Music Genre Classification and Recommendation

This paper presents the development of a Chatbot Music Recommendation System that utilizes artificial intelligence to enhance user engagement through personalized music suggestions. The system operates on two primary mechanisms: passive recommendations based on contextual factors such as time of day and weather conditions, and interactive suggestions during user engagement. By integrating real-time data from external APIs, the chatbot can provide relevant song recommendations tailored to the user's environment, such as upbeat tracks for sunny days or calming melodies for rainy weather. Additionally, the system employs natural language processing techniques to understand user preferences and emotions, allowing it to refine its recommendations over time. A comprehensive evaluation of the system's performance is conducted through user feedback and interaction metrics, demonstrating its effectiveness in improving the music discovery experience. The findings indicate that the chatbot not only enhances user satisfaction but also fosters a deeper emotional connection with music. This research contributes to the field of music recommendation systems by merging technology with user-centric design, ultimately transforming how individuals interact with music in their daily lives

Chatbot: Music Recommendation System

Amitkumar Shinde,

Ashwini Umbarge,

Neha Morkhande

et al. 2024

Attendance Marking using Face Detection

Hrushikesh Kale,

Kartik Aswar,

Kisan Yadav

et al. 2024

The demand for automated attendance management systems has significantly increased in educational institutions and corporate environments where manual methods are prone to inefficiencies and errors. This paper presents a real-time, facial recognition-based attendance system developed using Flask, OpenCV, and machine learning techniques. The system employs OpenCV’s Haar Cascade classifier for face detection and the K-Nearest Neighbours (KNN) algorithm for face recognition. During user registration, the system captures 10 images per individual to ensure robust recognition across various facial expressions. The web interface, built using Flask, facilitates user interaction for managing attendance records stored in CSV format. The system achieves an accuracy of 92% under optimal lighting conditions, though performance decreases to 80% in low-light environments. Recognition is completed in approximately 2 seconds, making the system suitable for real-time applications in classrooms or offices. Despite handling real-time image streaming efficiently, challenges such as reduced accuracy when handling obstructions like masks or glasses remain. Future enhancements will explore the integration of deep learning models, such as Convolutional Neural Networks (CNNs), to improve robustness and scalability. This system demonstrates the potential of facial recognition technology for automating attendance tracking, with significant applications in both educational and corporate sectors

Analysis of Lung Disease Prediction using Machine Learning Algorithms

Mr. Vishal Borate,

Dr. Alpana Adsul,

Ms. Palak Purohit

et al. 2024

Lung diseases are a notable in global health concern, requiring early diagnosis for better recovery and survival rates. Deep learning strategies, especially CNNs, have shown great promise in self learning lung disease diagnosis from medical images like chest X-rays. Ensemble learning methods using pretrained networks such as VGG16, InceptionV3, and MobileNetV2 have achieved up to 94% accuracy in identifying conditions like COVID-19, pneumonia, and lung opacity. Lightweight CNN models also performed well, with accuracy up to 89.89%. Traditional machine learning algorithms, including Random Forest and Logistic Regression, yielded accuracy rates between 88% and 90%. A hybrid deep learning approach, combining CNN based feature extraction with classifiers like AdaBoost, SVM, and Random Forest, improved classification accuracy by 3.1% and reduced computational complexity by 16.91%. This hybrid method highlights the main feature for integrating deep learning with traditional classifiers to enhance lung disease detection efficiency