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    Data on Machine Learning Reported by Researchers at Singidunum University (Optimizing Machine Learning for Space Weather Forecasting and Event Classification Using Modified Metaheuristics)

    28-29页
    查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting originating from Belgrade, Serbia, by NewsRx correspondents, research stated, "Space weather profoundly impacts Earth and its surrounding space environment, necessitating improved prediction to safeguard critical infrastructure such as communication and satellites. Solar flares can disrupt communications and pose radiation risks to airline passengers." Financial support for this research came from Ministarstvo Prosvete, Nauke i Tehnoloscaron;kog Razvoja.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Findings from Massachusetts Institute of Technology Yields New Data on Machine Translation (Machine Translation Between Bigsmiles Line Notation and Chemical Structure Diagrams)

    29-30页
    查看更多>>摘要：Data detailed on Machine Translation have been presented. According to news reporting originating in Cambridge, Massachusetts, by NewsRx journalists, research stated, "The representation of chemical structure forms a core component of polymer science, yet the chemical structure diagrams used to convey such information lack the machine processability vital for automating analysis, managing abundant data, and harnessing the potential of informatics. On the other hand, the usage of BigSMILES language & horbar;a machine-readable representation of polymer chemical structure & horbar;requires specialized knowledge of its grammar and syntax." Financial supporters for this research include International Business Machines (IBM), National Science Foundation Innovation and Technology Ecosystems, International Business Machines (IBM), Haley Beech.
      原文链接:
    • NETL
    • NSTL
    • Amer Chemical Soc

    Researchers' Work from University of Science and Technology of China Focuses on Machine Learning (Monitoring seismicity in the southern Sichuan Basin using a machine learning workflow)

    30-31页
    查看更多>>摘要：Researchers detail new data in artificial intelligence. According to news reporting from Hefei, People's Republic of China, by NewsRx journalists, research stated, "Monitoring seismicity in real time provides significant benefits for timely earthquake warning and analyses." Financial supporters for this research include National Key Research And Development Program of China. Our news editors obtained a quote from the research from University of Science and Technology of China: "In this study, we propose an automatic workflow based on machine learning (ML) to monitor seismicity in the southern Sichuan Basin of China. This workflow includes coherent event detection, phase picking, and earthquake location using three-component data from a seismic network. By combining PhaseNet, we develop an ML-based earthquake location model called PhaseLoc, to conduct real-time monitoring of the local seismicity. The approach allows us to use synthetic samples covering the entire study area to train PhaseLoc, addressing the problems of insufficient data samples, imbalanced data distribution, and unreliable labels when training with observed data. We apply the trained model to observed data recorded in the southern Sichuan Basin, China, between September 2018 and March 2019. The results show that the average differences in latitude, longitude, and depth are 5.7 km, 6.1 km, and 2 km, respectively, compared to the reference catalog."
      原文链接:
    • NETL
    • NSTL

    First Affiliated Hospital of Wannan Medical College Reports Findings in Machine Learning (Application of machine learning-based multi-sequence MRI radiomics in diagnosing anterior cruciate ligament tears)

    31-32页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating in Anhui, People's Republic of China, by NewsRx journalists, research stated, "To compare the diagnostic power among various machine learning algorithms utilizing multi-sequence magnetic resonance imaging (MRI) radiomics in detecting anterior cruciate ligament (ACL) tears. Additionally, this research aimed to create and validate the optimal diagnostic model." Financial support for this research came from Major Project of Scientific Research Project of Provincial Education Department of Anhui Province.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Texas A&M University Reports Findings in Artificial Intelligence [Impact of road transport system on groundwater quality inferred from eXplainable Artificial Intelligence (XAI)]

    32-33页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting out of College Station, Texas, by NewsRx editors, research stated, "Road networks constitute a vital component of modern society, facilitating rapid transportation and driving economic activities by enabling the smooth movement of goods and people. However, the expansion of road systems carries significant environmental considerations, particularly regarding its impact on groundwater quality." Our news journalists obtained a quote from the research from Texas A&M University, "Thus, it is crucial to understand the complex relationship between groundwater quality and the road traffic system. This paper aims to identify the impact of road transport systems on groundwater quality using a data-driven approach. Specifically, road network and groundwater chemistry data in Texas were obtained from an open data portal. This study was carried out in two phases: the explainable artificial intelligence (XAI) modeling phase and the multivariate analysis phase. In the XAI modeling phase, a prediction model was developed using eXtreme Gradient Boosting (XGB), with groundwater chemistry parameters as output features and road transport attributes as input features, i.e., elevation, annual average daily traffic, distance, lane-miles, speed limit and well depth. Furthermore, the relationships between groundwater chemistry parameters and road transport attributes were examined using feature importance and accumulated local effect (ALE). In the multivariate phase, Piper diagrams and principal component analysis (PCA) were utilized to identify the source of the selected groundwater chemistry parameters from the XAI models. The results of the prediction model showed that five groundwater chemistry parameters were significantly impacted by road transport systems with below a MAPE of 0.20, including, pH, temperature, aluminum (Al), bicarbonate (HCO), and alkalinity. Additionally, XAI models were developed to understand the relationship between the road transport attributes on five selected parameters. The findings collectively indicated that the Texas groundwater qualities are greatly impacted by road transport systems within a distance of 50-meters and a well depth of 100-meters."
      原文链接:
    • NETL
    • NSTL

    Researcher from University of Washington Reports Details of New Studies and Findings in the Area of Machine Learning (Machine Learning-Based Flood Forecasting System for Window Cliffs State Natural Area, Tennessee)

    33-34页
    查看更多>>摘要：Fresh data on artificial intelligence are presented in a new report. According to news originating from Seattle, Washington, by NewsRx editors, the research stated, "The prevalence of unforeseen floods has heightened the need for more accurate flood simulation and forecasting models." Funders for this research include Center For The Management, Utilization, Protection of Water Resources (Tntech Water Center) At The Tennessee Technological University.
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Johannes Kepler University Researchers Detail Research in Machine Learning (Polyolefin ductile-brittle transition temperature predictions by machine learning)

    34-35页
    查看更多>>摘要：Researchers detail new data in artificial intelligence. According to news reporting out of Linz, Austria, by NewsRx editors, research stated, "Polymers show a transition from ductile-to brittle fracture behavior at decreasing temperatures." Our news editors obtained a quote from the research from Johannes Kepler University: "Consequently, the material toughness has to be determined across wide temperature ranges in order to determine the Ductile-Brittle Transition Temperature This usually necessitates multiple impact experiments. We present a machine-learning methodology for the prediction of DBTTs from single Instrumented Puncture Tests Our dataset consists of 7,587 IPTs that comprise 181 Polyethylene and Polypropylene compounds. Based on a combination of feature engineering and Principal Component Analysis, relevant information of instrumentation signals is extracted. The transformed data is explored by unsupervised machine learning algorithms and is used as input for Random Forest Regressors to predict DBTTs. The proposed methodology allows for fast screening of new materials. Additionally, it offers estimations of DBTTs without thermal specimen conditioning."
      原文链接:
    • NETL
    • NSTL
    • Frontiers Media Sa

    Researchers from University of Toronto Describe Findings in Robotics (Moss: Monocular Shape Sensing for Continuum Robots)

    35-36页
    查看更多>>摘要：Data detailed on Robotics have been presented. According to news reporting from Mississauga, Canada, by NewsRx journalists, research stated, "Continuum robots are promising candidates for interactive tasks in medical and industrial applications due to their unique shape, compliance, and miniaturization capability. Accurate and real-time shape sensing is essential for such tasks yet remains a challenge." Financial support for this research came from CGIAR. The news correspondents obtained a quote from the research from the University of Toronto, "Embedded shape sensing has high hardware complexity and cost, while vision-based methods require stereo setup and struggle to achieve real-time performance. This letter proposes a novel eye-to-hand monocular approach to continuum robot shape sensing. Utilizing a deep encoder-decoder network, our method, MoSSNet, eliminates the computation cost of stereo matching and reduces requirements on sensing hardware. In particular, MoSSNet comprises an encoder and three parallel decoders to uncover spatial, length, and contour information from a single RGB image, and then obtains the 3D shape through curve fitting. A two-segment tendon-driven continuum robot is used for data collection and testing, demonstrating accurate (mean shape error of 0.91 mm, or 0.36% of robot length) and real-time (70 fps) shape sensing on real-world data."
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Report Summarizes Robotics and Automation Study Findings from Polytechnic University Torino (Jist: Joint Image and Sequence Training for Sequential Visual Place Recognition)

    36-37页
    查看更多>>摘要：A new study on Robotics - Robotics and Automation is now available. According to news originating from Turin, Italy, by NewsRx correspondents, research stated, "Visual Place Recognition aims at recognizing previously visited places by relying on visual clues, and it is used in robotics applications for SLAM and localization. Since typically a mobile robot has access to a continuous stream of frames, this task is naturally cast as a sequence-to-sequence localization problem." Financial support for this research came from Consorzio Interuniversitario Nazionale per l#x0027;Informatica. Our news journalists obtained a quote from the research from Polytechnic University Torino, "Nevertheless, obtaining sequences of labelled data is much more expensive than collecting isolated images, which can be done in an automated way with little supervision. As a mitigation to this problem, we propose a novel Joint Image and Sequence Training (JIST) protocol that leverages large uncurated sets of images through a multi-task learning framework. With JIST we also introduce SeqGeM, an aggregation layer that revisits the popular GeM pooling to produce a single robust and compact embedding from a sequence of single-frame embeddings."
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Study Findings from University of Anbar Update Knowledge in Engineering (A Comparative Performance Analysis of Malware Detection Algorithms Based on Various Texture Features and Classifiers)

    37-37页
    查看更多>>摘要：A new study on engineering is now available. According to news reporting originating from the University of Anbar by NewsRx correspondents, research stated, "Three frequent factors such as low classification accuracy, computational complexity, and resource consumption have an impact on malware evaluation methods." Funders for this research include Office of The Associate Provost For Research, United Arab Emirates (Uae) University.
      原文链接:
    • NETL
    • NSTL
    • IEEE