Songlin Yang^1,2, Xingjin Han^1,2, Chufeng Kuang^1,2, Weihua Fang³, Jianfei Zhang⁴, Tiantang Yu^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 49-72, 2022, DOI:10.32604/cmes.2022.018325

Abstract The deformation prediction models of Wuqiangxi concrete gravity dam are developed, including two statistical models and a deep learning model. In the statistical models, the reliable monitoring data are firstly determined with Lahitte criterion; then, the stepwise regression and partial least squares regression models for deformation prediction of concrete gravity dam are constructed in terms of the reliable monitoring data, and the factors of water pressure, temperature and time effect are considered in the models; finally, according to the monitoring data from 2006 to 2020 of five typical measuring points including J₂₃ (on dam section ), J₃₃ (on dam section… More >

Abdulqader M. Almars^*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3091-3106, 2022, DOI:10.32604/cmc.2022.022609

Abstract Depression is a common mental health issue that affects a large percentage of people all around the world. Usually, people who suffer from this mood disorder have issues such as low concentration, dementia, mood swings, and even suicide. A social media platform like Twitter allows people to communicate as well as share photos and videos that reflect their moods. Therefore, the analysis of social media content provides insight into individual moods, including depression. Several studies have been conducted on depression detection in English and less in Arabic. The detection of depression from Arabic social media lags behind due the complexity… More >

Muhammad Usman Younus^1,*, Rabia Shafi², Ammar Rafiq³, Muhammad Rizwan Anjum⁴, Sharjeel Afridi⁵, Abdul Aleem Jamali⁶, Zulfiqar Ali Arain⁷

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2617-2631, 2022, DOI:10.32604/cmc.2022.022236

Abstract The development of multimedia content has resulted in a massive increase in network traffic for video streaming. It demands such types of solutions that can be addressed to obtain the user's Quality-of-Experience (QoE). 360-degree videos have already taken up the user's behavior by storm. However, the users only focus on the part of 360-degree videos, known as a viewport. Despite the immense hype, 360-degree videos convey a loathsome side effect about viewport prediction, making viewers feel uncomfortable because user viewport needs to be pre-fetched in advance. Ideally, we can minimize the bandwidth consumption if we know what the user motion… More >

Shuo Liang¹, Dingcheng Wang^1,*, Jingrong Wu¹, Rui Wang¹, Ruiqi Wang²

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 701-714, 2021, DOI:10.32604/iasc.2021.020010

Abstract Atmospheric temperature forecast plays an important role in weather forecast and has a significant impact on human daily and economic life. However, due to the complexity and uncertainty of the atmospheric system, exploring advanced forecasting methods to improve the accuracy of meteorological prediction has always been a research topic for scientists. With the continuous improvement of computer performance and data acquisition technology, meteorological data has gained explosive growth, which creates the necessary hardware support conditions for more accurate weather forecast. The more accurate forecast results need advanced weather forecast methods suitable for hardware. Therefore, this paper proposes a deep learning… More >

Mei Sun¹, Qingtao Li², Peiguang Lin^2,*

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 369-378, 2021, DOI:10.32604/iasc.2021.014962

Abstract Stocks are the key components of most investment portfolios. The accurate forecasting of stock prices can help investors and investment brokerage firms make profits or reduce losses. However, stock forecasting is complex because of the intrinsic features of stock data, such as nonlinearity, long-term dependency, and volatility. Moreover, stock prices are affected by multiple factors. Various studies in this field have proposed ways to improve prediction accuracy. However, not all of the proposed features are valid, and there is often noise in the features—such as political, economic, and legal factors—which can lead to poor prediction results. To overcome such limitations,… More >

Ziyong Ran¹, Desheng Zheng^{1, *}, Yanling Lai¹, Lulu Tian²

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 309-320, 2020, DOI:10.32604/cmc.2020.010102

Abstract Nowadays, Internet has become an indispensable part of daily life and is used in many fields. Due to the large amount of Internet traffic, computers are subject to various security threats, which may cause serious economic losses and even endanger national security. It is hoped that an effective security method can systematically classify intrusion data in order to avoid leakage of important data or misuse of data. As machine learning technology matures, deep learning is widely used in various industries. Combining deep learning with network security and intrusion detection is the current trend. In this paper, the problem of data… More >

Bingjie Yan¹, Jun Wang¹, Zhen Zhang², Xiangyan Tang^{1, *}, Yize Zhou¹, Guopeng Zheng¹, Qi Zou¹, Yao Lu¹, Boyi Liu³, Wenxuan Tu⁴, Neal Xiong⁵

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1473-1490, 2020, DOI:10.32604/cmc.2020.011317

Abstract New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. Through understanding the development trend of confirmed cases in a region, the government can control the pandemic by using the corresponding policies. However, the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors. To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long-Short Term Memory) neural network. This work compares the deviation between the experimental results of the improved LSTM… More >