Mohammad Tabrez Quasim¹, Saad Alhuwaimel^2,*, Asadullah Shaikh³, Yousef Asiri³, Khairan Rajab³, Rihem Farkh^4,5, Khaled Al Jaloud⁴

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

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 achieved the component perception measured… More >

Daxing Zhang^1,*, 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 to a higher level for… More >

Dejia Shi¹, Hanzhong Zheng^2,*

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

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). However, sometimes, patients’ treatment can… More >

Wenxiao Wang^1,*, Xiaoyu Li^1,*, 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), denoted as RNN-LSTM-LSTM. This model… More >

Reem K. Alkhodhairi¹, Shahad R. Aljalhami¹, Norah K. Rusayni¹, Jowharah F. Alshobaili¹, Amal A. Al-Shargabi^1,*, Abdulatif Alabdulatif²

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3215-3233, 2021, DOI:10.32604/cmc.2021.016881

Abstract Currently, Bitcoin is the world’s most popular cryptocurrency. The price of Bitcoin is extremely volatile, which can be described as high-benefit and high-risk. To minimize the risk involved, a means of more accurately predicting the Bitcoin price is required. Most of the existing studies of Bitcoin prediction are based on historical (i.e., benchmark) data, without considering the real-time (i.e., live) data. To mitigate the issue of price volatility and achieve more precise outcomes, this study suggests using historical and real-time data to predict the Bitcoin candlestick—or open, high, low, and close (OHLC)—prices. Seeking a better prediction model, the present study… More >

Imran Ashraf, Soojung Hur, Yousaf Bin Zikria, Yongwan Park^*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2597-2620, 2021, DOI:10.32604/cmc.2021.016214

Abstract Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors: Smartphone heterogeneity and smaller data lengths. The use of multifarious smartphones cripples the performance of such approaches owing to the variability of the magnetic field data. In the same vein, smaller lengths of magnetic field data decrease the localization accuracy substantially. The current study proposes the use of multiple neural networks like deep neural network (DNN), long short term memory network (LSTM), and gated recurrent unit network (GRN) to perform indoor localization based on the embedded magnetic sensor of the smartphone. A voting scheme… More >

Abdullah Alharbi^1,*, Wael Alosaimi¹, M. Irfan Uddin²

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 303-317, 2021, DOI:10.32604/cmc.2021.016230

Abstract COVID-19 disease is spreading exponentially due to the rapid transmission of the virus between humans. Different countries have tried different solutions to control the spread of the disease, including lockdowns of countries or cities, quarantines, isolation, sanitization, and masks. Patients with symptoms of COVID-19 are tested using medical testing kits; these tests must be conducted by healthcare professionals. However, the testing process is expensive and time-consuming. There is no surveillance system that can be used as surveillance framework to identify regions of infected individuals and determine the rate of spread so that precautions can be taken. This paper introduces a… More >

Mustaqeem, Soonil Kwon^*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 4039-4059, 2021, DOI:10.32604/cmc.2021.015070

Abstract Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications, such as robotics, virtual reality, behavior assessments, and emergency call centers. Recently, researchers have developed many techniques in this field in order to ensure an improvement in the accuracy by utilizing several deep learning approaches, but the recognition rate is still not convincing. Our main aim is to develop a new technique that increases the recognition rate with reasonable cost computations. In this paper, we suggested a new technique, which is a one-dimensional dilated convolutional neural network (1D-DCNN) for… More >

Yonghua Huo¹, Yi Cao², Zhihao Wang¹, Yu Yan³, Zhongdi Ge³, Yang Yang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 1053-1091, 2021, DOI:10.32604/cmes.2021.013045

Abstract In recent years, with the continuous development of information technology and the rapid growth of network scale, network monitoring and management become more and more important. Network traffic is an important part of network state. In order to ensure the normal operation of the network, improve the availability of the network, find network faults in time and deal with network attacks; it is necessary to detect the abnormal traffic in the network. Abnormal traffic detection is of great significance in the actual network management. Therefore, in order to improve the accuracy and efficiency of network traffic anomaly detection, this paper… More >

Yasir Shahzad^1,*, 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 technique. The proposed model is… More >