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    New Machine Learning Study Findings Have Been Reported by Investigators at University of Bremen (Chained Machine Learning Model for Predicting Load Capacity and Ductility of Steel Fiberreinforced Concrete Beams)

    67-68页
    查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting originating in Bremen, Germany, by NewsRx journalists, research stated, "One of the main issues associated with steel fiber-reinforced concrete (SFRC) beams is the ability to anticipate their flexural response. With a comprehensive grid search, several stacked models (i.e., chained, parallel) consisting of various machine learning (ML) algorithms and artificial neural networks (ANNs) were developed to predict the flexural response of SFRC beams." Funders for this research include University of Bremen, National Research Foundation of Korea, Ministry of Science & ICT (MSIT), Republic of Korea, Yonsei University. The news reporters obtained a quote from the research from the University of Bremen, "The flexural performance of SFRC beams under bending was assessed based on 193 experimental specimens from reallife beam models. The ML techniques were applied to predict SFRC beam responses to bending load as functions of the steel fiber properties, concrete elastic modulus, beam dimensions, and reinforcement details. The accuracy of the models was evaluated using the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of actual versus predicted values. The findings revealed that the proposed technique exhibited notably superior performance, delivering faster and more accurate predictions compared to both the ANNs and parallel models. Shapley diagrams were used to analyze variable contributions quantitatively. Shapley values show that the chained model prediction of ductility index is highly affected by two other targets (peak load and peak deflection) that show the chained algorithm utilizing the prediction of previous steps for enhancing the prediction of the target feature."
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Study Results from Polytechnic University Torino Broaden Understanding of Machine Learning (A Scalable Approach For Automating Scan-to-bim Processes in The Heritage Field)

    68-69页
    查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news reporting out of Torino, Italy, by NewsRx editors, research stated, "In recent years, the demand for flexible and sustainable strategies in digitization processes has represented a significant challenge for the heritage documentation research community." Our news reporters obtained a quote from the research from Polytechnic University Torino: "In particular, the tasks of parametric modelling and AI-based semantic enrichment operations, necessary but traditionally time-consuming, is extremely onerous from a user-oriented perspective. Many efforts of the research community have been dedicated to enhancing efficiency through automation, and one of the possible solutions is represented by the employment of machine learning strategies. This study introduces an innovative methodology that integrates Visual Programming Language platforms and 3D Python libraries, thereby implementing the Scan-to-BIM approach. Two case studies - characterized by varying scales, resolutions, and accuracies - have been analysed to validate the proposed pipeline, demonstrating its flexibility and scalability across architectural objects and archaeological assets belonging to museum collections. The workflow involves several steps, starting from classified 3D and 2D data segmented using machine learning techniques with the aim of managing semantically enriched reality-based data in BIM/HBIM environment without scarifying accuracy criteria. Results highlight the methodology's efficiency and adaptability in diverse contexts, offering a compelling alternative to labour-intensive Scan-to-BIM processes."
      原文链接:
    • NETL
    • NSTL

    New Findings on Robotics Described by Investigators at University of South Carolina (Epistemic Agency In Preservice Teachers' Science Lessons With Robots)

    69-70页
    查看更多>>摘要：Researchers detail new data in Robotics. According to news reporting from Columbia, South Carolina, by NewsRx journalists, research stated, "Science teachers have been urged to use emerging technologies, such as robots, in ways that empower K-12 students as active participants responsible for their learning and knowledge development within the scientific domain. And yet, little is known about whether the use of robots effectively supports students' epistemic agency in science learning." Financial support for this research came from National Science Foundation (NSF). The news correspondents obtained a quote from the research from the University of South Carolina, "The purpose of this qualitative case study was to investigate to what extent elementary preservice teachers use educational robots in ways that promote epistemic agency in science lessons. Seven data sources were gathered for this study: individual reflections about lesson planning and lesson design, team reflection about teaching with robots, robotics-enhanced science lessons, posters, video-recorded presentations about designed lessons, and participant interview. A framework of epistemic practices for science inquiry was adopted to analyze the data followed by qualitative thematic analysis. Results indicate that the use of robots in science lessons promotes content assimilation rather than self-driven inquiry, robot movement rather than evidence drives science explanations, science activities with robots are situated in a social vacuum, and robot assembly and programming are underutilized in the lessons."
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Reports from University of Cadiz Add New Data to Findings in Artificial Intelligence (Supporting Skill Assessment In Learning Experiences Based On Serious Games Through Process Mining Techniques)

    70-71页
    查看更多>>摘要：A new study on Machine Learning - Artificial Intelligence is now available. According to news reporting out of Puerto Real, Spain, by NewsRx editors, research stated, "Learning experiences based on serious games are employed in multiple contexts. Players carry out multiple interactions during the gameplay to solve the different challenges faced." Financial support for this research came from MCIN/AEI. Our news journalists obtained a quote from the research from the University of Cadiz, "Those interactions can be registered in logs as large data sets providing the assessment process with objective information about the skills employed. Most assessment methods in learning experiences based on serious games rely on manual approaches, which do not scalewell when the amount of data increases. We propose an automated method to analyse students' interactions and assess their skills in learning experiences based on serious games. The method takes into account not only the final model obtained by the student, but also the process followed to obtain it, extracted from game logs. The assessment method groups students according to their in-game errors and ingame outcomes. Then, the models for the most and the least successful students are discovered using process mining techniques. Similarities in their behaviour are analysed through conformance checking techniques to compare all the students with the most successful ones. Finally, the similarities found are quantified to build a classification of the students' assessments. We have employed this method with Computer Science students playing a serious game to solve design problems in a course on databases."
      原文链接:
    • NETL
    • NSTL
    • Univ Int Rioja-Unir

    New Machine Learning Findings Reported from University of Nebraska (A Machine Learning-based Probabilistic Approach for Irrigation Scheduling)

    71-72页
    查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting originating from Lincoln, Nebraska, by NewsRx correspondents, research stated, "Accurate prediction of irrigation requirements ensures that water is applied only when necessary, reducing wastage and conserving this precious resource. This study provides a probabilistic framework for determining the irrigation requirements of crops, referred to as the Irrigation Factor (IF)." Our news editors obtained a quote from the research from the University of Nebraska, "IF was calculated based on three indicators - soil moisture (SM), leaf area index (LAI), and evapotranspiration (ET). Irrigation requirement is determined based on a three-step methodology. First, relevant variables for each indicator are identified using a Random Forest regressor, followed by the development of a Long Short-Term Memory (LSTM) model to predict the three indicators. Second, errors in the simulation are calculated for each indicator by comparing the predicted and actual values in the historical time period, which are then used to calculate the error weights (normalized) of the three indicators for each month to also capture the seasonal variations. Third, we calculate the lower and upper limits by adding the error values (5th and 95th percentiles) to a simulated value. Using these values, we determine the mean, minimum, and maximum levels of irrigation requirement based on the levels' threshold values. To determine the final levels of irrigation requirement at a daily time scale, we multiply the calculated levels (mean, minimum, and maximum) for each indicator by their respective weights. The outcome derived from the test case indicated that while certain variables exhibited no demand for water, there was a necessity for irrigation in other cases, and vice versa. This holistic approach to irrigation scheduling helps to ensure that plants receive adequate water while minimizing water wastage and promoting sustainability. It is especially valuable for agricultural operations, where optimizing water usage is essential economically and environmentally."
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Findings from Mayo Clinic Broaden Understanding of Machine Learning (Novel Machine Learning Model To Improve Performance of an Early Warning System In Hospitalized Patients: a Retrospective Multisite Cross-validation Study)

    72-73页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting out of Rochester, Minnesota, by NewsRx editors, research stated, "Threshold-based early warning systems (EWS) are used to predict adverse events (Aes). Machine learning (ML) algorithms that incorporate all EWS scores prior to an event may perform better in hospitalized patients." Our news journalists obtained a quote from the research from Mayo Clinic, "The deterioration index (DI) is a proprietary EWS. A threshold of DI ＞60 is used to predict a composite AE: all-cause mortality, cardiac arrest, transfer to intensive care, and evaluation by the rapid response team in practice. The DI scores were collected for adult patients (＞= 18 y-o) hospitalized on medical or surgical services during 8-23-2021 to 3-31-2022 from four different Mayo Clinic sites in the United States. A novel ML model was developed and trained on a retrospective cohort of hospital encounters. DI scores were represented in a high -dimensional space using random convolution kernels to facilitate training of a classifier and the area under the receiver operator characteristics curve (AUC) was calculated. Multiple time intervals prior to an AE were analyzed. A leave-one-out cross-validation protocol was used to evaluate performance across separate clinic sites. Three different classifiers were trained on 59,617 encounter-derived DI scores in highdimensional feature space and the AUCs were compared to two threshold models. All three tested classifiers improved the AUC over the threshold approaches from 0.56 and 0.57 to 0.76, 0.85 and 0.94. Time interval analysis of the top performing classifier showed best accuracy in the hour before an event occurred (AUC 0.91), but prediction held up even in the 12 h before an AE (AUC 0.80 at minus 12 h, 0.81 at minus 9 h, 0.85 at minus 6 h, and 0.88 at minus 3 h before an AE). Multisite cross-validation using leave-one-out approach on data from four different clinical sites showed broad generalization performance of the top performing ML model with AUC of 0.91, 0.91, 0.95, and 0.91. A novel ML model that incorporates all the longitudinal DI scores prior to an AE in a hospitalized patient performs better at outcome prediction than the currently used threshold model."
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Recent Findings from University of Belgrade Highlight Research in Artificial Intelligence (Implementing artificial intelligence tools for risk management in software projects)

    73-74页
    查看更多>>摘要：Current study results on artificial intelligence have been published. According to news reporting originating from Belgrade, Serbia, by NewsRx correspondents, research stated, "In recent years, there has been a significant surge in interest in the incorporation of artificial intelligence (AI) within the field of software engineering." The news reporters obtained a quote from the research from University of Belgrade: "This phenomenon can be attributed to the fact that AI has become ubiquitous and increasingly accessible, thereby finding effective application across various pivotal facets of software systems. Its contribution extends not only to the creation of novel functionalities but also to the enhancement of existing processes within software projects, often resulting in substantially improved adaptability to specific user requirements. Within this paper, we provide an overview of the application of AI tools within one of the critical domains of software project management - risk management. To achieve this objective, a bibliometric analysis of literature pertaining to risk management in software projects employing AI tools has been conducted. The primary aim of this study is to identify and analyze key trends, authors, journals, and keywords within this multidisciplinary domain, in order to gain a better understanding of the progress and relevance of research concerning risk management in software projects utilizing AI tools. The methodology encompasses a review of pertinent databases and the identification of relevant publications using keywords associated with software projects, risk management, and artificial intelligence."
      原文链接:
    • NETL
    • NSTL

    Recent Findings in Machine Learning Described by Researchers from Mbarara University of Science and Technology (A Review on Automated Detection and Assessment of Fruit Damage Using Machine Learning)

    74-75页
    查看更多>>摘要：Research findings on artificial intelligence are discussed in a new report. According to news reporting out of Mbarara, Uganda, by NewsRx editors, research stated, "Automation improves the quality of fruits through quick and accurate detection of pest and disease infections, thus contributing to the country's economic growth and productivity." Financial supporters for this research include Ademnea Project Under Norhed.. Our news reporters obtained a quote from the research from Mbarara University of Science and Technology: "Although humans can identify the fruit damage caused by pests and diseases, the methods used are inconsistent, time-consuming, and variable. The surface features of fruits typically observed by consumers who seek their health benefits affect their market value. The issue of pest and disease infections further deteriorates fruits' quality, becoming a mounting stressor on farmers since they reduce the potential revenue from fruit production, processing, and export. This article reviews various studies on detecting and classifying damages in fruits. Specifically, we review articles where state-of-the-art approaches under segmentation, image processing, machine learning, and deep learning have proved effective in developing automated systems that address hurdles associated with manual methods of assessing damage using visual experiences. This survey reviews thirty-two journal and conference papers from the past thirteen years that were found electronically through Google Scholar, Scopus, IEEE, ScienceDirect, and standard online searches."
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Researchers at National Institute of Technology Puducherry Release New Data on Machine Learning (Composition and Source Based Aerosol Classification Using Machine Learning Algorithms)

    75-76页
    查看更多>>摘要：Investigators discuss new findings in Machine Learning. According to news reporting originating from Karaikal, India, by NewsRx correspondents, research stated, "The aerosol particles present in the atmospheric region mainly affect the climate radiative forcing directly by scattering & absorbing the sunlight. Also, it indirectly influences the formation of clouds, precipitation and acts as a considerable uncertainty in assessing Earth's radiation budget." Our news editors obtained a quote from the research from the National Institute of Technology Puducherry, "Determination of aerosol type is significant in characterizing the aerosol role in the atmospheric processes, feedback, and climate models. This paper proposes two aerosol classification models, one based on the source and another based on the composition, to classify the aerosols using aerosol optical properties. The source based aerosol classification method helps to identify the sources which cause pollution in a particular region. Based on the results, proper control measures can be taken to reduce pollution. The composition based aerosol classification helps to identify the nature of aerosol types, such as absorbing or non-absorbing. This classification helps to study the climate of the Kanpur region. The aerosol data is taken from AERONET (AErosol RObotic NETwork) for the period 2002-2018 for the Kanpur region. The composition based aerosol classification model uses Single Scattering Albedo (SSA), Angstrom Exponent (AE), and Fine Mode Fraction (FMF) parameters to categorize aerosols based on their composition. The source based aerosol classification model classifies the aerosols based on values of AE and Aerosol Optical Depth (AOD) and describes the source of the aerosol particles. Knowledge of aerosol sources and compositions helps execute policies or controls to reduce aerosol concentrations. Machine learning algorithms, Nai center dot ve Bayes, K Nearest Neighbor, Decision Tree, Support Vector Machine, and Random Forest are used to validate classification schemes. The performance analysis of machine learning algorithms is compared using ten different metrics, and the results are also compared with the existing aerosol classification models. The results of the classification show that the source based aerosols of the desert and arid background and the composition based aerosols of types, Mixture Absorbing, Coarse absorbing (Dust), and Black Carbon are dominant over the Kanpur region during the study period considered. The Number of non -absorbing (scattering) type aerosols are least in the study region considered during the study period at all the seasons."
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Research from Imperial College London Provides New Study Findings on Artificial Intelligence (Audio Explainable Artificial Intelligence: A Review)

    76-76页
    查看更多>>摘要：A new study on artificial intelligence is now available. According to news reporting originating from London, United Kingdom, by NewsRx correspondents, research stated, "Artificial intelligence (AI) capabilities have grown rapidly with the introduction of cutting-edge deep-model architectures and learning strategies." The news correspondents obtained a quote from the research from Imperial College London: "Explainable AI (XAI) methods aim to make the capabilities of AI models beyond accuracy interpretable by providing explanations. The explanations are mainly used to increase model transparency, debug the model, and justify the model predictions to the end user. Most current XAI methods focus on providing visual and textual explanations that are prone to being present in visual media. However, audio explanations are crucial because of their intuitiveness in audio-based tasks and higher expressiveness than other modalities in specific scenarios, such as when understanding visual explanations requires expertise."
      原文链接:
    • NETL
    • NSTL