Rhodium-based catalysts offer remarkable selectivities toward higher alcohols, specifically ethanol, via syngas conversion. However, the addition of metal promoters is required to increase ...reactivity, augmenting the complexity of the system. Herein, we present an interpretable machine learning (ML) approach to predict and rationalize the performance of Rh-Mn-P/SiO2 catalysts (P = 19 promoters) using the open-source dataset on Rh-catalyzed higher alcohol synthesis (HAS) from Pacific Northwest National Laboratory (PNNL). A random forest model trained on this dataset comprising 19 alkali, transition, post-transition metals, and metalloid promoters, using catalytic descriptors and reaction conditions, predicts the higher alcohols space-time yield (STYHA) with an accuracy of R 2 = 0.76. The promoter’s cohesive energy and alloy formation energy with Rh are revealed as significant descriptors during posterior feature-importance analysis. Their interplay is captured as a dimensionless property, coined promoter affinity index (PAI), which exhibits volcano correlations for space-time yield. Based on this descriptor, we develop guidelines for the rational selection of promoters in designing improved Rh-Mn-P/SiO2 catalysts. This study highlights ML as a tool for computational screening and performance prediction of unseen catalysts and simultaneously draws insights into the property–performance relations of complex catalytic systems.
The corrosion resistance of low-alloy steel seriously influences its performance, particularly as a class of materials widely used in marine environments. In this study, we collected the marine ...corrosion data of low-alloy steels and established corrosion rate prediction models with machine learning algorithms. Both the chemical composition of low-alloy steel and environmental factors were used as input features, and the random forest algorithm was selected as the modeling algorithm. Feature reduction methods, including the gradient boosting decision tree, Kendall correlation analysis and principal component analysis, were first applied to select the dominating factors on the corrosion rate. Then, we proposed two feature creation methods to convert the chemical composition features into a set of atomic and physical property features. As a result, the feature creation method crafted a model no longer limited to materials with specific chemical compositions. The machine learning-based corrosion rate prediction model also showed good prediction accuracy of the corrosion rate. This study improved the generalization ability of the corrosion rate prediction model and proved the feasibility of machine learning in corrosion resistance evaluation.
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•Feature selection can effectively screen out the important factors related to corrosion rate.•The accuracy of the corrosion rate prediction model is improved through feature selection and feature creation.•Feature creation greatly increases the generalization ability of predictive models.
We are living in the information era. Therefore, intelligence-based researchers are hot-topic such as artificial intelligence. In the artificial intelligence research area, machine learning and deep ...learning models have frequently used to create intelligence assistants and deep learning is the shining star of the AI. Specifically, in the computer vision, numerous deep learning models have been proposed, leading to a competition between transformers and convolutional neural networks (CNNs). Since the introduction of Vision Transformers (ViT), many transformer models have been advocated for computer vision, often overshadowing CNNs. Therefore, it is crucial to propose CNNs to showcase their prowess in image classification. This research introduces a lightweight CNN named MobileDenseNeXt.
The proposed MobileDenseNeXt comprises four main blocks: (i) input, (ii) main, (iii) average pooling-based downsampling, and (iv) output. This research also incorporates convolution-based residual blocks and uses a depth concatenation layer to increase the number of filters. For downsampling, an average pooling operation has been employed, similar to the original DenseNet. Furthermore, the swish activation function is utilized in the presented CNN. MobileDenseNeXt has approximately 1.4 million learnable parameters, categorizing it as a lightweight CNN model. Additionally, a deep feature engineering approach has been developed using MobileDenseNeXt, incorporating two feature extractors with global average pooling and dropout layers, along with 10 feature selectors, to demonstrate the transfer learning capabilities of MobileDenseNeXt.
The recommended models achieved over 95% test classification accuracy on the used three datasets, unequivocally demonstrating the high image classification proficiency of the proposed MobileDenseNeXt. Moreover, to show general classification ability of the proposed model, MobileDenseNeXt was trained on the CIFAR10 dataset and reached 98.62% accuracy.
This research not only highlights the efficiency and effectiveness of MobileDenseNeXt in biomedical image classification but also highlights the competitive potential of this model for computer vision.
Social media communications offer valuable feedback to firms about their brands. We present a targeted approach to Twitter sentiment analysis for brands using supervised feature engineering and the ...dynamic architecture for artificial neural networks. The proposed approach addresses challenges associated with the unique characteristics of the Twitter language and brand-related tweet sentiment class distribution. We demonstrate its effectiveness on Twitter data sets related to two distinctive brands. The supervised feature engineering for brands offers final tweet feature representations of only seven dimensions with greater feature density. Reducing the dimensionality of the representations reduces the complexity of the classification problem and feature sparsity. Two sets of experiments are conducted for each brand in three-class and five-class tweet sentiment classification. We examine five-class classification to target the mild sentiment expressions that are of particular interest to firms and brand management practitioners. We compare the proposed approach to the performances of two state-of-the-art Twitter sentiment analysis systems from the academic and commercial domains. The results indicate that it outperforms these state-of-the-art systems by wide margins, with classification F
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-measures as high as 88 percent and excellent recall of tweets expressing mild sentiments. Furthermore, they demonstrate the tweet feature representations, though consisting of only seven dimensions, are highly effective in capturing indicators of Twitter sentiment expression. The proposed approach and vast majority of features identified through supervised feature engineering are applicable across brands, allowing researchers and brand management practitioners to quickly generate highly effective tweet feature representations for Twitter sentiment analysis on other brands.
On testing machine learning programs Braiek, Houssem Ben; Khomh, Foutse
The Journal of systems and software,
June 2020, 2020-06-00, Volume:
164
Journal Article
Peer reviewed
Open access
•We identify and explain ML testing challenges that should be addressed.•We report existing solutions found in the literature for testing ML programs.•We identify gaps in the literature related to ...the testing of ML programs.•We make recommendations of future research directions for the community.
Nowadays, we are witnessing a wide adoption of Machine learning (ML) models in many software systems. They are even being tested in safety-critical systems, thanks to recent breakthroughs in deep learning and reinforcement learning. Many people are now interacting with systems based on ML every day, e.g., voice recognition systems used by virtual personal assistants like Amazon Alexa or Google Home. As the field of ML continues to grow, we are likely to witness transformative advances in a wide range of areas, from finance, energy, to health and transportation. Given this growing importance of ML-based systems in our daily life, it is becoming utterly important to ensure their reliability. Recently, software researchers have started adapting concepts from the software testing domain (e.g., code coverage, mutation testing, or property-based testing) to help ML engineers detect and correct faults in ML programs. This paper reviews current existing testing practices for ML programs. First, we identify and explain challenges that should be addressed when testing ML programs. Next, we report existing solutions found in the literature for testing ML programs. Finally, we identify gaps in the literature related to the testing of ML programs and make recommendations of future research directions for the scientific community. We hope that this comprehensive review of software testing practices will help ML engineers identify the right approach to improve the reliability of their ML-based systems. We also hope that the research community will act on our proposed research directions to advance the state of the art of testing for ML programs.
Mechanical wave transmission through a material is influenced by the mechanical discontinuity in the material. The propagation of embedded discontinuities can be monitored by analyzing the ...wave-transmission measurements recorded by a multipoint sensor system placed on the surface of the material. The proposed workflow monitors the propagation of mechanical discontinuity through three stages, namely initial, intermediate, and final stages, by using supervised learning followed by data-driven causal discovery. To the end, the workflow processes the multipoint waveform measurements resulting from a single impulse source, while considering the effects of wave attenuation, dispersion and multiple wave-propagation modes due to the discontinuity and material boundaries. Among various feature reduction techniques ranging from decomposition methods to manifold approximation methods, the features derived based on statistical parameterizations of the measured waveforms lead to reliable monitoring that is robust to changes in precision, resolution, and signal-to-noise ratio of the multipoint sensor measurements. The numbers of zero-crossing, negative-turning, and positive turning in the waveforms are the strongest causal signatures of the propagation of mechanical discontinuity. Higher order moments of the waveforms, such as variance, skewness and kurtosis, are also strong causal signatures of the propagation. Finally, the newly discovered causal signatures confirm that the statistical correlations and conventional feature rankings are not always statistically significant indicators of causality.
Accurate battery lifetime estimates enable accelerated design of novel battery materials and determination of optimal use protocols for longevity in deployments. Unfortunately, traditional battery ...testing may take years to reach thousands of cycles. Recent studies have shown that machine learning (ML) tools can predict lithium-ion battery lifetimes from 100 or fewer preliminary cycles, representing only a few weeks of cycling. Until now, conclusions about the efficacy and broad applicability of these predictions across a variety of cathode chemistries have been limited by available experimental information. In this work, we leverage a battery cycling dataset representing six cathode chemistries (NMC111, NMC532, NMC622, NMC811, HE5050, and 5Vspinel), multiple electrolyte/anode compositions, and 300 total carefully prepared pouch batteries to explore feature selection and battery chemistry's role in ML battery lifetime predictions. A mean absolute error (MAE) of 78 cycles in prediction was seen for a chemistry-spanning test set from 100 preliminary cycles. Furthermore, an MAE of 103 cycles was seen when using only the first cycle. This study represents an in-depth investigation of strategies for feature selection for battery lifetime prediction, ML models' generalization across multiple battery chemistries, and predictions beyond the training set in the chemical space.
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•Unique Li-ion dataset comprised 6 metal oxide cathode chemistries and 300 batteries.•A single machine learning model accurately predicted cycle life across cathodes.•Useful predictions required as few as one preliminary cycle.•Broad feature sets are most accurate; categorically narrow sets are viable.•A diverse training set improved predictive performance for new cathode chemistries.
The accurate classification of endoscopic images is a challenging yet critical task in medical diagnostics, which directly affects the treatment and management of Gastrointestinal diseases. ...Misclassification can lead to incorrect treatment plans, adversely affecting patient outcomes. To address this challenge, our research aimed to develop a reliable computational model to improve the accuracy of classifying conditions of esophagitis and polyps. We focused on a subset of the Kvasir v1 secondary dataset, comprising 2000 endoscopic images evenly distributed across two classes: esophagitis and polyp. The goal was to leverage the strengths of both Machine Learning(ML) and Deep Learning(DL) to create a model that not only predicts with high accuracy but also integrates seamlessly into clinical workflows. To this end, we introduced a novel VRG-based ensemble image feature extraction technique, combining the powers of VGG, RF, and GB models to synthesize a robust feature set conducive to high-precision classification. The ensemble approach demonstrated a best-in-class performance with the GB model achieving an outstanding 99.73% accuracy in detecting esophagitis and polyps. The practical implications of these results are substantial, indicating that our method can significantly improve diagnostic accuracy in real-world settings, reduce the rate of misdiagnosis, and contribute to the efficient and effective treatment of patients, ultimately enhancing the quality of healthcare services. With the successful application of our proposed method to a controlled dataset, future work involves deploying the model in clinical environments and expanding its application to a broader spectrum of Gastrointestinal conditions across multi-class datasets.
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Food adulteration has emerged as a significant challenge in the food industry, impacting consumer health and trust in the market. Utilizing machine learning especially deep learning with ...spectroscopic methods has revolutionized food adulteration detection enabling the development of more sophisticated and automated solutions.
This review aims to provide a comprehensive overview of the challenges and opportunities in machine learning-based spectroscopic techniques for detecting food adulteration by exploring various spectroscopic techniques commonly employed in the food industry, such as infrared spectroscopy, Raman spectroscopy, NMR spectroscopy, fluorescence spectroscopy, multi-spectral imaging, and hyperspectral imaging. The article addresses data pre-processing, feature engineering, model complexity, interpretability and their performance, and the need for large-scale diverse datasets.
To develop a commercial spectroscopic adulteration detection system that uses machine learning, one needs to optimize not only the model, but also the dataset size, the combination of pre-processing methods, the feature selection and extraction methods, the model selection, the hyperparameters by validation and the performance criteria. In addition, new machine learning algorithms are growing rapidly but creating a specialized model for adulteration detection using spectroscopy is still an area of research.
•Data augmentation can handle data scarcity in adulteration detection.•Currently no reinforced learning algorithm is utilized for adulteration detection in food.•Commonly used pre-processing methods are SNV, MSC, and Savitzky-Golay algorithms.
•Accurate transferrable machine learning based load forecasting of machine tools.•Automated data preprocessing, feature construction and selection process.•Time lag and moving average feature ...construction increases forecasting accuracy.•Autocorrelation function is a valuable information source of feature construction.
With the ongoing integration of renewable energies into the electrical power grid, industrial energy flexibility gains importance. To enable demand response applications, knowledge about the future energy demand is necessary. This paper presents a machine learning process to forecast the very short term load of two machine tools, which can be utilized as a decision support basis for control schemes and measures to increase energy flexibility and decrease energy cost in manufacturing. The presented process is developed and evaluated on production machines in a research factory. The results indicate that the developed machine learning process is feasible and creates an accurate very short term load forecasting model for different production machines. It can be used as a blueprint to develop load forecasting models for other production machines using the historic load profile and various machine and process data. A combination of time series features and an Artificial Neural Network proves to be the most robust model regarding the presented machine tools with achieved coefficients of determination between 0.57 and 0.64 for a 100 step forecast. Improvements are still needed regarding the forecasting accuracy, especially of load peaks, for which different measures are proposed.
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