Jing He^1,2, Haonan Chen^3,*, Lingxiao Li⁴, Yebin Zou⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 211-237, 2023, DOI:10.32604/cmes.2022.020597

Abstract There are many sources of geographic big data, and most of them come from heterogeneous environments. The data sources obtained in this case contain attribute information of different spatial scales, different time scales and different complexity levels. It is worth noting that the emergence of new high-dimensional trajectory data types and the increasing number of details are becoming more difficult. In this case, visualizing high-dimensional spatiotemporal trajectory data is extremely challenging. Therefore, i-tStar and its extension i-tStar (3D) proposed, a trajectory behavior feature for moving objects that are integrated into a view with less effort to display and extract spatiotemporal… More > Graphic Abstract

V. Sridhar¹, K.V. Ranga Rao², Saddam Hussain^3,*, Syed Sajid Ullah⁴, Roobaea Alroobaea⁵, Maha Abdelhaq⁶, Raed Alsaqour⁷

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1693-1708, 2023, DOI:10.32604/cmc.2023.028129

Abstract Nonorthogonal Multiple Access (NOMA) is incorporated into the wireless network systems to achieve better connectivity, spectral and energy effectiveness, higher data transfer rate, and also obtain the high quality of services (QoS). In order to improve throughput and minimum latency, a Multivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access (MRRWPBA-NOMA) technique is introduced for network communication. In the downlink transmission, each mobile device's resources and their characteristics like energy, bandwidth, and trust are measured. Followed by, the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different… More >

Qiansheng Fang, Haojie Wang, Chenlei Xie^*, Jie Chen

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1237-1250, 2023, DOI:10.32604/cmes.2022.021131

Abstract At present, the leakage rate of the water distribution network in China is still high, and the waste of water resources caused by water distribution network leakage is quite serious every year. Therefore, the location of pipeline leakage is of great significance for saving water resources and reducing economic losses. Acoustic emission technology is the most widely used pipeline leak location technology. The traditional non-stationary random signal de-noising method mainly relies on the estimation of noise parameters, ignoring periodic noise and components unrelated to pipeline leakage. Aiming at the above problems, this paper proposes a leak location method for water… More >

M. Suresh Kumar^1,*, T. Purosothaman²

Computer Systems Science and Engineering, Vol.45, No.1, pp. 925-938, 2023, DOI:10.32604/csse.2023.027315

Abstract The expanding and ubiquitous availability of the Internet of Things (IoT) have changed everyone’s life easier and more convenient. Same time it also offers a number of issues, such as effectiveness, security, and excessive power consumption, which constitute a danger to intelligent IoT-based apps. Group managing is primarily used for transmitting and multi-pathing communications that are secured with a general group key and it can only be decrypted by an authorized group member. A centralized trustworthy system, which is in charge of key distribution and upgrades, is used to maintain group keys. To provide longitudinal access controls, Software Defined Network… More >

Ping Wen^*, Sheng-Duo Chen^*, Jia-Rui Wang^†, Ying-He Zeng^*

Oncology Research, Vol.27, No.5, pp. 583-592, 2019, DOI:10.3727/096504018X15368325811545

Abstract This study evaluated the difference in treatment response and survival profiles between drug-eluting bead transarterial chemoembolization (DEB-TACE) and conventional transarterial chemoembolization (cTACE) treatments in Chinese hepatocellular carcinoma (HCC) patients. A total of 120 HCC patients were consecutively enrolled in this prospective cohort study, which showed that DEB-TACE achieved higher complete response (CR) (30.8%) compared with cTACE (7.4%) with no difference in overall response rate (ORR) for patients treated with DEB-TACE and cTACE (80.8% vs. 73.5%). In addition, DEB-TACE was associated with a lower rate of progressive disease (PD) compared with cTACE (1.9% vs. 11.8%). With respect to survival, patients in… More >

Jinfu Wang¹, Shunyi Zhao^1,*, Fei Liu¹, Zhenyi Ma²

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.2, pp. 531-552, 2022, DOI:10.32604/cmes.2022.019521

Abstract In modern industry, process monitoring plays a significant role in improving the quality of process conduct. With the higher dimensional of the industrial data, the monitoring methods based on the latent variables have been widely applied in order to decrease the wasting of the industrial database. Nevertheless, these latent variables do not usually follow the Gaussian distribution and thus perform unsuitable when applying some statistics indices, especially the T2 on them. Variational AutoEncoders (VAE), an unsupervised deep learning algorithm using the hierarchy study method, has the ability to make the latent variables follow the Gaussian distribution. The partial least squares… More >

Narendran Sobanapuram Muruganandam, Umamakeswari Arumugam^*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 979-989, 2023, DOI:10.32604/csse.2023.024943

Abstract In forecasting real time environmental factors, large data is needed to analyse the pattern behind the data values. Air pollution is a major threat towards developing countries and it is proliferating every year. Many methods in time series prediction and deep learning models to estimate the severity of air pollution. Each independent variable contributing towards pollution is necessary to analyse the trend behind the air pollution in that particular locality. This approach selects multivariate time series and coalesce a real time updatable autoregressive model to forecast Particulate matter (PM) PM2.5. To perform experimental analysis the data from the Central Pollution… More >

Al-Batool Al-Ghamdi^1,*, Souad Kamel², Mashael Khayyat³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1365-1383, 2022, DOI:10.32604/cmc.2022.026246

Abstract Water is a vital resource. It supports a multitude of industries, civilizations, and agriculture. However, climatic conditions impact water availability, particularly in desert areas where the temperature is high, and rain is scarce. Therefore, it is crucial to forecast water demand to provide it to sectors either on regular or emergency days. The study aims to develop an accurate model to forecast daily water demand under the impact of climatic conditions. This forecasting is known as a multivariate time series because it uses both the historical data of water demand and climatic conditions to forecast the future. Focusing on the… More >

S. Kalyani^*, K. Venkata Rao, A. Mary Sowjanya

Structural Durability & Health Monitoring, Vol.15, No.4, pp. 317-334, 2021, DOI:10.32604/sdhm.2021.016975

Abstract Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration. Sensor data of all possible states of a system are used for building machine learning models. These models are further used to predict the possible downtime for proactive action on the system condition. Aircraft engine data from run to failure is used in the current study. The run to failure data includes states like new installation, stable operation, first reported issue, erroneous operation, and final failure. In the present work, the… More >

Jingtang Luo¹, Jingru Liao², Chenlin Zhang³, Ziqi Wang⁴, Yuhang Zhang², Jie Xu^2,*, Zhengwen Huang⁵

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1225-1239, 2022, DOI:10.32604/iasc.2022.022812

Abstract Accurate estimation of communication bandwidth is critical for the sensing and controlling applications of smart grid. Different from public network, the bandwidth requirements of smart grid communication network must be accurately estimated in prior to the deployment of applications or even the building of communication network. However, existing methods for smart grid usually model communication nodes in coarse-grained ways, so their estimations become inaccurate in scenarios where the same type of nodes have very different bandwidth requirements. To solve this issue, we propose a fine-grained estimation method based on multivariate nonlinear fitting. Firstly, we use linear fitting to calculate the… More >