Explore the vast range of titles available.

Communication in society had developed within cultural and geographical boundaries prior to the invention of digital technology. The latest advancements in communication technology have significantly surpassed the conventional constraints for communication with regards to time and location. These new platforms have ushered in a new age of user-generated content, online chats, social network and comprehensive data on individual behavior. However, the abuse of communication software such as social media websites, online communities, and chats has resulted in a new kind of online hostility and aggressive actions. Due to widespread use of the social networking platforms and technological gadgets, conventional bullying has migrated from physical form to online, where it is termed as Cyberbullying. However, recently the digital technologies as machine learning and deep learning have been showing their efficiency in identifying linguistic patterns used by cyberbullies and cyberbullying detection problem. In this research paper, we aimed to evaluate shallow machine learning and deep learning methods in cyberbullying detection problem. We deployed three deep and six shallow learning algorithms for cyberbullying detection problems. The results show that bidirectional long-short-term memory is the most efficient method for cyberbullying detection, in terms of accuracy and recall.

This research paper introduces a novel sign language recognition system developed using advanced deep learning (DL) techniques aimed at enhancing communication capabilities between deaf and hearing individuals. The system leverages a convolutional neural network (CNN) architecture, optimized for the real-time interpretation of dynamic hand gestures that constitute sign language. A comprehensive dataset was employed to train and validate the model, encompassing a diverse range of gestures across different environmental settings. Comparative analysis revealed that the deep learning-based model significantly outperforms traditional machine learning techniques in terms of recognition accuracy, particularly with the increase in the volume of training data. This was illustrated through various performance metrics, including a detailed confusion matrix and Levenshtein distance measurements, highlighting the system’s efficacy in accurately identifying complex gestures. Real-time application tests further demonstrated the model's robustness and adaptability to varying lighting conditions and backgrounds, essential for practical deployment. Key challenges identified include the need for broader linguistic diversity in training datasets and enhanced model sensitivity to subtle gestural distinctions. The paper concludes with suggestions for future research directions, emphasizing algorithm optimization, data diversification, and user-centric design improvements to foster wider adoption and usability. This study underscores the potential of deep learning technologies to revolutionize assistive communication tools, making them more accessible and effective for the deaf community.