scholarly journals KOMPARASI MODEL CELLULAR AUTOMATA DALAM MEMPREDIKSI PERUBAHAN LAHAN SAWAH DI KABUPATEN PURWOREJO

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Trida Ridho Fariz ◽  
Ely Nurhidayati ◽  
Hidhayah Nur Damayanti ◽  
Elvita Safitri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Cellular Automata ◽  
Paddy Field ◽  
Land Use Changes ◽  
Paddy Fields ◽  
False Alarms ◽  
Visual Interpretation ◽  
Artificial Neural

Penelitian ini bertujuan untuk mengkomparasikan akurasi metode ANN dan LR dalam memprediksi perubahan lahan sawah di Kabupaten Purworejo. Adapun data masukan yang dibutuhkan adalah peta lahan sawah tahun 2008, 2015 dan 2019 hasil interpretasi visual citra satelit resolusi tinggi dan faktor pendorong perubahan lahan sawah. Hasil penelitian menunjukkan bahwa model prediksi lahan sawah yang dibangun dari ANN dan LR secara umum memiliki akurasi yang sama-sama baik. Tetapi jika dilihat dari total nilai false alarm dan misses, model CA yang dibangun dari ANN lebih baik dari LR. Hasil penelitian ini juga menunjukkan bahwa dalam rentang tahun 2008 sampai 2019, luasan lahan sawah di Kabupaten Purworejo berkurang sekitar 194.01 Ha.  Kata kunci: artificial neural network; cellular automata; logistic regression; perubahan penggunaan lahan; sawah.  This research aims to compare the accuracy of the ANN and LR methods in predicting changes in paddy fields in Purworejo Regency. The input data required is a map of paddy fields in 2008, 2015 and 2019 as a result of visual interpretation of high-resolution satellite imagery and the driving factors for changes in paddy fields. The results showed that the paddy field prediction model built from ANN and LR generally has the same accuracy. But if it is seen from the total value of false alarms and misses, the CA model from ANN is better than LR. This study shows that from 2008 to 2019, the area of paddy fields in Purworejo Regency decreased around 194.01 Ha. Keywords: artificial neural network; cellular automata; land-use changes; logistic regression; paddy field.

Logistic Regression and Artificial Neural Network: A Comparative Study in Diagnosing Breast Cancer

2021 ◽  
pp. 6330-6334
Author(s):  
W. Abdul Hameed ◽  
Anuradha D. ◽  
Kaspar S.
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Breast Tumor ◽  
Diagnostic Performance ◽  
Malignant Breast Tumor ◽  
Malignant Breast ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Breast tumor is a common problem in gynecology. A reliable test for preoperative discrimination between benign and malignant breast tumor is highly helpful for clinicians in culling the malignant cells through felicitous treatment for patients. This paper is carried out to generate and estimate both logistic regression technique and Artificial Neural Network (ANN) technique to predict the malignancy of breast tumor, utilizing Wisconsin Diagnosis Breast Cancer Database (WDBC). Our aim in this Paper is: (i) to compare the diagnostic performance of both methods in distinguishing between malignant and benign patterns, (ii) to truncate the number of benign cases sent for biopsy utilizing the best model as an auxiliary implement, and (iii) to authenticate the capability of each model to recognize incipient cases as an expert system.

scholarly journals P055 THE USE OF ARTIFICIAL NEURAL NETWORK TO PREDICT THE RISK OF INCISIONAL HERNIA AFTER MIDLINE LAPAROTOMY

2021 ◽  
Vol 108 (Supplement_8) ◽  
Author(s):  
Edgard Efren Lozada Hernandez ◽  
Tania Aglae Ramírez del Real ◽  
Dagoberto Armenta Medina ◽  
Jose Francisco Molina Rodriguez ◽  
Juan ramon Varela Reynoso
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Incisional Hernia ◽  
False Negative ◽  
Wound Dehiscence ◽  
Midline Laparotomy ◽  
Significant Difference ◽  
Positive Rate ◽  
Artificial Neural

Abstract Aim “Incisional Hernia (IH) has an incidence of 10-23%, which can increase to 38% in specific risk groups. The objective of this study was developed and validated an artificial neural network (ANN) model for the prediction of IH after midline laparotomy (ML) and this model can be used by surgeons to help judge a patient’s risk for IH.” Material and Methods “A retrospective, single arm, observational cohort trial was conducted from January 2016 to December 2020. Study participants were recruited from patients undergoing ML for elective or urgent surgical indication. Using logistic regression and ANN models, we evaluated surgical treated IH, wound dehiscence, morbidity, readmission, and mortality using the area under the receiver operating characteristic curves, true-positive rate, true-negative rate, false-positive rate, and false-negative rates.” Results “There was no significant difference in the power of the ANN and logistic regression for predicting IH, wound dehiscence, mortality, readmission, and all morbidities after ML. The resulting model consisted of 4 variables: surgical site infection, emergency surgery, previous laparotomy, and BMI(Kg/m2) > 26. The patient with the four positive factors has a 73% risk of developing incisional hernia. The area under the curve was 0.82 (95% IC 0.76-0.87). Conclusions “ANNs perform comparably to logistic regression models in the prediction of IH. ANNs may be a useful tool in risk factor analysis of IH and clinical applications.”

Sign in / Sign up

Export Citation Format

Share Document