Indonesian Journal of Electrical Engineering and Computer Science, 2021
The grey wolf optimization (GWO) is a nature inspired and meta-heuristic algorithm, it has succes... more The grey wolf optimization (GWO) is a nature inspired and meta-heuristic algorithm, it has successfully solved many optimization problems and give better solution as compare to other algorithms. However, due to its poor exploration capability, it has imbalance relation between exploration and exploitation. Therefore, in this research work, the poor exploration part of GWO was improved through hybrid with whale optimization algorithm (WOA) exploration. The proposed grey wolf whale optimization algorithm (GWWOA) was evaluated on five unimodal and five multimodal benchmark functions. The results shows that GWWOA offered better exploration ability and able to solve the optimization problem and give better solution in search space. Additionally, GWWOA results were well balanced and gave the most optimal in search space as compare to the standard GWO and WOA algorithms.
Indonesian Journal of Electrical Engineering and Computer Science, 2020
Text classification is a fundamental task in several areas of natural language processing (NLP), ... more Text classification is a fundamental task in several areas of natural language processing (NLP), including words semantic classification, sentiment analysis, question answering, or dialog management. This paper investigates three basic architectures of deep learning models for the tasks of text classification: Deep Belief Neural (DBN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), these three main types of deep learning architectures, are largely explored to handled various classification tasks. DBN have excellent learning capabilities to extracts highly distinguishable features and good for general purpose. CNN have supposed to be better at extracting the position of various related features while RNN is modeling in sequential of long-term dependencies. This paper work shows the systematic comparison of DBN, CNN, and RNN on text classification tasks. Finally, we show the results of deep models by research experiment. The aim of this paper to provides basic ...
High dimensionality of the feature space is one of the difficulty that affect short message servi... more High dimensionality of the feature space is one of the difficulty that affect short message service(SMS) classification performance. Some studies used feature selection methods to pick up some features, while other studies used the full extracted features. In this work, we aim to analyse the relationship between features size and classification performance. For that, a classification performance comparison was carried outbetween ten features sizes selected by varies feature selection methods. The used methods were chi-square, Gini index and information gain (IG). Support vector machine was used as a classifier. Area Under the ROC (Receiver Operating Characteristics) Curve between true positive rate and false positive rate was used to measure the classification performance. We used the repeated measures ANOVA at p < 0.05 level to analyse the performance. Experimental results showed that IG method outperformed the other methods in all features sizes. The best result was with 50% of the extracted features. Furthermore, the results explicitly showed that using larger features size in the classification does not mean superior performance but sometimes leads to less classification performance. Therefore, feature selection step should be used. By reducing the used features for the classification, without degrading the classification performance, it means reducing memory usage and classification time.
Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2020
Handwritten Digit recognition is a challenging problem these days due to the widely used Arabic l... more Handwritten Digit recognition is a challenging problem these days due to the widely used Arabic language in the world, especially in the Middle East region. In this paper, sliding windows are used to enhance classification accuracies and implemented using random forests (RF) and support vector machine (SVM) classifiers for recognition of Arabic digit images. In order to study their effectiveness with and without using sliding windows, four different feature extraction techniques have been proposed which includes Mean-based, Gray-Level Co-occurrence Matrix (GLCM), Moment-based, and Edge Direction Histogram (EDH). The obtained accuracies show the significance of using sliding windows for classifying digit. The recognition rates acquired using the modified version of AHDBase dataset are 98% when Mean-based and Moment-based are applied with RF classifier, 98.33% and 99.13% when GLCM and EDH are used with linear-kernel SVM, respectively. Moreover, the performance of this study is compared against recent state-of-the-art approaches, namely Geometric-based, two-dimensional discrete cosine transform, Hierarchical features, Hetero-features, Discrete Fourier Transform and geometrical features, Gabor-based, gradient, structural, and concavity and Local Binary Convolutional Neural Networks.
Advances in Intelligent Systems and Computing, 2014
This book constitutes the refereed proceedings of the First International Conference on Soft Comp... more This book constitutes the refereed proceedings of the First International Conference on Soft Computing and Data Mining, SCDM 2014, held in Universiti Tun Hussein Onn Malaysia, in June 16th-18th, 2014. The 65 revised full papers presented in this book were carefully reviewed and selected from 145 submissions, and organized into two main topical sections; Data Mining and Soft Computing. The goal of this book is to provide both theoretical concepts and, especially, practical techniques on these exciting fields of soft computing and data mining, ready to be applied in real-world applications. The exchanges of views pertaining future research directions to be taken in this field and the resultant dissemination of the latest research findings makes this work of immense value to all those having an interest in the topics covered.
Prediction of time series grabs received much attention because of its effect on the vast range o... more Prediction of time series grabs received much attention because of its effect on the vast range of real life applications. This paper presents a survey of time series applications using Higher Order Neural Network (HONN) model. The basic motivation behind using HONN is the ability to expand the input space, to solve complex problems it becomes more efficient and perform high learning abilities of the time series forecasting. Pi-Sigma Neural Network (PSNN) includes indirectly the capabilities of higher order networks using product cells as the output units and less number of weights. The goal of this research is to present the reader awareness about PSNN for time series prediction, to highlight some benefits and challenges using PSNN. Possible fields of PSNN applications in comparison with existing methods are presented and future directions are also explored in advantage with the properties of error feedback and recurrent networks
Functional Link Neural Network (FLNN) has been becoming as an important tool used in many applica... more Functional Link Neural Network (FLNN) has been becoming as an important tool used in many applications task particularly in solving a non-linear separable problems. This is due to its modest architecture which required less tunable weights for training as compared to the standard multilayer feed forward network. The most common learning scheme for training the FLNN is a Backpropagation (BP-learning) algorithm. However, learning method by BP-learning algorithm tend to easily get trapped in local minima especially when dealing with non-linearly separable classification problems which affect the performance of FLNN. This paper discussed the implementation of modified Artificial Bee Colony with Firefly algorithm for training the FLNN network to overcome the drawback of BP-learning scheme. The aim is to introduce an alternative learning scheme that can provide a better solution for training the FLNN network for classification task.
Functional Link Neural Network (FLNN) has becoming as an important tool used in machine learning ... more Functional Link Neural Network (FLNN) has becoming as an important tool used in machine learning due to its modest architecture. FLNN requires less tunable weights for training as compared to the standard multilayer feed forward network such as Multilayer Perceptron (MLP). Since FLNN uses Backpropagation algorithm as the standard learning algorithm, the method however prone to get trapped in local minima which affect its performance. This paper proposed the implementation of Ant Lion Algorithm as learning algorithm to train the FLNN for classification tasks. The Ant Lion Optimizer (ALO) is the metaheuristic optimization algorithm that mimics the hunting mechanism of antlions in nature. The result of the classification made by FLNN-ALO is compared with the standard FLNN model to examine whether the ALO learning algorithm is capable of training the FLNN network and improve its performance. From the result achieved, it can be seen that the implementation of the proposed learning algori...
JOIV : International Journal on Informatics Visualization, 2019
Text classification has become very serious problem for big organization to manage the large amou... more Text classification has become very serious problem for big organization to manage the large amount of online data and has been extensively applied in the tasks of Natural Language Processing (NLP). Text classification can support users to excellently manage and exploit meaningful information require to be classified into various categories for further use. In order to best classify texts, our research efforts to develop a deep learning approach which obtains superior performance in text classification than other RNNs approaches. However, the main problem in text classification is how to enhance the classification accuracy and the sparsity of the data semantics sensitivity to context often hinders the classification performance of texts. In order to overcome the weakness, in this paper we proposed unified structure to investigate the effects of word embedding and Gated Recurrent Unit (GRU) for text classification on two benchmark datasets included (Google snippets and TREC). GRU is ...
Advances in Intelligent Systems and Computing, 2016
ANFIS performance depends on the parameters it is trained with. Therefore, the training mechanism... more ANFIS performance depends on the parameters it is trained with. Therefore, the training mechanism needs to be faster and reliable. Many have trained ANFIS parameters using GD, LSE, and metaheuristic techniques but the efficient one are still to be developed. Catfish-PSO algorithm is one of the latest successful swarm intelligence based technique which is used in this research for training ANFIS. As opposed to standard PSO, Catfish-PSO has string exploitation and exploration capability. The experimental results of training ANFIS network for classification problems show that Catfish-PSO algorithm achieved much better accuracy and satisfactory results.
Functional Link Neural Network (FLNN) is a type of Higher Order Neural Networks (HONNs) known to ... more Functional Link Neural Network (FLNN) is a type of Higher Order Neural Networks (HONNs) known to have the modest architecture as compared to other multilayer feedforward networks. FLNN employs less tunable weights which make the learning method in the network less complicated. The standard learning method used in FLNN network is the Backpropagation (BP) learning algorithm. This method however, is prone to easily get trapped in local minima which affect the performance of the FLNN network. Thus an alternative learning method named modified Bee-Firefly (MBF) algorithm is proposed for FLNN. This paper presents the implementation FLNN trained with MBF on mammographic mass classification task. The result of the classification made by FLNN-MBF is compared with the standard FLNN-BP model to examine whether the MBF learning algorithm is capable of training the FLNN network and improve its performance for the task of classification.
AWERProcedia Information Technology and Computer Science, Dec 24, 2012
The Back Propagation (BP) algorithm has been employed to solve wide range of practical problems. ... more The Back Propagation (BP) algorithm has been employed to solve wide range of practical problems. Despite many successful applications, it has some inevitable drawbacks includes getting trapped at local minima and slow convergence speeds. Thus, this paper reviews several improvements and modifications of the BP learning algorithm in order to overcome the drawbacks of conventional BP algorithm.
International Journal of Intelligent Systems and Applications, 2020
Financial time-series prediction has been long and the most challenging issues in financial marke... more Financial time-series prediction has been long and the most challenging issues in financial market analysis.The deep neural networks is one of the excellent data mining approach has received great attention by researchers in several areas of time-series prediction since last 10 years. Convolutional neural network (CNN) and recurrent neural network (RNN) models have become the mainstream methods for financial predictions. In this paper, we proposed to combine architectures, which exploit the advantages of CNN and RNN simultaneously, for the prediction of trading signals. Our model is essentially presented to financial time series predicting signals through a CNN layer, and directly fed into a gated recurrent unit (GRU) layer to capture long-term signals dependencies. GRU model perform better in sequential learning tasks and solve the vanishing gradients and exploding issue in standard RNNs. We evaluate our model on three datasets for stock indexes of the Hang Seng Indexes (HSI), the ...
Time series forecasting gets much attention due to its impact on many practical applications. Hig... more Time series forecasting gets much attention due to its impact on many practical applications. Higher-order neural network with recurrent feedback is a powerful technique which used successfully for forecasting. It maintains fast learning and the ability to learn the dynamics of the series over time. In general, the most used recurrent feedback is the network output. However, no much attention has been paid to use network error instead of the network output. For that, in this paper, we propose a novel model which is called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that combines the properties of higher order and error feedback recurrent neural network. Three signals have been used in this paper, namely heat wave temperature, IBM common stock closing price and Mackey–Glass equation. Simulation results show that RPNN-EF is significantly faster than other RPNN-based models for one-step ahead forecasting and its forecasting performance is more significant than these m...
Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015), 2019
ABSTRACT Abstract. Breast cancer has increased the mortality rate one out of every eight women; a... more ABSTRACT Abstract. Breast cancer has increased the mortality rate one out of every eight women; and as the second most-common type of cancer, it is a big threat for women’s health and survival. One of the popular methods to predict breast cancer is based on bio-inspired computing approaches. Bio-inspired approaches serve as global optimization algorithms which are motivated by the natural behaviors of swarms like ants, birds, fishes and bees. Artificial Bee Colony (ABC) is a well-known bio-inspired algo-rithm, which is robust, easy to implement and has few setting parameters. However, one of ABC disadvantages is that of slow convergence; which is due to the poor exploration and exploitation processes. In this paper, we propose Global Guided Artifi-cial Bee Colony (GGABC) algorithm; which employs a new hybrid population-based metaheuristic approach to overcome the deficiency of the traditional ABC algorithm. Further we use the proposed algorithm to predict patients having breast cancer, where we simulate by the foraging behavior of global best and guided honey bees. The simulation results of proposed algorithm were compared with other baseline algorithms including ABC, Guided ABC and Global ABC. The predicted results of breast cancer by GGABC model are highly accurate in contrast to the results by these algorithms.
Indonesian Journal of Electrical Engineering and Computer Science, 2021
The grey wolf optimization (GWO) is a nature inspired and meta-heuristic algorithm, it has succes... more The grey wolf optimization (GWO) is a nature inspired and meta-heuristic algorithm, it has successfully solved many optimization problems and give better solution as compare to other algorithms. However, due to its poor exploration capability, it has imbalance relation between exploration and exploitation. Therefore, in this research work, the poor exploration part of GWO was improved through hybrid with whale optimization algorithm (WOA) exploration. The proposed grey wolf whale optimization algorithm (GWWOA) was evaluated on five unimodal and five multimodal benchmark functions. The results shows that GWWOA offered better exploration ability and able to solve the optimization problem and give better solution in search space. Additionally, GWWOA results were well balanced and gave the most optimal in search space as compare to the standard GWO and WOA algorithms.
Indonesian Journal of Electrical Engineering and Computer Science, 2020
Text classification is a fundamental task in several areas of natural language processing (NLP), ... more Text classification is a fundamental task in several areas of natural language processing (NLP), including words semantic classification, sentiment analysis, question answering, or dialog management. This paper investigates three basic architectures of deep learning models for the tasks of text classification: Deep Belief Neural (DBN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), these three main types of deep learning architectures, are largely explored to handled various classification tasks. DBN have excellent learning capabilities to extracts highly distinguishable features and good for general purpose. CNN have supposed to be better at extracting the position of various related features while RNN is modeling in sequential of long-term dependencies. This paper work shows the systematic comparison of DBN, CNN, and RNN on text classification tasks. Finally, we show the results of deep models by research experiment. The aim of this paper to provides basic ...
High dimensionality of the feature space is one of the difficulty that affect short message servi... more High dimensionality of the feature space is one of the difficulty that affect short message service(SMS) classification performance. Some studies used feature selection methods to pick up some features, while other studies used the full extracted features. In this work, we aim to analyse the relationship between features size and classification performance. For that, a classification performance comparison was carried outbetween ten features sizes selected by varies feature selection methods. The used methods were chi-square, Gini index and information gain (IG). Support vector machine was used as a classifier. Area Under the ROC (Receiver Operating Characteristics) Curve between true positive rate and false positive rate was used to measure the classification performance. We used the repeated measures ANOVA at p < 0.05 level to analyse the performance. Experimental results showed that IG method outperformed the other methods in all features sizes. The best result was with 50% of the extracted features. Furthermore, the results explicitly showed that using larger features size in the classification does not mean superior performance but sometimes leads to less classification performance. Therefore, feature selection step should be used. By reducing the used features for the classification, without degrading the classification performance, it means reducing memory usage and classification time.
Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2020
Handwritten Digit recognition is a challenging problem these days due to the widely used Arabic l... more Handwritten Digit recognition is a challenging problem these days due to the widely used Arabic language in the world, especially in the Middle East region. In this paper, sliding windows are used to enhance classification accuracies and implemented using random forests (RF) and support vector machine (SVM) classifiers for recognition of Arabic digit images. In order to study their effectiveness with and without using sliding windows, four different feature extraction techniques have been proposed which includes Mean-based, Gray-Level Co-occurrence Matrix (GLCM), Moment-based, and Edge Direction Histogram (EDH). The obtained accuracies show the significance of using sliding windows for classifying digit. The recognition rates acquired using the modified version of AHDBase dataset are 98% when Mean-based and Moment-based are applied with RF classifier, 98.33% and 99.13% when GLCM and EDH are used with linear-kernel SVM, respectively. Moreover, the performance of this study is compared against recent state-of-the-art approaches, namely Geometric-based, two-dimensional discrete cosine transform, Hierarchical features, Hetero-features, Discrete Fourier Transform and geometrical features, Gabor-based, gradient, structural, and concavity and Local Binary Convolutional Neural Networks.
Advances in Intelligent Systems and Computing, 2014
This book constitutes the refereed proceedings of the First International Conference on Soft Comp... more This book constitutes the refereed proceedings of the First International Conference on Soft Computing and Data Mining, SCDM 2014, held in Universiti Tun Hussein Onn Malaysia, in June 16th-18th, 2014. The 65 revised full papers presented in this book were carefully reviewed and selected from 145 submissions, and organized into two main topical sections; Data Mining and Soft Computing. The goal of this book is to provide both theoretical concepts and, especially, practical techniques on these exciting fields of soft computing and data mining, ready to be applied in real-world applications. The exchanges of views pertaining future research directions to be taken in this field and the resultant dissemination of the latest research findings makes this work of immense value to all those having an interest in the topics covered.
Prediction of time series grabs received much attention because of its effect on the vast range o... more Prediction of time series grabs received much attention because of its effect on the vast range of real life applications. This paper presents a survey of time series applications using Higher Order Neural Network (HONN) model. The basic motivation behind using HONN is the ability to expand the input space, to solve complex problems it becomes more efficient and perform high learning abilities of the time series forecasting. Pi-Sigma Neural Network (PSNN) includes indirectly the capabilities of higher order networks using product cells as the output units and less number of weights. The goal of this research is to present the reader awareness about PSNN for time series prediction, to highlight some benefits and challenges using PSNN. Possible fields of PSNN applications in comparison with existing methods are presented and future directions are also explored in advantage with the properties of error feedback and recurrent networks
Functional Link Neural Network (FLNN) has been becoming as an important tool used in many applica... more Functional Link Neural Network (FLNN) has been becoming as an important tool used in many applications task particularly in solving a non-linear separable problems. This is due to its modest architecture which required less tunable weights for training as compared to the standard multilayer feed forward network. The most common learning scheme for training the FLNN is a Backpropagation (BP-learning) algorithm. However, learning method by BP-learning algorithm tend to easily get trapped in local minima especially when dealing with non-linearly separable classification problems which affect the performance of FLNN. This paper discussed the implementation of modified Artificial Bee Colony with Firefly algorithm for training the FLNN network to overcome the drawback of BP-learning scheme. The aim is to introduce an alternative learning scheme that can provide a better solution for training the FLNN network for classification task.
Functional Link Neural Network (FLNN) has becoming as an important tool used in machine learning ... more Functional Link Neural Network (FLNN) has becoming as an important tool used in machine learning due to its modest architecture. FLNN requires less tunable weights for training as compared to the standard multilayer feed forward network such as Multilayer Perceptron (MLP). Since FLNN uses Backpropagation algorithm as the standard learning algorithm, the method however prone to get trapped in local minima which affect its performance. This paper proposed the implementation of Ant Lion Algorithm as learning algorithm to train the FLNN for classification tasks. The Ant Lion Optimizer (ALO) is the metaheuristic optimization algorithm that mimics the hunting mechanism of antlions in nature. The result of the classification made by FLNN-ALO is compared with the standard FLNN model to examine whether the ALO learning algorithm is capable of training the FLNN network and improve its performance. From the result achieved, it can be seen that the implementation of the proposed learning algori...
JOIV : International Journal on Informatics Visualization, 2019
Text classification has become very serious problem for big organization to manage the large amou... more Text classification has become very serious problem for big organization to manage the large amount of online data and has been extensively applied in the tasks of Natural Language Processing (NLP). Text classification can support users to excellently manage and exploit meaningful information require to be classified into various categories for further use. In order to best classify texts, our research efforts to develop a deep learning approach which obtains superior performance in text classification than other RNNs approaches. However, the main problem in text classification is how to enhance the classification accuracy and the sparsity of the data semantics sensitivity to context often hinders the classification performance of texts. In order to overcome the weakness, in this paper we proposed unified structure to investigate the effects of word embedding and Gated Recurrent Unit (GRU) for text classification on two benchmark datasets included (Google snippets and TREC). GRU is ...
Advances in Intelligent Systems and Computing, 2016
ANFIS performance depends on the parameters it is trained with. Therefore, the training mechanism... more ANFIS performance depends on the parameters it is trained with. Therefore, the training mechanism needs to be faster and reliable. Many have trained ANFIS parameters using GD, LSE, and metaheuristic techniques but the efficient one are still to be developed. Catfish-PSO algorithm is one of the latest successful swarm intelligence based technique which is used in this research for training ANFIS. As opposed to standard PSO, Catfish-PSO has string exploitation and exploration capability. The experimental results of training ANFIS network for classification problems show that Catfish-PSO algorithm achieved much better accuracy and satisfactory results.
Functional Link Neural Network (FLNN) is a type of Higher Order Neural Networks (HONNs) known to ... more Functional Link Neural Network (FLNN) is a type of Higher Order Neural Networks (HONNs) known to have the modest architecture as compared to other multilayer feedforward networks. FLNN employs less tunable weights which make the learning method in the network less complicated. The standard learning method used in FLNN network is the Backpropagation (BP) learning algorithm. This method however, is prone to easily get trapped in local minima which affect the performance of the FLNN network. Thus an alternative learning method named modified Bee-Firefly (MBF) algorithm is proposed for FLNN. This paper presents the implementation FLNN trained with MBF on mammographic mass classification task. The result of the classification made by FLNN-MBF is compared with the standard FLNN-BP model to examine whether the MBF learning algorithm is capable of training the FLNN network and improve its performance for the task of classification.
AWERProcedia Information Technology and Computer Science, Dec 24, 2012
The Back Propagation (BP) algorithm has been employed to solve wide range of practical problems. ... more The Back Propagation (BP) algorithm has been employed to solve wide range of practical problems. Despite many successful applications, it has some inevitable drawbacks includes getting trapped at local minima and slow convergence speeds. Thus, this paper reviews several improvements and modifications of the BP learning algorithm in order to overcome the drawbacks of conventional BP algorithm.
International Journal of Intelligent Systems and Applications, 2020
Financial time-series prediction has been long and the most challenging issues in financial marke... more Financial time-series prediction has been long and the most challenging issues in financial market analysis.The deep neural networks is one of the excellent data mining approach has received great attention by researchers in several areas of time-series prediction since last 10 years. Convolutional neural network (CNN) and recurrent neural network (RNN) models have become the mainstream methods for financial predictions. In this paper, we proposed to combine architectures, which exploit the advantages of CNN and RNN simultaneously, for the prediction of trading signals. Our model is essentially presented to financial time series predicting signals through a CNN layer, and directly fed into a gated recurrent unit (GRU) layer to capture long-term signals dependencies. GRU model perform better in sequential learning tasks and solve the vanishing gradients and exploding issue in standard RNNs. We evaluate our model on three datasets for stock indexes of the Hang Seng Indexes (HSI), the ...
Time series forecasting gets much attention due to its impact on many practical applications. Hig... more Time series forecasting gets much attention due to its impact on many practical applications. Higher-order neural network with recurrent feedback is a powerful technique which used successfully for forecasting. It maintains fast learning and the ability to learn the dynamics of the series over time. In general, the most used recurrent feedback is the network output. However, no much attention has been paid to use network error instead of the network output. For that, in this paper, we propose a novel model which is called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that combines the properties of higher order and error feedback recurrent neural network. Three signals have been used in this paper, namely heat wave temperature, IBM common stock closing price and Mackey–Glass equation. Simulation results show that RPNN-EF is significantly faster than other RPNN-based models for one-step ahead forecasting and its forecasting performance is more significant than these m...
Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015), 2019
ABSTRACT Abstract. Breast cancer has increased the mortality rate one out of every eight women; a... more ABSTRACT Abstract. Breast cancer has increased the mortality rate one out of every eight women; and as the second most-common type of cancer, it is a big threat for women’s health and survival. One of the popular methods to predict breast cancer is based on bio-inspired computing approaches. Bio-inspired approaches serve as global optimization algorithms which are motivated by the natural behaviors of swarms like ants, birds, fishes and bees. Artificial Bee Colony (ABC) is a well-known bio-inspired algo-rithm, which is robust, easy to implement and has few setting parameters. However, one of ABC disadvantages is that of slow convergence; which is due to the poor exploration and exploitation processes. In this paper, we propose Global Guided Artifi-cial Bee Colony (GGABC) algorithm; which employs a new hybrid population-based metaheuristic approach to overcome the deficiency of the traditional ABC algorithm. Further we use the proposed algorithm to predict patients having breast cancer, where we simulate by the foraging behavior of global best and guided honey bees. The simulation results of proposed algorithm were compared with other baseline algorithms including ABC, Guided ABC and Global ABC. The predicted results of breast cancer by GGABC model are highly accurate in contrast to the results by these algorithms.
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Papers by Rozaida Ghazali