scholarly journals Semi-Supervised Classification of the State of Operation in Self-Lubricating Journal Bearings Using a Random Forest Classifier

Lubricants ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 50
Author(s):  
Josef Prost ◽  
Ulrike Cihak-Bayr ◽  
Ioana Adina Neacșu ◽  
Reinhard Grundtner ◽  
Franz Pirker ◽  
...  
Keyword(s):  
Random Forest ◽  
Lateral Force ◽  
Random Forest Classifier ◽  
The State ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Ex Post ◽  
The Individual ◽  
Oscillating Motion

For a tribological experiment involving a steel shaft sliding in a self-lubricating bronze bearing, a semi-supervised machine learning method for the classification of the state of operation is proposed. During the translatory oscillating motion, the system may undergo different states of operation from normal to critical, showing self-recovering behaviour. A Random Forest classifier was trained on individual cycles from the lateral force data from four distinct experimental runs in order to distinguish between four states of operation. The labelling of the individual cycles proved to be crucial for a high prediction accuracy of the trained RF classifier. The proposed semi-supervised approach allows choosing within a range between automatically generated labels and full manual labelling by an expert user. The algorithm was at the current state used for ex post classification of the state of operation. Considering the results from the ex post analysis and providing a sufficiently sized training dataset, online classification of the state of operation of a system will be possible. This will allow taking active countermeasures to stabilise the system or to terminate the experiment before major damage occurs.

scholarly journals Classification of EEG data for human mental state analysis using Random Forest Classifier

2018 ◽  
Vol 132 ◽  
pp. 1523-1532 ◽  
Author(s):  
Damodar Reddy Edla ◽  
Kunal Mangalorekar ◽  
Gauri Dhavalikar ◽  
Shubham Dodia
Keyword(s):  
Random Forest ◽  
Mental State ◽  
Random Forest Classifier ◽  
Eeg Data ◽  
State Analysis

scholarly journals Application of Natural Language Processing with Supervised Machine Learning Techniques to Predict the Overall Drugs Performance

2020 ◽  
Vol 11 (40) ◽  
pp. 8-23
Author(s):  
Pius MARTHIN ◽  
Duygu İÇEN
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Semantic Analysis ◽  
Classification Tree ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

Online product reviews have become a valuable source of information which facilitate customer decision with respect to a particular product. With the wealthy information regarding user's satisfaction and experiences about a particular drug, pharmaceutical companies make the use of online drug reviews to improve the quality of their products. Machine learning has enabled scientists to train more efficient models which facilitate decision making in various fields. In this manuscript we applied a drug review dataset used by (Gräβer, Kallumadi, Malberg,& Zaunseder, 2018), available freely from machine learning repository website of the University of California Irvine (UCI) to identify best machine learning model which provide a better prediction of the overall drug performance with respect to users' reviews. Apart from several manipulations done to improve model accuracy, all necessary procedures required for text analysis were followed including text cleaning and transformation of texts to numeric format for easy training machine learning models. Prior to modeling, we obtained overall sentiment scores for the reviews. Customer's reviews were summarized and visualized using a bar plot and word cloud to explore the most frequent terms. Due to scalability issues, we were able to use only the sample of the dataset. We randomly sampled 15000 observations from the 161297 training dataset and 10000 observations were randomly sampled from the 53766 testing dataset. Several machine learning models were trained using 10 folds cross-validation performed under stratified random sampling. The trained models include Classification and Regression Trees (CART), classification tree by C5.0, logistic regression (GLM), Multivariate Adaptive Regression Spline (MARS), Support vector machine (SVM) with both radial and linear kernels and a classification tree using random forest (Random Forest). Model selection was done through a comparison of accuracies and computational efficiency. Support vector machine (SVM) with linear kernel was significantly best with an accuracy of 83% compared to the rest. Using only a small portion of the dataset, we managed to attain reasonable accuracy in our models by applying the TF-IDF transformation and Latent Semantic Analysis (LSA) technique to our TDM.

scholarly journals To the Question on the Nature of Military Threats and Non-Military Responses

2015 ◽  
pp. 133-142
Author(s):  
S. R. Tsyrendorzhjyev
Keyword(s):  
National Security ◽  
The State ◽  
Theory And Practice ◽  
Structural Elements ◽  
Common Task ◽  
Military Security ◽  
Definition Of ◽  
The Individual ◽  
The Military

The notion of "military danger, military threats, military and non-military measures to Parry, and other definitions from the policy of the State to ensure the military security of the now widely used in journalism, conceptual, other documents and research. The attentive reader it is not difficult to notice the ambiguity in the interpretation of these concepts. This makes it difficult to not only the perception of the relevant topics for ensuring military security publications, but also the development of the theory and practice of ensuring the defence and security of the State. The author's view on the essence of the reasoning logic of non-military measures to counter military threats, as the ultimate goal of the article is the following.First the task of analyzing the concept of "national security", "object of national security" and understand the functions of the State, society and the individual to ensure national security. Decomposition of an object of national security, which is "national property" (the content of the concepts described in the article) has made it possible to substantiate the basis for classification of national security threats and with better understanding of the nature, variety, Genesis. This provided a rationale for the role and the place of the tasks ensuring military security in the common task of ensuring national security, the correlation of military and non-military threats.The final phase of the research, the results of which are set out in the article is devoted to analysis of military threats, which made it possible to identify their main structural elements: source, media, military-political and strategic nature, install the main factors defining the content of these elements and their interaction. Based on these results, the proposed definition of the essence of non-military measures for counteracting of military threats, as well as guidelines for developing these measures.

scholarly journals Classification of Agriculture Farm Machinery Using Machine Learning and Internet of Things

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 403
Author(s):  
Muhammad Waleed ◽  
Tai-Won Um ◽  
Tariq Kamal ◽  
Syed Muhammad Usman
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Farm Machinery ◽  
Learning Techniques

In this paper, we apply the multi-class supervised machine learning techniques for classifying the agriculture farm machinery. The classification of farm machinery is important when performing the automatic authentication of field activity in a remote setup. In the absence of a sound machine recognition system, there is every possibility of a fraudulent activity taking place. To address this need, we classify the machinery using five machine learning techniques—K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Gradient Boosting (GB). For training of the model, we use the vibration and tilt of machinery. The vibration and tilt of machinery are recorded using the accelerometer and gyroscope sensors, respectively. The machinery included the leveler, rotavator and cultivator. The preliminary analysis on the collected data revealed that the farm machinery (when in operation) showed big variations in vibration and tilt, but observed similar means. Additionally, the accuracies of vibration-based and tilt-based classifications of farm machinery show good accuracy when used alone (with vibration showing slightly better numbers than the tilt). However, the accuracies improve further when both (the tilt and vibration) are used together. Furthermore, all five machine learning algorithms used for classification have an accuracy of more than 82%, but random forest was the best performing. The gradient boosting and random forest show slight over-fitting (about 9%), but both algorithms produce high testing accuracy. In terms of execution time, the decision tree takes the least time to train, while the gradient boosting takes the most time.

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