scholarly journals Penerapan Artificial Neural Network (ANN) untuk Memprediksi Perubahan Derajat Miopia pada Manusia

2020 ◽  
Vol 10 (1) ◽  
pp. 53
Author(s):  
Ni Kadek Emik Sapitri ◽  
I Putu Eka N. Kencana ◽  
Luh Putu Ida Harini
Keyword(s):  
Training Data ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Individual Identity ◽  
Vision Disorder ◽  
Testing Data ◽  
Visual Disorders ◽  
Artificial Neural Network Ann ◽  
The Right ◽  
And Behavior

Myopia is a vision disorder that causes the sufferers unable to see distant objects. The degree of myopia in humans can changes, both increasing and decreasing. The increasing of myopia degree is proportional to the potential of other visual disorders, such as cataracts, retinal detachment, and glaucoma. Therefore, the increasing of myopia degree needs to be watched out. Several previous studies only considered the time factor in predicting the changes of myopia degree. In fact, the changes of myopia degree also influenced by some factors that related to individual identity and behavior. This study aims to predict the changes of myopia degree in humans based on some factors that causes myopia.. This study uses data that has been scaled with the fuzzy membership function to be processed with ANN for predicting the changes of myopia degree. By ANN 6-2-3 architecture that uses 80 training data, 20 testing data, and 1 predictive data, the prediction result of the changes of  myopia degree in the right eye is 1.1 dioptri, in the left eye is 1.2 dioptri and the accumulated of both is 2.3 dioptri with accuration values 87.79%, 78.47%, and 83.21%.

scholarly journals Deteksi Gulma Berdasarkan Warna HSV dan Fitur Bentuk Menggunakan Jaringan Syaraf Tiruan

2021 ◽  
Vol 8 (5) ◽  
pp. 929
Author(s):  
Hurriyatul Fitriyah ◽  
Rizal Maulana
Keyword(s):  
Learning Algorithm ◽  
Color Space ◽  
Outdoor Environment ◽  
Training Data ◽  
Distance Transform ◽  
Validation Data ◽  
Hsv Color Space ◽  
Testing Data ◽  
Artificial Neural Network Ann ◽  
Center Distance

<p class="Abstrak">Gulma merupakan tanaman pengganggu dalam lahan pertanian. Herbisida merupakan obat yang efektif membunuh gulma tersebut. Penyemprotan herbisida harus tepat sasaran kepada gulma saja dan tidak mengenai tanaman. Penelitian ini membuat sistem yang dapat mendeteksi gulma secara otomatis di antara tanaman pada lahan pertanian riil. Sistem ini menggunakan gambar lahan pertanian riil dimana tanaman tampak utuh (daun dapat lebih dari satu) yang diambil menggunakan kamera dengan posisi vertikal menghadap ke bawah. Algoritma yang dibuat menggunakan segmentasi berdasarkan warna hijau dalam ruang warna HSV untuk mendeteksi daun, baik gulma maupun tanaman pada beragam pencahayaan. Sebanyak tiga fitur bentuk domain spasial digunakan untuk membedakan gulma dengan tanaman yang memiliki karakteristik bentuk daun yang berbeda. Fitur bentuk yang digunakan adalah <em>Rectangularity, Edge-to-Center distances function</em>, dan <em>Distance Transform function</em>. Klasifikasi gulma dan tanaman menggunakan metode Jaringan syaraf tiruan (JST) yang dapat dilatih secara <em>offline. </em>Dari 149 tanaman yang terdeteksi dimana 70% sebagai data training, 15% data validasi dan 15% data uji, didapati akurasi pengujian sebesar 95.46%.</p><p class="Abstrak"><em><strong><br /></strong></em></p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Weed is a major challenge in a crop plantation. A herbicide is the most effective substance to kill this unwanted vegetation. Spraying the herbicide must be done carefully to target the weeds only. Here in this research, we develop an algorithm that detects weeds among the plants based on the shape of their leaves. The detection is based on images that were acquired using a camera. The leaves of weeds and plants were detected based on their green color using segmentation in HSV color-space as it is more effective to detect objects in various illumination. Three shape features were extracted, which are Rectangularity that is based on Rectangularity, Edge-to-Center distance function, and Distance Transform function. Those features were fed into a learning algorithm, Artificial Neural Network (ANN), to classify whether it is the plant or the weed. The testing on the weed classification in a real outdoor environment showed 95.46% accuracy using a total of 149 detected plants (70% as training data, 15%  as validation data, and 15% as testing data).<strong></strong></em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals Connected Component and Morphology Based Extraction of Arterial Centerlines of the Heart (CocomoBeach)

2008 ◽  
Author(s):  
Pieter Kitslaar ◽  
Michel Frenay ◽  
Elco Oost ◽  
Jouke Dijkstra ◽  
Berend Stoel ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Training Data ◽  
Data Sets ◽  
Fast Marching ◽  
Geometrical Properties ◽  
Testing Data ◽  
Heart Region ◽  
Extraction Scheme ◽  
The Right ◽  
Coronary Artery Tree

This document describes a novel scheme for the automated extraction of the central lumen lines of coronary arteries from computed tomography angiography (CTA) data. The scheme first obtains a seg- mentation of the whole coronary tree and subsequently extracts the centerlines from this segmentation. The first steps of the segmentation algorithm consist of the detection of the aorta and the entire heart region. Next, candidate coronary artery components are detected in the heart region after the masking of the cardiac blood pools. Based on their location and geometrical properties the structures representing the right and left arterties are selected from the candidate list. Starting from the aorta, connections between these structures are made resulting in a final segmentation of the whole coronary artery tree, A fast-marching level set method combined with a backtracking algorithm is employed to obtain the initial centerlines within this segmentation. For all vessels a curved multiplanar reformatted image (CMPR) is constructed and used to detect the lumen contours. The final centerline was then defined by determining the center of gravity of the detected lumen in the transversal CMPR slices. Within the scope of the MICCAI Challenge “Coronary Artery Tracking 2008”, the coronary tree segmentation and centerline extraction scheme was used to automatically detect a set of centerlines in 24 datasets. For 8 data sets reference centerlines were available. This training data was used during the development and tuning of the algorithm. Sixteen other data sets were provided as testing data. Evaluation of the proposed methodology was performed through submission of the resulting centerlines to the MICCAI Challenge website

scholarly journals Applying WPD and SVD to Classification of EM Wave Induced by Partial Discharge in Power Transformer

2013 ◽  
Vol 64 (4) ◽  
pp. 222-229 ◽  
Author(s):  
Xu Zhao ◽  
Yong-Hong Cheng ◽  
Yong-Peng Meng ◽  
Michael G. Danikas
Keyword(s):  
Power Transformer ◽  
Wavelet Packet ◽  
Partial Discharge ◽  
Training Data ◽  
Decomposition Level ◽  
Frequency Information ◽  
Testing Data ◽  
The Right ◽  
Wave Induced

Partial discharge (PD) current is an impulse signal at nanosecond level, which can generate electromagnetic (EM) wave containing broadband frequency information. The frequency band of EM signal is from MHz up to GHz. Due to different PD patterns, impulse currents with different shapes induce different EM waves containing different frequency information. Therefore, using the features extracted from frequency domain of EM signals, the classification of PD patterns can be effectively got. It is good to use wavelet or wavelet packet decomposition to select features. However, if the decomposition level is too shallow to find enough effective features, it cannot group the EM signals to the right pattern. On the contrary, although it is easier to find features to distinguish the PD pattern if the decomposition level is deep, there will be a lot of redundancy variables and it is hard to select features among so many variables. In this paper, a method is presented, which selected features in the whole decomposition tree instead of selecting among the leaf node of the tree, because more potential features can be found in the whole tree. With the present method, it is possible not only to get enough features, but also to eliminate the redundancy variables effectively. In order to validate the method, large EM signals from four PD patterns in a power transformer are acquired as the training data and testing data for feature selection and classification, and three common classification methods are introduced to classify the PD patterns using the features selected by the method. Most of the classification results are satisfactory indicating that the proposed method is effective.

scholarly journals Implementasi Jaringan Syaraf Tiruan Recurrent Dengan Metode Pembelajaran Gradient Descent Adaptive Learning Rate Untuk Pendugaan Curah Hujan Berdasarkan Peubah Enso

2010 ◽  
Vol 1 (2) ◽  
pp. 418
Author(s):  
Afan Galih Salman ◽  
Yen Lina Prasetio
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Gradient Descent ◽  
Learning Rate ◽  
Training Data ◽  
Testing Data ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Data Group

The use of technology of technology Artificial Neural Network (ANN) in prediction of rainfall can be done using the learning approach. ANN prediction accuracy measured by the coefficient of determination (R2) and Root Mean Square Error (RMSE).This research employ a recurrent optimized heuristic Artificial Neural Network (ANN) Recurrent Elman gradient descent adaptive learning rate approach using El-Nino Southern Oscilation (ENSO) variable, namely Wind, Southern Oscillation Index (SOI), Sea Surface Temperatur (SST) dan Outgoing Long Wave Radiation (OLR) to forecast regional monthly rainfall. The patterns of input data affect the performance of Recurrent Elman neural network in estimation process. The first data group that is 75% training data and 25% testing data produce the maximum R2 69.2% at leap 0 while the second data group that is 50% training data & 50% testing data produce the maximum R2 53.6%.at leap 0 Our result on leap 0 is better than leap 1,2 or 3. 

An Analog Circuit Fault Diagnosis Approach Based on Wavelet-based fractal analysis and Multiple Kernel SVM

2020 ◽  
Vol 13 ◽  
Author(s):  
Jianfeng Jiang
Keyword(s):  
Fault Diagnosis ◽  
Fractal Analysis ◽  
Analog Circuit ◽  
Training Data ◽  
Support Vector ◽  
Pass Filter ◽  
Multiple Kernel ◽  
Testing Data ◽  
Circuit Fault Diagnosis ◽  
Diagnosis Approach

Objective: In order to diagnose the analog circuit fault correctly, an analog circuit fault diagnosis approach on basis of wavelet-based fractal analysis and multiple kernel support vector machine (MKSVM) is presented in the paper. Methods: Time responses of the circuit under different faults are measured, and then wavelet-based fractal analysis is used to process the collected time responses for the purpose of generating features for the signals. Kernel principal component analysis (KPCA) is applied to reduce the features’ dimensionality. Afterwards, features are divided into training data and testing data. MKSVM with its multiple parameters optimized by chaos particle swarm optimization (CPSO) algorithm is utilized to construct an analog circuit fault diagnosis model based on the testing data. Results: The proposed analog diagnosis approach is revealed by a four opamp biquad high-pass filter fault diagnosis simulation. Conclusion: The approach outperforms other commonly used methods in the comparisons.

Questions to the Article: An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples (Preprint)

2020 ◽  
Author(s):  
Shu-Chun Kuo ◽  
CHIEN WEI ◽  
Willy Chou
Keyword(s):  
Data Entry ◽  
Ann Model ◽  
Model Accuracy ◽  
Data Format ◽  
Raw Data ◽  
Probability Of Survival ◽  
Testing Data ◽  
Laboratory Results ◽  
Data Yield ◽  
Artificial Neural Network Ann

UNSTRUCTURED The recent article published on December 23 27 in 2020 is well-written and of interest, but remains several questions that are required for clarifications, including (1) 30 feature variables with normalized format(mean=0 and SD=1) required to compare model accuracy with those with the raw-data format; (2)inconsistency in variable numbers between entry and preview panels in Figure 4 and reference typos; and (3) data-entry format with raw blood laboratory results in Figure 4 inconsistent with the model designed using normalized data to estimate parameters. We conducted a study using the training and testing data provided by the previous study. An artificial neural network(ANN) model was performed to estimate parameters and compare the model accuracy with those eight models provided by the previous study. We found that (1) normalized data yield higher accuracy than that with the raw data; (2) typos definitely exist at the bottom review (=32>30 variables in the entry) panels in Figure 4 and typos in Table 6; and (3)the ANN earns a probability of survival(=0.91) higher than that(=0.71) in the previous study using the similar entry data when the raw data are assumed in the app. We also demonstrated an author-made app using the visualization to display the prediction result, which is novel and innovative to make the result improved with a dashboard in comparison with the previous study.

Tickets for the Inclusive Museum

2020 ◽  
pp. 390-406
Author(s):  
Magda Nikolaraizi ◽  
Charikleia Kanari ◽  
Marc Marschark
Keyword(s):  
Hard Of Hearing ◽  
Learning Experiences ◽  
Learning Needs ◽  
Participatory Learning ◽  
Maximum Benefit ◽  
Wide Range ◽  
The Right ◽  
Physical Features ◽  
And Behavior

In recent years, museums of various kinds have broadened their mission and made systematic efforts to develop a dynamic role in learning by offering a wide range of less formal experiences for individuals with diverse characteristics, including individuals who are deaf or hard-of-hearing (DHH). Despite the worthwhile efforts, in the case of DHH individuals, museums frequently neglect to consider their unique communication, cognitive, cultural, and learning characteristics, thus limiting their access and opportunities for fully experiencing what museums have to offer. This chapter examines the potential for creating accessible museum environments and methods that reflect an understanding of the diverse communication, cognitive, cultural, and learning needs of DHH visitors, all of which enhance their access and participation in the museum activities. The role of the physical features of museum spaces for the access and behavior of DHH visitors is emphasized, together with attention to exhibition methods and the communication and cognitive challenges that need to be considered so DHH visitors can get the maximum benefit. The chapter emphasizes the right of individuals who are DHH to nonformal learning and analyzes how museums could become more accessible to DHH individuals by designing, from the beginning, participatory learning experiences that address their diverse needs.

Exploring User Information Needs in Online Pet Adoption Profiles

2020 ◽  
Vol 64 (1) ◽  
pp. 1308-1312
Author(s):  
Zoe M. Becerra ◽  
Sweta Parmar ◽  
Keenan May ◽  
Rachel E. Stuck
Keyword(s):  
Information Needs ◽  
Online Shopping ◽  
Physical Characteristics ◽  
User Needs ◽  
User Group ◽  
Animal Shelters ◽  
Types Of Information ◽  
The Right ◽  
And Behavior ◽  
Level Of Importance

With the increase of online shopping, animal shelters can use websites to allow potential adopters to view adoptable animals and increase the number of adoptions. However, little research has evaluated the information needs of this user group. This study conducted a user needs analysis to determine the types of information potential adopters want when searching for a new pet, specifically a cat or dog. Twenty-six participants ranked different behavioral and physical characteristics based on the level of importance and identified their top five overall characteristics. In general, cat adopters ranked the cat’s personality and behavior to be very important and dog adopters found physical characteristics highly important. This study shows the importance of understanding potential adopters’ needs to provide relevant and valued information on online pet adoption profiles. The recommendations and insights can be used to develop pet profiles that meet adopters’ needs and help adopters find the right pet.

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