scholarly journals Current Trends and Applications of Machine Learning in Tribology—A Review

Lubricants ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 86
Author(s):  
Max Marian ◽  
Stephan Tremmel
Keyword(s):  
Machine Learning ◽  
Surface Engineering ◽  
Numerical Algorithms ◽  
Support Vector ◽  
Current Trends ◽  
Scientific Disciplines ◽  
Vector Machines ◽  
Applications Of Machine Learning ◽  
Published Research ◽  
Selection Of

Machine learning (ML) and artificial intelligence (AI) are rising stars in many scientific disciplines and industries, and high hopes are being pinned upon them. Likewise, ML and AI approaches have also found their way into tribology, where they can support sorting through the complexity of patterns and identifying trends within the multiple interacting features and processes. Published research extends across many fields of tribology from composite materials and drive technology to manufacturing, surface engineering, and lubricants. Accordingly, the intended usages and numerical algorithms are manifold, ranging from artificial neural networks (ANN), decision trees over random forest and rule-based learners to support vector machines. Therefore, this review is aimed to introduce and discuss the current trends and applications of ML and AI in tribology. Thus, researchers and R&D engineers shall be inspired and supported in the identification and selection of suitable and promising ML approaches and strategies.

scholarly journals Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning

Science ◽  
2019 ◽  
Vol 363 (6424) ◽  
pp. eaau5631 ◽  
Author(s):  
Andrew F. Zahrt ◽  
Jeremy J. Henle ◽  
Brennan T. Rose ◽  
Yang Wang ◽  
William T. Darrow ◽  
...  
Keyword(s):  
Machine Learning ◽  
Catalyst Design ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Asymmetric Reaction ◽  
Stage Of Development ◽  
Chiral Phosphoric Acid ◽  
Vector Machines ◽  
Feed Forward Neural Networks ◽  
Selection Of

Catalyst design in asymmetric reaction development has traditionally been driven by empiricism, wherein experimentalists attempt to qualitatively recognize structural patterns to improve selectivity. Machine learning algorithms and chemoinformatics can potentially accelerate this process by recognizing otherwise inscrutable patterns in large datasets. Herein we report a computationally guided workflow for chiral catalyst selection using chemoinformatics at every stage of development. Robust molecular descriptors that are agnostic to the catalyst scaffold allow for selection of a universal training set on the basis of steric and electronic properties. This set can be used to train machine learning methods to make highly accurate predictive models over a broad range of selectivity space. Using support vector machines and deep feed-forward neural networks, we demonstrate accurate predictive modeling in the chiral phosphoric acid–catalyzed thiol addition toN-acylimines.

scholarly journals Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

2020 ◽  
Vol 10 (2) ◽  
pp. 21 ◽  
Author(s):  
Gopi Battineni ◽  
Getu Gamo Sagaro ◽  
Nalini Chinatalapudi ◽  
Francesco Amenta
Keyword(s):  
Machine Learning ◽  
Chronic Diseases ◽  
Predictive Models ◽  
Disease Diagnosis ◽  
Primary Diagnosis ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Vector Machines ◽  
Applications Of Machine Learning ◽  
Near Future

This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future.

scholarly journals Use of Machine Learning in Stock Market Prediction

2020 ◽  
Vol 4 (1) ◽  
pp. 60-73
Author(s):  
Memoona Shaheen ◽  
Mehreen Arshad
Keyword(s):  
Machine Learning ◽  
Policy Implications ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Hybrid Techniques ◽  
Current Trends ◽  
Time Period ◽  
Learning Techniques ◽  
Vector Machines ◽  
Prediction Problems

Objective: The objective of this study was to examine and determine future directions in regard to future machine learning techniques based on the review of the current literature. Methodology: A systematic review has been used to review the current trends from the peer-reviewed journal articles in the past twenty years. For this study, four categories have been categorized, the use of neural networks, support vector machines, the use of a genetic algorithm, and the combination of hybrid techniques. Studies in each of these categorize have been evaluated. Finding: Firstly, there is a strong link between machine learning methods and the prediction problems they are associated with. The second conclusion that we can conclude from this review is that past studies need to improve its generalizability results. Most of the studies that have been reviewed in this analysis has only used the machine learning systems through the use of one market or during only a one time period without taking into consideration whether the system would be adaptable in other situations and conditions. Limitations, future trends, as well as policy implications have been defined.

scholarly journals Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review

2018 ◽  
Vol 8 (12) ◽  
pp. 2570 ◽  
Author(s):  
Yves Rybarczyk ◽  
Rasa Zalakeviciute
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Deterministic Models ◽  
Learning Techniques ◽  
Vector Machines ◽  
Estimation Problems ◽  
Selection Of

Current studies show that traditional deterministic models tend to struggle to capture the non-linear relationship between the concentration of air pollutants and their sources of emission and dispersion. To tackle such a limitation, the most promising approach is to use statistical models based on machine learning techniques. Nevertheless, it is puzzling why a certain algorithm is chosen over another for a given task. This systematic review intends to clarify this question by providing the reader with a comprehensive description of the principles underlying these algorithms and how they are applied to enhance prediction accuracy. A rigorous search that conforms to the PRISMA guideline is performed and results in the selection of the 46 most relevant journal papers in the area. Through a factorial analysis method these studies are synthetized and linked to each other. The main findings of this literature review show that: (i) machine learning is mainly applied in Eurasian and North American continents and (ii) estimation problems tend to implement Ensemble Learning and Regressions, whereas forecasting make use of Neural Networks and Support Vector Machines. The next challenges of this approach are to improve the prediction of pollution peaks and contaminants recently put in the spotlights (e.g., nanoparticles).

scholarly journals Machine Learning Application in Genomic, Exercise, and Vital Datasets

2021 ◽  
Vol 30 (2) ◽  
pp. 131-146
Author(s):  
Kyung-Wan Baek ◽  
Jung-Jun Park ◽  
Jeong-An Gim
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Healthy Lifestyle ◽  
Support Vector ◽  
Computer Algorithms ◽  
Practical Application ◽  
Exercise Science ◽  
Vector Machines ◽  
Participatory Medicine ◽  
Selection Of

PURPOSE: Machine learning (ML) refers to newly developed computer algorithms that are improved through iterative experiences. ML applications are expected to assist humans in analyzing large amounts of data. This review has outlined the application of ML in analyzing variable vital data such as walking steps, exercise intensity, heart rate, sleeping hours, sleep quality, resting heart rate, blood pressure, and calorie consumption in a day. Vital data consist of different variables that are closely related to genomic or exercise data. The prediction of healthy traits from a vital dataset has become a necessity in personalized medicine.METHODS: Considerations and repeated tasks in supervised, semi-supervised, and unsupervised ML methods are presented. ML methods such as artificial neural networks, Bayesian networks, support vector machines, and decision trees have been widely used in biomedical studies to develop predictive models. Through vital data, these models can help in effective and accurate decision-making for a healthier life.<br/>PURPOSE: Models based on genomic, exercise, and vital datasets provide a healthy lifestyle through regular exercise. We have provided guidelines to help in the selection of these ML methods and their practical application for variable vital data analysis.CONCLUSIONS: Our guidelines could serve as a foundation for implementing both participatory medicine and data-driven exercise science.

scholarly journals A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers

2021 ◽  
Vol 17 (8) ◽  
pp. e1009247
Author(s):  
Frances L. Heredia ◽  
Abiel Roche-Lima ◽  
Elsie I. Parés-Matos
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector Machines ◽  
Dna Binding ◽  
Dna Sequences ◽  
Support Vector ◽  
Sequence Information ◽  
Dna Aptamers ◽  
Vector Machines ◽  
Selection Of

The selection of a DNA aptamer through the Systematic Evolution of Ligands by EXponential enrichment (SELEX) method involves multiple binding steps, in which a target and a library of randomized DNA sequences are mixed for selection of a single, nucleotide-specific molecule. Usually, 10 to 20 steps are required for SELEX to be completed. Throughout this process it is necessary to discriminate between true DNA aptamers and unspecified DNA-binding sequences. Thus, a novel machine learning-based approach was developed to support and simplify the early steps of the SELEX process, to help discriminate binding between DNA aptamers from those unspecified targets of DNA-binding sequences. An Artificial Intelligence (AI) approach to identify aptamers were implemented based on Natural Language Processing (NLP) and Machine Learning (ML). NLP method (CountVectorizer) was used to extract information from the nucleotide sequences. Four ML algorithms (Logistic Regression, Decision Tree, Gaussian Naïve Bayes, Support Vector Machines) were trained using data from the NLP method along with sequence information. The best performing model was Support Vector Machines because it had the best ability to discriminate between positive and negative classes. In our model, an Accuracy (A) of 0.995, the fraction of samples that the model correctly classified, and an Area Under the Receiving Operating Curve (AUROC) of 0.998, the degree by which a model is capable of distinguishing between classes, were observed. The developed AI approach is useful to identify potential DNA aptamers to reduce the amount of rounds in a SELEX selection. This new approach could be applied in the design of DNA libraries and result in a more efficient and faster process for DNA aptamers to be chosen during SELEX.

scholarly journals COMPARISON OF MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR PURCHASING FORECAST

2021 ◽  
pp. 59-68
Author(s):  
Rabia ÖZDEMİR ◽  
Münevver TURANLI
Keyword(s):  
Machine Learning ◽  
Support Vector ◽  
Classification Algorithms ◽  
Machine Learning Classification ◽  
Network Concept ◽  
Vector Machines ◽  
Accuracy Ratio ◽  
Future Data ◽  
Technological Developments ◽  
Selection Of

With the development of computer technologies and invention of internet, many concepts have entered our lives. With the starting of wide usage of globalized internet network, concept of machine learning has emerged in time for smarter management of data flow in big dimensions. In line with technological developments, all activities began to be carried to digital environment and as a result of this, concept of e-commerce has entered our lives. E-commerce is one of the areas where machine learning is used most widely. By examining product purchasing situations in accordance with data available at the enterprises, various researches have been made for selection of most appropriate model in order to predict future data. In the study it was mentioned about concepts of e-commerce and machine learning and by applying Logistic Regression, Naïve Bayes and Support Vector Machines being machine learning classification algorithms, it has been aimed to determine the model having best accuracy ratio.

Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-steps Rule and General Pseudo Components

2019 ◽  
Vol 19 (25) ◽  
pp. 2301-2317 ◽  
Author(s):  
Ruirui Liang ◽  
Jiayang Xie ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Hai Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Big Data ◽  
Human Genome Project ◽  
Genome Project ◽  
Support Vector ◽  
Successful Implementation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Vector Machines

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of ‘big data’ derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

scholarly journals Predictive modeling for peri-implantitis by using machine learning techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tomoaki Mameno ◽  
Masahiro Wada ◽  
Kazunori Nozaki ◽  
Toshihito Takahashi ◽  
Yoshitaka Tsujioka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Demographic Data ◽  
Risk Indicators ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Machine Learning Methods ◽  
Complex Interactions ◽  
Learning Techniques ◽  
Increased Risk ◽  
Vector Machines

AbstractThe purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

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