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    Studies from Ningbo No. 2 Hospital in the Area of Machine Learning Published (A new method applied for explaining the landing patterns: Interpretability analysis of machine learning)

    68-68页
    查看更多>>摘要：New study results on artificial intelligence have been published. According to news reporting originating from Ningbo, People’s Republic of China, by NewsRx correspondents, research stated, “As one of many fundamental sports techniques, the landing maneuver is also frequently used in clinical injury screening and diagnosis. However, the landing patterns are different under different constraints, which will cause great difficulties for clinical experts in clinical diagnosis.” Funders for this research include Zhejiang Province Natural Science Foundation. The news journalists obtained a quote from the research from Ningbo No. 2 Hospital: “Machine learning (ML) have been very successful in solving a variety of clinical diagnosis tasks, but they all have the disadvantage of being black boxes and rarely provide and explain useful information about the reasons for making a particular decision. The current work validates the feasibility of applying an explainable ML (XML) model constructed by Layer-wise Relevance Propagation (LRP) for landing pattern recognition in clinical biomechanics. This study collected 560 groups landing data. By incorporating these landing data into the XML model as input signals, the prediction results were interpreted based on the relevance score (RS) derived from LRP. The interpretation obtained from XML was evaluated comprehensively from the statistical perspective based on Statistical Parametric Mapping (SPM) and Effect Size. The RS has excellent statistical characteristics in the interpretation of landing patterns between classes, and also conforms to the clinical characteristics of landing pattern recognition.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Studies Conducted at Baylor University on Machine Learning Recently Reported (Analysis of an Optical Imaging System Prototype for Autonomously Monitoring Zooplankton In an Aquaculture Facility)

    69-70页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news originating from Waco, Texas, by NewsRx correspondents, research stated, “Traditional approaches to biomonitoring in aquatic systems, such as sample collection, sorting, and identification, require significant time and effort, thereby limiting the spatiotemporal resolution of sample collection. Additionally, collection and preservation of samples for subsequent taxonomic identification and enumeration leads to mortality of organisms.” Financial support for this research came from United States Department of Energy (DOE). Our news journalists obtained a quote from the research from Baylor University, “Recent advances in technologies that utilize optical imaging and machine learning have provided new opportunities to expedite biomonitoring and lead to significant cost savings. These technologies can be advantageous to scientists or managers that conduct routine biomonitoring to inform operations, as in the case of aquaculture facilities. The Small Aquatic Organism optical imaging system (SAO) is a high-throughput optical imaging and classification prototype system that relies on computer vision and machine learning (Support Vector Machines, or SVMs) to autonomously identify and enumerate aquatic organisms. The SAO provides a more sustainable method of collecting large volumes of data and has the benefit of being used in situ. In this study, we tested the performance of the SAO in providing comparable results to manual zooplankton community monitoring in ten ponds at an aquaculture facility. We performed a side-by-side study comparing the sampling methods of plankton tow nets, where major zooplankton taxonomic classes were manually identified and enumerated, to sampling with the SAO. Vouchered samples were used to develop a training library for the SAO, where classes consisted of water boatman and zooplankton groups: cladocerans, copepod adults, copepod nauplii, and rotifers. SAO imagery was manually classified and compared with predicted results for validation. Accuracy for the SVM classifier of the SAO was 37.4 %. Convolutional Neural Networks (CNN) and Random Forest classifiers were also applied to SAO imagery and image features for comparison. The best CNN model and our Random Forest model had accuracies of 80.4 % and 46.6 % respectively. Challenges faced included the small size of copepod nauplii and rotifers and the limited resolution of the imaging camera, although there are tradeoffs between imaging resolution and the sample processing rate.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Findings from Gonbad Kavous University Provide New Insights into Machine Learning (Uncertainty analysis of discharge coefficient predicted for rectangular side weir using machine learning methods)

    69-69页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting out of Gonbad Kavous, Iran, by NewsRx editors, research stated, “The present study used three machine learning models, including Least Square Support Vector Regression (LSSVR) and two non-parametric models, namely, Quantile Regression Forest (QRF) and Gaussian Process Regression (GPR), to quantify uncertainty and precisely predict the side weir discharge coefficient (Cd) in rectangular channels.” The news journalists obtained a quote from the research from Gonbad Kavous University: “So, 15 input structures were examined to develop the models. The results revealed that the machine learning models used in the study offered better accuracy compared to the classical equations. While the LSSVR and QRF models provided a good prediction performance, the GPR slightly outperformed them. The best input structure that was developed included all four dimensionless parameters. Sensitivity analysis was conducted to identify the effective parameters. To evaluate the uncertainty in the predictions, the LSSVR, QRF, and GPR were used to generate prediction intervals (PI), which quantify the uncertainty coupled with point prediction.”
      原文链接:
    • NETL
    • NSTL
    • Sciendo

    Studies from University of Georgia Yield New Data on Machine Learning (Involving Prediction of Dynamic Modulus In Asphalt Mix Design With Machine Learning and Mechanical-empirical Analysis)

    71-72页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news originating from Athens, Georgia, by NewsRx correspondents, research stated, “Dynamic modulus (|E*|) plays a dominant role in comprehensively capturing the mechanical behavior of asphalt mixture. Many researchers tried to consider |E*| as a performance indicator for a mix design stage, but the cost of time and labor for experimentally measuring the |E*| is high.” Financial support for this research came from Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems (CIAMTIS), a US Department of Transportation University Transportation Center. Our news journalists obtained a quote from the research from the University of Georgia, “Moreover, establishing harmonized |E*| criterion is unrealistic. To involve |E*| in an asphalt mix design, this paper improved the Superpave volumetric mixture design via controlling the performance (rutting) of mixtures, which were estimated by incorporating machine learning (ML) predictive models of |E*| into the mechanicalempirical approach. The data from the concatenation of the original |E*| database and volumetric properties data extracted from the NCHRP 9-19 report were involved. The different ML models including Support Vector Regression (SVR), kernel ridge regression (KRR), artificial Neural Networks (ANN), Gaussian process regression (GPR), gradient boosting (GB), and eXtreme gradient boosting (XGBoost) were optimized, trained and tested, and their performances were compared to empirical Witczak’s predictive equations. To illustrate the improved asphalt mix design, the case study was presented. Candidate asphalt mixtures were prepared by mixing the selected gradation and different asphalt content and subject to a series of |E*| predictions using the best ML model, which were then used to calculate the permanent deformation of these mixtures with elastic layered analysis and transfer functions in MATLAB. The results show that among all the ML models, XGBoost to predict the |E*| at actual scale has the largest prediction accuracy (R2 = 0.9867), and its prediction accuracy is significantly higher than Witczak’s equations. Feature importance analysis suggested that no matter which scale of |E*| is predicted, the main important factors are test conditions and asphalt binder properties.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Reports Outline Robotics Study Findings from Bandung Institute of Technology (Deep Learning for Crescent Detection and Recognition: Implementation of Mask R-cnn To the Observational Lunar Dataset Collected With the Robotic Lunar Telescope System)

    72-72页
    查看更多>>摘要：Research findings on Robotics are discussed in a new report. According to news originating from Bandung, Indonesia, by NewsRx correspondents, research stated, “The ability of the human eye to identify a crescent depends on its apparent object contrast versus the sky background, and inaccurate assessments are common when identifying it. The use of telescopes and cameras to monitor the crescent moon is becoming increasingly important as technology advances.” Funders for this research include Bandung Institute of Technology, Deputy for Strengthening Research and Development, Ministry of Research and Technology, Indonesia. Our news journalists obtained a quote from the research from the Bandung Institute of Technology, “Thus, in this study we developed an automated moon detection system with deep learning and integrated for the robotic telescope OZTALTS with an infrared camera. By utilizing a deep learning method called Mask R-CNN, we have created infrared camera software with the goal of identifying and recognizing the crescent moon. The result shows, a total of 3,202 manually annotated moon images were used for deeplearning- trained models. We tested several combinations of training hyperparameters and image distribution numbers. The results show that the crescent detection issue can be resolved using a Mask R-CNN. Using the top-performing Mask R-CNN configuration, the trained model achieved a mean averaged precision (mAP) at the intersection over union (IOU) of 0.5, with a 99% for the extreme condition of a young crescent concealed by clouds and a 99% for the normal case for each moon phase.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Studies Conducted at Technical University on Machine Learning Recently Published (Evaluating Different Deep Learning Approaches for Tree Health Classification Using High-Resolution Multispectral UAV Data in the Black Forest, Harz Region, and ...)

    73-73页
    查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news reporting from Wurzburg, Germany, by NewsRx journalists, research stated, “We present an evaluation of different deep learning and machine learning approaches for tree health classification in the Black Forest, the Harz Mountains, and the Gottinger Forest on a unique, highly accurate tree-level dataset. The multispectral UAV data were collected from eight forest plots with diverse tree species, mostly conifers.” Funders for this research include Federal Ministry of Research And Education. Our news journalists obtained a quote from the research from Technical University: “As ground truth data (GTD), nearly 1500 tree polygons with related attribute information on the health status of the trees were used. This data were collected during extensive fieldwork using a mobile application and subsequent individual tree segmentation. Extensive preprocessing included normalization, NDVI calculations, data augmentation to deal with the underrepresented classes, and splitting the data into training, validation, and test sets. We conducted several experiments using a classical machine learning approach (random forests), as well as different convolutional neural networks (CNNs)-ResNet50, ResNet101, VGG16, and Inception-v3-on different datasets and classes to evaluate the potential of these algorithms for tree health classification. Our first experiment was a binary classifier of healthy and damaged trees, which did not consider the degree of damage or tree species. The best results of a 0.99 test accuracy and an F1 score of 0.99 were obtained with ResNet50 on four band composites using the red, green, blue, and infrared bands (RGBI images), while VGG16 had the worst performance, with an F1 score of only 0.78. In a second experiment, we also distinguished between coniferous and deciduous trees. The F1 scores ranged from 0.62 to 0.99, with the highest results obtained using ResNet101 on derived vegetation indices using the red edge band of the camera (NDVIre images). Finally, in a third experiment, we aimed at evaluating the degree of damage: healthy, slightly damaged, and medium or heavily damaged trees.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Studies from Dalian University of Technology Update Current Data on Machine Learning (Analysis of Microplastics Release From Rice Package In Combination With Machine Learning and Hyperspectral Imaging Technique)

    74-74页
    查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news originating from Panjin, People’s Republic of China, by NewsRx correspondents, research stated, “Microplastics (MPs) release from rice package is an emerging issue since the ingestion of MPs might pose a serious threat to human health. However, the current methods for quantifying MPs in rice is laborious and time consuming.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from the Dalian University of Technology, “This study proposed a simple method to identify MPs in packaged rice, in combination of machine learning and hyperspectral image technology. The samples spectra demonstrate there are distinct differences between rice and MPs in near-infrared spectral region, and a support vector machine (SVM) model was developed to identify MPs, with an accurate rate ＞94.44%. Moreover, the developed model was applied to analyze the abundance of MPs release from rice package under simulated transportation conditions (e.g. transportation time, attrition rate, stackability pressure), demonstrating transportation conditions have an effect on the abundance of MPs release from rice package.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Researcher at Villanova University Reports Research in Machine Learning (AI Patent Approvals in Service Firms, Patent Radicalness, and Stock Market Reaction)

    75-75页
    查看更多>>摘要：A new study on artificial intelligence is now available. According to news reporting out of Villanova, Pennsylvania, by NewsRx editors, research stated, “Artificial intelligence (AI)-driven automation is of growing interest in the service sector.” Our news reporters obtained a quote from the research from Villanova University: “Using practice theory in service innovation and recombinant uncertainty frameworks, we ask whether AI patent approval for service firms is received positively by the stock market and whether patent radicalness strengthens or exacerbates the stock market reaction. We draw on 650 service industry firms from the years 1976 to 2019 with 133,813 non-AI patents and AI patents, including 7,543 (AI machine learning), 33,804 (AI hardware), and 53,419 (AI planning/control). The results show that the stock market reaction is positive for machine learning AI patents, and increasing radicalness strengthens the positive relationship; however, the reaction is negative to AI-related planning and control patents and increasing radicalness exacerbates the negative reaction. In addition, stock market reaction is insignificant to AI-related hardware patents and increasing radicalness does not influence this relationship. The findings are robust to excluding large firms representing a significant portion of the AI patents.”
      原文链接:
    • NETL
    • NSTL
    • Sage

    Tampere University Researcher Focuses on Machine Learning (High- Efficiency Compressor Trees for Latest AMD FPGAs)

    75-76页
    查看更多>>摘要：Current study results on artificial intelligence have been published. According to news reporting from Dresden, Germany, by NewsRx journalists, research stated, “High-fan-in dot product computations are ubiquitous in highly relevant application domains, such as signal processing and machine learning.” Our news editors obtained a quote from the research from Tampere University: “Particularly, the diverse set of data formats used in machine learning poses a challenge for flexible efficient design solutions. Ideally, a dot product summation is composed from a carry-free compressor tree followed by a terminal carrypropagate addition. On FPGA, these compressor trees are constructed from generalized parallel counters (GPCs) whose architecture is closely tied to the underlying reconfigurable fabric. This work reviews known counter designs and proposes new ones in the context of the new AMD Versal™ fabric. On this basis, we develop a compressor generator featuring variable-sized counters, novel counter composition heuristics, explicit clustering strategies, and case-specific optimizations like logic gate absorption.”
      原文链接:
    • NETL
    • NSTL

    Data on Machine Learning Described by Researchers at St Petersburg State University (Unsupervised Machine Learning Methods for Determining Special Points of the Polymerase Chain Reaction Fluorescence Accumulation Curve)

    76-77页
    查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting from St. Petersburg, Russia, by NewsRx journalists, research stated, “The polymerase chain reaction (PCR) method is a cyclic process based on the repeated copying of a certain fragment of DNA using enzymes in vitro. The main molecular mechanism of PCR is amplification, that is, the accumulation of copies of the selected nucleotide sequence.” The news correspondents obtained a quote from the research from St Petersburg State University, “A real-time polymerase chain reaction, which is one of the varieties of the PCR method, allows determining not only the presence of the target nucleotide sequence in the sample, but also measuring the number of its copies. The efficiency of a real-time polymerase chain reaction is characterized by the exponential section of the fluorescence accumulation curve (PCR kinetic curve). This curve consists of a baseline, an exponential phase and a plateau phase. Of both theoretical and practical interest is the analytical determination of the moments of the transition of the PCR kinetic curve from linear to exponential growth, and then reaching a plateau. Unsupervised machine learning methods can be used to solve this problem. If we consider amplification as a quasi-deterministic discrete random process, for which the fluorescence accumulation curves are monotonically increasing trajectories, then the moments of the transition from the baseline to the exponential phase and from the exponential phase to the plateau phase are trajectory anomalies.”
      原文链接:
    • NETL
    • NSTL
    • Pleiades Publishing Inc