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8 results for “Recurrent Neural Networks (RNN)”
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Research ArticleOpen accessIn the keywordsProtein Secondary Structure Prediction using Hybrid Recurrent Neural NetworksRomana Rahman Ema, Akhi Khatun, Md. Alam Hossain et al.The most important and challenging problem in bioinformatics is protein secondary structure prediction. The molecules of all protein organisms have three-dimensional (primary, secondary, 3-D) structures which are completely recognized by the sequence of amino acids. Protein secondary structure attributes to the polypeptide backbone of the local configuration of proteins. Most generally, the second-level prediction is indicated such as: If there … …Journal of Computer Science, Vol. 18, Issue 7 · 14 Jul 2022 · 7,339 accessesView PDFCite
Research ArticleOpen accessIn the keywordsEpoxy Insulators’ Lifetime Prediction Implementing Neural Network TechniqueL. S. Nasrat, A. M. IbrahimDue to wide implementation of Epoxy insulators in industrial applications and its economic implications; development of various Epoxy insulator materials has to be evaluated along with a reliable prediction methodology of their lifetimes. In this study, a new methodology based on Artificial-Neural-Networks (ANN) is developed to predict Epoxy insulators lifetime using laboratory measurements of their surface leakage current under accelerated … …American Journal of Engineering and Applied Sciences, Vol. 5, Issue 2 · 7 Aug 2012 · 7,791 accessesView PDFCite
Research ArticleOpen accessIn the abstractDeep Learning Analysis of Drug Reviews: Identifying Patient Conditions, Predicting Drug Ratings, and Sentiment AnalysisRafat Hammad, Mohammad Ashraf Ottom, Ayah Bani BakerUnderstanding patients' experiences with medications is essential for improving drug effectiveness, detecting potential side effects, and enhancing overall patient satisfaction. This research investigates applying machine learning to patient drug reviews across three main tasks: Identifying patient conditions, predicting drug ratings, and performing sentiment analysis. We implemented and evaluated Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and Recurrent Neural Networks … …Journal of Computer Science, Vol. 22, Issue 9 · 7 Sep 2026 · 747 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Intelligent Hybrid Machine Learning Model for Paddy Disease DetectionS. Hrushikesava Raju, S. Adinarayna, U. Sesadri et al.Most countries rely on paddy/rice as a preferred staple crop due to its desirable agronomic characteristics. However, during the growth cycle, undetected or delayed identification of diseases can significantly reduce crop yield. To address this, machine learning approaches are increasingly employed for early-stage disease detection. Traditional Recurrent Neural Networks (RNNs), while useful for sequential data, suffer from limitations such as … …Journal of Computer Science, Vol. 22, Issue 4 · 29 Apr 2026 · 1,754 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing Fleet Management with GPS Data in Port Terminals: A Deep Learning Architecture for Truck Position PredictionMohammed El Karkraoui, Hicham AttariuasEffective truck fleet management in port terminals is crucial for improving operational efficiency and reducing delays. However, GPS-based positioning systems often face limitations in port environments due to signal obstructions and network issues. This study explores the utilization of advanced methods for deep learning, particularly Recurrent Neural Networks (RNNs) for predicting truck positions when GPS signals are unavailable or unreliable. … …Journal of Computer Science, Vol. 21, Issue 10 · 16 Dec 2025 · 2,279 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 · 6,978 accessesView PDFCite
Review ArticleOpen accessIn the abstractDeep Learning Approach for Human Action Recognition Using Gated Recurrent Unit Neural Networks and Motion AnalysisNeziha Jaouedi, Noureddine Boujnah, Med Salim BouhlelHuman action recognition is a computer vision task. The evaluation of action recognition algorithms relies on the proper extraction and learning of the data. The success of the deep learning and especially learning layer by layer led to many imposing results in several contexts that include neural network. Here the Recurrent Neural Networks (RNN) with hidden unit has demonstrated advanced … …Journal of Computer Science, Vol. 15, Issue 7 · 29 Jul 2019 · 8,023 accessesView PDFCite
Research ArticleOpen accessAll words, across the recordAn Enhanced Training- Based Arabic Sign Language Virtual Interpreter Using Parallel Recurrent Neural NetworksMohamed A. AbdouIntelligent machine translation systems have a remarkable importance in integrating people with disabilities in community. Arabic to Arabic sign language systems are limited. Deep Learning (DL) was successfully applied to problems related to music information retrieval, image recognition and text recognition, but its use in sign language recognition is rare. This paper introduces an automatic virtual translation system from Arabic … …Journal of Computer Science, Vol. 14, Issue 2 · 20 Feb 2018 · 7,633 accessesView PDFCite