Solvent Selection Scheme Using Machine Learning Based on Physicochemical Description of Solvent Molecules: Application to Cyclic Organometallic Reaction

Boiler waterwall tube leakage is the most probable cause of failure in steam power plants (SPPs). The development of an intelligent tube leak detection system can increase the efficiency and reliability of modern power plants. The idea of e-maintenance based on multivariate algorithms was recently introduced for intelligent fault detection and diagnosis in SPPs. However, these multivariate algorithms are highly dependent on the number of input process variables (sensors). Therefore, this work proposes a machine learning-based model integrated with an optimal sensor selection scheme to analyze boiler waterwall tube leakage. Finally, a real SPP test case is employed to validate the proposed model’s effectiveness. The results indicate that the proposed model can successfully detect waterwall tube leakage with improved accuracy vs. other comparable models.

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Low Complexity Beam Selection Scheme for Terahertz Systems: A Machine Learning Approach

2019 IEEE International Conference on Communications Workshops (ICC Workshops) ◽

10.1109/iccw.2019.8757148 ◽

2019 ◽

Cited By ~ 1

Author(s):

Xinying Ma ◽

Zhi Chen ◽

Zhuoxun Li ◽

Wenjie Chen ◽

Ke Liu

Keyword(s):

Machine Learning ◽

Low Complexity ◽

Learning Approach ◽

Selection Scheme ◽

Machine Learning Approach ◽

Beam Selection

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Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T1-weighted Contrast-enhanced Imaging

BMC Medical Imaging ◽

10.1186/s12880-020-00545-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Ying-Zhi Sun ◽

Lin-Feng Yan ◽

Yu Han ◽

Hai-Yan Nan ◽

Gang Xiao ◽

...

Keyword(s):

Machine Learning ◽

Sensitivity And Specificity ◽

Standard Treatment ◽

Learning Strategy ◽

Critical Issue ◽

Classification Performance ◽

Selection Scheme ◽

Contrast Enhanced ◽

True Progression ◽

Enhanced Imaging

Abstract Background Based on conventional MRI images, it is difficult to differentiatepseudoprogression from true progressionin GBM patients after standard treatment, which isa critical issue associated with survival. The aim of this study was to evaluate the diagnostic performance of machine learning using radiomics modelfrom T1-weighted contrast enhanced imaging(T1CE) in differentiating pseudoprogression from true progression after standard treatment for GBM. Methods Seventy-sevenGBM patients, including 51 with true progression and 26 with pseudoprogression,who underwent standard treatment and T1CE, were retrospectively enrolled.Clinical information, including sex, age, KPS score, resection extent, neurological deficit and mean radiation dose, were also recorded collected for each patient. The whole tumor enhancementwas manually drawn on the T1CE image, and a total of texture 9675 features were extracted and fed to a two-step feature selection scheme. A random forest (RF) classifier was trained to separate the patients by their outcomes.The diagnostic efficacies of the radiomics modeland radiologist assessment were further compared by using theaccuracy (ACC), sensitivity and specificity. Results No clinical features showed statistically significant differences between true progression and pseudoprogression.The radiomic classifier demonstrated ACC, sensitivity, and specificity of 72.78%(95% confidence interval [CI]: 0.45,0.91), 78.36%(95%CI: 0.56,1.00) and 61.33%(95%CI: 0.20,0.82).The accuracy, sensitivity and specificity of three radiologists’ assessment were66.23%(95% CI: 0.55,0.76), 61.50%(95% CI: 0.43,0.78) and 68.62%(95% CI: 0.55,0.80); 55.84%(95% CI: 0.45,0.66),69.25%(95% CI: 0.50,0.84) and 49.13%(95% CI: 0.36,0.62); 55.84%(95% CI: 0.45,0.66), 69.23%(95% CI: 0.50,0.84) and 47.06%(95% CI: 0.34,0.61), respectively. Conclusion T1CE–based radiomics showed better classification performance compared with radiologists’ assessment.The radiomics modelwas promising in differentiating pseudoprogression from true progression.

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Machine Learning-Based Antenna Selection in Untrusted Relay Networks

10.20944/preprints201903.0160.v1 ◽

2019 ◽

Cited By ~ 2

Author(s):

Rugui Yao ◽

Yuxin Zhang ◽

Nan Qi ◽

Theodoros A. Tsiftsis

Keyword(s):

Machine Learning ◽

Antenna Selection ◽

Relay Networks ◽

Support Vector ◽

Secrecy Outage Probability ◽

Secrecy Rate ◽

Transmit Antenna Selection ◽

K Nearest Neighbors ◽

Selection Scheme ◽

Untrusted Relay

This paper studies the transmit antenna selection based on machine learning (ML) schemes in untrusted relay networks. First, we state the conventional antenna selection scheme. Then, we implement three ML schemes, namely, the support vector machine-based scheme, the naive-Bayes-based scheme, and the k-nearest neighbors-based scheme, which are applied to select the best antenna with the highest secrecy rate. The simulation results are presented in terms of system secrecy rate and secrecy outage probability. From the simulation, we can conclude that the proposed ML-based antenna selection schemes can achieve the same performance without amplification at the relay, or small performance degradation with transmitted power constraint at the relay, comparing with conventional schemes. However, when the training is completed, the proposed schemes can perform the antenna selection with a small computational complexity.

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A Human-Machine Coupled System for Efficient Sleep Spindle Detection by Iterative Revision

10.1101/454488 ◽

2018 ◽

Cited By ~ 1

Author(s):

Dasheng Bi

Keyword(s):

Machine Learning ◽

Automatic Detection ◽

Coupled System ◽

Sleep Spindles ◽

Sleep Spindle ◽

Learning Models ◽

Selection Scheme ◽

Detection Algorithms ◽

Low Threshold ◽

Machine Learning Models

AbstractSleep spindles are characteristic events in EEG signals during non-REM sleep, and are known to be important biological markers. Manually labeling spindles by visual inspection, however, has proved to be a tedious task. Automatic detection algorithms generalize weakly for versatile spindle forms, and machine-learning methods require large datasets to train, which are unfeasible to acquire particularly for experimental animal groups. Here, a novel, integrated system based on a process of iterative “Selection-Revision” (iSR) is introduced to aid in the efficient detection of spindles. By coupling low-threshold automatic detection of spindle events based on selected parameters with manual “Revision,” the human task is effectively simplified from searching across signal traces to binary verification. Convergence was observed between resulting spindle sets through iSR, largely independent of their initial labeling, demonstrating the robustness of the method. Although possible breakdown of the revised spindle sets could be seen after multiple rounds of Revision, due to overfitting of the revised set to the initial human labeling, this could be compensated for by a Selection scheme tolerant to higher False-Negative rates of the machine labeling relative to the standard set. It was also found that iSR is generalizable to different datasets, and that initial human labeling could be substituted by low-threshold machine detection. Overall, this human-machine coupled approach allows for fast labeling to obtain consistent spindle sets, which can also be used to train machine-learning models in the future. The principle of iSR may also be applied for many different data types to assist with other pattern detection tasks.Significance StatementElectroencephalography (EEG) recordings are widely adopted in brain research. Abnormalities in the occurrence of particular EEG waveforms, such as sleep spindles, can be used to diagnose psychiatric diseases. Traditionally, human experts have labeled EEG traces for sleep spindles, a time consuming process; automated detection algorithms, however, often yield inaccurate results. This study introduces a new method for efficient sleep spindle detection with a human-machine coupled system that can iteratively revise labeled datasets, enabling convergence towards a robust, accurate spindle labeling. This system eases large-scale sleep spindle detection, which can yield datasets for both biological analyses and for training machine-learning models. Furthermore, the underlying method of iterative revision can be used to analyze other types of patterns efficiently.

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Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis

Chemical Science ◽

10.1039/c9sc01844a ◽

2019 ◽

Vol 10 (27) ◽

pp. 6697-6706 ◽

Cited By ~ 16

Author(s):

Yehia Amar ◽

Artur M. Schweidtmann ◽

Paul Deutsch ◽

Liwei Cao ◽

Alexei Lapkin

Keyword(s):

Machine Learning ◽

Asymmetric Catalysis ◽

Molecular Descriptors ◽

Process Development ◽

Solvent Selection ◽

Significant Challenge

Rational solvent selection remains a significant challenge in process development.

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Phase Segmentation of X-Ray Computer Tomography Rock Images using Machine Learning Techniques: an Accuracy and Performance Study

10.5194/se-2016-44 ◽

2016 ◽

Cited By ~ 1

Author(s):

Swarup Chauhan ◽

Wolfram Rühaak ◽

Hauke Anbergen ◽

Alen Kabdenov ◽

Marcus Freise ◽

...

Keyword(s):

Machine Learning ◽

Least Square ◽

Classification Model ◽

Machine Learning Techniques ◽

Support Vector ◽

Ensemble Classifiers ◽

Synthetic Sample ◽

Selection Scheme ◽

X Ray ◽

Learning Techniques

Abstract. Performance and accuracy of machine learning techniques to segment rock grains, matrix and pore voxels, from a 3D volume of X-ray tomographic (XCT) grey-scale rock images was evaluated. The segmentation and classification capability of unsupervised (k-means, fuzzy c-means, self-organized maps), supervised (artificial neural networks, least square support vector machines) and ensemble classifiers (bragging and boosting) was tested using XCT images of Andesite volcanic rock, Berea sandstone, Rotliegend sandstone and a synthetic sample. The averaged porosity obtained for Andesite (0.15 ± 0.017), Barea sandstone (0.15 ± 0.02), Rotliegend sandstone (0.14 ± 0.08), synthetic sample (0.50 ± 0.13) is in very good agreement to the respective laboratory measurement data and varies by a factor of 0.2. The k-means algorithm is the fastest of all machine learning algorithms, whereas least square support vector machine is the most computationally expensive. Assessment of accuracy by entropy and purity values for unsupervised techniques; mean squared root error, receiver operational characteristics (to train the classification model) for supervised techniques; and 10-fold cross validation for the ensemble classifiers was performed. In general, the accuracy was found to be largely affected by the feature vector selection scheme. As it is always a trade-off between performance and accuracy, it is difficult to isolate one particular machine learning algorithm which is best suited for the complex phase segmentation problem. Therefore, our investigation provides parameters that can help selecting the appropriate machine learning techniques for phase segmentation.

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Phase segmentation of X-ray computer tomography rock images using machine learning techniques: an accuracy and performance study

Solid Earth ◽

10.5194/se-7-1125-2016 ◽

2016 ◽

Vol 7 (4) ◽

pp. 1125-1139 ◽

Cited By ~ 15

Author(s):

Swarup Chauhan ◽

Wolfram Rühaak ◽

Hauke Anbergen ◽

Alen Kabdenov ◽

Marcus Freise ◽

...

Keyword(s):

Machine Learning ◽

Least Squares ◽

Machine Learning Techniques ◽

Support Vector ◽

Operational Characteristic ◽

Berea Sandstone ◽

Synthetic Sample ◽

Selection Scheme ◽

X Ray ◽

Learning Techniques

Abstract. Performance and accuracy of machine learning techniques to segment rock grains, matrix and pore voxels from a 3-D volume of X-ray tomographic (XCT) grayscale rock images was evaluated. The segmentation and classification capability of unsupervised (k-means, fuzzy c-means, self-organized maps), supervised (artificial neural networks, least-squares support vector machines) and ensemble classifiers (bragging and boosting) were tested using XCT images of andesite volcanic rock, Berea sandstone, Rotliegend sandstone and a synthetic sample. The averaged porosity obtained for andesite (15.8 ± 2.5 %), Berea sandstone (16.3 ± 2.6 %), Rotliegend sandstone (13.4 ± 7.4 %) and the synthetic sample (48.3 ± 13.3 %) is in very good agreement with the respective laboratory measurement data and varies by a factor of 0.2. The k-means algorithm is the fastest of all machine learning algorithms, whereas a least-squares support vector machine is the most computationally expensive. Metrics entropy, purity, mean square root error, receiver operational characteristic curve and 10 K-fold cross-validation were used to determine the accuracy of unsupervised, supervised and ensemble classifier techniques. In general, the accuracy was found to be largely affected by the feature vector selection scheme. As it is always a trade-off between performance and accuracy, it is difficult to isolate one particular machine learning algorithm which is best suited for the complex phase segmentation problem. Therefore, our investigation provides parameters that can help in selecting the appropriate machine learning techniques for phase segmentation.

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