scholarly journals Website-Based Application for Classification of Diabetes Using Logistic Regression Method

2021 ◽  
pp. 23
Author(s):  
Muhamad Soleh ◽  
Naufal Ammar ◽  
Indrati Sukmadi
Keyword(s):  
Machine Learning ◽  
Regression Method ◽  
Training Data ◽  
Support Vector ◽  
Application Development ◽  
Science Field ◽  
Logistics Regression ◽  
Vector Machines ◽  
Logistic Regression Method

Machine learning is a one of computer science field, machine-learning studies how computers are able to learn from data to improve their intelligence. Machine learning consists of many classification methods, including Neural Networks, Support Vector Machines, Logistics Regression, and others. In this study, a classification process carried out using the Logistics Regression method for cases of Diabetes. Diabetes is an increase in glucose in the bloodstream due to a lack of insulin, which is responsible for the transfer of glucose from the blood to tissues or cells. This study created with the aim of improving previous paper. The data used in this study are the same data as previous studies published by the Pima Indian Diabetes Dataset. In this study, several stages used, those are pre-processing, processing, evaluation, and website-based application development. The data in this study divided into two, 75% for training data, and 25% for testing data. This study produces an evaluation with an accuracy 80%, which means it is better than the previous paper, which is 75, 97%.

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

scholarly journals Prediksi Not Operational Transaction Menggunakan Logistic Regression pada Bank XYZ di Kota Kupang

AITI ◽  
2020 ◽  
Vol 17 (1) ◽  
pp. 42-55
Author(s):  
Radius Tanone ◽  
Arnold B Emmanuel
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Method ◽  
Learning Approach ◽  
Know How ◽  
Independent Variables ◽  
Machine Learning Approach ◽  
Logistic Regression Method ◽  
Python Programming

Bank XYZ is one of the banks in Kupang City, East Nusa Tenggara Province which has several ATM machines and is placed in several merchant locations. The existing ATM machine is one of the goals of customers and non-customers in conducting transactions at the ATM machine. The placement of the ATM machines sometimes makes the machine not used optimally by the customer to transact, causing the disposal of machine resources and a condition called Not Operational Transaction (NOP). With the data consisting of several independent variables with numeric types, it is necessary to know how the classification of the dependent variable is NOP. Machine learning approach with Logistic Regression method is the solution in doing this classification. Some research steps are carried out by collecting data, analyzing using machine learning using python programming and writing reports. The results obtained with this machine learning approach is the resulting prediction value of 0.507 for its classification. This means that in the future XYZ Bank can classify NOP conditions based on the behavior of customers or non-customers in making transactions using Bank XYZ ATM machines.  

A Comparison of Machine Learning Algorithms for the Segmentation and Classification of Snow Micro Penetrometer Profiles on Arctic Sea Ice

2021 ◽  
Author(s):  
Julia Kaltenborn ◽  
Viviane Clay ◽  
Amy R. Macfarlane ◽  
Joshua Michael Lloyd King ◽  
Martin Schneebeli
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Snow Layer ◽  
Arctic Sea ◽  
Execution Speed

<p>Snow-layer classification is an essential diagnostic task for a wide variety of cryospheric science and climate research applications. Traditionally, these measurements are made in snow pits, requiring trained operators and a substantial time commitment. The SnowMicroPen (SMP), a portable high-resolution snow penetrometer, has been demonstrated as a capable tool for rapid snow grain classification and layer type segmentation through statistical inversion of its mechanical signal. The manual classification of the SMP profiles requires time and training and becomes infeasible for large datasets.</p><p>Here, we introduce a novel set of SMP measurements collected during the MOSAiC expedition and apply Machine Learning (ML) algorithms to automatically classify and segment SMP profiles of snow on Arctic sea ice. To this end, different supervised and unsupervised ML methods, including Random Forests, Support Vector Machines, Artificial Neural Networks, and k-means Clustering, are compared. A subsequent segmentation of the classified data results in distinct layers and snow grain markers for the SMP profiles. The models are trained with the dataset by King et al. (2020) and the MOSAiC SMP dataset. The MOSAiC dataset is a unique and extensive dataset characterizing seasonal and spatial variation of snow on the central Arctic sea-ice.</p><p>We will test and compare the different algorithms and evaluate the algorithms’ effectiveness based on the need for initial dataset labeling, execution speed, and ease of implementation. In particular, we will compare supervised to unsupervised methods, which are distinguished by their need for labeled training data.</p><p>The implementation of different ML algorithms for SMP profile classification could provide a fast and automatic grain type classification and snow layer segmentation. Based on the gained knowledge from the algorithms’ comparison, a tool can be built to provide scientists from different fields with an immediate SMP profile classification and segmentation. </p><p> </p><p>King, J., Howell, S., Brady, M., Toose, P., Derksen, C., Haas, C., & Beckers, J. (2020). Local-scale variability of snow density on Arctic sea ice. <em>The Cryosphere</em>, <em>14</em>(12), 4323-4339, https://doi.org/10.5194/tc-14-4323-2020.</p>

Using Remote Monitoring and Machine Learning to Classify Slam Events of Wave Piercing Catamarans

2021 ◽  
Vol 163 (A3) ◽  
Author(s):  
B Shabani ◽  
J Ali-Lavroff ◽  
D S Holloway ◽  
S Penev ◽  
D Dessi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
Probability Distributions ◽  
Feature Space ◽  
Support Vector ◽  
Acceleration Threshold ◽  
Vector Machines ◽  
Structural Responses ◽  
As Stress

An onboard monitoring system can measure features such as stress cycles counts and provide warnings due to slamming. Considering current technology trends there is the opportunity of incorporating machine learning methods into monitoring systems. A hull monitoring system has been developed and installed on a 111 m wave piercing catamaran (Hull 091) to remotely monitor the ship kinematics and hull structural responses. Parallel to that, an existing dataset of a similar vessel (Hull 061) was analysed using unsupervised and supervised learning models; these were found to be beneficial for the classification of bow entry events according to key kinematic parameters. A comparison of different algorithms including linear support vector machines, naïve Bayes and decision tree for the bow entry classification were conducted. In addition, using empirical probability distributions, the likelihood of wet-deck slamming was estimated given a vertical bow acceleration threshold of 1  in head seas, clustering the feature space with the approximate probabilities of 0.001, 0.030 and 0.25.

scholarly journals Enhanced Changeover Detection in Industry 4.0 Environments with Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5896
Author(s):  
Eddi Miller ◽  
Vladyslav Borysenko ◽  
Moritz Heusinger ◽  
Niklas Niedner ◽  
Bastian Engelmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Binary Classification ◽  
Model Performance ◽  
Support Vector ◽  
Milling Machine ◽  
Vector Machines ◽  
Changeover Times ◽  
Flow Power

Changeover times are an important element when evaluating the Overall Equipment Effectiveness (OEE) of a production machine. The article presents a machine learning (ML) approach that is based on an external sensor setup to automatically detect changeovers in a shopfloor environment. The door statuses, coolant flow, power consumption, and operator indoor GPS data of a milling machine were used in the ML approach. As ML methods, Decision Trees, Support Vector Machines, (Balanced) Random Forest algorithms, and Neural Networks were chosen, and their performance was compared. The best results were achieved with the Random Forest ML model (97% F1 score, 99.72% AUC score). It was also carried out that model performance is optimal when only a binary classification of a changeover phase and a production phase is considered and less subphases of the changeover process are applied.

scholarly journals Extraction of Sea Ice Cover by Sentinel-1 SAR Based on SVM with Unsupervised Generation of Training Data

2020 ◽  
Author(s):  
Xiaoming Li ◽  
Yan Sun ◽  
Qiang Zhang
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Learning Algorithm ◽  
Texture Features ◽  
Open Water ◽  
Ice Cover ◽  
Training Data ◽  
Support Vector ◽  
Training Samples

In this paper, we focus on developing a novel method to extract sea ice cover (i.e., discrimination/classification of sea ice and open water) using Sentinel-1 (S1) cross-polarization (vertical-horizontal, VH or horizontal-vertical, HV) data in extra wide (EW) swath mode based on the machine learning algorithm support vector machine (SVM). The classification basis includes the S1 radar backscatter coefficients and texture features that are calculated from S1 data using the gray level co-occurrence matrix (GLCM). Different from previous methods where appropriate samples are manually selected to train the SVM to classify sea ice and open water, we proposed a method of unsupervised generation of the training samples based on two GLCM texture features, i.e. entropy and homogeneity, that have contrasting characteristics on sea ice and open water. We eliminate the most uncertainty of selecting training samples in machine learning and achieve automatic classification of sea ice and open water by using S1 EW data. The comparison shows good agreement between the SAR-derived sea ice cover using the proposed method and a visual inspection, of which the accuracy reaches approximately 90% - 95% based on a few cases. Besides this, compared with the analyzed sea ice cover data Ice Mapping System (IMS) based on 728 S1 EW images, the accuracy of extracted sea ice cover by using S1 data is more than 80%.

scholarly journals Supervised Learning Based Classification of Cardiovascular Diseases

2021 ◽  
Vol 20 ◽  
pp. 24-34
Author(s):  
Arif Hussain ◽  
Hassaan Malik ◽  
Muhammad Umar Chaudhry
Keyword(s):  
Machine Learning ◽  
Model Building ◽  
Matthews Correlation Coefficient ◽  
Early Stage ◽  
Support Vector ◽  
Logistics Regression ◽  
Efficiency And Effectiveness ◽  
Sensitivity Specificity ◽  
Better Than

Detecting cardiovascular disease (CVD) in the early stage is a difficult and crucial process. The objective of this study is to test the capability of machine learning (ML) methods for accurately diagnosing the CVD outcomes. For this study, the efficiency and effectiveness of four well renowned ML classifiers, i.e., support vector machine (SVM), logistics regression (LR), naive Bayes (NB), and decision tree (J48), are measured in terms of precision, sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), correctly and incorrectly classified instances, and model building time. These ML classifiers are applied on publically available CVD dataset. In accordance with the measured result, J48 performs better than its competitor classifiers, providing significant assistance to the cardiologists.

Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines

2020 ◽  
Vol 16 (6) ◽  
pp. 703-710 ◽  
Author(s):  
Jebasonia Jebamony ◽  
Dheeba Jacob
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Misclassification Rate ◽  
Support Vector ◽  
Mass Detection ◽  
Training Process ◽  
Vector Machines

Background: Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. Objective: The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. Methods: The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. Results: Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. Conclusion: Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.

scholarly journals A Neural Basis for the Implementation of Deep Learning and Arti cial Intelligence

2018 ◽  
Vol 7 (4.36) ◽  
pp. 444 ◽  
Author(s):  
Alan F. Smeaton ◽  
. .
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Training Data ◽  
Computer Architectures ◽  
Support Vector ◽  
Neural Basis ◽  
Mathematical Basis ◽  
Unseen Data ◽  
Vector Machines ◽  
Conventional Computer

One of the mathematical cornerstones of modern data ana-lytics is machine learning whereby we automatically learn subtle patterns which may be hidden in training data, we associate those patterns with outcomes and we apply these patterns to new and unseen data and make predictions about as yet unseen outcomes. This form of data analytics al-lows us to bring value to the huge volumes of data that is collected from people, from the environment, from commerce, from online activities, from scienti c experiments, from many other sources. The mathematical basis for this form of machine learning has led to tools like Support Vector Machines which have shown moderate e ectiveness and good e ciency in their implementation. Recently, however, these have been usurped by the emergence of deep learning based on convolutional neural networks. In this presentation we will examine the basis for why such deep net-works are remarkably successful and accurate, their similarity to ways in which the human brain is organised, and the challenges of implementing such deep networks on conventional computer architectures.  

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