Sait Can Yucebas^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 49-71, 2023, DOI:10.32604/iasc.2023.036871

Abstract The number of studies in the literature that diagnose cancer with machine learning using genome data is quite limited. These studies focus on the prediction performance, and the extraction of genomic factors that cause disease is often overlooked. However, finding underlying genetic causes is very important in terms of early diagnosis, development of diagnostic kits, preventive medicine, etc. The motivation of our study was to diagnose bladder cancer (BCa) based on genetic data and to reveal underlying genetic factors by using machine-learning models. In addition, conducting hyper-parameter optimization to get the best performance from different models, which is overlooked in… More >

Shekaina Justin^1,*, Wafaa Saleh^1,2, Tasneem Al Ghamdi¹, J. Shermina³

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1091-1109, 2023, DOI:10.32604/iasc.2023.032589

Abstract Renewable energy has become a solution to the world’s energy concerns in recent years. Photovoltaic (PV) technology is the fastest technique to convert solar radiation into electricity. Solar-powered buses, metros, and cars use PV technology. Such technologies are always evolving. Included in the parameters that need to be analysed and examined include PV capabilities, vehicle power requirements, utility patterns, acceleration and deceleration rates, and storage module type and capacity, among others. PVPG is intermittent and weather-dependent. Accurate forecasting and modelling of PV system output power are key to managing storage, delivery, and smart grids. With unparalleled data granularity, a data-driven… More >

Sheren Sadiq Hasan¹, Zainab Salih Agee², Bareen Shamsaldeen Tahir³, Subhi R. M. Zeebaree^4,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3225-3238, 2023, DOI:10.32604/csse.2023.037434

Abstract Solar energy will be a great alternative to fossil fuels since it is clean and renewable. The photovoltaic (PV) mechanism produces sunbeams’ green energy without noise or pollution. The PV mechanism seems simple, seldom malfunctioning, and easy to install. PV energy productivity significantly contributes to smart grids through many small PV mechanisms. Precise solar radiation (SR) prediction could substantially reduce the impact and cost relating to the advancement of solar energy. In recent times, several SR predictive mechanism was formulated, namely artificial neural network (ANN), autoregressive moving average, and support vector machine (SVM). Therefore, this article develops an optimal Modified… More >

Po-Yuan Yang^1,*, Yu-Cheng Liao², Fu-I Chou²

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2861-2880, 2023, DOI:10.32604/csse.2023.036810

Abstract Aquaculture has long been a critical economic sector in Taiwan. Since a key factor in aquaculture production efficiency is water quality, an effective means of monitoring the dissolved oxygen content (DOC) of aquaculture water is essential. This study developed an internet of things system for monitoring DOC by collecting essential data related to water quality. Artificial intelligence technology was used to construct a water quality prediction model for use in a complete system for managing water quality. Since aquaculture water quality depends on a continuous interaction among multiple factors, and the current state is correlated with the previous state, a… More >

Haijian Shao¹, Edwin Ma², Ming Zhu¹, Xing Deng³, Shengjie Zhai^1,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3595-3606, 2023, DOI:10.32604/iasc.2023.036323

Abstract Accurate handwriting recognition has been a challenging computer vision problem, because static feature analysis of the text pictures is often inadequate to account for high variance in handwriting styles across people and poor image quality of the handwritten text. Recently, by introducing machine learning, especially convolutional neural networks (CNNs), the recognition accuracy of various handwriting patterns is steadily improved. In this paper, a deep CNN model is developed to further improve the recognition rate of the MNIST handwritten digit dataset with a fast-converging rate in training. The proposed model comes with a multi-layer deep arrange structure, including 3 convolution and… More >

Hui Fang¹, Jingsen Zhou¹, Hanjie Liu^2,*, Yanxu Wang², Hongji Xiang¹, Yechun Xin²

Energy Engineering, Vol.120, No.4, pp. 931-948, 2023, DOI:10.32604/ee.2023.025163

Abstract The voltage source converter based high voltage direct current (VSC-HVDC) system is based on voltage source converter, and its control system is more complex. Also affected by the fast control of power electronics, oscillation phenomenon in wide frequency domain may occur. To address the problem of small signal stability of the VSC-HVDC system, a converter control strategy is designed to improve its small signal stability, and the risk of system oscillation is reduced by attaching a damping controller and optimizing the control parameters. Based on the modeling of the VSC-HVDC system, the general architecture of the inner and outer loop… More >

N. Sharmili¹, Saud Yonbawi², Sultan Alahmari³, E. Laxmi Lydia⁴, Mohamad Khairi Ishak⁵, Hend Khalid Alkahtani^6,*, Ayman Aljarbouh⁷, Samih M. Mostafa⁸

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2247-2262, 2023, DOI:10.32604/csse.2023.036377

Abstract Facial expression recognition (FER) remains a hot research area among computer vision researchers and still becomes a challenge because of high intra-class variations. Conventional techniques for this problem depend on hand-crafted features, namely, LBP, SIFT, and HOG, along with that a classifier trained on a database of videos or images. Many execute perform well on image datasets captured in a controlled condition; however not perform well in the more challenging dataset, which has partial faces and image variation. Recently, many studies presented an endwise structure for facial expression recognition by utilizing DL methods. Therefore, this study develops an earthworm optimization… More >

Mrim M. Alnfiai^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5723-5740, 2023, DOI:10.32604/cmc.2023.033408

Abstract In recent years, the need for a fast, efficient and a reliable wireless network has increased dramatically. Numerous 5G networks have already been tested while a few are in the early stages of deployment. In non-cooperative communication scenarios, the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them. The recent advancements in both Machine Learning (ML) and Deep Learning (DL) models demand the development of effective modulation recognition models with self-learning capability. In this background, the current research article designs a Deep Learning enabled Intelligent Modulation Recognition of Communication Signal… More >

Wenbin Luo^1,*, Lingfeng Tang¹, Yuting Yan², Yifang Shi¹

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.4, pp. 869-899, 2023, DOI:10.32604/fdmp.2022.022280

Abstract The aim of the study is to determine the optimal structural parameters for a plastic centrifugal pump inducer within the framework of an orthogonal experimental method. For this purpose, a numerical study of the related flow field is performed using CFX. The shaft power and the head of the pump are taken as the evaluation indicators. Accordingly, the examined variables are the thickness (S), the blade cascade degree (t), the blade rim angle (β1), the blade hub angle (β2) and the hub length (L). The impact of each structural parameter on each evaluation index is examined and special attention is… More >

Fatih Ahmet Şenel^*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4587-4600, 2023, DOI:10.32604/cmc.2023.033155

Abstract In this study, the morphological galaxy classification process was carried out with a hybrid approach. Since the Galaxy classification process may contain detailed information about the universe’s formation, it remains the current research topic. Researchers divided more than 100 billion galaxies into ten different classes. It is not always possible to understand which class the galaxy types belong. However, Artificial Intelligence (AI) can be used for successful classification. There are studies on the automatic classification of galaxies into a small number of classes. As the number of classes increases, the success of the used methods decreases. Based on the literature,… More >