查看更多>>摘要：New study results on artificial intelligence have been published. According to news reporting out of Lima, Peru, by NewsRx editors, research stated, “One of the main causes of having low crop efficiency in Peru is the poor management of water resources; which is why the main objective of this article is to estimate the amount of irrigation water required in quinoa crops through a comparison between the machine learning and AquaCrop models.” Our news reporters obtained a quote from the research from Faculty of Engineering: “For the development of this study, meteorological data from the province of Jauja and descriptive data of quinoa crops were processed and a simulation period was established from June to December 2020. From the simulation carried out, it was determined that the best model to predict the required irrigation water is the Adaptive Boosting (AdaBoost) model in which it was observed that the mean and standard deviation of the AdaBoost models (mean = 19.681 and SD = 4.665) behave similarly to AquaCrop (mean = 19.838 and SD = 5.04). In addition, the result of ANOVA was that the AdaBoost model has the best P-value indicator with a value of 0.962 and a smaller margin of error in relation to the mean absolute error (MAE) indicator with a value of 0.629.”
查看更多>>摘要：Research findings on artificial intelligence are discussed in a new report. According to news reporting from Lanzhou, People’s Republic of China, by NewsRx journalists, research stated, “Because of the complex nonlinear relationship between working conditions, the prediction of tribological properties has become a difficult problem in the field of tribology.” Funders for this research include Scientific Research Project of The Lanzhou Petrochemical University of Vocational Technology. Our news journalists obtained a quote from the research from College of Information Engineering: “In this study, we employed three distinct machine learning (ML) models, namely random forest regression (RFR), gradient boosting regression (GBR), and extreme gradient boosting (XGBoost), to predict the tribological properties of polytetrafluoroethylene (PTFE) composites under high-speed and high-temperature conditions. Firstly, PTFE composites were successfully prepared, and tribological properties under different temperature, speed, and load conditions were studied in order to explore wear mechanisms. Then, the investigation focused on establishing correlations between the friction and wear of PTFE composites by testing these parameters through the prediction of the friction coefficient and wear rate. Importantly, the correlation results illustrated that the friction coefficient and wear rate gradually decreased with the increase in speed, which was also proven by the correlation coefficient.”
查看更多>>摘要：A new study on artificial intelligence is now available. According to news reporting from Beijing, People’s Republic of China, by NewsRx journalists, research stated, “Long-term exposure to high concentrations of fine particles can cause irreversible damage to people’s health.” Funders for this research include Forestry Technological Developments And Monitoring And Assessment of Terrestrial Ecosystem Research. The news reporters obtained a quote from the research from Chinese Academy of Sciences: “Therefore, it is of extreme significance to conduct large-scale continuous spatial fine particulate matter (PM2.5) concentration prediction for air pollution prevention and control in China. The distribution of PM2.5 ground monitoring stations in China is uneven with a larger number of stations in southeastern China, while the number of ground monitoring sites is also insufficient for air quality control. Remote sensing technology can obtain information quickly and macroscopically. Therefore, it is possible to predict PM2.5 concentration based on multi-source remote sensing data. Our study took China as the research area, using the Pearson correlation coefficient and GeoDetector to select auxiliary variables. In addition, a long shortterm memory neural network and random forest regression model were established for PM2.5 concentration estimation.”
查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting originating from Zurich, Switzerland, by NewsRx correspondents, research stated, “The pystacked command implements stacked generalization (Wolpert, 1992, Neural Networks 5: 241-259) for regression and binary classification via Python’s scikit-learn.” Our news editors obtained a quote from the research from the Swiss Federal Institute of Technology, “Stacking combines multiple supervised machine learners-the ‘base’ or ‘level-0’ learners-into one learner.” According to the news editors, the research concluded: “The currently supported base learners include regularized regression, random forest, gradient boosted trees, support vector machines, and feed-forward neural nets (multilayer perceptron). pystacked can also be used as a ‘regular’ machine learning program to fit one base learner and thus provides an easy-to-use application programming interface for scikit-learn’s machine learning algorithms.”
查看更多>>摘要：Researchers detail new data in engineering. According to news reporting originating from Haripur, Pakistan, by NewsRx correspondents, research stated, “Code smell indicates inadequacies in design and implementation choices.” The news reporters obtained a quote from the research from University of Haripur: “Code smells harm software maintainability including effects on components’ bug proneness and code quality has been demonstrated in previous studies. This study aims to investigate the importance of code smell metrics in prediction models for detecting bug-prone code modules. For improvement of the bug prediction model, in this study, smell-based metrics of code have been used. For the training of our model, we employed 14 different open-source projects from the PROMISE repository. Every project file consists of source code as well as smell code metrics and was written in Java. We examined different evaluation metrics such as F1_score, accuracy, precision, recall, the area under the receiver operating characteristic curve, and the area under the precision-recall curve of the five methods within the version, within the project, and across the projects.”
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating from Minneapolis, Minnesota, by NewsRx correspondents, research stated, “Discriminating between volatile organic compounds (VOCs) for applications including disease diagnosis and environmental monitoring, is often complicated by the presence of interfering compounds such as oxygen. Graphene sensors are effective at detecting VOCs; however, they are also known to be highly sensitive to oxygen.” Our news editors obtained a quote from the research from the University of Minnesota, “Therefore, the combined effects of each of these gases on graphene sensors must be understood. In this work, we use graphene variable capacitor (varactor) sensors to examine the cross-selectivity of oxygen at 3 concentrations and 3 VOCs (ethanol, methanol, and methyl ethyl ketone) at 5 concentrations each. The sensor responses exhibit distinct shapes dependent on the relative concentrations in mixtures of oxygen and VOCs. Because the entire response shape is therefore informative for distinguishing between each gas mixture, a classification algorithm that utilizes entire sequences of data is needed. Accordingly, a long short-term memory (LSTM) network is used to classify the mixtures and VOC concentrations. The model achieves 100% accurate classification of the VOC type, even in the presence of varying levels of oxygen. When the VOC type and VOC concentration are classified, we show that the sensors can provide VOC concentration resolution within approximately 200 ppm. Throughout this work, we also demonstrate that an effective gas mixture classification can be achieved, even while the sensors exhibit varied drift patterns typical of graphene sensors.”
查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting originating from North Ryde, Australia, by NewsRx correspondents, research stated, “A new machine learning (ML)-based large-signal parameter extraction for ASM-HEMT model has been presented for the first time. The proposed technique uses a 20k training sample generated by Monte Carlo simulations.” Our news editors obtained a quote from the research from Macquarie University, “The training samples of simulated output power P-out and power-added efficiency (PAE) are used to train an ML extractor to extract the ASM-HEMT model parameters. The trained ML extractor has been evaluated on measurements performed on a commercial GaN device which was previously modeled using ASM-HEMT using manual extraction. The results show that the ML extractor could extract ASM-HEMT large-signal parameters to model P-out , gain, and PAE, producing a level of accuracy comparable to the conventional manual parameter extraction. The proposed parameter extraction technique takes less than a second while removing the complexity and the need for expertise for extraction.”
查看更多>>摘要：Fresh data on artificial intelligence are presented in a new report. According to news originating from Glasgow, United Kingdom, by NewsRx correspondents, research stated, “The field of explainable Artificial Intelligence (AI) has gained growing attention over the last few years due to the potential for making accurate data-based predictions of asset health.” Our news reporters obtained a quote from the research from University of Strathclyde: “One of the current research aims in AI is to address challenges associated with adopting machine learning (ML) (i.e., data-driven) AI that is, understanding how and why ML predictions are made. Despite ML models successfully providing accurate predictions in many applications, such as condition monitoring, there are still concerns about the transparency of the prediction-making process. Therefore, ensuring that the models used are explainable to human users is essential to build trust in the approaches proposed. Consequently, AI and ML practitioners need to be able to evaluate any available eXplainable AI (XAI) tools’ suitability for their intended domain and end users, while simultaneously being aware of the tools’ limitations. This paper provides insight into various existing XAI approaches and their limitations to be considered by practitioners in condition monitoring applications during the design process for an MLbased prediction. The aim is to assist practitioners in engineering applications in building interpretable and explainable models intended for end users who wish to improve a system’s reliability and help users make better-informed decisions based upon a predictive ML algorithm output.”
查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting out of Wuhan, People’s Republic of China, by NewsRx editors, research stated, “Many significant viral infections have been recorded in human history, which have caused enormous negative impacts worldwide. Human-virus protein-protein interactions (PPIs) mediate viral infection and immune processes in the host.” Our news journalists obtained a quote from the research from Central China Normal University, “The identification, quantification, localization, and construction of human-virus PPIs maps are critical prerequisites for understanding the biophysical basis of the viral invasion process and characterising the framework for all protein functions. With the technological revolution and the introduction of artificial intelligence, the human-virus PPIs maps have been expanded rapidly in the past decade and shed light on solving complicated biomedical problems. However, there is still a lack of prospective insight into the field. In this work, we comprehensively review and compare the effectiveness, potential, and limitations of diverse approaches for constructing large-scale PPIs maps in human-virus, including experimental methods based on biophysics and biochemistry, databases of human-virus PPIs, computational methods based on artificial intelligence, and tools for visualising PPIs maps.”
查看更多>>摘要：Researchers detail new data in Machine Learning. According to news originating from Knoxville, Tennessee, by NewsRx correspondents, research stated, “In this research work, three practical correction methods are proposed to mitigate the impact of defective input features in power system data measurement for machine learning (ML) applications. A well-trained ML tool may become ineffective due to defective input features, which may originate from measurement issues, such as monitor malfunction, cyberattack, communication failure, or others.” Financial support for this research came from National Science Foundation (NSF). Our news journalists obtained a quote from the research from the University of Tennessee, “It is crucial to correct defective input features to enable ML tools with desirable performances. This letter first introduces the mechanism of three correction methods, i.e., statistical-value-based method, minimalerror-based method, and DNN-based adaptive method. Then, the methods are validated via a deep neural network (DNN) case for power system stability enhancement. Validation results demonstrate that the adaptive method achieves the best performance, enabling the well-trained ML tool with a similar accuracy level to the case of no data defects.
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