scholarly journals Detection and Classification of Genetically Transfered Idiopathic Partial Epilepsy to Child:a Four Rule ANFIS based SWT-EBFO Approach

2018 ◽  
Vol 7 (3.34) ◽  
pp. 499
Author(s):  
Debasis Mohanta ◽  
Sakuntala Mahapatra ◽  
Santanu Kumar Nayak
Keyword(s):  
Correlation Coefficient ◽  
Partial Epilepsy ◽  
Lms Algorithm ◽  
Idiopathic Epilepsy ◽  
Test Results ◽  
Inference System ◽  
Detection Ratio ◽  
Matthew’S Correlation Coefficient ◽  
Statistical Results

Background:-Hereditary qualities have an influence in numerous sorts of epilepsy. In the event that a parent has idiopathic epilepsy, there is around 5% to 8% chance that the youngster upto 8 years will likewise have epilepsy called as idiopathic partial epilepsy (IPE).Methods:-This exploration work breaks down the epilepsy issue exchange hereditarily by coordinating the best properties of Enhanced Bacterial Foraging Optimization (EBFO) and Least-mean-square (LMS) algorithm with four rule Adaptive Neuro-fuzzy Inference System (ANFIS) Network. Stockwell Transform (SWT) strategy searched for the extraction of decomposed signal. In this work, quantitative tests and statistical tests are performed by utilizing SWT-ANFIS-EBFO and SWT-ANFIS-LMS strategies.Results:-Our proposed statistical results(Accuracy (98.30%), sensitivity (98.23%), specificity (99.53%)   and Matthew’s correlation coefficient (97.08%),G-mean (98.88%) and average detection ratio (98.93%)) are calculated withthe network SWT-ANFIS-LMS. Proposed statistical results (accuracy (99.49%), sensitivity (98.78%), specificity (98.56%), and Matthew’s correlation coefficient (97.907%), G-mean (99.172) and average detection ratio (99.174%)) are calculated SWT-ANFIS-EBFO beats. The calculated quantitative test results for network SWT-ANFIS-LMS are (SNR 18.42±0.18, RE 0.11±0.02, CC 61±0.012, MFRE 0.41±0.02) and for network SWT-ANFIS-MFRE are (SNR 18.42±0.18, RE 0.11±0.02, CC 61±0.012, MFRE 0.41±0.02).Conclusion:-In this paper we endeavor to investigate the best capability of SWT based ANFISnetwork trained with EBFO and LMS algorithms for classification of IPE EEG signals.Calculated statisticaland quantative test results of the proposed method outperforms as compared to existing methods. It will end up being a significant trial device in clinical application and advantageous application towards IPE influenced patients. 

PENGEMBANGAN PERANGKAT PEMBELAJARAN MENGGUNAKAN PENDEKATAN CONTEXTUAL TEACHING AND LEARNING (CTL) DENGAN INTEGRASI NILAI-NILAI KARAKTER DI SMAN I TUGUMULYO

2014 ◽  
Vol 13 (3) ◽  
Author(s):  
Sri Wahyu Widyaningsih ◽  
Irfan Yusuf
Keyword(s):  
Teaching And Learning ◽  
Lesson Plan ◽  
Development Stage ◽  
Design Stage ◽  
Learning Tools ◽  
Test Results ◽  
Learning Materials ◽  
Is Research ◽  
Efficacy Trials

<p>The research is motivated not yet using CTL approach. In addition, the study provided yet foster the character value of students. This study aimed to the development of learning materials by using CTL approach with the integration of character value are valid, practical, and effective. The type of this research is research and development by using 4-D models. The stages of this research are define, design, and development. The define stage consists of analyzing of curriculum, students, and concept. Then, the learning materials as lesson plan, handout, student’s worksheet, and evaluation, were designed at design stage. The development stage was doing validity, practicality, and effectiveness test. The data of this research was collected by using validation instruments, questionnaire of students and teacher, observation and test instruments. The result of research with validity of the test results showed that the syllabus, lesson plans, teaching materials, worksheets and assessment sheets (cognitive, affective and psychomotor) developed very valid. The test results showed that the learning practicalities developed very practical. Based on the results of efficacy trials, it was stated that the developed learning very effectively used as learning tools are developed to improve the activity and competence of students in the cognitive, affective and psychomotor and behavioral character. And Those, learning materials by using CTL approach with the integration of character values are classification of very valid, very practical, and effective.</p>

Modeling of cutting parameters in turning of PEEK composite using artificial neural networks and adaptive-neural fuzzy inference systems

2021 ◽  
pp. 089270572110130
Author(s):  
Gökçe Özden ◽  
Mustafa Özgür Öteyaka ◽  
Francisco Mata Cabrera
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cutting Forces ◽  
Fuzzy Inference ◽  
Test Results ◽  
Inference System ◽  
Good Prediction Accuracy ◽  
Peek Composite ◽  
Artificial Neural ◽  
Neural Fuzzy

Polyetheretherketone (PEEK) and its composites are commonly used in the industry. Materials with PEEK are widely used in aeronautical, automotive, mechanical, medical, robotic and biomechanical applications due to superior properties, such as high-temperature work, better chemical resistance, lightweight, good absorbance of energy and high strength. To enhance the tribological and mechanical properties of unreinforced PEEK, short fibers are added to the matrix. In this study, Artificial Neural Networks (ANNs) and the Adaptive-Neural Fuzzy Inference System (ANFIS) are employed to predict the cutting forces during the machining operation of unreinforced and reinforced PEEK with30 v/v% carbon fiber and 30 v/v% glass fiber machining. The cutting speed, feed rate, material type, and cutting tools are defined as input parameters, and the cutting force is defined as the system output. The experimental results and test results that are predicted using the ANN and ANFIS models are compared in terms of the coefficient of determination ( R2) and mean absolute percentage error. The test results reveal that the ANFIS and ANN models provide good prediction accuracy and are convenient for predicting the cutting forces in the turning operation of PEEK.

Adaptive Neuro-Fuzzy Inference System for Classification of ACL-Ruptured Knees Using Arthrometric Data

2008 ◽  
Vol 36 (9) ◽  
pp. 1449-1457 ◽  
Author(s):  
Zoya Heydari ◽  
Farzam Farahmand ◽  
Hossein Arabalibeik ◽  
Mohamad Parnianpour
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Rare Diseases, Common Problems: Recognition and Management

PEDIATRICS ◽  
1978 ◽  
Vol 62 (6) ◽  
pp. 1056-1060
Author(s):  
Neil A. Holtzman
Keyword(s):  
Johns Hopkins Hospital ◽  
Full Time ◽  
Test Results ◽  
Irreversible Damage ◽  
Negative Results ◽  
Initial Symptoms ◽  
Metabolic Conditions ◽  
Common Problems

Although a growing number of inherited metaboic diseases can be treated effectively, diagnosis often comes too late to benefit the patient. There are at least two ways, however, in which diagnosis can be made before irreversible damage occurs. First, physicians whose services are sought when a patient becomes ill could be attuned to the possibility of metabolic conditions. This is difficult when the initial symptoms, for example, vomiting or poor feeding, resemble those of common, self-limited illnesses, or when they suggest, as with respiratory distress, other categories of serious illness. Second, all infants could be screened for indicators of some of these conditions. Then the primary physician has a responsibility to determine the significance of both positive and negative results and to decide whether follow-up is needed. This study had three objectives: (1) to determine whether physicians are aware of the common problems with which inherited metabolic conditions often present; (2) to determine whether their management of common problems facilitates the early diagnosis of such conditions; and (3) to assess their evaluation of screening test results. METHODS Physicians who were participating in three continuing education programs were asked to answer, anonymously, several questions dealing with recognition and in management of geneticmetaboic diseases before they were given any instruction on the subject. The same questionnaire was distributed to the pediatric house staff at The Johns Hopkins Hospital. The tabulated results were discussed with the respondents collectively during hour-long conferences. RESULTS AND COMMENTS Classification of Respondents One hundred fifty-six physicians returned the questionnaire: 67 pediatricians (in practice, 56; full-time faculty, 6: unknown, 5), 30 general or family practitioners, and 59 pediatric house officers.

GPS Signal Reception Classification Using Adaptive Neuro-Fuzzy Inference System

2018 ◽  
Vol 72 (3) ◽  
pp. 685-701 ◽  
Author(s):  
Rui Sun ◽  
Li-Ta Hsu ◽  
Dabin Xue ◽  
Guohao Zhang ◽  
Washington Yotto Ochieng
Keyword(s):  
Fuzzy Inference System ◽  
Urban Areas ◽  
Fuzzy Inference ◽  
Receiver Design ◽  
Single Variable ◽  
Gps Measurements ◽  
Inference System ◽  
Static Test ◽  
Neuro Fuzzy

The multipath effect and Non-Line-Of-Sight (NLOS) reception of Global Positioning System (GPS) signals both serve to degrade performance, particularly in urban areas. Although receiver design continues to evolve, residual multipath errors and NLOS signals remain a challenge in built-up areas. It is therefore desirable to identify direct, multipath-affected and NLOS GPS measurements in order improve ranging-based position solutions. The traditional signal strength-based methods to achieve this, however, use a single variable (for example, Signal to Noise Ratio (C/N0)) as the classifier. As this single variable does not completely represent the multipath and NLOS characteristics of the signals, the traditional methods are not robust in the classification of signals received. This paper uses a set of variables derived from the raw GPS measurements together with an algorithm based on an Adaptive Neuro Fuzzy Inference System (ANFIS) to classify direct, multipath-affected and NLOS measurements from GPS. Results from real data show that the proposed method could achieve rates of correct classification of 100%, 91% and 84%, respectively, for LOS, Multipath and NLOS based on a static test with special conditions. These results are superior to the other three state-of-the-art signal reception classification methods.

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