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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 · 8,071 accessesView PDFCite
Research ArticleOpen accessIn the abstractFall Detection Using the Histogram of Oriented Gradients and Decision-Based FusionMohamed Maher Ben Ismail, Arwa AlGabas, Ouiem BchirAs the number of fall incidents among elderly people and patients are continuously growing, researches boosted their researches to propose efficient automatic fall detection systems. In particular, they formulated the fall detection problem as a supervised learning task where some visual features are extracted from the video frames and used to automatically identify the position of a human as “Fall” … …Journal of Computer Science, Vol. 16, Issue 2 · 26 Feb 2020 · 7,505 accessesView PDFCite
Research ArticleOpen accessIn the abstractPerformance of Hybrid GANN in Comparison with Other Standalone Models on Dengue Outbreak PredictionNor Azura Husin, Norwati Mustapha, Md. Nasir Sulaiman et al.Early prediction of diseases especially dengue fever in the case of Malaysia, is very crucial to enable health authorities to develop response strategies and context preventive intervention programs such as awareness campaigns for the high risk population before an outbreak occurs. Some of the deficiencies in dengue epidemiology are insufficient awareness on the parameter as well as the combination among … …Journal of Computer Science, Vol. 12, Issue 6 · 2 Jul 2016 · 8,167 accessesView PDFCite
InvestigationOpen accessIn the abstractBackpropagation Vs. Radial Basis Function Neural Model: Rainfall Intensity Classification For Flood Prediction Using Meteorology DataSoo See Chai, Wei Keat Wong, Kok Luong GohRainfall is one of the important weather variables that vary in space and time. High mean daily rainfall (>30 mm) has a high possibility of resulting in flood. Accurate prediction of this variable would save human lives and properties. Soft computing methods have been widely applied in this field. Among the various soft computing methods, Artificial Neural Network (ANN) is … …Journal of Computer Science, Vol. 12, Issue 4 · 15 Apr 2016 · 8,710 accessesView PDFCite
Research ArticleOpen accessIn the abstractINCORPORATING PRIOR KNOWLEDGE INTO TEMPORAL DIFFERENCE NETWORKSBritton Wolfe, James HarpeDeveloping general purpose algorithms for learning an accurate model of dynamical systems from example traces of the system is still a challenging research problem. Predictive State Representation (PSR) models represent the state of a dynamical system as a set of predictions about future events. Our work focuses on improving Temporal Difference Networks (TD Nets), a general class of predictive state … …Journal of Computer Science, Vol. 10, Issue 11 · 18 Dec 2014 · 7,842 accessesView PDFCite
Research ArticleOpen accessIn the abstractUsing Neutral Network in Predicting Corporate FailureHuong G. NguyenThis study investigates the predictive power of three neutral network models: Multi-layer neural network, probabilistic neural network, and logistic regression model in predicting corporate failure. Basing on the database provided by The Corporate Scorecard Group (CSG), we combine financial ratios which deem to be significant predictors of corporate bankruptcy in many previous empirical studies to build our predictive models and … …Journal of Social Sciences, Vol. 1, Issue 4 · 31 Dec 2005 · 7,327 accessesView PDFCite
Research ArticleOpen accessIn the abstractAutism Detection from 2D Transformed EEG Signal using Convolutional Neural NetworkZahrul Jannat Peya, M. A. H. Akhand, Jannatul Ferdous Srabonee, Nazmul SiddiqueAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder relating to speech complications, nonverbal and social communication, and repetitive behaviors. There is no remedy for ASD but early diagnosis, mediation, and supportive care can aid the development of language, conduct, and communication skills. As the cause of ASD is a neurodevelopmental disorder, its diagnosis based on brain function analyzing different brain … …Journal of Computer Science, Vol. 18, Issue 8 · 31 Jul 2022 · 7,927 accessesView PDFCite
Research ArticleOpen accessIn the abstractIdentification of Microbe-Drug Association based on Weighted Profile and Collaborative Matrix FactorizationZhu Ling-Zhi, Guixiang Li, Chunhua Li, Jun WangPrevious studies have shown that diseases are associated with microbe. To explore a more effective treatment for these diseases, unknown microbe-drug associations must be identified. However, existing models to identify microbe-drug association are limited. In our article, a predictive model (WPCMF) is presented for identifying microbe-drug associations based on weighted profile and collaborative matrix factorization. In WPCMF, the Gaussian Interaction … …American Journal of Biochemistry and Biotechnology, Vol. 17, Issue 4 · 23 Jan 2022 · 5,741 accessesView PDFCite
Research ArticleOpen accessIn the abstractPredicting Microbe-Drug Association based on Similarity and Semi-Supervised LearningLingzhi Zhu, Jun Wang, Guixiang Li et al.Increasing clinic evidences have showed that microbial communities play important roles in human health and disease. Predicting hidden microbe-drug associations can be helpful in understanding the microbe-drug association mechanisms in clinical treatment, drug discovery, combinations and repositioning. Some computational methods were proposed to predict the associations of microbes and drugs. However, the prediction performance of these methods needs to be … …American Journal of Biochemistry and Biotechnology, Vol. 17, Issue 1 · 9 Mar 2021 · 7,539 accessesView PDFCite
Research ArticleOpen accessIn the abstractFeature Extraction Method for Improving Speech Recognition in Noisy EnvironmentsYoussef Zouhir, Kaïs OuniThe paper presents a feature extraction method, named as Normalized Gammachirp Cepstral Coefficients (NGCC) that incorporates the properties of the peripheral auditory system to improve robustness in noisy speech recognition. The proposed method is based on a second order low-pass filter and normalized gammachirp filterbank to emulate the mechanisms performed in the outer/middle ear and cochlea. The speech recognition performance … …Journal of Computer Science, Vol. 12, Issue 2 · 25 Mar 2016 · 7,873 accessesView PDFCite