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This paper is aimed at the usage of an augmented reality assisted system set up on the smart-glasses for training activities. Literature review leads us to a comparison among related technologies, yielding that Mask Regions with Convolutional Neural Network (R-CNN) oriented approach fits the study needs. The proposed method including (1) pointing gesture capture, (2) finger-pointing analysis, and (3) virtual tool positioning and rotation angle are developed. Results show that the recognition of object detection is 95.5%, the Kappa value of recognition of gesture detection is 0.93, and the average time for detecting pointing gesture is 0.26 seconds. Furthermore, even under different lighting, such as indoor and outdoor, the pointing analysis accuracy is up to 79%. The error between the analysis angle and the actual angle is only 1.32 degrees. The results proved that the system is well suited to present the effect of augmented reality, making it applicable for real world usage.
Mu-Chun Su; Jieh-Haur Chen; Vidya Trisandini Azzizi; Hsiang-Ling Chang; Hsi-Hsien Wei. Smart training: Mask R-CNN oriented approach. Expert Systems with Applications 2021, 185, 115595 .
AMA StyleMu-Chun Su, Jieh-Haur Chen, Vidya Trisandini Azzizi, Hsiang-Ling Chang, Hsi-Hsien Wei. Smart training: Mask R-CNN oriented approach. Expert Systems with Applications. 2021; 185 ():115595.
Chicago/Turabian StyleMu-Chun Su; Jieh-Haur Chen; Vidya Trisandini Azzizi; Hsiang-Ling Chang; Hsi-Hsien Wei. 2021. "Smart training: Mask R-CNN oriented approach." Expert Systems with Applications 185, no. : 115595.
Data-driven housing-market segmentation has been given increasing prominence for its objectiveness in identifying submarkets based on the housing data’s underlying structures. However, when handling high-dimensionality housing dataset, traditional statistical-clustering methods have been found to tend to lose low-variance information of the dataset and be deficient in deriving the globally optimal number of submarkets. Accordingly, with the intention of achieving more rigorous high-dimensionality housing market segmentation, a swarm-inspired projection (SIP) algorithm is introduced by this study. Using a high-dimensionality Taipei city’s housing dataset in a case study, a comparison of the proposed SIP algorithm and a statistical-clustering method using the combination of principal component analysis (PCA) and K-means clustering is conducted in evaluating the predictive accuracy of hedonic price models of the housing submarkets. The results show that, as compared to the original single market, the segmented submarkets resulting from SIP algorithm are more homogenous and distinctive, where the resulted hedonic price models have high-level statistical explanation and disparate sets of hedonic prices for different submarkets. In addition, as compared to the use of a statistical-clustering method, SIP algorithm is found to obtain a more optimal number of submarkets, where the resulted hedonic price models are found to achieve greater improvement of statistical explanation and more stable reduction of prediction error. These findings highlight the advantages of our proposed SIP algorithm in high-dimensionality housing market segmentation, and thus it is hoped that the present research will serve as a practical tool to better inform further studies aimed at market-segmentation-related problems.
Jieh-Haur Chen; Tingting Ji; Mu-Chun Su; Hsi-Hsien Wei; Vidya Trisandini Azzizi; Shu-Chien Hsu. Swarm-inspired data-driven approach for housing market segmentation: a case study of Taipei city. Journal of Housing and the Built Environment 2021, 1 -25.
AMA StyleJieh-Haur Chen, Tingting Ji, Mu-Chun Su, Hsi-Hsien Wei, Vidya Trisandini Azzizi, Shu-Chien Hsu. Swarm-inspired data-driven approach for housing market segmentation: a case study of Taipei city. Journal of Housing and the Built Environment. 2021; ():1-25.
Chicago/Turabian StyleJieh-Haur Chen; Tingting Ji; Mu-Chun Su; Hsi-Hsien Wei; Vidya Trisandini Azzizi; Shu-Chien Hsu. 2021. "Swarm-inspired data-driven approach for housing market segmentation: a case study of Taipei city." Journal of Housing and the Built Environment , no. : 1-25.
Technological developments have made the construction industry efficient. The aim of this research is to solve communication interaction problems to build a project management platform using the interactive concept of natural language processing technology. A comprehensive literature review and expert interviews associated with techniques dealing with natural languages suggests the proposed system containing the Progressive Scale Expansion Network (PSENet), Convolutional Recurrent Neural Network (CRNN), and Bi-directional Recurrent Neutral Networks Convolutional Recurrent Neural Network (BRNN-CNN) toolboxes to extract the key words for construction projects contracts. The results show that a fully automatic platform facilitating contract management is achieved. For academic domains, the Contract Keyword Detection (CKD) mechanism integrating PSENet, CRNN, and BRNN-CNN approaches to cope with real-time massive document flows is novel in the construction industry. For practice, the proposed approach brings significant reduction for manpower and human error, an alternative for settling down misunderstanding or disputes due to real-time and precise communication, and a solution for efficient documentary management. It connects all contract stakeholders proficiently.
Jieh-Haur Chen; Mu-Chun Su; Vidya-Trisandini Azzizi; Ting-Kwei Wang; Wei-Jen Lin. Smart Project Management: Interactive Platform Using Natural Language Processing Technology. Applied Sciences 2021, 11, 1597 .
AMA StyleJieh-Haur Chen, Mu-Chun Su, Vidya-Trisandini Azzizi, Ting-Kwei Wang, Wei-Jen Lin. Smart Project Management: Interactive Platform Using Natural Language Processing Technology. Applied Sciences. 2021; 11 (4):1597.
Chicago/Turabian StyleJieh-Haur Chen; Mu-Chun Su; Vidya-Trisandini Azzizi; Ting-Kwei Wang; Wei-Jen Lin. 2021. "Smart Project Management: Interactive Platform Using Natural Language Processing Technology." Applied Sciences 11, no. 4: 1597.
Hand-gesture based control has enormous potential both theoretically and for practical applications due to its convenience and intuitiveness. This study presents a real-time interactive control system for household appliances. The interactive control system allowing wireless control of household appliances using a combination of 11 hand gestures and 2 waving motions is tested on hundreds of samples. It is implemented using a regular personal computer (PC) and existing digital single processing (DSP) platforms. The evaluation results show that the system performs efficiently reaching an accuracy recognition rate of 91% and spending around 30 seconds to complete the control operation for household appliances. The contributions of this work are both academic (1) successful demonstration of the integration of algorithms for solving image detection, processing, and pattern recognition, and practical (2) showing its feasibility and using commonly available hardware and software configurations for practical uses, and finally (3) establishing a mechanism for intuitively interactive control system that facilitates smart living.
Mu-Chun Su; Jieh-Haur Chen; Achmad Muhyidin Arifai; Sung-Yang Tsai; Hsi-Hsien Wei. Smart living: an interactive control system for household appliances. IEEE Access 2021, 9, 1 -1.
AMA StyleMu-Chun Su, Jieh-Haur Chen, Achmad Muhyidin Arifai, Sung-Yang Tsai, Hsi-Hsien Wei. Smart living: an interactive control system for household appliances. IEEE Access. 2021; 9 ():1-1.
Chicago/Turabian StyleMu-Chun Su; Jieh-Haur Chen; Achmad Muhyidin Arifai; Sung-Yang Tsai; Hsi-Hsien Wei. 2021. "Smart living: an interactive control system for household appliances." IEEE Access 9, no. : 1-1.
Health care for independently living elders is more important than ever. Automatic recognition of their Activities of Daily Living (ADL) is the first step to solving the health care issues faced by seniors in an efficient way. The paper describes a Deep Neural Network (DNN)-based recognition system aimed at facilitating smart care, which combines ADL recognition, image/video processing, movement calculation, and DNN. An algorithm is developed for processing skeletal data, filtering noise, and pattern recognition for identification of the 10 most common ADL including standing, bending, squatting, sitting, eating, hand holding, hand raising, sitting plus drinking, standing plus drinking, and falling. The evaluation results show that this DNN-based system is suitable method for dealing with ADL recognition with an accuracy rate of over 95%. The findings support the feasibility of this system that is efficient enough for both practical and academic applications.
Muchun Su; Diana Wahyu Hayati; Shaowu Tseng; Jiehhaur Chen; Hsihsien Wei. Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition. Applied Sciences 2020, 11, 10 .
AMA StyleMuchun Su, Diana Wahyu Hayati, Shaowu Tseng, Jiehhaur Chen, Hsihsien Wei. Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition. Applied Sciences. 2020; 11 (1):10.
Chicago/Turabian StyleMuchun Su; Diana Wahyu Hayati; Shaowu Tseng; Jiehhaur Chen; Hsihsien Wei. 2020. "Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition." Applied Sciences 11, no. 1: 10.
Neural networks have achieved great results in sound recognition, and many different kinds of acoustic features have been tried as the training input for the network. However, there is still doubt about whether a neural network can efficiently extract features from the raw audio signal input. This study improved the raw-signal-input network from other researches using deeper network architectures. The raw signals could be better analyzed in the proposed network. We also presented a discussion of several kinds of network settings, and with the spectrogram-like conversion, our network could reach an accuracy of 73.55% in the open-audio-dataset “Dataset for Environmental Sound Classification 50” (ESC50). This study also proposed a network architecture that could combine different kinds of network feeds with different features. With the help of global pooling, a flexible fusion way was integrated into the network. Our experiment successfully combined two different networks with different audio feature inputs (a raw audio signal and the log-mel spectrum). Using the above settings, the proposed ParallelNet finally reached the accuracy of 81.55% in ESC50, which also reached the recognition level of human beings.
Yu-Kai Lin; Mu-Chun Su; Yi-Zeng Hsieh. The Application and Improvement of Deep Neural Networks in Environmental Sound Recognition. Applied Sciences 2020, 10, 5965 .
AMA StyleYu-Kai Lin, Mu-Chun Su, Yi-Zeng Hsieh. The Application and Improvement of Deep Neural Networks in Environmental Sound Recognition. Applied Sciences. 2020; 10 (17):5965.
Chicago/Turabian StyleYu-Kai Lin; Mu-Chun Su; Yi-Zeng Hsieh. 2020. "The Application and Improvement of Deep Neural Networks in Environmental Sound Recognition." Applied Sciences 10, no. 17: 5965.
The human eye is a vital sensory organ that provides us with visual information about the world around us. It can also convey such information as our emotional state to people with whom we interact. In technology, eye tracking has become a hot research topic recently, and a growing number of eye-tracking devices have been widely applied in fields such as psychology, medicine, education, and virtual reality. However, most commercially available eye trackers are prohibitively expensive and require that the user’s head remain completely stationary in order to accurately estimate the direction of their gaze. To address these drawbacks, this paper proposes an inner corner-pupil center vector (ICPCV) eye-tracking system based on a deep neural network, which does not require that the user’s head remain stationary or expensive hardware to operate. The performance of the proposed system is compared with those of other currently available eye-tracking estimation algorithms, and the results show that it outperforms these systems.
Mu-Chun Su; Tat-Meng U; Yi-Zeng Hsieh; Zhe-Fu Yeh; Shu-Fang Lee; Shih-Syun Lin. An Eye-Tracking System based on Inner Corner-Pupil Center Vector and Deep Neural Network. Sensors 2019, 20, 25 .
AMA StyleMu-Chun Su, Tat-Meng U, Yi-Zeng Hsieh, Zhe-Fu Yeh, Shu-Fang Lee, Shih-Syun Lin. An Eye-Tracking System based on Inner Corner-Pupil Center Vector and Deep Neural Network. Sensors. 2019; 20 (1):25.
Chicago/Turabian StyleMu-Chun Su; Tat-Meng U; Yi-Zeng Hsieh; Zhe-Fu Yeh; Shu-Fang Lee; Shih-Syun Lin. 2019. "An Eye-Tracking System based on Inner Corner-Pupil Center Vector and Deep Neural Network." Sensors 20, no. 1: 25.
Magnetic resonance imaging (MRI) offers the most detailed brain structure image available today; it can identify tiny lesions or cerebral cortical abnormalities. The primary purpose of the procedure is to confirm whether there is structural variation that causes epilepsy, such as hippocampal sclerotherapy, local cerebral cortical dysplasia, and cavernous hemangioma. Cerebrovascular disease, the second most common factor of death in the world, is also the fourth leading cause of death in Taiwan, with cerebrovascular disease having the highest rate of stroke. Among the most common are large vascular atherosclerotic lesions, small vascular lesions, and cardiac emboli. The purpose of this thesis is to establish a computer-aided diagnosis system based on small blood vessel lesions in MRI images, using the method of Convolutional Neural Network and deep learning to analyze brain vascular occlusion by analyzing brain MRI images. Blocks can help clinicians more quickly determine the probability and severity of stroke in patients. We analyzed MRI data from 50 patients, including 30 patients with stroke, 17 patients with occlusion but no stroke, and 3 patients with dementia. This system mainly helps doctors find out whether there are cerebral small vessel lesions in the brain MRI images, and to output the found results into labeled images. The marked contents include the position coordinates of the small blood vessel blockage, the block range, the area size, and if it may cause a stroke. Finally, all the MRI images of the patient are synthesized, showing a 3D display of the small blood vessels in the brain to assist the doctor in making a diagnosis or to provide accurate lesion location for the patient.
Yi-Zeng Hsieh; Yu-Cin Luo; Chen Pan; Mu-Chun Su; Chi-Jen Chen; Kevin Li-Chun Hsieh. Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep Learning. Sensors 2019, 19, 2573 .
AMA StyleYi-Zeng Hsieh, Yu-Cin Luo, Chen Pan, Mu-Chun Su, Chi-Jen Chen, Kevin Li-Chun Hsieh. Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep Learning. Sensors. 2019; 19 (11):2573.
Chicago/Turabian StyleYi-Zeng Hsieh; Yu-Cin Luo; Chen Pan; Mu-Chun Su; Chi-Jen Chen; Kevin Li-Chun Hsieh. 2019. "Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep Learning." Sensors 19, no. 11: 2573.
In some hospitals, rehabilitation professionals usually adopt the visual assessment incorporated with some posture charts to assess whether a patient has good postures while he or she is standing still or stretching some joints. While the advantages of the visual assessment are its simplicity and no need of expensive equipment, its disadvantages are imprecise, subjective, inefficient, etc. In this paper, we report the implement of a Kinect-based postural assessment system which is able to perform postural assessment and create an analysis report with 62 measurements including 22 angles, 35 distances, and 5 postural rotations. Based on the proposed Kinect-based postural assessment system, rehabilitation specialists are then able to objectively assess the treatment effect after each individual course of treatment.Some experiments were designed to measure the accuracy of the proposed system to verify whether it has the potential of being adopted at hospitals.
Mu-Chun Su; Sheng-Hung Lin; Shu-Fang Lee; Yu-Shiang Huang; Huang-Ren Chen. The Implementation of a Kinect-Based Postural Assessment System. Transactions on Petri Nets and Other Models of Concurrency XV 2016, 503 -513.
AMA StyleMu-Chun Su, Sheng-Hung Lin, Shu-Fang Lee, Yu-Shiang Huang, Huang-Ren Chen. The Implementation of a Kinect-Based Postural Assessment System. Transactions on Petri Nets and Other Models of Concurrency XV. 2016; ():503-513.
Chicago/Turabian StyleMu-Chun Su; Sheng-Hung Lin; Shu-Fang Lee; Yu-Shiang Huang; Huang-Ren Chen. 2016. "The Implementation of a Kinect-Based Postural Assessment System." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 503-513.
In this paper, we treat optimization problems as a kind of reinforcement learning problems regarding an optimization procedure for searching an optimal solution as a reinforcement learning procedure for finding the best policy to maximize the expected rewards. This viewpoint motivated us to propose a Q-learning-based swarm optimization (QSO) algorithm. The proposed QSO algorithm is a population-based optimization algorithm which integrates the essential properties of Q-learning and particle swarm optimization. The optimization procedure of the QSO algorithm proceeds as each individual imitates the behavior of the global best one in the swarm. The best individual is chosen based on its accumulated performance instead of its momentary performance at each evaluation. Two data sets including a set of benchmark functions and a real-world problem—the economic dispatch (ED) problem for power systems—were used to test the performance of the proposed QSO algorithm. The simulation results on the benchmark functions show that the proposed QSO algorithm is comparable to or even outperforms several existing optimization algorithms. As for the ED problem, the proposed QSO algorithm has found solutions better than all previously found solutions.
Yi-Zeng Hsieh; Mu-Chun Su. A Q-learning-based swarm optimization algorithm for economic dispatch problem. Neural Computing and Applications 2015, 27, 2333 -2350.
AMA StyleYi-Zeng Hsieh, Mu-Chun Su. A Q-learning-based swarm optimization algorithm for economic dispatch problem. Neural Computing and Applications. 2015; 27 (8):2333-2350.
Chicago/Turabian StyleYi-Zeng Hsieh; Mu-Chun Su. 2015. "A Q-learning-based swarm optimization algorithm for economic dispatch problem." Neural Computing and Applications 27, no. 8: 2333-2350.
In this paper, we propose a self-organizing feature map-based (SOM) monitoring system which is able to evaluate whether the physiotherapeutic exercise performed by a patient matches the corresponding assigned exercise. It allows patients to be able to perform their physiotherapeutic exercises on their own, but their progress during exercises can be monitored. The performance of the proposed the SOM-based monitoring system is tested on a database consisting of 12 different types of physiotherapeutic exercises. An average 98.8% correct rate was achieved.
Mu-Chun Su; Jhih-Jie Jhang; Yi-Zeng Hsieh; Shih-Ching Yeh; Shih-Chieh Lin; Shu-Fang Lee; Kai-Ping Tseng. Depth-Sensor-Based Monitoring of Therapeutic Exercises. Sensors 2015, 15, 25628 -25647.
AMA StyleMu-Chun Su, Jhih-Jie Jhang, Yi-Zeng Hsieh, Shih-Ching Yeh, Shih-Chieh Lin, Shu-Fang Lee, Kai-Ping Tseng. Depth-Sensor-Based Monitoring of Therapeutic Exercises. Sensors. 2015; 15 (10):25628-25647.
Chicago/Turabian StyleMu-Chun Su; Jhih-Jie Jhang; Yi-Zeng Hsieh; Shih-Ching Yeh; Shih-Chieh Lin; Shu-Fang Lee; Kai-Ping Tseng. 2015. "Depth-Sensor-Based Monitoring of Therapeutic Exercises." Sensors 15, no. 10: 25628-25647.
This paper presents a computational-intelligence-based model to predict the survival rate of critically ill patients who were admitted to an intensive care unit (ICU). The prediction input variables were based on the first 24 h admission physiological data of ICU patients to forecast whether the final outcome was survival or not. The prediction model was based on a particle swarm optimization (PSO)-based Fuzzy Hyper-Rectangular Composite Neural Network (PFHRCNN) that integrates three computational intelligence tools including hyper-rectangular composite neural networks, fuzzy systems and PSO. It could help doctors to make appropriate treatment decisions without excessive laboratory tests. The performance of the proposed prediction model was evaluated on the data set collected from 300 ICU patients in the Cathy General Hospital in 2012. There were 10 input variables in total for the prediction model. Nine of these variables (e.g. systolic arterial blood pressures, systolic non-invasive blood pressures, respiratory rate, heart rate, and body temperature) were routinely available for 24 h in ICU and the last variable is patient's age. The proposed model could achieve a 96% and 86% accuracy rate for the training data and testing data, respectively.
Yi-Zeng Hsieh; Mu-Chun Su; Chen-Hsu Wang; Pa-Chun Wang. Prediction of survival of ICU patients using computational intelligence. Computers in Biology and Medicine 2014, 47, 13 -19.
AMA StyleYi-Zeng Hsieh, Mu-Chun Su, Chen-Hsu Wang, Pa-Chun Wang. Prediction of survival of ICU patients using computational intelligence. Computers in Biology and Medicine. 2014; 47 ():13-19.
Chicago/Turabian StyleYi-Zeng Hsieh; Mu-Chun Su; Chen-Hsu Wang; Pa-Chun Wang. 2014. "Prediction of survival of ICU patients using computational intelligence." Computers in Biology and Medicine 47, no. : 13-19.
In this paper, a multi-layer perceptron based on 6 important features is proposed for predicting the time needed by our aircraft to approach enemy aircraft when they are in a combat situation. Then the prediction module is incorporated into a SOMO-based decision aid for solving multi-aircraft assignment problems. The problem of assigning our aircraft to meet head-on enemy aircraft in a combat situation is formulated as a constrained resource allocation problem. The objective function of the multi-aircraft assignment problem is a measure for computing the expected overall predominance value of aircraft over enemy aircraft based on four predominance factors. The SOMO algorithm which is an extension version of the SOM algorithm for optimization is executed to find an assignment such that the expected overall predominance value of aircraft can be maximized. Four hundred combat situations are simulated to test the effectiveness of the proposed SOMO-based decision aid. The simulation results demonstrate that the SOMO-based decision aid could provide appealing solutions within 0.5 s even for combat situations with a large number of aircraft.
Mu-Chun Su; Shih-Chang Lai; Shih-Chieh Lin; Liang-Fu You. A new approach to multi-aircraft air combat assignments. Swarm and Evolutionary Computation 2012, 6, 39 -46.
AMA StyleMu-Chun Su, Shih-Chang Lai, Shih-Chieh Lin, Liang-Fu You. A new approach to multi-aircraft air combat assignments. Swarm and Evolutionary Computation. 2012; 6 ():39-46.
Chicago/Turabian StyleMu-Chun Su; Shih-Chang Lai; Shih-Chieh Lin; Liang-Fu You. 2012. "A new approach to multi-aircraft air combat assignments." Swarm and Evolutionary Computation 6, no. : 39-46.
The conventional self-organizing feature map (SOM) algorithm is usually interpreted as a computational model, which can capture main features of computational maps in the brain. In this paper, we present a variant of the SOM algorithm called the SOM-based optimization (SOMO) algorithm. The development of the SOMO algorithm was motivated by exploring the possibility of applying the SOM algorithm in continuous optimization problems. Through the self-organizing process, good solutions to an optimization problem can be simultaneously explored and exploited by the SOMO algorithm. In our opinion, the SOMO algorithm not only can be regarded as a biologically inspired computational model but also may be regarded as a new approach to a model of social influence and social learning. Several simulations are used to illustrate the effectiveness of the proposed optimization algorithm.
Mu-Chun Su; Yu-Xiang Zhao. A variant of the SOM algorithm and its interpretation in the viewpoint of social influence and learning. Neural Computing and Applications 2009, 18, 1043 -1055.
AMA StyleMu-Chun Su, Yu-Xiang Zhao. A variant of the SOM algorithm and its interpretation in the viewpoint of social influence and learning. Neural Computing and Applications. 2009; 18 (8):1043-1055.
Chicago/Turabian StyleMu-Chun Su; Yu-Xiang Zhao. 2009. "A variant of the SOM algorithm and its interpretation in the viewpoint of social influence and learning." Neural Computing and Applications 18, no. 8: 1043-1055.
A fuzzy ARTMAP system is a system for incremental supervised learning of recognition categories and multi-dimensional maps in response to an arbitrary sequence of analog or binary input vectors. Fuzzy ARTMAP systems have been benchmarked against a variety of machine learning, neural networks, and genetic algorithms with considerable success. Owing to many appealing properties, fuzzy ARTMAP systems provide a natural basis for many researchers. Many different approaches have been proposed to modify fuzzy ARTMAP systems. In this paper, we propose a new approach to modifying a fuzzy ARTMAP system. We refer to the new system as the modified and simplified fuzzy ARTMAP (MSFAM) system. The aims of MSFAM systems are not only to reduce the architectural redundancy of the fuzzy ARTMAP system, but also to make extracted rules more comprehensible and concise. Four data sets were used for demonstrating the performance of the proposed MSFAM system.
Mu-Chun Su; Wei-Zhe Lu; Jonathan Lee; Gwo-Dong Chen; Chen-Chiung Hsieh. The MSFAM: a modified fuzzy ARTMAP system. Pattern Analysis and Applications 2005, 8, 1 -16.
AMA StyleMu-Chun Su, Wei-Zhe Lu, Jonathan Lee, Gwo-Dong Chen, Chen-Chiung Hsieh. The MSFAM: a modified fuzzy ARTMAP system. Pattern Analysis and Applications. 2005; 8 (1-2):1-16.
Chicago/Turabian StyleMu-Chun Su; Wei-Zhe Lu; Jonathan Lee; Gwo-Dong Chen; Chen-Chiung Hsieh. 2005. "The MSFAM: a modified fuzzy ARTMAP system." Pattern Analysis and Applications 8, no. 1-2: 1-16.