scholarly journals A Method for the Definition of Immunological Non-response to Antiretroviral Therapy Based on Review Analysis and Supervised Classification Model

2021 ◽  
Author(s):  
Yong Shuai
Keyword(s):  
Supervised Learning ◽  
Supervised Classification ◽  
Absolute Number ◽  
Learning Model ◽  
Classification Model ◽  
Support Vector ◽  
Infected People ◽  
Machine Learning Classification ◽  
Visualization Technology ◽  
Definition Of

Abstract BackgroundImmunological non-response (INR) accelerated the progression of AIDS disease and brought serious difficulties to the treatment of HIV-1 infected people. The current definition of INR lacked a credible consensus, which affected the diagnosis, treatment and scientific research of INR. MethodWe systematically analyzed the open source INR related references, used visualization techniques and machine learning classification models to propose the features, models and criteria that define INR. ResultWe summarized some consensus on the definition of INR. Among the features that defined INR, CD4+ T-cell absolute number and ART time were the best feature to define INR . The supervised learning classification model had high accuracy in defining INR, and the support vector machine (SVM) had the highest accuracy in the commonly used supervised classification learning model. Based on supervised learning model and visualization technology, we proposed some criteria that could help to reach a consensus on INR definition. ConclusionsThis study provided consensus, features, model and criteria for defining INR.

scholarly journals A New Epigenetic Model to Stratify Glioma Patients According to Their Immunosuppressive State

Cells ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 576
Author(s):  
Maurizio Polano ◽  
Emanuele Fabbiani ◽  
Eva Adreuzzi ◽  
Federica Di Cintio ◽  
Luca Bedon ◽  
...  
Keyword(s):  
The Cancer Genome Atlas ◽  
Classification Model ◽  
Low Grade ◽  
Machine Learning Classification ◽  
Immune State ◽  
Out Of Sample ◽  
Cancer Genome Atlas ◽  
The Central Nervous System ◽  
Neural Network Classifiers ◽  
Definition Of

Gliomas are the most common primary neoplasm of the central nervous system. A promising frontier in the definition of glioma prognosis and treatment is represented by epigenetics. Furthermore, in this study, we developed a machine learning classification model based on epigenetic data (CpG probes) to separate patients according to their state of immunosuppression. We considered 573 cases of low-grade glioma (LGG) and glioblastoma (GBM) from The Cancer Genome Atlas (TCGA). First, from gene expression data, we derived a novel binary indicator to flag patients with a favorable immune state. Then, based on previous studies, we selected the genes related to the immune state of tumor microenvironment. After, we improved the selection with a data-driven procedure, based on Boruta. Finally, we tuned, trained, and evaluated both random forest and neural network classifiers on the resulting dataset. We found that a multi-layer perceptron network fed by the 338 probes selected by applying both expert choice and Boruta results in the best performance, achieving an out-of-sample accuracy of 82.8%, a Matthews correlation coefficient of 0.657, and an area under the ROC curve of 0.9. Based on the proposed model, we provided a method to stratify glioma patients according to their epigenomic state.

Evaluating disaster-related tweet credibility using content-based and user-based features

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nasser Assery ◽  
Yuan (Dorothy) Xiaohong ◽  
Qu Xiuli ◽  
Roy Kaushik ◽  
Sultan Almalki
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Emergency Response ◽  
Learning Model ◽  
Performance Comparison ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Content Type ◽  
Machine Learning Classification

Purpose This study aims to propose an unsupervised learning model to evaluate the credibility of disaster-related Twitter data and present a performance comparison with commonly used supervised machine learning models. Design/methodology/approach First historical tweets on two recent hurricane events are collected via Twitter API. Then a credibility scoring system is implemented in which the tweet features are analyzed to give a credibility score and credibility label to the tweet. After that, supervised machine learning classification is implemented using various classification algorithms and their performances are compared. Findings The proposed unsupervised learning model could enhance the emergency response by providing a fast way to determine the credibility of disaster-related tweets. Additionally, the comparison of the supervised classification models reveals that the Random Forest classifier performs significantly better than the SVM and Logistic Regression classifiers in classifying the credibility of disaster-related tweets. Originality/value In this paper, an unsupervised 10-point scoring model is proposed to evaluate the tweets’ credibility based on the user-based and content-based features. This technique could be used to evaluate the credibility of disaster-related tweets on future hurricanes and would have the potential to enhance emergency response during critical events. The comparative study of different supervised learning methods has revealed effective supervised learning methods for evaluating the credibility of Tweeter data.

Semi-Supervised Classification and its Application to Filtering IDS False Positives

2013 ◽  
Vol 427-429 ◽  
pp. 2309-2312
Author(s):  
Hai Bin Mei ◽  
Ming Hua Zhang
Keyword(s):  
Supervised Learning ◽  
Supervised Classification ◽  
Classification Performance ◽  
False Positives ◽  
Training Data ◽  
Classification Model ◽  
Classification Technique

Alert classifiers built with the supervised classification technique require large amounts of labeled training alerts. Preparing for such training data is very difficult and expensive. Thus accuracy and feasibility of current classifiers are greatly restricted. This paper employs semi-supervised learning to build alert classification model to reduce the number of needed labeled training alerts. Alert context properties are also introduced to improve the classification performance. Experiments have demonstrated the accuracy and feasibility of our approach.

Self-Trained Stacking Model for Semi-Supervised Learning

2017 ◽  
Vol 26 (02) ◽  
pp. 1750001 ◽  
Author(s):  
Stamatis Karlos ◽  
Nikos Fazakis ◽  
Sotiris Kotsiantis ◽  
Kyriakos Sgarbas
Keyword(s):  
Supervised Learning ◽  
Supervised Classification ◽  
The Other ◽  
Support Vector ◽  
Training Phase ◽  
Classification Methods ◽  
Bayesian Algorithm ◽  
Benchmark Datasets ◽  
Supervised Methods ◽  
Supervised Classification Methods

The most important characteristic of semi-supervised learning methods is the combination of available unlabeled data along with an enough smaller set of labeled examples, so as to increase the learning accuracy compared with the default procedure of supervised methods, which on the other hand use only the labeled data during the training phase. In this work, we have implemented a hybrid Self-trained system that combines a Support Vector Machine, a Decision Tree, a Lazy Learner and a Bayesian algorithm using a Stacking variant methodology. We performed an in depth comparison with other well-known Semi-Supervised classification methods on standard benchmark datasets and we finally reached to the point that the presented technique had better accuracy in most cases.

scholarly journals Daily Living Movement Recognition for Pedestrian Dead Reckoning Applications

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Alessio Martinelli ◽  
Simone Morosi ◽  
Enrico Del Re
Keyword(s):  
Supervised Learning ◽  
Activity Recognition ◽  
Sport Activity ◽  
Dead Reckoning ◽  
Support Vector ◽  
Navigation Systems ◽  
Cyclic Movements ◽  
Movement Recognition ◽  
Definition Of ◽  
Pedestrian Dead Reckoning

Nowadays, activity recognition is a central topic in numerous applications such as patient and sport activity monitoring, surveillance, and navigation. By focusing on the latter, in particular Pedestrian Dead Reckoning navigation systems, activity recognition is generally exploited to get landmarks on the map of the buildings in order to permit the calibration of the navigation routines. The present work aims to provide a contribution to the definition of a more effective movement recognition for Pedestrian Dead Reckoning applications. The signal acquired by a belt-mounted triaxial accelerometer is considered as the input to the movement segmentation procedure which exploits Continuous Wavelet Transform to detect and segment cyclic movements such as walking. Furthermore, the segmented movements are provided to a supervised learning classifier in order to distinguish between activities such as walking and walking downstairs and upstairs. In particular, four supervised learning classification families are tested: decision tree, Support Vector Machine,k-nearest neighbour, and Ensemble Learner. Finally, the accuracy of the considered classification models is evaluated and the relative confusion matrices are presented.

scholarly journals SupportNet: a novel incremental learning framework through deep learning and support data

2018 ◽  
Author(s):  
Yu Li ◽  
Zhongxiao Li ◽  
Lizhong Ding ◽  
Yuhui Hu ◽  
Wei Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Incremental Learning ◽  
Learning Model ◽  
Biological Data ◽  
Classification Model ◽  
Support Vector ◽  
Data Sets ◽  
Essential Information ◽  
New Information ◽  
Deep Learning Model

ABSTRACTMotivationIn most biological data sets, the amount of data is regularly growing and the number of classes is continuously increasing. To deal with the new data from the new classes, one approach is to train a classification model, e.g., a deep learning model, from scratch based on both old and new data. This approach is highly computationally costly and the extracted features are likely very different from the ones extracted by the model trained on the old data alone, which leads to poor model robustness. Another approach is to fine tune the trained model from the old data on the new data. However, this approach often does not have the ability to learn new knowledge without forgetting the previously learned knowledge, which is known as the catastrophic forgetting problem. To our knowledge, this problem has not been studied in the field of bioinformatics despite its existence in many bioinformatic problems.ResultsHere we propose a novel method, SupportNet, to solve the catastrophic forgetting problem efficiently and effectively. SupportNet combines the strength of deep learning and support vector machine (SVM), where SVM is used to identify the support data from the old data, which are fed to the deep learning model together with the new data for further training so that the model can review the essential information of the old data when learning the new information. Two powerful consolidation regularizers are applied to ensure the robustness of the learned model. Comprehensive experiments on various tasks, including enzyme function prediction, subcellular structure classification and breast tumor classification, show that SupportNet drastically outperforms the state-of-the-art incremental learning methods and reaches similar performance as the deep learning model trained from scratch on both old and new data.AvailabilityOur program is accessible at: https://github.com/lykaust15/SupportNet.

scholarly journals Revealing the unique features of each individual’s muscle activation signatures

2020 ◽  
Author(s):  
Jeroen Aeles ◽  
Fabian Horst ◽  
Sebastian Lapuschkin ◽  
Lilian Lacourpaille ◽  
François Hug
Keyword(s):  
Muscle Activation ◽  
Conclusive Evidence ◽  
Classification Model ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Individual Muscle ◽  
Lower Limb Muscles ◽  
Model Predictions ◽  
Exact Origin ◽  
Individual Input

AbstractThere is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear Support Vector Machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision making by the machine learning classification model, a Layer-wise Relevance Propagation (LRP) approach was implemented. This enabled the model predictions to be decomposed into relevance scores for each individual input value. In other words, it provided information regarding which features of the time-varying EMG profiles were unique to each individual. Through extensive testing, we have shown that the LRP results, and by extent the activation signatures, are highly consistent between conditions and across days. In addition, they are minimally influenced by the dataset used to train the model. Additionally, we proposed a method for visualising each individual’s muscle activation signature, which has several potential clinical and scientific applications. This is the first study to provide conclusive evidence of the existence of individual muscle activation signatures.

scholarly journals Detection of coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine

2020 ◽  
Vol 5 (4) ◽  
pp. 643-651 ◽  
Author(s):  
Prabira Kumar Sethy ◽  
Santi Kumari Behera ◽  
Pradyumna Kumar Ratha ◽  
Preesat Biswas
Keyword(s):  
Support Vector Machine ◽  
Medical Practitioner ◽  
Local Binary Patterns ◽  
Classification Model ◽  
Classification Method ◽  
Support Vector ◽  
Data Set ◽  
X Ray ◽  
Infected People ◽  
Deep Feature

The detection of coronavirus (COVID-19) is now a critical task for the medical practitioner. The coronavirus spread so quickly between people and approaches 100,000 people worldwide. In this consequence, it is very much essential to identify the infected people so that prevention of spread can be taken. In this paper, the deep feature plus support vector machine (SVM) based methodology is suggested for detection of coronavirus infected patient using X-ray images. For classification, SVM is used instead of deep learning based classifier, as the later one need a large dataset for training and validation. The deep features from the fully connected layer of CNN model are extracted and fed to SVM for classification purpose. The SVM classifies the corona affected X-ray images from others. The methodology consists of three categories of Xray images, i.e., COVID-19, pneumonia and normal. The method is beneficial for the medical practitioner to classify among the COVID-19 patient, pneumonia patient and healthy people. SVM is evaluated for detection of COVID-19 using the deep features of different 13 number of CNN models. The SVM produced the best results using the deep feature of ResNet50. The classification model, i.e. ResNet50 plus SVM achieved accuracy, sensitivity, FPR and F1 score of 95.33%,95.33%,2.33% and 95.34% respectively for detection of COVID-19 (ignoring SARS, MERS and ARDS). Again, the highest accuracy achieved by ResNet50 plus SVM is 98.66%. The result is based on the Xray images available in the repository of GitHub and Kaggle. As the data set is in hundreds, the classification based on SVM is more robust compared to the transfer learning approach. Also, a comparison analysis of other traditional classification method is carried out. The traditional methods are local binary patterns (LBP) plus SVM, histogram of oriented gradients (HOG) plus SVM and Gray Level Co-occurrence Matrix (GLCM) plus SVM. In traditional image classification method, LBP plus SVM achieved 93.4% of accuracy.

scholarly journals Performance Evaluation of Machine Learning Predictive Analytical Model for Determining the Job Applicants Employment Status

2021 ◽  
Vol 6 (1) ◽  
pp. 67-79
Author(s):  
Olalekan Awujoola ◽  
Philip O Odion ◽  
Martins E Irhebhude ◽  
Halima Aminu
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Job Applicants ◽  
Classification Model ◽  
Management Decision ◽  
Support Vector ◽  
Tertiary Institution ◽  
Machine Learning Classification ◽  
Higher Institution ◽  
Management Decision Making

Several higher institution of learning faces issue or difficulty of turning out more than 90% of their graduates who can competently satisfy and meet the requirements of the industry. However, the industry is also confronted with the difficulty of sourcing skilled tertiary institution graduates that match their needs. Failure or success of any organization depends mostly on how its workforce is recruited and retained. Therefore, the selection of an acceptable or satisfactory candidate for the job position is one of the major and vital problems of management decision-making. This work, therefore, proposes a modern, accurate and worthy machine learning classification model that can be deployed, implemented, and put to use when making predictions and assessments on job applicant's attributes from their academic performance datasets in other to meet the selection criteria for the industry. Both supervised and unsupervised machine learning classifiers were considered in this work. Naïve Bayes, Logistic Regression, support vector machine (SVM). Random Forest and Decision tree performed well, but Logistic Regression outperformed others with 93% accuracy.

Attribute-based structural damage identification by few-shot meta learning with inter-class knowledge transfer

2020 ◽  
pp. 147592172092113
Author(s):  
Yang Xu ◽  
Yuequan Bao ◽  
Yufeng Zhang ◽  
Hui Li
Keyword(s):  
Supervised Learning ◽  
Structural Damage ◽  
Damage Identification ◽  
Learning Model ◽  
Classification Model ◽  
Parameter Study ◽  
Image Dataset ◽  
Structural Damage Identification ◽  
Meta Learning ◽  
Damage Types

Image archives of multi-class structural damages can be collected by manual inspection and then used for structural damage identification. On one hand, conventional image-processing-based approaches rely on optimal designs of hand-crafted feature detectors and lack universal adaptability for various application cases; on the other hand, regular supervised learning techniques require complete damage types and sufficient training examples to establish a robust damage recognition model, which brings up a time-labor-consuming image collection process. To solve these problems, this study proposes a nested attribute-based few-shot meta learning paradigm for structural damage identification. First, an external few-shot meta learning module is established based on different classification tasks named as meta-batches to produce robust classifiers for new damage types, in which support and query subsets including partial damage types and a few examples are randomly sampled from the original image dataset. Second, an embedded internal attribute-based transfer learning model is trained by minimizing the l2-norm and angular losses of attribute representation vectors in an end-to-end manner, where damage attributes act as the common inter-class knowledge and are transferred from the source damage space of support set to the target damage space of query set. Finally, the proposed approach is validated on a real-world structural damage image dataset, which contains 1000 examples of 10 representative damage types in total. Results show the proposed approach produces an overall accuracy of 93.5% and an average area under the ROC curve of 0.96 for 10 damage types. The general equilibrium of average precision and recall indicates that the trained model is balanced to both positive and negative examples for each damage type. Compared with a regular supervised learning model by directly classifying input images with one-hot vector labels, the proposed approach generates higher accuracy and better robustness. Parameter study suggests the proposed paradigm enables to train a stable and reliable meta learning classification model that can perform well across a series of settings about the ratio between support and query subsets. Theoretical analysis is also performed to explain why meta learning surpasses regular supervised learning.

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