scholarly journals Diagnosis of Diseases Based on Iridology Using Fuzzy Logic

2021 ◽  
Author(s):  
Zakaria Madhouse ◽  
Ammar Kayli ◽  
Luna Himmami
Keyword(s):  
Fuzzy Logic ◽  
Disease Rate ◽  
Accuracy Rate ◽  
The Public ◽  
Fuzzy Models ◽  
Average Accuracy ◽  
Automatic Methods ◽  
Input Variables ◽  
The Brain ◽  
Diagnosis Of Diseases

Many automatic methods have been introduced in iridology to predict diseases according to the iridology chart. This is important to prevent diseases before they develop. This research aims to find a computer model for the early diagnosis of diseases in the brain, back, pelvis, abdomen, and chest using the iridology chart based on fuzzy logic. Image preprocessing for the iris aims to find the ring, code, and features of the iris. Five fuzzy models have been built for diagnosis and to determine a person's disease rate based on specific features that were extracted from the iris as the input variables. Each model contains four membership functions for each input or output variables and 64 fuzzy rules for fuzzification and defuzzification. The five models that were built to diagnose the five diseases of iridology have an accuracy rate of over 98%, with an average accuracy of 98.6223%. The results mean that the models are qualified for use by doctors as medical tools to diagnose specific diseases or as a tool for the public to reassure them about their health.

scholarly journals Artificial Neural Network with Backpropogation Method in Predicting Sukuk Retail State Based on Age Categories in Promoting Economic Growth

2018 ◽  
Author(s):  
dedisuhendro
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Series ◽  
Accuracy Rate ◽  
Public Funds ◽  
The Public ◽  
Artificial Neural ◽  
Input Variables ◽  
The Government ◽  
Age Category

Sukuk Retail State has fixed remuneration that paid every month. The government gains equity from the useof public funds, while the public gets a profit from the investment. The contribution of this researchprovides benefits for promoting optimally on the next sukuk issuance. Referral data sourced from Ministryof Finance through website www.djppr.kemenkeu.go.id. The data are sukuk sales data series 003 - 009which are grouped into several categories namely geography, profession and age category. The method usedis Artificial Neural Network Backpropogation. The input variables used are age category <25 (X1), agecategory 25 - 40 (X2), age category 41 - 55 (X3), and age category> 55 (X4) with model of trainingarchitecture and test of 4 architecture ie 4-2-1, 4-5-1, 4-2-5-1 and 4-5-2-1. The results of this study providethe best architecture 4-2-1 with epoch 1593, MSE 0.00099950214 and 71% accuracy rate. Furthermore, thesensitivity analysis was performed to determine the best performing variables, resulting in the 41-55 (X3)age category variable with a score of 0.4089. Thus obtained the prediction of most investors on the purchaseof sukuk series 010 is the age category 41 - 55.

scholarly journals Prediksi Pendonor Darah Potensial Menggunakan Algoritma Learning Vector Quantitation (LVQ) (Studi Kasus : Unit Transfusi Darah PMI Kota Palu, Sigi Dan Donggala)

2020 ◽  
Vol 17 (1) ◽  
pp. 29-40
Author(s):  
N K Susiani ◽  
A I Jaya
Keyword(s):  
Blood Donors ◽  
Blood Donation ◽  
Training Data ◽  
Rate Reduction ◽  
Accuracy Rate ◽  
The Public ◽  
Average Accuracy ◽  
Testing Data ◽  
Stage 1 ◽  
Health Test

Potential blood donors are blood donors who can donate their blood back after success through 2 stages of blood donation such as the physical health test (active) and the screening test (laboratory test). The purpose of this study are to obtain an application that can be used to predict potential blood donors who will donate their blood back at the PMI Palu, Sigi and Donggala Blood Transfusion Units, and to obtain their level of accuracy using the Learning Vector Quantitation algorithm. This prediction application for potential blood donors makes it easier for the public to know whether they can donate their blood or not. Classification is done using 300 data consisting of 70% training data and 30% testing data. The data used in this study are data taken in 2018. The accuracy of the best weighting in stage 1 is 95.56% obtained using the training rate (α) of 0.1≤α≤0.25 and the rate reduction training (decα) which is varied. While the best weighting results in stage 2 have an average accuracy rate of 100% obtained by using a training rate (α) of 0.000001≤α≤0.5 and a reduction in the rate of training (decα) which varies.

scholarly journals Logika Fuzzy dengan Metode Mamdani dalam Menentukan Tingkat Peminatan Tipe Motor Honda

2020 ◽  
Author(s):  
J Jufriadi ◽  
Gunadi Widi Nurcahyo ◽  
S Sumijan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Programming Language ◽  
Engine Speed ◽  
Fuel Tank ◽  
The Public ◽  
Decision Making System ◽  
Tank Capacity ◽  
Input Variables ◽  
Oil Fuel

Honda motorcycles are in demand by the public as a cheap means of land transportation. CV Hayati is the main motorcycle dealer company in Padang. In carrying out its activities, CV Hayati needs to consider several factors when selling motorcycles that are in demand by consumers. However, CV Hayati still uses manual means in looking at the interest in the motor that will be purchased by consumers. To solve the problem, a system is needed that can help with decision-making by consumers in purchasing motors according to their interests. In this study, the decision to buy a motor that consumers were interested in was done using the fuzzy logic of mamdani method. With the decision-making system in motor interest, it is expected to help and facilitate consumers in determining the motor they are interested in buying. The results of this study can be viewed using the PHP programming language and MySQL database, with the fuzzy logic of the mamdani method. Where in the fuzzyfication process consider several input variables namely: price, oil fuel tank capacity, engine speed, baggage capacity and vehicle weight. So that by defuzzification can be determined the recomedation of motors that are in demand by consumers.

scholarly journals Voltage & Current Magnitude Pattern Recognization by Using Fuzzy Logic Toolbox for Fault Types Classification

2018 ◽  
Vol 12 (1) ◽  
pp. 326
Author(s):  
Lilik J. Awalin ◽  
Fatini Fatini ◽  
M. N. Abdullah ◽  
L.T. Tay ◽  
M. Fairuz Ab. Hamid ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Distribution Network ◽  
Voltage Sag ◽  
Logic Design ◽  
Fault Resistance ◽  
Fault Type ◽  
Research Technique ◽  
Average Accuracy ◽  
Input Variables ◽  
Pscad Simulation

<p>This research introduces the appropriate input pattern of Fuzzy Logic design for fault type classification of Single Line to Ground Fault at distribution network. The proposed design is solely using Fuzzy Logic as the research technique with input data from PSCAD simulation. PSCAD software simulate the circuit configuration for fault disturbance at the distribution network. The research technique was applied with multiples input values of voltage and current that extracted from the PSCAD simulation. This research testifies the output result by using different fault resistance values; 0.01Ω, 10Ω, 30Ω, 50Ω and 70Ω. Voltage sag and current swell of phase a, b and c that were obtained from the PSCAD simulation have been used as the input variables for Fuzzy Logic design. The acquired results that represented in average accuracy shown that voltage sag and current swell can draw a satisfying accuracy in classifying the fault type.</p>

scholarly journals Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

2021 ◽  
Vol 9 (1) ◽  
pp. 49
Author(s):  
Tanja Brcko ◽  
Andrej Androjna ◽  
Jure Srše ◽  
Renata Boć
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Decision Model ◽  
Fuzzy Inference ◽  
Weather Conditions ◽  
Target Vessel ◽  
Planning Tool ◽  
Inference System ◽  
International Regulations ◽  
Input Variables

The application of fuzzy logic is an effective approach to a variety of circumstances, including solutions to maritime anti-collision problems. The article presents an upgrade of the radar navigation system, in particular, its collision avoidance planning tool, using a decision model that combines dynamic parameters into one decision—the collision avoidance course. In this paper, a multi-parametric decision model based on fuzzy logic is proposed. The model calculates course alteration in a collision avoidance situation. First, the model collects input data of the target vessel and assesses the collision risk. Using time delay, four parameters are calculated for further processing as input variables for a fuzzy inference system. Then, the fuzzy logic method is used to calculate the course alteration, which considers the vessel’s safety domain and International Regulations for Preventing Collisions at Sea (COLREGs). The special feature of the decision model is its tuning with the results of the database of correct solutions obtained with the manual radar plotting method. The validation was carried out with six selected cases simulating encounters with the target vessel in the open sea from different angles and at any visibility. The results of the case studies have shown that the decision model computes well in situations where the own vessel is in a give-way position. In addition, the model provides good results in situations when the target vessel violates COLREG rules. The collision avoidance planning tool can be automated and serve as a basis for further implementation of a model that considers the manoeuvrability of the vessels, weather conditions, and multi-vessel encounter situations.

scholarly journals Generalization of Patterns by Identification with Polynomial Neural Network

2010 ◽  
Vol 61 (2) ◽  
pp. 120-124 ◽  
Author(s):  
Ladislav Zjavka
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Functional Dependence ◽  
Polynomial Neural Network ◽  
New Type ◽  
Input Variables ◽  
The Brain ◽  
Functional Output ◽  
Simplified Form

Generalization of Patterns by Identification with Polynomial Neural Network Artificial neural networks (ANN) in general classify patterns according to their relationship, they are responding to related patterns with a similar output. Polynomial neural networks (PNN) are capable of organizing themselves in response to some features (relations) of the data. Polynomial neural network for dependence of variables identification (D-PNN) describes a functional dependence of input variables (not entire patterns). It approximates a hyper-surface of this function with multi-parametric particular polynomials forming its functional output as a generalization of input patterns. This new type of neural network is based on GMDH polynomial neural network and was designed by author. D-PNN operates in a way closer to the brain learning as the ANN does. The ANN is in principle a simplified form of the PNN, where the combinations of input variables are missing.

Split Sciences: The Legal Accountability of Patients With Dissociative Identity Disorder

2021 ◽  
Vol 10 (4) ◽  
Author(s):  
Madhalasa Iyer ◽  
James Neve
Keyword(s):  
American Psychiatric Association ◽  
Dissociative Identity Disorder ◽  
Insanity Defense ◽  
Psychological Disorder ◽  
The Public ◽  
Course Of Action ◽  
Legal Accountability ◽  
The Brain ◽  
Multiple Personalities

The thriller “Split” by M. Night Shyamalan showed a glimpse into the multiple personalities of the antagonist in the film. While many elements were added for intense suspense, the existence of such a disorder was factual. Dissociative Identity Disorder is defined by the American Psychiatric Association as a “psychological illness with 2 or more distinct identities, each accompanied by changes in behavior, memory, and thinking” (American Psychiatric Association). In a legal setting, the actions of the patients with DID have numerous ramifications. This paper aims to illustrate how the accountability of DID patients during a crime should be assessed. To find out how DID patients could be held accountable, we analyzed the disorder by researching the transformations in the brain, identified its origins, and explored the consequences in a judicial milieu. After conducting this research, we identified the solution that could be seamlessly embedded into our current society and benefit the patient as well as the courts. Through the analysis of the psychological disorder with a social lens, we evaluated that the jury and the public should be made more aware of the disorder and the court should not automatically assume innocence based on just the Insanity Defense. This plan is the best course of action for patients and the court systems and also aims to adapt societal thought to be more aware of DID’s difficulties. 

Prediction of State of Charge (SOC) of Battery Electric Vehicle

2021 ◽  
Vol 104 ◽  
pp. 65-71
Author(s):  
Illa Rizianiza ◽  
Dian Mart Shoodiqin
Keyword(s):  
Fuzzy Logic ◽  
Maximum Energy ◽  
State Of Charge ◽  
Battery Management ◽  
Charge Condition ◽  
Available Energy ◽  
Energy Needs ◽  
Fuzzy Logic Method ◽  
Input Variables ◽  
Charging And Discharging Processes

Batteries have an important thing in development of energy needs. A good performance battery, will support the device it supports. The energy that can save a battery is limited, so the battery will increase its charge and discharge cycles. Incorrect charging and discharging processes can cause battery performance to decrease. Therefore battery management is needed so that the battery can reach the maximum. One aspect of battery management is setting the state which is the ratio of available energy capacitance to maximum energy capacity. One method for estimating load states is the fuzzy logic method, namely by assessing the input and output systems of prediction. Predictor of State of Charge use Mamdani Fuzzy Logic that have temperature and voltage as input variables and State of Charge as output variable. A result of prediction State of Charge battery is represented by the number of Root Mean Square Error. Battery in charge condition has 2.7 for RMSE and level of accuracy 81.5%. Whereas Battery in discharge condition has RMSE 1.5 and level of accuracy 84.7%.

scholarly journals Streamflow forecasting by combining neural networks and fuzzy models using advanced methods of input variable selection

2017 ◽  
Vol 20 (2) ◽  
pp. 520-532 ◽  
Author(s):  
A. B. Dariane ◽  
Sh. Azimi
Keyword(s):  
Wavelet Transform ◽  
Multistep Method ◽  
Test Period ◽  
Fast Method ◽  
Streamflow Forecasting ◽  
Input Selection ◽  
Fuzzy Models ◽  
Monthly Streamflow ◽  
Input Variables ◽  
Learning Machine

Abstract In this paper the performance of extreme learning machine (ELM) training method of radial basis function artificial neural network (RBF-ANN) is evaluated using monthly hydrological data from Ajichai Basin. ELM is a newly introduced fast method and here we show a novel application of this method in monthly streamflow forecasting. ELM may not work well for a large number of input variables. Therefore, an input selection is applied to overcome this problem. The Nash–Sutcliffe efficiency (NSE) of ANN trained by backpropagation (BP) and ELM algorithm using initial input selection was found to be 0.66 and 0.72, respectively, for the test period. However, when wavelet transform, and then genetic algorithm (GA)-based input selection are applied, the test NSE increase to 0.76 and 0.86, respectively, for ANN-BP and ANN-ELM. Similarly, using singular spectral analysis (SSA) instead, the coefficients are found to be 0.88 and 0.90, respectively, for the test period. These results show the importance of input selection and superiority of ELM and SSA over BP and wavelet transform. Finally, a proposed multistep method shows an outstanding NSE value of 0.97, which is near perfect and well above the performance of the previous methods.

FUZZY DYNAMIC LOGIC

2006 ◽  
Vol 02 (01) ◽  
pp. 43-55 ◽  
Author(s):  
LEONID I. PERLOVSKY
Keyword(s):  
Fuzzy Logic ◽  
Dynamic Logic ◽  
Learning System ◽  
Dynamic Adaptation ◽  
Fuzzy Models ◽  
Uncertain Knowledge ◽  
Model Improvement ◽  
The Mind ◽  
Fuzzy State ◽  
Theory Of Forms

Fuzzy logic is extended toward dynamic adaptation of the degree of fuzziness. The motivation is to explain the process of learning as a joint model improvement and fuzziness reduction. A learning system with fuzzy models is introduced. Initially, the system is in a highly fuzzy state of uncertain knowledge, and it dynamically evolves into a low-fuzzy state of certain knowledge. We present an image recognition example of patterns below clutter. The paper discusses relationships to formal logic, fuzzy logic, complexity and draws tentative connections to Aristotelian theory of forms and working of the mind.

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