The Identification of Oreochromis niloticus Feeding Behaviour Through the Integration of Photoelectric Sensor and Logistic Regression Classifier

In today’s modern world, the world population is affected with some kind of heart diseases. With the vast knowledge and advancement in applications, the analysis and the identification of the heart disease still remain as a challenging issue. Due to the lack of awareness in the availability of patient symptoms, the prediction of heart disease is a questionable task. The World Health Organization has released that 33% of population were died due to the attack of heart diseases. With this background, we have used Heart Disease Prediction dataset extracted from UCI Machine Learning Repository for analyzing and the prediction of heart disease by integrating the ensembling methods. The prediction of heart disease classes are achieved in four ways. Firstly, The important features are extracted for the various ensembling methods like Extra Trees Regressor, Ada boost regressor, Gradient booster regress, Random forest regressor and Ada boost classifier. Secondly, the highly importance features of each of the ensembling methods is filtered from the dataset and it is fitted to logistic regression classifier to analyze the performance. Thirdly, the same extracted important features of each of the ensembling methods are subjected to feature scaling and then fitted with logistic regression to analyze the performance. Fourth, the Performance analysis is done with the performance metric such as Mean Squared error (MSE), Mean Absolute error (MAE), R2 Score, Explained Variance Score (EVS) and Mean Squared Log Error (MSLE). The implementation is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that before applying feature scaling, the feature importance extracted from the Ada boost classifier is found to be effective with the MSE of 0.04, MAE of 0.07, R2 Score of 92%, EVS of 0.86 and MSLE of 0.16 as compared to other ensembling methods. Experimental results shows that after applying feature scaling, the feature importance extracted from the Ada boost classifier is found to be effective with the MSE of 0.09, MAE of 0.13, R2 Score of 91%, EVS of 0.93 and MSLE of 0.18 as compared to other ensembling methods.

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An Improved Model Based Detection of Urban Impervious Surfaces Using Multiple Features Extracted from ROSIS-3 Hyperspectral Images

Remote Sensing ◽

10.3390/rs11020136 ◽

2019 ◽

Vol 11 (2) ◽

pp. 136 ◽

Cited By ~ 2

Author(s):

Yuliang Wang ◽

Huiyi Su ◽

Mingshi Li

Keyword(s):

Feature Extraction ◽

Logistic Regression ◽

Dimensionality Reduction ◽

Spectral Feature ◽

Hyperspectral Images ◽

Nonlinear Dimensionality Reduction ◽

Impervious Surfaces ◽

Urban Impervious Surfaces ◽

Improved Model ◽

Logistic Regression Classifier

Hyperspectral images (HSIs) provide unique capabilities for urban impervious surfaces (UIS) extraction. This paper proposes a multi-feature extraction model (MFEM) for UIS detection from HSIs. The model is based on a nonlinear dimensionality reduction technique, t-distributed stochastic neighbor embedding (t-SNE), and the deep learning method convolutional deep belief networks (CDBNs). We improved the two methods to create a novel MFEM consisting of improved t-SNE, deep compression CDBNs (d-CDBNs), and a logistic regression classifier. The improved t-SNE method provides dimensionality reduction and spectral feature extraction from the original HSIs and the d-CDBNs algorithm extracts spatial feature and edges using the reduced dimensional datasets. Finally, the extracted features are combined into multi-feature for the impervious surface detection using the logistic regression classifier. After comparing with the commonly used methods, the current experimental results demonstrate that the proposed MFEM model provides better performance for UIS extraction and detection from HSIs.

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Improving vertical positioning accuracy with the weighted multinomial logistic regression classifier

SN Applied Sciences ◽

10.1007/s42452-020-03240-w ◽

2020 ◽

Vol 2 (8) ◽

Author(s):

Yiyan Yao ◽

Xin-long Luo

Keyword(s):

Logistic Regression ◽

Multinomial Logistic Regression ◽

Positioning Accuracy ◽

Vertical Positioning ◽

Logistic Regression Classifier

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Anomaly Detection and Severity Prediction of Air Leakage in Train Braking Pipes

International Journal of Prognostics and Health Management ◽

10.36001/ijphm.2017.v8i3.2662 ◽

2020 ◽

Vol 8 (3) ◽

Author(s):

Wan-Jui Lee

Keyword(s):

Logistic Regression ◽

Anomaly Detection ◽

Detection Method ◽

Idle Time ◽

Remaining Useful Life ◽

False Alarms ◽

Air Leakage ◽

Severity Prediction ◽

Run Time ◽

Logistic Regression Classifier

Air leakage in braking pipes is a commonly encountered mechanical defect on trains. A severe air leakage will lead to braking issues and therefore decrease the reliability and cause train delays or stranding. However, air leakage is difficult to be detected via visual inspection and therefore most air leakage defects are run to fail. In this research we present a framework that not only can detect air leakages but also predicts the severity of air leakages so that action plans can be determined based on the severity level. The proposed contextual anomaly detection method detects air leakages based on the on/off logs of a compressor. Air leakage causes failure in the context when the compressor idle time is short than the compressor run time, that is, the speed of air consumption is faster than air generation. In our method the logistic regression classifier is adopted to model two different classes of compressor behavior for each train separately. The logistic regression classifier defines the boundary separating the two classes under normal situations and models the distribution of the compressor idle time and run time separately using logistic functions. The air leakage anomaly is further detected in the context that when a compressor idle time is erroneously classified as a compressor run time. To distinguish anomalies from outliers and detect anomalies based on the severity degree, a density-based clustering method with a dynamic density threshold is developed for anomaly detection. The results have demonstrated that most air leakages can be detected one to four weeks before the braking failure and therefore can be prevented in time. Most importantly, the contextual anomaly detection method can pre-filter anomaly candidates and therefore avoid generating false alarms. To facilitate the decisionmaking process, the logistic function built on the compressor run time is further used together with the duration of an air leakage to model the severity of the air leakage. By building the prediction model on the severity, the remaining useful life of the air braking pipe until it reaches a certain level of severity can be estimated.

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Perbandingan Algoritma Klasifikasi Sentimen Twitter Terhadap Insiden Kebocoran Data Tokopedia

JISKA (Jurnal Informatika Sunan Kalijaga) ◽

10.14421/jiska.2021.6.2.120-129 ◽

2021 ◽

Vol 6 (2) ◽

pp. 120-129

Author(s):

Nadhif Ikbar Wibowo ◽

Tri Andika Maulana ◽

Hamzah Muhammad ◽

Nur Aini Rakhmawati

Keyword(s):

Support Vector Machine ◽

Logistic Regression ◽

Random Forest ◽

Supervised Learning ◽

Support Vector ◽

Data Set ◽

Logistic Regression Classifier

Public responses, posted on Twitter reacting to the Tokopedia data leak incident, were used as a data set to compare the performance of three different classifiers, trained using supervised learning modeling, to classify sentiment on the text. All tweets were classified into either positive, negative, or neutral classes. This study compares the performance of Random Forest, Support-Vector Machine, and Logistic Regression classifier. Data was scraped automatically and used to evaluate several models; the SVM-based model has the highest f1-score 0.503583. SVM is the best performing classifier.

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Feasibility Testing of a Wearable Behavioral Aid for Social Learning in Children with Autism

Applied Clinical Informatics ◽

10.1055/s-0038-1626727 ◽

2018 ◽

Vol 09 (01) ◽

pp. 129-140 ◽

Cited By ~ 13

Author(s):

Jena Daniels ◽

Nick Haber ◽

Catalin Voss ◽

Jessey Schwartz ◽

Serena Tamura ◽

...

Keyword(s):

Logistic Regression ◽

Emotion Recognition ◽

Recognition Task ◽

Affective Learning ◽

Autism Spectrum ◽

Therapeutic Interventions ◽

Emotion Classification ◽

Google Glass ◽

Children With Asd ◽

Logistic Regression Classifier

Background Recent advances in computer vision and wearable technology have created an opportunity to introduce mobile therapy systems for autism spectrum disorders (ASD) that can respond to the increasing demand for therapeutic interventions; however, feasibility questions must be answered first. Objective We studied the feasibility of a prototype therapeutic tool for children with ASD using Google Glass, examining whether children with ASD would wear such a device, if providing the emotion classification will improve emotion recognition, and how emotion recognition differs between ASD participants and neurotypical controls (NC). Methods We ran a controlled laboratory experiment with 43 children: 23 with ASD and 20 NC. Children identified static facial images on a computer screen with one of 7 emotions in 3 successive batches: the first with no information about emotion provided to the child, the second with the correct classification from the Glass labeling the emotion, and the third again without emotion information. We then trained a logistic regression classifier on the emotion confusion matrices generated by the two information-free batches to predict ASD versus NC. Results All 43 children were comfortable wearing the Glass. ASD and NC participants who completed the computer task with Glass providing audible emotion labeling (n = 33) showed increased accuracies in emotion labeling, and the logistic regression classifier achieved an accuracy of 72.7%. Further analysis suggests that the ability to recognize surprise, fear, and neutrality may distinguish ASD cases from NC. Conclusion This feasibility study supports the utility of a wearable device for social affective learning in ASD children and demonstrates subtle differences in how ASD and NC children perform on an emotion recognition task.

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Classifying Autism Spectrum Disorder using Machine Learning Models

10.5121/csit.2021.110306 ◽

2021 ◽

Author(s):

Tingyan Deng

Keyword(s):

Logistic Regression ◽

Autistic Spectrum Disorder ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Autistic Spectrum ◽

Time Treatment ◽

Proposed Model ◽

Related Feature ◽

And Behavior ◽

Logistic Regression Classifier

Autistic Spectrum Disorder (ASD) is a developmental disability, which can affect communication and behavior, causing significant social, communication, and behavior challenge. From a rare childhood disorder, ASD has evolved into a disorder that is found, according to the National Institute of Health, in 1% to 2% of the population in high income countries. A potential early and accurate diagnosis can not only help doctors to find the disease early, leading to a more on time treatment to the patient, but also can save significant healthcare costs for the patients. With the rapid growth of ASD cases, many open-source ASD related datasets were created for scientists and doctors to investigate this disease. Autistic Spectrum Disorder Screening Data for Adult is a well-known dataset, which contains 20 features to be utilized for further analysis on the potential cause and prediction of ASD. In this paper, we developed an Autism classification algorithm based on logistic regression model. Our model starts with featuring engineering to extract deep information from the dataset and then applied a modified logistic regression classifier to the data. The model can predict the ASD in an average F1 score of 0.97, which displays the superiority and feasibility of the proposed model. Besides, the data visualization technique was used to displays several feature distributions images for people to better understand the data and related feature engineering.

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