Jurgita Markevičiūtė^1,*, Jolita Bernatavičienė², Rūta Levulienė¹, Viktor Medvedev², Povilas Treigys², Julius Venskus²

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 695-714, 2022, DOI:10.32604/cmc.2022.018735

Abstract The growing number of COVID-19 cases puts pressure on healthcare services and public institutions worldwide. The pandemic has brought much uncertainty to the global economy and the situation in general. Forecasting methods and modeling techniques are important tools for governments to manage critical situations caused by pandemics, which have negative impact on public health. The main purpose of this study is to obtain short-term forecasts of disease epidemiology that could be useful for policymakers and public institutions to make necessary short-term decisions. To evaluate the effectiveness of the proposed attention-based method combining certain data mining algorithms and the classical ARIMA… More >

Desheng Zheng¹, Xiaolan Tang^1,*, Xinlong Wu¹, Kexin Zhang¹, Chao Lu², Lulu Tian³

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 815-826, 2021, DOI:10.32604/iasc.2021.017737

Abstract Aeroengine surge fault is one of the main causes of flight accidents. When a surge occurs, it is hard to detect it in time and take anti-surge measures correctly. Recently, people have been applying detection methods based on mathematical models and expert knowledge. Due to difficult modeling and limited failure-mode coverage of these methods, early surge detection cannot be achieved. To address these problems, firstly, this paper introduced the data of six main sensors related to the aeroengine surge fault, such as, total pressure at compressor (high pressure rotor) outlet (Pt3), high pressure compressor rotor speed (N2), power level angle… More >

Tian Liwei^1,2,*, Feng Li¹, Sun Yu³, Guo Yuankai⁴

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 855-868, 2021, DOI:10.32604/iasc.2021.016805

Abstract The prediction of the “ups and downs” of stock market prices is one of the important undertakings of the financial market. Since accurate prediction helps foster considerable economic benefits, stock market prediction has attracted significant interest by both investors and researchers. Efforts into building an accurate, stable and effective model to predict stock prices’ movements have been proliferating at a fast pace, to meet such a challenge. Firstly, this paper uses a correlation analysis to analyze the attributes of a stock dataset, processing missing values, determining the data attributes to be retained data, then divide it in a training set… More >

Qura-Tul-Ain Khan¹, Sagheer Abbas¹, Muhammad Adnan Khan^2,*, Areej Fatima³, Saad Alanazi⁴, Nouh Sabri Elmitwally^4,5

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3061-3077, 2021, DOI:10.32604/cmc.2021.015441

Abstract In vehicular systems, driving is considered to be the most complex task, involving many aspects of external sensory skills as well as cognitive intelligence. External skills include the estimation of distance and speed, time perception, visual and auditory perception, attention, the capability to drive safely and action-reaction time. Cognitive intelligence works as an internal mechanism that manages and holds the overall driver’s intelligent system.These cognitive capacities constitute the frontiers for generating adaptive behaviour for dynamic environments. The parameters for understanding intelligent behaviour are knowledge, reasoning, decision making, habit and cognitive skill. Modelling intelligent behaviour reveals that many of these parameters… More >

Ajla Kulaglic^1,*, Burak Berk Ustundag²

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3577-3593, 2021, DOI:10.32604/cmc.2021.014768

Abstract Machine Learning (ML) algorithms have been widely used for financial time series prediction and trading through bots. In this work, we propose a Predictive Error Compensated Wavelet Neural Network (PEC-WNN) ML model that improves the prediction of next day closing prices. In the proposed model we use multiple neural networks where the first one uses the closing stock prices from multiple-scale time-domain inputs. An additional network is used for error estimation to compensate and reduce the prediction error of the main network instead of using recurrence. The performance of the proposed model is evaluated using six different stock data samples… More >

Jaekyu Lee^1,2, Sangyub Lee¹, Hyosub Choi¹, Hyeonjoong Cho^2,*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2709-2727, 2021, DOI:10.32604/cmc.2021.015174

Abstract In this study, we developed software for vehicle big data analysis to analyze the time-series data of connected vehicles. We designed two software modules: The first to derive the Pearson correlation coefficients to analyze the collected data and the second to conduct exploratory data analysis of the collected vehicle data. In particular, we analyzed the dangerous driving patterns of motorists based on the safety standards of the Korea Transportation Safety Authority. We also analyzed seasonal fuel efficiency (four seasons) and mileage of vehicles, and identified rapid acceleration, rapid deceleration, sudden stopping (harsh braking), quick starting, sudden left turn, sudden right… More >

Mashael Khayyat^1,*, Kaouther Laabidi², Nada Almalki¹, Maysoon Al-zahrani¹

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3781-3793, 2021, DOI:10.32604/cmc.2021.014918

Abstract COVID-19 comes from a large family of viruses identified in 1965; to date, seven groups have been recorded which have been found to affect humans. In the healthcare industry, there is much evidence that Al or machine learning algorithms can provide effective models that solve problems in order to predict confirmed cases, recovered cases, and deaths. Many researchers and scientists in the field of machine learning are also involved in solving this dilemma, seeking to understand the patterns and characteristics of virus attacks, so scientists may make the right decisions and take specific actions. Furthermore, many models have been considered… More >

Xiaoli Li¹, Shuailing Zhou¹, Zixu An^2,*, Zhenlong Du¹

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3765-3780, 2021, DOI:10.32604/cmc.2021.012422

Abstract Rough set theory has been widely researched for time series prediction problems such as rainfall runoff. Accurate forecasting of rainfall runoff is a long standing but still mostly significant problem for water resource planning and management, reservoir and river regulation. Most research is focused on constructing the better model for improving prediction accuracy. In this paper, a rainfall runoff forecast model based on the variable-precision fuzzy neighborhood rough set (VPFNRS) is constructed to predict Watershed runoff value. Fuzzy neighborhood rough set define the fuzzy decision of a sample by using the concept of fuzzy neighborhood. The fuzzy neighborhood rough set… More >

Bilal Shoaib¹, Yasir Javed², Muhammad Adnan Khan^3,*, Fahad Ahmad⁴, Rizwan Majeed⁵, Muhammad Saqib Nawaz¹, Muhammad Adeel Ashraf⁶, Abid Iqbal², Muhammad Idrees⁷

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1413-1427, 2021, DOI:10.32604/cmc.2021.015099

Abstract For the unforced dynamical non-linear statespace model, a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article. The proposed algorithm lends itself towards the parallel implementation as in the FPGA systems. With the help of an ortho-normal triangularization method, which relies on numerically stable givens rotation, matrix inversion causes a computational burden, is reduced. Matrix computation possesses many excellent numerical properties such as singularity, symmetry, skew symmetry, and triangularity is achieved by using this algorithm. The proposed method is validated for the prediction of stationary and non-stationary MackeyGlass Time Series, along… More >

Anh H. Vo¹, Trang Nguyen², Tuong Le^1,3,*

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1307-1317, 2020, DOI:10.32604/iasc.2020.013189

Abstract Brent oil price fluctuates continuously causing instability in the economy. Therefore, it is essential to accurately predict the trend of oil prices, as it helps to improve profits for investors and benefits the community at large. Oil prices usually fluctuate over time as a time series and as such several sequence-based models can be used to predict them. Hence, this study proposes an efficient model named BOP-BL based on Bidirectional Long Short-Term Memory (Bi-LSTM) for oil price prediction. The proposed framework consists of two modules as follows: The first module has three Bi-LSTM layers which help learning useful information features… More >