A new algorithm of modified binary particle swarm optimization based on the Gustafson-Kessel for credit risk assessment

Sameer, Fadhaa O.; Bakar, Mohd Rizam Abu; Zaidan, A. A.; Zaidan, B. B.

doi:10.1007/s00521-017-3018-4

Cited by 29 publications

(5 citation statements)

References 39 publications

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“…Other than that, the relationship between each input variable and the target variable is evaluated using statistical feature selection techniques, and the input variables with the most robust relationships to the target variable are selected. Even though the choice of statistical measures is dependent on the data types of both the input and output variables, these techniques can be quick and effective [ 45 , 46 , 47 ].…”

Section: Methodsmentioning

confidence: 99%

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Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver

et al. 2022

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Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS–BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS–BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS–BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample.

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Section: Methodsmentioning

confidence: 99%

“…Note that BPSO sets the feature positions of a particle based on the discrete values of binary ‘0’ and ‘1’ values instead of continuous values [ 48 , 49 ]. A Sigmoid function is used to map the continuous-valued velocity given by Equation (13) to the range [0, 1], as shown in Equation (14) [ 45 ].

…”

Section: Methodsmentioning

confidence: 99%

Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver

et al. 2022

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“…Step 4: Sorting of solutions: The bald eagles are ordered according to their capacity measurements, and options with lower fitness values are ignored [17][18][19][20]. The best fitness metric is substituted with the current one.…”

Section: Proposed Bald Eagle Search Optimization Algorithm In Optical...mentioning

confidence: 99%

Design of Optical Filter Using Bald Eagle Search Optimization Algorithm

Marcilin¹,

Nandhitha²

2023

Intelligent Automation &Amp; Soft Computing

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Controlled thermonuclear reactors require consistent monitoring of plasma in the toroidal chamber. Better working conditions of such machines can be monitored by analyzing its radiations. Various wavelengths such as 656.3, 486.1, 464.7 nm are quite significant which are used for health monitoring of thermonuclear machines. The optical thin film filters which work on constructive and destructive interference are the ideal choices. These filters are multilayered with a pair of high and low refractive index dielectric materials. Significantly high transmission index at the desired wavelength and relatively low transmission at the other wavelengths are desired. With this as the objective, it is necessary to design the filter. Various optimization techniques are used for identifying the suitable design of the filters. To choose the parameter combination that provides the most excellent performance, optimization of the design parameters is entailed. The goal of this work is to improve the optical band filter using the Bald eagle search optimization (BES) method. The ideal design is determined by assessing several characteristics such as thickness, refractive index, Full-Width at Half-Maximum (FWHM), and the impact of choosing optical properties, which increases transmission potential. Initially, an alternate multi-layer stack with 28, 30, and 32 layers is created by altering the thickness while keeping the dielectric substances high and low refractive indices constant. By adjusting the thickness of each layer, the BES algorithm achieves the best practical solution. The proposed method is implemented using MATLAB and the outcomes show the efficacy of the proposed technique. The transmittance, reflectance, and FWHM using the proposed BES are found to be 99.9356%, 0.065%, and 1.2 nm respectively.

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“…Nazari-Heris et al [26], Zhao et al [27], Sameer et al [28], Reddy et al [29], Stützle et al [30], Pijarski and Piotr [31], Yapici and Cetinkaya [32], Dede et al [33], Grzywiński et al [34], Kaveh et al [35], Kaveh et al [36] and Kaveh and Ilchi Ghazaan [37]. Following a description of the EVPS algorithm with a brief description of six other metaheuristic algorithms including VPS, GWO, HS, SSA, ECBO and GOA algorithms, is presented in the following subsections.…”

Section: Optimization Algorithmsmentioning

confidence: 99%

Optimal Design of Steel Curved Roof Frames by Enhanced Vibrating Particles System Algorithm

Kaveh¹,

Vaez²,

Hosseini³

et al. 2019

Period. Polytech. Civil Eng.

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The paper presents an optimal design of steel curved roof frames with its roof being part of a circular arc. The elements of frames are tapered I-section members. In the objective function for optimization, two factors affecting the weight of frames are considered simultaneously. First, the roof slope angle as an effective variable on the values of the structural loading and second, the cross-section of members that are considered as continuous and discrete variables, respectively. In the range of 3 to 70 degrees, the optimum range of roof slope angles for steel curved roof frames, as well as precise value of the best roof slope angle, will be reported. Enhanced Vibrating Particles System (EVPS) algorithm is utilized for the optimal design of steel curved roof frames with tapered members. The performance and efficiency of the EVPS algorithm is compared with six other recently developed optimization algorithms including VPS, GWO, HS, SSA, ECBO and GOA algorithms. The effectiveness and performance of EVPS algorithm is proven. Frames design are performed using ANSI/AISC 360-05 speciﬁcations which strength, displacement and stability constraints are imposed on the frames.

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A new algorithm of modified binary particle swarm optimization based on the Gustafson-Kessel for credit risk assessment

Cited by 29 publications

References 39 publications

Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver

Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver

Design of Optical Filter Using Bald Eagle Search Optimization Algorithm

Optimal Design of Steel Curved Roof Frames by Enhanced Vibrating Particles System Algorithm

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