Journal of Knowledge in Data Science and Information Management

AI-Based Attendance System

K. Jeyagurudev, M. Sanmugapriya, P. Babu — Mon, 13 May 2024 00:00:00 +0000

This paper presents an AI-based face recognition system for efficient and accurate attendance tracking. The system employs state-of-the-art computer vision algorithms to detect and recognize faces in real-time. A database is maintained to store and manage the facial features of registered individuals for comparison during recognition. The system automatically marks attendance based on recognized faces, eliminating the need for manual entry and reducing errors. Real-time monitoring is provided to track attendance status and generate alerts for anomalies. The system is designed to integrate seamlessly with existing systems, such as Student Information Systems (SIS) or Human Resource Management Systems (HRMS). Security and privacy measures are implemented to protect the system's integrity and ensure individuals' facial data privacy. The system is scalable and flexible, allowing for easy expansion to accommodate growing users or locations. The proposed system offers a reliable, efficient, and user-friendly solution for tracking attendance in various organizations and settings.

Development of Real-Time Automobile Monitoring System based on IoT and Cloud

S. V. Balshetwar, Shreyash Dude, Nishant Bodare, Om Kadam — Wed, 22 May 2024 00:00:00 +0000

In a fascinating research endeavour, a team of experts has dedicated their efforts to developing a cutting-edge IoT cloud-based system that offers a time knight of vehicles. Equipped with sensor-integrated units specializing in level sensing, temperature monitoring, and infrared barrier detecting, the system is on the road to providing cutting-edge solutions. By utilizing the power of IoT technology and cloud computing, this system collects and analyzes data from these units to improve vehicle safety, performance, and efficiency. The research article delves into the intricate details of the system's design, implementation, and benefits, aiming to revolutionize the vehicle monitoring landscape through proactive insights and predictive capabilities. This Internet of Things cloud-based real-time vehicle monitoring technology alters the game, especially with India's transportation business expanding quickly and the country's vehicle population constantly rising. Effectively monitoring various metrics such as fuel level, Temperature, and barrier detection offers a comprehensive solution for optimal vehicle performance. This innovative system transforms our vehicle monitoring approach, significantly enhancing efficiency and safety.

Classification and Categorization of Music Genre Using Machine Learning Algorithms

Fri, 31 May 2024 00:00:00 +0000

Music genre classification is crucial in music information retrieval for organizing large music libraries and enhancing recommendation systems. This research presents an innovative web-based application for music genre classification. Users can upload WAV format music files, which are processed to extract audio features such as spectral characteristics, rhythmic attributes, and Mel-Frequency Cepstral Coefficients (MFCCs) using the Librosa library. Preprocessing includes handling missing values, normalization, and dimensionality reduction via Principal Component Analysis (PCA). Machine learning classifiers—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest—are trained on labelled music samples to predict genres accurately.

Classifier performance is evaluated using accuracy, precision, recall, and F1-score, with results visualized graphically. Experimental results demonstrate the approach's efficacy in inaccurate genre classification, highlighting its potential for improving music recommendation systems and genre-based analysis. In summary, the proposed music genre classification system represents a significant advancement in automated music categorization, providing a robust, efficient, and user-friendly solution that revolutionizes genre identification and classification.

Enhanced Women Safety and Security System based on Internet of Things

S. V. Balshetwar, Ankita Phalke, Sanjana Jagdale, Divya Chikodi — Fri, 31 May 2024 00:00:00 +0000

This project proposes an IoT-based women's security system that combines voice activation, camera surveillance, heart rate monitoring, and a panic button to assure total safety. The system's core hub coordinates the smooth operation of its components, allowing for hands-free contact via voice commands to initiate alarms and orders. Real-time camera surveillance provides remote monitoring by capturing suspicious activity, while a heart rate sensor identifies physiological irregularities that indicate distress. A panic button initiates an urgent emergency by initiating predetermined protocols that notify authorized contacts or authorities of the user's location and live audio-video feed. The IoT design enables remote access and management using mobile apps or web interfaces, allowing users to modify security settings and receive quick notifications from anywhere. By combining these innovative technologies, the system intends to provide women with a proactive and efficient method of improving their safety and peace of mind in various settings.

Hotel Booking Analysis and Prediction Using Data Mining

Wed, 05 Jun 2024 00:00:00 +0000

The project aims to analyze hotel booking data using various mining algorithms to extract meaningful patterns and insights. The dataset includes customer demographics, booking dates, room preferences, and cancellation history. Subsequently, preprocessing techniques such as data cleaning, normalization, and feature selection are employed to prepare the data for modelling. The insights derived from this analysis can assist hotel management in optimizing pricing strategies, enhancing customer segmentation, and implementing proactive measures to reduce booking cancellations. Additionally, the findings contribute to a deeper understanding of customer behaviour and preferences in the hotel booking domain, ultimately leading to improved service delivery and customer satisfaction. Several data mining algorithms, including but not limited to decision trees, association rule mining, clustering, and neural networks, are applied to uncover patterns related to booking patterns, customer segmentation, and cancellation predictors.
By understanding customer behaviour and preferences, hotels can enhance customer satisfaction, optimize resource utilization, and maximize revenue.

FOSSFOLIO: A Seamless Event Operations Platform

Nandagopan P, Sreehari Jayaraj, Surya N, Swathi P P, Nithya VP — Fri, 21 Jun 2024 00:00:00 +0000

Efficient event co-ordination is paramount in today's fast-paced world, as it can significantly impact the success of any gathering, from educational events to entertainment functions. In response to this need, the proposed system offers a modern, user-friendly solution that effortlessly empowers organizations to register and create multiple events through dedicated dashboards. With a strong focus on catering to students and a diverse user base, this system is designed to catalyze improved collaboration, engagement, and overall event-planning practices. It places a particular emphasis on student-centric event discovery, ensuring that events are tailored to the specific interests and needs of the student community. The system also facilitates seamless financial transactions for paid events. It incorporates QR-enhanced ticketing to enhance the attendee experience, ensuring easy ticket generation and verification via a dedicated mobile app. Moreover, the system provides secure collaboration through Role-Based Access Control and leverages AI-driven event materials to enhance engagement and visibility. Built as a cloud-based platform with universal accessibility and a user-friendly interface, it is poised to transform event co-ordination into a more efficient and engaging process.