A study of micromanufacturing process fingerprints in micro-injection moulding for machine learning and Industry 4.0 applications

Process Conditions ◽

Learning Models ◽

Micro Injection Moulding ◽

AbstractThis paper discusses micromanufacturing process quality proxies called “process fingerprints” in micro-injection moulding for establishing in-line quality assurance and machine learning models for Industry 4.0 applications. Process fingerprints that we present in this study are purely physical proxies of the product quality and need tangible rationale regarding their selection criteria such as sensitivity, cost-effectiveness, and robustness. Proposed methods and selection reasons for process fingerprints are also justified by analysing the temporally collected data with respect to the microreplication efficiency. Extracted process fingerprints were also used in a multiple linear regression scenario where they bring actionable insights for creating traceable and cost-effective supervised machine learning models in challenging micro-injection moulding environments. Multiple linear regression model demonstrated %84 accuracy in predicting the quality of the process, which is significant as far as the extreme process conditions and product features are concerned.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

Current Medicinal Chemistry ◽

10.2174/2213275912666191102162959 ◽

2020 ◽

Vol 28 (2) ◽

pp. 253-265 ◽

Cited By ~ 3

Author(s):

Gabriela Bitencourt-Ferreira ◽

Amauri Duarte da Silva ◽

Walter Filgueira de Azevedo

Keyword(s):

Machine Learning ◽

Binding Affinity ◽

Predictive Performance ◽

Machine Learning Techniques ◽

Scoring Functions ◽

Cyclin Dependent Kinase ◽

Learning Models ◽

Learning Techniques ◽

Background: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. Objective: Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. Methods: We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. Results: Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. Conclusion: Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

QUBO formulations for training machine learning models

Scientific Reports ◽

10.1038/s41598-021-89461-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Prasanna Date ◽

Davis Arthur ◽

Lauren Pusey-Nazzaro

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Large Scale ◽

Support Vector ◽

Quantum Computers ◽

Np Hard ◽

Learning Models ◽

Moore’S Law ◽

Moore's Law ◽

AbstractTraining machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models efficiently. Adiabatic quantum computers can approximately solve NP-hard problems, such as the quadratic unconstrained binary optimization (QUBO), faster than classical computers. Since many machine learning problems are also NP-hard, we believe adiabatic quantum computers might be instrumental in training machine learning models efficiently in the post Moore’s law era. In order to solve problems on adiabatic quantum computers, they must be formulated as QUBO problems, which is very challenging. In this paper, we formulate the training problems of three machine learning models—linear regression, support vector machine (SVM) and balanced k-means clustering—as QUBO problems, making them conducive to be trained on adiabatic quantum computers. We also analyze the computational complexities of our formulations and compare them to corresponding state-of-the-art classical approaches. We show that the time and space complexities of our formulations are better (in case of SVM and balanced k-means clustering) or equivalent (in case of linear regression) to their classical counterparts.

Prediction of Graduate Admission using Multiple Supervised Machine Learning Models

2020 SoutheastCon ◽

10.1109/southeastcon44009.2020.9249747 ◽

2020 ◽

Author(s):

Zain Bitar ◽

Amjed Al-Mousa

Keyword(s):

Machine Learning ◽

Learning Models ◽

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 ◽

Machine Learning Algorithms ◽

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.

A machine learning approach to inform developmental milestone achievement for children with autism (Preprint)

10.2196/preprints.29242 ◽

2021 ◽

Author(s):

Munirul M. Haque ◽

Masud Rabbani ◽

Dipranjan Das Dipal ◽

Md Ishrak Islam Zarif ◽

Anik Iqbal ◽

...

Keyword(s):

Machine Learning ◽

Autism Spectrum Disorder ◽

Children With Autism ◽

Autism Spectrum ◽

The Other ◽

Learning Models ◽

Children With Asd ◽

Socio Demographic Factors ◽

BACKGROUND Care for children with autism spectrum disorder (ASD) can be challenging for families and medical care systems. This is especially true in Low-and-Middle-Income-countries (LMIC) like Bangladesh. To improve family-practitioner communication and developmental monitoring of children with ASD, [spell out] (mCARE) was developed. Within this study, mCARE was used to track child milestone achievement and family socio-demographic assets to inform mCARE feasibility/scalability and family-asset informed practitioner recommendations. OBJECTIVE The objectives of this paper are three-fold. First, document how mCARE can be used to monitor child milestone achievement. Second, demonstrate how advanced machine learning models can inform our understanding of milestone achievement in children with ASD. Third, describe family/child socio-demographic factors that are associated with earlier milestone achievement in children with ASD (across five machine learning models). METHODS Using mCARE collected data, this study assessed milestone achievement in 300 children with ASD from Bangladesh. In this study, we used four supervised machine learning (ML) algorithms (Decision Tree, Logistic Regression, k-Nearest Neighbors, Artificial Neural Network) and one unsupervised machine learning (K-means Clustering) to build models of milestone achievement based on family/child socio-demographic details. For analyses, the sample was randomly divided in half to train the ML models and then their accuracy was estimated based on the other half of the sample. Each model was specified for the following milestones: Brushes teeth, Asks to use the toilet, Urinates in the toilet or potty, and Buttons large buttons. RESULTS This study aimed to find a suitable machine learning algorithm for milestone prediction/achievement for children with ASD using family/child socio-demographic characteristics. For, Brushes teeth, the three supervised machine learning models met or exceeded an accuracy of 95% with Logistic Regression, KNN, and ANN as the most robust socio-demographic predictors. For Asks to use toilet, 84.00% accuracy was achieved with the KNN and ANN models. For these models, the family socio-demographic predictors of “family expenditure” and “parents’ age” accounted for most of the model variability. The last two parameters, Urinates in toilet or potty and Buttons large buttons had an accuracy of 91.00% and 76.00%, respectively, in ANN. Overall, the ANN had a higher accuracy (Above ~80% on average) among the other algorithms for all the parameters. Across the models and milestones, “family expenditure”, “family size/ type”, “living places” and “parent’s age and occupation” were the most influential family/child socio-demographic factors. CONCLUSIONS mCARE was successfully deployed in an LMIC (i.e., Bangladesh), allowing parents and care-practitioners a mechanism to share detailed information on child milestones achievement. Using advanced modeling techniques this study demonstrates how family/child socio-demographic elements can inform child milestone achievement. Specifically, families with fewer socio-demographic resources reported later milestone attainment. Developmental science theories highlight how family/systems can directly influence child development and this study provides a clear link between family resources and child developmental progress. Clinical implications for this work could include supporting the larger family system to improve child milestone achievement. CLINICALTRIAL We took the IRB from Marquette University Institutional Review Board on July 9, 2020, with the protocol number HR-1803022959, and titled “MOBILE-BASED CARE FOR CHILDREN WITH AUTISM SPECTRUM DISORDER USING REMOTE EXPERIENCE SAMPLING METHOD (MCARE)” for recruiting a total of 316 subjects, of which we recruited 300. (Details description of participants in Methods section)

2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) ◽

Classification and Success Investigation of Biomedical Data Sets Using Supervised Machine Learning Models

10.1109/ismsit.2019.8932734 ◽

2019 ◽

Author(s):

Sarmad N. Mohammed ◽

Mehmet Serdar Guzel ◽

Erkan Bostanci

Keyword(s):

Machine Learning ◽

Data Sets ◽

Biomedical Data ◽

Learning Models ◽

2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW) ◽

MMP - A Platform to Manage Machine Learning Models in Industry 4.0 Environments

10.1109/edocw49879.2020.00025 ◽

2020 ◽

Author(s):

Christian Weber ◽

Peter Reimann

Keyword(s):

Machine Learning ◽

Industry 4.0 ◽

Learning Models ◽

Development of Combined Heavy Rain Damage Prediction Models with Machine Learning

Water ◽

10.3390/w11122516 ◽

2019 ◽

Vol 11 (12) ◽

pp. 2516 ◽

Cited By ~ 1

Author(s):

Changhyun Choi ◽

Jeonghwan Kim ◽

Jungwook Kim ◽

Hung Soo Kim

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Prediction Model ◽

Prediction Models ◽

Predictive Performance ◽

Heavy Rain ◽

Learning Models ◽

Damage Prediction ◽

Natural Disaster Management ◽

Adequate forecasting and preparation for heavy rain can minimize life and property damage. Some studies have been conducted on the heavy rain damage prediction model (HDPM), however, most of their models are limited to the linear regression model that simply explains the linear relation between rainfall data and damage. This study develops the combined heavy rain damage prediction model (CHDPM) where the residual prediction model (RPM) is added to the HDPM. The predictive performance of the CHDPM is analyzed to be 4–14% higher than that of HDPM. Through this, we confirmed that the predictive performance of the model is improved by combining the RPM of the machine learning models to complement the linearity of the HDPM. The results of this study can be used as basic data beneficial for natural disaster management.