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  • Open Access

    REVIEW

    Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

    Baydaa Abdul Kareem1,2, Salah L. Zubaidi2,3, Nadhir Al-Ansari4,*, Yousif Raad Muhsen2,5

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 1-41, 2024, DOI:10.32604/cmes.2023.027954

    Abstract Forecasting river flow is crucial for optimal planning, management, and sustainability using freshwater resources. Many machine learning (ML) approaches have been enhanced to improve streamflow prediction. Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches. Current researchers have also emphasised using hybrid models to improve forecast accuracy. Accordingly, this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years, summarising data preprocessing, univariate machine learning modelling strategy, advantages and disadvantages of standalone ML techniques, hybrid models, and performance… More > Graphic Abstract

    Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

  • Open Access

    ARTICLE

    CT-NET: A Novel Convolutional Transformer-Based Network for Short-Term Solar Energy Forecasting Using Climatic Information

    Muhammad Munsif1,2, Fath U Min Ullah1,2, Samee Ullah Khan1,2, Noman Khan1,2, Sung Wook Baik1,2,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1751-1773, 2023, DOI:10.32604/csse.2023.038514

    Abstract Photovoltaic (PV) systems are environmentally friendly, generate green energy, and receive support from policies and organizations. However, weather fluctuations make large-scale PV power integration and management challenging despite the economic benefits. Existing PV forecasting techniques (sequential and convolutional neural networks (CNN)) are sensitive to environmental conditions, reducing energy distribution system performance. To handle these issues, this article proposes an efficient, weather-resilient convolutional-transformer-based network (CT-NET) for accurate and efficient PV power forecasting. The network consists of three main modules. First, the acquired PV generation data are forwarded to the pre-processing module for data refinement. Next, to carry out data encoding, a… More >

  • Open Access

    ARTICLE

    Hybridized Intelligent Neural Network Optimization Model for Forecasting Prices of Rubber in Malaysia

    Shehab Abdulhabib Alzaeemi1, Saratha Sathasivam2,*, Majid Khan bin Majahar Ali2, K. G. Tay1, Muraly Velavan3

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1471-1491, 2023, DOI:10.32604/csse.2023.037366

    Abstract Rubber producers, consumers, traders, and those who are involved in the rubber industry face major risks of rubber price fluctuations. As a result, decision-makers are required to make an accurate estimation of the price of rubber. This paper aims to propose hybrid intelligent models, which can be utilized to forecast the price of rubber in Malaysia by employing monthly Malaysia’s rubber pricing data, spanning from January 2016 to March 2021. The projected hybrid model consists of different algorithms with the symbolic Radial Basis Functions Neural Network k-Satisfiability Logic Mining (RBFNN-kSAT). These algorithms, including Grey Wolf Optimization Algorithm, Artificial Bee Colony… More >

  • Open Access

    ARTICLE

    A Model Average Algorithm for Housing Price Forecast with Evaluation Interpretation

    Jintao Fu1, Yong Zhou1,*, Qian Qiu2, Guangwei Xu3, Neng Wan3

    Journal of Quantum Computing, Vol.4, No.3, pp. 147-163, 2022, DOI:10.32604/jqc.2022.038358

    Abstract In the field of computer research, the increase of data in result of societal progress has been remarkable, and the management of this data and the analysis of linked businesses have grown in popularity. There are numerous practical uses for the capability to extract key characteristics from secondary property data and utilize these characteristics to forecast home prices. Using regression methods in machine learning to segment the data set, examine the major factors affecting it, and forecast home prices is the most popular method for examining pricing information. It is challenging to generate precise forecasts since many of the regression… More >

  • Open Access

    REVIEW

    Recent Advances of Deep Learning in Geological Hazard Forecasting

    Jiaqi Wang1, Pengfei Sun1, Leilei Chen2, Jianfeng Yang3, Zhenghe Liu1, Haojie Lian1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1381-1418, 2023, DOI:10.32604/cmes.2023.023693

    Abstract Geological hazard is an adverse geological condition that can cause loss of life and property. Accurate prediction and analysis of geological hazards is an important and challenging task. In the past decade, there has been a great expansion of geohazard detection data and advancement in data-driven simulation techniques. In particular, great efforts have been made in applying deep learning to predict geohazards. To understand the recent progress in this field, this paper provides an overview of the commonly used data sources and deep neural networks in the prediction of a variety of geological hazards. More >

  • Open Access

    ARTICLE

    Railway Passenger Flow Forecasting by Integrating Passenger Flow Relationship and Spatiotemporal Similarity

    Song Yu*, Aiping Luo, Xiang Wang

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1877-1893, 2023, DOI:10.32604/iasc.2023.039132

    Abstract Railway passenger flow forecasting can help to develop sensible railway schedules, make full use of railway resources, and meet the travel demand of passengers. The structure of passenger flow in railway networks and the spatiotemporal relationship of passenger flow among stations are two distinctive features of railway passenger flow. Most of the previous studies used only a single feature for prediction and lacked correlations, resulting in suboptimal performance. To address the above-mentioned problem, we proposed the railway passenger flow prediction model called Flow-Similarity Attention Graph Convolutional Network (F-SAGCN). First, we constructed the passenger flow relations graph (RG) based on the… More >

  • Open Access

    ARTICLE

    Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

    Doaa Sami Khafaga1, El-Sayed M. El-kenawy2, Amel Ali Alhussan1,*, Marwa M. Eid3

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2117-2132, 2023, DOI:10.32604/iasc.2023.038811

    Abstract The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments. Meanwhile, the accurate prediction can be realized using the recent advances in machine learning and predictive models. This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory (LSTM) units. The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy. This optimization algorithm is based on the recently emerged dipper-throated optimization (DTO) and stochastic… More >

  • Open Access

    ARTICLE

    Flow Direction Level Traffic Flow Prediction Based on a GCN-LSTM Combined Model

    Fulu Wei1, Xin Li1, Yongqing Guo1,*, Zhenyu Wang2, Qingyin Li1, Xueshi Ma3

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2001-2018, 2023, DOI:10.32604/iasc.2023.035799

    Abstract Traffic flow prediction plays an important role in intelligent transportation systems and is of great significance in the applications of traffic control and urban planning. Due to the complexity of road traffic flow data, traffic flow prediction has been one of the challenging tasks to fully exploit the spatiotemporal characteristics of roads to improve prediction accuracy. In this study, a combined flow direction level traffic flow prediction graph convolutional network (GCN) and long short-term memory (LSTM) model based on spatiotemporal characteristics is proposed. First, a GCN model is employed to capture the topological structure of the data graph and extract… More >

  • Open Access

    ARTICLE

    Statistical Time Series Forecasting Models for Pandemic Prediction

    Ahmed ElShafee1, Walid El-Shafai2,3, Abeer D. Algarni4,*, Naglaa F. Soliman4, Moustafa H. Aly5

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 349-374, 2023, DOI:10.32604/csse.2023.037408

    Abstract COVID-19 has significantly impacted the growth prediction of a pandemic, and it is critical in determining how to battle and track the disease progression. In this case, COVID-19 data is a time-series dataset that can be projected using different methodologies. Thus, this work aims to gauge the spread of the outbreak severity over time. Furthermore, data analytics and Machine Learning (ML) techniques are employed to gain a broader understanding of virus infections. We have simulated, adjusted, and fitted several statistical time-series forecasting models, linear ML models, and nonlinear ML models. Examples of these models are Logistic Regression, Lasso, Ridge, ElasticNet,… More >

  • Open Access

    ARTICLE

    CBOE Volatility Index Forecasting under COVID-19: An Integrated BiLSTM-ARIMA-GARCH Model

    Min Hyung Park1, Dongyan Nan2,3, Yerin Kim1, Jang Hyun Kim1,2,3,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 121-134, 2023, DOI:10.32604/csse.2023.033247

    Abstract After the outbreak of COVID-19, the global economy entered a deep freeze. This observation is supported by the Volatility Index (VIX), which reflects the market risk expected by investors. In the current study, we predicted the VIX using variables obtained from the sentiment analysis of data on Twitter posts related to the keyword “COVID-19,” using a model integrating the bidirectional long-term memory (BiLSTM), autoregressive integrated moving average (ARIMA) algorithm, and generalized autoregressive conditional heteroskedasticity (GARCH) model. The Linguistic Inquiry and Word Count (LIWC) program and Valence Aware Dictionary for Sentiment Reasoning (VADER) model were utilized as sentiment analysis methods. The… More >

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