Yi-Zeng Hsieh

A PSO-based rule extractor for medical diagnosis

One of the major bottlenecks in applying conventional neural networks to the medical field is that it is very difficult to interpret, in a physically meaningful way, because the learned knowledge is numerically encoded in the trained synaptic weights. In one of our previous works, we proposed a class of Hyper-Rectangular Composite Neural Networks (HRCNNs) of which synaptic weights can be interpreted as a set of crisp If-Then rules; however, a trained HRCNN may result in some ineffective If-Then rules which can only justify very few positive examples (i.e., poor generalization). This motivated us to propose a PSO-based Fuzzy Hyper-Rectangular Composite Neural Network (PFHRCNN) which applies particle swarm optimization (PSO) to trim the rules generated by a trained HRCNN while the recognition performance will not be degraded or even be improved. The performance of the proposed PFHRCNN is demonstrated on three benchmark medical databases including liver disorders data set, the breast cancer data set and the Parkinsons disease data set.

A SOMO-based approach to the operating room scheduling problem

In most hospitals, operating rooms are the most costly facilities and consume a large percentage of the hospital recourses. Therefore, an efficient and effective operating room scheduling system is highly demanded for hospitals. In this paper, a SOMO-based approach to solving the operating room scheduling problem is proposed. Computational experiments performed on 100 randomly generated simulations are conducted to test whether the proposed scheduling algorithm can provide appealing arrangements in a reasonable computation time.

Prediction of survival of ICU patients using computational intelligence

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 patients age. The proposed model could achieve a 96% and 86% accuracy rate for the training data and testing data, respectively.

A Q-learning-based swarm optimization algorithm for economic dispatch problem

Abstract 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.

To design an interactive learning system for child by integrating blocks with Kinect

In this study, an interactive block-building system named as e-Block system is developed for children to learn the concepts of geometric structures and space. First, the system displays a picture (e.g., car or house) of the target object intended for the child to assemble. The child then follows the instructions provided by the system and uses various blocks to build the object. After the child has completed the task, the system employs a pattern recognition algorithm to automatically compare the assembled object with the picture and determine whether the shape is identical. The experimental results show that the proposed system achieves high accuracy rate, and children in testing are enjoy this system and have more motivation to play with building blocks.

A Simple Approach to Stereo Matching and Its Application in Developing a Travel Aid for the Blind

In this paper, we present an idea of using stereo matching to develop a travel aid for the blind. In this approach, images are segmented into several nonoverlapping homogeneous regions using a color segmentation algorithm. For each homogeneous region, a rectangular window, which is large enough to cover the region, is found. A local match with the found rectangular window size is then executed to find the disparity for the considered region. A clustering algorithm is adopted to cluster the disparities into several major different values. Finally, a piece-wise disparity map is constructed. Based on the disparity map, information about the unfamiliar environments in front of the blind will be output to them. With the information about the environment the blind will have less fear in walking through unfamiliar environments via white canes.

A New Measure of Cluster Validity Using Line Symmetry

Many real-world and man-made objects are symmetry, therefore, it is reasonable to assume that some kind of symmetry may exist in data clusters. In this paper a new cluster validity measure which adopts a non-metric distance measure based on the idea of ”line symmetry” is presented. The proposed validity measure can be applied in finding the number of clusters of different geometrical structures. Several data sets are used to illustrate the performance of the proposed measure.

To Develop the Virtual Physics Laboratory by Integrating Kinect with Gesture Classification Algorithm

Physics is an experimental science, which is through experiments for initiating students into physical concepts and principles. To motivate students in learning physics, in this study, a virtual physics laboratory was developed by using the techniques of Kinect, Unity3D and a gesture classification algorithm. The visual physics experiments were designed in the virtual physics laboratory. The experimental results show that the user can accurately interact with the virtual objects in the virtual physics laboratory, and the developed system provides an interesting way to assist students in learning physics.

A neural-network-based sketch recognition system

Children really enjoy painting. Painting is not only full of fun but also beneficial for children to further develop their physical skills (e.g., hand eye coordination, fine motor skills, and gross motor skills), express their creativity, boost their self-confidence, etc. On the other hand, storytelling is also a joyful and educational activity for children. In this paper, we develop an interactive platform called An Interactive Painting and Storytelling Platform (AIPSP) on which the two activities of painting and storytelling mingle. Via the proposed AIPSP, children can create a story simply via sketching some main characters of the story on a drawing board. Via these characters and interactions, some creative and funny stories can be created.

Depth-Sensor-Based Monitoring of Therapeutic Exercises.

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.