• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.306-314
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• 2000

In this paper we present a new approach to estimate link travel speed based on the hybrid neuro-fuzzy network. It combines the fuzzy ART algorithm for structure learning and the backpropagation algorithm for parameter adaptation. At first, the fuzzy ART algorithm partitions the input/output space using the training data set in order to construct initial neuro-fuzzy inference network. After the initial network topology is completed, a backpropagation learning scheme is applied to optimize parameters of fuzzy membership functions. An initial neuro-fuzzy network can be applicable to any other link where the probe car data are available. This can be realized by the network adaptation and add/modify module. In the network adaptation module, a CBR(Case-Based Reasoning) approach is used. Various experiments show that proposed methodology has better performance for estimating link travel speed comparing to the existing method.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.123-131
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• 2015

Training neural networks is a complex task with great importance in the field of supervised learning. In the training process, a set of input-output patterns is repeated to an artificial neural network (ANN). From those patterns weights of all the interconnections between neurons are adjusted until the specified input yields the desired output. In this paper, a new hybrid algorithm is proposed for global optimization of connection weights in an ANN. Dynamic swarms are shown to converge rapidly during the initial stages of a global search, but around the global optimum, the search process becomes very slow. In contrast, the gradient descent method can achieve faster convergence speed around the global optimum, and at the same time, the convergence accuracy can be relatively high. Therefore, the proposed hybrid algorithm combines the dynamic particle swarm optimization (DPSO) algorithm with the backpropagation (BP) algorithm, also referred to as the DPSO-BP algorithm, to train the weights of an ANN. In this paper, we intend to show the superiority (time performance and quality of solution) of the proposed hybrid algorithm (DPSO-BP) over other more standard algorithms in neural network training. The algorithms are compared using two different datasets, and the results are simulated.

• Journal of the Korean Society of Industry Convergence
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• v.6 no.1
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• pp.23-29
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• 2003

This paper proposes an efficient feature extraction of the images by using independent component analysis(ICA) based on neural networks of the hybrid learning algorithm. The proposed learning algorithm is the fixed point(FP) algorithm based on Newton method and moment. The Newton method, which uses to the tangent line for estimating the root of function, is applied for fast updating the inverse mixing matrix. The moment is also applied for getting the better speed-up by restraining an oscillation due to compute the tangent line. The proposed algorithm has been applied to the 10,000 image patches of $12{\times}12$-pixel that are extracted from 13 natural images. The 144 features of $12{\times}12$-pixel and the 160 features of $16{\times}16$-pixel have been extracted from all patches, respectively. The simulation results show that the extracted features have a localized characteristics being included in the images in space, as well as in frequency and orientation. And the proposed algorithm has better performances of the learning speed than those using the conventional FP algorithm based on Newton method.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.11
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• pp.997-1004
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• 2011

A fault detection algorithm of a charge and discharge system to ensure the safe use of hybrid electric vehicle is proposed in this paper. This algorithm can be used as a complementary way to existing fault detection technique for a charge and discharge system. The proposed algorithm uses a SVDD technique, which additionally utilizes two methods for learning a large amount of data; one is to incrementally learn a large amount of data, the other one is to remove the data that does not affect the next learning using a new data reduction technique. Removal of data is selected by using lines connecting support vectors. In the proposed method, the data processing speed is drastically improved and the storage space used is remarkably reduced than the conventional methods using the SVDD technique only. A battery data and speed data of a commercial hybrid electrical vehicle are utilized in this study. A fault boundary is produced via SVDD techniques using the input and output in normal operation of the system without using mathematical modeling. A fault detection simulation is performed using both an artificial fault data and the obtained fault boundary via SVDD techniques. In the fault detection simulation, fault detection time via proposed algorithm is compared with that of the peak-peak method. Also the proposed algorithm is revealed to detect fault in the region where conventional peak-peak method is never able to do.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1782-1791
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• 2017

In this paper, an optimal design of hybrid magnetic levitation(Maglev) system using intelligent optimization algorithms is proposed. The proposed maglev system adopts hybrid suspension system with permanent-magnet(PM) and electro magnet(EM) to reduce the suspension power loss and the teaching-learning based optimization(TLBO) that can overcome the drawbacks of conventional intelligent optimization algorithm is used. To obtain the mathematical model of hybrid suspension system, the magnetic equivalent circuit including leakage fluxes are used. Also, design restrictions such as cross section areas of PM and EM, the maximum length of PM, magnetic force are considered to choose the optimal parameters by intelligent optimization algorithm. To meet desired suspension power and lower power loss, the multi object function is proposed. To verify the proposed object function and intelligent optimization algorithms, we analyze the performance using the mean value and standard error of 10 simulation results. The simulation results show that the proposed method is more effective than conventional optimization methods.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.10
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• pp.819-826
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• 2001

Genetic algorithms are becoming more popular because of their relative simplicity and robustness. Genetic algorithms are global search techniques for nonlinear optimization. However, traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain because they are poor at hill-climbing, whereas simulated annealing has the ability of probabilistic hill-climbing. Therefore, hybridizing a genetic algorithm with other algorithms can produce better performance than using the genetic algorithm or other algorithms independently. In this paper, we propose an efficient hybrid optimization algorithm named the adaptive random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural networks. This paper describes the application of genetic algorithms and simulated annealing to a random signal-based learning in order to generate the parameters and reinforcement signal of the random signal-based learning, respectively. The validity of the proposed algorithm is confirmed by applying it to two different examples.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.4
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• pp.234-239
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• 2014

This paper presents an empirical study on selecting a small amount of useful unlabeled data to improve the classification accuracy of semi-supervised learning algorithms. In particular, a hybrid method of unifying the simply recycled selection method and the incrementally-reinforced selection method was considered and evaluated empirically. The experimental results, which were obtained from well-known benchmark data sets using semi-supervised support vector machines, demonstrated that the hybrid method works better than the traditional ones in terms of the classification accuracy.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.477-484
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• 2001

This paper proposes a hybrid learning algorithm combined momentum and kernel-adatron for improving the performance of regression support vector machine. The momentum is utilized for high-speed convergence by restraining the oscillation in the process of converging to the optimal solution, and the kernel-adatron algorithm is also utilized for the capability by working in nonlinear feature spaces and the simple implementation. The proposed algorithm has been applied to the 1-dimension and 2-dimension nonlinear function regression problems. The simulation results show that the proposed algorithm has better the learning speed and performance of the regression, in comparison with those quadratic programming and kernel-adatron algorithm.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.669-680
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• 2001

Genetic Algorithms are optimization algorithm that mimics biological evolution to solve optimization problems. Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex fitness landscapes. Hybrid genetic algorithm that is combined with local search called learning can sustain the balance between exploration and exploitation. The genetic traits that each individual in the population learns through evolution are transferred back to the next generation, and when this learning is combined with genetic algorithm we can expect the improvement of the search speed. This paper proposes a genetic algorithm based Cellular Learning with accelerated learning capability for function optimization. Proposed Cellular Learning strategy is based on periodic and convergent behaviors in cellular automata, and on the theory of transmitting to offspring the knowledge and experience that organisms acquire in their lifetime. We compared the search efficiency of Cellular Learning strategy with those of Lamarckian and Baldwin Effect in hybrid genetic algorithm. We showed that the local improvement by cellular learning could enhance the global performance higher by evaluating their performance through the experiment of various test bed functions and also showed that proposed learning strategy could find out the better global optima than conventional method.