scholarly journals Classification of skin cancer images by applying simple evolving connectionist system

2021 ◽  
Vol 10 (2) ◽  
pp. 421
Author(s):  
Al-Khowarizmi Al-Khowarizmi ◽  
Suherman Suherman
Keyword(s):  
Skin Cancer ◽  
Learning Rate ◽  
Training Data ◽  
Sensitivity Threshold ◽  
Data Binding ◽  
Connectionist System ◽  
Binding Data ◽  
Hidden Node ◽  
Malignant Skin ◽  
Hidden Layer

<span id="docs-internal-guid-eea5616b-7fff-5d26-eeb4-1d8c084ec93d"><span>Simple evolving connectionist system (SECoS) is one of data mining classification techniques that recognizing data based on the tested and the training data binding. Data recognition is achieved by aligning testing data to trained data pattern. SECoS uses a feedforward neural network but its hidden layer evolves so that each input layer does not perform epoch. SECoS distance has been modified with the normalized Euclidean distance formula to reduce error in training. This paper recognizes skin cancer by classifying benign malignant skin moles images using SECoS based on parameter combinations. The skin cancer classification has learning rate 1 of 0.3, learning rate 2 of 0.3, sensitivity threshold of 0.5, error threshold of 0.1 and MAPE is 0.5184845 with developing hidden node of 23. Skin cancer recognition by applying modified SECoS algorithm is proven more acceptable. Compared to other methods, SECoS is more robust to error variations.</span></span>

scholarly journals Implementasi Jaringan Syaraf Tiruan Perambatan Balik untuk Memprediksi Harga Logam Mulia Emas Menggunakan Algoritma Lavenberg Marquardt

2013 ◽  
Vol 1 (2) ◽  
pp. 49
Author(s):  
Reza Najib Hidayat ◽  
R. Rizal Isnanto ◽  
Oky Dwi Nurhayati
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Tolerance ◽  
Stock Market ◽  
Error Rate ◽  
Learning Rate ◽  
Training Data ◽  
Accuracy Rate ◽  
Rate Minimum ◽  
Hidden Layer

Gold is one of commodities investment which its value continue to increase by year. The rising price of gold will encourage investors to choose to invest in gold rather than the stock market. With the risks that are relatively low, gold can give better resultsin accordance with its increasing price. In addition, gold can also be a safe value protector in the future.The Objectives of the research are to predict the price of gold using artificial neural networks backpropagations methods and to analyze best network used in prediction. In the process of training data, it is used some training parameters to decide the best gold prediction architecture. Comparative parameters that is used are the variation of the number of hidden layers, number of neurons in each hidden layer, learning rate, minimum gradients and fault tolerance. The results showed that the best architecture has an accuracy rate of 99,7604% of data training and test data at 98,849% with architecture combinations are have two hidden layer neurons combined 10-30, the error rate 0.00001 and 0.00001 of learning rate.

scholarly journals Peramalan Harga Telur Ayam Ras Di Jakarta Timur Berbasis Jaringan Syaraf Tiruan

2020 ◽  
Vol 5 (1) ◽  
pp. 65
Author(s):  
Nurfia Oktaviani Syamsiah
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Learning Rate ◽  
Training Cycle ◽  
Hidden Node ◽  
Hidden Layer

Penelitian ini membahas tentang pemanfaatan jaringan syaraf tiruan untuk peramalan harga telur ayam ras di Jakarta Timur. Data harga yang digunakan adalah data time series harian.  Metode yang dipilih adalah Jaringan syaraf tiruan dengna 2 fungsi aktivasi, yakni sigmoid biner dan sigmoid bipolar dengan memanfaatkan Tools Rapidminer Studio mulai dari tahapan pertama hingga tahapan akhir. Eksperimen dilakukan dengan melakukan perubahan pada beberapa parameter neural network seperti jumlah hidden node, training cycle, learning rate maupun jumlah momentum. Penentuan hidden layer diupayakan semaksimal mungkin bertujuan untuk menghindari terjadinya permasalahan Overfitting dan Underfitting. Hasil yang dicapai, bahwasanya RMSE terkecil diperoleh dari penggunaan fungsi aktivasi sigmoid biner dengan nilai 0.033. dan arsitektur terbaik yakni 7 input, 2 hidden node dan 1 output. Penelitian ini menunjukkan hasil bahwa jaringan syaraf tiruan memberikan hasil yang cukup baik bagi peramalan data harga telur ayam ras di Jakarta Timur yang datanya bersifat time series univariat.Kata kunci: Jaringan Syaraf Tiruan, Telur, Harga, Peramalan

scholarly journals Exploiting heterogeneity in operational neural networks by synaptic plasticity

2021 ◽  
Author(s):  
Serkan Kiranyaz ◽  
Junaid Malik ◽  
Habib Ben Abdallah ◽  
Turker Ince ◽  
Alexandros Iosifidis ◽  
...  
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Network Model ◽  
Neuron Model ◽  
Linear Operators ◽  
Training Data ◽  
Learning Performance ◽  
Minimal Network ◽  
Hidden Layer ◽  
Hidden Neurons

AbstractThe recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional Neural Networks (CNNs) that are homogenous only with a linear neuron model. As a heterogenous network model, ONNs are based on a generalized neuron model that can encapsulate any set of non-linear operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data. However, the default search method to find optimal operators in ONNs, the so-called Greedy Iterative Search (GIS) method, usually takes several training sessions to find a single operator set per layer. This is not only computationally demanding, also the network heterogeneity is limited since the same set of operators will then be used for all neurons in each layer. To address this deficiency and exploit a superior level of heterogeneity, in this study the focus is drawn on searching the best-possible operator set(s) for the hidden neurons of the network based on the “Synaptic Plasticity” paradigm that poses the essential learning theory in biological neurons. During training, each operator set in the library can be evaluated by their synaptic plasticity level, ranked from the worst to the best, and an “elite” ONN can then be configured using the top-ranked operator sets found at each hidden layer. Experimental results over highly challenging problems demonstrate that the elite ONNs even with few neurons and layers can achieve a superior learning performance than GIS-based ONNs and as a result, the performance gap over the CNNs further widens.

scholarly journals Epidemiology of basal cell carcinomas and squamous cell carcinomas in a Department of Dermatology: a 5 year review

2012 ◽  
Vol 87 (2) ◽  
pp. 212-219 ◽  
Author(s):  
Pedro Andrade ◽  
Maria Manuel Brites ◽  
Ricardo Vieira ◽  
Angelina Mariano ◽  
José Pedro Reis ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Squamous Cell ◽  
Basal Cell ◽  
Skin Neoplasms ◽  
Squamous Cell Carcinomas ◽  
Exposed Skin ◽  
Non Melanoma Skin Cancer ◽  
Basal Cell Carcinomas ◽  
Malignant Skin ◽  
Melanoma Skin

BACKGROUND: Non-melanoma skin cancer, a common designation for both basal cell carcinomas and squamous cell carcinomas, is the most frequent malignant skin neoplasm. OBJECTIVE: Epidemiologic characterization of the population with Non-melanoma skin cancer. METHODS: Retrospective analysis of all patients diagnosed with Non-melanoma skin cancer based on histopathologic analysis of all incisional or excisional skin biopsies performed between 2004 and 2008 in a Department of Dermatology. RESULTS: A total of 3075 Non-melanoma skin cancers were identified, representing 88% of all malignant skin neoplasms (n=3493) diagnosed in the same period. Of those, 68,3% were basal cell carcinomas. Most Non-melanoma skin cancer patients were female and over 60 years old. Of all Non-melanoma skin cancer, 81,7% (n=1443) were located in sun-exposed skin, and represented 95,1% of malignant skin neoplasms in sun-exposed skin. Non-melanoma skin cancer was the most frequent malignant skin neoplasm in most topographic locations, except for abdomen and pelvis - over 95% of all malignant skin neoplasms in the face, neck and scalp were Non-melanoma skin cancer. Basal cell carcinomas were clearly predominant in all locations, except in upper and lower limbs, lower lip and genitals, where squamous cell carcinomas represented respectively 77,7%, 77,4%, 94,7% and 95,3% of the Non-melanoma skin cancers. CONCLUSION: Being the most common skin cancer, Non-melanoma skin cancer should be under constant surveillance, in order to monitor its epidemiologic dynamics, the efficiency of preventive measures and the adaptation of the healthcare resources.

An Effective Solution to Regression Problem by RBF Neuron Network

2015 ◽  
Vol 6 (4) ◽  
pp. 57-74 ◽  
Author(s):  
Dang Thi Thu Hien ◽  
Hoang Xuan Huan ◽  
Le Xuan Minh Hoang
Keyword(s):  
Regression Function ◽  
Training Data ◽  
Neuron Network ◽  
Open Problems ◽  
Regression Problem ◽  
Rbf Network ◽  
Network Training ◽  
Hidden Layer ◽  
Definition Of ◽  
Selection Of

Radial Basis Function (RBF) neuron network is being applied widely in multivariate function regression. However, selection of neuron number for hidden layer and definition of suitable centre in order to produce a good regression network are still open problems which have been researched by many people. This article proposes to apply grid equally space nodes as the centre of hidden layer. Then, the authors use k-nearest neighbour method to define the value of regression function at the center and an interpolation RBF network training algorithm with equally spaced nodes to train the network. The experiments show the outstanding efficiency of regression function when the training data has Gauss white noise.

scholarly journals Teaching Skin Cancer Detection to Medical Students Using a Dermoscopic Algorithm

PRiMER ◽  
2021 ◽  
Vol 5 ◽  
Author(s):  
Peggy R. Cyr ◽  
Wendy Craig ◽  
Hadjh Ahrns ◽  
Kathryn Stevens ◽  
Caroline Wight ◽  
...  
Keyword(s):  
Primary Care ◽  
Medical Students ◽  
Skin Cancer ◽  
Primary Care Physicians ◽  
Survival Rates ◽  
Family Physicians ◽  
Skin Lesions ◽  
Posttest Score ◽  
Malignant Skin ◽  
Attending Physicians

Introduction: Early detection of melanoma skin cancer improves survival rates. Training family physicians in dermoscopy with the triage amalgamated dermoscopic algorithm (TADA) has high sensitivity and specificity for identifying malignant skin neoplasms. In this study we evaluated the effectiveness of TADA training among medical students, compared with practicing clinicians. Methods: We incorporated the TADA framework into 90-minute workshops that taught dermoscopy to family physicians, primary care residents, and first- and second-year medical students. The workshop reviewed the clinical and dermoscopic features of benign and malignant skin lesions and included a hands-on interactive session using a dermatoscope. All participants took a 30-image pretest and a different 30-image posttest. Results: Forty-six attending physicians, 25 residents, and 48 medical students participated in the workshop. Mean pretest scores were 20.1, 20.3, and 15.8 for attending physicians, resident physicians and students, respectively (P&lt;.001); mean posttest scores were 24.5, 25.9, and 24.1, respectively (P=.11). Pre/posttest score differences were significant (P&lt;.001) for all groups. The medical students showed the most gain in their pretest and posttest scores. Conclusion: After short dermoscopy workshop, medical students perform as well as trained physicians in identifying images of malignant skin lesions. Dermoscopy training may be a valuable addition to the medical school curriculum as this skill can be used by primary care physicians as well as multiple specialists including dermatologists, gynecologists, otolaryngologists, plastic surgeons, and ophthalmologists, who often encounter patients with concerning skin lesions.

scholarly journals Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

2021 ◽  
Vol 2 (01) ◽  
pp. 41-51
Author(s):  
Jwan Saeed ◽  
Subhi Zeebaree
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Skin Cancer ◽  
Cancer Detection ◽  
Deep Convolutional Neural Networks ◽  
Color Structure ◽  
Malignant Skin ◽  
Dermatological Disorders ◽  
Detection And Diagnosis ◽  
Skin Cancer Detection

Skin cancer is among the primary cancer types that manifest due to various dermatological disorders, which may be further classified into several types based on morphological features, color, structure, and texture. The mortality rate of patients who have skin cancer is contingent on preliminary and rapid detection and diagnosis of malignant skin cancer cells. Limitations in current dermoscopic images, including shadow, artifact, and noise, affect image quality, which may hamper detection effort. Attempts to overcome these challenges have been made by analyzing the images using deep learning neural networks to perform skin cancer detection. In this paper, the authors review the state-of-the-art in authoritative deep learning concepts pertinent to skin cancer detection and classification.

Evaluating the Effects of Size and Precision of Training Data on ANN Training Performance for the Prediction of Chaotic Time Series Patterns

2022 ◽  
pp. 266-282
Author(s):  
Lei Zhang
Keyword(s):  
Time Series ◽  
Open Loop ◽  
Chaotic Time Series ◽  
Training Data ◽  
Acquisition Time ◽  
Lorenz Attractor ◽  
Ann Model ◽  
Training Performance ◽  
Hidden Layer ◽  
Input Delays

In this research, artificial neural networks (ANN) with various architectures are trained to generate the chaotic time series patterns of the Lorenz attractor. The ANN training performance is evaluated based on the size and precision of the training data. The nonlinear Auto-Regressive (NAR) model is trained in open loop mode first. The trained model is then used with closed loop feedback to predict the chaotic time series outputs. The research goal is to use the designed NAR ANN model for the simulation and analysis of Electroencephalogram (EEG) signals in order to study brain activities. A simple ANN topology with a single hidden layer of 3 to 16 neurons and 1 to 4 input delays is used. The training performance is measured by averaged mean square error. It is found that the training performance cannot be improved by solely increasing the training data size. However, the training performance can be improved by increasing the precision of the training data. This provides useful knowledge towards reducing the number of EEG data samples and corresponding acquisition time for prediction.

scholarly journals Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

2020 ◽  
Vol 10 (5) ◽  
pp. 1657 ◽  
Author(s):  
Jieun Baek ◽  
Yosoon Choi
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Deep Neural Network ◽  
Training Data ◽  
Open Pit ◽  
Percentage Error ◽  
Open Pit Mines ◽  
Determination Coefficient ◽  
Packet Data ◽  
Hidden Layer

This paper proposes a deep neural network (DNN)-based method for predicting ore production by truck-haulage systems in open-pit mines. The proposed method utilizes two DNN models that are designed to predict ore production during the morning and afternoon haulage sessions, respectively. The configuration of the input nodes of the DNN models is based on truck-haulage conditions and corresponding operation times. To verify the efficacy of the proposed method, training data for the DNN models were generated by processing packet data collected over the two-month period December 2018 to January 2019. Subsequently, following training under different hidden-layer conditions, it was observed that the prediction accuracy of morning ore production was highest when the number of hidden layers and number of corresponding nodes were four and 50, respectively. The corresponding values of the determination coefficient and mean absolute percentage error (MAPE) were 0.99% and 4.78%, respectively. Further, the prediction accuracy of afternoon ore production was highest when the number of hidden layers was four and the corresponding number of nodes was 50. This yielded determination coefficient and MAPE values of 0.99% and 5.26%, respectively.

RECOGNITION OF CONTACT STATE BY USING NEURAL NETWORK FOR MICROMACHINED ARRAY TYPE TACTILE SENSOR

2005 ◽  
Vol 02 (03) ◽  
pp. 181-190 ◽  
Author(s):  
SEIJI AOYAGI ◽  
TAKAAKI TANAKA ◽  
KENJI MAKIHIRA
Keyword(s):  
Neural Network ◽  
Rotation Angle ◽  
Simulated Data ◽  
Tactile Sensor ◽  
Training Data ◽  
Sensing Element ◽  
Array Type ◽  
Tactile Pattern ◽  
Hidden Layer ◽  
Contact Position

In this paper, a force sensing element having a pillar and a diaphragm is proposed and thereafter fabricated by micromachining. Piezo resistors are fabricated on a silicon diaphragm for detecting distortions caused by a force input to a pillar on the diaphragm. Since a practical arrayed sensor consisting of many of this element is still under development, the output of an assumed arrayed type tactile sensor is simulated by FEM (finite element method). Using simulated data, the possibility of tactile pattern recognition using a neural network (NN) is investigated. The learning method of NN, the number of units of the input layer and the hidden layer, as well as the number of training data are investigated for realizing high probability of recognition. The 14 subjects having different shape and size are recognized. This recognition succeeded even if the contact position and the rotation angle of these objects are changed.

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