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  • Open Access


    Recurrent Convolutional Neural Network MSER-Based Approach for Payable Document Processing

    Suliman Aladhadh1, Hidayat Ur Rehman2, Ali Mustafa Qamar3,4,*, Rehan Ullah Khan1

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3399-3411, 2021, DOI:10.32604/cmc.2021.018724

    Abstract A tremendous amount of vendor invoices is generated in the corporate sector. To automate the manual data entry in payable documents, highly accurate Optical Character Recognition (OCR) is required. This paper proposes an end-to-end OCR system that does both localization and recognition and serves as a single unit to automate payable document processing such as cheques and cash disbursement. For text localization, the maximally stable extremal region is used, which extracts a word or digit chunk from an invoice. This chunk is later passed to the deep learning model, which performs text recognition. The deep… More >

  • Open Access


    Improving Stock Price Forecasting Using a Large Volume of News Headline Text

    Daxing Zhang1,*, Erguan Cai2

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3931-3943, 2021, DOI:10.32604/cmc.2021.012302

    Abstract Previous research in the area of using deep learning algorithms to forecast stock prices was focused on news headlines, company reports, and a mix of daily stock fundamentals, but few studies achieved excellent results. This study uses a convolutional neural network (CNN) to predict stock prices by considering a great amount of data, consisting of financial news headlines. We call our model N-CNN to distinguish it from a CNN. The main concept is to narrow the diversity of specific stock prices as they are impacted by news headlines, then horizontally expand the news headline data… More >

  • Open Access


    Adapted Long Short-Term Memory (LSTM) for Concurrent\\ Human Activity Recognition

    Keshav Thapa, Zubaer Md. Abdhulla AI, Yang Sung-Hyun*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1653-1670, 2021, DOI:10.32604/cmc.2021.015660

    Abstract In this era, deep learning methods offer a broad spectrum of efficient and original algorithms to recognize or predict an output when given a sequence of inputs. In current trends, deep learning methods using recent long short-term memory (LSTM) algorithms try to provide superior performance, but they still have limited effectiveness when detecting sequences of complex human activity. In this work, we adapted the LSTM algorithm into a synchronous algorithm (sync-LSTM), enabling the model to take multiple parallel input sequences to produce multiple parallel synchronized output sequences. The proposed method is implemented for simultaneous human… More >

  • Open Access


    A Prediction Method of Trend-Type Capacity Index Based on Recurrent Neural Network

    Wenxiao Wang1,*, Xiaoyu Li1,*, Yin Ding1, Feizhou Wu2, Shan Yang3

    Journal of Quantum Computing, Vol.3, No.1, pp. 25-33, 2021, DOI:10.32604/jqc.2021.016346

    Abstract Due to the increase in the types of business and equipment in telecommunications companies, the performance index data collected in the operation and maintenance process varies greatly. The diversity of index data makes it very difficult to perform high-precision capacity prediction. In order to improve the forecasting efficiency of related indexes, this paper designs a classification method of capacity index data, which divides the capacity index data into trend type, periodic type and irregular type. Then for the prediction of trend data, it proposes a capacity index prediction model based on Recurrent Neural Network (RNN), More >

  • Open Access


    Optimized Predictive Framework for Healthcare Through Deep Learning

    Yasir Shahzad1,*, Huma Javed1, Haleem Farman2, Jamil Ahmad2, Bilal Jan3, Abdelmohsen A. Nassani4

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2463-2480, 2021, DOI:10.32604/cmc.2021.014904

    Abstract Smart healthcare integrates an advanced wave of information technology using smart devices to collect health-related medical science data. Such data usually exist in unstructured, noisy, incomplete, and heterogeneous forms. Annotating these limitations remains an open challenge in deep learning to classify health conditions. In this paper, a long short-term memory (LSTM) based health condition prediction framework is proposed to rectify imbalanced and noisy data and transform it into a useful form to predict accurate health conditions. The imbalanced and scarce data is normalized through coding to gain consistency for accurate results using synthetic minority oversampling… More >

  • Open Access


    An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment

    Ayesha Jabeen1, Sitara Afzal1, Muazzam Maqsood1, Irfan Mehmood2, Sadaf Yasmin1, Muhammad Tabish Niaz3, Yunyoung Nam4,*

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1191-1206, 2021, DOI:10.32604/cmc.2021.014598

    Abstract Stock market forecasting is an important research area, especially for better business decision making. Efficient stock predictions continue to be significant for business intelligence. Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices, moving averages, or daily returns. However, major events’ news also contains significant information regarding market drivers. An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market. This research proposes an efficient model for stock market prediction. The current proposed study explores the 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 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)… More >

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