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  • Open Access


    Estimating Construction Material Indices with ARIMA and Optimized NARNETs

    Ümit Işıkdağ1, Aycan Hepsağ2, Süreyya İmre Bıyıklı3, Derya Öz4, Gebrail Bekdaş5, Zong Woo Geem6,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 113-129, 2023, DOI:10.32604/cmc.2023.032502

    Abstract Construction Industry operates relying on various key economic indicators. One of these indicators is material prices. On the other hand, cost is a key concern in all operations of the construction industry. In the uncertain conditions, reliable cost forecasts become an important source of information. Material cost is one of the key components of the overall cost of construction. In addition, cost overrun is a common problem in the construction industry, where nine out of ten construction projects face cost overrun. In order to carry out a successful cost management strategy and prevent cost overruns, it is very important to… More >

  • Open Access


    Application of Time Serial Model in Water Quality Predicting

    Jiang Wu1, Jianjun Zhang1, Wenwu Tan1, Hao Lan1,*, Sirao Zhang1, Ke Xiao2, Li Wang2, Haijun Lin1, Guang Sun3, Peng Guo4

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 67-82, 2023, DOI:10.32604/cmc.2023.030703

    Abstract Water resources are an indispensable and valuable resource for human survival and development. Water quality predicting plays an important role in the protection and development of water resources. It is difficult to predict water quality due to its random and trend changes. Therefore, a method of predicting water quality which combines Auto Regressive Integrated Moving Average (ARIMA) and clustering model was proposed in this paper. By taking the water quality monitoring data of a certain river basin as a sample, the water quality Total Phosphorus (TP) index was selected as the prediction object. Firstly, the sample data was cleaned, stationary… More >

  • Open Access


    An Accurate Dynamic Forecast of Photovoltaic Energy Generation

    Anoir Souissi1,*, Imen Guidara1, Maher Chaabene1, Giuseppe Marco Tina2, Moez Bouchouicha3

    FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.6, pp. 1683-1698, 2022, DOI:10.32604/fdmp.2022.022051

    Abstract The accurate forecast of the photovoltaic generation (PVG) process is essential to develop optimum installation sizing and pragmatic energy planning and management. This paper proposes a PVG forecast model for a PVG/Battery installation. The forecasting strategy is built on a Medium-Term Energy Forecasting (MTEF) approach refined dynamically every hour (Dynamic Medium-Term Energy Forecasting (DMTEF)) and adjusted by means of a Short-Term Energy Forecasting (STEF) strategy. The MTEF predicts the generated energy for a day ahead based on the PVG of the last 15 days. As for STEF, it is a combination between PVG Short-Term (ST) forecasting and DMTEF methods obtained… More >

  • Open Access


    Air Quality Predictions in Urban Areas Using Hybrid ARIMA and Metaheuristic LSTM

    S. Gunasekar*, G. Joselin Retna Kumar, G. Pius Agbulu

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1271-1284, 2022, DOI:10.32604/csse.2022.024303

    Abstract Due to the development of transportation, population growth and industrial activities, air quality has become a major issue in urban areas. Poor air quality leads to rising health issues in the human’s life in many ways especially respiratory infections, heart disease, asthma, stroke and lung cancer. The contaminated air comprises harmful ingredients such as sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter of PM10, PM2.5, and an Air Quality Index (AQI). These pollutant ingredients are very harmful to human’s health and also leads to death. So, it is necessary to develop a prediction model for air quality as regular on… More >

  • Open Access


    Modeling of Chaotic Political Optimizer for Crop Yield Prediction

    Gurram Sunitha1,*, M. N. Pushpalatha2, A. Parkavi3, Prasanthi Boyapati4, Ranjan Walia5, Rachna Kohar6, Kashif Qureshi7

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 423-437, 2022, DOI:10.32604/iasc.2022.024757

    Abstract Crop yield is an extremely difficult trait identified using many factors like genotype, environment and their interaction. Accurate Crop Yield Prediction (CYP) necessitates the basic understanding of the functional relativity among yields and the collaborative factor. Disclosing such connection requires both wide-ranging datasets and an efficient model. The CYP is important to accomplish irrigation scheduling and assessing labor necessities for reaping and storing. Predicting yield using various kinds of irrigation is effective for optimizing resources, but CYP is a difficult process owing to the existence of distinct factors. Recently, Deep Learning (DL) approaches offer solutions to complicated data like weather… More >

  • Open Access


    Rainfall Forecasting Using Machine Learning Algorithms for Localized Events

    Ganapathy Pattukandan Ganapathy1, Kathiravan Srinivasan2, Debajit Datta2, Chuan-Yu Chang3,4,*, Om Purohit5, Vladislav Zaalishvili6, Olga Burdzieva6

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6333-6350, 2022, DOI:10.32604/cmc.2022.023254

    Abstract A substantial amount of the Indian economy depends solely on agriculture. Rainfall, on the other hand, plays a significant role in agriculture–while an adequate amount of rainfall can be considered as a blessing, if the amount is inordinate or scant, it can ruin the entire hard work of the farmers. In this work, the rainfall dataset of the Vellore region, of Tamil Nadu, India, in the years 2021 and 2022 is forecasted using several machine learning algorithms. Feature engineering has been performed in this work in order to generate new features that remove all sorts of autocorrelation present in the… More >

  • Open Access


    An Intelligent Forecasting Model for Disease Prediction Using Stack Ensembling Approach

    Shobhit Verma1 , Nonita Sharma1 , Aman Singh2 , Abdullah Alharbi3 , Wael Alosaimi3 , Hashem Alyami4, Deepali Gupta5, Nitin Goyal5 ,*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6041-6055, 2022, DOI:10.32604/cmc.2022.021747

    Abstract This research work proposes a new stack-based generalization ensemble model to forecast the number of incidences of conjunctivitis disease. In addition to forecasting the occurrences of conjunctivitis incidences, the proposed model also improves performance by using the ensemble model. Weekly rate of acute Conjunctivitis per 1000 for Hong Kong is collected for the duration of the first week of January 2010 to the last week of December 2019. Pre-processing techniques such as imputation of missing values and logarithmic transformation are applied to pre-process the data sets. A stacked generalization ensemble model based on Auto-ARIMA (Autoregressive Integrated Moving Average), NNAR (Neural… More >

  • Open Access


    SutteARIMA: A Novel Method for Forecasting the Infant Mortality Rate in Indonesia

    Ansari Saleh Ahmar1,2,*, Eva Boj del Val3, M. A. El Safty4, Samirah AlZahrani4, Hamed El-Khawaga5,6

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6007-6022, 2022, DOI:10.32604/cmc.2022.021382

    Abstract This study focuses on the novel forecasting method (SutteARIMA) and its application in predicting Infant Mortality Rate data in Indonesia. It undertakes a comparison of the most popular and widely used four forecasting methods: ARIMA, Neural Networks Time Series (NNAR), Holt-Winters, and SutteARIMA. The data used were obtained from the website of the World Bank. The data consisted of the annual infant mortality rate (per 1000 live births) from 1991 to 2019. To determine a suitable and best method for predicting Infant Mortality rate, the forecasting results of these four methods were compared based on the mean absolute percentage error… More >

  • Open Access


    Price Prediction of Seasonal Items Using Machine Learning and Statistical Methods

    Mohamed Ali Mohamed, Ibrahim Mahmoud El-Henawy, Ahmad Salah*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3473-3489, 2022, DOI:10.32604/cmc.2022.020782

    Abstract Price prediction of goods is a vital point of research due to how common e-commerce platforms are. There are several efforts conducted to forecast the price of items using classic machine learning algorithms and statistical models. These models can predict prices of various financial instruments, e.g., gold, oil, cryptocurrencies, stocks, and second-hand items. Despite these efforts, the literature has no model for predicting the prices of seasonal goods (e.g., Christmas gifts). In this context, we framed the task of seasonal goods price prediction as a regression problem. First, we utilized a real online trailer dataset of Christmas gifts and then… More >

  • Open Access


    Attention-Based and Time Series Models for Short-Term Forecasting of COVID-19 Spread

    Jurgita Markevičiūtė1,*, Jolita Bernatavičienė2, Rūta Levulienė1, Viktor Medvedev2, Povilas Treigys2, Julius Venskus2

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 695-714, 2022, DOI:10.32604/cmc.2022.018735

    Abstract The growing number of COVID-19 cases puts pressure on healthcare services and public institutions worldwide. The pandemic has brought much uncertainty to the global economy and the situation in general. Forecasting methods and modeling techniques are important tools for governments to manage critical situations caused by pandemics, which have negative impact on public health. The main purpose of this study is to obtain short-term forecasts of disease epidemiology that could be useful for policymakers and public institutions to make necessary short-term decisions. To evaluate the effectiveness of the proposed attention-based method combining certain data mining algorithms and the classical ARIMA… More >

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