Ehsan Momeni¹, Biao He², Yasin Abdi^3,*, Danial Jahed Armaghani⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2527-2550, 2023, DOI:10.32604/cmes.2023.026531

Abstract When building geotechnical constructions like retaining walls and dams is of interest, one of the most important factors to consider is the soil’s shear strength parameters. This study makes an effort to propose a novel predictive model of shear strength. The study implements an extreme gradient boosting (XGBoost) technique coupled with a powerful optimization algorithm, the salp swarm algorithm (SSA), to predict the shear strength of various soils. To do this, a database consisting of 152 sets of data is prepared where the shear strength (τ) of the soil is considered as the model output and some soil index tests… More >

Hanan T. Halawani^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6775-6788, 2023, DOI:10.32604/cmc.2023.030814

Abstract Biomedical image processing acts as an essential part of several medical applications in supporting computer aided disease diagnosis. Magnetic Resonance Image (MRI) is a commonly utilized imaging tool used to save glioma for clinical examination. Biomedical image segmentation plays a vital role in healthcare decision making process which also helps to identify the affected regions in the MRI. Though numerous segmentation models are available in the literature, it is still needed to develop effective segmentation models for BT. This study develops a salp swarm algorithm with multi-level thresholding based brain tumor segmentation (SSAMLT-BTS) model. The presented SSAMLT-BTS model initially employs… More >

Xuhui Zhu^1,3, Pingfan Xia^1,3, Qizhi He^2,4,*, Zhiwei Ni^1,3, Liping Ni^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 653-671, 2023, DOI:10.32604/cmes.2022.022985

Abstract Multiple classifier system exhibits strong classification capacity compared with single classifiers, but they require significant computational resources. Selective ensemble system aims to attain equivalent or better classification accuracy with fewer classifiers. However, current methods fail to identify precise solutions for constructing an ensemble classifier. In this study, we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm (ECDPB). Considering that extreme learning machines (ELMs) have rapid learning rates and good generalization ability, they can serve as the basic classifier for creating multiple candidates while using fewer computational resources. Meanwhile, we introduce a combined diversity measure… More >

Li Ding^1,*, Rui Ma¹, Zhengtian Wu², Rongzhi Qi¹, Wenrui Ruan¹

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 441-464, 2023, DOI:10.32604/cmes.2022.022642

Abstract This article proposes a novel method for maintaining the trajectory of an aerial manipulator by utilizing a fast nonsingular terminal sliding mode (FNTSM) manifold and a linear extended state observer (LESO). The developed control method applies an FNTSM to ensure the tracking performance’s control accuracy, and an LESO to estimate the system’s unmodeled dynamics and external disturbances. Additionally, an improved salp swarm algorithm (ISSA) is employed to parameter tune the suggested controller by integrating the salp swarm technique with a cloud model. This approach also uses a model-free scheme to reduce the complexity of controller design without relying on complex… More >

A. Durgadevi^*, N. Shanmugavadivoo

Computer Systems Science and Engineering, Vol.44, No.3, pp. 1951-1971, 2023, DOI:10.32604/csse.2023.026810

Abstract

Contemporarily, the development of distributed generations (DGs) technologies is fetching more, and their deployment in power systems is becoming broad and diverse. Consequently, several glitches are found in the recent studies due to the inappropriate/inadequate penetrations. This work aims to improve the reliable operation of the power system employing reliability indices using a metaheuristic-based algorithm before and after DGs penetration with feeder system. The assessment procedure is carried out using MATLAB software and Modified Salp Swarm Algorithm (MSSA) that helps assess the Reliability indices of the proposed integrated IEEE RTS79 system for seven different configurations. This algorithm modifies two control… More >

Mariem Ayedi^1,2,*, Walaa H. ElAshmawi^3,4, Esraa Eldesouky^1,3

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2963-2980, 2022, DOI:10.32604/cmc.2022.025741

Abstract Resource management in Underground Wireless Sensor Networks (UWSNs) is one of the pillars to extend the network lifetime. An intriguing design goal for such networks is to achieve balanced energy and spectral resource utilization. This paper focuses on optimizing the resource efficiency in UWSNs where underground relay nodes amplify and forward sensed data, received from the buried source nodes through a lossy soil medium, to the aboveground base station. A new algorithm called the Hybrid Chaotic Salp Swarm and Crossover (HCSSC) algorithm is proposed to obtain the optimal source and relay transmission powers to maximize the network resource efficiency. The… More >

Fadwa Alrowais¹, Ahmed S. Almasoud², Radwa Marzouk³, Fahd N. Al-Wesabi^4,5, Anwer Mustafa Hilal^6,*, Mohammed Rizwanullah⁶, Abdelwahed Motwakel⁶, Ishfaq Yaseen⁶

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2783-2795, 2022, DOI:10.32604/cmc.2022.025204

Abstract Mobile edge computing (MEC) provides effective cloud services and functionality at the edge device, to improve the quality of service (QoS) of end users by offloading the high computation tasks. Currently, the introduction of deep learning (DL) and hardware technologies paves a method in detecting the current traffic status, data offloading, and cyberattacks in MEC. This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks (SNN) to determine the traffic status in the MEC system. Also, an adaptive sampling… More >

Nitin Mittal¹, Harbinder Singh¹, Vikas Mittal², Shubham Mahajan³, Amit Kant Pandit³, Mehedi Masud⁴, Mohammed Baz⁵, Mohamed Abouhawwash^6,7,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3821-3835, 2022, DOI:10.32604/cmc.2022.020592

Abstract Cognitive Radio (CR) has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency. To improve the overall performance of the CR system it is extremely important to adapt or reconfigure the system parameters. The Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter adaptation. As meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches, the performance of these algorithms is investigated in order to design an efficient CR system that is able… More >

Suresh Krishnamoorthy^1,*, Kumaratharan Narayanaswamy²

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 89-102, 2021, DOI:10.32604/iasc.2021.013643

Abstract Increase in demand for multimedia and quality services requires 5G networks to resolve issues such as slicing, allocation, forwarding, and control using techniques such as software-defined networking (SDN) and network function virtualization. In this study, the optimum number of SDN multi-controllers are implemented based on a multi-criterion advanced genetic algorithm that takes into consideration three key parameters: Switch controller latency, hopcount, and link utilization. Preprocessing is the first step, in which delay, delay paths, hopcount, and hoppaths are computed as an information matrix (Infomat). Randomization is the second step, and consists of initially placing controllers randomly, followed by an analytical… More >

Mohammad Behrouzian Nejad¹, Mohammad Ebrahim Shiri^1,2,*

Computer Systems Science and Engineering, Vol.34, No.2, pp. 91-100, 2019, DOI:10.32604/csse.2019.34.091

Abstract In this paper we propose a new image classification technique. According to this note that most research focuses on extraction of features in the frequency domain, location, and reduction of feature dimensions, in this research we focused on learning step in image classification. The main aim is to use the heuristic methods to increase the function of the estimator of the learning algorithm and continue to achieve the desired state, as well as categorization without user interference and automatically performed by the model produced from the above steps. So, in this paper, a new learning approach based on the Salp… More >