scholarly journals Investigation of the effectiveness of metric classification methods in identifying attacks in VANET

2021 ◽  
Vol 2094 (3) ◽  
pp. 032066
Author(s):  
D Parfenov ◽  
I Bolodurina ◽  
L Grishina ◽  
A Zhigalov
Keyword(s):  
Machine Learning ◽  
Main Idea ◽  
Nearest Neighbour ◽  
Classification Methods ◽  
Nonlinear Functions ◽  
Machine Learning Methods ◽  
Parzen Window ◽  
Window Method ◽  
Different Types ◽  
Adhoc Networks

Abstract This paper discusses the problem of improving the efficiency of metric machine learning methods of identification attacks in vehicular adhoc networks (VANETs). The main idea of this research is to select the type of nonlinear functions for calculating the distances between the objects of the sample, describing the traffic of VANET using metric methods, such as the method of k-nearest neighbour with linearly decreasing weights and the Parzen window method. The analysis of the effectiveness of the methods considered was carried out on a synthetically generated sample with three different types of attacks on the network. Computational experiments have shown that the k-nearest neighbour method with decreasing weights based on an exponential function with base a < 1 is more efficient than the Parzen window method by about 0.3% and has an accuracy of 84.15%.

scholarly journals A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

2014 ◽  
Vol 5 (3) ◽  
pp. 82-96 ◽  
Author(s):  
Marijana Zekić-Sušac ◽  
Sanja Pfeifer ◽  
Nataša Šarlija
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Classification Accuracy ◽  
Classification Problem ◽  
High Dimensional ◽  
Nearest Neighbour ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Dimensional Classification ◽  
Artificial Neural

Abstract Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

scholarly journals Machine Learning Approach to Classify Rain Type Based on Thies Disdrometers and Cloud Observations

Atmosphere ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 251 ◽  
Author(s):  
Wael Ghada ◽  
Nicole Estrella ◽  
Annette Menzel
Keyword(s):  
Machine Learning ◽  
Geographical Location ◽  
Classification Performance ◽  
Classification Methods ◽  
Reference Dataset ◽  
Machine Learning Classification ◽  
Machine Learning Approach ◽  
Different Types ◽  
Microstructure Parameters

Rain microstructure parameters assessed by disdrometers are commonly used to classify rain into convective and stratiform. However, different types of disdrometer result in different values for these parameters. This in turn potentially deteriorates the quality of rain type classifications. Thies disdrometer measurements at two sites in Bavaria in southern Germany were combined with cloud observations to construct a set of clear convective and stratiform intervals. This reference dataset was used to study the performance of classification methods from the literature based on the rain microstructure. We also explored the possibility of improving the performance of these methods by tuning the decision boundary. We further identified highly discriminant rain microstructure parameters and used these parameters in five machine-learning classification models. Our results confirm the potential of achieving high classification performance by applying the concepts of machine learning compared to already available methods. Machine-learning classification methods provide a concrete and flexible procedure that is applicable regardless of the geographical location or the device. The suggested procedure for classifying rain types is recommended prior to studying rain microstructure variability or any attempts at improving radar estimations of rain intensity.

scholarly journals Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
İlhan Umut ◽  
Güven Çentik
Keyword(s):  
Machine Learning ◽  
Multilayer Perceptron ◽  
Digital Signal ◽  
Machine Learning Algorithms ◽  
Classification Error ◽  
Nearest Neighbour ◽  
Learning Methods ◽  
Classification Rate ◽  
Periodic Leg Movements ◽  
Machine Learning Methods

The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron,K-nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that whileK-nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present.

scholarly journals Harassment Detection Using Machine Learning and Fuzzy Logic Techniques

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 27
Author(s):  
Jezabel Molina-Gil ◽  
José A. Concepción-Sánchez ◽  
Pino Caballero-Gil
Keyword(s):  
Machine Learning ◽  
Fuzzy Logic ◽  
Mobile Application ◽  
Instant Messaging ◽  
Main Idea ◽  
Technological Tools ◽  
Different Types ◽  
Using Data ◽  
Fuzzy Logic Technique ◽  
A Current

Social networks, instant messaging applications, smartphones and the Internet are the main technological tools used by adolescents for communication. While they can benefit from those tools, they can also be used as a weapon for harassment. Cyberbullying is the name used for a current global social problem derived from harassment that uses offensive messages, which is severely affecting the youngest. Different types of software to identify and filter offensive contents have been developed in the last years. However, most of them are time consuming, not scalable and focused on very specific environments. To address this problem, we propose a mobile application for smartphones that provides a potential offensive content detection in order to determine whether a cyberbullying attack exists or not. In particular, we have developed an application that combines data pre-processing, fuzzy logic and machine learning to predict cyberbullying content. The main idea is to install a mobile application on the smartphone of a possible victim, so that it runs in the background. The system analyzes all received messages and notifications using data processing and decision-making algorithms. Finally, a fuzzy logic technique helps the system to reach a conclusion under a certain degree of imprecision.

scholarly journals A post-method condition analysis of using ensemble machine learning for cancer prognosis and diagnosis: a systematic review

2019 ◽  
Author(s):  
Leila Mirsadeghi ◽  
Ali Mohammad Banaei-Moghaddam ◽  
Seyed Reza Beh-Afarin ◽  
Reza Haji Hosseini ◽  
Kaveh Kavousi
Keyword(s):  
Machine Learning ◽  
Empirical Studies ◽  
Ensemble Methods ◽  
Feature Space ◽  
Ensemble Classifier ◽  
Cancer Prognosis ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Different Types

Abstract Background: Ensemble methods are supervised learning approaches that integrate different types of data or multiple individual classifiers. It has been shown that these methods can improve professional performance.Methods: This study is an attempt to provide an in-depth review on 45 most relevant articles and aims to introduce 42 ensemble classifier (EC) machine learning methods used for the detection of 18 different types of cancer. Compared to other types of cancer, breast cancer, and the 22 ensemble methods introduced for its identification, is extensively investigated. The purpose of this study is to identify, map, and analyze the current academic discourse on EC machine learning methods in order to: 1. identify overarching themes emerging from empirical studies as regards EC methods, 2. determine their input data and decision-making strategies, and 3. evaluate relevant statistical procedures.Results: By comparing various approaches, we can introduce Relevance Vector Machine (RVM)-based ensemble learning method that can provide optimal solutions for problems such as curse the dimensionality and high-dimensionality of feature space without missing data values.Conclusions: To obtain robust performance and achieve better results, it is tactfully suggested to use multi-omics data integration, which has demonstrated to identify cancers and their subtypes more efficiently.

scholarly journals A Post-Method Condition Analysis of Using Ensemble Machine Learning for Cancer Prognosis and Diagnosis: a systematic review

2019 ◽  
Author(s):  
Kaveh Kavousi ◽  
Leila Mirsadeghi ◽  
Reza Haji Hosseini ◽  
Ali Mohammad Banaei-Moghaddam ◽  
Seyed Reza Beh-Afarin
Keyword(s):  
Machine Learning ◽  
Empirical Studies ◽  
Ensemble Methods ◽  
Feature Space ◽  
Ensemble Classifier ◽  
Cancer Prognosis ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Different Types

Abstract Background Ensemble methods are supervised learning approaches that integrate different types of data or multiple individual classifiers. It has been shown that these methods can improve professional performance. Methods This study is an attempt to provide an in-depth review on 45 most relevant articles and aims to introduce 42 ensemble classifier (EC) machine learning methods used for the detection of 18 different types of cancer. Compared to other types of cancer, breast cancer, and the 22 ensemble methods introduced for its identification, is extensively investigated. The purpose of this study was to identify, map, and analyze the current academic discourse on EC machine learning methods in order to: 1. identify overarching themes emerging from empirical studies regarding EC methods, 2. determine their input data and decision-making strategies, and 3. evaluate relevant statistical procedures. Results By comparing various approaches, we can introduce Relevance Vector Machine (RVM)-based ensemble learning method that can provide optimal solutions for problems such as curse the dimensionality and high-dimensionality of feature space without missing data values. Conclusions To obtain robust performance and achieve better results, it is tactfully suggested to use multi-omics data integration, which has demonstrated to identify cancers and their subtypes more efficiently.

scholarly journals Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges

2020 ◽  
Vol 66 (6) ◽  
pp. 2495-2522 ◽  
Author(s):  
Duncan Simester ◽  
Artem Timoshenko ◽  
Spyros I. Zoumpoulis
Keyword(s):  
Machine Learning ◽  
Field Experiment ◽  
Large Scale ◽  
Field Experiments ◽  
Training Data ◽  
Classification Methods ◽  
Learning Methods ◽  
Model Driven ◽  
Machine Learning Methods

We investigate how firms can use the results of field experiments to optimize the targeting of promotions when prospecting for new customers. We evaluate seven widely used machine-learning methods using a series of two large-scale field experiments. The first field experiment generates a common pool of training data for each of the seven methods. We then validate the seven optimized policies provided by each method together with uniform benchmark policies in a second field experiment. The findings not only compare the performance of the targeting methods, but also demonstrate how well the methods address common data challenges. Our results reveal that when the training data are ideal, model-driven methods perform better than distance-driven methods and classification methods. However, the performance advantage vanishes in the presence of challenges that affect the quality of the training data, including the extent to which the training data captures details of the implementation setting. The challenges we study are covariate shift, concept shift, information loss through aggregation, and imbalanced data. Intuitively, the model-driven methods make better use of the information available in the training data, but the performance of these methods is more sensitive to deterioration in the quality of this information. The classification methods we tested performed relatively poorly. We explain the poor performance of the classification methods in our setting and describe how the performance of these methods could be improved. This paper was accepted by Matthew Shum, marketing.

scholarly journals Machine Learning Methods for Quality Prediction in Production

Logistics ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 35
Author(s):  
Sidharth Sankhye ◽  
Guiping Hu
Keyword(s):  
Machine Learning ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Classification Methods ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Production Stages ◽  
Smart Factories

The rising popularity of smart factories and Industry 4.0 has made it possible to collect large amounts of data from production stages. Thus, supervised machine learning methods such as classification can viably predict product compliance quality using manufacturing data collected during production. Elimination of uncertainty via accurate prediction provides significant benefits at any stage in a supply chain. Thus, early knowledge of product batch quality can save costs associated with recalls, packaging, and transportation. While there has been thorough research on predicting the quality of specific manufacturing processes, the adoption of classification methods to predict the overall compliance of production batches has not been extensively investigated. This paper aims to design machine learning based classification methods for quality compliance and validate the models via case study of a multi-model appliance production line. The proposed classification model could achieve an accuracy of 0.99 and Cohen’s Kappa of 0.91 for the compliance quality of unit batches. Thus, the proposed method would enable implementation of a predictive model for compliance quality. The case study also highlights the importance of feature construction and dataset knowledge in training classification models.

Defect Classification Using Machine Learning Techniques for Flat Display Panels

2013 ◽  
Vol 365-366 ◽  
pp. 720-724
Author(s):  
Du Hyung Cho ◽  
Seok Lyong Lee
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Defect Classification ◽  
Gaussian Filter ◽  
Classification Methods ◽  
Production Environment ◽  
Comparison Result ◽  
Learning Techniques ◽  
Different Types ◽  
Test Sets

Defect classification for a flat display panel (FDP) is the crucial process that identifies and classifies defects automatically during the final step of its manufacturing process. It plays an important role since it prevents possible malfunction by inspecting defects timely and reduces time for identifying inferior products. In this paper, we propose the defect classification methods for FDP using various machine learning techniques and provide the comparison among them for practical use in production environment. First, we identify defects through Gaussian filter and threshold technique. Then, those defects are classified into different types based on geometric characteristics of them using four machine learning techniques that are widely used. The experimental results using training and test sets of FDP images show considerable effectiveness in classifying defect types. We also believe that the comparison result might be quite useful when engineers determine methods for defect classification during FDP manufacturing.

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