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  • Open Access

    ARTICLE

    UNSTEADY MHD ROTATING AND CHEMICALLY REACTING FLUID FLOW OVER AN OSCILLATING VERTICAL SURFACE IN A DARCIAN POROUS REGIME

    Utpal Jyoti Dasa , Mira Dasb,*

    Frontiers in Heat and Mass Transfer, Vol.17, pp. 1-8, 2021, DOI:10.5098/hmt.17.18

    Abstract An unsteady mixed convection flow of an electrically conducting, viscous, incompressible fluid over an oscillating vertical surface in a Darcian porous regime in presence of heat generation/absorption and thermal radiation have been studied. The liquid and the surface is moving with constant angular velocity as a rigid body about an axis. The fluid is taken here to be gray, absorbing/emitting radiation but non scattering medium. Here firstorder chemical reaction and a transverse magnetic field have been considered. The presence of Hall current and Soret effect are considered. The governing coupled partial differential equations are solved More >

  • Open Access

    ARTICLE

    A Deep Learning Breast Cancer Prediction Framework

    Asmaa E. E. Ali*, Mofreh Mohamed Salem, Mahmoud Badway, Ali I. EL Desouky

    Journal on Artificial Intelligence, Vol.3, No.3, pp. 81-96, 2021, DOI:10.32604/jai.2021.022433 - 25 January 2022

    Abstract Breast cancer (BrC) is now the world’s leading cause of death for women. Early detection and effective treatment of this disease are the only rescues to reduce BrC mortality. The prediction of BrC diseases is very difficult because it is not an individual disease but a mixture of various diseases. Many researchers have used different techniques such as classification, Machine Learning (ML), and Deep Learning (DL) of the prediction of the breast tumor into Benign and Malignant. However, still there is a scope to introduce appropriate techniques for developing and implementing a more effective diagnosis… More >

  • Open Access

    REVIEW

    The Current Status of Chlorin e6-Based Nanoscale Delivery Systems for Cancer Therapy

    Zhengyi Li1,2,3, Lihua Qiu1,2,3,*

    Oncologie, Vol.23, No.4, pp. 515-531, 2021, DOI:10.32604/oncologie.2021.019856 - 31 December 2021

    Abstract Improving the effectiveness of cancer treatment has become a central concern for the public. In recent years, in order to maximize the efficiency of cancer treatment, photodynamic therapy (PDT) and sonodynamic therapy (SDT) have received widespread attention. Chlorin e6 (Ce6) is a fluorescent dye with strong optical properties and excellent photoconversion efficiency under near-infrared light irradiation, which has been widely used in PDT in recent decades due to its superior antitumor ability. Of note, Ce6 can be used as a sonosensitizer for SDT, which generates large amounts of reactive oxygen species (ROS) for tumor treatment More >

  • Open Access

    ARTICLE

    A Novel Starting Impulse Suppression Method for Active Power Filter Based on Slowly Rising Curve

    Jianfeng Yang*, Yu Ding

    Energy Engineering, Vol.118, No.6, pp. 1839-1853, 2021, DOI:10.32604/EE.2021.015930 - 10 September 2021

    Abstract With the widespread application of power electronic equipment in the power grid, the harmonic problem of the power grid becomes more pronounced, reducing the efficiency of power production, transmission, and utilization, and interfering with the normal operation of the power grid. Based on the requirements of harmonic suppression and power system protection, a shunt active power filter (SAPF) is proposed as an effective harmonic suppression method. However, there are problems with impulse current and impulse voltage in the starting process of SAPF. Impulse current and impulse voltage cause the power grid and switchgear to bear… More >

  • Open Access

    ARTICLE

    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 - 24 August 2021

    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

    ARTICLE

    An Improved Machine Learning Technique with Effective Heart Disease Prediction System

    Mohammad Tabrez Quasim1, Saad Alhuwaimel2,*, Asadullah Shaikh3, Yousef Asiri3, Khairan Rajab3, Rihem Farkh4,5, Khaled Al Jaloud4

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4169-4181, 2021, DOI:10.32604/cmc.2021.015984 - 24 August 2021

    Abstract Heart disease is the leading cause of death worldwide. Predicting heart disease is challenging because it requires substantial experience and knowledge. Several research studies have found that the diagnostic accuracy of heart disease is low. The coronary heart disorder determines the state that influences the heart valves, causing heart disease. Two indications of coronary heart disorder are strep throat with a red persistent skin rash, and a sore throat covered by tonsils or strep throat. This work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial stiffness. At first, we More >

  • Open Access

    ARTICLE

    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 - 24 August 2021

    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

    ARTICLE

    Cluster Analysis for IR and NIR Spectroscopy: Current Practices to Future Perspectives

    Simon Crase1,2, Benjamin Hall2, Suresh N. Thennadil3,*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1945-1965, 2021, DOI:10.32604/cmc.2021.018517 - 21 July 2021

    Abstract Supervised machine learning techniques have become well established in the study of spectroscopy data. However, the unsupervised learning technique of cluster analysis hasn’t reached the same level maturity in chemometric analysis. This paper surveys recent studies which apply cluster analysis to NIR and IR spectroscopy data. In addition, we summarize the current practices in cluster analysis of spectroscopy and contrast these with cluster analysis literature from the machine learning and pattern recognition domain. This includes practices in data pre-processing, feature extraction, clustering distance metrics, clustering algorithms and validation techniques. Special consideration is given to the More >

  • Open Access

    ARTICLE

    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 - 21 July 2021

    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

    ARTICLE

    A Mortality Risk Assessment Approach on ICU Patients Clinical Medication Events Using Deep Learning

    Dejia Shi1, Hanzhong Zheng2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 161-181, 2021, DOI:10.32604/cmes.2021.014917 - 28 June 2021

    Abstract ICU patients are vulnerable to medications, especially infusion medications, and the rate and dosage of infusion drugs may worsen the condition. The mortality prediction model can monitor the real-time response of patients to drug treatment, evaluate doctors’ treatment plans to avoid severe situations such as inverse Drug-Drug Interactions (DDI), and facilitate the timely intervention and adjustment of doctor’s treatment plan. The treatment process of patients usually has a time-sequence relation (which usually has the missing data problem) in patients’ treatment history. The state-of-the-art method to model such time-sequence is to use Recurrent Neural Network (RNN).… More >

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