作者：
Asha P.Hemamalini V.Poongodaia.Swapna N.Soujanya K. L. S.Vaishali Gaikwad (Mohite)Associate Professor
Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai TN India Assistant Professor
Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur India Assistant Professor
School of Computers Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India. Associate Professor
Head of the Department Department of Computer Science and Engineering Vijay Rural Engineering College Nizamabad Telangana India Professor
Department of Computer Science and Engineering CMR College of Engineering & Technology Hyderabad Telangana India Associate Professor
Department of Computer Engineering Xavier Institute of Engineering Mumbai Maharashtra India
It is difficult to discover significant audio elements and conduct systematic comparison analyses when trying to automatically detect emotions in speech. In situations when it is desirable to reduce memory and process...
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It is difficult to discover significant audio elements and conduct systematic comparison analyses when trying to automatically detect emotions in speech. In situations when it is desirable to reduce memory and processing constraints, this research deals with emotion recognition. One way to achieve this is by reducing the amount of features. In this study, propose "Active Feature Selection" (AFS) method and compares it against different state-of-the-art techniques. According to the results, smaller subsets of features than the complete feature set can produce accuracy that is comparable to or better than the full feature set. The memory and processing requirements of an emotion identification system will be reduced, which can minimise the hurdles to using health monitoring technology. The results show by using 696 characteristics, the AFS technique for emobase yields a Unweighted average recall (UAR) of 75.8%.
ICT is widely adopted by Asian youth and is utilized by people of all ages across the continent. Despite its many advantages, unethical ICT usage can lead to many complications. A harmful application of ICT for social...
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ICT is widely adopted by Asian youth and is utilized by people of all ages across the continent. Despite its many advantages, unethical ICT usage can lead to many complications. A harmful application of ICT for social communication and engagement is cyberbullying. Simply adhering to the generally accepted norms and guidelines for cybersecurity will not protect you from cybercrime. Even well-known social media stages like Twitter are safe from this attack. Natural language processing (NLP) research on cyberbullying detection has become popular recently. Even though old-style NLP procedures have become highly cyberbullying, there are still hurdles to overcome. These include the limited character count allowed by social media platforms, an imbalance among comments, ambiguity, and unnecessary use of slang. Models based on (CNNs), Multilayer Perceptrons (MLPs), and (RNNs), have recently shown encouraging results in a variety of NLP tasks. With this motivation, this research develops an African vulture optimization algorithm with a graph neural network-based cyberbullying detection and classification (AVOAGNN-CBDC) model. The proposed AVOAGNN-CBDC technique mainly intends to detect and classify cyberbullying. The AVOAGNN-CBDC technique undergoes data preprocessing in different stages and a FastText-based word embedding process to achieve this. Besides, the AVOAGNN-CBDC technique employs the GNN model for cyberbullying detection and classification. Finally, the AVOA is used for the optimal parameter selection of the GNN model, which helps achieve improved classification performance. The experimental result investigation of the AVOAGNN-CBDC technique is tested on the cyberbullying dataset, and the outcomes highlighted the supremacy of the AVOAGNN-CBDC technique in terms of several measures.
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