Satisfiability Logic Analysis Via Radial Basis Function Neural Network with Artificial Bee Colony Algorithm

Kasihmuddin, Mohd Shareduwan Bin Mohd; Mansor, Mohd. Asyraf; Alzaeemi, Shehab Abdulhabib; Sathasivam, Saratha

doi:10.9781/ijimai.2020.06.002

Cited by 8 publications

(8 citation statements)

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“…The result of the RBFNN-2SATRAPSO model seems quite promising. However, the aspect of the control of the efficient local search is lacking [44]. The search space, in this case, for each of the particles, magnifies indefinitely, thereby leading to the suboptimal output weight between the attributes.…”

Section: Methodsmentioning

confidence: 99%

Examining the Forecasting Movement of Palm Oil Price Using RBFNN-2SATRA Metaheuristic Algorithms for Logic Mining

Alzaeemi

Sathasivam

2021

IEEE Access

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RBFNN with different algorithms and the logic mining method for forecasting constitute the most significant tools and techniques, which are used to demonstrate the economic growth in the country. Upon using monthly data spanning from Jan 2016 to March 2020 for the manufacturing of palm oil, the results mainly revealed that RBFNN-2SATRAAIS is the most accurate and efficient model compared to RBFNN-2SATRAPSO and RBFNN-2SATRAGA in forecasting the price of palm oil. RBFNN-2SATRAAIS had the highest average overall accuracy (90.476190 %), followed by RBFNN-2SATRAPSO (85.71%), and RBFNN-2SATRAGA (76.19%). The results also showed that the spot price of palm oil is highly influenced by the total exports and imports, as well as the production of palm oil, its end-stocks, and Malaysia's real, effective exchange rate. By using the data mining technique based on the energy minimization technique, the logical mining task was carried out. The empirical findings provided useful insights into decision-making and policy implementations, including the formulation of strategies to help the industry in dealing with constant price fluctuations and, thereby, enabling the Malaysian palm oil industry to continue its domination over the international market.

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Section: Methodsmentioning

confidence: 99%

Examining the Forecasting Movement of Palm Oil Price Using RBFNN-2SATRA Metaheuristic Algorithms for Logic Mining

Alzaeemi

Sathasivam

2021

IEEE Access

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“…In the literature, a significant amount of traditional [30,40,41] and modern (CNN) [9,11,42,43] medical segmentation techniques have been used. Implementation of the traditional segmentation techniques is time consuming, and most of the existing traditional techniques are semi-automated methods and frequently need operator assistance.…”

Section: Cnn Schemementioning

confidence: 99%

Automated segmentation of leukocyte from hematological images—a study using various CNN schemes

Kadry¹,

Rajinikanth

Taniar

et al. 2021

J Supercomput

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Medical images play a fundamental role in disease screening, and automated evaluation of these images is widely preferred in hospitals. Recently, Convolutional Neural Network (CNN) supported medical data assessment is widely adopted to inspect a set of medical imaging modalities. Extraction of the leukocyte section from a thin blood smear image is one of the essential procedures during the preliminary disease screening process. The conventional segmentation needs complex/hybrid procedures to extract the necessary section and the results achieved with conventional methods sometime tender poor results. Hence, this research aims to implement the CNN-assisted image segmentation scheme to extract the leukocyte section from the RGB scaled hematological images. The proposed work employs various CNN-based segmentation schemes, such as SegNet, U-Net, and VGG-UNet. We used the images from the Leukocyte Images for Segmentation and Classification (LISC) database. In this work, five classes of the leukocytes are considered, and each CNN segmentation scheme is separately implemented and evaluated with the ground-truth image. The experimental outcome of the proposed work confirms that the overall results accomplished with the VGG-UNet are better (Jaccard-Index = 91.5124%, Dice-Coefficient = 94.4080%, and Accuracy = 97.7316%) than those of the SegNet and U-Net schemes Finally, the merit of the proposed scheme is also confirmed using other similar image datasets, such as Blood Cell Count and Detection (BCCD) database and ALL-IDB2. The attained result confirms that the proposed scheme works well on hematological images and offers better performance measure values.

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“…Artificial Bee Colony Algorithm is applied to neural network. [3,4] applied Ant Colony Optimization to jobs Scheduling and energy Optimization. Fault diagnosis is an application domain of optimization algorithm [5].…”

Section: Introductionmentioning

confidence: 99%

Research on a new optimization algorithm simulating multi- states of matter inspired by finite element analysis approach

2021

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A new optimization algorithm is proposed, since a huge problem that many algorithms faced was not being able to effectively balance the global and local search ability. Matter exists in three states: solid, liquid, and gas, which presents different motion characteristics. Inspired by multi-states of matter, individuals of optimization algorithm have different motion characteristics of matter, which could present different search ability. The Finite Element Analysis (FEA) approach can simulate multi-states of matter, which can be adopted to effectively balance the global search ability and local search ability in new optimization algorithm. The new algorithm is creative application of Finite Element Analysis at optimization algorithm field. Artificial Physics Optimization (APO) and Gravitational Search Algorithm (GSA) belongs to the algorithm types defined by force and mass. According to FEA approach, node displacement caused by force and stiffness could be equivalent to motion caused by force and mass of APO and GSA. In the new algorithm framework, stiffness replaces mass of APO and GSA algorithm. This paper performs research on two different algorithms based on APO and GSA respectively. The individuals of new optimization algorithm are divided into solid state, liquid state, and gas state. The effects of main parameters on the performance were studied through experiments of 6 static test functions. The performance is compared with PSO, basic APO, or GSA for four complex models which made up of solid individual, liquid individual, and gas individual in iterative process. The reasonable complex model can be confirmed experimentally. Based on the reasonable complex model, the article conducted complete experiments against Enhancing artificial bee colony algorithm with multi-elite guidance (MGABC), Artificial bee colony algorithm with an adaptive greedy position update strategy (AABC), Multi-strategy ensemble artificial bee colony (MEABC), Self-adaptive heterogeneous PSO (fk-PSO), and APO with 28 CEC2013 test problem. Experimental results show that the proposed method achieves a good performance in comparison to its counterparts as a consequence of its better exploration-exploitation balance. The algorithm supplies a new method to improve physics optimization algorithm.

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Satisfiability Logic Analysis Via Radial Basis Function Neural Network with Artificial Bee Colony Algorithm

Cited by 8 publications

References 0 publications

Examining the Forecasting Movement of Palm Oil Price Using RBFNN-2SATRA Metaheuristic Algorithms for Logic Mining

Examining the Forecasting Movement of Palm Oil Price Using RBFNN-2SATRA Metaheuristic Algorithms for Logic Mining

Automated segmentation of leukocyte from hematological images—a study using various CNN schemes

Research on a new optimization algorithm simulating multi- states of matter inspired by finite element analysis approach

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