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396 results for “Learning Based Approach”
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Research ArticleOpen accessIn the abstractAspect Category Detection Using Bi-LSTM-Based Deep Learning for Sentiment Analysis for HindiAshwani Gupta, Utpal SharmaIn the field of sentiment analysis, the notion of aspect category identification lays emphasis on identifying the aspect categories in a specific review phrase. The purpose of this research is to propose a novel approach to the identification of aspect categories in reviews that are published in Hindi. The training and evaluation of a supervised model that is based on … …Journal of Computer Science, Vol. 20, Issue 11 · 18 Sep 2024 · 4,615 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Deep Ensemble Framework for DDoS Attack Recognition and Mitigation in Cloud SDN EnvironmentS. Annie Christila, R. SivakumarMuch research has been done in the recent past on the absolute shift of Internet infrastructure in order to make it more significantly programmable, configurable and make it more conveniently feasible. Software Defined Networking (SDN) forms the basis for this absolute shift in Internet infrastructure. When you look at the benefits of an SDN-based cloud environment they are monumental. Namely, … …Journal of Computer Science, Vol. 20, Issue 10 · 5 Aug 2024 · 5,344 accessesView PDFCite
Research ArticleOpen accessIn the abstractAspect Based Sentiment Analysis Using Self-Attention Based LSTM Model with Word EmbeddingVimalkumar B. Vaghela, ZishanHaider Y. Noorani, Kalpesh Patel et al.Sentiment analysis is in advance more attention for research due to incremental convention in affairs of state, online marketing and social networking. Users afford their opinions for exacting object as reviews. Analysts categorize these reviews into prejudiced information. Consequently sentiment pulling out is the development of take out human awareness from amorphous text reviews. Extracted sentiment will assist to recognize … …Journal of Computer Science, Vol. 20, Issue 10 · 29 Jul 2024 · 6,521 accessesView PDFCite
Research ArticleOpen accessIn the abstractAutism Spectrum Disorder Prediction Using Hybrid Deep Learning Model and a Recommendation System Application for Autistic PatientBiplov Paneru, Bishwash Paneru, Krishna Bikram Shah et al.The goal of this study is to create machine learning models that use a big dataset to predict Autism Spectrum Disorder (ASD). To achieve optimal performance, a number of algorithms were employed and refined, including Support Vector Machines (SVM), Random Forest, XGBoost, Multi-Layer Perceptron (MLP) and a hybrid model that combines MLP and SVM. To evaluate the durability of the … …Journal of Computer Science, Vol. 20, Issue 9 · 6 Jul 2024 · 6,226 accessesView PDFCite
Research ArticleOpen accessIn the abstractDeep Learning Approach to Mitigate DDoS Attacks in SDNHema Surendrakumar Dhadhal, Paresh Kotak, Parvez Faruki, Atul GonsaiThe rise of Distributed Denial of Service (DDoS) attacks remains a significant obstacle to network security and availability. This abstract presents a new hybrid model for DDoS mitigation in Software-Defined Networking (SDN) environments, combining a Semi-supervised Deep Extreme Learning Machine (Semi-Deep ELM) with a hybrid architecture. SDN's centralized control and programmability create an ideal platform for implementing advanced mitigation strategies. … …Journal of Computer Science, Vol. 20, Issue 9 · 5 Jul 2024 · 5,345 accessesView PDFCite
Research ArticleOpen accessIn the abstractPM2.5 Prediction Using Homogenous and Heterogenous Ensemble Learning: A Comprehensive EvaluationShrabani Medhi, Minakshi GogoiAir pollution is a global issue. PM2.5 is considered to be the most dangerous pollutant. Prediction of PM2.5 concentration is important so that effective measures can be taken beforehand. A multitude of machine learning methodologies have been employed in forecasting PM2.5 levels, utilizing diverse combinations of ensemble classifiers and regressors. However, there are three important issues that need to be … …Journal of Computer Science, Vol. 20, Issue 9 · 20 Jun 2024 · 5,186 accessesView PDFCite
Research ArticleOpen accessIn the abstractSemSimp: A Parametric Method for Evaluating the Semantic Similarity of Digital ResourcesAntonio De Nicola, Anna Formica, Ida Mele, Francesco TaglinoAbioui, H., Idarrou, A., Bouzit, A., & Mammass, D. (2018). Towards a Novel and Generic Approach for OWL Ontology Weighting. Procedia Computer Science, 127, 426–435. https://doi.org/10.1016/j.procs.2018.01.140 Adhikari, A., Dutta, B., Dutta, A., Mondal, D., & Singh, S. (2018). An intrinsic information content-based semantic similarity measure considering the disjoint common subsumers of concepts of an ontology. Journal of the Association for … …Journal of Computer Science, Vol. 20, Issue 8 · 27 May 2024 · 5,778 accessesView PDFCite
Research ArticleOpen accessIn the abstractPrompt-Based Data Augmentation with Large Language Models for Indonesian Gender-Based Hate Speech DetectionMuhammad Amien Ibrahim, Faisal, Zefanya Delvin Sulistiya, Tora Sangputra Yopie WinartoThe increasing amount of content on social media content makes the use of automatic moderation crucial for preserving a healthy online community and reducing the spread of offensive and abusive content, such as hate speech based on gender. Developing automated social media moderation using machine learning demands a large and balanced dataset. However, difficulties such as data scarcity and class … …Journal of Computer Science, Vol. 20, Issue 8 · 27 May 2024 · 5,886 accessesView PDFCite
Research ArticleOpen accessIn the abstractFed-Deep CTRL (Light GBM)-Scalable, Interpretable, and Privacy-Preserving Federated Learning Framework for Wireless Intrusion Detection SystemSudaroli Vijayakumar, V. MuthumanikandanA learning-based intrusion detection system automates the understanding and reporting of network traffic information. The systems using neural networks and machine learning have demonstrated good detection accuracy, but the accuracy is entirely reliant on the type and volume of data. Additionally, there are issues with its scalability, privacy, efficiency, and interpretability. With the innovative fed-deep CTRL (light GBM) method, this … …Journal of Computer Science, Vol. 20, Issue 4 · 29 Feb 2024 · 5,327 accessesView PDFCite
Research ArticleOpen accessIn the abstractFuel Prediction Model for Driving Patterns Using Machine Learning TechniquesManjunath TK, Ashok Kumar PSIn recent years, the demand for fuel efficiency has become a crucial aspect of the automotive industry. Predicting fuel economy accurately is essential for optimizing vehicle performance and reducing environmental impact. This proposed model presents a machine learning-based approach for developing a fuel prediction model using the real dataset. The model aims to predict fuel efficiency for different drivers by … …Journal of Computer Science, Vol. 20, Issue 3 · 29 Jan 2024 · 5,488 accessesView PDFCite