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  • Open Access


    Short-term Forecasting of Air Passengers Based on the Hybrid Rough Set and the Double Exponential Smoothing Model

    Haresh Kumar Sharma, Kriti Kumari, Samarjit Kar

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 1-14, 2019, DOI:10.31209/2018.100000036

    Abstract This article focuses on the use of the rough set theory in modeling of time series forecasting. In this paper, we have used the double exponential smoothing (DES) model for forecasting. The classical DES model has been improved by using the rough set technique. The improved double exponential smoothing (IDES) method can be used for the time series data without any statistical assumptions. The proposed method is applied on tourism demand of the air transportation passenger data set in Australia and the results are compared with the classical DES model. It has been observed that the forecasting accuracy of the… More >

  • Open Access


    Accurate Multi-Scale Feature Fusion CNN for Time Series Classification in Smart Factory

    Xiaorui Shao1, Chang Soo Kim1, *, Dae Geun Kim2

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 543-561, 2020, DOI:10.32604/cmc.2020.011108

    Abstract Time series classification (TSC) has attracted various attention in the community of machine learning and data mining and has many successful applications such as fault detection and product identification in the process of building a smart factory. However, it is still challenging for the efficiency and accuracy of classification due to complexity, multi-dimension of time series. This paper presents a new approach for time series classification based on convolutional neural networks (CNN). The proposed method contains three parts: short-time gap feature extraction, multi-scale local feature learning, and global feature learning. In the process of short-time gap feature extraction, large kernel… More >

  • Open Access


    COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining

    Yixian Zhang1, Jieren Cheng2, *, Yifan Yang2, Haocheng Li2, Xinyi Zheng2, Xi Chen2, Boyi Liu3, Tenglong Ren4, Naixue Xiong5

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1415-1434, 2020, DOI:10.32604/cmc.2020.011316

    Abstract With the spread and development of new epidemics, it is of great reference value to identify the changing trends of epidemics in public emotions. We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining. A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment are proposed. Establish a “Scrapy-Redis-Bloomfilter” distributed crawler framework to collect data. The system can judge the positive and negative emotions of the reviewer based on the comments, and can also reflect the… More >

  • Open Access


    Machine Learning and Classical Forecasting Methods Based Decision Support Systems for COVID-19

    Ramazan Ünlü1, Ersin Namlı2, *

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1383-1399, 2020, DOI:10.32604/cmc.2020.011335

    Abstract From late 2019 to the present day, the coronavirus outbreak tragically affected the whole world and killed tens of thousands of people. Many countries have taken very stringent measures to alleviate the effects of the coronavirus disease 2019 (COVID-19) and are still being implemented. In this study, various machine learning techniques are implemented to predict possible confirmed cases and mortality numbers for the future. According to these models, we have tried to shed light on the future in terms of possible measures to be taken or updating the current measures. Support Vector Machines (SVM), Holt-Winters, Prophet, and Long-Short Term Memory… More >

  • Open Access


    Anomaly IoT Node Detection Based on Local Outlier Factor and Time Series

    Fang Wang1, *, Zhe Wei1, Xu Zuo2

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1063-1073, 2020, DOI:10.32604/cmc.2020.09774

    Abstract The heterogeneous nodes in the Internet of Things (IoT) are relatively weak in the computing power and storage capacity. Therefore, traditional algorithms of network security are not suitable for the IoT. Once these nodes alternate between normal behavior and anomaly behavior, it is difficult to identify and isolate them by the network system in a short time, thus the data transmission accuracy and the integrity of the network function will be affected negatively. Based on the characteristics of IoT, a lightweight local outlier factor detection method is used for node detection. In order to further determine whether the nodes are… More >

  • Open Access


    Functional Causality between Oil Prices and GDP Based on Big Data

    Ibrahim Mufrah Almanjahie1, 2, Zouaoui Chikr Elmezouar1, 2, 3, *, Ali Laksaci1, 2

    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 593-604, 2020, DOI:10.32604/cmc.2020.08901

    Abstract This paper examines the causal relationship between oil prices and the Gross Domestic Product (GDP) in the Kingdom of Saudi Arabia. The study is carried out by a data set collected quarterly, by Saudi Arabian Monetary Authority, over a period from 1974 to 2016. We seek how a change in real crude oil price affects the GDP of KSA. Based on a new technique, we treat this data in its continuous path. Precisely, we analyze the causality between these two variables, i.e., oil prices and GDP, by using their yearly curves observed in the four quarters of each year. We… More >

  • Open Access


    Forecasting Damage Mechanics By Deep Learning

    Duyen Le Hien Nguyen1, Dieu Thi Thanh Do2, Jaehong Lee2, Timon Rabczuk3, Hung Nguyen-Xuan1,4,*

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 951-977, 2019, DOI:10.32604/cmc.2019.08001

    Abstract We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems. The methodologies that are able to work accurately for less computational and resolving attempts are a significant demand nowadays. Relied on learning an amount of information from given data, the long short-term memory (LSTM) method and multi-layer neural networks (MNN) method are applied to predict solutions. Numerical examples are implemented for predicting fracture growth rates of L-shape concrete specimen under load ratio, single-edge-notched beam forced by 4-point shear and hydraulic fracturing in permeable porous media problems such as storage-toughness fracture regime and fracture-height growth… More >

  • Open Access


    A Self-Organizing Memory Neural Network for Aerosol Concentration Prediction

    Qiang Liu1,*, Yanyun Zou2,3, Xiaodong Liu4

    CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 617-637, 2019, DOI:10.32604/cmes.2019.06272

    Abstract Haze-fog, which is an atmospheric aerosol caused by natural or man-made factors, seriously affects the physical and mental health of human beings. PM2.5 (a particulate matter whose diameter is smaller than or equal to 2.5 microns) is the chief culprit causing aerosol. To forecast the condition of PM2.5, this paper adopts the related the meteorological data and air pollutes data to predict the concentration of PM2.5. Since the meteorological data and air pollutes data are typical time series data, it is reasonable to adopt a machine learning method called Single Hidden-Layer Long Short-Term Memory Neural Network (SSHL-LSTMNN) containing memory capability… More >

  • Open Access


    Monitoring Multiple Cropping Index of Henan Province, China Based on MODIS-EVI Time Series Data and Savitzky-Golay Filtering Algorithm

    Lihui Wang1, Feng Qi1, Xin Shen2,3,*, Jinliang Huang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 331-348, 2019, DOI:10.32604/cmes.2019.04268

    Abstract Multiple cropping index (MCI) is a very important indicator in crop production and agricultural intensification, which represents the utilizing degree of agriculture resources at time scale and the effective utilization situation of arable land. The objective of this paper is monitoring multiple cropping index of Henan province of China according to the time series of MODIS (Moderate-Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) after Savitzky-Golay filter processing from the year 2006 to 2011.The results revealed that this method could provide an effective way to monitor multiple cropping index, and the method of no additional authentication data is independent and reliable.… More >

  • Open Access


    Time Series Analysis for Vibration-Based Structural Health Monitoring: A Review

    Kong Fah Tee 1,*

    Structural Durability & Health Monitoring, Vol.12, No.3, pp. 129-147, 2018, DOI: 10.3970/sdhm.2018.04316

    Abstract Structural health monitoring (SHM) is a vast, interdisciplinary research field whose literature spans several decades with focusing on condition assessment of different types of structures including aerospace, mechanical and civil structures. The need for quantitative global damage detection methods that can be applied to complex structures has led to vibration-based inspection. Statistical time series methods for SHM form an important and rapidly evolving category within the broader vibration-based methods. In the literature on the structural damage detection, many time series-based methods have been proposed. When a considered time series model approximates the vibration response of a structure and model coefficients… More >

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