In-situ identification of material batches using machine learning for machining operations

AbstractIn subtractive manufacturing, differences in machinability among batches of the same material can be observed. Ignoring these deviations can potentially reduce product quality and increase manufacturing costs. To consider the influence of the material batch in process optimization models, the batch needs to be efficiently identified. Thus, a smart service is proposed for in-situ material batch identification. This service is driven by a supervised machine learning model, which analyzes the signals of the machine’s control, especially torque data, for batch classification. The proposed approach is validated by cutting experiments with five different batches of the same specified material at various cutting conditions. Using this data, multiple classification models are trained and optimized. It is shown that the investigated batches can be correctly identified with close to 90% prediction accuracy using machine learning. Out of all the investigated algorithms, the best results are achieved using a Support Vector Machine with 89.0% prediction accuracy for individual batches and 98.9% while combining batches of similar machinability.

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Predicting Sentiment Polarity of Microblogs using an LSTM – CNN Deep Learning Model

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8933.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 4368-4373

Keyword(s):

Neural Network ◽

Machine Learning ◽

Prediction Accuracy ◽

Short Term Memory ◽

Learning Model ◽

Supervised Machine Learning ◽

Machine Learning Model ◽

Proposed Model ◽

Network Approaches

In this paper we propose a novel supervised machine learning model to predict the polarity of sentiments expressed in microblogs. The proposed model has a stacked neural network structure consisting of Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) layers. In order to capture the long-term dependencies of sentiments in the text ordering of a microblog, the proposed model employs an LSTM layer. The encodings produced by the LSTM layer are then fed to a CNN layer, which generates localized patterns of higher accuracy. These patterns are capable of capturing both local and global long-term dependences in the text of the microblogs. It was observed that the proposed model performs better and gives improved prediction accuracy when compared to semantic, machine learning and deep neural network approaches such as SVM, CNN, LSTM, CNN-LSTM, etc. This paper utilizes the benchmark Stanford Large Movie Review dataset to show the significance of the new approach. The prediction accuracy of the proposed approach is comparable to other state-of-art approaches.

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Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models

Remote Sensing ◽

10.3390/rs12030502 ◽

2020 ◽

Vol 12 (3) ◽

pp. 502 ◽

Cited By ~ 18

Author(s):

Zhilu Chang ◽

Zhen Du ◽

Fan Zhang ◽

Faming Huang ◽

Jiawu Chen ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Landslide Susceptibility ◽

Prediction Accuracy ◽

Aerial Images ◽

Supervised Machine Learning ◽

Support Vector ◽

Unsupervised Machine Learning ◽

Advantages And Disadvantages ◽

Interaction Detection

Landslide susceptibility prediction (LSP) has been widely and effectively implemented by machine learning (ML) models based on remote sensing (RS) images and Geographic Information System (GIS). However, comparisons of the applications of ML models for LSP from the perspectives of supervised machine learning (SML) and unsupervised machine learning (USML) have not been explored. Hence, this study aims to compare the LSP performance of these SML and USML models, thus further to explore the advantages and disadvantages of these ML models and to realize a more accurate and reliable LSP result. Two representative SML models (support vector machine (SVM) and CHi-squared Automatic Interaction Detection (CHAID)) and two representative USML models (K-means and Kohonen models) are respectively used to scientifically predict the landslide susceptibility indexes, and then these prediction results are discussed. Ningdu County with 446 recorded landslides obtained through field investigations is introduced as case study. A total of 12 conditioning factors are obtained through procession of Landsat TM 8 images and high-resolution aerial images, topographical and hydrological spatial analysis of Digital Elevation Modeling in GIS software, and government reports. The area value under the curve of receiver operating features (AUC) is applied for evaluating the prediction accuracy of SML models, and the frequency ratio (FR) accuracy is then introduced to compare the remarkable prediction performance differences between SML and USML models. Overall, the receiver operation curve (ROC) results show that the AUC of the SVM is 0.892 and is slightly greater than the AUC of the CHAID model (0.872). The FR accuracy results show that the SVM model has the highest accuracy for LSP (77.80%), followed by the CHAID model (74.50%), the Kohonen model (72.8%) and the K-means model (69.7%), which indicates that the SML models can reach considerably better prediction capability than the USML models. It can be concluded that selecting recorded landslides as prior knowledge to train and test the LSP models is the key reason for the higher prediction accuracy of the SML models, while the lack of a priori knowledge and target guidance is an important reason for the low LSP accuracy of the USML models. Nevertheless, the USML models can also be used to implement LSP due to their advantages of efficient modeling processes, dimensionality reduction and strong scalability.

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Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17197032 ◽

2020 ◽

Vol 17 (19) ◽

pp. 7032

Author(s):

Thu T. Nguyen ◽

Shaniece Criss ◽

Pallavi Dwivedi ◽

Dina Huang ◽

Jessica Keralis ◽

...

Keyword(s):

Machine Learning ◽

Application Programming Interface ◽

Learning Model ◽

Systematic Investigation ◽

Supervised Machine Learning ◽

Support Vector ◽

Time Period ◽

Machine Learning Model ◽

Life Impact ◽

Application Programming

Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter’s Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019–June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment.

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FoRex Trading Using Supervised Machine Learning

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.15.23024 ◽

2018 ◽

Vol 7 (4.15) ◽

pp. 400 ◽

Cited By ~ 1

Author(s):

Thuy Nguyen Thi Thu ◽

Vuong Dang Xuan

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Exchange Rate ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Support Vector ◽

Machine Learning Algorithm ◽

Machine Learning Model

The exchange rate of each money pair can be predicted by using machine learning algorithm during classification process. With the help of supervised machine learning model, the predicted uptrend or downtrend of FoRex rate might help traders to have right decision on FoRex transactions. The installation of machine learning algorithms in the FoRex trading online market can automatically make the transactions of buying/selling. All the transactions in the experiment are performed by using scripts added-on in transaction application. The capital, profits results of use support vector machine (SVM) models are higher than the normal one (without use of SVM).

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A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain

Diagnostics ◽

10.3390/diagnostics12010082 ◽

2021 ◽

Vol 12 (1) ◽

pp. 82

Author(s):

Chun-Chuan Hsu ◽

Cheng-CJ Chu ◽

Ching-Heng Lin ◽

Chien-Hsiung Huang ◽

Chip-Jin Ng ◽

...

Keyword(s):

Machine Learning ◽

Emergency Department ◽

Abdominal Pain ◽

Learning Model ◽

Supervised Machine Learning ◽

Support Vector ◽

Research Database ◽

Machine Learning Model ◽

Extreme Gradient Boosting ◽

Return Visits

Seventy-two-hour unscheduled return visits (URVs) by emergency department patients are a key clinical index for evaluating the quality of care in emergency departments (EDs). This study aimed to develop a machine learning model to predict 72 h URVs for ED patients with abdominal pain. Electronic health records data were collected from the Chang Gung Research Database (CGRD) for 25,151 ED visits by patients with abdominal pain and a total of 617 features were used for analysis. We used supervised machine learning models, namely logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC), to predict URVs. The VC model achieved more favorable overall performance than other models (AUROC: 0.74; 95% confidence interval (CI), 0.69–0.76; sensitivity, 0.39; specificity, 0.89; F1 score, 0.25). The reduced VC model achieved comparable performance (AUROC: 0.72; 95% CI, 0.69–0.74) to the full models using all clinical features. The VC model exhibited the most favorable performance in predicting 72 h URVs for patients with abdominal pain, both for all-features and reduced-features models. Application of the VC model in the clinical setting after validation may help physicians to make accurate decisions and decrease URVs.

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A machine learning approach for single cell interphase cell cycle staging

Scientific Reports ◽

10.1038/s41598-021-98489-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hemaxi Narotamo ◽

Maria Sofia Fernandes ◽

Ana Margarida Moreira ◽

Soraia Melo ◽

Raquel Seruca ◽

...

Keyword(s):

Machine Learning ◽

Cell Cycle ◽

Single Cell ◽

Cell Function ◽

Ground Truth ◽

Supervised Machine Learning ◽

Support Vector ◽

Svm Classifier ◽

Interphase Cell

AbstractThe cell nucleus is a tightly regulated organelle and its architectural structure is dynamically orchestrated to maintain normal cell function. Indeed, fluctuations in nuclear size and shape are known to occur during the cell cycle and alterations in nuclear morphology are also hallmarks of many diseases including cancer. Regrettably, automated reliable tools for cell cycle staging at single cell level using in situ images are still limited. It is therefore urgent to establish accurate strategies combining bioimaging with high-content image analysis for a bona fide classification. In this study we developed a supervised machine learning method for interphase cell cycle staging of individual adherent cells using in situ fluorescence images of nuclei stained with DAPI. A Support Vector Machine (SVM) classifier operated over normalized nuclear features using more than 3500 DAPI stained nuclei. Molecular ground truth labels were obtained by automatic image processing using fluorescent ubiquitination-based cell cycle indicator (Fucci) technology. An average F1-Score of 87.7% was achieved with this framework. Furthermore, the method was validated on distinct cell types reaching recall values higher than 89%. Our method is a robust approach to identify cells in G1 or S/G2 at the individual level, with implications in research and clinical applications.

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Machine Learning Assisted Chimera and Solitary States in Networks

Frontiers in Physics ◽

10.3389/fphy.2021.513969 ◽

2021 ◽

Vol 9 ◽

Author(s):

Niraj Kushwaha ◽

Naveen Kumar Mendola ◽

Saptarshi Ghosh ◽

Ajay Deep Kachhvah ◽

Sarika Jalan

Keyword(s):

Machine Learning ◽

Single Layer ◽

Dynamical Evolution ◽

Network Models ◽

Learning Model ◽

Supervised Machine Learning ◽

Support Vector ◽

Svm Classifier ◽

Chimera States ◽

Machine Learning Model

Chimera and Solitary states have captivated scientists and engineers due to their peculiar dynamical states corresponding to co-existence of coherent and incoherent dynamical evolution in coupled units in various natural and artificial systems. It has been further demonstrated that such states can be engineered in systems of coupled oscillators by suitable implementation of communication delays. Here, using supervised machine learning, we predict (a) the precise value of delay which is sufficient for engineering chimera and solitary states for a given set of system's parameters, as well as (b) the intensity of incoherence for such engineered states. Ergo, using few initial data points we generate a machine learning model which can then create a more refined phase plot as well as by including new parameter values. We demonstrate our results for two different examples consisting of single layer and multi layer networks. First, the chimera states (solitary states) are engineered by establishing delays in the neighboring links of a node (the interlayer links) in a 2-D lattice (multiplex network) of oscillators. Then, different machine learning classifiers, K-nearest neighbors (KNN), support vector machine (SVM) and multi-layer perceptron neural network (MLP-NN) are employed by feeding the data obtained from the network models. Once a machine learning model is trained using the limited amount of data, it predicts the precise value of critical delay as well as the intensity of incoherence for a given unknown systems parameters values. Testing accuracy, sensitivity, and specificity analysis reveal that MLP-NN classifier is better suited than Knn or SVM classifier for the predictions of parameters values for engineered chimera and solitary states. The technique provides an easy methodology to predict critical delay values as well as intensity of incoherence for that delay value for designing an experimental setup to create solitary and chimera states.

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Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model

Computational Intelligence and Neuroscience ◽

10.1155/2020/8878681 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Pavel Stefanovič ◽

Rokas Štrimaitis ◽

Olga Kurasova

Keyword(s):

Machine Learning ◽

Probabilistic Neural Network ◽

Forest Tree ◽

Learning Model ◽

Flight Time ◽

Supervised Machine Learning ◽

Support Vector ◽

Tree Model ◽

Machine Learning Model ◽

Random Forest Tree

In the paper, the flight time deviation of Lithuania airports has been analyzed. The supervised machine learning model has been implemented to predict the interval of time delay deviation of new flights. The analysis has been made using seven algorithms: probabilistic neural network, multilayer perceptron, decision trees, random forest, tree ensemble, gradient boosted trees, and support vector machines. To find the best parameters which give the highest accuracy for each algorithm, the grid search has been used. To evaluate the quality of each algorithm, the five measures have been calculated: sensitivity/recall, precision, specificity, F-measure, and accuracy. All experimental investigation has been made using the newly collected dataset from Lithuania airports and weather information on departure/landing time. The departure flights and arrival flights have been investigated separately. To balance the dataset, the SMOTE technique is used. The research results showed that the highest accuracy is obtained using the tree model classifiers and the best algorithm of this type to predict is gradient boosted trees.

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Identification and transfer of spatial transcriptomics signatures for cancer diagnosis

Breast Cancer Research ◽

10.1186/s13058-019-1242-9 ◽

2020 ◽

Vol 22 (1) ◽

Cited By ~ 3

Author(s):

Niyaz Yoosuf ◽

José Fernández Navarro ◽

Fredrik Salmén ◽

Patrik L. Ståhl ◽

Carsten O. Daub

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Ductal Carcinoma ◽

Supervised Machine Learning ◽

Support Vector ◽

Cancer Tissue ◽

Diagnostic Challenge ◽

Expression Signature ◽

Tissue Sections

Abstract Background Distinguishing ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) regions in clinical biopsies constitutes a diagnostic challenge. Spatial transcriptomics (ST) is an in situ capturing method, which allows quantification and visualization of transcriptomes in individual tissue sections. In the past, studies have shown that breast cancer samples can be used to study their transcriptomes with spatial resolution in individual tissue sections. Previously, supervised machine learning methods were used in clinical studies to predict the clinical outcomes for cancer types. Methods We used four publicly available ST breast cancer datasets from breast tissue sections annotated by pathologists as non-malignant, DCIS, or IDC. We trained and tested a machine learning method (support vector machine) based on the expert annotation as well as based on automatic selection of cell types by their transcriptome profiles. Results We identified expression signatures for expert annotated regions (non-malignant, DCIS, and IDC) and build machine learning models. Classification results for 798 expression signature transcripts showed high coincidence with the expert pathologist annotation for DCIS (100%) and IDC (96%). Extending our analysis to include all 25,179 expressed transcripts resulted in an accuracy of 99% for DCIS and 98% for IDC. Further, classification based on an automatically identified expression signature covering all ST spots of tissue sections resulted in prediction accuracy of 95% for DCIS and 91% for IDC. Conclusions This concept study suggest that the ST signatures learned from expert selected breast cancer tissue sections can be used to identify breast cancer regions in whole tissue sections including regions not trained on. Furthermore, the identified expression signatures can classify cancer regions in tissue sections not used for training with high accuracy. Expert-generated but even automatically generated cancer signatures from ST data might be able to classify breast cancer regions and provide clinical decision support for pathologists in the future.

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A Novel Amino Acid Sequence-based Computational Approach to Predicting Cell-penetrating Peptides

Current Computer - Aided Drug Design ◽

10.2174/1573409914666180925100355 ◽

2019 ◽

Vol 15 (3) ◽

pp. 206-211 ◽

Cited By ~ 2

Author(s):

Jihui Tang ◽

Jie Ning ◽

Xiaoyan Liu ◽

Baoming Wu ◽

Rongfeng Hu

Keyword(s):

Machine Learning ◽

Amino Acid ◽

Amino Acid Position ◽

Cell Penetrating Peptides ◽

Support Vector ◽

Cell Penetration ◽

Drug Candidates ◽

Machine Learning Model ◽

Cell Penetrating ◽

Novel Method

Introduction: Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates. Materials and Methods: In this study, we developed a novel method for predicting Cell-Penetrating Peptides (CPPs) membrane penetrating capability. For this, we used orthogonal encoding to encode amino acid and each amino acid position as one variable. Then a software of IBM spss modeler and a dataset including 533 CPPs, were used for model screening. Results: The results indicated that the machine learning model of Support Vector Machine (SVM) was suitable for predicting membrane penetrating capability. For improvement, the three CPPs with the most longer lengths were used to predict CPPs. The penetration capability can be predicted with an accuracy of close to 95%. Conclusion: All the results indicated that by using amino acid position as a variable can be a perspective method for predicting CPPs membrane penetrating capability.

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