Journal of Science Engineering Technology and Management Science

The Workforce Productivity Trend Dashboard is a data-driven system designed to monitor, analyze, and visualize workforce productivity over different periods. Organizations need to understand how efficiently employees and teams are utilizing their working time and completing assigned tasks. Productivity information is often generated from attendance records, task management systems, project activities, work hours, and completed assignments. The proposed dashboard brings these different productivity-related indicators into a centralized platform. The system focuses on measurable indicators such as tasks completed, working hours, attendance, task completion rate, project progress, workload, and productivity score. These metrics can be collected from organizational systems and processed to generate useful productivity information. By analyzing these measurements over time, managers can identify productivity trends, workload differences, and areas that may require operational improvements. The dashboard presents workforce information through interactive charts, graphs, tables, rankings, and summary cards. Users can analyze productivity by employee, team, department, project, or time period, depending on the available data and organizational requirements. Trend charts can show whether productivity is increasing, decreasing, or remaining stable, while comparison charts can help identify differences between teams or departments. The system is intended to support workforce planning and operational decisionmaking, rather than simply ranking individual employees. Managers can use aggregated productivity information to identify workload imbalances, project delays, resource requirements, and process bottlenecks. Historical analysis can also help organizations understand productivity changes during different periods and evaluate the effectiveness of process improvements. Overall, the Workforce Productivity Trend Dashboard provides a centralized and visual approach to workforce analytics. It reduces the effort required to manually combine productivity information from multiple sources and provides a consistent view of operational performance. The system can help organizations make more informed decisions about resource allocation, workload planning, project management, and process improvement.

Keywords : Workforce productivity is an important factor in the successful operation of an organization.

Author : 1 A Veerender, 2 M Shruthi, 3 U Narsimulu, 4 U Ranjith, 5 G Vivekanand

Title : WORKFORCE PRODUCTIVITY TREND DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 189-196

The Employee Attrition Trend Dashboard is a data-driven analytics system designed to monitor, analyze, and visualize employee attrition patterns within an organization. Employee attrition refers to employees leaving an organization due to various professional, organizational, or personal circumstances. High or increasing attrition can affect workforce stability, recruitment costs, productivity, and knowledge retention. Therefore, organizations require an effective method to understand historical attrition patterns and identify areas that need attention. The proposed dashboard collects relevant workforce information such as employee status, department, job role, tenure, joining date, exit date, experience level, and other authorized organizational attributes. The system processes this information to calculate metrics such as attrition rate, employee count, voluntary and involuntary exits, department-wise attrition, tenure-wise attrition, and monthly or yearly trends. These metrics provide a structured view of workforce movement. The dashboard presents the analyzed information using interactive charts, graphs, tables, KPI cards, and trend visualizations. Users can examine attrition according to department, job role, tenure group, age range, experience level, or selected time periods where such analysis is appropriate and permitted. Historical comparisons help users identify periods with unusually high attrition and understand how workforce turnover changes over time. The system is intended to support HR teams and organizational managers in understanding workforce trends rather than making decisions about individual employees automatically. By identifying aggregated patterns, organizations can investigate possible causes through appropriate HR processes and develop suitable retention strategies. The dashboard can also help with workforce planning and resource forecasting. Overall, the Employee Attrition Trend Dashboard provides a centralized and visual approach to workforce attrition analysis. It reduces the effort required to manually prepare HR reports and enables users to identify important trends more efficiently. The system can be further enhanced with forecasting, alerts, employee-engagement analysis, and integration with HR management systems while maintaining appropriate privacy and access controls.

Keywords : Employee retention is an important concern for organizations because frequent workforce changes can affect productivity, operational continuity, recruitment expenses, and organizational knowledge.

Author : 1 MD Riaz, 2 B Roopa, 3 A Shiva kumar, 4 K Nithin, 5 N Yogitha

Title : EMPLOYEE ATTRITION TREND DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 197-204

The Disease Case Count Trend Tracker is a data-driven monitoring and visualization system designed to collect, analyze, and display disease case information over different periods. Monitoring disease cases is important for understanding how the number of reported cases changes across time and geographical areas. When case information is maintained in separate records or manually prepared reports, identifying trends and changes can become difficult. The proposed system provides a centralized platform for organizing disease-related case-count information. The system focuses on measurable indicators such as total reported cases, new cases, recovered cases, active cases, deaths, disease categories, geographical distribution, and time-based trends, depending on the available dataset. The collected information is processed and grouped according to date, location, and disease type. This allows users to observe increases, decreases, and recurring patterns in reported disease cases. The dashboard presents the processed information using interactive charts, graphs, maps, tables, and summary cards. Users can select a particular disease, date range, or geographical area to examine the corresponding case trends. Historical comparisons can help users understand how reported cases have changed between different periods. The visual presentation makes large datasets easier to interpret than manually examining individual records. The system is intended primarily as an information and monitoring tool, rather than a diagnostic or treatment system. It provides summarized case-count information that can support public-health reporting, research, planning, and administrative analysis. Appropriate data validation and access controls can be applied to maintain the quality and privacy of the information used by the system. Overall, the Disease Case Count Trend Tracker provides a centralized approach to disease-case monitoring and trend analysis. By converting collected records into understandable visual insights, the system can reduce manual reporting effort and improve awareness of changing case patterns. Future versions can incorporate automated data updates, geographical visualization, anomaly detection, forecasting, and integration with authorized health-information sources.

Keywords : Disease surveillance and case monitoring are important components of public-health information management.

Author : 1 S Sudarshan, 2 V Benny, 3 M Manvith kumar, 4 G Mohan Krishna, 5 S Dinesh

Title : DISEASE CASE COUNT TREND TRACKER

Volume/Issue : 2026;03(09)

Page No : 205-212

The Health Insurance Volume Dashboard is a data-driven analytics system designed to monitor, analyze, and visualize health insurance-related transaction volumes in a centralized platform. Health insurance organizations handle large amounts of information related to policies, claims, premiums, customers, renewals, settlements, and other operational activities. When this information is maintained across multiple systems or manually prepared reports, obtaining a clear view of overall business volume can be difficult. The proposed dashboard provides a centralized solution for analyzing these insurance activities. The system focuses on important volume-related indicators such as total policies, new policies, renewed policies, total claims, approved claims, rejected claims, premium volume, and claim volume. These metrics can be analyzed across different periods, policy categories, regions, customer segments, and other authorized dimensions. The system processes available insurance records and converts them into meaningful aggregate metrics for business analysis. The dashboard presents the processed information through interactive charts, graphs, tables, KPI cards, and trend visualizations. Users can examine policy and claim volumes over daily, monthly, quarterly, or yearly periods. Comparative charts can show differences between insurance products or regions, while trend graphs can help identify increases or decreases in business activity. Filters allow users to focus on specific categories or reporting periods. The proposed system is intended to support insurance administrators, managers, and analysts in understanding operational volume and business trends. By providing a centralized view of insurance activities, the dashboard can help identify periods of high claim activity, changes in policy volumes, renewal patterns, and other operational trends. This information can support planning, reporting, resource allocation, and business analysis. Overall, the Health Insurance Volume Dashboard provides a practical and visual approach to analyzing insurance transaction volumes. It reduces the effort required to manually consolidate information from different sources and makes important trends easier to understand. The system can be further enhanced with forecasting, automated alerts, advanced claim analytics, and integration with insurance management systems.

Keywords : Health insurance organizations manage large volumes of transactions involving policies, premiums, claims, renewals, settlements, and customer services.

Author : 1 Vijayata Ramteke, 2 M Nithin, 3 M Anjanna, 4 Ch Shravani, 5 M Abhiram

Title : HEALTH INSURANCE VOLUME DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 213-220

The Student Performance and Management System is a web-based application designed to manage student academic information and analyze performance in a centralized platform. Educational institutions handle large amounts of student data, including personal details, attendance, marks, subjects, assignments, examinations, and academic progress. Managing this information manually can be time-consuming and may make it difficult for teachers and administrators to obtain a complete view of student performance. The proposed system provides an organized solution for storing and analyzing student-related academic information. The system maintains important academic information such as student details, subjects, marks, attendance, examination results, assignment performance, grades, and overall academic progress. The collected information is stored in a structured database and can be accessed by authorized users. Performance metrics can be calculated automatically based on examination marks, attendance, assignments, and other institution-defined criteria. The system provides dashboards and reports that allow teachers and administrators to understand student performance through charts, tables, graphs, and summary indicators. Users can view subject-wise marks, attendance percentages, grade distributions, and overall performance trends. Students can also access their academic information through an appropriate user interface, subject to the institution's access policies. The proposed system helps teachers identify students who may need additional academic support and allows them to monitor progress over different assessment periods. Administrators can use aggregated academic information to understand class or department-level performance. The system is intended to support academic monitoring and management rather than automatically making decisions about students. Overall, the Student Performance and Management System provides a centralized and efficient approach to managing academic information and monitoring student progress. It reduces dependence on manual record keeping and improves access to organized academic data. The system can be further enhanced with notifications, performance forecasting, learning analytics, online examination integration, and personalized academic-support features.

Keywords : Student performance monitoring is an important activity in educational institutions because academic progress needs to be tracked throughout the academic year.

Author : 1 M Bharath Kumar, 2 S Abhishek, 3 N Ajay Kumar, 4 T Pavan Sai, 5 P Shynitha

Title : STUDENT PERFORMANCE AND MANAGEMENT SYSTEM

Volume/Issue : 2026;03(09)

Page No : 221-228

The Bank Customer Segment Overview Dashboard is a data-driven analytics system designed to analyze and visualize customer segments within a banking organization. Banks maintain large amounts of customer information related to demographics, account types, transaction behavior, balances, products, and service usage. Analyzing this information manually can be difficult when the customer base is large. The proposed dashboard provides a centralized platform for understanding customer segments and their characteristics. The system categorizes customers based on relevant attributes such as age group, income range, account type, geographic region, product usage, transaction activity, and customer relationship characteristics. The collected data is processed and transformed into meaningful aggregate metrics. These metrics help users understand the distribution and characteristics of different customer segments without requiring them to manually examine individual customer records. The dashboard presents customer-segment information through interactive charts, graphs, tables, KPI cards, and comparison visualizations. Users can analyze the number of customers in each segment, average balances, product adoption, transaction activity, and other approved indicators. Filters can be applied based on region, customer category, account type, age group, or selected time periods. The system can support banking managers, analysts, and authorized business teams in understanding customer patterns and identifying opportunities for improving services. For example, aggregated segment analysis can help identify customer groups with higher product adoption or different service requirements. The dashboard is intended to support analytical decision-making and does not automatically determine customer eligibility for financial products. Overall, the Bank Customer Segment Overview Dashboard provides a centralized and visual approach to customer segmentation analysis. It reduces the effort required to manually prepare customer reports and makes customer patterns easier to understand. The system can be further enhanced with predictive analytics, customer-lifecycle analysis, personalized recommendations, and integration with authorized banking systems while maintaining strong privacy and security controls.

Keywords : Customer segmentation is an important activity in the banking industry because banks serve customers with different financial needs, behaviors, and product preferences.

Author : 1 Dr M Dileep, 2 S Srivani, 3 T Uday Raj, 4 K Samatha, 5 T Akshitha Bhai

Title : BANK CUSTOMER SEGMENT OVERVIEW DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 229-236

The Product Pricing vs Sales Volume Analysis is a data-driven analytics system designed to examine the relationship between product prices and sales volume. Pricing is an important factor that can influence customer purchasing behavior, revenue generation, and overall business performance. Organizations often maintain large amounts of sales and pricing information, but manually analyzing the relationship between these variables can be difficult. The proposed system provides a centralized platform for analyzing pricing and sales patterns. The system collects relevant information such as product name, product category, selling price, quantity sold, revenue, discount, sales date, and geographical or customer segments where available. The collected data is cleaned, processed, and organized to calculate useful business metrics. Price ranges can be compared with corresponding sales volumes to understand how sales change at different price levels. The dashboard presents the analysis through interactive charts, graphs, tables, KPI cards, and comparison visualizations. A price-versus-sales-volume chart can help users observe whether higher or lower prices are associated with changes in sales quantity. Additional visualizations can show product-wise revenue, average selling price, sales volume trends, discount impact, and category-level comparisons. The proposed system can support business managers, sales teams, and analysts in evaluating pricing performance. By comparing historical pricing and sales data, users can identify products with strong sales at particular price ranges and products whose sales volume changes significantly after price adjustments. The system provides analytical information rather than automatically determining the ideal selling price. Overall, the Product Pricing vs Sales Volume Analysis provides a centralized and visual approach to understanding pricing and sales relationships. It reduces manual data analysis and helps users identify important patterns in product performance. The system can be further enhanced with price elasticity estimation, demand forecasting, scenario analysis, and machine-learning-based pricing recommendations.

Keywords : Pricing plays an important role in determining how customers respond to products and services.

Author : 1 K Srikanth, 2 E Chaitanaya, 3 B Varsha, 4 K Chandrakanth

Title : PRODUCT PRICING VS SALES VOLUME ANALYSIS

Volume/Issue : 2026;03(09)

Page No : 237-244

Hospital bed occupancy is an important operational indicator that helps healthcare organizations understand how efficiently available beds are being utilized. Monitoring occupancy trends enables hospital administrators to identify periods of high demand, underutilization, and potential capacity constraints. However, large amounts of hospital data can be difficult to interpret when maintained through spreadsheets or basic reports. A dashboard-based approach can provide a clearer and more efficient way to analyze bed occupancy information. The Hospital Bed Occupancy Trend Dashboard is designed to collect, process, analyze, and visually represent hospital bed utilization data. The system can display important metrics such as total beds, occupied beds, available beds, occupancy percentage, admissions, discharges, and occupancy trends over different time periods. Interactive charts and graphical representations allow users to understand changes in occupancy without manually examining large datasets. The proposed dashboard can organize occupancy information by departments, wards, dates, and other relevant categories. Users can apply filters to examine specific periods or hospital units and compare their occupancy patterns. Trend analysis can help identify recurring high-occupancy periods and areas where bed utilization is relatively low. This information can support better operational planning and resource allocation. The system uses data processing and visualization techniques to transform raw hospital records into meaningful analytical information. Appropriate calculations are performed to determine occupancy-related indicators, while charts and summary cards provide a concise view of the results. The dashboard can therefore serve as a decisionsupport tool for hospital management rather than simply functioning as a datareporting interface. Overall, the Hospital Bed Occupancy Trend Dashboard provides a structured and visual method for analyzing hospital bed utilization. It can improve the accessibility of operational information, reduce dependence on manual analysis, and help administrators make more informed decisions regarding bed allocation and hospital capacity management.

Keywords : Hospitals continuously manage limited resources such as beds, medical staff, equipment, and treatment facilities.

Author : 1 N Ramya Krishna, 2 B Vamshi, 3 P Lokesh, 4 A Sheshank, 5 M Rakesh

Title : HOSPITAL BED OCCUPANCY TREND DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 245-252

The rapid development of artificial intelligence and deeplearning technologies has enabled the generation of highly realistic DeepFake videos, creating significant challenges for media authenticity, cybersecurity, public trust, and digital forensic investigations. DeepFake manipulation can alter facial appearance, expressions, movements, and audio-visual characteristics, making conventional verification approaches increasingly ineffective. This survey reviews existing artificial intelligence and deep-learning techniques for DeepFake video detection, including convolutional neural networks, recurrent neural networks, spatial-temporal analysis, multimodal learning, attention mechanisms, artifact-based analysis, and transformer-based approaches. Existing methods are examined with respect to their detection capabilities, robustness, generalization, computational requirements, and limitations in realworld forensic scenarios. Based on the surveyed literature, an integrated AI-based DeepFake detection framework is presented that combines video acquisition, preprocessing, face detection, spatial feature extraction, temporal inconsistency analysis, multimodal forensic evidence assessment, and explainable detection. The framework further considers confidence estimation, robustness against unseen manipulation techniques, and human-assisted forensic verification. The survey identifies important research challenges related to dataset diversity, cross-domain generalization, adversarial manipulation, computational efficiency, interpretability, and reliable forensic evidence. The proposed framework provides a structured foundation for developing intelligent and trustworthy DeepFake detection systems capable of supporting digital forensic investigations and improving the reliability of digital video evidence.

Keywords : DeepFake Detection, Artificial Intelligence, Deep Learning, Video Forgery Detection, Digital Forensics, Convolutional Neural Networks, Spatial-Temporal Analysis, Multimodal Learning, Facial Forgery, Transformer, Explainable AI, Video Authentication, Forensic Evidence.

Author : Mulaparthi Anu,Dr. A. Ramamurthy

Title : An Artificial Intelligence Framework for Detecting Deepfake Videos to Enhance Digital Forensic Investigations

Volume/Issue : 2026;03(09)

Page No : 253-258

Aquaculture productivity and sustainability are strongly influenced by water-quality conditions, making continuous monitoring and timely management essential. Conventional monitoring approaches often depend on periodic measurements and manual interpretation, limiting their ability to identify rapidly changing conditions. Recent advances in machine learning provide opportunities for water-quality prediction, forecasting, anomaly identification, and intelligent aquaculture management. This survey reviews machine-learning and deep-learning approaches for aquaculture water-quality assessment, considering parameters such as pH, temperature, dissolved oxygen, turbidity, and related environmental indicators. Existing approaches including traditional machine learning, neural networks, hybrid models, time-series forecasting, and sensor-assisted monitoring are examined with respect to their capabilities and limitations. Based on the surveyed literature, an integrated AI-based aquaculture recommendation framework is presented that connects data preprocessing, waterquality prediction, anomaly detection, and management-oriented recommendations. The survey further discusses evaluation requirements, research challenges, and future directions toward intelligent, adaptive, and sustainable aquaculture systems.

Keywords : Aquaculture, Water Quality Prediction, Machine Learning, Deep Learning, Water Quality Forecasting, Anomaly Detection, IoT-Based Monitoring, Aquaculture Recommendation, Intelligent Aquaculture, Sustainable Aquaculture.

Author : Naga Raju Pechetti,Mr. B. Nandana Kumar

Title : AI-Based Aquaculture Recommendation System Using Machine Learning

Volume/Issue : 2026;03(09)

Page No : 259-264

The rapid growth of social media platforms has transformed global communication but has also enabled a sharp rise in fake and malicious accounts that spread spam, misinformation, and phishing attacks, thereby undermining user trust and platform integrity. Traditional rule-based and manual moderation techniques are inadequate for identifying such accounts across large-scale, rapidly evolving social networks. This project proposes an AI-driven system for detecting malicious and fake profiles by analyzing multiple behavioral and structural features, including account activity patterns, posting frequency, follower-to-following ratio, and profile metadata. Machine learning algorithms such as Random Forest, Support Vector Machines (SVM), and Neural Networks are employed to classify accounts as genuine or fake, while deep learning models capture complex, nonlinear behavioral patterns that traditional classifiers may overlook. Natural Language Processing (NLP) techniques are further applied to analyze the textual content of posts for spam indicators and malicious intent. Ensemble learning methods combine multiple classifiers to improve overall detection accuracy and robustness. The proposed system supports real-time detection to curb the spread of misinformation, along with automated alerting and reporting of suspicious accounts. Cloud-based deployment ensures scalability to accommodate millions of users, while visualization dashboards provide analytics on detected threats. Adaptive learning mechanisms allow the model to evolve alongside changing fake-account strategies. System performance is evaluated using accuracy, precision, recall, and F1-score. Overall, the proposed AI-driven framework offers a reliable, scalable, and proactive approach to enhancing social media security.

Keywords : Fake Profile Detection, Malicious Account Detection, Machine Learning, Deep Learning, Natural Language Processing, Random Forest, Support Vector Machine, Neural Networks, Ensemble Learning, Social Media Security, Spam Detection, RealTime Detection, Adaptive Learning.

Author : Mutyala srisailakshmi,Dr.B.V.S.Varma

Title : AI-Driven Techniques for Detecting Malicious and Fake Profiles Across Social Media Networks

Volume/Issue : 2026;03(09)

Page No : 265-270

Fraud detection in financial transactions is a critical challenge, requiring advanced techniques to identify and prevent fraudulent activities in real time. This study explores the use of machine learning models, including Multilayer Perceptron (MLP) and Extreme Gradient Boosting (XGBoost), for detecting fraudulent transactions. The proposed approach involves data preprocessing, feature engineering, and model training on historical transaction data. Key transaction features such as amount, frequency, location, and user behaviour patterns are analysed to distinguish between legitimate and fraudulent activities. MLP captures complex transactional relationships, while XGBoost enhances accuracy through ensemble learning and effectively detects sequential fraud patterns. The models are evaluated using metrics such as accuracy, precision, recall, and F1-score. The study demonstrates that AIpowered fraud detection significantly improves security by minimizing false positives and identifying fraudulent behaviour effectively. The proposed hybrid framework achieves an overall detection accuracy of 98%, making it suitable for real-time financial fraud prevention and risk mitigation.

Keywords : Financial Fraud Detection, Machine Learning, Multilayer Perceptron, XGBoost, Ensemble Learning, Feature Engineering, Anomaly Detection, Transaction Analysis, Artificial Intelligence, Risk Mitigation.

Author : Mudundi Revathi Sowjanya,Dr A Ramamurthy

Title : An Artificial Intelligence Framework for Detecting a Fraudulent Activities in Financial Transactions

Volume/Issue : 2026;03(09)

Page No : 271-276

Music streaming platforms have transformed listening from an album-oriented activity into a continuously personalized, data-driven experience. Spotify generates a rich behavioural footprint involving listening histories, track characteristics, artist preferences, playlist interactions, temporal patterns, discovery activity, and contextual signals. However, existing research frequently treats music consumption as separate problems such as recommendation, popularity prediction, user profiling, or audio-content analysis. This review synthesizes recent research on music-streaming behaviour with particular emphasis on Spotify data and identifies the limitations of isolated analytical approaches. Six interrelated dimensions are examined: multi-source music-behaviour representation; multi-temporal user preference modelling; content-driven and collaborative recommendation; contextual, social, and new-release discovery; explainability, diversity, and popularity bias; and adaptive learning for changing user preferences. Recent Spotify research demonstrates that generalized user representations can combine multimodal signals across different temporal scales and support multiple downstream personalization tasks, while studies of new-release and socially motivated listening demonstrate that recommendation relevance depends on content age, community, culture, and timing. Based on this synthesis, this review develops a Unified Spotify Behaviour Analytics Framework (USBAF) consisting of data acquisition, behavioural representation, intelligence, adaptive explanation, and insight layers. The review concludes with open challenges and a staged roadmap toward context-aware, explainable, diverse, continually adaptive, and generative music-streaming intelligence.

Keywords : Spotify, music streaming behaviour, music recommendation, user profiling, personalization, recommender systems, music discovery, preference modelling, explainable AI, streaming analytics.

Author : Kokkirigadda Ratna Priyanka,Mr. Nandana Kumar

Title : Comprehensive Analysis of Music Streaming Behaviour Using Spotify data and Insights

Volume/Issue : 2026;03(09)

Page No : 277-282

Financial markets are highly dynamic and influenced by numerous factors, including historical price movements, market sentiment, economic indicators, and global events. Predicting stock and cryptocurrency prices accurately remains a challenging task due to their volatility and complex patterns. This project presents an AI and Machine Learning-based Financial Market Prediction System that enhances forecasting accuracy by leveraging advanced data analysis and predictive modeling techniques. The system collects historical market data, preprocesses and normalizes it, extracts relevant features, and applies machine learning algorithms such as Random Forest, Support Vector Machine (SVM), XGBoost, and Long Short-Term Memory (LSTM) neural networks to predict future price trends. Additionally, technical indicators such as Moving Average (MA), Relative Strength Index (RSI), and MACD are incorporated to improve prediction performance. The proposed system provides investors with data-driven insights, trend analysis, and buy/sell recommendations, enabling informed investment decisions while reducing financial risk. Experimental results demonstrate that AI-based forecasting models outperform traditional statistical approaches in terms of prediction accuracy, adaptability, and real-time market analysis. This intelligent prediction framework offers a reliable decision-support tool for stock and cryptocurrency trading, contributing to smarter financial investments and enhanced market forecasting.

Keywords : Financial markets, including stock exchanges and cryptocurrency trading platforms, generate vast amounts of data every day.

Author : NagiriMadugu Reddy Nikhilesh1 , S.Mohammed Hussain2

Title : FINANCIAL MARKET PREDICTION – ENHANCED STOCK AND CRYPTO CURRENCY FORECASTING WITH AI MACHINE LEARNING

Volume/Issue : 2026;03(09)

Page No : 283-289

mHealth Bot is an intelligent healthcare chatbot designed to assist users in identifying potential diseases based on their reported symptoms using Natural Language Processing (NLP) and Machine Learning (ML) techniques. The system provides an interactive conversational interface through which users can describe their symptoms in natural language rather than selecting from predefined medical options. The NLP module processes and interprets the user's input, extracts relevant symptoms, and converts the information into a structured format suitable for machine learning prediction. Multiple disease prediction models are integrated into the system to analyze symptom patterns and predict possible health conditions. The chatbot is designed to support the prediction of multiple diseases, providing users with preliminary information about possible conditions based on the symptoms they provide. The system can also present relevant precautions, general health recommendations, and guidance on seeking professional medical assistance when appropriate. By combining conversational AI, NLP, and machine learning, mHealthBot aims to make preliminary health information more accessible, interactive, and user-friendly. The proposed system is intended as a decision-support and educational tool, not as a replacement for professional medical diagnosis. Overall, mHealthBot demonstrates the potential of AI-driven healthcare chatbots to improve accessibility to preliminary disease-risk information and enhance user engagement with digital health services.

Keywords : Healthcare is one of the most important areas where Artificial Intelligence (AI) and Machine Learning (ML) can provide valuable support.

Author : A. Jabiullah , V.Abdul Razzaq

Title : HEALTHBOT-MULTI_DISEASE PREDICTION CHATBOT USING NLP AND MACHINE LEARNING MODELS

Volume/Issue : 2026;03(09)

Page No : 290-294

The Employee Attrition Analysis and Prediction system is designed to analyze employee-related data and predict the likelihood of employees leaving an organization. Employee attrition can negatively affect productivity, increase recruitment and training costs, and create difficulties in maintaining an experienced workforce. Therefore, identifying employees who may have a higher probability of leaving can help organizations take preventive actions. The proposed system uses data analysis and machine learning techniques to examine various factors associated with employee attrition, such as age, job role, salary, job satisfaction, overtime, years of experience, work environment, distance from home, and job involvement. Historical employee data is collected and preprocessed by handling missing values, encoding categorical variables, and preparing the data for machine-learning algorithms. Different classification algorithms such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine can be applied to the prepared dataset. The models are trained using historical employee information and evaluated using performance measures such as accuracy, precision, recall, F1-score, and confusion matrix. The best-performing model can then be used to predict whether an employee is likely to stay or leave. The system can also provide visualizations that help management understand the major factors influencing employee attrition. These insights can support HR teams in developing suitable strategies such as improving job satisfaction, workload management, career growth opportunities, and employee engagement. Overall, the Employee Attrition Analysis and Prediction system provides a datadriven approach to understanding and predicting employee turnover. It can help organizations make informed HR decisions, reduce avoidable attrition, improve employee retention, and support better workforce planning.

Keywords :

Author : M Shirisha, P Vamshi, A Nagamani, M Kranthi, K Swayam

Title : EMPLOYEE ATTRITION ANALYSIS AND PREDICTION

Volume/Issue : 2026;03(09)

Page No : 295-302

The HR-Analytics Recruitment Tracker is a data-driven system designed to simplify and improve the management of the employee recruitment process. Recruitment involves handling a large amount of information related to job openings, candidates, applications, interviews, selections, and joining status. Managing this information manually can be time-consuming and may make it difficult for HR teams to track recruitment progress and evaluate hiring performance. The proposed system collects and organizes recruitment data in a centralized platform and provides real-time tracking and analysis of hiring activities. Important parameters such as job position, department, candidate status, application source, interview stage, selection status, hiring time, and recruitment cost can be monitored. The system uses data analytics and visualization techniques to generate meaningful insights from recruitment data. An interactive HR dashboard can display key performance indicators such as total applicants, shortlisted candidates, interviews conducted, selected candidates, rejected candidates, open positions, and hiring conversion rate. Charts and graphs can also be used to analyze recruitment trends by department, job role, recruitment source, and time period. This helps HR professionals identify successful recruitment channels and areas requiring improvement. The system provides a centralized and systematic way to track candidates throughout the recruitment lifecycle, from application to final selection and joining. It reduces manual record keeping, improves data accessibility, and supports faster decisionmaking. Overall, the HR-Analytics Recruitment Tracker provides an efficient and userfriendly solution for recruitment monitoring and workforce planning. It can be further enhanced with AI-based candidate screening, recruitment forecasting, automated interview scheduling, resume analysis, predictive hiring analytics, and automated notifications to make the recruitment process more intelligent and efficient.

Keywords :

Author : Malleswar Rao, P Bhanu Prakash, P Fairoz khan, R Prem Kumar, M Surekha

Title : HR-Analytics Recruitment Tracker

Volume/Issue : 2026;03(09)

Page No : 303-310

The Regional Sales Analysis and Prediction system is designed to analyze historical sales data across different geographical regions and predict future sales performance. Regional sales can vary due to factors such as customer demand, product category, pricing, seasonality, marketing activities, and regional market conditions. Analyzing these variations helps organizations identify high-performing regions and improve sales planning. The proposed system collects historical sales information such as region, sales amount, quantity sold, product category, order date, profit, and customer details. The collected data is cleaned and preprocessed before performing exploratory data analysis. Statistical analysis and visualization techniques are used to identify regional sales trends, compare performance between regions, and determine factors influencing sales. Machine-learning algorithms such as Linear Regression, Random Forest, XGBoost, and other suitable forecasting models can be used to predict future regional sales. Recent research shows that machine-learning models can incorporate multiple input factors for sales forecasting, while regional and spatial information can further improve sales prediction and market analysis. The prediction results can be evaluated using metrics such as MAE, RMSE, MAPE, and R² to determine model performance. An interactive dashboard can display regionwise sales, profit, sales trends, top-performing regions, and predicted future sales. This allows managers to make data-driven decisions regarding inventory, marketing, resource allocation, and sales strategies.

Keywords :

Author : B Vijay Kumar, Sai Teja, Rathod Atharv, R Sai Charan Reddy, Saniya Meerja

Title : REGIONAL SALES ANALYSIS AND PREDICTION

Volume/Issue : 2026;03(09)

Page No : 311-318

The Store Stock and Sales Analysis and Prediction system is designed to monitor, analyze, and predict store inventory and sales performance using data analytics and machine-learning techniques. Effective stock management is essential for maintaining the right quantity of products, avoiding stock shortages, reducing excess inventory, and improving overall store performance. The proposed system collects historical information such as product name, category, stock quantity, sales quantity, selling price, purchase price, sales date, revenue, and store or region. The collected data is cleaned and preprocessed to handle missing values, duplicate records, and inconsistent information. Exploratory data analysis is then performed to identify sales trends, fast-moving products, slow-moving products, seasonal demand, and inventory patterns. Machine-learning and forecasting algorithms can be applied to historical sales data to predict future product demand and sales. The system can estimate the required stock levels for upcoming periods and help identify products that may require restocking. Prediction models can be evaluated using metrics such as MAE, RMSE, MAPE, and R² to measure their performance. An interactive dashboard can display important information such as total sales, current stock, revenue, top-selling products, low-stock products, sales trends, predicted demand, and inventory status. These insights enable store managers to make better decisions regarding purchasing, inventory planning, product availability, and sales strategies.

Keywords :

Author : MD Raiz, M Akash, A Bharath, N Bunny, T Kavya Sri

Title : STORE STOCKS AND SALES ANALYSIS AND PREDICTION

Volume/Issue : 2026;03(09)

Page No : 319-326

The Personal Finance Tracker Analysis and Prediction system is designed to help individuals manage, analyze, and predict their personal financial activities in an organized manner. Managing income, expenses, savings, and financial goals manually can be difficult and may lead to poor spending decisions. The proposed system provides a digital platform for recording financial transactions and analyzing spending patterns. The system allows users to record income, expenses, savings, loans, and different spending categories such as food, transportation, shopping, education, bills, and entertainment. The collected financial data is stored and processed to generate useful summaries of the user's financial activities. Data analysis techniques are used to identify spending patterns, frequently used categories, monthly expenses, and savings trends. The system can use machine learning and statistical prediction techniques to forecast future expenses, income, and possible savings based on historical financial records. The predicted information can help users understand their expected financial position and plan their future spending more effectively. Visualizations such as pie charts, bar charts, line graphs, and monthly summaries can make financial information easier to understand. The proposed system can also provide budget monitoring and alerts when spending approaches or exceeds a predefined budget. Users can compare their actual expenses with planned budgets and identify areas where unnecessary spending can be reduced. Overall, the Personal Finance Tracker Analysis and Prediction system provides a convenient and intelligent approach to personal financial management. By combining transaction tracking, data analysis, visualization, budgeting, and prediction, the system can help users make better financial decisions, improve saving habits, and achieve their financial goals.

Keywords :

Author : R Saritha, Kamal Suthar, Sowmya, P Ganesh, Keerthan

Title : PERSONAL FINANCE TRACKER ANALYSIS AND PREDICTION

Volume/Issue : 2026;03(09)

Page No : 327-334

The Deloitte Expense Analysis and Prediction system is designed to analyze organizational expense data and predict future expenditure using data analytics and machine learning techniques. Managing large volumes of employee and business expense records manually can be time-consuming and may make it difficult to identify spending patterns, unusual transactions, and future budget requirements. The proposed system provides a data-driven approach to improving expense monitoring and financial planning. The system collects and processes expense-related information such as employee details, expense categories, transaction amounts, dates, departments, locations, and payment information. The collected data is cleaned and transformed to remove inconsistencies and prepare it for analysis. Exploratory data analysis is then performed to identify major spending categories, departmental expenses, monthly trends, and variations in expenditure. Machine-learning algorithms such as Linear Regression, Decision Tree, Random Forest, and other suitable prediction models can be applied to historical expense data to forecast future expenditure. The prediction results can help organizations estimate upcoming expenses and improve budget planning. The system can also identify unusually high or abnormal expenses that may require further review. Interactive dashboards, charts, graphs, and reports can be used to present expense trends, category-wise spending, department-wise expenditure, and predicted future expenses. These visualizations allow users and management to understand financial patterns quickly and support informed decision-making.

Keywords :

Author : M Jyothi Reddy, T Sri Apoorva, K Venkatesh, M Harshika, S Sidhartha

Title : DELOITEE EXPENSE ANALYSIS AND PREDICTION

Volume/Issue : 2026;03(09)

Page No : 335-342

The Marketing Campaign Spending Analysis and Prediction system is designed to analyze marketing campaign expenses and predict future spending to support effective budget planning and decision-making. Organizations spend money across different marketing channels such as social media, search advertising, email marketing, television, print media, and online campaigns. Without proper analysis, it can be difficult to determine which campaigns are cost-effective and how much should be allocated to future campaigns. The proposed system collects and processes campaign-related data such as campaign name, marketing channel, campaign duration, budget, actual spending, impressions, clicks, conversions, revenue, and campaign performance. The collected data is cleaned and prepared using data preprocessing techniques to handle missing values, duplicate records, and inconsistent information. Data analysis is performed to identify campaign-wise spending, channel-wise expenditure, monthly spending trends, cost per conversion, return on investment (ROI), and overall campaign performance. Machine-learning and statistical techniques can then be applied to historical campaign data to predict future marketing expenditure and estimate expected campaign performance. The system provides interactive dashboards, charts, graphs, and reports to help marketing teams understand spending patterns and compare campaign performance. It can also identify campaigns with high spending and low returns, allowing organizations to optimize their marketing budgets.

Keywords :

Author : Dr.B Anil, P Sanjeevini, P Ankith Kumar, P Alekhya, Jay Kumar

Title : Marketing Campaign Spending analysis and Prediction

Volume/Issue : 2026;03(09)

Page No : 343-350

The Online Order Analysis and Prediction for E-Commerce system is designed to analyze e-commerce order data and predict future sales, customer orders, and purchasing trends using data analytics and machine-learning techniques. With the rapid growth of online shopping, e-commerce platforms generate large volumes of information related to customers, products, orders, payments, and deliveries. Analyzing this data can help businesses understand customer behavior, optimize inventory, and improve sales performance. The proposed system collects and processes information such as order ID, customer details, product category, product price, quantity, order date, payment method, shipping location, delivery status, and total order value. The collected data is cleaned and transformed to handle missing values, duplicate records, and inconsistent information before analysis. The system performs order-wise, product-wise, category-wise, customer-wise, and time-based analysis to identify important sales patterns. Machine-learning and statistical techniques can be applied to historical order data to predict future order volume, sales revenue, product demand, and customer purchasing trends. The system can also identify frequently purchased products and high-demand categories. Interactive dashboards, charts, graphs, and reports are used to display total orders, revenue, average order value, popular products, monthly sales trends, and prediction results. These insights can help e-commerce businesses improve inventory planning, marketing strategies, sales forecasting, and customer management.

Keywords :

Author : G Praveen, A Nithin, B Abhinav, S Hemanth, B Anjali

Title : ONLINE ORDER ANALYSIS AND PREDICTION ECOMMERCE

Volume/Issue : 2026;03(09)

Page No : 351-358

The Loan Approval Analysis and Prediction system is designed to analyze loan application data and predict whether a loan application is likely to be approved or rejected. Loan approval is an important process for financial institutions, as it requires evaluating several factors such as applicant income, credit history, loan amount, employment status, education, marital status, and other financial details. Traditional loan evaluation methods can be time-consuming and may involve manual assessment of large amounts of applicant information. The proposed system uses data analysis and machine learning techniques to process historical loan application data and identify patterns associated with loan approval decisions. The dataset is first preprocessed by handling missing values, removing inconsistencies, encoding categorical variables, and selecting relevant features. Exploratory data analysis is then performed to understand the relationship between applicant characteristics and loan approval outcomes. Machine learning algorithms such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine can be trained and evaluated to identify a suitable prediction model. The selected model predicts the approval status of new loan applications based on the information provided by applicants. The system can also present analytical results through charts, graphs, and performance metrics such as accuracy, precision, recall, and F1-score. By automating the initial analysis and prediction process, the system can reduce manual effort and support faster, more consistent decision-making. Overall, the Loan Approval Analysis and Prediction system demonstrates how machine learning can be applied to financial data to assist loan evaluation and improve the efficiency of the loan approval process.

Keywords :

Author : Sachin Chowhan, B Ram Charan, M Abhinav, R Suresh, K Hyma

Title : LOAN APPROVAL ANALYSIS AND PREDICTION

Volume/Issue : 2026;03(09)

Page No : 359-366

The HOTSTAR CUSTOMER CHURN ANALYSIS AND PREDICTION system is designed to analyze customer behavior and predict whether a customer is likely to stop using or subscribing to the Hotstar streaming platform. With the rapid growth of online entertainment services, customer retention has become an important challenge for streaming platforms. Customers may discontinue their subscriptions due to factors such as subscription cost, content availability, viewing frequency, service experience, and competition from other platforms. The proposed system uses historical customer data to identify patterns and factors associated with customer churn. The collected data may include customer details, subscription plan, subscription duration, viewing frequency, watch time, payment method, content preferences, and engagement level. The data is first preprocessed by handling missing values, duplicate records, inconsistent data, and categorical attributes. Exploratory data analysis is performed to understand customer behavior and identify important churn patterns. Machinelearning algorithms such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine can be used to build the prediction model. The trained model predicts whether a customer is likely to Churn or Stay based on their characteristics and usage behavior. Model performance can be evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. The system can also provide charts, graphs, and dashboards to visualize customer demographics, subscription patterns, engagement levels, and churn rates. The prediction results can help identify high-risk customers and support targeted customer-retention strategies. Overall, the HOTSTAR CUSTOMER CHURN ANALYSIS AND PREDICTION system demonstrates how data analytics and machine learning can be used to understand customer behavior, predict potential churn, and support effective customer retention and data-driven business decisions.

Keywords :

Author : A Veerender, L Alekhya, G Shravani, M Sreekanth, Y Venkatesh

Title : HOT STAR CUSTOMER CHURN ANALYSIS AND PREDICTION

Volume/Issue : 2026;03(09)

Page No : 367-375