scholarly journals A Systematic Methodology to Evaluate Prediction Models for Driving Style Classification

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1692 ◽  
Author(s):  
Iván Silva ◽  
José Eugenio Naranjo
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Performance Metrics ◽  
Prediction Models ◽  
Statistical Tests ◽  
Area Under The Curve ◽  
The Other ◽  
Support Vector ◽  
Classification Models ◽  
K Nearest Neighbor

Identifying driving styles using classification models with in-vehicle data can provide automated feedback to drivers on their driving behavior, particularly if they are driving safely. Although several classification models have been developed for this purpose, there is no consensus on which classifier performs better at identifying driving styles. Therefore, more research is needed to evaluate classification models by comparing performance metrics. In this paper, a data-driven machine-learning methodology for classifying driving styles is introduced. This methodology is grounded in well-established machine-learning (ML) methods and literature related to driving-styles research. The methodology is illustrated through a study involving data collected from 50 drivers from two different cities in a naturalistic setting. Five features were extracted from the raw data. Fifteen experts were involved in the data labeling to derive the ground truth of the dataset. The dataset fed five different models (Support Vector Machines (SVM), Artificial Neural Networks (ANN), fuzzy logic, k-Nearest Neighbor (kNN), and Random Forests (RF)). These models were evaluated in terms of a set of performance metrics and statistical tests. The experimental results from performance metrics showed that SVM outperformed the other four models, achieving an average accuracy of 0.96, F1-Score of 0.9595, Area Under the Curve (AUC) of 0.9730, and Kappa of 0.9375. In addition, Wilcoxon tests indicated that ANN predicts differently to the other four models. These promising results demonstrate that the proposed methodology may support researchers in making informed decisions about which ML model performs better for driving-styles classification.

scholarly journals Classification model for accuracy and intrusion detection using machine learning approach

2021 ◽  
Vol 7 ◽  
pp. e437
Author(s):  
Arushi Agarwal ◽  
Purushottam Sharma ◽  
Mohammed Alshehri ◽  
Ahmed A. Mohamed ◽  
Osama Alfarraj
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Nearest Neighbor ◽  
Performance Metrics ◽  
Detection System ◽  
Confusion Matrix ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
K Nearest Neighbor

In today’s cyber world, the demand for the internet is increasing day by day, increasing the concern of network security. The aim of an Intrusion Detection System (IDS) is to provide approaches against many fast-growing network attacks (e.g., DDoS attack, Ransomware attack, Botnet attack, etc.), as it blocks the harmful activities occurring in the network system. In this work, three different classification machine learning algorithms—Naïve Bayes (NB), Support Vector Machine (SVM), and K-nearest neighbor (KNN)—were used to detect the accuracy and reducing the processing time of an algorithm on the UNSW-NB15 dataset and to find the best-suited algorithm which can efficiently learn the pattern of the suspicious network activities. The data gathered from the feature set comparison was then applied as input to IDS as data feeds to train the system for future intrusion behavior prediction and analysis using the best-fit algorithm chosen from the above three algorithms based on the performance metrics found. Also, the classification reports (Precision, Recall, and F1-score) and confusion matrix were generated and compared to finalize the support-validation status found throughout the testing phase of the model used in this approach.

scholarly journals Predicting Learning Outcomes with MOOC Clickstreams

2019 ◽  
Vol 9 (2) ◽  
pp. 104 ◽  
Author(s):  
Chen-Hsiang Yu ◽  
Jungpin Wu ◽  
An-Chi Liu
Keyword(s):  
Machine Learning ◽  
Learning Outcomes ◽  
Prediction Accuracy ◽  
Nearest Neighbor ◽  
Prediction Models ◽  
Video Data ◽  
Support Vector ◽  
Completion Rates ◽  
K Nearest Neighbor ◽  
Learning Behaviors

Massive Open Online Courses (MOOCs) have gradually become a dominant trend in education. Since 2014, the Ministry of Education in Taiwan has been promoting MOOC programs, with successful results. The ability of students to work at their own pace, however, is associated with low MOOC completion rates and has recently become a focus. The development of a mechanism to effectively improve course completion rates continues to be of great interest to both teachers and researchers. This study established a series of learning behaviors using the video clickstream records of students, through a MOOC platform, to identify seven types of cognitive participation models of learners. We subsequently built practical machine learning models by using K-nearest neighbor (KNN), support vector machines (SVM), and artificial neural network (ANN) algorithms to predict students’ learning outcomes via their learning behaviors. The ANN machine learning method had the highest prediction accuracy. Based on the prediction results, we saw a correlation between video viewing behavior and learning outcomes. This could allow teachers to help students needing extra support successfully pass the course. To further improve our method, we classified the course videos based on their content. There were three video categories: theoretical, experimental, and analytic. Different prediction models were built for each of these three video types and their combinations. We performed the accuracy verification; our experimental results showed that we could use only theoretical and experimental video data, instead of all three types of data, to generate prediction models without significant differences in prediction accuracy. In addition to data reduction in model generation, this could help teachers evaluate the effectiveness of course videos.

Comparison of Machine Learning Techniques for Software Quality Prediction

2020 ◽  
Vol 11 (2) ◽  
pp. 20-40
Author(s):  
Somya Goyal ◽  
Pradeep Kumar Bhatia
Keyword(s):  
Machine Learning ◽  
Software Quality ◽  
Software Metrics ◽  
Nearest Neighbor ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Quality Prediction ◽  
K Nearest Neighbor ◽  
Software Quality Prediction

Software quality prediction is one the most challenging tasks in the development and maintenance of software. Machine learning (ML) is widely being incorporated for the prediction of the quality of a final product in the early development stages of the software development life cycle (SDLC). An ML prediction model uses software metrics and faulty data from previous projects to detect high-risk modules for future projects, so that the testing efforts can be targeted to those specific ‘risky' modules. Hence, ML-based predictors contribute to the detection of development anomalies early and inexpensively and ensure the timely delivery of a successful, failure-free and supreme quality software product within budget. This article has a comparison of 30 software quality prediction models (5 technique * 6 dataset) built on five ML techniques: artificial neural network (ANN); support vector machine (SVMs); Decision Tree (DTs); k-Nearest Neighbor (KNN); and Naïve Bayes Classifiers (NBC), using six datasets: CM1, KC1, KC2, PC1, JM1, and a combined one. These models exploit the predictive power of static code metrics, McCabe complexity metrics, for quality prediction. All thirty predictors are compared using a receiver operator curve (ROC), area under the curve (AUC), and accuracy as performance evaluation criteria. The results show that the ANN technique for software quality prediction is promising for accurate quality prediction irrespective of the dataset used.

scholarly journals An Automatic Detection and Localization of Mammographic Microcalcifications ROI with Multi-Scale Features Using the Radiomics Analysis Approach

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 5916
Author(s):  
Tariq Mahmood ◽  
Jianqiang Li ◽  
Yan Pei ◽  
Faheem Akhtar ◽  
Azhar Imran ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Nearest Neighbor ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Fusion Algorithm ◽  
Enhancement Method ◽  
Experimental Findings

Microcalcifications in breast tissue can be an early sign of breast cancer, and play a crucial role in breast cancer screening. This study proposes a radiomics approach based on advanced machine learning algorithms for diagnosing pathological microcalcifications in mammogram images and provides radiologists with a valuable decision support system (in regard to diagnosing patients). An adaptive enhancement method based on the contourlet transform is proposed to enhance microcalcifications and effectively suppress background and noise. Textural and statistical features are extracted from each wavelet layer’s high-frequency coefficients to detect microcalcification regions. The top-hat morphological operator and wavelet transform segment microcalcifications, implying their exact locations. Finally, the proposed radiomic fusion algorithm is employed to classify the selected features into benign and malignant. The proposed model’s diagnostic performance was evaluated on the MIAS dataset and compared with traditional machine learning models, such as the support vector machine, K-nearest neighbor, and random forest, using different evaluation parameters. Our proposed approach outperformed existing models in diagnosing microcalcification by achieving an 0.90 area under the curve, 0.98 sensitivity, and 0.98 accuracy. The experimental findings concur with expert observations, indicating that the proposed approach is most effective and practical for early diagnosing breast microcalcifications, substantially improving the work efficiency of physicians.

scholarly journals MCDM APPROACH TO EVALUATING BANK LOAN DEFAULT MODELS

2014 ◽  
Vol 20 (2) ◽  
pp. 292-311 ◽  
Author(s):  
Gang Kou ◽  
Yi Peng ◽  
Chen Lu
Keyword(s):  
Nearest Neighbor ◽  
Performance Metrics ◽  
Prediction Models ◽  
Real Life ◽  
Multiple Criteria Decision Making ◽  
Bank Loan ◽  
Classification Models ◽  
K Nearest Neighbor ◽  
Loan Default ◽  
Default Prediction

Banks and financial institutions rely on loan default prediction models in credit risk management. An important yet challenging task in developing and applying default classification models is model evaluation and selection. This study proposes an evaluation approach for bank loan default classification models based on multiple criteria decision making (MCDM) methods. A large real-life Chinese bank loan dataset is used to validate the proposed approach. Specifically, a set of performance metrics is utilized to measure a selection of statistical and machine-learning default models. The technique for order preference by similarity to ideal solution (TOPSIS), a MCDM method, takes the performances of default classification models on multiple performance metrics as inputs to generate a ranking of default risk models. In addition, feature selection and sampling techniques are applied to the data pre-processing step to handle high dimensionality and class unbalancedness of bank loan default data. The results show that K-Nearest Neighbor algorithm has a good potential in bank loan default prediction.

scholarly journals Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine Learning

Machines ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 56
Author(s):  
Pringgo Widyo Laksono ◽  
Takahide Kitamura ◽  
Joseph Muguro ◽  
Kojiro Matsushita ◽  
Minoru Sasaki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Energy Operator ◽  
Classification Model ◽  
Robotic Arm ◽  
Support Vector ◽  
Classification Models ◽  
Elbow Extension ◽  
K Nearest Neighbor

This research focuses on the minimum process of classifying three upper arm movements (elbow extension, shoulder extension, combined shoulder and elbow extension) of humans with three electromyography (EMG) signals, to control a 2-degrees of freedom (DoF) robotic arm. The proposed minimum process consists of four parts: time divisions of data, Teager–Kaiser energy operator (TKEO), the conventional EMG feature extraction (i.e., the mean absolute value (MAV), zero crossings (ZC), slope-sign changes (SSC), and waveform length (WL)), and eight major machine learning models (i.e., decision tree (medium), decision tree (fine), k-Nearest Neighbor (KNN) (weighted KNN, KNN (fine), Support Vector Machine (SVM) (cubic and fine Gaussian SVM), Ensemble (bagged trees and subspace KNN). Then, we compare and investigate 48 classification models (i.e., 47 models are proposed, and 1 model is the conventional) based on five healthy subjects. The results showed that all the classification models achieved accuracies ranging between 74–98%, and the processing speed is below 40 ms and indicated acceptable controller delay for robotic arm control. Moreover, we confirmed that the classification model with no time division, with TKEO, and with ensemble (subspace KNN) had the best performance in accuracy rates at 96.67, recall rates at 99.66, and precision rates at 96.99. In short, the combination of the proposed TKEO and ensemble (subspace KNN) plays an important role to achieve the EMG classification.

scholarly journals Metabolic Syndrome Prediction Models Using Machine Learning and Sasang Constitution Type

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ji-Eun Park ◽  
Sujeong Mun ◽  
Siwoo Lee
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Prediction Models ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Sasang Constitution ◽  
Constitution Type ◽  
Conventional Regression

Background. Machine learning may be a useful tool for predicting metabolic syndrome (MetS), and previous studies also suggest that the risk of MetS differs according to Sasang constitution type. The present study investigated the development of MetS prediction models utilizing machine learning methods and whether the incorporation of Sasang constitution type could improve the performance of those prediction models. Methods. Participants visiting a medical center for a health check-up were recruited in 2005 and 2006. Six kinds of machine learning were utilized (K-nearest neighbor, naive Bayes, random forest, decision tree, multilayer perceptron, and support vector machine), as was conventional logistic regression. Machine learning-derived MetS prediction models with and without the incorporation of Sasang constitution type were compared to investigate whether the former would predict MetS with higher sensitivity. Age, sex, education level, marital status, body mass index, stress, physical activity, alcohol consumption, and smoking were included as potentially predictive factors. Results. A total of 750/2,871 participants had MetS. Among the six types of machine learning methods investigated, multiplayer perceptron and support vector machine exhibited the same performance as the conventional regression method, based on the areas under the receiver operating characteristic curves. The naive-Bayes method exhibited the highest sensitivity (0.49), which was higher than that of the conventional regression method (0.39). The incorporation of Sasang constitution type improved the sensitivity of all of the machine learning methods investigated except for the K-nearest neighbor method. Conclusion. Machine learning-derived models may be useful for MetS prediction, and the incorporation of Sasang constitution type may increase the sensitivity of such models.

scholarly journals Detection of Ovarian Tumor Using Machine Learning Approaches A Review

2020 ◽  
pp. 471-476
Author(s):  
Gitanjali Wadhwa ◽  
Mansi Mathur
Keyword(s):  
Machine Learning ◽  
Ovarian Cancer ◽  
Deep Learning ◽  
Nearest Neighbor ◽  
Performance Metrics ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Female Sex Hormones

The important part of female reproductive system is ovaries. The importance of these tiny glands is derived from the production of female sex hormones and female gametes. The place of these ductless almond shaped tiny glandular organs is on just opposite sides of uterus attached with ovarian ligament. There are several reasons due to which ovarian cancer can arise but it can be classified by using different number of techniques. Early prediction of ovarian cancer will decrease its progress rate and may possibly save countless lives. CAD systems (Computer-aided diagnosis) is a noninvasive routine for finding ovarian cancer in its initial stages of cancer which can keep away patients’ anxiety and unnecessary biopsy. This review paper states us about how we can use different techniques to classify the ovarian cancer tumor. In this survey effort we have also deliberate about the comparison of different machine learning algorithms like K-Nearest Neighbor, Support Vector Machine and deep learning techniques used in classification process of ovarian cancer. Later comparing the different techniques for this type of cancer detection, it gives the impression that Deep Learning Technique has provided good results and come out with good accuracy and other performance metrics.

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

scholarly journals Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)

2021 ◽  
Vol 10 (4) ◽  
pp. 199
Author(s):  
Francisco M. Bellas Aláez ◽  
Jesus M. Torres Palenzuela ◽  
Evangelos Spyrakos ◽  
Luis González Vilas
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Prediction Models ◽  
Support Vector ◽  
False Alarms ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Rías Baixas ◽  
New Algorithms

This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). The models predict Pseudo-nitzschia spp. blooms in the Galician Rias Baixas. This work builds on a previous study by the authors (doi.org/10.1016/j.pocean.2014.03.003) but uses an extended database (from 2002 to 2012) and new algorithms. Our results show that RF and AdaBoost provide better prediction results compared to SVMs and NNs, as they show improved performance metrics and a better balance between sensitivity and specificity. Classical machine learning approaches show higher sensitivities, but at a cost of lower specificity and higher percentages of false alarms (lower precision). These results seem to indicate a greater adaptation of new algorithms (RF and AdaBoost) to unbalanced datasets. Our models could be operationally implemented to establish a short-term prediction system.

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