scholarly journals A Feature-Based Analysis for Time-Series Classification of COVID-19 Incidence in Chile: A Case Study

2021 ◽  
Vol 11 (15) ◽  
pp. 7080
Author(s):  
Christopher Flores ◽  
Carla Taramasco ◽  
Maria Elena Lagos ◽  
Carla Rimassa ◽  
Rosa Figueroa
Keyword(s):  
Short Term Memory ◽  
National Level ◽  
Point Of View ◽  
Support Vector ◽  
Mechanical Ventilators ◽  
Feature Based ◽  
Risk Categories ◽  
Better Than

The 2019 Coronavirus disease (COVID-19) pandemic is a current challenge for the world’s health systems aiming to control this disease. From an epidemiological point of view, the control of the incidence of this disease requires an understanding of the influence of the variables describing a population. This research aims to predict the COVID-19 incidence in three risk categories using two types of machine learning models, together with an analysis of the relative importance of the available features in predicting the COVID-19 incidence in the Chilean urban commune of Concepción. The classification results indicate that the ConvLSTM (Convolutional Long Short-Term Memory) classifier performed better than the SVM (Support Vector Machine), with results between 93% and 96% in terms of accuracy (ACC) and F-measure (F1) metrics. In addition, when considering each one of the regional and national features as well as the communal features (DEATHS and MOBILITY), it was observed that at the regional level the CRITICAL BED OCCUPANCY and PATIENTS IN ICU features positively contributed to the performance of the classifiers, while at the national level the features that most impacted the performance of the SVM and ConvLSTM were those related to the type of hospitalization of patients and the use of mechanical ventilators.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

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.

Automatic Classification of ECG Using Multi-Classifiers with a New Fusion Method

2014 ◽  
Vol 687-691 ◽  
pp. 3917-3922
Author(s):  
Yi Chang Wang ◽  
Feng Qi Yan ◽  
Yu Fang
Keyword(s):  
Traditional Method ◽  
Heart Beat ◽  
Vector Model ◽  
Support Vector ◽  
Beat Classification ◽  
Computer Aided Analysis ◽  
Important Basis ◽  
Voting Method ◽  
Better Than

ECG signal contains abundant information of human heart activity. It is important basis of doctors’ diagnose. With the development of computer technology, computer aided analysis has been widely applied in the field of ECG analysis. Most of the traditional method is based on single classifier and too complex. Also, the accuracy is not high. This paper focuses on ECG heart beat classification, extracting different types of feature, training different classifiers by vector model and support vector machine (SVM), merging the result of multiple classifiers. In this paper, we used the advanced voting method (voting by weight) to fusion the result of different classifier, having compared it with the traditional voting method.It performed better than traditional method in term of accuracy

scholarly journals Identification and Classification of Real Estate Features for the Purpose of an Algorithm-Based Valuation– Case Study within Szczecin

2019 ◽  
Vol 19 (2) ◽  
pp. 38-55
Author(s):  
Sebastian Kokot ◽  
Sebastian Gnat
Keyword(s):  
Statistical Analysis ◽  
Real Estate ◽  
Point Of View ◽  
Graphical Methods ◽  
Scientific Discussion ◽  
Valuation Process ◽  
Deductive Method ◽  
Research Criteria

Abstract Research background: The article discusses the issue of the identification and measurement of market characteristics of real estate for valuation purposes. This problem is the most difficult stage of the whole valuation process in terms of both a substantive, methodological and analytical basis. Goal: The aim of the research is to outline and explore on the basis of literature studies as well as developed problems to be solved in the process of mass valuation together with the presentation of an exemplary solution. Methodology: In the theoretical part a hypothetical-deductive method which consists of developing a certain hypothesis and deducing its consequences was applied. The empirical section uses the method of scientific discussion among scientists and practicing valuers and, for the presentation of the results; some graphical methods were used for a statistical analysis. Results: As a result of the conducted research, criteria to be used in identifying and classifying market characteristics for the purposes of valuation were identified and a set of market characteristics of properties was developed along with a method of identifying their states for the purposes of mass valuation. Novelty: The article proves that the problem of the identification and classification of market characteristics of real estate for valuation purposes is extremely important from the point of view of the valuation process and the results obtained as a result of it as well. In addition, for some features, it is proposed to develop special measures such as the plot shape attractiveness ratio. This meets the problem of the objective measurement of market features of real estate. In relation to other features, the legitimacy of the expert approach was pointed out.

scholarly journals Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen

2017 ◽  
Vol 9 (4) ◽  
pp. 416 ◽  
Author(s):  
Nelly Indriani Widiastuti ◽  
Ednawati Rainarli ◽  
Kania Evita Dewi
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Support Vector ◽  
Good Reputation ◽  
Multiclass Support Vector Machine ◽  
Simple Logistic ◽  
Better Than

Classification is the process of grouping objects that have the same features or characteristics into several classes. The automatic documents classification use words frequency that appears on training data as features. The large number of documents cause the number of words that appears as a feature will increase. Therefore, summaries are chosen to reduce the number of words that used in classification. The classification uses multiclass Support Vector Machine (SVM) method. SVM was considered to have a good reputation in the classification. This research tests the effect of summary as selection features into documents classification. The summaries reduce text into 50%. A result obtained that the summaries did not affect value accuracy of classification of documents that use SVM. But, summaries improve the accuracy of Simple Logistic Classifier. The classification testing shows that the accuracy of Naïve Bayes Multinomial (NBM) better than SVM

Investing in Sustainability

2013 ◽  
Vol 4 (2) ◽  
pp. 99-120 ◽  
Author(s):  
Christoph Beckers ◽  
Oliver Marz ◽  
Lutz M. Kolbe
Keyword(s):  
Enterprise Resource Planning ◽  
Sustainability Reporting ◽  
Resource Planning ◽  
Point Of View ◽  
Management Approach ◽  
Meat Industry ◽  
Erp System ◽  
Environmental Information Processing

Given the increased stakeholder focus on sustainability, companies are facing the challenge of systematically collecting and processing sustainability information. Identifying sustainability indicators will become imperative for small and medium-sized enterprises (SME), especially in the extremely segmented and highly specialized food industry – entailing opportunities, but also costs and risks. This article is a management approach and a basis for discussing the classification of sustainability reporting systems in existing Information Technology (IT) infrastructures from an economical point of view. Supported by a case study from the meat industry, the authors discuss to what extent sustainability information is reflected by an existing Enterprise Resource Planning (ERP) System, or which investments would be required for companies to facilitate the communication of sustainability data. Additionally and as a result, the article gives a schematic explanation how process changes can be evaluated economically by capturing and processing sustainability information, and thus how to decide in favor of or against a certain level of IT-based environmental information processing functionality.

scholarly journals Using AI-Based Classification Techniques to Process EEG Data Collected during the Visual Short-Term Memory Assessment

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Milos Antonijevic ◽  
Miodrag Zivkovic ◽  
Sladjana Arsic ◽  
Aleksandar Jevremovic
Keyword(s):  
Visual Information ◽  
Short Term Memory ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Memory Assessment ◽  
Visual Short Term Memory ◽  
Eeg Data ◽  
Short Period

Visual short-term memory (VSTM) is defined as the ability to remember a small amount of visual information, such as colors and shapes, during a short period of time. VSTM is a part of short-term memory, which can hold information up to 30 seconds. In this paper, we present the results of research where we classified the data gathered by using an electroencephalogram (EEG) during a VSTM experiment. The experiment was performed with 12 participants that were required to remember as many details as possible from the two images, displayed for 1 minute. The first assessment was done in an isolated environment, while the second assessment was done in front of the other participants, in order to increase the stress of the examinee. The classification of the EEG data was done by using four algorithms: Naive Bayes, support vector, KNN, and random forest. The results obtained show that AI-based classification could be successfully used in the proposed way, since we were able to correctly classify the order of the images presented 90.12% of the time and type of the displayed image 90.51% of the time.

scholarly journals Comparison of Computational Algorithms for the Classification of Liver Cancer using SELDI Mass Spectrometry: A Case Study

2007 ◽  
Vol 3 ◽  
pp. 117693510700300 ◽  
Author(s):  
Changyu Shen ◽  
Timothy E Breen ◽  
Lacey E Dobrolecki ◽  
C. Max Schmidt ◽  
George W. Sledge ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Hepatocellular Carcinoma ◽  
Support Vector Machine ◽  
Random Forest ◽  
Radial Function ◽  
Support Vector ◽  
Laser Desorption Ionization ◽  
Prediction Analysis

Introduction As an alternative to DNA microarrays, mass spectrometry based analysis of proteomic patterns has shown great potential in cancer diagnosis. The ultimate application of this technique in clinical settings relies on the advancement of the technology itself and the maturity of the computational tools used to analyze the data. A number of computational algorithms constructed on different principles are available for the classification of disease status based on proteomic patterns. Nevertheless, few studies have addressed the difference in the performance of these approaches. In this report, we describe a comparative case study on the classification accuracy of hepatocellular carcinoma based on the serum proteomic pattern generated from a Surface Enhanced Laser Desorption/Ionization (SELDI) mass spectrometer. Methods Nine supervised classification algorithms are implemented in R software and compared for the classification accuracy. Results We found that the support vector machine with radial function is preferable as a tool for classification of hepatocellular carcinoma using features in SELDI mass spectra. Among the rest of the methods, random forest and prediction analysis of microarrays have better performance. A permutation-based technique reveals that the support vector machine with a radial function seems intrinsically superior in learning from the training data since it has a lower prediction error than others when there is essentially no differential signal. On the other hand, the performance of the random forest and prediction analysis of microarrays rely on their capability of capturing the signals with substantial differentiation between groups. Conclusions Our finding is similar to a previous study, where classification methods based on the Matrix Assisted Laser Desorption/Ionization (MALDI) mass spectrometry are compared for the prediction accuracy of ovarian cancer. The support vector machine, random forest and prediction analysis of microarrays provide better prediction accuracy for hepatocellular carcinoma using SELDI proteomic data than six other approaches.

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