Doaa Sami Khafaga¹, Amel Ali Alhussan^1,*, El-Sayed M. El-kenawy^2,3, Abdelhameed Ibrahim⁴, Said H. Abd Elkhalik³, Shady Y. El-Mashad⁵, Abdelaziz A. Abdelhamid^6,7

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 865-881, 2022, DOI:10.32604/cmc.2022.028550

Abstract The design of an antenna requires a careful selection of its parameters to retain the desired performance. However, this task is time-consuming when the traditional approaches are employed, which represents a significant challenge. On the other hand, machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended performance. In this paper, we propose a novel approach for accurately predicting the bandwidth of metamaterial antenna. The proposed approach is based on employing the recently emerged guided whale… More >

Lina Wang^1,2,*, Xilin Deng¹, Peng Ge¹, Changming Dong^2,3, Brandon J. Bethel³, Leqing Yang¹, Jinyue Xia⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2151-2168, 2022, DOI:10.32604/cmc.2022.027415

Abstract Though numerical wave models have been applied widely to significant wave height prediction, they consume massive computing memory and their accuracy needs to be further improved. In this paper, a two-dimensional (2D) significant wave height (SWH) prediction model is established for the South and East China Seas. The proposed model is trained by Wave Watch III (WW3) reanalysis data based on a convolutional neural network, the bi-directional long short-term memory and the attention mechanism (CNN-BiLSTM-Attention). It adopts the convolutional neural network to extract spatial features of original wave height to reduce the redundant information input into the BiLSTM network. Meanwhile,… More >

Pranav Pushkar¹, Christo Ananth², Preeti Nagrath¹, Jehad F. Al-Amri⁵, Vividha¹, Anand Nayyar^3,4,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1601-1619, 2022, DOI:10.32604/cmc.2022.026205

Abstract The study of viruses and their genetics has been an opportunity as well as a challenge for the scientific community. The recent ongoing SARS-Cov2 (Severe Acute Respiratory Syndrome) pandemic proved the unpreparedness for these situations. Not only the countermeasures for the effect caused by virus need to be tackled but the mutation taking place in the very genome of the virus is needed to be kept in check frequently. One major way to find out more information about such pathogens is by extracting the genetic data of such viruses. Though genetic data of viruses have been cultured and stored as… More >

S. Gunasekar^*, G. Joselin Retna Kumar, G. Pius Agbulu

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1271-1284, 2022, DOI:10.32604/csse.2022.024303

Abstract Due to the development of transportation, population growth and industrial activities, air quality has become a major issue in urban areas. Poor air quality leads to rising health issues in the human’s life in many ways especially respiratory infections, heart disease, asthma, stroke and lung cancer. The contaminated air comprises harmful ingredients such as sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter of PM10, PM2.5, and an Air Quality Index (AQI). These pollutant ingredients are very harmful to human’s health and also leads to death. So, it is necessary to develop a prediction model for air quality as regular on… More >

Chitra Paulpandi^*, Murukesh Chinnasamy, Shanker Nagalingam Rajendiran

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1341-1355, 2022, DOI:10.32604/csse.2022.023882

Abstract The current pandemic highlights the significance and impact of air pollution on individuals. When it comes to climate sustainability, air pollution is a major challenge. Because of the distinctive nature, unpredictability, and great changeability in the reality of toxins and particulates, detecting air quality is a puzzling task. Simultaneously, the ability to predict or classify and monitor air quality is becoming increasingly important, particularly in urban areas, due to the well documented negative impact of air pollution on resident’s health and the environment. To better comprehend the current condition of air quality, this research proposes predicting air pollution levels from… More >

S. Prabha Kumaresan¹, Chee Keong Tan^2,*, Yin Hoe Ng¹

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6119-6140, 2022, DOI:10.32604/cmc.2022.027223

Abstract Non-orthogonal multiple access (NOMA) has been a key enabling technology for the fifth generation (5G) cellular networks. Based on the NOMA principle, a traditional neural network has been implemented for user clustering (UC) to maximize the NOMA system’s throughput performance by considering that each sample is independent of the prior and the subsequent ones. Consequently, the prediction of UC for the future ones is based on the current clustering information, which is never used again due to the lack of memory of the network. Therefore, to relate the input features of NOMA users and capture the dependency in the clustering… More >

Wei Fang^1,2,*, Liang Shen¹, Victor S. Sheng³, Qiongying Xue¹

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4867-4877, 2022, DOI:10.32604/cmc.2022.027197

Abstract Precipitation nowcasting is of great significance for severe convective weather warnings. Radar echo extrapolation is a commonly used precipitation nowcasting method. However, the traditional radar echo extrapolation methods are encountered with the dilemma of low prediction accuracy and extrapolation ambiguity. The reason is that those methods cannot retain important long-term information and fail to capture short-term motion information from the long-range data stream. In order to solve the above problems, we select the spatiotemporal long short-term memory (ST-LSTM) as the recurrent unit of the model and integrate the 3D convolution operation in it to strengthen the model's ability to capture… More >

Yingnan Zhao^1,*, Guanlan Ji¹, Fei Chen¹, Peiyuan Ji¹, Yi Cao²

Computer Systems Science and Engineering, Vol.43, No.2, pp. 719-735, 2022, DOI:10.32604/csse.2022.027288

Abstract Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting researchers. This paper proposes a new variational mode decomposition (VMD)-attention-based spatio-temporal network (VASTN) method that takes advantage of both temporal and spatial correlations of wind speed. First, VASTN is a hybrid wind speed prediction model that combines VMD, squeeze-and-excitation network (SENet), and attention mechanism (AM)-based bidirectional long short-term memory (BiLSTM). VASTN initially employs VMD to decompose the wind speed matrix into a series of intrinsic mode functions (IMF). Then, to extract the spatial features at the bottom of the model, each IMF employs an improved convolutional… More >

S. Karthic^1,*, S. Manoj Kumar²

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 439-453, 2022, DOI:10.32604/iasc.2022.025153

Abstract In recent years, due to the rapid progress of various technologies, wireless computer networks have developed. However, the activities of the security threats and attackers affect the data communication of these technologies. So, to protect the network against these security threats, an efficient IDS (Intrusion Detection System) is presented in this paper. Namely, optimized long short-term memory (OLSTM) network with a stacked auto-encoder (SAE) network is proposed as an IDS system. Using SAE, significant features are extracted from the databases such as input NSL-KDD database and the UNSW-NB15 database. Then extracted features are given as input to the optimized LSTM… More >

Sandeep Kumar¹, Arpit Jain^2,*, Shilpa Rani³, Hammam Alshazly⁴, Sahar Ahmed Idris⁵, Sami Bourouis⁶

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 119-131, 2022, DOI:10.32604/iasc.2022.024812

Abstract The detection of the objects in the ariel image has a significant impact on the field of parking space management, traffic management activities and surveillance systems. Traditional vehicle detection algorithms have some limitations as these algorithms are not working with the complex background and with the small size of object in bigger scenes. It is observed that researchers are facing numerous problems in vehicle detection and classification, i.e., complicated background, the vehicle’s modest size, other objects with similar visual appearances are not correctly addressed. A robust algorithm for vehicle detection and classification has been proposed to overcome the limitation of… More >