Accident Emergency Notification System
Emotion Based Music Recommendation System
Pneumonia Detection using CNN: A Deep Learning Approach
Adaptive Question Answering System
Comparative Analysis of Drought Detection using Satellite Imagery with Deep Learning Models
A Secure and Scalable Framework for Optimizing Library Repository Exchange using VPN Technology
This paper introduces the concept of an Accident Emergency Notification System (AENS) designed for improving the process of accident reporting. Timely accident reporting is vital for saving lives, and AENS serves as a dedicated platform to facilitate immediate accident notifications while ensuring an organized workflow. It addresses key challenges, such as notifying the accident victim's family, retrieving vehicle insurance information, and providing essential personal details to assist the law enforcement agencies in managing the situation. Accident Emergency Notification System integrates various technologies such as QR code generation, incident reporting, and real-time communication, RCNN (Region based Convolutional Neural Network) which is used to predict whether accidents occurred or not, Further the intimation is sent to medical services, family and insurance agencies about the accident.
Music has a unique emotional connection with human beings. It is a medium of unifying people all around the globe. On the other hand, it is challenging to generalize music and assert that all individuals would enjoy the same type. Emotion-based music is important as it can de-stress and de-anxiety human beings. Its purpose is to identify the user's emotions appropriately in real time and play music accordingly based on the user's mood. The system implemented here identifies the facial features from the face of the user based on facial parameters like lips and eyes from a real-time webcam with the assistance of the CNN Algorithm. The suggested system identifies 7 different types of human emotions like happy, sad, neutral, fearful, disgust, angry, and surprise. The identified emotion is then used to recommend music from a trained dataset, unlike previous systems where manually curated playlists are used. By training, the CNN model using facial emotion data and K-means using music data playlists, the suggested system becomes more efficient and flexible and provides an easy solution for user-personalized music recommendations based on the user's emotional state.
Pneumonia is a highly contagious lung infection characterized by inflammation of air sacs in one or both the lungs. The air sacs get filled with fluid resulting in fever, cough and difficult breathing. Chest X-ray images are used to detect pneumonia. The manual identification of pneumonia using chest X-ray images is typically time-consuming and prone to errors, which may delay diagnosis and treatment. So, a deep learning model is used for detecting the pneumonia without any delays. A Convolutional Neural Network is a type of deep learning model specifically designed for processing images. Two Convolutional Neural Network models, EfficientNet-B0 and VGG16, are trained. The model that gives the best accuracy is chosen as the final model. The VGG16 model provides an accuracy value of 89.90% and EfficientNet- B0 model provides an accuracy of 91.51%. This research outcome exhibits that EfficientNet-B0 provides better performance than VGG16. And the study indicates that the CNN model EfficientNet-B0 can make pneumonia detection more accurate and reliable, helping doctors in their diagnosis and treatment.
This paper presents an Adaptive Question Answering System designed to enhance information retrieval from text-heavy documents. The system integrates Natural Language Processing (NLP) techniques such as tokenization, stop word removal, and lemmatization to preprocess extracted text. By leveraging BM25 for document ranking and transformer-based models like BERT and T5 for answer generation, the system ensures accurate and contextually relevant responses. The backend is implemented using Python (Flask or FastAPI); while the frontend utilizes JavaScript, frameworks like React or Vue.js. This architecture facilitates an efficient and user-friendly interface for document uploads and querying, making complex information more accessible.
Drought detection is essential for effective environmental and agricultural planning. In this study, satellite imagery from the USGS Landsat8 dataset, focusing on RGB bands, was used to develop a deep learning-based model for identifying drought conditions. To classify the data, images with few cows were labeled as Class 0, while those with more cows were categorized as Class 1. Drought conditions are detected if an image falls under Class 0, whereas Class 1 indicates no drought. Four deep learning models including EfficientNetB0, MobileNetV2, VGG16, and a custom CNN were trained and evaluated. By analyzing performance metrics such as accuracy, precision, recall, and F1-score, EfficientNetB0 was found to be the most effective. The study demonstrates the potential of deep learning for satellite-based drought detection, offering insights for environmental monitoring and resource management.
Library repositories are essential for providing secure and seamless digital access to academic and research materials. However, challenges related to data security, scalability, and efficient repository exchange persist, particularly with the rise of cyber threats and increasing remote access demands. This paper proposes a secure and scalable framework for optimizing library repository exchanges using Virtual Private Network (VPN) technology. The framework integrates advanced encryption mechanisms, multi-factor authentication (MFA), AI-driven anomaly detection, and load balancing techniques to ensure confidential, high-performance, and uninterrupted access to digital resources. A comparative analysis of VPN-based solutions versus traditional repository exchange methods demonstrates that VPN-based architectures significantly enhance security, ensure data integrity, and improve accessibility across distributed library networks. The study highlights the role of AI-powered threat detection in mitigating unauthorized access while maintaining scalability through dynamic resource allocation. By implementing this structured approach, academic institutions can fortify their digital repositories against evolving cybersecurity threats while ensuring efficient, scalable, and user-friendly remote access to library resources.
