . Additionally, the system provides functionalities such as playback and download of generated music, making it practical for real-world applications in entertainment, gaming, and content creation. This approach reduces the dependency on manual composition and enables faster music production while maintaining creativity and diversity. The project highlights the integration of AI with music theory, demonstrating how intelligent systems can assist both beginners and professionals in music composition. Overall, the system contributes to advancements in AI-driven creativity and opens new opportunities for innovation in digital music production
1Mrs.O. NAGA KUMARI,2 R. NISMHITHA, 3T. LAKSHMI, 4P. NAVEEN 1
The rapid advancement of Artificial Intelligence has significantly increased the demand for intelligent systems capable of assisting users in everyday computing tasks. Most existing virtual assistants rely heavily on cloud-based architectures, which introduce challenges related to data privacy, network dependency, and latency. To overcome these limitations, this project proposes an Offline Smart AI Assistant designed to operate entirely on local hardware, ensuring secure, private, and lowlatency interaction. The system integrates a quantized Large Language Model, specifically Mistral-7B, to enable efficient natural language understanding and response generation without requiring internet connectivity. Speech recognition is implemented using an optimized offline model that converts user voice input into text, while a text-to-speech module provides audible responses. The assistant processes contextual information, including conversation history and system state, to deliver accurate and meaningful outputs. A Pythonbased backend coordinates all modules, ensuring smooth interaction between speech processing, language modeling, and user interface components. The graphical interface, developed using a modern UI framework, enhances usability by displaying real-time system states such as listening, processing, and responding. The system achieves high contextual accuracy while efficiently utilizing local computational resources through model optimization techniques. Additionally, all user data is stored locally, ensuring complete data sovereignty and security. This project demonstrates the feasibility of deploying advanced conversational AI systems on edge devices, offering a reliable, privacy-focused alternative to cloud-based assistants.
1Mr. R. BHARATH, 2JATOTHU ANAND, 3G. ANUGNA REDDY,4JAKKA. SAIKUMAR
The rapid growth of digital communication has significantly increased the demand for efficient and user-friendly social networking platforms. This project presents the design and development of a web-based social media website that enables users to communicate, share content, and interact in a secure and scalable environment. The system allows users to register, authenticate, create profiles, and post content such as text, images, and videos. It incorporates essential features including real-time interaction, messaging, notifications, and personalized content feeds to enhance user engagement. The platform is developed using modern web technologies such as HTML, CSS, and JavaScript for the frontend, along with backend technologies like PHP and database management using MySQL. Security mechanisms such as password encryption, session management, and access control are implemented to ensure data protection and user privacy. The proposed system addresses common issues in existing platforms, including privacy concerns, spam, fake accounts, and inefficient content management. Additionally, the system emphasizes usability, scalability, and performance to handle increasing user demands effectively. This project provides a foundational understanding of client-server architecture, database integration, and web application security practices. It also serves as a base for future enhancements such as artificial intelligence-based recommendations, real-time communication features, and mobile application integration. Overall, the developed system demonstrates a practical and efficient solution for modern social networking requirements.
1Mr. T. JAYARAJAN, 2B. PRANAV BHARADWAJ, 3B. BHANU PRAKASH, 4E. NARSA REDDY
CYBER FRAUD APP DETECTION USING MACHINE LEARNING
The rapid growth of digital transactions and mobile applications has significantly increased the risk of cyber fraud, making security a critical concern for individuals and organizations. Traditional fraud detection systems, which rely on rule-based mechanisms, often fail to adapt to evolving fraud patterns and result in delayed or inaccurate detection. This project presents a machine learningbased Cyber Fraud App Detection System designed to identify fraudulent applications and suspicious activities efficiently. The system utilizes supervised learning algorithms such as Logistic Regression, Random Forest, and Gradient Boosting to analyze application features including permissions, API calls, behavioral patterns, and metadata. Data preprocessing techniques such as normalization, feature extraction, and transformation are applied to ensure high-quality input for model training. The trained model classifies applications as fraudulent or legitimate and provides confidence scores for prediction accuracy. The system is designed to operate in real time, enabling fast detection and alert generation while ensuring data privacy through local processing. Additionally, a userfriendly interface is integrated to allow users to input data, view results, and analyze fraud patterns visually. Experimental results demonstrate improved accuracy, reduced false positives, and efficient performance on large datasets. The proposed system offers a scalable, cost-effective, and reliable solution for detecting cyber fraud in modern digital environments.
1Mrs. L. SHIRISHA, 2G. ABHINAV REDDY, 3K. KUNAL, 4G. NIKHIL REDDY
The To-Do List Application is a web-based productivity tool designed to help users efficiently organize and manage their daily tasks. The system enables users to create, update, delete, and monitor tasks with features such as prioritization, deadlines, reminders, and categorization, ensuring improved task visibility and time management. The application addresses the limitations of traditional task management methods, such as manual lists and basic digital tools, by providing real-time updates, interactive user interfaces, and seamless data synchronization. The frontend is developed using HTML, CSS, and JavaScript, offering a responsive and userfriendly interface, while the backend integrates with databases such as PostgreSQL or SQLite to ensure secure data storage and persistence. The system follows a modular architecture that supports scalability and future enhancements such as collaborative task sharing and analytics. Additionally, features like notifications and reminders reduce the chances of missing important tasks and enhance productivity. The application also demonstrates full-stack development principles by combining frontend responsiveness with backend efficiency. By incorporating modern web technologies and structured system design, the To-Do List Application provides a reliable and scalable solution for personal and professional task management. Overall, the system enhances user productivity, improves organization, and simplifies daily task tracking through an intuitive and efficient digital platform.
1Mrs.O. NAGA KUMARI, 2A. AKSHITHA, 3D. SURYA KIRAN, 4J. SRIRAM 1
The Voice Stress Detection System (VSDS) is an intelligent and efficient solution designed to identify stress levels in human speech by analyzing acoustic and speech-based features. Human emotions such as stress, anxiety, and nervousness significantly influence voice characteristics, including pitch, tone, frequency, amplitude, and speech rate. This system utilizes advanced digital signal processing and machine learning techniques to detect these variations and classify stress levels accurately. The process begins with capturing voice input through a microphone or audio file, followed by preprocessing to remove noise and enhance signal quality. Key features such as Mel-Frequency Cepstral Coefficients (MFCC), jitter, shimmer, pitch, and energy are extracted to represent speech patterns. These features are then analyzed using machine learning algorithms such as Support Vector Machine (SVM), Random Forest, and deep learning models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The system is designed with a modular architecture, ensuring scalability, efficiency, and real-time performance. It provides fast and accurate results while being user-friendly and adaptable to multiple languages and environments. The proposed system overcomes limitations of traditional stress detection methods, which are often intrusive and time-consuming. VSDS has wide applications in healthcare, security, call centers, and human-computer interaction. Overall, the system offers a reliable, cost-effective, and noninvasive approach for stress detection and contributes to advancements in emotion recognition technologies.
1Mrs.O. SHRAVANI, 2K. RAVITHREYANI, 3C. SHIVA KUMAR, 4D. TEJA
Real Time Hazardous Gas Detection And Warning System Via Embedded Technology
The real-time hazardous gas detection and warning system using embedded technology is designed to monitor and detect toxic and flammable gases in industrial and residential environments. Hazardous gases such as carbon monoxide, methane, and ammonia pose serious risks to human health and safety, making early detection crucial. The proposed system uses gas sensors integrated with embedded systems like microcontrollers to continuously monitor gas concentration levels in the environment. When gas levels exceed predefined safety thresholds, the system generates immediate alerts through alarms and notifications. The integration of embedded technology ensures fast processing, low power consumption, and real-time response. Modern gas detection systems utilize sensors such as metal oxide semiconductor (MOS) and electrochemical sensors to detect various gases with improved accuracy . Additionally, the system can be enhanced using IoT for remote monitoring and data logging. This project aims to provide a cost-effective, reliable, and efficient solution for preventing accidents caused by gas leaks. The system is particularly useful in industries, mines, and smart homes, ensuring safety and reducing risks associated with hazardous gas exposure.
1 Mrs. D. Anuradha,2 B.Divya,3G.Mounika, 4 B.Raksha, 5A.Ankitha
Secure and Scalable Blockchain-Based Federated Learning for Cryptocurrency Fraud Detection: A Systematic Review
Cryptocurrency fraud has become a significant threat in the digital financial ecosystem, with traditional detection systems often relying on centralized datasets and conventional machine learning techniques. These methods face challenges in terms of scalability, privacy, and the ability to effectively handle distributed and dynamic data sources. In this project, a secure and scalable approach is implemented for cryptocurrency fraud detection by leveraging a dataset stored on a blockchain. By utilizing the blockchain dataset, the system benefits from reliable and tamper-proof data while avoiding the need to implement blockchain infrastructure directly. Federated learning is employed to collaboratively train an XGBoost model across multiple data sources, enabling the detection of fraudulent transactions without centralizing sensitive information. This approach improves model accuracy, ensures data integrity, and provides a systematic framework for analyzing cryptocurrency transactions efficiently. The project demonstrates the potential of integrating advanced machine learning techniques with blockchainbased datasets for robust and privacy-preserving fraud detection.
1Vasavi Sanku,2T.Anusha,3B.Anusha,4 J.Godhavari,5N.Harshitha,6D.Madhuri dikss
ROBOTIC HOODIE FOR BLIND PERSON WITH NAVIGATION
The increasing challenges faced by visually impaired individuals in navigating their surroundings safely and independently highlight the need for assistive technologies that enhance mobility and awareness. Traditional aids such as white canes and guide dogs provide limited information about obstacles and do not offer real-time navigation assistance. This paper proposes a robotic hoodie for blind persons equipped with intelligent navigation and obstacle detection capabilities to improve safety and autonomy. The proposed system integrates sensors such as ultrasonic and infrared sensors embedded within a wearable hoodie to detect obstacles in real time. A microcontroller processes the sensor data and provides feedback to the user through vibration motors or audio signals, indicating the presence and direction of obstacles. Additionally, the system can incorporate GPS and IoT technologies for route guidance and emergency alerts. The robotic hoodie is designed to be lightweight, comfortable, and user-friendly, making it suitable for daily use. Experimental results demonstrate that the system effectively assists visually impaired individuals in avoiding obstacles and navigating safely, thereby enhancing their independence and quality of life.
1Mrs. A. Hari Priya, 2B.Aishwarya, 3Nandini, 4E.Maheshwari, 5E.Shravani
Optimizing Electric Vehicle Charging Considering Driver Satisfaction Through ML Algorithm
The rapid adoption of electric vehicles (EVs) has increased the demand for efficient and intelligent charging infrastructure. However, long waiting times, station congestion, and varying electricity prices often reduce driver satisfaction during charging sessions. To address these challenges, this study proposes a machine learning–based optimization system for electric vehicle charging that considers driver satisfaction as a primary factor. The system is implemented using a Django-based web framework integrated with advanced machine learning models to analyze charging conditions and predict driver satisfaction levels. The proposed system collects multiple parameters related to EV charging, including state of charge before charging, queue length, waiting time, electricity price, distance to charging station, station rating, charger power, historical satisfaction, price fairness, and charging speed stability, along with contextual factors such as weather conditions, crowd level, preferred charging time, charging station type, and trip urgency. These features are processed using data preprocessing techniques such as standardization, categorical encoding, and feature alignment before training machine learning models. Multiple algorithms, including Random Forest, Gradient Boosting, and XGBoost, are trained and evaluated to determine the most effective model for predicting driver satisfaction levels. The system compares models using performance metrics such as accuracy, precision, recall, and F1-score, and selects the bestperforming model for deployment. Once trained, the system predicts the expected satisfaction level of a driver during a charging session and provides intelligent recommendations to improve the charging experience, such as selecting less crowded stations, choosing off-peak charging times, or using faster chargers. By integrating machine learning prediction with real-time user interaction through a web interface, the proposed system helps drivers make informed charging decisions and assists service providers in optimizing charging station operations. Overall, the proposed approach improves charging efficiency, reduces waiting time, and enhances driver satisfaction, contributing to a smarter and more sustainable EV charging ecosystem.
1K. Yakoob,2Kodem Bharathi,3Gugulothu Shwetha,4R. Nikhitha Reddy,5Boini Shivani,6Kotagiri Shravani
OVER SPEED INDICATION AND ACCIDENT PREVENTION SYSTEM
The increasing number of road accidents caused by over-speeding has become a major concern for public safety and transportation systems. Excessive speed not only reduces the driver’s ability to react to sudden obstacles but also increases the severity of accidents. Traditional speed monitoring systems rely heavily on manual enforcement and external surveillance, which are often inefficient and unable to provide real-time alerts to drivers. This paper proposes an intelligent over-speed indication and accident prevention system designed to enhance road safety through continuous monitoring and timely warnings. The proposed system utilizes sensors and embedded technologies such as GPS modules and speed detection units to monitor the vehicle’s speed in real time. When the vehicle exceeds predefined speed limits, the system generates alerts through visual or auditory signals to notify the driver. Additionally, the system can be integrated with accident prevention mechanisms such as automatic braking, obstacle detection, and collision warning systems to minimize the risk of accidents. Experimental results demonstrate that the system effectively reduces over-speeding incidents and improves driver awareness. This approach provides a cost-effective and reliable solution for enhancing road safety and preventing accidents, with potential applications in smart transportation systems and intelligent vehicles.
1Mr. Srikanth.V, 2B.Sarthika, 3D.Mythri, 4G.Anitha, 5Ch.Diya Sri
Blood Pressure Controlled By Machine Learning Algorithms
Personalized blood pressure control has become a critical focus in modern healthcare due to the rising prevalence of hypertension and its associated complications such as cardiovascular diseases and stroke. Traditional treatment approaches often rely on generalized guidelines, which may not effectively address individual patient variability. This paper proposes a machine learning-based approach for personalized blood pressure management within remote patient monitoring systems. By leveraging patient-specific data such as historical blood pressure readings, lifestyle patterns, medication adherence, and physiological parameters, machine learning models can predict trends and recommend optimized interventions. The integration of wearable devices and IoT-enabled health monitoring systems allows continuous data collection and real-time analysis. This approach enhances early detection of anomalies, improves treatment accuracy, and supports proactive healthcare delivery. The proposed system aims to reduce hospital visits, improve patient outcomes, and enable efficient healthcare management through intelligent decision support.
1 I. Swapna,2 Pamula Sravani,3Annepally Manimala,4Karre Sharanya,5Vemula Sravanthi,6M. Maithily
Colorectal Cancer Detection Using Pre-Trained Ensemble Algorithms
Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide, emphasizing the need for early and accurate detection methods. Traditional diagnostic procedures, such as colonoscopy and biopsy, are often invasive, time-consuming, and prone to human error. Therefore, the integration of machine learning techniques in medical diagnosis has gained significant attention for enhancing the precision and speed of colorectal cancer detection. This project proposes a novel approach that utilizes pre-trained ensemble machine learning algorithms to detect colorectal cancer from medical imaging and clinical data. Ensemble learning, which combines multiple individual models, has been shown to improve predictive performance by reducing variance and bias. Leveraging pre-trained models accelerates the training process and allows the system to benefit from learned feature representations from large-scale datasets. The methodology involves collecting a comprehensive dataset of colorectal images and related patient information, followed by data preprocessing steps such as normalization and augmentation to improve model generalization. Multiple pre-trained base learners, including decision trees, random forests, and gradient boosting machines, are fine-tuned and ensembled using techniques like voting, stacking, or boosting. The ensemble model is then evaluated on unseen test data to assess its accuracy, sensitivity, specificity, and overall robustness. Experimental results demonstrate that the proposed ensemble approach outperforms individual classifiers and traditional diagnostic methods in identifying colorectal cancer. The system shows promise in reducing false negatives and improving early-stage detection, which is critical for patient prognosis and treatment planning. Additionally, the use of pre-trained models significantly reduces computational costs and training time, making the solution feasible for real-world medical applications. In conclusion, this study highlights the potential of ensemble machine learning frameworks combined with transfer learning for effective colorectal cancer detection. Future work will focus on expanding the dataset, integrating multi-modal data sources, and deploying the model in clinical settings to assist healthcare professionals in timely and accurate diagnosis, ultimately improving patient outcomes.
1K. Manjula,2Chenchu Jyothi,3Angoth Kavyasree,4Diddiga Prasanna,5Varikuppala Sarala,6Boina Anusha
A Hybrid Deep Learning Model To Predict High-Risk Students In Virtual Learning Environment
The rapid growth of virtual learning environments (VLEs) has increased the need for intelligent systems that can identify students who are at risk of poor academic performance or disengagement. This study proposes a hybrid deep learning–based predictive framework for detecting high-risk students by analyzing behavioral and engagement data collected from online learning platforms. The system is implemented as a web-based application using a Flask framework, where student interaction data such as time spent weekly, quiz score average, forum participation, video watching percentage, assignment submission rate, login frequency, session duration, device type, course difficulty, and region are utilized as predictive features. Data preprocessing includes missing value removal, label encoding for categorical attributes, and feature normalization using a standard scaler. The dataset is then divided into training and testing subsets to evaluate model performance. Multiple machine learning and deep learning models—including Convolutional Neural Networks (CNN), XGBoost, Random Forest, and Decision Tree classifiers—are trained and compared to determine the most effective approach for predicting student engagement risk levels. The system classifies students into low, medium, or high engagement risk categories, enabling early identification of learners who may require academic support. Experimental results demonstrate that integrating deep learning with traditional machine learning models enhances prediction accuracy and provides a scalable solution for improving student retention and personalized learning interventions in virtual education systems.
1Hemalatha,2Dodali Jyothi,3Bayya Indhu,4Bandari Sruthi,5Baindla Kalpana,6Manchala Sandhya