Designing optimized ternary catalytic alloy electrode for efficiency improvement of semiconductor gas sensors using a machine learning approach

Catalytic noble metal (s) or its alloy (s) has long been used as the electrode material to enhance the sensing performance of the semiconducting oxide based gas sensors. In the present paper, design of optimized ternary metal alloy electrode, while the database is in pure or binary alloy compositions, using a machine learning methodology is reported for detection of CH4 gas as a test case. Pure noble metals or their binary alloys as the electrode on the semiconducting ZnO sensing layer were investigated by the earlier researchers to enhance the sensitivity towards CH4. Based on those research findings, an artificial neural network (ANN) models were developed considering the three main features of the gas sensor devices, viz. response magnitude, response time and recovery time as a function of ZnO particle size and the composition of the catalytic alloy. A novel methodology was introduced by using ANN models considered for optimized ternary alloy with enriched presentation through the multi-objective genetic algorithm (GA) wherever the generated pareto front was used. The prescriptive data analytics methodology seems to offer more or less convinced evidences for future experimental studies.

Download Full-text

Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning—A Review

Sensors ◽

10.3390/s21082877 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2877

Author(s):

Usman Yaqoob ◽

Mohammad I. Younis

Keyword(s):

Machine Learning ◽

Gas Sensors ◽

Cross Validation ◽

Experimental Studies ◽

Future Generation ◽

Machine Learning Techniques ◽

Healthcare Applications ◽

Highly Sensitive ◽

Metal Metal ◽

Smart Machine

Nowadays, there is increasing interest in fast, accurate, and highly sensitive smart gas sensors with excellent selectivity boosted by the high demand for environmental safety and healthcare applications. Significant research has been conducted to develop sensors based on novel highly sensitive and selective materials. Computational and experimental studies have been explored in order to identify the key factors in providing the maximum active location for gas molecule adsorption including bandgap tuning through nanostructures, metal/metal oxide catalytic reactions, and nano junction formations. However, there are still great challenges, specifically in terms of selectivity, which raises the need for combining interdisciplinary fields to build smarter and high-performance gas/chemical sensing devices. This review discusses current major gas sensing performance-enhancing methods, their advantages, and limitations, especially in terms of selectivity and long-term stability. The discussion then establishes a case for the use of smart machine learning techniques, which offer effective data processing approaches, for the development of highly selective smart gas sensors. We highlight the effectiveness of static, dynamic, and frequency domain feature extraction techniques. Additionally, cross-validation methods are also covered; in particular, the manipulation of the k-fold cross-validation is discussed to accurately train a model according to the available datasets. We summarize different chemresistive and FET gas sensors and highlight their shortcomings, and then propose the potential of machine learning as a possible and feasible option. The review concludes that machine learning can be very promising in terms of building the future generation of smart, sensitive, and selective sensors.

Download Full-text

Classification by a stacking model using CNN features for COVID-19 infection diagnosis

Journal of X-Ray Science and Technology ◽

10.3233/xst-211031 ◽

2021 ◽

pp. 1-16

Author(s):

Yavuz Selim Taspinar ◽

Ilkay Cinar ◽

Murat Koklu

Keyword(s):

Machine Learning ◽

Classification Performance ◽

Support Vector ◽

Clinical Environment ◽

X Ray ◽

Left Behind ◽

Study Results ◽

Chest X Ray ◽

Ann Models ◽

Artificial Neural Network Ann

Affecting millions of people all over the world, the COVID-19 pandemic has caused the death of hundreds of thousands of people since its beginning. Examinations also found that even if the COVID-19 patients initially survived the coronavirus, pneumonia left behind by the virus may still cause severe diseases resulting in organ failure and therefore death in the future. The aim of this study is to classify COVID-19, normal and viral pneumonia using the chest X-ray images with machine learning methods. A total of 3,486 chest X-ray images from three classes were first classified by three single machine learning models including the support vector machine (SVM), logistics regression (LR), artificial neural network (ANN) models, and then by a stacking model that was created by combining these 3 single models. Several performance evaluation indices including recall, precision, F-score, and accuracy were computed to evaluate and compare classification performance of 3 single four models and the final stacking model used in the study. As a result of the evaluations, the models namely, SVM, ANN, LR, and stacking, achieved 90.2%, 96.2%, 96.7%, and 96.9%classification accuracy, respectively. The study results indicate that the proposed stacking model is a fast and inexpensive method for assisting COVID-19 diagnosis, which can have potential to assist physicians and nurses to better and more efficiently diagnose COVID-19 infection cases in the busy clinical environment.

Download Full-text

Automatic genome segmentation with HMM-ANN hybrid models

10.1101/034579 ◽

2015 ◽

Author(s):

Li Shen

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Markov Model ◽

State Of The Art ◽

Hidden Markov ◽

Machine Learning Algorithms ◽

Ann Models ◽

Artificial Neural Network Ann ◽

Genomic Regions

AbstractWe consider the problem of automatic genome segmentation (AGS) that aims to assign discrete labels to all genomic regions based on multiple ChIP-seq samples. We propose to use a hybrid model that combines a hidden Markov model (HMM) with an artificial neural network (ANN) to overcome the weaknesses of a standard HMM. Our contributions are threefold: first, we benchmark two approaches to generate targets for ANN training on an example dataset; second, we investigate many different ANN models to identify the ones with best predictions on chromatin states; third, we test different hyper-parameters and discuss how they affect the machine learning algorithms’ performance. We find our best performing models to beat two pervious state-of-the-art methods for AGS by large margins.

Download Full-text

Cross-Validation and Comparison of Energy Expenditure Prediction Models Using Count-Based and Raw Accelerometer Data in Youth

Journal for the Measurement of Physical Behaviour ◽

10.1123/jmpb.2018-0011 ◽

2019 ◽

Vol 2 (4) ◽

pp. 237-246

Author(s):

Alexander H.K. Montoye ◽

Kimberly A. Clevenger ◽

Kelly A. Mackintosh ◽

Melitta A. McNarry ◽

Karin A. Pfeiffer

Keyword(s):

Machine Learning ◽

Energy Expenditure ◽

Cross Validation ◽

Prediction Models ◽

Calibration Data ◽

Regression Equations ◽

Accelerometer Data ◽

Raw Data ◽

Ann Models ◽

Artificial Neural Network Ann

Background: Machine learning may improve energy expenditure (EE) prediction from body-worn accelerometers. However, machine learning models are rarely cross-validated in an independent sample, and the use of machine learning raises additional questions including the effect of accelerometer placement and data type (count vs. raw) for optimal EE prediction. Purpose: To assess the accuracy of artificial neural network (ANN) models for EE prediction in youth using count-based or raw data from accelerometers worn on the hip, wrist, or in combination, and compare these to count-based, EE regression equations. Methods: Data were collected in two settings; one (n = 27) to calibrate the EE prediction models, and the other (n = 34) for model cross-validation. Participants wore a portable metabolic analyzer (EE criterion) and accelerometers on the left wrist and right hip while completing 30 minutes of exergames (calibration, cross-validation) and a maximal exercise test (calibration only). Six ANNs were created from the calibration data, separately by accelerometer placement (hip, wrist, combination) and data format (count-based, raw) to predict EE (15-second epochs). Three count-based linear regression equations were also developed for comparison to the ANNs. Results: The count-based, hip ANN demonstrated lower error (RMSE: 1.2 METs) than all other ANNs (RMSE: 1.7–3.6 METs) and EE regression equations (RMSE: 1.5–3.2 METs). However, all models showed bias toward the mean. Conclusion: An ANN developed for hip-worn accelerometers had higher accuracy for EE prediction during an exergame session than wrist or combination ANNs, and ANNs developed using count-based data had higher accuracy than ANNs developed using raw data.

Download Full-text

Machine Learning-Based Prediction of Air Quality

Applied Sciences ◽

10.3390/app10249151 ◽

2020 ◽

Vol 10 (24) ◽

pp. 9151

Author(s):

Yun-Chia Liang ◽

Yona Maimury ◽

Angela Hsiang-Ling Chen ◽

Josue Rodolfo Cuevas Juarez

Keyword(s):

Machine Learning ◽

Air Quality ◽

Random Forest ◽

Prediction Models ◽

Superior Performance ◽

Support Vector ◽

Economic Activities ◽

Adaptive Boosting ◽

Series Of Experiments ◽

Artificial Neural Network Ann

Air, an essential natural resource, has been compromised in terms of quality by economic activities. Considerable research has been devoted to predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. Several prediction models have been developed using an 11-year dataset collected by Taiwan’s Environmental Protection Administration (EPA). Machine learning methods, including adaptive boosting (AdaBoost), artificial neural network (ANN), random forest, stacking ensemble, and support vector machine (SVM), produce promising results for air quality index (AQI) level predictions. A series of experiments, using datasets for three different regions to obtain the best prediction performance from the stacking ensemble, AdaBoost, and random forest, found the stacking ensemble delivers consistently superior performance for R2 and RMSE, while AdaBoost provides best results for MAE.

Download Full-text

Cross-Predicting Essential Genes between Two Model Eukaryotic Species Using Machine Learning

International Journal of Molecular Sciences ◽

10.3390/ijms22105056 ◽

2021 ◽

Vol 22 (10) ◽

pp. 5056

Author(s):

Tulio L. Campos ◽

Pasi K. Korhonen ◽

Neil D. Young

Keyword(s):

Machine Learning ◽

Experimental Studies ◽

Essential Genes ◽

Subcellular Localisation ◽

Cross Prediction ◽

Gene Essentiality ◽

C Elegans ◽

Cellular Processes ◽

Eukaryotic Species ◽

The Cross

Experimental studies of Caenorhabditis elegans and Drosophila melanogaster have contributed substantially to our understanding of molecular and cellular processes in metazoans at large. Since the publication of their genomes, functional genomic investigations have identified genes that are essential or non-essential for survival in each species. Recently, a range of features linked to gene essentiality have been inferred using a machine learning (ML)-based approach, allowing essentiality predictions within a species. Nevertheless, predictions between species are still elusive. Here, we undertake a comprehensive study using ML to discover and validate features of essential genes common to both C. elegans and D. melanogaster. We demonstrate that the cross-species prediction of gene essentiality is possible using a subset of features linked to nucleotide/protein sequences, protein orthology and subcellular localisation, single-cell RNA-seq, and histone methylation markers. Complementary analyses showed that essential genes are enriched for transcription and translation functions and are preferentially located away from heterochromatin regions of C. elegans and D. melanogaster chromosomes. The present work should enable the cross-prediction of essential genes between model and non-model metazoans.

Download Full-text

Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Materials ◽

10.3390/ma14154068 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4068

Author(s):

Xu Huang ◽

Mirna Wasouf ◽

Jessada Sresakoolchai ◽

Sakdirat Kaewunruen

Keyword(s):

Machine Learning ◽

Search Algorithm ◽

Weather Conditions ◽

Prediction Performance ◽

Machine Learning Algorithms ◽

Coefficient Of Determination ◽

Gradient Boosting ◽

Support Vector ◽

Self Healing ◽

Artificial Neural Network Ann

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

Download Full-text

EXPRESS: When Algorithms Fail: Consumers’ Responses to Brand Harm Crises Caused by Algorithm Errors

Journal of Marketing ◽

10.1177/0022242921997082 ◽

2021 ◽

pp. 002224292199708

Author(s):

Raji Srinivasan ◽

Gülen Sarial-Abi

Keyword(s):

Machine Learning ◽

Theory Of Mind ◽

Human Error ◽

Learning Algorithm ◽

Experimental Studies ◽

Task Characteristics ◽

Machine Learning Algorithm ◽

Incentive Compatible ◽

Mind Perception ◽

Interactive Task

Algorithms increasingly used by brands sometimes fail to perform as expected or even worse, cause harm, causing brand harm crises. Unfortunately, algorithm failures are increasing in frequency. Yet, we know little about consumers’ responses to brands following such brand harm crises. Extending developments in the theory of mind perception, we hypothesize that following a brand harm crisis caused by an algorithm error (vs. human error), consumers will respond less negatively to the brand. We further hypothesize that consumers’ lower mind perception of agency of the algorithm (vs. human) for the error that lowers their perceptions of the algorithm’s responsibility for the harm caused by the error will mediate this relationship. We also hypothesize four moderators of this relationship: two algorithm characteristics, anthropomorphized algorithm and machine learning algorithm and two task characteristics where the algorithm is deployed, subjective (vs. objective) task and interactive (vs. non-interactive) task. We find support for the hypotheses in eight experimental studies including two incentive-compatible studies. We examine the effects of two managerial interventions to manage the aftermath of brand harm crises caused by algorithm errors. The research’s findings advance the literature on brand harm crises, algorithm usage, and algorithmic marketing and generate managerial guidelines to address the aftermath of such brand harm crises.

Download Full-text

Inverse Design of Fe-Based Bulk Metallic Glasses Using Machine Learning

Metals ◽

10.3390/met11050729 ◽

2021 ◽

Vol 11 (5) ◽

pp. 729

Author(s):

Junhyub Jeon ◽

Namhyuk Seo ◽

Hwi-Jun Kim ◽

Min-Ha Lee ◽

Hyun-Kyu Lim ◽

...

Keyword(s):

Thermal Properties ◽

Metallic Glasses ◽

Alloy Composition ◽

Relevant Literature ◽

Bulk Metallic Glasses ◽

X Ray Diffraction ◽

X Ray ◽

Ann Models ◽

Wide Scale ◽

Artificial Neural Network Ann

Fe-based bulk metallic glasses (BMGs) are a unique class of materials that are attracting attention in a wide variety of applications owing to their physical properties. Several studies have investigated and designed the relationships between alloy composition and thermal properties of BMGs using an artificial neural network (ANN). The limitation of the wide-scale use of these models is that the required composition is yet to be found despite numerous case studies. To address this issue, we trained an ANN to design Fe-based BMGs that predict the thermal properties. Models were trained using only the composition of the alloy as input and were created from a database of more than 150 experimental data of Fe-based BMGs from relevant literature. We adopted these ANN models to design BMGs with thermal properties to satisfy the intended purpose using particle swarm optimization. A melt spinner was employed to fabricate the designed alloys. X-ray diffraction and differential thermal analysis tests were used to evaluate the specimens.

Download Full-text

CuxO Nanostructure-Based Gas Sensors for H2S Detection: An Overview

Chemosensors ◽

10.3390/chemosensors9060127 ◽

2021 ◽

Vol 9 (6) ◽

pp. 127

Author(s):

Sachin Navale ◽

Mehrdad Shahbaz ◽

Sanjit Manohar Majhi ◽

Ali Mirzaei ◽

Hyoun-Woo Kim ◽

...

Keyword(s):

Metal Oxides ◽

Gas Sensors ◽

Noble Metals ◽

Oil And Gas ◽

Detection Mechanism ◽

Reliable Detection ◽

Electrical Sensing ◽

Semiconducting Metal Oxides ◽

H2s Detection

H2S gas is a toxic and hazardous byproduct of the oil and gas industries. It paralyzes the olfactory nerves, with concentrations above 100 ppm, resulting in loss of smell; prolonged inhalation may even cause death. One of the most important semiconducting metal oxides for the detection of H2S is CuxO (x = 1, 2), which is converted to CuxS upon exposure to H2S, leading to a remarkable modulation in the resistance and appearance of an electrical sensing signal. In this review, various morphologies of CuxO in the pristine form, composites of CuxO with other materials, and decoration/doping of noble metals on CuxO nanostructures for the reliable detection of H2S gas are thoroughly discussed. With an emphasis to the detection mechanism of CuxO-based gas sensors, this review presents findings that are of considerable value as a reference.

Download Full-text