Efficient hyperparameter-tuned machine learning approach for estimation of supercapacitor performance attributes

Abstract Recent years have witnessed the rise of supercapacitor as effective energy storage device. Specifically, carbon-based electrodes have been experimentally well studied and used in the fabrication of supercapacitors due to their excellent electrochemical properties. This work reports the development and utilization of highly tuned and efficient Machine Learning (ML) models that give insights into correlation between structural features of electrodes and supercapacitor performance metrics namely specific capacitance, power density and energy density. Artificial Neural Networks (ANN) and Random Forest (RF) models have been employed to predict the various in-operando performance metrics of carbon-based supercapacitors based on three input features such as mesopore surface area, micropore surface area and scan rate. Experimentally measured values of these parameters used for training and testing these two models have been extracted from a set of research papers reported in literature. The optimization techniques and various tuning methodologies adopted for identifying model hyperparameters are discussed in this paper. The authors demonstrate the importance of hyperparameter tuning and optimization in building accurate and reliable computational models.

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Machine Learning for the Design and Development of Biofilm Regulators

10.20944/preprints201803.0118.v1 ◽

2018 ◽

Author(s):

Benjamin Stone ◽

Erik Sapper

Keyword(s):

Machine Learning ◽

Quorum Sensing ◽

Computational Models ◽

Quantitative Structure Activity Relationship ◽

Structural Features ◽

Machine Learning Algorithms ◽

Chemical Signaling ◽

Biochemical System ◽

Structure Activity ◽

Qsar Models

Biofilms are congregations of bacteria on a surface, and they grow into obstacles for the functionalities of any device or machinery involves anything biological. Biofilms are developed through a biochemical system known as ‘Quorum Sensing’ that accounts for the chemical signaling that direct either biofilm formation or inhibition. Computational models that relate chemical and structural features of compounds to their performance properties have been used to aide in the discovery of active small molecules for many decades. These quantitative structure-activity relationship (QSAR) models are also important for predicting the activity of molecules that can have a range of effectiveness in biological systems. This study uses QSAR methodologies combined with and different machine learning algorithms to predict and assess the performance of several different compounds acting in Quorum Sensing. Through computational probing of the quorum sensing molecular interaction, new design rules can be elucidated for countering biofilms.

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A Machine Learning Model to Predict Drug Transfer Across the Human Placenta Barrier

Frontiers in Chemistry ◽

10.3389/fchem.2021.714678 ◽

2021 ◽

Vol 9 ◽

Author(s):

Juan I. Di Filippo ◽

Mariela Bollini ◽

Claudio N. Cavasotto

Keyword(s):

Machine Learning ◽

Computational Models ◽

Placental Transfer ◽

Learning Model ◽

Structural Features ◽

Placental Barrier ◽

Analysis Model ◽

Drug Transfer ◽

Linear Discriminant ◽

Machine Learning Model

The development of computational models for assessing the transfer of chemicals across the placental membrane would be of the utmost importance in drug discovery campaigns, in order to develop safe therapeutic options. We have developed a low-dimensional machine learning model capable of classifying compounds according to whether they can cross or not the placental barrier. To this aim, we compiled a database of 248 compounds with experimental information about their placental transfer, characterizing each compound with a set of ∼5.4 thousand descriptors, including physicochemical properties and structural features. We evaluated different machine learning classifiers and implemented a genetic algorithm, in a five cross validation scheme, to perform feature selection. The optimization was guided towards models displaying a low number of false positives (molecules that actually cross the placental barrier, but are predicted as not crossing it). A Linear Discriminant Analysis model trained with only four structural features resulted to be robust for this task, exhibiting only one false positive case across all testing folds. This model is expected to be useful in predicting placental drug transfer during pregnancy, and thus could be used as a filter for chemical libraries in virtual screening campaigns.

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Machine Learning-Based Scoring Functions. Development and Applications with SAnDReS.

Current Medicinal Chemistry ◽

10.2174/0929867327666200515101820 ◽

2020 ◽

Vol 27 ◽

Author(s):

Gabriela Bitencourt-Ferreira ◽

Camila Rizzotto ◽

Walter Filgueira de Azevedo Junior

Keyword(s):

Machine Learning ◽

Binding Affinity ◽

Drug Targets ◽

Computational Models ◽

Factor Xa ◽

Coagulation Factor ◽

Predictive Performance ◽

Machine Learning Techniques ◽

Scoring Functions ◽

Molegro Virtual Docker

Background: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs. Objective: Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes. Method: SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding, and thermodynamic data to create targeted scoring functions. Results: Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases, and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions. Conclusion: Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker, and AutoDock Vina.

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Computing Possible Futures

10.1093/oso/9780198846420.001.0001 ◽

2019 ◽

Cited By ~ 2

Author(s):

William B. Rouse

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Computational Modeling ◽

Mental Model ◽

Data Analytics ◽

Computational Models ◽

Senior Managers ◽

Interactive Visualizations ◽

Use Of Models ◽

Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

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Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs

Pharmaceuticals ◽

10.3390/ph14050472 ◽

2021 ◽

Vol 14 (5) ◽

pp. 472

Author(s):

Tyler C. Beck ◽

Kyle R. Beck ◽

Jordan Morningstar ◽

Menny M. Benjamin ◽

Russell A. Norris

Keyword(s):

United States ◽

Machine Learning ◽

Drug Interactions ◽

The United States ◽

Structural Features ◽

Physiochemical Properties ◽

Drug Dosing ◽

Therapeutic Outcomes ◽

Cyp Inhibition ◽

Cyp Inhibitors

Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug–drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of adverse interactions, such as altering drug dosing schemes and/or minimizing the number of drugs prescribed; however, often, a reduction in the number of medications cannot be achieved without impacting therapeutic outcomes. Nearly 80% of drugs fail in development due to pharmacokinetic issues, outlining the importance of examining cytochrome interactions during preclinical drug design. In this review, we examined the physiochemical and structural properties of small molecule inhibitors of CYPs 3A4, 2D6, 2C19, 2C9, and 1A2. Although CYP inhibitors tend to have distinct physiochemical properties and structural features, these descriptors alone are insufficient to predict major cytochrome inhibition probability and affinity. Machine learning based in silico approaches may be employed as a more robust and accurate way of predicting CYP inhibition. These various approaches are highlighted in the review.

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The Disagreement Deconvolution: Bringing Machine Learning Performance Metrics In Line With Reality

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems ◽

10.1145/3411764.3445423 ◽

2021 ◽

Author(s):

Mitchell L. Gordon ◽

Kaitlyn Zhou ◽

Kayur Patel ◽

Tatsunori Hashimoto ◽

Michael S. Bernstein

Keyword(s):

Machine Learning ◽

Performance Metrics ◽

Learning Performance

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Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10040199 ◽

2021 ◽

Vol 10 (4) ◽

pp. 199

Author(s):

Francisco M. Bellas Aláez ◽

Jesus M. Torres Palenzuela ◽

Evangelos Spyrakos ◽

Luis González Vilas

Keyword(s):

Machine Learning ◽

Performance Metrics ◽

Prediction Models ◽

Support Vector ◽

False Alarms ◽

Learning Approaches ◽

Learning Methods ◽

Machine Learning Methods ◽

Rías Baixas ◽

New Algorithms

This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). The models predict Pseudo-nitzschia spp. blooms in the Galician Rias Baixas. This work builds on a previous study by the authors (doi.org/10.1016/j.pocean.2014.03.003) but uses an extended database (from 2002 to 2012) and new algorithms. Our results show that RF and AdaBoost provide better prediction results compared to SVMs and NNs, as they show improved performance metrics and a better balance between sensitivity and specificity. Classical machine learning approaches show higher sensitivities, but at a cost of lower specificity and higher percentages of false alarms (lower precision). These results seem to indicate a greater adaptation of new algorithms (RF and AdaBoost) to unbalanced datasets. Our models could be operationally implemented to establish a short-term prediction system.

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Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review

BMJ Health & Care Informatics ◽

10.1136/bmjhci-2020-100262 ◽

2021 ◽

Vol 28 (1) ◽

pp. e100262

Author(s):

Mustafa Khanbhai ◽

Patrick Anyadi ◽

Joshua Symons ◽

Kelsey Flott ◽

Ara Darzi ◽

...

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Social Media ◽

Natural Language Processing ◽

Natural Language ◽

Patient Experience ◽

Language Processing ◽

Performance Metrics ◽

Free Text ◽

Patient Feedback

ObjectivesUnstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of the literature on the use of natural language processing (NLP) and machine learning (ML) to process and analyse free-text patient experience data.MethodsDatabases were systematically searched to identify articles published between January 2000 and December 2019 examining NLP to analyse free-text patient feedback. Due to the heterogeneous nature of the studies, a narrative synthesis was deemed most appropriate. Data related to the study purpose, corpus, methodology, performance metrics and indicators of quality were recorded.ResultsNineteen articles were included. The majority (80%) of studies applied language analysis techniques on patient feedback from social media sites (unsolicited) followed by structured surveys (solicited). Supervised learning was frequently used (n=9), followed by unsupervised (n=6) and semisupervised (n=3). Comments extracted from social media were analysed using an unsupervised approach, and free-text comments held within structured surveys were analysed using a supervised approach. Reported performance metrics included the precision, recall and F-measure, with support vector machine and Naïve Bayes being the best performing ML classifiers.ConclusionNLP and ML have emerged as an important tool for processing unstructured free text. Both supervised and unsupervised approaches have their role depending on the data source. With the advancement of data analysis tools, these techniques may be useful to healthcare organisations to generate insight from the volumes of unstructured free-text data.

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Assessment of Machine Learning-Based Audiovisual Quality Predictors

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3430376 ◽

2021 ◽

Vol 17 (2) ◽

pp. 1-22

Author(s):

Mythili K. ◽

Manish Narwaria

Keyword(s):

Machine Learning ◽

Signal Processing ◽

Quality Assessment ◽

Computational Models ◽

Model Development ◽

Multimedia Communication ◽

Current Approach ◽

Learning Ability ◽

Data Uncertainty ◽

Attractive Alternative

Quality assessment of audiovisual (AV) signals is important from the perspective of system design, optimization, and management of a modern multimedia communication system. However, automatic prediction of AV quality via the use of computational models remains challenging. In this context, machine learning (ML) appears to be an attractive alternative to the traditional approaches. This is especially when such assessment needs to be made in no-reference (i.e., the original signal is unavailable) fashion. While development of ML-based quality predictors is desirable, we argue that proper assessment and validation of such predictors is also crucial before they can be deployed in practice. To this end, we raise some fundamental questions about the current approach of ML-based model development for AV quality assessment and signal processing for multimedia communication in general. We also identify specific limitations associated with the current validation strategy which have implications on analysis and comparison of ML-based quality predictors. These include a lack of consideration of: (a) data uncertainty, (b) domain knowledge, (c) explicit learning ability of the trained model, and (d) interpretability of the resultant model. Therefore, the primary goal of this article is to shed some light into mentioned factors. Our analysis and proposed recommendations are of particular importance in the light of significant interests in ML methods for multimedia signal processing (specifically in cases where human-labeled data is used), and a lack of discussion of mentioned issues in existing literature.

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