Qi Zhuang^a,* , Dong Liu^b, Bo Liu^c, Mei Liu^a

Frontiers in Heat and Mass Transfer, Vol.20, pp. 1-6, 2023, DOI:10.5098/hmt.20.13

Abstract In the actual operation of wet gas pipeline, liquid accumulation is easy to form in the low-lying and uphill sections of the pipeline, which leads to a series of problems such as reduced pipeline transportation efficiency, increased pipeline pressure drop, hydrate formation, slug flow and intensified corrosion in the pipeline. Accurate calculation of liquid holdup is of great significance to the research of flow pattern identification, pipeline corrosion evaluation and prediction, and gas pipeline transportation efficiency calculation. Based on the experimental data of liquid holdup in horizontal pipeline, a commonly used BP neural network (BPNN) model is established in this… More >

Ping-Huan Kuo^1,2, Yu-Tsun Chen¹, Her-Terng Yau^1,2,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2687-2709, 2023, DOI:10.32604/csse.2023.036293

Abstract The power factor is the ratio between the active and apparent power, and it is available to determine the operational capability of the intended circuit or the parts. The excitation current of the synchronous motor is an essential parameter required for adjusting the power factor because it determines whether the motor is under the optimal operating status. Although the excitation current should predict with the experimental devices, such a method is unsuitable for online real-time prediction. The artificial intelligence algorithm can compensate for the defect of conventional measurement methods requiring the measuring devices and the model optimization is compared during… More >

Sarkar Hasan Ahmed¹, Adel Al-Zebari², Rizgar R. Zebari³, Subhi R. M. Zeebaree^4,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3993-4008, 2023, DOI:10.32604/cmc.2023.037464

Abstract Remote sensing (RS) presents laser scanning measurements, aerial photos, and high-resolution satellite images, which are utilized for extracting a range of traffic-related and road-related features. RS has a weakness, such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic features. This article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images (ODLTCP-HRRSI) to resolve these issues. The presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart cities. To attain this, the presented ODLTCP-HRRSI model performs two major processes. At the initial stage, the… More >

Hamza Safwan¹, Zeshan Iqbal¹, Rashid Amin¹, Muhammad Attique Khan², Majed Alhaisoni³, Abdullah Alqahtani⁴, Ye Jin Kim⁵, Byoungchol Chang^6,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2365-2381, 2023, DOI:10.32604/cmc.2023.033896

Abstract Software-defined networking (SDN) represents a paradigm shift in network traffic management. It distinguishes between the data and control planes. APIs are then used to communicate between these planes. The controller is central to the management of an SDN network and is subject to security concerns. This research shows how a deep learning algorithm can detect intrusions in SDN-based IoT networks. Overfitting, low accuracy, and efficient feature selection is all discussed. We propose a hybrid machine learning-based approach based on Random Forest and Long Short-Term Memory (LSTM). In this study, a new dataset based specifically on Software Defined Networks is used… More >

Ping-Huan Kuo^1,2, Sing-Yan Chen¹, Her-Terng Yau^1,2,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1577-1596, 2023, DOI:10.32604/csse.2023.036292

Abstract Cars are regarded as an indispensable means of transportation in Taiwan. Several studies have indicated that the automotive industry has witnessed remarkable advances and that the market of used cars has rapidly expanded. In this study, a price prediction system for used BMW cars was developed. Nine parameters of used cars, including their model, registration year, and transmission style, were analyzed. The data obtained were then divided into three subsets. The first subset was used to compare the results of each algorithm. The predicted values produced by the two algorithms with the most satisfactory results were used as the input… More >

Jiahao Wen, Zhijian Wang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 749-765, 2023, DOI:10.32604/cmes.2023.023865

Abstract Since the existing prediction methods have encountered difficulties in processing the multiple influencing factors in short-term power load forecasting, we propose a bidirectional long short-term memory (BiLSTM) neural network model based on the temporal pattern attention (TPA) mechanism. Firstly, based on the grey relational analysis, datasets similar to forecast day are obtained. Secondly, the bidirectional LSTM layer models the data of the historical load, temperature, humidity, and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network, so that the influencing factors (with different characteristics) can select relevant information from different time steps… More >

V. Saikumari^*, V. Sunitha

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1797-1810, 2023, DOI:10.32604/iasc.2023.034038

Abstract Recently, gender equality and women’s entrepreneurship have gained considerable attention in global economic development. Prior to the design of any policy interventions to increase women’s entrepreneurship, it is significant to comprehend the factors motivating women to become entrepreneurs. The non-understanding of the factors can result in the endurance of low living standards and the design of expensive and ineffectual policies. But female involvement in entrepreneurship becomes higher in developing economies compared to developed economies. Women Entrepreneurship Index (WEI) plays a vital role in determining the factors that enable the flourishment of high potential female entrepreneurs which enhances economic welfare and… More >

P. Sumathi^1,*, V. V. Karthikeyan², M. S. Kavitha³, S. Karthik³

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1545-1559, 2023, DOI:10.32604/csse.2023.027580

Abstract Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around. Hence, the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper yield. In present decade, the application of deep learning models in many fields of research has created greater impact. The increasing soil data availability of soil data there is a greater demand for the remotely avail open source model, leads to the incorporation of deep learning method to predict the soil quality. With that concern, this… More >

P. S. S. Gopi^*, M. Karthikeyan

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 313-326, 2023, DOI:10.32604/iasc.2023.029756

Abstract Agriculture plays a vital role in the Indian economy. Crop recommendation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters. At the same time, crop yield prediction was based on several features like area, irrigation type, temperature, etc. The recent advancements of artificial intelligence (AI) and machine learning (ML) models pave the way to design effective crop recommendation and crop prediction models. In this view, this paper presents a novel Multimodal Machine Learning Based Crop Recommendation and Yield Prediction (MMML-CRYP) technique. The proposed MMML-CRYP model mainly… More >

K. Kalyani¹, Velmurugan Subbiah Parvathy², Hikmat A. M. Abdeljaber³, T. Satyanarayana Murthy⁴, Srijana Acharya⁵, Gyanendra Prasad Joshi⁶, Sung Won Kim^7,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2303-2316, 2023, DOI:10.32604/cmc.2023.033162

Abstract In recent times, financial globalization has drastically increased in different ways to improve the quality of services with advanced resources. The successful applications of bitcoin Blockchain (BC) techniques enable the stockholders to worry about the return and risk of financial products. The stockholders focused on the prediction of return rate and risk rate of financial products. Therefore, an automatic return rate bitcoin prediction model becomes essential for BC financial products. The newly designed machine learning (ML) and deep learning (DL) approaches pave the way for return rate predictive method. This study introduces a novel Jellyfish search optimization based extreme learning… More >