Model Comparison for Esp Run-Life Prediction: Classic Statistics Vs. Machine Learning

Slight Reduction ◽

Learning Models ◽

Validation Data ◽

Operational Conditions ◽

Abstract Electrical Submersible Pumps reliability and run-life analysis has been extensively studied since its development. Current machine learning algorithms allow to correlate operational conditions to ESP run-life in order to generate predictions for active and new wells. Four machine learning models are compared to a linear proportional hazards model, used as a baseline for comparison purposes. Proper accuracy metrics for survival analysis problems are calculated on run-life predictions vs. actual values over training and validation data subsets. Results demonstrate that the baseline model is able to produce more consistent predictions with a slight reduction in its accuracy, compared to current machine learning models for small datasets. This study demonstrates that the quality of the date and it pre-processing supports the current shift from model-centric to data-centric approach to machine and deep learning problems.

Prediction of aircraft estimated time of arrival using machine learning methods

The Aeronautical Journal ◽

10.1017/aer.2021.13 ◽

2021 ◽

pp. 1-15

Author(s):

O. Basturk ◽

C. Cetek

Keyword(s):

Machine Learning ◽

Web Application ◽

Absolute Error ◽

Weather Data ◽

Time Of Arrival ◽

Learning Models ◽

Trajectory Data ◽

Different Sources ◽

ABSTRACT In this study, prediction of aircraft Estimated Time of Arrival (ETA) is proposed using machine learning algorithms. Accurate prediction of ETA is important for management of delay and air traffic flow, runway assignment, gate assignment, collaborative decision making (CDM), coordination of ground personnel and equipment, and optimisation of arrival sequence etc. Machine learning is able to learn from experience and make predictions with weak assumptions or no assumptions at all. In the proposed approach, general flight information, trajectory data and weather data were obtained from different sources in various formats. Raw data were converted to tidy data and inserted into a relational database. To obtain the features for training the machine learning models, the data were explored, cleaned and transformed into convenient features. New features were also derived from the available data. Random forests and deep neural networks were used to train the machine learning models. Both models can predict the ETA with a mean absolute error (MAE) less than 6min after departure, and less than 3min after terminal manoeuvring area (TMA) entrance. Additionally, a web application was developed to dynamically predict the ETA using proposed models.

Characterizing and Evaluating the Zoonotic Potential of Novel Viruses Discovered in Vampire Bats

Viruses ◽

10.3390/v13020252 ◽

2021 ◽

Vol 13 (2) ◽

pp. 252

Author(s):

Laura M. Bergner ◽

Nardus Mollentze ◽

Richard J. Orton ◽

Carlos Tello ◽

Alice Broos ◽

...

Keyword(s):

Machine Learning ◽

Phylogenetic Analyses ◽

Human Infection ◽

Zoonotic Potential ◽

Metagenomic Sequencing ◽

Learning Models ◽

Sequencing Data ◽

Vampire Bats ◽

The contemporary surge in metagenomic sequencing has transformed knowledge of viral diversity in wildlife. However, evaluating which newly discovered viruses pose sufficient risk of infecting humans to merit detailed laboratory characterization and surveillance remains largely speculative. Machine learning algorithms have been developed to address this imbalance by ranking the relative likelihood of human infection based on viral genome sequences, but are not yet routinely applied to viruses at the time of their discovery. Here, we characterized viral genomes detected through metagenomic sequencing of feces and saliva from common vampire bats (Desmodus rotundus) and used these data as a case study in evaluating zoonotic potential using molecular sequencing data. Of 58 detected viral families, including 17 which infect mammals, the only known zoonosis detected was rabies virus; however, additional genomes were detected from the families Hepeviridae, Coronaviridae, Reoviridae, Astroviridae and Picornaviridae, all of which contain human-infecting species. In phylogenetic analyses, novel vampire bat viruses most frequently grouped with other bat viruses that are not currently known to infect humans. In agreement, machine learning models built from only phylogenetic information ranked all novel viruses similarly, yielding little insight into zoonotic potential. In contrast, genome composition-based machine learning models estimated different levels of zoonotic potential, even for closely related viruses, categorizing one out of four detected hepeviruses and two out of three picornaviruses as having high priority for further research. We highlight the value of evaluating zoonotic potential beyond ad hoc consideration of phylogeny and provide surveillance recommendations for novel viruses in a wildlife host which has frequent contact with humans and domestic animals.

Machine Learning Models for Finger Bend Evaluation using Implemented Low cost Flex Sensor

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35742 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 3605-3611

Author(s):

Pratyush Kaware

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Low Cost ◽

Learning Algorithms ◽

Cost Effective ◽

Support Vector ◽

Learning Models ◽

In this paper a cost-effective sensor has been implemented to read finger bend signals, by attaching the sensor to a finger, so as to classify them based on the degree of bent as well as the joint about which the finger was being bent. This was done by testing with various machine learning algorithms to get the most accurate and consistent classifier. Finally, we found that Support Vector Machine was the best algorithm suited to classify our data, using we were able predict live state of a finger, i.e., the degree of bent and the joints involved. The live voltage values from the sensor were transmitted using a NodeMCU micro-controller which were converted to digital and uploaded on a database for analysis.

Comparison of the Performance of Machine Learning Algorithms in Predicting Heart Disease

Frontiers in Health Informatics ◽

10.30699/fhi.v10i1.349 ◽

2021 ◽

Vol 10 (1) ◽

pp. 99

Author(s):

Sajad Yousefi

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Heart Disease ◽

Decision Tree ◽

Roc Curve ◽

Supervised Machine Learning ◽

Learning Models ◽

Algorithm Performance ◽

Introduction: Heart disease is often associated with conditions such as clogged arteries due to the sediment accumulation which causes chest pain and heart attack. Many people die due to the heart disease annually. Most countries have a shortage of cardiovascular specialists and thus, a significant percentage of misdiagnosis occurs. Hence, predicting this disease is a serious issue. Using machine learning models performed on multidimensional dataset, this article aims to find the most efficient and accurate machine learning models for disease prediction.Material and Methods: Several algorithms were utilized to predict heart disease among which Decision Tree, Random Forest and KNN supervised machine learning are highly mentioned. The algorithms are applied to the dataset taken from the UCI repository including 294 samples. The dataset includes heart disease features. To enhance the algorithm performance, these features are analyzed, the feature importance scores and cross validation are considered.Results: The algorithm performance is compared with each other, so that performance based on ROC curve and some criteria such as accuracy, precision, sensitivity and F1 score were evaluated for each model. As a result of evaluation, Accuracy, AUC ROC are 83% and 99% respectively for Decision Tree algorithm. Logistic Regression algorithm with accuracy and AUC ROC are 88% and 91% respectively has better performance than other algorithms. Therefore, these techniques can be useful for physicians to predict heart disease patients and prescribe them correctly.Conclusion: Machine learning technique can be used in medicine for analyzing the related data collections to a disease and its prediction. The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of machine learning to evaluate heart disease and indeed, the prediction of heart disease is compared to determine the most appropriate classification. As a result of evaluation, better performance was observed in both Decision Tree and Logistic Regression models.

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

10.1101/724310 ◽

2019 ◽

Author(s):

Edward W Huang ◽

Ameya Bhope ◽

Jing Lim ◽

Saurabh Sinha ◽

Amin Emad

Keyword(s):

Machine Learning ◽

Drug Response ◽

Drug Sensitivity ◽

Learning Models ◽

Large Databases ◽

Tissue Of Origin ◽

Molecular Profiles ◽

Clinical Drug ◽

ABSTRACTPrediction of clinical drug response (CDR) of cancer patients, based on their clinical and molecular profiles obtained prior to administration of the drug, can play a significant role in individualized medicine. Machine learning models have the potential to address this issue, but training them requires data from a large number of patients treated with each drug, limiting their feasibility. While large databases of drug response and molecular profiles of preclinical in-vitro cancer cell lines (CCLs) exist for many drugs, it is unclear whether preclinical samples can be used to predict CDR of real patients.We designed a systematic approach to evaluate how well different algorithms, trained on gene expression and drug response of CCLs, can predict CDR of patients. Using data from two large databases, we evaluated various linear and non-linear algorithms, some of which utilized information on gene interactions. Then, we developed a new algorithm called TG-LASSO that explicitly integrates information on samples’ tissue of origin with gene expression profiles to improve prediction performance. Our results showed that regularized regression methods provide significantly accurate prediction. However, including the network information or common methods of including information on the tissue of origin did not improve the results. On the other hand, TG-LASSO improved the predictions and distinguished resistant and sensitive patients for 7 out of 13 drugs. Additionally, TG-LASSO identified genes associated with the drug response, including known targets and pathways involved in the drugs’ mechanism of action. Moreover, genes identified by TG-LASSO for multiple drugs in a tissue were associated with patient survival. In summary, our analysis suggests that preclinical samples can be used to predict CDR of patients and identify biomarkers of drug sensitivity and survival.AUTHOR SUMMARYCancer is among the leading causes of death globally and perdition of the drug response of patients to different treatments based on their clinical and molecular profiles can enable individualized cancer medicine. Machine learning algorithms have the potential to play a significant role in this task; but, these algorithms are designed based the premise that a large number of labeled training samples are available, and these samples are accurate representation of the profiles of real tumors. However, due to ethical and technical reasons, it is not possible to screen humans for many drugs, significantly limiting the size of training data. To overcome this data scarcity problem, machine learning models can be trained using large databases of preclinical samples (e.g. cancer cell line cultures). However, due to the major differences between preclinical samples and real tumors, it is unclear how accurately such preclinical-to-clinical computational models can predict the clinical drug response of cancer patients.Here, first we systematically evaluate a variety of different linear and nonlinear machine learning algorithms for this particular task using two large databases of preclinical (GDSC) and tumor samples (TCGA). Then, we present a novel method called TG-LASSO that utilizes a new approach for explicitly incorporating the tissue of origin of samples in the prediction task. Our results show that TG-LASSO outperforms all other algorithms and can accurately distinguish resistant and sensitive patients for the majority of the tested drugs. Follow-up analysis reveal that this method can also identify biomarkers of drug sensitivity in each cancer type.

Limitations and Future Applications of Quantum Cryptography - Advances in Information Security, Privacy, and Ethics ◽

Recent Progress in Quantum Machine Learning

10.4018/978-1-7998-6677-0.ch012 ◽

2021 ◽

pp. 232-256

Author(s):

Amandeep Singh Bhatia ◽

Renata Wong

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Quantum Computing ◽

Recent Progress ◽

Learning Models ◽

Exciting Field ◽

Quantum Machine Learning ◽

The Subject ◽

Quantum computing is a new exciting field which can be exploited to great speed and innovation in machine learning and artificial intelligence. Quantum machine learning at crossroads explores the interaction between quantum computing and machine learning, supplementing each other to create models and also to accelerate existing machine learning models predicting better and accurate classifications. The main purpose is to explore methods, concepts, theories, and algorithms that focus and utilize quantum computing features such as superposition and entanglement to enhance the abilities of machine learning computations enormously faster. It is a natural goal to study the present and future quantum technologies with machine learning that can enhance the existing classical algorithms. The objective of this chapter is to facilitate the reader to grasp the key components involved in the field to be able to understand the essentialities of the subject and thus can compare computations of quantum computing with its counterpart classical machine learning algorithms.

Assessing Continuous Operator Workload With a Hybrid Scaffolded Neuroergonomic Modeling Approach

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/0018720816672308 ◽

2017 ◽

Vol 59 (1) ◽

pp. 134-146 ◽

Cited By ~ 8

Author(s):

Brett J. Borghetti ◽

Joseph J. Giametta ◽

Christina F. Rusnock

Keyword(s):

Machine Learning ◽

Adaptive Systems ◽

Model Performance ◽

Training Data ◽

State Assessments ◽

Learning Models ◽

Dynamic Task ◽

Operator Workload ◽

Objective: We aimed to predict operator workload from neurological data using statistical learning methods to fit neurological-to-state-assessment models. Background: Adaptive systems require real-time mental workload assessment to perform dynamic task allocations or operator augmentation as workload issues arise. Neuroergonomic measures have great potential for informing adaptive systems, and we combine these measures with models of task demand as well as information about critical events and performance to clarify the inherent ambiguity of interpretation. Method: We use machine learning algorithms on electroencephalogram (EEG) input to infer operator workload based upon Improved Performance Research Integration Tool workload model estimates. Results: Cross-participant models predict workload of other participants, statistically distinguishing between 62% of the workload changes. Machine learning models trained from Monte Carlo resampled workload profiles can be used in place of deterministic workload profiles for cross-participant modeling without incurring a significant decrease in machine learning model performance, suggesting that stochastic models can be used when limited training data are available. Conclusion: We employed a novel temporary scaffold of simulation-generated workload profile truth data during the model-fitting process. A continuous workload profile serves as the target to train our statistical machine learning models. Once trained, the workload profile scaffolding is removed and the trained model is used directly on neurophysiological data in future operator state assessments. Application: These modeling techniques demonstrate how to use neuroergonomic methods to develop operator state assessments, which can be employed in adaptive systems.

Forecasting residential gas consumption with machine learning algorithms on weather data

E3S Web of Conferences ◽

10.1051/e3sconf/201911105019 ◽

2019 ◽

Vol 111 ◽

pp. 05019

Author(s):

Brian de Keijzer ◽

Pol de Visser ◽

Víctor García Romillo ◽

Víctor Gómez Muñoz ◽

Daan Boesten ◽

...

Keyword(s):

Machine Learning ◽

Energy Consumption ◽

Energy Use ◽

Weather Data ◽

Computational Time ◽

Percentage Error ◽

Learning Models ◽

Gas Consumption ◽

Machine learning models have proven to be reliable methods in the forecasting of energy use in commercial and office buildings. However, little research has been done on energy forecasting in dwellings, mainly due to the difficulty of obtaining household level data while keeping the privacy of inhabitants in mind. Gaining insight into the energy consumption in the near future can be helpful in balancing the grid and insights in how to reduce the energy consumption can be received. In collaboration with OPSCHALER, a measurement campaign on the influence of housing characteristics on energy costs and comfort, several machine learning models were compared on forecasting performance and the computational time needed. Nine months of data containing the mean gas consumption of 52 dwellings on a one hour resolution was used for this research. The first 6 months were used for training, whereas the last 3 months were used to evaluate the models. The results showed that the Deep Neural Network (DNN) performed best with a 50.1 % Mean Absolute Percentage Error (MAPE) on a one hour resolution. When comparing daily and weekly resolutions, the Multivariate Linear Regression (MVLR) outperformed other models, with a 20.1 % and 17.0 % MAPE, respectively. The models were programmed in Python.

Cyber-Physical LPG Debutanizer Distillation Columns: Machine Learning-Based Soft Sensors for Product Quality Monitoring

10.20944/preprints202110.0364.v1 ◽

2021 ◽

Author(s):

Jože M. Rožanec ◽

Elena Trajkova ◽

Jinzhi Lu ◽

Nikolaos Sarantinoudis ◽

Georgios Arampatzis ◽

...

Keyword(s):

Machine Learning ◽

Product Quality ◽

Learning Models ◽

State Monitoring ◽

Soft Sensors ◽

Distillation Columns ◽

Operational Conditions ◽

Equipment State ◽

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables to provide equipment state monitoring services and to generate decision-making for equipment operations. In this paper, we propose two machine learning models: 1) to forecast the amount of pentane (C5) content in the final product mixture; 2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach by using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models.

Imputation of PaO2 from SpO2 values from the MIMIC-III Critical Care Database Using Machine-Learning Based Algorithms

10.1101/2021.04.21.21255877 ◽

2021 ◽

Cited By ~ 1

Author(s):

Shuangxia Ren ◽

Jill Zupetic ◽

Mehdi Nouraie ◽

Xinghua Lu ◽

Richard D. Boyce ◽

...

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Superior Performance ◽

Learning Models ◽

Mechanically Ventilated ◽

Mechanically Ventilated Patients ◽

Mimic Iii ◽

Ventilated Patients ◽

AbstractBackgroundThe partial pressure of oxygen (PaO2)/fraction of oxygen delivered (FIO2) ratio is the reference standard for assessment of hypoxemia in mechanically ventilated patients. Non-invasive monitoring with the peripheral saturation of oxygen (SpO2) is increasingly utilized to estimate PaO2 because it does not require invasive sampling. Several equations have been reported to impute PaO2/FIO2 from SpO2 /FIO2. However, machine-learning algorithms to impute the PaO2 from the SpO2 has not been compared to published equations.Research QuestionHow do machine learning algorithms perform at predicting the PaO2 from SpO2 compared to previously published equations?MethodsThree machine learning algorithms (neural network, regression, and kernel-based methods) were developed using 7 clinical variable features (n=9,900 ICU events) and subsequently 3 features (n=20,198 ICU events) as input into the models from data available in mechanically ventilated patients from the Medical Information Mart for Intensive Care (MIMIC) III database. As a regression task, the machine learning models were used to impute PaO2 values. As a classification task, the models were used to predict patients with moderate-to-severe hypoxemic respiratory failure based on a clinically relevant cut-off of PaO2/FIO2 ≤ 150. The accuracy of the machine learning models was compared to published log-linear and non-linear equations. An online imputation calculator was created.ResultsCompared to seven features, three features (SpO2, FiO2 and PEEP) were sufficient to impute PaO2/FIO2 ratio using a large dataset. Any of the tested machine learning models enabled imputation of PaO2/FIO2 from the SpO2/FIO2 with lower error and had greater accuracy in predicting PaO2/FIO2 ≤ 150 compared to published equations. Using three features, the machine learning models showed superior performance in imputing PaO2 across the entire span of SpO2 values, including those ≥ 97%.InterpretationThe improved performance shown for the machine learning algorithms suggests a promising framework for future use in large datasets.