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International Journal of Advanced Computer Science and Applications
ISSN
2156-5570
2158-107X
Издатель
Science and Information Organization
2 результаты
Показано: 1 - 2 из 2
- Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, Offensive Language Detection on Social Media using Machine Learning(Science and Information Organization, 2024-01-01); ; ; Elmira AbdrashovaThis research paper addresses the critical issue of cyberbullying detection within the realm of social networks, employing a comprehensive examination of various machine learning and deep learning techniques. The study investigates the performance of these methodologies through rigorous evaluation using standard metrics, including Accuracy, Precision, Recall, F-measure, and AUC-ROC. The findings highlight the notable efficacy of deep learning models, particularly the Bidirectional Long Short-Term Memory (BiLSTM) architecture, in consistently outperforming alternative methods across diverse classification tasks. Confusion matrices and graphical representations further elucidate model performance, emphasizing the BiLSTM-based model's remarkable capacity to discern and classify cyberbullying instances accurately. These results underscore the significance of advanced neural network structures in capturing the complexities of online hate speech and offensive content. This research contributes valuable insights toward fostering safer and more inclusive online communities by facilitating early identification and mitigation of cyberbullying. Future investigations may explore hybrid approaches, additional feature integration, or real-time detection systems to further refine and advance the state-of-the-art in addressing this critical societal concern.Цитирования (OpenAlex) 11Цитирования (OpenCitations) 7 - Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, Development of a Framework for Classification of Impulsive Urban Sounds using BiLSTM Network(Science and Information Organization, 2023-01-01); ; Zh. MadibaiulyUrban environments are awash with myriad sounds, among which impulsive noises stand distinct due to their brief and often disruptive nature. As cities evolve and expand, the accurate classification and management of these impulsive sounds become paramount for urban planners, environmental scientists, and public health advocates. This paper introduces a novel framework leveraging the Bidirectional Long Short-Term Memory (BiLSTM) Network for the systematic categorization of impulsive urban sounds. Traditional methodologies often falter in recognizing the nuanced intricacies of such noises. In contrast, the presented BiLSTM-based approach adapts to the temporal variability intrinsic to these sounds, thereby enhancing classification accuracy. The research harnesses an expansive dataset, curated from various urban settings, to train and validate the model. Preliminary findings suggest that our BiLSTM framework outperforms existing models, with a marked increase in both specificity and sensitivity metrics. The outcome of this study holds profound implications for city acoustics management, noise pollution control, and urban health interventions. Moreover, the framework's adaptability paves the way for its application across diverse acoustic landscapes beyond the urban realm. Future endeavors should seek to further optimize the model by integrating more diverse soundscapes and addressing potential biases in data collection.Цитирования (OpenAlex) 3Цитирования (OpenCitations) 3