scholarly journals Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks

2021 ◽  
Author(s):  
Seher ARSLANKAYA ◽  
Miraç ÇELİK
Keyword(s):  
Fuzzy Logic ◽  
Heart Attack ◽  
Factors Affecting ◽  
Heart Attacks ◽  
Fuzzy Logic Method ◽  
Logic Method

FUZZY LOGIC APPROACH IN DETERMINING POOR FAMILIES IN THE POVERTY DATABASE IN MALANG DISTRICS

Tibuana ◽  
2019 ◽  
Vol 2 (01) ◽  
pp. 58-65
Author(s):  
Prihono Prihono
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Multi Criteria Decision Making ◽  
Poor Families ◽  
Fuzzy Logic Approach ◽  
Fuzzy Logic Method ◽  
Logic Approach ◽  
Family Classification ◽  
Logic Method

Determination of poor families in the poverty database is still less than perfect. There is still no multi criteria decision making (MCDM) technique in the grouping of poor families, making the results of the criteria in grouping poor families still far from expectations. So, this article discusses the use of the multi criteria decision making (MCDM) technique for grouping poor families in the poverty database in the Malang district. Fuzzy logic is one technique of MCDM which is commonly used for affirmation of decisions. In a random sampling of 35 families taken from the Malang District poverty database, the classification that was originally obtained was only obtained by 2 (two) classifications of poor families, namely: very poor families and poor families. But after it was calculated using the Fuzzy Logic method, it was found 3 (three) classifications of poor families, namely very poor families, poor families, and almost poor families. The magnitude of the distribution of the poor family classification is: 17 (seventeen) very poor families which previously were 14 (fourteen), 17 (seventeen) families were categorized as poor families that were previously 21 (twenty one), and 1 (one) family in the category of near-poor families that were not previously found. With these results, it can be concluded that the Fuzzy Logic method can and is able to provide better and more diverse results in determining poor families in the Malang District poverty database.

scholarly journals Using WSR-88D Polarimetric Data to Identify Bird-Contaminated Doppler Velocities

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Yuan Jiang ◽  
Qin Xu ◽  
Pengfei Zhang ◽  
Kang Nai ◽  
Liping Liu
Keyword(s):  
Fuzzy Logic ◽  
Real Data ◽  
Radar Data ◽  
Second Step ◽  
Doppler Velocity ◽  
Data Quality Control ◽  
Fuzzy Logic Method ◽  
Automated Technique ◽  
Migrating Birds ◽  
Logic Method

As an important part of Doppler velocity data quality control for radar data assimilation and other quantitative applications, an automated technique is developed to identify and remove contaminated velocities by birds, especially migrating birds. This technique builds upon the existing hydrometeor classification algorithm (HCA) for dual-polarimetric WSR-88D radars developed at the National Severe Storms Laboratory, and it performs two steps. In the first step, the fuzzy-logic method in the HCA is simplified and used to identify biological echoes (mainly from birds and insects). In the second step, another simple fuzzy logic method is developed to detect bird echoes among the biological echoes identified in the first step and thus remove bird-contaminated velocities. The membership functions used by the fuzzy logic method in the second step are extracted from normalized histograms of differential reflectivity and differential phase for birds and insects, respectively, while the normalized histograms are constructed by polarimetric data collected during the 2012 fall migrating season and sorted for bird and insects, respectively. The performance and effectiveness of the technique are demonstrated by real-data examples.

scholarly journals Feature Extraction in Six Blocks to Detect and Recognize English Numbers

2021 ◽  
pp. 3790-3803
Author(s):  
Heba Kh. Abbas ◽  
Haidar J. Mohamad
Keyword(s):  
Fuzzy Logic ◽  
Feature Extraction ◽  
Computational Cost ◽  
Absolute Error ◽  
Suggested Method ◽  
Support Vector ◽  
Horizontal Line ◽  
Svm Algorithm ◽  
Fuzzy Logic Method ◽  
Logic Method

    The Fuzzy Logic method was implemented to detect and recognize English numbers in this paper. The extracted features within this method make the detection easy and accurate. These features depend on the crossing point of two vertical lines with one horizontal line to be used from the Fuzzy logic method, as shown by the Matlab code in this study. The font types are Times New Roman, Arial, Calabria, Arabic, and Andalus with different font sizes of 10, 16, 22, 28, 36, 42, 50 and 72. These numbers are isolated automatically with the designed algorithm, for which the code is also presented. The number’s image is tested with the Fuzzy algorithm depending on six-block properties only. Groups of regions (High, Medium, and Low) for each number showed unique behavior to recognize any number. Normalized Absolute Error (NAE) equation was used to evaluate the error percentage for the suggested algorithm. The lowest error was 0.001% compared with the real number. The data were checked by the support vector machine (SVM) algorithm to confirm the quality and the efficiency of the suggested method, where the matching was found to be 100% between the data of the suggested method and SVM. The six properties offer a new method to build a rule-based feature extraction technique in different applications and detect any text recognition with a low computational cost.

scholarly journals Pemetaan Tingkat Kerawanan Longsor Berdasarkan Curah Hujan dan Geologi Menggunakan Metode Fuzzy Logic Di Kecamatan Leupung Kabupaten Aceh Besar

2018 ◽  
Vol 3 (2) ◽  
pp. 434-441
Author(s):  
Rasyid Alkhoir Lubis ◽  
Muhammad Rusdi ◽  
Hairul Basri
Keyword(s):  
Fuzzy Logic ◽  
Data Analysis ◽  
Analysis Data ◽  
Fuzzy Logic Method ◽  
Input Variables ◽  
Logic Method

Abstrak. Penelitian ini bertujuan untuk mengetahui tingkat kerawanan longsor di Kecamatan Leupung Kabupaten Aceh Besar. Penelitian ini dilakukan menggunakan SIG dengan Metode Fuzzy Logic. Curah Hujan dan Geologi sebagai variabel input dan tingkat kerawanan longsor sebagai variabel output metode fuzzy logic. Beberapa tahapan yang dilakukan dalam metode ini antara lain : fuzzyfication, inferensi dan defuzzyfication. Secara umum, tahapan penelitian persiapan, pra analisis data, analisis data dan output.. Penelitian ini dilakukan karena Kecamatan Leupung berbukit, berlereng, tersusun dari material sedimen termasuk batuan pegunungan dan memiliki curah hujan yang lebih tinggi dibandingkan dengan kecamatan lainnya di lingkup Kabupaten Aceh Besar.Hasil penelitian memperoleh hasil bahwa Kecamatan Leupung didominasi dengan tingkat kerawanan longsor kategori rendah dan sedang. Tingkat kerawanan longsor rendah seluas 16.486,01 ha (97,97 %) dan tingkat kerawanan longsor sedang seluas 342,37 ha (2,03 %). Kedua faktor yaitu curah hujan dan geologi saling mempengaruhi sehingga membedakan nilai defuzzyfication serta kelas kerawanan longsor. Abstract. This study aims to determine the level of landslide vulnerability in Leupung District, Aceh Besar District. This research was conducted using GIS with Fuzzy Logic Method. Rainfall and Geology as input variables and landslide vulnerability as output variables fuzzy logic method. Some of the steps performed in this method include: fuzzyfication, inference and defuzzyfication. In general, the stages of preparatory research, pre-data analysis, data analysis and output. This research was conducted because the hilly Leupung District, the slopes, composed of sedimentary materials including mountainous rocks and had higher rainfall compared to other sub-districts in Aceh Besar .The result of this research is that Leupung District is dominated by low and medium category avalanche vulnerability. Low landslide vulnerability of 16,486.01 ha (97.97%) and moderate landslide vulnerability of 342.37 ha (2.03%). Both factors are rainfall and geology influence each other so as to distinguish the defuzzyfication value and the class of landslide vulnerability.

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