Adaptive Dual-Layer Resource Allocation for Maximizing Spectral Efficiency in 5G Using Hybrid NOMA-RSMA Techniques

Social networks Cyber bullying has become another common problem in online social networks (OSNs) which exposes individuals to high risks of their mental health and interacting with others. Previous work in cyber bullying detection is often confronted with limitations in accurately detecting abusive behavior because of the intricacies in cyber space and evolution of cyber bullying practices. A new approach of Cyber bullying detection and classification network (CBDC- Net) for improving the effectiveness of detection of cyber bullying in OSNs based on natural language processing features, feature selection techniques, and deep learning algorithms is also presented in this study. CBDC-Net can overcome these challenges to existing detection methods of cyber bullying using innovative Natural Language Processing (NLP) and Deep Learning approaches. In the data preprocessing step, CBDC-Net filter and normalize the text data that is openly collected from OSNs. After that, CBDC-Net extracts features using a Synonym Level N-Gram (SLNG) approach and it incorporates both the word and character-based information to make the synonyms of text much better than the other method. After that, CSI of CBDC-Net applied Textual Similarity Resilient Sand Cat Swarm Optimization (TSR-SCSO) for feature selection to give an iterative value of their features’ importance level to detect cyber bullying. Last, in CBDC-Net, a Recurrent Bidirectional Long Short-Term Memory (LSTM)Neural Network for classification (RBLNN) is used as classification approach is applied, which recognizes the sequential nature of textual data enabling proper distinction between cyber bullying cases. Last but not the least, the CBDC Net provides a promising solution for solving the mentioned problems of cyber bullying detection in OSNs.

CBDC-Net: Recurrent Bidirectional LSTM Neural Networks Based Cyberbullying Detection with Synonym-Level N-Gram and TSR-SCSOFeatures

P. Padma,

G. Siva Nageswara Rao

2024

An Interpretable PyCaret Approach for Alzheimer's Disease Prediction

P.,

R. GUNASUNDARI

2024

Alzheimer's Disease (AD) is a major global health concern. The research focuses on early and accurate diagnosis of AD for its effective treatment and management. This study presents a novel Machine Learning (ML) approach utilizing PyCaret and SHAP for early and interpretable AD prediction. PyCaret employs a span of classification algorithms and the study identifies the best model. SHAP value determines the contribution of individual features for the final prediction thereby enhancing the model’s interpretability. The feature selection using SHAP improves the overall performance of the model. The proposed XAI framework improves clinical decision making and patient care by providing a reliable and transparent method for early AD detection.

Navigating the Future with YOLOv9 for Advanced Traffic Sign Recognition in Autonomous Vehicles

N. Sriram,

Jayalakshmi V.,

P. Preethi

et al. 2024

In the realm of autonomous and self-driving vehicles, accurate traffic sign detection is critical for ensuring road safety, efficient navigation, and compliance with traffic regulations. This paper presents an advanced traffic sign detection system based on YOLOv9, an enhanced form of the YOLO (You Only Look Once) architecture. YOLOv9 offers significant enhancements over its predecessor, YOLOv8, through advanced feature extraction, multi-scale feature fusion, and optimized detection heads. The suggested YOLOv9 variant provides a notable accuracy of 95.0%, surpassing YOLOv8's 90.5%. This improvement is complemented by enhanced performance metrics, including a precision of 93.0%, recall of 94.0%, and an F1 score of 93.5%, compared to YOLOv8's precision of 88.0%, recall of 87.5%, and F1 score of 87.7%. The mean Average Precision (mAP) also increases from 85.5% in YOLOv8 to 91.0% in YOLOv9, reflecting superior detection and classification capabilities. The YOLOv9 model demonstrates superior efficiency with reduced training time (12 hours compared to YOLOv8's 15 hours) and faster inference (30 ms compared to YOLOv8's 40 ms). It utilizes a more comprehensive dataset with a greater number of images, traffic sign classes, and varied conditions, enhancing its robustness and generalization in real-world scenarios. Key parameter adjustments, including a lower learning rate, smaller batch size, and refined IoU threshold for non-maximum suppression, contribute to YOLOv9's improved performance. These enhancements make YOLOv9 a highly effective solution for real-time traffic sign detection in autonomous driving systems, offering a safer and more efficient driving experience. This work demonstrates the potential of YOLOv9 in advancing traffic sign detection technologies and provides a solid structure for further R&D in autonomous vehicle systems.