scholarly journals Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Rivindu Weerasekera ◽  
Mohan Sridharan ◽  
Prakash Ranjitkar
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Data Aggregation ◽  
Transportation Systems ◽  
Machine Learning Algorithms ◽  
Recursive Feature Elimination ◽  
Traffic Prediction ◽  
Support Vector ◽  
Short Term ◽  
Open Problems

Short-term traffic prediction is a key component of Intelligent Transportation Systems. It uses historical data to construct models for reliably predicting traffic state at specific locations in road networks in the near future. Despite being a mature field, short-term traffic prediction still poses some open problems related to the choice of optimal data resolution, prediction of nonrecurring congestion, and the modelling of relevant spatiotemporal dependencies. As a step towards addressing these problems, this paper investigates the ability of Artificial Neural Networks, Random Forests, and Support Vector Regression algorithms to reliably model traffic flow at different data resolutions and respond to unexpected traffic incidents. We also explore different feature selection methods to identify and better understand the spatiotemporal attributes that most influence the reliability of these models. Experimental results indicate that data aggregation does not necessarily achieve good performance for multivariate spatiotemporal machine learning models. The models learned using high-resolution 30-second input data outperformed the corresponding baseline ARIMA models by 8%. Furthermore, feature selection based on Recursive Feature Elimination resulted in models that outperformed those based on linear correlation-based feature selection.

scholarly journals Comparing Methods of Feature Extraction of Brain Activities for Octave Illusion Classification Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6407
Author(s):  
Nina Pilyugina ◽  
Akihiko Tsukahara ◽  
Keita Tanaka
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Selection ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Selection Methods ◽  
Automatic Feature Extraction ◽  
Octave Illusion

The aim of this study was to find an efficient method to determine features that characterize octave illusion data. Specifically, this study compared the efficiency of several automatic feature selection methods for automatic feature extraction of the auditory steady-state responses (ASSR) data in brain activities to distinguish auditory octave illusion and nonillusion groups by the difference in ASSR amplitudes using machine learning. We compared univariate selection, recursive feature elimination, principal component analysis, and feature importance by testifying the results of feature selection methods by using several machine learning algorithms: linear regression, random forest, and support vector machine. The univariate selection with the SVM as the classification method showed the highest accuracy result, 75%, compared to 66.6% without using feature selection. The received results will be used for future work on the explanation of the mechanism behind the octave illusion phenomenon and creating an algorithm for automatic octave illusion classification.

Phishing web site detection using diverse machine learning algorithms

2020 ◽  
Vol 38 (1) ◽  
pp. 65-80 ◽  
Author(s):  
Ammara Zamir ◽  
Hikmat Ullah Khan ◽  
Tassawar Iqbal ◽  
Nazish Yousaf ◽  
Farah Aslam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Classification Accuracy ◽  
Information Gain ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Data Set ◽  
Content Type

Purpose This paper aims to present a framework to detect phishing websites using stacking model. Phishing is a type of fraud to access users’ credentials. The attackers access users’ personal and sensitive information for monetary purposes. Phishing affects diverse fields, such as e-commerce, online business, banking and digital marketing, and is ordinarily carried out by sending spam emails and developing identical websites resembling the original websites. As people surf the targeted website, the phishers hijack their personal information. Design/methodology/approach Features of phishing data set are analysed by using feature selection techniques including information gain, gain ratio, Relief-F and recursive feature elimination (RFE) for feature selection. Two features are proposed combining the strongest and weakest attributes. Principal component analysis with diverse machine learning algorithms including (random forest [RF], neural network [NN], bagging, support vector machine, Naïve Bayes and k-nearest neighbour) is applied on proposed and remaining features. Afterwards, two stacking models: Stacking1 (RF + NN + Bagging) and Stacking2 (kNN + RF + Bagging) are applied by combining highest scoring classifiers to improve the classification accuracy. Findings The proposed features played an important role in improving the accuracy of all the classifiers. The results show that RFE plays an important role to remove the least important feature from the data set. Furthermore, Stacking1 (RF + NN + Bagging) outperformed all other classifiers in terms of classification accuracy to detect phishing website with 97.4% accuracy. Originality/value This research is novel in this regard that no previous research focusses on using feed forward NN and ensemble learners for detecting phishing websites.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

scholarly journals Recognition Technology of Athlete’s Limb Movement Combined Based on the Integrated Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fei Tan ◽  
Xiaoqing Xie
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Algorithm ◽  
Human Motion ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Recording Device ◽  
Table Tennis ◽  
Movement Recognition ◽  
Random Forest Tree

Human motion recognition based on inertial sensor is a new research direction in the field of pattern recognition. It carries out preprocessing, feature selection, and feature selection by placing inertial sensors on the surface of the human body. Finally, it mainly classifies and recognizes the extracted features of human action. There are many kinds of swing movements in table tennis. Accurately identifying these movement modes is of great significance for swing movement analysis. With the development of artificial intelligence technology, human movement recognition has made many breakthroughs in recent years, from machine learning to deep learning, from wearable sensors to visual sensors. However, there is not much work on movement recognition for table tennis, and the methods are still mainly integrated into the traditional field of machine learning. Therefore, this paper uses an acceleration sensor as a motion recording device for a table tennis disc and explores the three-axis acceleration data of four common swing motions. Traditional machine learning algorithms (decision tree, random forest tree, and support vector) are used to classify the swing motion, and a classification algorithm based on the idea of integration is designed. Experimental results show that the ensemble learning algorithm developed in this paper is better than the traditional machine learning algorithm, and the average recognition accuracy is 91%.

scholarly journals Techniques for Detecting Malware Traffic: A Comprehensive Approach to Feature Selection and Classification

2021 ◽  
Vol 9 (12) ◽  
pp. 1-10
Author(s):  
Harsha A K
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Steady Increase ◽  
Extreme Gradient Boosting

Abstract: Since the advent of encryption, there has been a steady increase in malware being transmitted over encrypted networks. Traditional approaches to detect malware like packet content analysis are inefficient in dealing with encrypted data. In the absence of actual packet contents, we can make use of other features like packet size, arrival time, source and destination addresses and other such metadata to detect malware. Such information can be used to train machine learning classifiers in order to classify malicious and benign packets. In this paper, we offer an efficient malware detection approach using classification algorithms in machine learning such as support vector machine, random forest and extreme gradient boosting. We employ an extensive feature selection process to reduce the dimensionality of the chosen dataset. The dataset is then split into training and testing sets. Machine learning algorithms are trained using the training set. These models are then evaluated against the testing set in order to assess their respective performances. We further attempt to tune the hyper parameters of the algorithms, in order to achieve better results. Random forest and extreme gradient boosting algorithms performed exceptionally well in our experiments, resulting in area under the curve values of 0.9928 and 0.9998 respectively. Our work demonstrates that malware traffic can be effectively classified using conventional machine learning algorithms and also shows the importance of dimensionality reduction in such classification problems. Keywords: Malware Detection, Extreme Gradient Boosting, Random Forest, Feature Selection.

scholarly journals Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data

2020 ◽  
Vol 9 (9) ◽  
pp. 507
Author(s):  
Sanjiwana Arjasakusuma ◽  
Sandiaga Swahyu Kusuma ◽  
Stuart Phinn
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Forest Height ◽  
Extreme Gradient Boosting

Machine learning has been employed for various mapping and modeling tasks using input variables from different sources of remote sensing data. For feature selection involving high- spatial and spectral dimensionality data, various methods have been developed and incorporated into the machine learning framework to ensure an efficient and optimal computational process. This research aims to assess the accuracy of various feature selection and machine learning methods for estimating forest height using AISA (airborne imaging spectrometer for applications) hyperspectral bands (479 bands) and airborne light detection and ranging (lidar) height metrics (36 metrics), alone and combined. Feature selection and dimensionality reduction using Boruta (BO), principal component analysis (PCA), simulated annealing (SA), and genetic algorithm (GA) in combination with machine learning algorithms such as multivariate adaptive regression spline (MARS), extra trees (ET), support vector regression (SVR) with radial basis function, and extreme gradient boosting (XGB) with trees (XGbtree and XGBdart) and linear (XGBlin) classifiers were evaluated. The results demonstrated that the combinations of BO-XGBdart and BO-SVR delivered the best model performance for estimating tropical forest height by combining lidar and hyperspectral data, with R2 = 0.53 and RMSE = 1.7 m (18.4% of nRMSE and 0.046 m of bias) for BO-XGBdart and R2 = 0.51 and RMSE = 1.8 m (15.8% of nRMSE and −0.244 m of bias) for BO-SVR. Our study also demonstrated the effectiveness of BO for variables selection; it could reduce 95% of the data to select the 29 most important variables from the initial 516 variables from lidar metrics and hyperspectral data.

scholarly journals Diagnostic Performance of 2D and 3D T2WI-Based Radiomics Features With Machine Learning Algorithms to Distinguish Solid Solitary Pulmonary Lesion

2021 ◽  
Vol 11 ◽  
Author(s):  
Qi Wan ◽  
Jiaxuan Zhou ◽  
Xiaoying Xia ◽  
Jianfeng Hu ◽  
Peng Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Diagnostic Performance ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
Selection Methods ◽  
Linear Discriminant ◽  
2D And 3D

ObjectiveTo evaluate the performance of 2D and 3D radiomics features with different machine learning approaches to classify SPLs based on magnetic resonance(MR) T2 weighted imaging (T2WI).Material and MethodsA total of 132 patients with pathologically confirmed SPLs were examined and randomly divided into training (n = 92) and test datasets (n = 40). A total of 1692 3D and 1231 2D radiomics features per patient were extracted. Both radiomics features and clinical data were evaluated. A total of 1260 classification models, comprising 3 normalization methods, 2 dimension reduction algorithms, 3 feature selection methods, and 10 classifiers with 7 different feature numbers (confined to 3–9), were compared. The ten-fold cross-validation on the training dataset was applied to choose the candidate final model. The area under the receiver operating characteristic curve (AUC), precision-recall plot, and Matthews Correlation Coefficient were used to evaluate the performance of machine learning approaches.ResultsThe 3D features were significantly superior to 2D features, showing much more machine learning combinations with AUC greater than 0.7 in both validation and test groups (129 vs. 11). The feature selection method Analysis of Variance(ANOVA), Recursive Feature Elimination(RFE) and the classifier Logistic Regression(LR), Linear Discriminant Analysis(LDA), Support Vector Machine(SVM), Gaussian Process(GP) had relatively better performance. The best performance of 3D radiomics features in the test dataset (AUC = 0.824, AUC-PR = 0.927, MCC = 0.514) was higher than that of 2D features (AUC = 0.740, AUC-PR = 0.846, MCC = 0.404). The joint 3D and 2D features (AUC=0.813, AUC-PR = 0.926, MCC = 0.563) showed similar results as 3D features. Incorporating clinical features with 3D and 2D radiomics features slightly improved the AUC to 0.836 (AUC-PR = 0.918, MCC = 0.620) and 0.780 (AUC-PR = 0.900, MCC = 0.574), respectively.ConclusionsAfter algorithm optimization, 2D feature-based radiomics models yield favorable results in differentiating malignant and benign SPLs, but 3D features are still preferred because of the availability of more machine learning algorithmic combinations with better performance. Feature selection methods ANOVA and RFE, and classifier LR, LDA, SVM and GP are more likely to demonstrate better diagnostic performance for 3D features in the current study.

scholarly journals Diagnosis of COVID-19 Using CT image Radiomics Features: A Comprehensive Machine Learning Study Involving 26,307 Patients

2021 ◽  
Author(s):  
Isaac Shiri ◽  
Yazdan Salimi ◽  
Abdollah Saberi ◽  
Masoumeh Pakbin ◽  
Ghasem Hajianfar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Strategies ◽  
Lung Diseases ◽  
Pearson Correlation ◽  
Characteristic Curve ◽  
Univariate Analysis ◽  
Machine Learning Algorithms ◽  
Recursive Feature Elimination ◽  
Classifier Combination

AbstractPurposeTo derive and validate an effective radiomics-based model for differentiation of COVID-19 pneumonia from other lung diseases using a very large cohort of patients.MethodsWe collected 19 private and 5 public datasets, accumulating to 26,307 individual patient images (15,148 COVID-19; 9,657 with other lung diseases e.g. non-COVID-19 pneumonia, lung cancer, pulmonary embolism; 1502 normal cases). Images were automatically segmented using a validated deep learning (DL) model and the results carefully reviewed. Images were first cropped into lung-only region boxes, then resized to 296×216 voxels. Voxel dimensions was resized to 1×1×1mm3 followed by 64-bin discretization. The 108 extracted features included shape, first-order histogram and texture features. Univariate analysis was first performed using simple logistic regression. The thresholds were fixed in the training set and then evaluation performed on the test set. False discovery rate (FDR) correction was applied to the p-values. Z-Score normalization was applied to all features. For multivariate analysis, features with high correlation (R2>0.99) were eliminated first using Pearson correlation. We tested 96 different machine learning strategies through cross-combining 4 feature selectors or 8 dimensionality reduction techniques with 8 classifiers. We trained and evaluated our models using 3 different datasets: 1) the entire dataset (26,307 patients: 15,148 COVID-19; 11,159 non-COVID-19); 2) excluding normal patients in non-COVID-19, and including only RT-PCR positive COVID-19 cases in the COVID-19 class (20,697 patients including 12,419 COVID-19, and 8,278 non-COVID-19)); 3) including only non-COVID-19 pneumonia patients and a random sample of COVID-19 patients (5,582 patients: 3,000 COVID-19, and 2,582 non-COVID-19) to provide balanced classes. Subsequently, each of these 3 datasets were randomly split into 70% and 30% for training and testing, respectively. All various steps, including feature preprocessing, feature selection, and classification, were performed separately in each dataset. Classification algorithms were optimized during training using grid search algorithms. The best models were chosen by a one-standard-deviation rule in 10-fold cross-validation and then were evaluated on the test sets.ResultsIn dataset #1, Relief feature selection and RF classifier combination resulted in the highest performance (Area under the receiver operating characteristic curve (AUC) = 0.99, sensitivity = 0.98, specificity = 0.94, accuracy = 0.96, positive predictive value (PPV) = 0.96, and negative predicted value (NPV) = 0.96). In dataset #2, Recursive Feature Elimination (RFE) feature selection and Random Forest (RF) classifier combination resulted in the highest performance (AUC = 0.99, sensitivity = 0.98, specificity = 0.95, accuracy = 0.97, PPV = 0.96, and NPV = 0.98). In dataset #3, the ANOVA feature selection and RF classifier combination resulted in the highest performance (AUC = 0.98, sensitivity = 0.96, specificity = 0.93, accuracy = 0.94, PPV = 0.93, NPV = 0.96).ConclusionRadiomic features extracted from entire lung combined with machine learning algorithms can enable very effective, routine diagnosis of COVID-19 pneumonia from CT images without the use of any other diagnostic test.

scholarly journals A Hybrid Feature Selection Method for Improve the Accuracy of Medical Classification Process

2021 ◽  
Vol 11 (1) ◽  
pp. 50-55
Author(s):  
Maria Mohammad Yousef ◽  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Fitness Function ◽  
Machine Learning Algorithms ◽  
Feature Subset Selection ◽  
High Dimensionality ◽  
Support Vector ◽  
Feature Subset

Generally, medical dataset classification has become one of the biggest problems in data mining research. Every database has a given number of features but it is observed that some of these features can be redundant and can be harmful as well as disrupt the process of classification and this problem is known as a high dimensionality problem. Dimensionality reduction in data preprocessing is critical for increasing the performance of machine learning algorithms. Besides the contribution of feature subset selection in dimensionality reduction gives a significant improvement in classification accuracy. In this paper, we proposed a new hybrid feature selection approach based on (GA assisted by KNN) to deal with issues of high dimensionality in biomedical data classification. The proposed method first applies the combination between GA and KNN for feature selection to find the optimal subset of features where the classification accuracy of the k-Nearest Neighbor (kNN) method is used as the fitness function for GA. After selecting the best-suggested subset of features, Support Vector Machine (SVM) are used as the classifiers. The proposed method experiments on five medical datasets of the UCI Machine Learning Repository. It is noted that the suggested technique performs admirably on these databases, achieving higher classification accuracy while using fewer features.

scholarly journals Crime Data Forecasting Using Machine Learning and Big Data Analytics

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 591-606
Author(s):  
R. Brindha ◽  
Dr.M. Thillaikarasi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Geographical Information ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Crime Data

Big data analytics (BDA) is a system based method with an aim to recognize and examine different designs, patterns and trends under the big dataset. In this paper, BDA is used to visualize and trends the prediction where exploratory data analysis examines the crime data. “A successive facts and patterns have been taken in following cities of California, Washington and Florida by using statistical analysis and visualization”. The predictive result gives the performance using Keras Prophet Model, LSTM and neural network models followed by prophet model which are the existing methods used to find the crime data under BDA technique. But the crime actions increases day by day which is greater task for the people to overcome the challenging crime activities. Some ignored the essential rate of influential aspects. To overcome these challenging problems of big data, many studies have been developed with limited one or two features. “This paper introduces a big data introduces to analyze the influential aspects about the crime incidents, and examine it on New York City. The proposed structure relates the dynamic machine learning algorithms and geographical information system (GIS) to consider the contiguous reasons of crime data. Recursive feature elimination (RFE) is used to select the optimum characteristic data. Exploitation of gradient boost decision tree (GBDT), logistic regression (LR), support vector machine (SVM) and artificial neural network (ANN) are related to develop the optimum data model. Significant impact features were then reviewed by applying GBDT and GIS”. The experimental results illustrates that GBDT along with GIS model combination can identify the crime ranking with high performance and accuracy compared to existing method.”

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