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  • Open Access

    ARTICLE

    Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classification

    Ayesha Bin T. Tahir1, Muhamamd Attique Khan1, Majed Alhaisoni2, Junaid Ali Khan1, Yunyoung Nam3,*, 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 >

  • Open Access

    ARTICLE

    Feasibility-Guided Constraint-Handling Techniques for Engineering Optimization Problems

    Muhammad Asif Jan1,*, Yasir Mahmood1, Hidayat Ullah Khan2, Wali Khan Mashwani1, Muhammad Irfan Uddin3, Marwan Mahmoud4, Rashida Adeeb Khanum5, Ikramullah6, Noor Mast3

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2845-2862, 2021, DOI:10.32604/cmc.2021.015294

    Abstract The particle swarm optimization (PSO) algorithm is an established nature-inspired population-based meta-heuristic that replicates the synchronizing movements of birds and fish. PSO is essentially an unconstrained algorithm and requires constraint handling techniques (CHTs) to solve constrained optimization problems (COPs). For this purpose, we integrate two CHTs, the superiority of feasibility (SF) and the violation constraint-handling (VCH), with a PSO. These CHTs distinguish feasible solutions from infeasible ones. Moreover, in SF, the selection of infeasible solutions is based on their degree of constraint violations, whereas in VCH, the number of constraint violations by an infeasible solution is of more importance. Therefore,… More >

  • Open Access

    ARTICLE

    A User-Transformer Relation Identification Method Based on QPSO and Kernel Fuzzy Clustering

    Yong Xiao1, Xin Jin1, Jingfeng Yang2, Yanhua Shen3,*, Quansheng Guan4

    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 1293-1313, 2021, DOI:10.32604/cmes.2021.012562

    Abstract User-transformer relations are significant to electric power marketing, power supply safety, and line loss calculations. To get accurate user-transformer relations, this paper proposes an identification method for user-transformer relations based on improved quantum particle swarm optimization (QPSO) and Fuzzy C-Means Clustering. The main idea is: as energy meters at different transformer areas exhibit different zero-crossing shift features, we classify the zero-crossing shift data from energy meters through Fuzzy C-Means Clustering and compare it with that at the transformer end to identify user-transformer relations. The proposed method contributes in three main ways. First, based on the fuzzy C-means clustering algorithm (FCM),… More >

  • Open Access

    ARTICLE

    Medical Image Compression Based on Wavelets with Particle Swarm Optimization

    Monagi H. Alkinani1,*, E. A. Zanaty2, Sherif M. Ibrahim3

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1577-1593, 2021, DOI:10.32604/cmc.2021.014803

    Abstract This paper presents a novel method utilizing wavelets with particle swarm optimization (PSO) for medical image compression. Our method utilizes PSO to overcome the wavelets discontinuity which occurs when compressing images using thresholding. It transfers images into subband details and approximations using a modified Haar wavelet (MHW), and then applies a threshold. PSO is applied for selecting a particle assigned to the threshold values for the subbands. Nine positions assigned to particles values are used to represent population. Every particle updates its position depending on the global best position (gbest) (for all details subband) and local best position (pbest) (for… More >

  • Open Access

    ARTICLE

    A New Population Initialization of Particle Swarm Optimization Method Based on PCA for Feature Selection

    Shichao Wang, Yu Xue*, Weiwei Jia

    Journal on Big Data, Vol.3, No.1, pp. 1-9, 2021, DOI:10.32604/jbd.2021.010364

    Abstract In many fields such as signal processing, machine learning, pattern recognition and data mining, it is common practice to process datasets containing huge numbers of features. In such cases, Feature Selection (FS) is often involved. Meanwhile, owing to their excellent global search ability, evolutionary computation techniques have been widely employed to the FS. So, as a powerful global search method and calculation fast than other EC algorithms, PSO can solve features selection problems well. However, when facing a large number of feature selection, the efficiency of PSO drops significantly. Therefore, plenty of works have been done to improve this situation.… More >

  • Open Access

    ARTICLE

    Realization of IoT Integration System of LED Based on Particle Swarm Optimization

    Sung-Jung Hsiao1, Wen-Tsai Sung2,*

    Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 499-517, 2021, DOI:10.32604/iasc.2021.014911

    Abstract The purpose of this research is to design an intelligent LED lighting control system with wireless remote control. Since the designed system is mainly based on Android applications, it controls wireless lighting modules through a sever by means of a smartphone, tablet computer, or another handheld mobile device, and the values displayed on the handheld device are used to monitor the establishment and design of the electricity state and multiple-LED light control scenarios in real time. The server is connected with a touch human-machine interface, digital meter, wireless lighting controller, infrared learning module, and other devices through RS-232 and RS-485… More >

  • Open Access

    ARTICLE

    Improved Channel Allocation Scheme for Cognitive Radio Networks

    Shahzad Latif1, Suhail Akraam2, Arif Jamal Malik3, Aaqif Afzaal Abbasi3, Muhammad Habib3, Sangsoon Lim4,*

    Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 103-114, 2021, DOI:10.32604/iasc.2021.014388

    Abstract

    In recent years, wireless channel optimization technologies witnessed tremendous improvements. In this regard, research for developing wireless spectrum for accommodating a wider range of wireless devices increased. This also helped in resolving spectrum scarcity issues. Cognitive Radio (CR) is a type of wireless communication in which a transceiver can intelligently detect which communication channels are being used. To avoid interference, it instantly moves traffic into vacant channels by avoiding the occupied ones. Cognitive Radio (CR) technology showed the potential to deal with the spectrum shortage problem. The spectrum assignment is often considered as a key research challenge in Cognitive Radio… More >

  • Open Access

    ARTICLE

    Highway Cost Prediction Based on LSSVM Optimized by Intial Parameters

    Xueqing Wang1, Shuang Liu1,*, Lejun Zhang2

    Computer Systems Science and Engineering, Vol.36, No.1, pp. 259-269, 2021, DOI:10.32604/csse.2021.014343

    Abstract The cost of highway is affected by many factors. Its composition and calculation are complicated and have great ambiguity. Calculating the cost of highway according to the traditional highway engineering estimation method is a completely tedious task. Constructing a highway cost prediction model can forecast the value promptly and improve the accuracy of highway engineering cost. This work sorts out and collects 60 sets of measured data of highway engineering; establishes an expressway cost index system based on 10 factors, including main route mileage, roadbed width, roadbed earthwork, and number of bridges; and processes the data through principal component analysis… More >

  • Open Access

    ARTICLE

    Study on the Application of an Improved Intelligent Group Algorithm

    Fengjuan Wang1,2,*

    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1073-1080, 2020, DOI:10.32604/iasc.2020.010138

    Abstract The Particle swarm optimization algorithm (PSO) and the simulated annealing algorithm (SA) are two well-known stochastic and intelligent methods used for optimization. Both methods have some shortcomings. On the basis of the shortcomings of PSO and SA, this paper offers an enhanced intelligent group algorithm on the basis of the roulette rule to improve the parameter velocity ν of (PSO) and the initial temperature of SA algorithm. This paper gives a detailed introduction to the principle and flow of the new algorithm and introduces the application status of the new algorithm. More >

  • Open Access

    ARTICLE

    A PSO-XGBoost Model for Estimating Daily Reference Evapotranspiration in the Solar Greenhouse

    Jingxin Yu1,3, Wengang Zheng1,*, Linlin Xu3, Lili Zhangzhong1, Geng Zhang2, Feifei Shan1

    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 989-1003, 2020, DOI:10.32604/iasc.2020.010130

    Abstract Accurate estimation of reference evapotranspiration (ET0) is a critical prerequisite for the development of agricultural water management strategies. It is challenging to estimate the ET0 of a solar greenhouse because of its unique environmental variations. Based on the idea of ensemble learning, this paper proposed a novel ET0i estimation model named PSO-XGBoost, which took eXtreme Gradient Boosting (XGBoost) as the main regression model and used Particle Swarm Optimization (PSO) algorithm to optimize the parameters of XGBoost. Using the meteorological and soil moisture data during the two-crop planting process as the experimental data, and taking ET0i calculated based on the improved… More >

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