Fuad Ali Mohammed Al-Yarimi^*

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1729-1742, 2022, DOI:10.32604/iasc.2022.022418

Abstract Computer-aided arrhythmia prediction from ECG (electrocardiograms) is essential in clinical practices, which promises to reduce the mortality caused by inexperienced clinical practitioners. Moreover, computer-aided methods often succeed in the early detection of arrhythmia scope from electrocardiogram reports. Machine learning is the buzz of computer-aided clinical practices. Particularly, computer-aided arrhythmia prediction methods highly adopted machine learning methods. However, the high dimensionality in feature values considered for the machine learning models’ training phase often causes false alarming. This manuscript addressed the high dimensionality in the learning phase and proposed an (Ensemble Learning method for Arrhythmia Prediction) ELAP (ensemble learning-based arrhythmia prediction). The… More >

Manh Duc Nguyen¹, Ha Nguyen Hai¹, Nadhir Al-Ansari^2,*, Mahdis Amiri³, Hai-Bang Ly⁴, Indra Prakash⁵, Binh Thai Pham^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 149-166, 2022, DOI:10.32604/cmes.2022.017355

Abstract One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil. In this study, the main purpose is to develop a novel hybrid Machine Learning (ML) model (ANFIS-DE), which used Differential Evolution (DE) algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System (ANFIS), for estimating soil Compression coefficient (Cc) from other geotechnical parameters namely Water Content, Void Ratio, Specific Gravity, Liquid Limit, Plastic Limit, Clay content and Depth of Soil Samples. Validation of the predictive capability of the novel model was carried out using statistical indices: Root Mean Square… More >

Yousef Sanjalawe^1,2, Mohammed Anbar^1,*, Salam Al-E’mari¹, Rosni Abdullah¹, Iznan Hasbullah¹, Mohammed Aladaileh¹

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3179-3204, 2021, DOI:10.32604/cmc.2021.018546

Abstract The interest in selecting an appropriate cloud data center is exponentially increasing due to the popularity and continuous growth of the cloud computing sector. Cloud data center selection challenges are compounded by ever-increasing users’ requests and the number of data centers required to execute these requests. Cloud service broker policy defines cloud data center’s selection, which is a case of an NP-hard problem that needs a precise solution for an efficient and superior solution. Differential evolution algorithm is a metaheuristic algorithm characterized by its speed and robustness, and it is well suited for selecting an appropriate cloud data center. This… More >

Heming Jia^1,2,*, Yao Li², Kangjian Sun², Ning Cao¹, Helen Min Zhou³

Computer Systems Science and Engineering, Vol.39, No.3, pp. 321-335, 2021, DOI:10.32604/csse.2021.017536

Abstract In this paper, a hybrid model based on sooty tern optimization algorithm (STOA) is proposed to optimize the parameters of the support vector machine (SVM) and identify the best feature sets simultaneously. Feature selection is an essential process of data preprocessing, and it aims to find the most relevant subset of features. In recent years, it has been applied in many practical domains of intelligent systems. The application of SVM in many fields has proved its effectiveness in classification tasks of various types. Its performance is mainly determined by the kernel type and its parameters. One of the most challenging… More >

Bin Xu^1,2,3, Lu Zhang¹, Zipeng Xu¹, Yichuan Liu¹, Jinming Chai¹, Sichong Qin⁴, Yanfei Sun^1,*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1671-1686, 2021, DOI:10.32604/cmc.2021.018395

Abstract In the IoT (Internet of Things) system, the introduction of UAV (Unmanned Aerial Vehicle) as a new data collection platform can solve the problem that IoT devices are unable to transmit data over long distances due to the limitation of their battery energy. However, the unreasonable distribution of UAVs will still lead to the problem of the high total energy consumption of the system. In this work, to deal with the problem, a deployment model of a mobile edge computing (MEC) system based on multi-UAV is proposed. The goal of the model is to minimize the energy consumption of the… More >

Bin Xu^1,2,3,*, Jianming Zhu¹, Junzhe Wen¹, Shanshan Lin¹, Yunkai Zhao¹, Jin Qi^1,2, Yu Xue⁴, Sichong Qin⁵

Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 525-537, 2021, DOI:10.32604/iasc.2021.018338

Abstract The optimization of wind farm layouts is very important for the effective utilization of wind resources. A fixed wind turbine hub height in the layout of wind farms leads to a low wind energy utilization and a higher LCOE (levelized cost of electricity). WOMH (Wind Farm Layout Optimization Model Considering Multiple Hub Heights) is proposed in this paper to tackle the above problem. This model is different from the traditional fixed hub height model, as it uses a variable height wind turbine. In WOMH, the Jensen wake and Weibull distribution are used to describe the wake effect on the wind… More >

Annan Jiang^*, Xinping Guo, Shuai Zheng, Mengfei Xu

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 1177-1199, 2021, DOI:10.32604/cmes.2021.014199

Abstract Due to the geological body uncertainty, the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability. The ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes, but conventional methods have certain problems, such as a large number of parameters and large time consumption. To solve the problems, this study combines the orthogonal design, Gaussian process (GP) regression, and difference evolution (DE) optimization, and it constructs the parameters identification method of the jointed surrounding rock. The calculation process of parameters… More >

V. Nivedhitha^1,*, A. Gopi Saminathan², P. Thirumurugan³

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 603-616, 2021, DOI:10.32604/iasc.2021.016780

Abstract Several protocols strive to improve network longevity but fail to ameliorate the uneven overhead imparted upon the sensor nodes that lead to temporal deaths. The proposed work uses a metaheuristic approach that promotes load balancing and energy-efficient data transmission using the fruit fly optimization algorithm (FFOA). The approach combines the LEACH protocol with differential evolution (DE) to select an optimum cluster head in every cluster. The algorithm is designed to provide energy-efficient data transmissions based on the smell and vision foraging behavior of fruit flies. The approach considers the compactness of nodes, energy capacity, and the distance of sensor nodes… More >

Aiyun Hu¹, Sunli Cong^1,*, Jian Ding², Yao Cheng¹, Enock Mpofu³

Computer Systems Science and Engineering, Vol.38, No.1, pp. 65-77, 2021, DOI:10.32604/csse.2021.016404

Abstract In the fed-batch cultivation of Saccharomyces cerevisiae, excessive glucose addition leads to increased ethanol accumulation, which will reduce the efficiency of glucose utilization and inhibit product synthesis. Insufficient glucose addition limits cell growth. To properly regulate glucose feed, a different evolution algorithm based on self-adaptive control strategy was proposed, consisting of three modules (PID, system identification and parameter optimization). Performance of the proposed and conventional PID controllers was validated and compared in simulated and experimental cultivations. In the simulation, cultivation with the self-adaptive control strategy had a more stable glucose feed rate and concentration, more stable ethanol concentration around the… More >

Hui Sheng Lim^1,*, Christopher K. H. Chin¹, Shuhong Chai¹, Neil Bose^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 33-50, 2020, DOI:10.32604/cmes.2020.011648

Abstract This paper presents an online AUV (autonomous underwater vehicle) path planner that employs path replanning approach and the SDEQPSO (selective differential evolution-hybridized quantum-behaved particle swarm optimization) algorithm to optimize an AUV mission conducted in an unknown, dynamic and cluttered ocean environment. The proposed path replanner considered the effect of ocean currents in path optimization to generate a Pareto-optimal path that guides the AUV to its target within minimum time. The optimization was based on the onboard sensor data measured from the environment, which consists of a priori unknown dynamic obstacles and spatiotemporal currents. Different sensor arrangements for the forward-looking sonar… More >