A comprehensive insight into the application of machine learning approaches in predicting the separation efficiency of hydrocyclones

2021 ◽  
Vol 236 ◽  
pp. 123-143
Author(s):  
Mohammad Zandieh ◽  
Alireza Kazemi ◽  
Mohammad Ahmadi
Keyword(s):  
Machine Learning ◽  
Separation Efficiency ◽  
Learning Approaches ◽  
Insight Into

Learning and Explaining the Impact of Enterprises’ Organizational Quality on their Economic Results

2012 ◽  
pp. 228-248 ◽  
Author(s):  
Marko Pregeljc ◽  
Erik Štrumbelj ◽  
Miran Mihelcic ◽  
Igor Kononenko
Keyword(s):  
Machine Learning ◽  
Learning Approaches ◽  
Data Set ◽  
Economic Interpretation ◽  
Organizational Quality ◽  
Social Units ◽  
The Impact ◽  
Insight Into ◽  
General Explanation

The authors employed traditional and novel machine learning to improve insight into the connections between the quality of an organization of enterprises as a type of formal social units and the results of enterprises’ performance in this chapter. The analyzed data set contains 72 Slovenian enterprises’ economic results across four years and indicators of their organizational quality. The authors hypothesize that a causal relationship exists between the latter and the former. In the first part of a two-part process, they use several classification algorithms to study these relationships and to evaluate how accurately they predict the target economic results. However, the most successful models were often very complex and difficult to interpret, especially for non-technical users. Therefore, in the second part, the authors take advantage of a novel general explanation method that can be used to explain the influence of individual features on the model’s prediction. Results show that traditional machine-learning approaches are successful at modeling the dependency relationship. Furthermore, the explanation of the influence of the input features on the predicted economic results provides insights that have a meaningful economic interpretation.

scholarly journals Prediction of prokaryotic transposases from protein features with machine learning approaches

2021 ◽  
Vol 7 (7) ◽  
Author(s):  
Qian Wang ◽  
Jun Ye ◽  
Teng Xu ◽  
Ning Zhou ◽  
Zhongqiu Lu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Antibiotic Resistance ◽  
Mutual Information ◽  
Ensemble Classifier ◽  
Training Dataset ◽  
Command Line ◽  
Learning Approaches ◽  
Command Line Tool ◽  
Selection Operator ◽  
Insight Into

Identification of prokaryotic transposases (Tnps) not only gives insight into the spread of antibiotic resistance and virulence but the process of DNA movement. This study aimed to develop a classifier for predicting Tnps in bacteria and archaea using machine learning (ML) approaches. We extracted a total of 2751 protein features from the training dataset including 14852 Tnps and 14852 controls, and selected 75 features as predictive signatures using the combined mutual information and least absolute shrinkage and selection operator algorithms. By aggregating these signatures, an ensemble classifier that integrated a collection of individual ML-based classifiers, was developed to identify Tnps. Further validation revealed that this classifier achieved good performance with an average AUC of 0.955, and met or exceeded other common methods. Based on this ensemble classifier, a stand-alone command-line tool designated TnpDiscovery was established to maximize the convenience for bioinformaticians and experimental researchers toward Tnp prediction. This study demonstrates the effectiveness of ML approaches in identifying Tnps, facilitating the discovery of novel Tnps in the future.

Machine Learning for Big Data

2021 ◽  
pp. 56-76
Author(s):  
Supriya M. S. ◽  
Keerthana Sasidaran
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Learning Algorithms ◽  
Full Scale ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Data Process ◽  
Machine Learning Model ◽  
To Come ◽  
Insight Into

Big data and machine learning currently play an important role in various applications and in research. These approaches are explored in depth in this chapter. The chapter starts with a summary of big data and its implementation in a number of fields, and then deals with the problems that big data presents and the need for other technology to resolve these issues/challenges. Big data can best be used with the aid of the machine learning model, even though they are not directly related. Thus, the paradigms of machine learning that support big data can be combined with big data technology, thus providing insight into a range of big data machine learning approaches and techniques. Although big data cannot rely solely on the few paradigms of machine learning, the underlying problems are addressed. New machine learning algorithms are needed that can explore the full scale of the big data process and enable software engineering firms to come up with better solutions.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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