Rashed Iqbal¹, Hazlie Mokhlis¹, Anis Salwa Mohd Khairuddin^1,*, Syafiqah Ismail¹, Munir Azam Muhammad²

Computer Systems Science and Engineering, Vol.43, No.2, pp. 817-832, 2022, DOI:10.32604/csse.2022.023617

Abstract Electricity price forecasting (EPF) is important for energy system operations and management which include strategic bidding, generation scheduling, optimum storage reserves scheduling and systems analysis. Moreover, accurate EPF is crucial for the purpose of bidding strategies and minimizing the risk for market participants in the competitive electricity market. Nevertheless, accurate time-series prediction of electricity price is very challenging due to complex nonlinearity in the trend of electricity price. This work proposes a mid-term forecasting model based on the demand and price data, renewable and non-renewable energy supplies, the seasonality and peak and off-peak hours of… More >

Muhammad Zulqarnain¹, Rozaida Ghazali^1,*, Habib Shah², Lokman Hakim Ismail¹, Abdullah Alsheddy³, Maqsood Mahmud⁴

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3051-3068, 2022, DOI:10.32604/cmc.2022.021629

Abstract Air pollution is a significant problem in modern societies since it has a serious impact on human health and the environment. Particulate Matter (PM_2.5) is a type of air pollution that contains of interrupted elements with a diameter less than or equal to 2.5 m. For risk assessment and epidemiological investigations, a better knowledge of the spatiotemporal variation of PM_2.5 concentration in a constant space-time area is essential. Conventional spatiotemporal interpolation approaches commonly relying on robust presumption by limiting interpolation algorithms to those with explicit and basic mathematical expression, ignoring a plethora of hidden but crucial… More >

Wei Pei¹, Yan Li¹, Siuly Siuly^1,*, Peng Wen²

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 889-905, 2022, DOI:10.32604/cmc.2022.021830

Abstract Sleep stage classification plays a significant role in the accurate diagnosis and treatment of sleep-related diseases. This study aims to develop an efficient deep learning based scheme for correctly identifying sleep stages using multi-biological signals such as electroencephalography (EEG), electrocardiogram (ECG), electromyogram (EMG), and electrooculogram (EOG). Most of the prior studies in sleep stage classification focus on hand-crafted feature extraction methods. Traditional hand-crafted feature extraction methods choose features manually from raw data, which is tedious, and these features are limited in their ability to balance efficiency and accuracy. Moreover, most of the existing works on… More >

Jinho Bang¹, SongEe Park², Haemin Jeon^2,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1503-1519, 2022, DOI:10.32604/cmc.2022.020485

Abstract Conductive cementitious composites are innovated materials that have improved electrical conductivity compared to general types of cement, and are expected to be used in a variety of future infrastructures with unique functionalities such as self-heating, electromagnetic shielding, and piezoelectricity. In the present study, machine learning methods that have been recently applied in various fields were proposed for the prediction of piezoelectric characteristics of carbon nanotubes (CNTs)-incorporated cement composites. Data on the resistivity change of CNTs/cement composites according to various water/binder ratios, loading types, and CNT content were considered as training values. These data were applied 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… 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… 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… 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… More >

Yugang Li^{1, 2, *}, Yongbin Wang¹, Zhe Chen², Yuting Zhu³

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1575-1589, 2020, DOI:10.32604/cmc.2020.07451

Abstract Understanding an image goes beyond recognizing and locating the objects in it, the relationships between objects also very important in image understanding. Most previous methods have focused on recognizing local predictions of the relationships. But real-world image relationships often determined by the surrounding objects and other contextual information. In this work, we employ this insight to propose a novel framework to deal with the problem of visual relationship detection. The core of the framework is a relationship inference network, which is a recurrent structure designed for combining the global contextual information of the object to More >