Performance Analysis of Different Machine Learning Algorithms for Identifying and Classifying the Failures of Traction Motors

Abstract This paper addresses electric motor fault diagnosis using supervised machine learning classification. A total of 15 distinct fault types are classified and multilabel strategies are used to classify concurrent faults. we explored, developed, and compared the performance of different types of binary (fault/non-fault), multi-class (fault type) and multi-label (single fault versus combination fault) classifiers. To evaluate the effectiveness of fault identification and classification, we used different supervised machine learning methods, including Random forest classification, support vector machine and neural network classification. Through experiment, we compared these methods over 4 classification regimes and finally summarize the most suitable machine learning algorithms for different aspects of health diagnosis in traction motors area.

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Encrypted DNP3 Traffic Classification Using Supervised Machine Learning Algorithms

Machine Learning and Knowledge Extraction ◽

10.3390/make1010022 ◽

2019 ◽

Vol 1 (1) ◽

pp. 384-399 ◽

Cited By ~ 2

Author(s):

Thais de Toledo ◽

Nunzio Torrisi

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Decision Tree ◽

Smart Grids ◽

Learning Algorithms ◽

Electric Utility ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Support Vector ◽

Communication Link

The Distributed Network Protocol (DNP3) is predominately used by the electric utility industry and, consequently, in smart grids. The Peekaboo attack was created to compromise DNP3 traffic, in which a man-in-the-middle on a communication link can capture and drop selected encrypted DNP3 messages by using support vector machine learning algorithms. The communication networks of smart grids are a important part of their infrastructure, so it is of critical importance to keep this communication secure and reliable. The main contribution of this paper is to compare the use of machine learning techniques to classify messages of the same protocol exchanged in encrypted tunnels. The study considers four simulated cases of encrypted DNP3 traffic scenarios and four different supervised machine learning algorithms: Decision tree, nearest-neighbor, support vector machine, and naive Bayes. The results obtained show that it is possible to extend a Peekaboo attack over multiple substations, using a decision tree learning algorithm, and to gather significant information from a system that communicates using encrypted DNP3 traffic.

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Deep Learning and Conventional Machine Learning for Image-Based in-Situ Fault Detection During Laser Welding: A Comparative Study

10.20944/preprints202105.0272.v1 ◽

2021 ◽

Author(s):

Christian Knaak ◽

Moritz Kröger ◽

Frederic Schulze ◽

Peter Abels ◽

Arnold Gillner

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Near Infrared ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Support Vector ◽

Detection Rates ◽

Feature Extraction And Selection ◽

Welding Defects

An effective process monitoring strategy is a requirement for meeting the challenges posed by increasingly complex products and manufacturing processes. To address these needs, this study investigates a comprehensive scheme based on classical machine learning methods, deep learning algorithms, and feature extraction and selection techniques. In a first step, a novel deep learning architecture based on convolutional neural networks (CNN) and gated recurrent units (GRU) is introduced to predict the local weld quality based on mid-wave infrared (MWIR) and near-infrared (NIR) image data. The developed technology is used to discover critical welding defects including lack of fusion (false friends), sagging and lack of penetration, and geometric deviations of the weld seam. Additional work is conducted to investigate the significance of various geometrical, statistical, and spatio-temporal features extracted from the keyhole and weld pool regions. Furthermore, the performance of the proposed deep learning architecture is compared to that of classical supervised machine learning algorithms, such as multi-layer perceptron (MLP), logistic regression (LogReg), support vector machines (SVM), decision trees (DT), random forest (RF) and k-Nearest Neighbors (kNN). Optimal hyperparameters for each algorithm are determined by an extensive grid search. Ultimately, the three best classification models are combined into an ensemble classifier that yields the highest detection rates and achieves the most robust estimation of welding defects among all classifiers studied, which is validated on previously unknown welding trials.

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Framework for Providing Security in Private Cloud using Machine Learning Techniques

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9121.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 7641-7645

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Learning Algorithms ◽

Feature Reduction ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Cyber Attack ◽

Learning Techniques

The advancement in cyber-attack technologies have ushered in various new attacks which are difficult to detect using traditional intrusion detection systems (IDS).Existing IDS are trained to detect known patterns because of which newer attacks bypass the current IDS and go undetected. In this paper, a two level framework is proposed which can be used to detect unknown new attacks using machine learning techniques. In the first level the known types of classes for attacks are determined using supervised machine learning algorithms such as Support Vector Machine (SVM) and Neural networks (NN). The second level uses unsupervised machine learning algorithms such as K-means. The experimentation is carried out with four models with NSL- KDD dataset in Openstack cloud environment. The Model with Support Vector Machine for supervised machine learning, Gradual Feature Reduction (GFR) for feature selection and K-means for unsupervised algorithm provided the optimum efficiency of 94.56 %.

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Predictive model construction for prediction of soil fertility using decision tree machine learning algorithm

Kongunadu Research Journal ◽

10.26524/krj.2021.5 ◽

2021 ◽

Vol 8 (1) ◽

pp. 30-35

Author(s):

Jayalakshmi R ◽

Savitha Devi M

Keyword(s):

Machine Learning ◽

Decision Tree ◽

Soil Fertility ◽

Learning Algorithms ◽

Crop Productivity ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Support Vector ◽

Severe Problem ◽

Agriculture Sector

Agriculture sector is recognized as the backbone of the Indian economy that plays a crucial role in the growth of the nation’s economy. It imparts on weather and other environmental aspects. Some of the factors on which agriculture is reliant are Soil, climate, flooding, fertilizers, temperature, precipitation, crops, insecticides, and herb. The soil fertility is dependent on these factors and hence difficult to predict. However, the Agriculture sector in India is facing the severe problem of increasing crop productivity. Farmers lack the essential knowledge of nutrient content of the soil, selection of crop best suited for the soil and they also lack efficient methods for predicting crop well in advance so that appropriate methods have been used to improve crop productivity. This paper presents different Supervised Machine Learning Algorithms such as Decision tree, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) to predict the fertility of soil based on macro-nutrients and micro-nutrients status found in the dataset. Supervised Machine Learning algorithms are applied on the training dataset and are tested with the test dataset, and the implementation of these algorithms is done using R Tool. The performance analysis of these algorithms is done using different evaluation metrics like mean absolute error, cross-validation, and accuracy. Result analysis shows that the Decision tree is produced the best accuracy of 99% with a very less mean square error (MSE) rate.

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Comparative Analysis of Supervised Machine Learning Algorithms to Build a Predictive Model for Evaluating Students’ Performance

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v17i02.20025 ◽

2021 ◽

Vol 17 (02) ◽

pp. 90

Author(s):

Inssaf El Guabassi ◽

Zakaria Bousalem ◽

Rim Marah ◽

Aimad Qazdar

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Predictive Model ◽

Learning Algorithms ◽

Research Work ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Support Vector ◽

The Future ◽

Log Linear

In recent years, the world's population is increasingly demanding to predict the future with certainty, predicting the right information in any area is becoming a necessity. One of the ways to predict the future with certainty is to determine the possible future. In this sense, machine learning is a way to analyze huge datasets to make strong predictions or decisions. The main objective of this research work is to build a predictive model for evaluating students’ performance. Hence, the contributions are threefold. The first is to apply several supervised machine learning algorithms (i.e. ANCOVA, Logistic Regression, Support Vector Regression, Log-linear Regression, Decision Tree Regression, Random Forest Regression, and Partial Least Squares Regression) on our education dataset. The second purpose is to compare and evaluate algorithms used to create a predictive model based on various evaluation metrics. The last purpose is to determine the most important factors that influence the success or failure of the students. The experimental results showed that the Log-linear Regression provides a better prediction as well as the behavioral factors that influence students’ performance.

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Leveraging Machine Learning Algorithms For Zero-Day Ransomware Attack

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8694.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 4104-4107

Keyword(s):

Machine Learning ◽

Random Forest ◽

Nearest Neighbor ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Support Vector ◽

K Nearest Neighbor ◽

Supervised Learning Algorithms ◽

Microsoft Windows

Current global huge cyber protection attacks resulting from Infected Encryption ransomware structures over all international locations and businesses with millions of greenbacks lost in paying compulsion abundance. This type of malware encrypts consumer files, extracts consumer files, and charges higher ransoms to be paid for decryption of keys. An attacker could use different types of ransomware approach to steal a victim's files. Some of ransomware attacks like Scareware, Mobile ransomware, WannaCry, CryptoLocker, Zero-Day ransomware attack etc. A zero-day vulnerability is a software program security flaw this is regarded to the software seller however doesn’t have patch in vicinity to restore a flaw. Despite the fact that machine learning algorithms are already used to find encryption Ransomware. This is based on the analysis of a large number of PE file data Samples (benign software and ransomware utility) makes use of supervised machine learning algorithms for ascertain Zero-day attacks. This work was done on a Microsoft Windows operating system (the most attacked os through encryption ransomware) and estimated it. We have used four Supervised learning Algorithms, Random Forest Classifier , K-Nearest Neighbor, Support Vector Machine and Logistic Regression. Tests using machine learning algorithms evaluate almost null false positives with a 99.5% accuracy with a random forest algorithm.

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An analysis of PCOS disease prediction model using machine learning classification algorithms

Recent Patents on Engineering ◽

10.2174/1872212115999201224130204 ◽

2020 ◽

Vol 15 ◽

Author(s):

Shivani Aggarwal ◽

Kavita Pandey

Keyword(s):

Machine Learning ◽

Insulin Resistance ◽

Feature Selection ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Classification Algorithms ◽

Metabolic Abnormalities ◽

Related Disorder ◽

Machine Learning Classification

Background: Polycystic ovary syndrome is commonly known as PCOS and it is surprising that it affects up to 18% of women in reproductive age. PCOS is the most usually occurring hormone-related disorder. Some of the symptoms of PCOS are irregular periods, increased facial and body hair growth, attain more weight, darkening of skin, diabetes and trouble conceiving (infertility). It also came into light that patients suffering from PCOS also possess a range of metabolic abnormalities. Due to metabolic abnormalities, some disorder may occur which increase the risk of insulin resistance, type 2 diabetes and impaired glucose tolerance (a sign of prediabetes). Family members of women suffering from PCOS are also at higher hazardous level for developing the same metabolic abnormalities. Obesity and overweight status contribute to insulin resistance in PCOS. Objective: In the modern era, there are several new technologies available to diagnose PCOS and one of them is Machine learning algorithms because they are exposed to new data. These algorithms learn from past experiences to produce reliable and repeatable decisions. In this article, Machine learning algorithms are used to identify the important features to diagnose PCOS. Methods: Several classification algorithms like Support vector machine (SVM), Logistic Regression, Gradient Boosting, Random Forest, Decision Tree and K-Nearest Neighbor (KNN) are uses well organized test datasets for classify huge records. Initially a dataset of 541 instances and 41 attributes has been taken to apply the prediction models and a manual feature selection is done over it. Results: After the feature selection, a set of 12 attributes has been identified which plays a crucial role in diagnosing PCOS. Conclusion: There are several researches progressing in the direction of diagnosing PCOS but till now the relevant features are not identify for the same.

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Comparing different supervised machine learning algorithms for disease prediction

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-019-1004-8 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 19

Author(s):

Shahadat Uddin ◽

Arif Khan ◽

Md Ekramul Hossain ◽

Mohammad Ali Moni

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Relative Performance ◽

Supervised Machine Learning ◽

Support Vector ◽

Disease Prediction ◽

Machine Learning Algorithm ◽

Different Types

Abstract Background Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. This study aims to identify the key trends among different types of supervised machine learning algorithms, and their performance and usage for disease risk prediction. Methods In this study, extensive research efforts were made to identify those studies that applied more than one supervised machine learning algorithm on single disease prediction. Two databases (i.e., Scopus and PubMed) were searched for different types of search items. Thus, we selected 48 articles in total for the comparison among variants supervised machine learning algorithms for disease prediction. Results We found that the Support Vector Machine (SVM) algorithm is applied most frequently (in 29 studies) followed by the Naïve Bayes algorithm (in 23 studies). However, the Random Forest (RF) algorithm showed superior accuracy comparatively. Of the 17 studies where it was applied, RF showed the highest accuracy in 9 of them, i.e., 53%. This was followed by SVM which topped in 41% of the studies it was considered. Conclusion This study provides a wide overview of the relative performance of different variants of supervised machine learning algorithms for disease prediction. This important information of relative performance can be used to aid researchers in the selection of an appropriate supervised machine learning algorithm for their studies.

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Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data—Part II

Sensors ◽

10.3390/s20071806 ◽

2020 ◽

Vol 20 (7) ◽

pp. 1806

Author(s):

Silvio Semanjski ◽

Ivana Semanjski ◽

Wim De Wilde ◽

Sidharta Gautama

Keyword(s):

Machine Learning ◽

Real World ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Added Value ◽

Supervised Machine Learning ◽

Training Dataset ◽

Support Vector ◽

Correlation Pattern ◽

The Real

Global Navigation Satellite System (GNSS) meaconing and spoofing are being considered as the key threats to the Safety-of-Life (SoL) applications that mostly rely upon the use of open service (OS) signals without signal or data-level protection. While a number of pre and post correlation techniques have been proposed so far, possible utilization of the supervised machine learning algorithms to detect GNSS meaconing and spoofing is currently being examined. One of the supervised machine learning algorithms, the Support Vector Machine classification (C-SVM), is proposed for utilization at the GNSS receiver level due to fact that at that stage of signal processing, a number of measurements and observables exists. It is possible to establish the correlation pattern among those GNSS measurements and observables and monitor it with use of the C-SVM classification, the results of which we present in this paper. By adding the real-world spoofing and meaconing datasets to the laboratory-generated spoofing datasets at the training stage of the C-SVM, we complement the experiments and results obtained in Part I of this paper, where the training was conducted solely with the use of laboratory-generated spoofing datasets. In two experiments presented in this paper, the C-SVM algorithm was cross-fed with the real-world meaconing and spoofing datasets, such that the meaconing addition to the training was validated by the spoofing dataset, and vice versa. The comparative analysis of all four experiments presented in this paper shows promising results in two aspects: (i) the added value of the training dataset enrichment seems to be relevant for real-world GNSS signal manipulation attempt detection and (ii) the C-SVM-based approach seems to be promising for GNSS signal manipulation attempt detection, as well as in the context of potential federated learning applications.

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COVID-19 Prediction Applying Supervised Machine Learning Algorithms with Comparative Analysis Using WEKA

Algorithms ◽

10.3390/a14070201 ◽

2021 ◽

Vol 14 (7) ◽

pp. 201

Author(s):

Charlyn Nayve Villavicencio ◽

Julio Jerison Escudero Macrohon ◽

Xavier Alphonse Inbaraj ◽

Jyh-Horng Jeng ◽

Jer-Guang Hsieh

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Mean Absolute Error ◽

Learning Algorithms ◽

Absolute Error ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Support Vector ◽

K Nearest Neighbors ◽

Use Of Technology

Early diagnosis is crucial to prevent the development of a disease that may cause danger to human lives. COVID-19, which is a contagious disease that has mutated into several variants, has become a global pandemic that demands to be diagnosed as soon as possible. With the use of technology, available information concerning COVID-19 increases each day, and extracting useful information from massive data can be done through data mining. In this study, authors utilized several supervised machine learning algorithms in building a model to analyze and predict the presence of COVID-19 using the COVID-19 Symptoms and Presence dataset from Kaggle. J48 Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes algorithms were applied through WEKA machine learning software. Each model’s performance was evaluated using 10-fold cross validation and compared according to major accuracy measures, correctly or incorrectly classified instances, kappa, mean absolute error, and time taken to build the model. The results show that Support Vector Machine using Pearson VII universal kernel outweighs other algorithms by attaining 98.81% accuracy and a mean absolute error of 0.012.

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