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47 results for “Long Short Term Memory (LSTM)”
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Research ArticleOpen accessIn the abstractMultimodal Face Expression Recognition Using Parametric Exponential Linear Unit-Long Short-Term MemoryKampa Ratna Babu, Akula Suneetha, Kampa Kanthi KumarMultimodal facial expression recognition combines information from multiple modalities of audio and video to achieve the required accuracy and robustness. By integrating different data sources, multimodal systems capture different aspects of human expression. However, accurately recognizing facial expressions across audio and video modalities causes challenges due to variations in expression representation. In this research, Parametric Exponential Linear Unit-Long Short-Term Memory … …Journal of Computer Science, Vol. 20, Issue 10 · 17 Aug 2024 · 4,612 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 · 5,337 accessesView PDFCite
Research ArticleOpen accessIn the abstractSales Forecasting Models: Comparison between ARIMA, LSTM and ProphetDmitry BrykinSales forecasting is crucial for business planning and resource allocation. Data-driven approaches have become popular in this field. This study compares the performance of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) and Prophet within the context of specific sales categories derived from acquiring data provided by a bank. This study uses a time series dataset … …Journal of Computer Science, Vol. 20, Issue 10 · 1 Aug 2024 · 7,981 accessesView PDFCite
Research ArticleOpen accessIn the abstractMeasuring Customers’ Satisfaction Using Sentiment Analysis: Model and ToolAhmed Alqurafi, Tawfeeq AlsanoosyCustomer reviews are a valuable data resource for business owners and companies. Customers frequently write reviews on many platforms, such as eBay or Amazon. These reviews show, for instance, to what extent customers are satisfied with a service or a product. Therefore, these reviews can be used by companies and business owners to improve their services or products. However, numerous … …Journal of Computer Science, Vol. 20, Issue 4 · 7 Feb 2024 · 6,471 accessesView PDFCite
Research ArticleOpen accessIn the abstractMachine Learning and Deep Learning for Phishing Email Classification using One-Hot EncodingSikha Bagui, Debarghya Nandi, Subhash Bagui, Robert Jamie WhiteRepresentation of text is a significant task in Natural Language Processing (NLP) and in recent years Deep Learning (DL) and Machine Learning (ML) have been widely used in various NLP tasks like topic classification, sentiment analysis and language translation. Until very recently, little work has been devoted to semantic analysis in phishing detection or phishing email detection. The novelty of … …Journal of Computer Science, Vol. 17, Issue 7 · 23 Jul 2021 · 10,936 accessesView PDFCite
Research ArticleOpen accessIn the abstractPredicting Support and Resistance Indicators for Stock Market with Fibonacci Sequence in Long Short-Term MemoryThambusamy Velmurugan, Thiruvalluvan IndhumathyPredictive data analytics is a branch of data analytics where models are designed that effectively interpret; anticipate outcomes by analyzing present data to make predictions about future. One of the major attributes for prediction is time and there have been a considerable number of time series analytical models that are used for forecasting. Long Short-Term Memory (LSTM) is a deep … …Journal of Computer Science, Vol. 16, Issue 10 · 28 Oct 2020 · 7,316 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparative Analysis of Deep Learning Models for Multi-Step Prediction of Financial Time SeriesSaugat Aryal, Dheynoshan Nadarajah, Prabath Lakmal Rupasinghe et al.Financial time series prediction has been a key topic of interest among researchers considering the complexity of the domain and also due to its significant impact on a wide range of applications. In contrast to one-step ahead prediction, multi-step forecasting is more desirable in the industry but the task is more challenging. In recent days, advancement in deep learning has … …Journal of Computer Science, Vol. 16, Issue 10 · 27 Oct 2020 · 6,967 accessesView PDFCite
Research ArticleOpen accessIn the abstractPrediction of Data Traffic in Telecom Networks based on Deep Neural NetworksQuang Hung Do, Thi Thanh Hang Doan, Thi Van Anh Nguyen et al.Accurate prediction of data traffic in telecom network is a challenging task for a better network management. It advances dynamic resource allocation and power management. This study employs deep neural networks including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) techniques to one-hour-ahead forecast the volume of expected traffic and compares this approach to other methods including Adaptive Neuro-Fuzzy … …Journal of Computer Science, Vol. 16, Issue 9 · 30 Sep 2020 · 8,059 accessesView PDFCite
Research ArticleOpen accessIn the abstractForecasting Air Passenger Demand: A Comparison of LSTM and SARIMAQuang Hung Do, Shih-Kuei Lo, Jeng-Fung Chen et al.All airports need to have an accurate prediction of the number of passengers for their efficient management. An accurate prediction of the number of air passengers is crucial task since it provides information for planning decisions in the airport infrastructure to stabilize the service and maximize the profit. This study proposes a novel air passenger demand forecasting model based on … …Journal of Computer Science, Vol. 16, Issue 7 · 25 Jul 2020 · 8,555 accessesView PDFCite
Research ArticleOpen accessIn the abstractEmail Spam Classification Using Gated Recurrent Unit and Long Short-Term MemoryIqbal Basyar, Adiwijaya, Danang Triantoro MurdiansyahHigh numbers of spam emails have led to an increase in email triage, causing losses amounting to USD 355 million per year. One way to reduce this loss is to classify spam email into categories including fraud or promotions made by unwanted parties. The initial development of spam email classification was based on simple methods such as word filters. Now, … …Journal of Computer Science, Vol. 16, Issue 4 · 3 Apr 2020 · 8,323 accessesView PDFCite