A. Manjula^1,*, L. Kalaivani², M. Gengaraj²

Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 451-465, 2021, DOI:10.32604/iasc.2021.016088

Abstract The fast-growing field of mechanical robotization necessitates a well-designed and controlled version of electric drives. The concept of control concerning mechanical characteristics also requires a methodology in which the system needs to be modeled precisely and deals with uncertainty. The proposed method provides the enhanced performance of Switched Reluctance Motor (SRM) by controlling its speed and minimized torque ripple. Proportional-Integral-Derivative (PID) controllers have drawn more attention in industry automation due to their ease and robustness. The performances are further improved by using fractional order (Non-integer) controllers. The Modified Particle Swarm Optimization (MPSO) based optimization approach is employed to acquire the… More >

Zhenzhou Wang¹, Yan Zhang¹, Pingping Yu^1,*, Ning Cao², Heiner Dintera³

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 503-519, 2021, DOI:10.32604/cmc.2021.017624

Abstract To better regulate the speed of brushless DC motors, an improved algorithm based on the original Glowworm Swarm Optimization is proposed. The proposed algorithm solves the problems of poor robustness, slow convergence, and low accuracy exhibited by traditional PID controllers. When selecting the glowworm neighborhood set, an optimization scheme based on the growth and competition behavior of weeds is applied to a single glowworm to prevent falling into a local optimal solution. After the glowworm’s position is updated, the league selection operator is introduced to search for the global optimal solution. Combining the local search ability of the invasive weed… More >

Abdullah Ali H. Ahmadini¹, Amal S. Hassan², Rokaya E. Mohamed^3,*, Shokrya S. Alshqaq⁴, Heba F. Nagy⁵

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 131-146, 2021, DOI:10.32604/iasc.2021.017652

Abstract In this research article, we propose and study a new model the so-called Marshal-Olkin Kumaraswamy moment exponential distribution. The new distribution contains the moment exponential distribution, exponentiated moment exponential distribution, Marshal Olkin moment exponential distribution and generalized exponentiated moment exponential distribution as special sub-models. Some significant properties are acquired such as expansion for the density function and explicit expressions for the moments, generating function, Bonferroni and Lorenz curves. The probabilistic definition of entropy as a measure of uncertainty called Shannon entropy is computed. Some of the numerical values of entropy for different parameters are given. The method of maximum likelihood… More >

Ahmed S. Ghiduk^1,*, Moheb R. Girgis³, Eman Hassan^2,4, Sultan Aljahdali¹

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 147-164, 2021, DOI:10.32604/iasc.2021.015708

Abstract Path-based testing involves two main steps: 1) finding all paths throughout the code under test; 2) creating a test suite to cover these paths. Unfortunately, covering all paths in the code under test is impossible. Path-based testing could be achieved by targeting a subset of all feasible paths that satisfy a given testing criterion. Then, a test suite is created to execute this paths subset. Generating those paths is a key problem in path testing. In this paper, a new path testing technique is presented. This technique employs Particle Swarm Optimization (PSO) for generating a set of paths to satisfy… More >

Rajalakshmi Shenbaga Moorthy^1,*, P. Pabitha²

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3101-3119, 2021, DOI:10.32604/cmc.2021.016489

Abstract Parkinson’s disease is a neurogenerative disorder and it is difficult to diagnose as no therapies may slow down its progression. This paper contributes a novel analytic system for Parkinson’s Disease Prediction mechanism using Improved Radial Basis Function Neural Network (IRBFNN). Particle swarm optimization (PSO) with K-means is used to find the hidden neuron’s centers to improve the accuracy of IRBFNN. The performance of RBFNN is seriously affected by the centers of hidden neurons. Conventionally K-means was used to find the centers of hidden neurons. The problem of sensitiveness to the random initial centroid in K-means degrades the performance of RBFNN.… More >

M. S. Maharajan¹, T. Abirami², Irina V. Pustokhina³, Denis A. Pustokhin⁴, K. Shankar^5,*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2995-3013, 2021, DOI:10.32604/cmc.2021.016139

Abstract Wireless Sensor Networks (WSN) started gaining attention due to its wide application in the fields of data collection and information processing. The recent advancements in multimedia sensors demand the Quality of Service (QoS) be maintained up to certain standards. The restrictions and requirements in QoS management completely depend upon the nature of target application. Some of the major QoS parameters in WSN are energy efficiency, network lifetime, delay and throughput. In this scenario, clustering and routing are considered as the most effective techniques to meet the demands of QoS. Since they are treated as NP (Non-deterministic Polynomial-time) hard problem, Swarm… More >

Haibo Liu^1,*, Yujie Dong², Fuzhong Wang¹

Energy Engineering, Vol.118, No.3, pp. 679-689, 2021, DOI: 10.32604/EE.2021.014630

Abstract For the problems of nonlinearity, uncertainty and low prediction accuracy in the gas outburst prediction of coal mining face, the least squares support vector machine (LSSVM) is proposed to establish the prediction model. Firstly, considering the inertia coefficients as global parameters lacks the ability to improve the solution for the traditional particle swarm optimization (PSO), an improved PSO (IPSO) algorithm is introduced to adjust different inertia weights in updating the particle swarm and solve the fitness to stagnate. Secondly, the penalty factor and kernel function parameter of LSSVM are searched automatically, and the regression accuracy and generalization performance is enhanced… More >

Amir Parnianifard¹, Muhammad Saadi², Manus Pengnoo¹, Muhammad Ali Imran³, Sattam Al Otaibi⁴, Pruk Sasithong¹, Pisit Vanichchanunt⁵, Tuchsanai Polysuwan⁶, Lunchakorn Wuttisittikulkij^1,*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 569-587, 2021, DOI:10.32604/cmc.2021.015730

Abstract With every passing day, the demand for data traffic is increasing, and this urges the research community not only to look for an alternating spectrum for communication but also urges radio frequency planners to use the existing spectrum efficiently. Cell sizes are shrinking with every upcoming communication generation, which makes base station placement planning even more complex and cumbersome. In order to make the next-generation cost-effective, it is important to design a network in such a way that it utilizes the minimum number of base stations while ensuring seamless coverage and quality of service. This paper aims at the development… More >

Ayesha Bin T. Tahir¹, Muhamamd Attique Khan¹, Majed Alhaisoni², Junaid Ali Khan¹, Yunyoung Nam^3,*, Shui-Hua Wang⁴, Kashif Javed⁵

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1099-1116, 2021, DOI:10.32604/cmc.2021.015154

Abstract Background: A brain tumor reflects abnormal cell growth. Challenges: Surgery, radiation therapy, and chemotherapy are used to treat brain tumors, but these procedures are painful and costly. Magnetic resonance imaging (MRI) is a non-invasive modality for diagnosing tumors, but scans must be interpretated by an expert radiologist. Methodology: We used deep learning and improved particle swarm optimization (IPSO) to automate brain tumor classification. MRI scan contrast is enhanced by ant colony optimization (ACO); the scans are then used to further train a pretrained deep learning model, via transfer learning (TL), and to extract features from two dense layers. We fused… More >

Amal S. Hassan¹, Ehab M. Almetwally^2,*, Gamal M. Ibrahim³

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 337-358, 2021, DOI:10.32604/cmc.2021.013971

Abstract In this paper, an attempt is made to discover the distribution of COVID-19 spread in different countries such as; Saudi Arabia, Italy, Argentina and Angola by specifying an optimal statistical distribution for analyzing the mortality rate of COVID-19. A new generalization of the recently inverted Topp Leone distribution, called Kumaraswamy inverted Topp–Leone distribution, is proposed by combining the Kumaraswamy-G family and the inverted Topp–Leone distribution. We initially provide a linear representation of its density function. We give some of its structure properties, such as quantile function, median, moments, incomplete moments, Lorenz and Bonferroni curves, entropies measures and stress-strength reliability. Then,… More >