International Journal of Biomedical Soft Computing and Human Sciences: the official journal of the Biomedical Fuzzy Systems Association

Distributed Resource Sharing System for Hospitals in Nigeria

P. A Idowu, E. R. Adagunodo, E. O. Ogunbodede

原稿種別: Article
2003 年 9 巻 2 号 p. 1-7
発行日: 2003年
公開日: 2017/09/04

DOIhttps://doi.org/10.24466/ijbschs.9.2_1

ジャーナルオープンアクセス

抄録を表示する抄録を非表示にする

With the goal of providing high quality and cost effective health care services to all the resident of Osun State, Nigeria, we have developed a distributed resource sharing system (SHONET) for hospitals in Osun State, Nigeria. The system allows sharing of hospital equipment and medical personnel (such as pathologist and radiologist) among the hospitals in the six geopolitical zones of the state. Pathological data, endoscopic data, supersonic tomographic images and Computerized Tomography (CT) scan were transmitted over the network successfully. In conclusion, the system (SHONET) has the potential to increase medical personnel productivity, reduce prenatal and neonatal mortality rates, improve the efficiency of medical care and minimize the cost of running hospitals among other benefits.

抄録全体を表示

PDF形式でダウンロード (837K)
On Reasoning Conditions of Koczy's Interpolative Reasoning

Baowen WANG, Xia LI, Wenyaun LIU, Yan SHI, Masaharu MIZUMOTO

原稿種別: Article
2003 年 9 巻 2 号 p. 9-15
発行日: 2003年
公開日: 2017/09/04

DOIhttps://doi.org/10.24466/ijbschs.9.2_9

ジャーナルオープンアクセス

抄録を表示する抄録を非表示にする

In this paper, we shall briefly introduce Koczy's interpolative reasoning method, and improve the reasoning conditions of interpolative reasoning method given by authors. It will be illustrated that these reasoning conditions are imperfect, and the inference consequence by these conditions is not always a normal triangular-type even if the disjoint fuzzy rules A_1=>B_1, A_2=>B_2 and an observation A^* (A_1<A^*<A_2) to be defined by triangular-type membership functions, when one consider both left and right sides. Moreover, we shall give the perfect reasoning conditions which guarantee the normality and convexity of the inference consequence by combining inf{B^*_α} and sup{B^*_α}, in general.

抄録全体を表示

PDF形式でダウンロード (583K)
A Note on Designing Learning Rates in a Class of Neuro-Fuzzy Learning Algorithms

Yan SHI, Masaharu MIZUMOTO, Hirofumi SASAKI

原稿種別: Article
2003 年 9 巻 2 号 p. 17-23
発行日: 2003年
公開日: 2017/09/04

DOIhttps://doi.org/10.24466/ijbschs.9.2_17

ジャーナルオープンアクセス

抄録を表示する抄録を非表示にする

In this paper, we study the problem of designing of learning rates in a class of neuro-fuzzy learning algorithms, which are widely used in recent fuzzy applications for tuning fuzzy inference rules. The analysis on the convergence of the learning algorithm in the cases of tuning parameters of fuzzy rules is conducted. Sufficient conditions for choosing appropriate learning rates have been presented such that an optimal fuzzy system model can be constructed.

抄録全体を表示

PDF形式でダウンロード (507K)
Optimization of Fuzzy Cognitive Map Model in Clinical Radiotherapy Through Differential Evolution Algorithm

Elpiniki PAPAGEORGIOU, Peter GROUMPOS

原稿種別: Article
2003 年 9 巻 2 号 p. 25-31
発行日: 2003年
公開日: 2017/09/04

DOIhttps://doi.org/10.24466/ijbschs.9.2_25

ジャーナルオープンアクセス

抄録を表示する抄録を非表示にする

In this article, a Fuzzy Cognitive Map model used for the supervision and monitoring of the radiotherapy process, is optimized through the minimization of an objective function, using the Differential Evolution algorithm. The Differential Evolution algorithm is a Computational Intelligence technique and belongs to the fields of Evolutionary Computation. The proposed approach determines the appropriate values of the causal links (weights) of the Supervisor-Fuzzy Cognitive Map model of the system in order to succeed acceptable results for the radiation therapy. This method is useful for doctors-radiotherapists to manage a clinical case and make decisions for the successful or not of the radiotherapy.

抄録全体を表示

PDF形式でダウンロード (870K)
Grading Urinary Bladder Tumors Using Unsupervised Hebbian Algorithm for Fuzzy Cognitive Maps

E. I. PAPAGEORGIOU, P. P. SPYRIDONOS, C. D. STYLIOS, G. C. NIKIFORIDIS ...

原稿種別: Article
2003 年 9 巻 2 号 p. 33-39
発行日: 2003年
公開日: 2017/09/04

DOIhttps://doi.org/10.24466/ijbschs.9.2_33

ジャーナルオープンアクセス

抄録を表示する抄録を非表示にする

The application of Fuzzy Cognitive Maps as a modeling and classification tool, for assessing tumors grade for urinary bladder, is examined in this research work. One hundred twenty nine cases were classified according to the WHO grading system in two classes, by experienced pathologists : Low Grade and High Grade, based on eight significant histopathological features that histopathologists selected for each case. This research work incorporates doctor's knowledge in developing the FCM model for tumor grading and utilizes the Nonlinear Hebbian Learning algorithm to further train the FCM and thus to achieve tumor malignancy classification. The classification is based on the histopathological characteristics of tissue that features are the concepts of the Fuzzy Cognitive Map model that was trained using the unsupervised learning algorithm. The classification accuracy is 93.18% for High Grade tumor cases and 90.59%, for tumors of Low Grade.

抄録全体を表示

PDF形式でダウンロード (844K)

J-STAGEへの登録はこちら（無料）