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Author

Manabu Nii

Other affiliations: Hyogo University, Osaka University
Bio: Manabu Nii is an academic researcher from University of Hyogo. The author has contributed to research in topics: Nursing care & Fuzzy logic. The author has an hindex of 15, co-authored 121 publications receiving 671 citations. Previous affiliations of Manabu Nii include Hyogo University & Osaka University.


Papers
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Proceedings ArticleDOI
03 Jun 1996
TL;DR: A fuzzy-arithmetic-based approach for extracting fuzzy if-then rules from multilayer feedforward neural networks for pattern classification problems and determining the consequent class and the grade of certainty of a fuzzyIf-then rule.
Abstract: We propose a fuzzy-arithmetic-based approach for extracting fuzzy if-then rules from multilayer feedforward neural networks. For pattern classification problems, our approach extracts fuzzy if-then rules such as "If x/sub 1/ is small and x/sub 2/ is large then Class 1 with CF=0.9" where CF is the grade of certainty. In order to determine the consequent class and the grade of certainty of a fuzzy if-then rule, first an input vector of linguistic values is presented to a trained neural network. The input vector consists of linguistic values in the antecedent part of the fuzzy if-then rule (e.g. (small, large) in the case of the above fuzzy if-then rule). Next fuzzy outputs from the neural network are calculated by fuzzy arithmetic. Then the consequent class and the grade of certainty of the fuzzy if-then rule are determined by an inequality relation between the fuzzy outputs.

35 citations

Proceedings ArticleDOI
02 Nov 2007
TL;DR: In order to reduce workloads evaluating nursing-care data, a support vector machine(SVM) based classification system is proposed, which will help to improve nursing care quality.
Abstract: The nursing care quality improvement is very important in the medical field. Currently, nursing-care freestyle texts (nursing-care data) are collected from many hospitals in Japan by using Web applications. Some nursing-care experts evaluate the collected data to improve nursing care quality. For evaluating the nursing-care data, experts need to read all freestyle texts carefully. However, it is a hard task for an expert to evaluate the data because of huge number of nursing-care data in the database. In order to reduce workloads evaluating nursing-care data, we propose a support vector machine(SVM) based classification system.

28 citations

Proceedings ArticleDOI
25 Jul 2001
TL;DR: This paper examines the effect of instance and feature selection on the generalization ability of trained neural networks through computer simulations on various artificial and real-world pattern classification problems.
Abstract: We examine the effect of instance and feature selection on the generalization ability of trained neural networks for pattern classification problems. Before the learning of neural networks, a genetic-algorithm-based instance and feature selection method is applied for reducing the size of training data. Nearest neighbor classification is used for evaluating the classification ability of subsets of training data in instance and feature selection. Neural networks are trained by the selected subset (i.e., reduced training data). In this paper, we first explain our GA-based instance and feature selection method. Then we examine the effect of instance and feature selection on the generalization ability of trained neural networks through computer simulations on various artificial and real-world pattern classification problems.

27 citations

Proceedings ArticleDOI
09 Jun 1997
TL;DR: A hybrid approach to the design of a compact fuzzy rule-based classification system with a small number of linguistic rules through rule extraction from a trained neural network and rule selection by a genetic algorithm is proposed.
Abstract: This paper proposes a hybrid approach to the design of a compact fuzzy rule-based classification system with a small number of linguistic rules. The proposed approach consists of two procedures: rule extraction from a trained neural network and rule selection by a genetic algorithm. We first describe how linguistic rules can be extracted from a multilayer feedforward neural network that has been already trained for a classification problem with many continuous attributes. In our rule extraction procedure, a linguistic input vector corresponding to the antecedent part of a linguistic rule is presented to the trained neural network, and the fuzzy output vector front the trained neural network is examined for determining the consequent part and the grade of certainty of that linguistic rule. Next we explain how a genetic algorithm can be utilized for selecting a small number of significant linguistic rules from a large number of extracted rules. Our rule selection problem has two objectives: to minimize the number of selected linguistic rules and to maximize the number of correctly classified patterns by the selected linguistic rules. A multi-objective genetic algorithm is employed for finding a set of non-dominated solutions with respect to these two objectives. Finally we illustrate our hybrid approach by computer simulations on real-world test problems.

25 citations

Proceedings ArticleDOI
09 Jul 2017
TL;DR: The finger joint detection method and the mTS score estimation method using support vector machine are proposed and Experimental results showed that the proposed method detects finger joints with accuracy of 81.4 % and estimated the erosion, JSN, and JSN score withuracy of 50.9, 64.3 %, respectively.
Abstract: There are 700,000 Rheumatoid Arthritis (RA) patients in Japan, and the number of patients is increased by 30,000 annually. The early detection and appropriate treatment according to the progression of RA are effective to improve the patient's prognosis. The modified Total Sharp (mTS) score is widely used for the progression evaluation of Rheumatoid Arthritis. The mTS score assessments on hand or foot X-ray image is required several times a year, and it takes very long time. The automatic mTS score calculation system is required. This paper proposes the finger joint detection method and the mTS score estimation method using support vector machine. Experimental results on 45 RA patient's X-ray images showed that the proposed method detects finger joints with accuracy of 81.4 %, and estimated the erosion and JSN score with accuracy of 50.9, 64.3 %, respectively.

19 citations


Cited by
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Journal ArticleDOI
TL;DR: This article proposes to bring the various neuro-fuzzy models used for rule generation under a unified soft computing framework, and includes both rule extraction and rule refinement in the broader perspective of rule generation.
Abstract: The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule generation algorithms. Rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a more human comprehensible or natural form, and can handle uncertainties at various levels. The neuro-fuzzy approach, symbiotically combining the merits of connectionist and fuzzy approaches, constitutes a key component of soft computing at this stage. To date, there has been no detailed and integrated categorization of the various neuro-fuzzy models used for rule generation. We propose to bring these together under a unified soft computing framework. Moreover, we include both rule extraction and rule refinement in the broader perspective of rule generation. Rules learned and generated for fuzzy reasoning and fuzzy control are also considered from this wider viewpoint. Models are grouped on the basis of their level of neuro-fuzzy synthesis. Use of other soft computing tools like genetic algorithms and rough sets are emphasized. Rule generation from fuzzy knowledge-based networks, which initially encode some crude domain knowledge, are found to result in more refined rules. Finally, real-life application to medical diagnosis is provided.

726 citations

Journal ArticleDOI
TL;DR: A narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends, and discusses the many innovations that have advanced in deep learning and how these tools facilitate U-nets.
Abstract: U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

425 citations

Journal ArticleDOI
TL;DR: This paper will give a systematic review on advanced IoT enabled PHS, and key enabling technologies, major IoT enabled applications and successful case studies in healthcare, and finally point out future research trends and challenges.

301 citations

Journal ArticleDOI
TL;DR: In this article, the role of machine learning applications and algorithms in investigating and various purposes that deals with COVID-19 was detected and the purpose of this study is to detect the role machine learning application and algorithms.
Abstract: Today world thinks about coronavirus disease that which means all even this pandemic disease is not unique. The purpose of this study is to detect the role of machine-learning applications and algorithms in investigating and various purposes that deals with COVID-19. Review of the studies that had been published during 2020 and were related to this topic by seeking in Science Direct, Springer, Hindawi, and MDPI using COVID-19, machine learning, supervised learning, and unsupervised learning as keywords. The total articles obtained were 16,306 overall but after limitation; only 14 researches of these articles were included in this study. Our findings show that machine learning can produce an important role in COVID-19 investigations, prediction, and discrimination. In conclusion, machine learning can be involved in the health provider programs and plans to assess and triage the COVID-19 cases. Supervised learning showed better results than other Unsupervised learning algorithms by having 92.9% testing accuracy. In the future recurrent supervised learning can be utilized for superior accuracy.

202 citations

Journal ArticleDOI
TL;DR: In this study neural network and genetic algorithm fuzzy rule induction systems have been developed and applied to three classification problems and it is indicated that the genetic/fuzzy approach compares more than favourably with the neuro/ fuzzy and rough set approaches.

149 citations