scholarly journals IMPLEMENTASI METODE KOHONEN UNTUK PREDIKSI CURAH HUJAN (STUDI KASUS : KOTA PONTIANAK)

2017 ◽  
Vol 4 (2) ◽  
pp. 198
Author(s):  
Fatma Agus Setyanngsih
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Self Organizing Map ◽  
Rainfall Prediction ◽  
The Neural Network ◽  
Network Time ◽  
Neural Network Algorithm ◽  
Network Prediction ◽  
The City ◽  
Self Organizing

<p><em>The prediction to determine the rainfall in Pontianak is much needed. One of them is using a neural network algorithm using SOM (Self Organizing Maping) with the data used in January 2010-2013. The purpose of this study was to determine the rainfall prediction in the city of Pontianak with parameters of air temperature, relative humidity, air pressure and wind speed. The results showed that the value of MSE is obtained when studying the data network prediction in January of 2010 until 2013 using the Neural Network-SOM learning process with the amount of 1 neuron and using 124 datas, with MSE value 0,0148.</em><strong> </strong></p><p><strong><em>Keywords</em></strong><em>: </em><em>Rainfall, Neural Network, Time Series, Self Organizing Map</em></p><p><em>Prediksi untuk mengetahui curah hujan yang terjadi di Pontianak sangat dibutuhkan salah satunya yaitu menggunakan algoritma jaringan syaraf tiruan dengan pengelompokkannya menggunakan SOM (Self Organizing Map) dengan data yang digunakan adalah data di bulan januari tahun 2010-2013. Tujuan dari penelitian ini adalah untuk mengetahui prediksi curah hujan di kota Pontianak dengan parameter suhu udara, kelembababn relative, tekanan udara dan kecepatan angin. Hasil penelitian menunjukkan bahwa nilai MSE ini didapatkan saat jaringan mempelajari data prediksi pada bulan januari di tahun 2010 sampai tahun 2013 dengan menggunakan proses pembelajaran JST SOM dengan jumlah neuron 1 dan menggunakan 124 data, dengan nilai MSE 0,0148. </em></p><p><em></em><em><strong><em>Kata kunci</em></strong><strong><em>:</em></strong><em> </em><em>Curah Hujan, Jaringan Syaraf Tiruan, Time Series, Self Organizing Map</em></em></p>

scholarly journals An Application of Neural Network Technique to Correct the Dome Temperature Effects on Pyrgeometer Measurements

2006 ◽  
Vol 23 (1) ◽  
pp. 80-89 ◽  
Author(s):  
Amauri P. Oliveira ◽  
Jacyra Soares ◽  
Marija Z. Božnar ◽  
Primož Mlakar ◽  
João F. Escobedo
Keyword(s):  
Neural Network ◽  
Temperature Effects ◽  
Longwave Radiation ◽  
Global Solar Radiation ◽  
Time Of Day ◽  
Network Training ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Using Data ◽  
The City

Abstract This work describes an application of a multilayer perceptron neural network technique to correct dome emission effects on longwave atmospheric radiation measurements carried out using an Eppley Precision Infrared Radiometer (PIR) pyrgeometer. It is shown that approximately 7-month-long measurements of dome and case temperatures and meteorological variables available in regular surface stations (global solar radiation, air temperature, and air relative humidity) are enough to train the neural network algorithm and correct the observed longwave radiation for dome temperature effects in surface stations with climates similar to that of the city of São Paulo, Brazil. The network was trained using data from 15 October 2003 to 7 January 2004 and verified using data, not present during the network-training period, from 8 January to 30 April 2004. The longwave radiation values generated by the neural network technique were very similar to the values obtained by Fairall et al., assumed here as the reference approach to correct dome emission effects in PIR pyrgeometers. Compared to the empirical approach the neural network technique is less limited to sensor type and time of day (allows nighttime corrections).

FLEXIBLE STRUCTURE MULTIPLE MODELING USING IRREGULAR SELF-ORGANIZING MAPS NEURAL NETWORK

2008 ◽  
Vol 18 (03) ◽  
pp. 233-256 ◽  
Author(s):  
ALIREZA FATEHI ◽  
KENICHI ABE
Keyword(s):  
Neural Network ◽  
Minimum Spanning Tree ◽  
Model Space ◽  
Self Organizing Map ◽  
Local Models ◽  
Tree Graph ◽  
Self Organizing Maps ◽  
The Neural Network ◽  
The Neural Networks ◽  
Self Organizing

The MMSOM identification method, which had been presented by the authors, is improved to the multiple modeling by the irregular self-organizing map (MMISOM) using the irregular SOM (ISOM). Inputs to the neural networks are parameters of the instantaneous model computed adaptively at every instant. The neural network learns these models. The reference vectors of its output nodes are estimation of the parameters of the local models. At every instant, the model with closest output to the plant output is selected as the model of the plant. ISOM used in this paper is a graph of all the nodes and some of the weighted links between them to make a minimum spanning tree graph. It is shown in this paper that it is possible to add new models if the number of models is initially less than the appropriate one. The MMISOM shows more flexibility to cover the linear model space of the plant when the space is concave.

scholarly journals Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 299
Author(s):  
Birgitta Dresp-Langley ◽  
John M. Wandeto
Keyword(s):  
Neural Network ◽  
Mirror Symmetry ◽  
Image Data ◽  
Choice Response ◽  
Local Contrast ◽  
Self Organizing Map ◽  
The Neural Network ◽  
Unsupervised Neural Network ◽  
Ambiguous Data ◽  
Self Organizing

Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. To this end, we exploit a neural network metric in the output of a biologically inspired Self-Organizing Map Quantization Error (SOM-QE). Shape pairs with perfect geometry mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, a longer choice response time (RT) for “yes” responses relative to symmetry. These data are consistently mirrored by the variations in the SOM-QE from unsupervised neural network analysis of the same stimulus images. The neural network metric is thus capable of detecting and scaling human symmetry uncertainty in response to patterns. Such capacity is tightly linked to the metric’s proven selectivity to local contrast and color variations in large and highly complex image data.

scholarly journals Artificial intelligence for advance requesting of immunohistochemistry in diagnostically uncertain prostate biopsies

2021 ◽  
Author(s):  
Andrea Chatrian ◽  
Richard T. Colling ◽  
Lisa Browning ◽  
Nasullah Khalid Alham ◽  
Korsuk Sirinukunwattana ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Area Under The Curve ◽  
Validation Dataset ◽  
Prostate Biopsies ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Neural Network Algorithm ◽  
Network Prediction ◽  
Whole Slide Images

AbstractThe use of immunohistochemistry in the reporting of prostate biopsies is an important adjunct when the diagnosis is not definite on haematoxylin and eosin (H&E) morphology alone. The process is however inherently inefficient with delays while waiting for pathologist review to make the request and duplicated effort reviewing a case more than once. In this study, we aimed to capture the workflow implications of immunohistochemistry requests and demonstrate a novel artificial intelligence tool to identify cases in which immunohistochemistry (IHC) is required and generate an automated request. We conducted audits of the workflow for prostate biopsies in order to understand the potential implications of automated immunohistochemistry requesting and collected prospective cases to train a deep neural network algorithm to detect tissue regions that presented ambiguous morphology on whole slide images. These ambiguous foci were selected on the basis of the pathologist requesting immunohistochemistry to aid diagnosis. A gradient boosted trees classifier was then used to make a slide-level prediction based on the outputs of the neural network prediction. The algorithm was trained on annotations of 219 immunohistochemistry-requested and 80 control images, and tested by threefold cross-validation. Validation was conducted on a separate validation dataset of 222 images. Non IHC-requested cases were diagnosed in 17.9 min on average, while IHC-requested cases took 33.4 min over multiple reporting sessions. We estimated 11 min could be saved on average per case by automated IHC requesting, by removing duplication of effort. The tool attained 99% accuracy and 0.99 Area Under the Curve (AUC) on the test data. In the validation, the average agreement with pathologists was 0.81, with a mean AUC of 0.80. We demonstrate the proof-of-principle that an AI tool making automated immunohistochemistry requests could create a significantly leaner workflow and result in pathologist time savings.

scholarly journals Artificial Intelligence for Advance Requesting of Immunohistochemistry in Diagnostically Uncertain Prostate Biopsies

2021 ◽  
Author(s):  
Andrea Chatrian ◽  
Richard Colling ◽  
Lisa Browning ◽  
Nasullah Khalid Alham ◽  
Korsuk Sirinukunwattana ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Area Under The Curve ◽  
Validation Dataset ◽  
Prostate Biopsies ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Neural Network Algorithm ◽  
Network Prediction ◽  
Whole Slide Images

The use of immunohistochemistry in the reporting of prostate biopsies is an important adjunct when the diagnosis is not definite on haematoxylin and eosin (H&E) morphology alone. The process is however inherently inefficient with delays while waiting for pathologist review to make the request and duplicated effort reviewing a case more than once. In this study, we aimed to capture the workflow implications of immunohistochemistry requests and demonstrate a novel artificial intelligence tool to identify cases in which immunohistochemistry (IHC) is required and generate an automated request. We conducted audits of the workflow for prostate biopsies in order to understand the potential implications of automated immunohistochemistry requesting and collected prospective cases to train a deep neural network algorithm to detect tissue regions that presented ambiguous morphology on whole slide images. These ambiguous foci were selected on the basis of the pathologist requesting immunohistochemistry to aid diagnosis. A gradient boosted trees classifier was then used to make a slide level prediction based on the outputs of the neural network prediction. The algorithm was trained on annotations of 219 immunohistochemistry-requested and 80 control images, and tested by 3-fold cross-validation. Validation was conducted on a separate validation dataset of 212 images. Non IHC-requested cases were diagnosed in 17.9 minutes on average, while IHC-requested cases took 33.4 minutes over multiple reporting sessions. We estimated 11 minutes could be saved on average per case by automated IHC requesting, by removing duplication of effort. The tool attained 99% accuracy and 0.99 Area Under the Curve (AUC) on the test data. In the validation, the average agreement with pathologists was 0.81, with a mean AUC of 0.80. We demonstrate the proof-of-principle that an AI tool making automated immunohistochemistry requests could create a significantly leaner workflow and result in pathologist time savings.

Line Segment-Based Clustering Approach With Self-Organizing Maps

2021 ◽  
Vol 14 (4) ◽  
pp. 33-44
Author(s):  
G. Chamundeswari ◽  
G. P. S. Varma ◽  
C. Satyanarayana
Keyword(s):  
Neural Network ◽  
Line Segment ◽  
Feature Vector ◽  
Input Image ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Clustering Techniques ◽  
Hough Transformation ◽  
The Neural Network ◽  
Self Organizing

Clustering techniques are used widely in computer vision and pattern recognition. The clustering techniques are found to be efficient with the feature vector of the input image. So, the present paper uses an approach for evaluating the feature vector by using Hough transformation. With the Hough transformation, the present paper mapped the points to line segment. The line features are considered as the feature vector and are given to the neural network for performing clustering. The present paper uses self-organizing map (SOM) neural network for performing the clustering process. The proposed method is evaluated with various leaf images, and the evaluated performance measures show the efficiency of the proposed method.

scholarly journals ERRATUM: "FLEXIBLE STRUCTURE MULTIPLE MODELING USING IRREGULAR SELF-ORGANIZING MAPS NEURAL NETWORK"

2008 ◽  
Vol 18 (04) ◽  
pp. 347-370 ◽  
Author(s):  
ALIREZA FATEHI ◽  
KENICHI ABE
Keyword(s):  
Neural Network ◽  
Minimum Spanning Tree ◽  
Model Space ◽  
Self Organizing Map ◽  
Local Models ◽  
Tree Graph ◽  
Self Organizing Maps ◽  
The Neural Network ◽  
The Neural Networks ◽  
Self Organizing

The MMSOM identification method, which had been presented by the authors, is improved to the multiple modeling by the irregular self-organizing map (MMISOM) using the irregular SOM (ISOM). Inputs to the neural networks are parameters of the instantaneous model computed adaptively at every instant. The neural network learns these models. The reference vectors of its output nodes are estimation of the parameters of the local models. At every instant, the model with closest output to the plant output is selected as the model of the plant. ISOM used in this paper is a graph of all the nodes and some of the weighted links between them to make a minimum spanning tree graph. It is shown in this paper that it is possible to add new models if the number of models is initially less than the appropriate one. The MMISOM shows more flexibility to cover the linear model space of the plant when the space is concave.

Self-Organising Features of Rotor-Dynamic Faults

2007 ◽  
Author(s):  
Nalinaksh S. Vyas ◽  
Ankur Kumar
Keyword(s):  
Neural Network ◽  
Three Dimensional ◽  
Self Organizing Map ◽  
Dynamic Faults ◽  
Monitoring Strategies ◽  
Orderly Fashion ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Low Dimensional ◽  
Self Organizing

Artificial Neural Networks offer an efficient platform for devising condition monitoring strategies in machinery and plants where the number of components and processes are too many and complex to be mathematically modeled appropriately. Self-Organizing Map (SOM) is an interesting artificial neural network algorithm which produces an ordered low dimensional representation of an input data space. The basic Self-Organizing Map (SOM) can be visualized as a sheet-like neural-network array, the cells (or nodes) of which become specifically tuned to various input signal patterns or classes of patterns in an orderly fashion. This work describes a study on application of such topographic mapping for rotordynamic faults. A variety of rotor faults like unbalance, bearing damage, cocked rotor etc., are simulated on an experimental rig in a controlled manner. Vibration Signals obtained from various sensors on the rig are processed to train a Self-Organising Neural Network. It is has been shown that input neurons from the same fault get mapped together in clusters in both two-dimensional and three-dimensional grid spaces and each individual fault occupies a distinct zone on the grid.

scholarly journals Forecasting Number of COVID-19 in Bali Province Using Neural Network Algorithm

2021 ◽  
pp. 72
Author(s):  
Ida Ayu Utari Dewi ◽  
I Kadek Noppi Adi Jaya ◽  
Kadek Oky Sanjaya
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Large Family ◽  
Series Data ◽  
Severe Illness ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
The Common

COVID-19 (coronavirus disease 2019) is a large family of viruses that cause mild to severe illness, such as the common cold or colds and serious illnesses such as MERS and SARS. COVID-19 has become a pandemic, meaning that there has been an increase in cases of the disease which is quite fast and there has been spread between countries and caused enormous losses in various countries. The increasing number of COVID-19 cases every day in Indonesia, including in Bali Province and the resulting losses underlie the forecasting of the number of COVID-19 in Bali Province. Forecasting is carried out using the Neural Network algorithm for time series data on the number of COVID-19 in Bali Province. The data used is data on the number of COVID-19 in the Bali Province in the form of time series data sourced from the Bali Provincial Health Office. The entire forecasting process uses the Rapidminer Studio tools starting from preprocessing, modeling, testing and validation. The results of the RMSE (Root Mean Square Error) evaluation value based on testing for the positive patients were 18.956, the patients recovered were 15.413, the patients under treatment were 5.066 and the patients who died was 0.233.

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