American Journal of Management and IOT Medical Computing
Editor- In-Chief: Dr. K M Jhon Title proper: American Journal of Management and IoT Medical Computing (AJMIMC) Subject: AI ML, Data Science, Management, Medical, Pharmacy, Cyber Security, Computer Science, IOT, Engineering All, Science and Technology Publisher Name: GreenMS Publications ISSN: 3069-0110 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. 3069-0110
SMART COMMUNITY HEALTH MONITORING AND EARLY WARNING SYSTEM FOR WATER-BORNE DISEASES
Water-borne diseases such as cholera, typhoid, dysentery and diarrhea are major public health concerns, particularly in developing countries where access to clean drinking water and proper sanitation is limited. Traditional water quality monitoring methods rely on manual sampling and laboratory testing, which are time-consuming and often fail to provide early warning before disease outbreaks occur. This project proposes a Smart Community Health Monitoring and Early Warning System that integrates Internet of Things (IoT) technology and machine learning techniques to continuously monitor water quality and predict potential disease risks. The system uses to IoT sensors such as PH sensors, turbidity sensor and temperature sensors to monitor important water quality parameters in real time. The collected data is transmitted to a cloud platform where it is stored and analyzed. Machine learning algorithms analyze both environmental data and community health reports to detect abnormal patterns and identify possible contamination risks.
Mrs. B. Archana1 , Ms. S. Saitejaswi2 , Ms. N. Divyasri3 , Ms. V. Susmitha4 , Ms. P. Savithri5
CUSTOMER CHURN PREDICTION USING MACHINE LEARNING
Customer churn is a major concern for organizations in industries such as telecom, banking, e commerce, 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 analysis and manual monitoring, which are often reactive, less accurate, and costly. This project proposes 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.Helini, T. Sathvika, G.Shiva Joshna, D.Vijaya Laxmi, A.Sruthi
AI-POWERED VIRTUAL TRY-ON SYSTEM FOR FASHION E-COMMERCE
Fashion e-commerce is growing really fast these days. Customers can’t actually try on clothes before buying them which makes things tricky. That uncertainty probably causes a lot of returns I suppose. This paper suggests using AI for virtual try-on setup. Users can see clothes on their own pictures that way. It pulls in computer vision and deep learning stuff along with pose estimation to make the simulations look real. The system mixes image processing and those diffusion models to keep the body shape right and the pose natural plus the fabric texture. It does not mess up the ouput much. Experiments show people feel more confident about buying and they engage better. Return go down too it seems. For e-commerce sites this could be a good scalable fix. It makes shopping more personal and interactive kind of like trying things on without the hassle. Some details might get overlooked but overall it works.
1Mrs. D. Kousari Kumari, 2Ms. A. Meghana, 3 Ms. Ch.Akhila, 4Ms. D. Amulya, 5Ms. S. Shushma
Web-Based Document Classification System Using Deep Learning
Managing large volumes of digital documents is often time-consuming . This project presents a Web-Based Document Classification System that automates the sorting of digital documents. With secure authentication and bulk upload support, users can easily manage files through a drag-and-drop interface. A ResNet18 deep learning model extracts features and classifies documents into categories such as Email, Resume, and Scientific Publication. Results with confidence scores are displayed in an interactive dashboard, offering search, filter, and export options. The system enhances efficiency in corporate, academic, and HR domains by delivering a secure, scalable, and userfriendly solution. Furthermore, the system is designed with modular architecture, allowing easy integration of additional document categories and continuous model improvement for enhanced accuracy over time.
Mrs. A. Eenaja¹, Koyaguri Sravani², Vermareddy Harini³, Kammari Shravani⁴, Nimmaraboina Srilekha⁵
Sensitive information like financial data, personal information and personal identities are more likely to be revealed due to the explosive growth of digital documents. The majority of traditional methods for redacting data are labor-intensive and manual, which often leads to errors and lack of data protection. An automated redaction system based on artificial intelligence that can effectively identify and redaction of sensitive information from digital documents and photos. To protect privacy and security, the system automatically redacts sensitive data after identifying it using machine learning techniques. An efficient and scalable solution is built by using technologies like Python, MongoDB, and web frameworks. This helps in increasing data protection, decreases manual work and improves accuracy in digital documents.
1 Mrs. K. Helini, 2 B. Hindu, 3 D. Sindhuja, 4 L. Chandana, 5 B. Shailaja, 6 M. Charitha
AI BASED SMART ENVIRONMENTAL MONITORING SYSTEM
This project introduces an AI-based Smart Environmental Monitoring System that automatically detects and reports public safety issues in urban areas. It uses Artificial Intelligence (AI) and Internet of Things (IoT) technologies. The main goal of the system is to monitor environmental conditions and identify hazardous situations without the need for constant human supervision. The system addresses two common problems in public spaces: gatherings of stray dogs and open manholes. A camera module captures images of the environment, and a machine learning model analyses these images to detect dogs. When several dogs are identified in the same area, signaling a gathering, the system records the event and sends the information to a web-based complaint management system. Additionally, an ultrasonic sensor measures the distance between the sensor and the ground to check the condition of a manhole. If the distance suggests that the manhole is open, the system creates a complaint and sends it to the monitoring dashboard. There is also a web interface where administrators and officers can log in to view detected problems, check details like location, date, and time, and update the complaint status to resolved or pending. By combining sensor technology, machine learning, and web monitoring, this system improves the efficiency of detecting environmental hazards, reduces reliance on manual reporting, and allows for quicker responses from authorities. This approach supports smart city efforts by offering a practical solution for monitoring safety-related environmental conditions in urban areas.
1 Dr. D. Shanthi, 2 S. Srivarshini, 3 B. Shilpa, 4 D. Sravanthi, 5 T. LakshmiPriya
SMART LITTER DETECTION IN MARKET STREETS USING AI-BASED MONITORING SYSTEMS
The rapid growth of urban populations and commercial activities in market streets has led to increasing challenges in maintaining cleanliness and effective waste management. Traditional monitoring methods rely heavily on manual inspection, which is time-consuming, inefficient, and often inconsistent. To address these issues, this paper proposes a Smart Litter Detection in Market Streets Using AI-Based Monitoring Systems, which leverages artificial intelligence and computer vision techniques to automatically identify and monitor litter in public spaces. The proposed system utilizes surveillance cameras integrated with deep learning models, such as convolutional neural networks (CNNs), to detect various types of waste materials including plastics, paper, and organic debris in real-time. The system processes video feeds to identify litter presence, location, and accumulation patterns, enabling timely intervention by municipal authorities. Additionally, the platform can generate alerts and visual reports, improving response efficiency and resource allocation for cleaning operations. The model is trained on diverse datasets representing different environmental conditions, ensuring robustness against variations in lighting, weather, and crowd density. Experimental results demonstrate high detection accuracy and real-time performance, making the system suitable for deployment in busy market environments. Furthermore, the solution supports smart city initiatives by promoting automation, sustainability, and improved urban hygiene. Overall, the proposed AI-based monitoring system provides an efficient, scalable, and cost-effective approach to litter detection and management, contributing to cleaner and healthier urban environments.
Artificial Intelligence, Litter Detection, Smart City, Computer Vision, Convolutional Neural Networks (CNN), Waste Management, Real-Time Monitoring, Urban Cleanliness
Knowledge Management Systems and Organizational Learning in High-Performance Enterprises
Knowledge Management Systems (KMS) have become a general strategic enhancer within organizations on how to capture, store, and share and even utilize organization knowledge. Simultaneously, organizational learning has emerged as a vital competency, which improves innovation, flexibility, staff growth and decision making. This is research on the connection learning in high performance organizations. technological infrastructure and learning-oriented organizational cultures in enhancing productivity of employees and effectiveness of organization. Structured survey response whose residents were employees of high-performance organizations in various fields was utilized to develop a quantitative research approach. Descriptive analysis and hypothesis tests were statistical methods that the results indicate that Knowledge Management Systems performance can be greatly improved with proper implementation of Knowledge Management Systems promoting organization learning ability, collaborative decision making and innovation performance. The research also points out that companies that have great knowledge sharing cultures have increased sustainability in growth and employee motivation.
Customer Relationship Management Strategies for Enhancing Brand Loyalty in Competitive Markets
In a marketplace that is highly competitive, organizations are tirelessly searching to acquire effective strategies to ensure that they maintain its customers and even enhance its brand loyalty. Customer Relationship Management (CRM) has turned out to be a very crucial dimension in which businesses can establish long term relationships with customers by engaging in one-on-one communication, enhancing service quality, and making decisions based on data. The current paper examines the effects of CRM strategy on brand loyalty considering customer satisfaction, customer trust, customer engagement, and customer perceived value. The paper emphasizes the need to incorporate the use of digital tools, feedback, and customer-focused practices in maintaining a competitive advantage. The results indicate that the successful implementation of CRM can promote customer retention and adherence to customer loyalty considerably, thus making a business viable in the long term.
Customer Relationship Management, Brand Loyalty, Customer Satisfaction, Customer Engagement, Competitive Markets, Customer Retention
The Smart Digital Complaint Box is a web-based system designed to modernize and improve the traditional complaint handling process in educational institutions and organizations. The existing manual complaint system relies on paper submissions, which often results in delays, poor record management, lack of transparency, and inefficiency. This project introduces a digital platform where users can submit complaints online easily through a user-friendly interface. The system supports multi-language functionality, allowing users from diverse backgrounds to access the platform conveniently. Each complaint is assigned a unique tracking ID, enabling users to monitor the progress and status of their complaints in real time. One of the key features of the system is automatic escalation, where unresolved complaints are forwarded to higher authorities after a fixed time period to ensure timely action. The system securely stores all complaint data in a centralized database, reducing paperwork and improving data management. Administrators can access a dashboard to view, manage, and update complaint statuses such as pending, in progress, resolved, or escalated. This enhances transparency, accountability, and communication between users and management. Overall, the Smart Digital Complaint Box provides an efficient, secure, and reliable solution for complaint management, ensuring better service quality, faster resolution, and improved user satisfaction.
1Mrs.K. DURGA BHAVANI, 2M. PRATEEKSHA, 3 P. SURYA TEJA, 4 S. RASHMITHA
The increasing concerns regarding women’s safety in public and private spaces have highlighted the urgent need for intelligent and real-time security solutions. This project proposes a Women Safety Hotspot Analysis System integrated with an SOS emergency alert mechanism to enhance personal security. The system enables users to send instant emergency alerts by pressing an SOS button, which transmits their live GPS location, time, and details to pre-registered emergency contacts, ensuring immediate assistance during critical situations. Additionally, the system incorporates a hotspot analysis module that collects and analyzes incident data to identify unsafe areas using clustering techniques. These high-risk zones are displayed on an interactive map, allowing users to make informed decisions and avoid dangerous locations. The application utilizes technologies such as GPS, Google Maps API, and cloud-based databases to provide real-time tracking and visualization of safety information. Unlike traditional systems that rely on manual communication, this system offers automated alerting and proactive safety awareness. It also supports community-based incident reporting, which enhances data accuracy and improves hotspot detection over time. The system is designed to be user-friendly, cost-effective, and scalable for deployment in various environments such as colleges, workplaces, and smart cities. Overall, this project demonstrates how integrating real-time emergency response with data-driven hotspot analysis can significantly improve women’s safety and reduce response time in emergencies.
1Mrs.S. GUNASREE, 2 S. DIVYA, 3 S. MANISHA, 4M. KALYAN
Campus Connect is a web-based campus management system developed to integrate multiple institutional services into a single unified platform. In many educational institutions, services such as communication, academic management, library access, cafeteria services, and event tracking operate independently, leading to inefficiencies and inconvenience for users. The proposed system addresses these challenges by providing a centralized solution that enhances connectivity and streamlines operations. It offers a personalized dashboard through which users can access academic information, announcements, schedules, and notifications in real time. The system includes modules for students, faculty, library management, cafeteria services, and campus events, enabling users to perform various activities efficiently within one platform. Students can manage library resources, view cafeteria menus, and stay updated with campus events and announcements. The system is developed using Django, ensuring secure authentication, data protection, and reliable performance. It is designed to be scalable and capable of handling peak usage without performance degradation. By automating manual processes and enabling seamless communication, Campus Connect reduces errors, saves time, and improves overall productivity. The platform provides a user-friendly interface that is accessible across multiple devices, enhancing usability and engagement. Overall, Campus Connect contributes to the development of a smart digital campus by improving efficiency, communication, and user experience.
1V.LALITHA LAVANYA, 2T.SAI KEERTHI, 3 S. SHAKSHI, 4 P. RISHI
The Student Performance Prediction System is a web-based intelligent application developed using Django and Machine Learning techniques to predict academic outcomes effectively. Student performance is influenced by multiple academic and behavioral factors such as attendance, internal marks, study hours, assignment completion, and sleep patterns. The system collects these inputs through a user-friendly interface and processes them using a trained machine learning model. The Random Forest Classification algorithm is used due to its ability to handle complex datasets and improve prediction accuracy by combining multiple decision trees. Data preprocessing techniques such as normalization, encoding, and handling missing values ensure that input data is suitable for model prediction. Based on the processed data, the system classifies students into three categories: Excellent, Average, and Needs Improvement. The results are displayed instantly, allowing educators to identify weak students early and take corrective measures. The system reduces manual effort, minimizes errors, and supports datadriven decision-making in education. Additionally, the application is scalable, efficient, and easy to use, making it suitable for schools and colleges. By integrating machine learning with web technologies, the system enhances academic monitoring and improves overall student outcomes.
1Mrs.E. ARUNA, 2T. JYOTHI, 3T. VISHNU CHARAN, 4T. ANJAN
The Smart Traffic Signal Control System is an intelligent traffic management solution designed to optimize vehicle movement at intersections using real-time data analysis and machine learning techniques. Traditional traffic systems operate on fixed time intervals, leading to inefficient traffic flow, increased congestion, and unnecessary waiting times . This project introduces a dynamic approach where traffic signals are adjusted based on real-time vehicle density captured through cameras and sensors . Using image processing techniques such as OpenCV, the system detects vehicles and calculates traffic density on each road . Machine learning models analyze these patterns and predict optimal signal timings to improve efficiency . The system prioritizes high-density roads by allocating longer green signals, thereby reducing congestion and improving road utilization . Additionally, the system incorporates emergency vehicle detection, allowing immediate signal clearance for faster response times . Data visualization tools provide real-time monitoring and performance insights . The implementation of this system reduces fuel consumption, minimizes environmental pollution, and enhances road safety . It also contributes to smart city development by integrating automation and intelligent decisionmaking . Overall, the system provides a scalable and efficient solution for modern traffic management challenges
1Dr.S. PAVANI, 2 S. SUPRIYA, 3 S. PURUSHOTHAM, 4V. SONY
The Answer Evaluation System is an intelligent AIbased application designed to automate the evaluation of descriptive answers by analyzing their semantic meaning rather than relying solely on keyword matching . The system uses Natural Language Processing (NLP) techniques to process textual data efficiently and generate meaningful insights . It employs a pretrained SentenceTransformer model to convert textual answers into vector embeddings, enabling accurate comparison between student responses and predefined model answers . Cosine similarity is used as the primary metric to measure the closeness between answer representations and determine evaluation scores . The system is implemented using Python and integrated with libraries such as NumPy and Scikit-learn to support efficient computation and model performance. A Streamlitbased web interface allows users to input answers and receive real-time feedback and scores in an interactive manner . The system addresses limitations of traditional evaluation methods, which are time-consuming and prone to inconsistency due to human involvement . By providing instant and unbiased evaluation, it enhances efficiency in educational systems and online examination platforms . Additionally, the system supports scalability by handling large volumes of student responses with minimal computational overhead . The architecture includes modules for preprocessing, embedding generation, similarity computation, and result display to ensure a seamless workflow. Overall, the system improves accuracy, fairness, and speed in answer evaluation, making it a valuable tool for modern education systems .
1Dr.M. RAMU, 2RY.NAVEENA, 3O. RAKESH, 4M. RANADHEER
The AI-Powered Summarization Application is a full-stack intelligent system designed to address the increasing challenge of information overload in modern digital environments . With the exponential growth of textual, document-based, and multimedia content, users face difficulty in extracting meaningful insights efficiently. This application provides a unified platform that enables users to generate concise, context-aware summaries from multiple input formats including raw text, PDF documents, and YouTube video transcripts. The system integrates Natural Language Processing techniques with advanced Generative AI models to produce coherent and accurate summaries that preserve the core meaning of the original content. The architecture follows a client–server model, where a responsive frontend built using React.js interacts with a Python Flask backend for processing requests and managing AI interactions. Libraries such as PyMuPDF and YouTube Transcript API facilitate efficient content extraction from different sources. A key feature of the system is its customization capability, allowing users to define summary length, tone, and output language according to their needs. This flexibility enhances usability across academic, professional, and research domains. The system significantly improves productivity by reducing manual reading time and transforming unstructured data into structured knowledge. Overall, the application represents a scalable, efficient, and user-friendly solution for intelligent content summarization.
1Dr.J. PRAVEEN KUMAR, 2 P. ANJUM AZRA, 3M. A. AQIB ARFATH, 4 S. SRIRAM, 5T. PRATHYUSHA
The rapid advancement of digital learning environments has significantly increased the demand for efficient and organized platforms for sharing academic resources among students. Traditional note-sharing methods, such as physical exchange, emails, and messaging applications, often result in unstructured data, limited accessibility, and security concerns. To address these issues, this project proposes a Peer-to-Peer Notes Sharing Platform designed as a web-based system that enables students to upload, manage, and access academic materials in a centralized and secure manner. The system integrates a Flask-based backend with a relational database to ensure efficient data handling and seamless communication between users and storage systems. It provides essential functionalities including user authentication, file upload, keyword-based search, and secure download features. The platform enhances accessibility by allowing users to retrieve study materials anytime and from anywhere. Additionally, it promotes collaborative learning by encouraging students to contribute and utilize shared knowledge resources. The structured organization of notes based on subjects and metadata improves retrieval efficiency and user experience. The system is designed to be scalable, reliable, and user-friendly, with potential for future enhancements such as recommendation systems, rating mechanisms, and cloud integration. Overall, the proposed solution addresses the limitations of existing systems and contributes to improving academic productivity through an efficient and collaborative digital learning environment.
1Ms. S. GUNASREE, 2D. KARTHIK, 3D. MANISH PRANAY,4 J. JAYANTH
Skin cancer is one of the most common and lifethreatening diseases worldwide, where early detection plays a crucial role in improving survival rates. This project presents an intelligent system for skin cancer detection using Convolutional Neural Networks (CNN), a powerful deep learning technique for image classification. The proposed system aims to automate the diagnosis process by analyzing dermoscopic images of skin lesions and classifying them into benign or malignant categories. The system reduces reliance on manual examination by dermatologists, which is often time-consuming and prone to human error. The model is trained using a dataset of labeled skin lesion images, enabling it to learn complex patterns such as color variation, texture, and shape. Preprocessing techniques like resizing, normalization, and noise removal enhance the quality of images before feeding them into the model. Feature extraction is automatically handled by CNN layers, improving classification accuracy. The system is designed to be user-friendly, allowing users to upload images and receive instant predictions. Experimental results demonstrate that the CNN-based approach achieves high accuracy compared to traditional machine learning methods. The proposed solution provides a fast, costeffective, and reliable diagnostic tool, assisting healthcare professionals in early detection and treatment planning. This system highlights the importance of artificial intelligence in modern healthcare and its potential to revolutionize medical diagnostics by providing scalable and efficient solutions.
1Mrs. LALITHA LAVANYA,2 SAIPRIYANKA.B, 3BHUVANENDHAR.K,4 POOJITHA.A
The AI Travel Planner is an intelligent, automated system designed to simplify and enhance the travel planning process using advanced artificial intelligence and multi-agent architectures. Traditional travel planning involves navigating multiple platforms to compare flights, hotels, and weather conditions, resulting in a fragmented and time-consuming experience. This project addresses these challenges by integrating a centralized platform powered by agentic AI workflows, enabling seamless coordination between travel components. The system utilizes a local Large Language Model to convert user-friendly inputs into structured travel data, such as standardized airport codes, ensuring accuracy and efficiency. It employs parallel agents to retrieve real-time data on flights, accommodations, and weather conditions, while a smart caching mechanism reduces redundant API calls and improves response time. The backend is developed using FastAPI, while the frontend is implemented with Next.js to provide a responsive and user-friendly interface. The system generates personalized, day-by-day itineraries based on user preferences such as destination, budget, and travel dates. Additionally, the solution supports downloadable reports, enhancing usability for travelers. By combining real-time data processing, AI-driven decisionmaking, and scalable architecture, the AI Travel Planner offers a highly efficient and personalized travel planning experience. The project demonstrates the practical application of artificial intelligence in automating complex decisionmaking tasks and improving user satisfaction in travel management systems.
1Mrs. B.NIRUPAMA, 2B.SAI KRISHNA, 3AMAN KUMAR, 4K. SUMANTH
The rapid evolution of digital technology has significantly increased the importance of online presence and personal branding for students and professionals. This project presents a Dynamic Portfolio Website Generator, a web-based system designed to simplify the creation of professional portfolios without requiring extensive programming knowledge. The system enables users to register, log in, and input personal details such as education, skills, projects, certifications, and contact information through an intuitive dashboard interface. Based on the entered data, the system dynamically generates a structured and visually appealing multi-page portfolio website that can be accessed from any device. The application focuses on automation, usability, and customization, thereby reducing the time and effort required for manual portfolio development. It is developed using HTML, CSS, JavaScript for frontend design and PHP with MySQL for backend processing and data management. Key features include user authentication, responsive design, theme selection, real-time updates, and portfolio download functionality. The system ensures efficient data handling and scalability for future enhancements such as analytics and advanced templates. Overall, the proposed solution provides a practical and userfriendly platform that enhances professional visibility and improves career opportunities in a competitive digital environment.
1Dr. S. PAVANI, 2C. SRUTHI, 3C. SUMANA, 4 J. AKSHAY, 5G. VINAY
The Food Waste Management System is a webbased solution designed to address the critical issue of food wastage while simultaneously tackling hunger and environmental concerns. In modern society, large quantities of surplus food generated by restaurants, hotels, households, and events are often discarded due to the lack of proper coordination and distribution mechanisms. This system provides a centralized digital platform that connects food donors with NGOs and volunteers in real time, enabling efficient collection and redistribution of excess food to needy individuals. The application allows donors to upload details such as food type, quantity, location, and expiry time, ensuring transparency and accessibility. Volunteers and NGOs can view available donations, request pickups, and track delivery status, ensuring timely utilization before spoilage. Additionally, the system incorporates features such as secure user authentication, role-based access, real-time notifications, and an administrative dashboard for monitoring activities and generating reports. It also addresses non-edible waste by promoting eco-friendly disposal methods such as composting and biogas production. By integrating modern web technologies and automation, the system minimizes manual effort, enhances coordination, and improves resource utilization. Overall, the Food Waste Management System contributes to social welfare by reducing hunger, promotes sustainability by minimizing waste, and supports environmental protection by reducing landfill impact and greenhouse gas emissions.
1Dr. M. RAMU, 2K. PAVANI, 3CH. ESHWAR, 4K. KARTHIK
The AI Virtual Interview Application is an intelligent system designed to enhance interview preparation through automated and interactive mock interview sessions. The application provides users with various skill categories such as communication, technical skills, aptitude, and behavioral questions, enabling a realistic simulation of interview environments. By leveraging artificial intelligence, machine learning, and natural language processing techniques, the system dynamically generates interview questions and evaluates user responses based on accuracy, relevance, fluency, and confidence . The application supports both text and voice inputs, offering flexibility and accessibility to users. A key feature of the system is its real-time feedback mechanism, which provides detailed performance analysis, including strengths, weaknesses, and improvement suggestions. This enables users to track their progress over time and continuously improve their interview skills. The system also eliminates the dependency on manual mock interviews and reduces the need for human evaluators, making it a cost-effective and scalable solution. By creating a risk-free and user-friendly environment, the application helps reduce interview anxiety and boosts confidence among students and job seekers. Furthermore, the integration of data storage and reporting modules ensures proper tracking and evaluation of user performance. Overall, the AI Virtual Interview Application bridges the gap between academic learning and real-world interview expectations, making it a valuable tool for career development and skill enhancement.
1Dr.J.PRAVEEN KUMAR, 2CH.MEGHANA, 3M.SAI VAISHNAVI,4 B.VENUMADHAV REDDY, 5A.ABHINAY
Financial Fraud Detection Using Value At Risk With Machine Learning In Skewed Data
Financial fraud has become a major challenge for banking and financial institutions due to the rapid growth of digital transactions and online payment systems. Detecting fraudulent activities is particularly difficult when transaction datasets are highly imbalanced or skewed, where legitimate transactions significantly outnumber fraudulent ones. Traditional rule-based systems often fail to identify complex and evolving fraud patterns, making it necessary to adopt intelligent data-driven approaches. This study presents a machine learning-based financial fraud detection framework that incorporates the concept of Value at Risk (VaR) to improve the identification of suspicious transactions in skewed datasets. The proposed approach utilizes VaR as a financial risk indicator to estimate potential losses in transaction amounts and integrate it as an additional feature in the fraud detection process. By combining risk-based financial analysis with machine learning techniques, the system is able to capture abnormal transaction behaviors more effectively. The dataset undergoes preprocessing steps including data cleaning, feature selection, categorical encoding, and normalization to prepare the data for model training. Multiple machine learning algorithms such as Logistic Regression, Gradient Boosting, and XGBoost are employed to classify transactions as fraudulent or legitimate. Since fraud detection datasets are typically imbalanced, appropriate techniques such as class weighting and stratified data splitting are used to address skewed data distribution and improve model performance. The models are evaluated based on accuracy and predictive capability, and the best-performing model is selected for final fraud prediction. Experimental results demonstrate that incorporating Value at Risk as a feature enhances the detection capability of machine learning models in identifying risky transactions. The proposed framework provides an effective and scalable solution for financial fraud detection by integrating financial risk metrics with machine learning techniques. This approach can help financial institutions detect fraudulent transactions more accurately, reduce financial losses, and strengthen the security of digital financial systems
1M. Madhavi Latha,2K.Varshitha,3 P.Haripriya,4K.Shireesha,5CH.Sadhana,6R.Vennala
Criminal investigate tracker with suspect prediction
Crime investigation is a complex and time-consuming process that requires the analysis of large volumes of data, including crime records, suspect information, evidence, and location details. Traditional investigation methods often rely on manual analysis, which can lead to delays and inefficiencies in identifying suspects and solving cases. With the rapid advancement of data analytics and machine learning technologies, intelligent systems can now assist investigators in analyzing crime patterns and predicting potential suspects. The Criminal Investigation Tracker With Suspect Prediction system is designed to support law enforcement agencies by providing an intelligent platform for managing crime records and predicting suspect involvement using machine learning techniques. The system stores detailed information about crime incidents, suspects, evidence, and investigation activities. By analyzing this data, the system can identify patterns and relationships that may not be easily detected through manual investigation. Machine learning algorithms such as Random Forest, Decision Tree, and Logistic Regression are used to analyze historical crime data and predict whether a suspect is likely involved in a particular crime. The system evaluates various factors including crime location, time, suspect history, distance from the crime scene, and evidence type to generate predictions. In addition to prediction capabilities, the system also functions as an investigation management platform that helps track crime cases, monitor investigation progress, and maintain organized records of suspects and evidence. By combining crime tracking with predictive analytics, the system assists investigators in making faster, more informed decisions. Ultimately, this project aims to improve investigation efficiency, reduce investigation time, and support law enforcement agencies in solving crimes more effectively.
1 P. Narsimha,2L.pooja,3N.Akhila,4K.Shireesha,5A.Mounika,6M.Shireesha
RASPBERRY PI BASED WEATHER REPORTING OVER IOT
The increasing need for real-time environmental monitoring has led to the development of smart weather reporting systems using Internet of Things (IoT) technologies. Traditional weather monitoring methods often rely on centralized systems that may not provide localized and real-time data efficiently. To address these limitations, this paper proposes a Raspberry Pi-based weather reporting system that collects, processes, and transmits environmental data over the internet for remote monitoring and analysis. The proposed system utilizes sensors such as temperature, humidity, pressure, and rainfall sensors connected to a Raspberry Pi, which acts as the central processing unit. The collected data is processed and transmitted to cloud platforms using IoT protocols, enabling users to access real-time weather information through web or mobile applications. The system is costeffective, scalable, and capable of continuous monitoring, making it suitable for applications in agriculture, environmental monitoring, and smart cities. Experimental results demonstrate that the system provides accurate and reliable weather data, enhancing decision-making and improving efficiency in weather-dependent activities.
1Mr. S. Chandra Bhanu, 2M.Gouthami, 3R.Rajeshwari, 4T.Mounika, 5B.Divya
ARTIFICIAL INTELLIGENCE TECHNIQUES FOR LAND SLIDE PREDICTION USING SATELLITE IMAGINORY
Landslides are one of the most destructive natural disasters, causing significant loss of life, infrastructure damage, and environmental degradation worldwide. Accurate and timely prediction of landslides remains a challenging task due to the complex interaction of geological, hydrological, and climatic factors. In recent years, advancements in Artificial Intelligence (AI) and the availability of high-resolution satellite imagery have opened new avenues for improving landslide prediction systems. This study explores the application of AI techniques such as Machine Learning (ML) and Deep Learning (DL) for landslide prediction using satellite imagery. Various models, including Convolutional Neural Networks (CNNs), Random Forest, and Support Vector Machines (SVM), are employed to analyze spatial and temporal patterns in satellite data. These models integrate multiple influencing factors such as rainfall, slope, soil type, vegetation cover, and land use. The proposed approach aims to enhance prediction accuracy by leveraging feature extraction capabilities of deep learning models combined with geospatial analysis. Experimental results demonstrate that AI-based models outperform traditional statistical methods in identifying landslide-prone areas. This research contributes to disaster management by providing an efficient and scalable solution for early warning systems and risk assessment.
1 J. Santhosh,2A. Jayavardhini,3D. Ananda,4Aileni Shivani,5Molla Komali,6Kamaluri Sri Lakshmi
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
Improving Cloud Data Communication Security Through Intelligent Machine Learning Models
Cloud computing has become an essential platform for data storage and communication due to its scalability, flexibility, and cost efficiency. However, the increasing volume of cloudbased data communication also raises significant security concerns, including unauthorized access, malicious attacks, and network intrusions. To address these challenges, this work proposes an intelligent machine learning–based framework for improving cloud data communication security. The proposed system analyzes network traffic attributes such as source IP, destination IP, protocol type, packet size, login time, country of origin, and risk score to detect potential attack patterns in cloud environments. Feature engineering techniques are applied to extract meaningful attributes from IP addresses, followed by preprocessing steps including label encoding and data normalization. Multiple machine learning algorithms, namely Random Forest, Gradient Boosting, and Extreme Gradient Boosting (XGBoost), are employed to classify and identify various types of cyber-attacks in cloud networks. The models are trained and evaluated using a cloud security dataset, and their performance is measured using metrics such as accuracy, classification report, and confusion matrix analysis. Among the implemented models, XGBoost demonstrates superior performance in predicting attack categories and identifying malicious activities. The proposed intelligent ML-based security framework enables early detection of suspicious network behavior and supports administrators in strengthening cloud communication security. This approach contributes to enhancing reliability, minimizing cyber threats, and improving the overall resilience of cloud computing environments.
1 P. Renuka,2Gaddam Kusumanjali,3Kedasu Vikitha,4Dhandagala Jansi,5M. Meenakshi,6Macha Bhargavi
Model & Implementation Of Robotic Arm Using Joystick(Mems)
Robotic arms play a significant role in automation and remote manipulation tasks in industries, laboratories, and hazardous environments. This project presents the model and implementation of a robotic arm controlled using a joystick based on MEMS (Micro-ElectroMechanical Systems) technology. The system is designed to provide intuitive and real-time control of a multi-axis robotic arm by translating the tilt and motion of a MEMS sensor into corresponding movements of servo motors. In this system, a MEMS accelerometer sensor mounted on a joystick detects the orientation and tilt of the controller. These signals are processed using a microcontroller, which converts the sensor data into control commands for the robotic arm. The robotic arm consists of multiple servo motors that enable movements such as gripping, lifting, rotating, and placing objects. The joystick movements allow the user to control each axis of the robotic arm with high precision and minimal effort. The proposed system offers advantages such as low cost, ease of control, flexibility, and improved accuracy compared to conventional wired control systems. It can be effectively used in applications like industrial automation, pick-and-place operations, medical assistance, space exploration, and handling hazardous materials. The implementation demonstrates that MEMS-based joystick control provides a simple yet efficient interface for robotic manipulation, making the system suitable for both educational and practical robotic applications.
1Dr. A. Krishnamurthy, 2G. Sarayu, 3M. Nikhitha, 4B. Anjali, 5K. Sai Prasanna
Fine Tuned Understanding Enhancing Social Bot Detection With Transformer Based Classification
The rapid growth of social media platforms has led to the widespread presence of automated accounts, commonly known as social bots, which can manipulate public opinion, spread misinformation, and disrupt online communities. Detecting such bots has become a critical challenge for maintaining the integrity of digital communication. This study proposes a finetuned framework for enhancing social bot detection using advanced machine learning and transformer-based classification techniques. The developed system integrates a web-based application using the Django framework to manage user interactions, dataset processing, and prediction services. The dataset is preprocessed by removing missing values, balancing class distribution through resampling, and normalizing features using a standard scaler. Multiple classification algorithms, including Logistic Regression, Support Vector Machine, Random Forest, XGBoost, and an Artificial Neural Network (ANN), are implemented and evaluated to identify the most effective model for bot detection. The system extracts behavioral features such as tweet frequency, follower-following ratio, account age, retweet ratio, and spam content indicators to classify accounts as human users or social bots. Model performance is assessed using accuracy metrics, and the best-performing classifier is deployed for real-time prediction within the application. Experimental results demonstrate that the proposed approach significantly improves detection accuracy and provides an efficient mechanism for identifying suspicious accounts. The framework supports scalable deployment and offers practical assistance in combating automated malicious activities on social media platforms. The implementation details of the classification pipeline and prediction workflow are demonstrated through the developed Django-based system.
1Ravali,2Gandam Niharika,3Godisela Deekshitha,4Chiluka Rashmitha,5 Pasula Sai Archana,6Chegonda Samatha