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    Studies from Hong Kong University of Science and Technology Reveal New Findings on Machine Learning (Diurnal Carbon Monoxide Retrieval from FY-4B/GIIRS Using a Novel Machine Learning Method)

    57-58页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news reporting out of Hong Kong, People's Republ ic of China, by NewsRx editors, research stated, "Carbon monoxide (CO) is one of the primary reactive trace gases in the Earth's atmosphere and plays an importa nt role in atmospheric chemistry." Financial supporters for this research include Hong Kong Research Grants Council ; Guangdong Provincial Department of Science And Technology; Strategic Priority Research Program of The Chinese Academy of Sciences.
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    Findings from University of North Carolina Chapel Hill Broaden Understanding of Machine Learning (Estimating Classification Consistency of Machine Learning Mode ls for Screening Measures)

    58-59页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting out of Chapel Hill, North Carolina, by NewsRx editors, research stated, "This article illustrates novel quantitativ e methods to estimate classification consistency in machine learning models used for screening measures. Screening measures are used in psychology and medicine to classify individuals into diagnostic classifications." Financial supporters for this research include UNC Ann Rankin Cowan Excellence A ward, NIH National Institute on Drug Abuse (NIDA), NIH National Institute on Dru g Abuse (NIDA), NIH National Institute on Alcohol Abuse & Alcoholi sm (NIAAA), NIH National Institute of Mental Health (NIMH).
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    Research on Machine Learning Published by a Researcher at Iowa State University (A Sequence-Based Hybrid Ensemble Approach for Estimating Trail Pavement Roughne ss Using Smartphone and Bicycle Data)

    59-60页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news originating from Ames, Iowa, by NewsRx correspondents, research stated, "Trail pavement roughness significan tly impacts user experience and safety." Funders for this research include Des Moines Area Metropolitan Planning Organiza tion. Our news correspondents obtained a quote from the research from Iowa State Unive rsity: "Measuring roughness over large areas using traditional equipment is chal lenging and expensive. The utilization of smartphones and bicycles offers a more feasible approach to measuring trail roughness, but the current methods to capt ure data using these have accuracy limitations. While machine learning has the p otential to improve accuracy, there have been few applications of real-time roug hness evaluation. This study proposes a hybrid ensemble machine learning model t hat combines sequence-based modeling with support vector regression (SVR) to est imate trail roughness using smartphone sensor data mounted on bicycles. The hybr id model outperformed traditional methods like double integration and whole-body vibration in roughness estimation. For the 0.031 mi (50 m) segments, it reduced RMSE by 54-74% for asphalt concrete (AC) trails and 50-59% for Portland cement concrete (PCC) trails."
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    Investigators from University of Stellenbosch Report New Data on Machine Learnin g (Mapping Soil Thickness By Accounting for Right-censored Data With Survival Pr obabilities and Machine Learning)

    60-61页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news reporting originating from Stellenbosch, South Afr ica, by NewsRx correspondents, research stated, "In digital soil mapping, modell ing soil thickness poses a challenge due to the prevalent issue of right-censore d data. This means that the true soil thickness exceeds the depth of sampling, a nd neglecting to account for the censored nature of the data can lead to poor mo del performance and underestimation of the true soil thickness." Financial support for this research came from National Research Foundation - Sou th Africa.
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    Reports Outline Machine Learning Study Results from Chengdu University of Inform ation Technology (Change in Fractional Vegetation Cover and Its Prediction durin g the Growing Season Based on Machine Learning in Southwest China)

    61-62页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news originating from Chengdu , People's Republic of China, by NewsRx correspondents, research stated, "Fracti onal vegetation cover (FVC) is a crucial indicator for measuring the growth of s urface vegetation. The changes and predictions of FVC significantly impact biodi versity conservation, ecosystem health and stability, and climate change respons e and prediction." Our news editors obtained a quote from the research from Chengdu University of I nformation Technology: "Southwest China (SWC) is characterized by complex topogr aphy, diverse climate types, and rich vegetation types. This study first analyze d the spatiotemporal variation of FVC at various timescales in SWC from 2000 to 2020 using FVC values derived from pixel dichotomy model. Next, we constructed f our machine learning models-light gradient boosting machine (LightGBM), support vector regression (SVR), * * k* * -nearest neighbor (KNN), and ridge regression (RR)-along with a weighted average heterogeneous ensemble model (WAHEM) to predi ct growing-season FVC in SWC from 2000 to 2023. Finally, the performance of the different ML models was comprehensively evaluated using tenfold cross-validation and multiple performance metrics. The results indicated that the overall FVC in SWC predominantly increased from 2000 to 2020. Over the 21 years, the FVC spati al distribution in SWC generally showed a high east and low west pattern, with e xtremely low FVC in the western plateau of Tibet and higher FVC in parts of east ern Sichuan, Chongqing, Guizhou, and Yunnan. The determination coefficient * * R * * 2 scores from tenfold cross-validation for the four ML models indicated that Ligh tGBM had the strongest predictive ability whereas RR had the weakest. WAHEM and LightGBM models performed the best overall in the training, validation, and test sets, with RR performing the worst. The predicted spatial change trends were co nsistent with the MODIS-MOD13A3-FVC and FY3D-MERSI-FVC, although the predicted F VC values were slightly higher but closer to the MODIS-MOD13A3-FVC. The feature importance scores from the LightGBM model indicated that digital elevation model (DEM) had the most significant influence on FVC among the six input features. I n contrast, soil surface water retention capacity (SSWRC) was the most influenti al climate factor."
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    Studies from Pontifical Catholic University in the Area of Machine Learning Desc ribed (Craving for a Robust Methodology: a Systematic Review of Machine Learning Algorithms On Substance-use Disorders Treatment Outcomes)

    62-63页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Machine Learn ing. According to news reporting originating from Porto Alegre, Brazil, by NewsR x correspondents, research stated, "Substance use disorders (SUDs) pose signific ant mental health challenges due to their chronic nature, health implications, i mpact on quality of life, and variability of treatment response. This systematic review critically examines the application of machine learning (ML) algorithms in predicting and analyzing treatment outcomes in SUDs." Financial supporters for this research include Aarhus Universitet, Conselho Naci onal de Desenvolvimento Cientifico e Tecnologico (CNPQ), Coordenacao de Aperfeic oamento de Pessoal de Nivel Superior (CAPES), National Institutes of Health (NIH ) - USA. Our news editors obtained a quote from the research from Pontifical Catholic Uni versity, "Conducting a thorough search across PubMed, Embase, Scopus, and Web of Science, we identified 28 studies that met our inclusion criteria from an initi al pool of 362 articles. The MI-CLand CHARMS instruments were utilized for metho dological quality and bias assessment. Reviewed studies encompass an array of SU Ds, mainly opioids, cocaine, and alcohol use, predicting outcomes such as treatm ent adherence, relapse, and severity assessment. Our analysis reveals a signific ant potential of ML models in enhancing predictive accuracy and clinical decisio n-making in SUD treatment. However, we also identify critical gaps in methodolog ical consistency, transparency, and external validation among the studies review ed."
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    New Findings from Complutense University Madrid in the Area of Artificial Intell igence Described (Isee: a Case-based Reasoning Platform for the Design of Explan ation Experiences)

    63-64页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Artificial Intelligence are presented in a new report. According to news reporting out of Madrid, Spain, by NewsRx editors, research stated, "Explainable Artificial Intelligence (XAI) is an emerging field within Artificial Intelligence (AI) that has provided many methods that enable humans to understand and interpret the outcomes of AI system s. However, deciding on the best explanation approach for a given AI problem is currently a challenging decision-making task." Funders for this research include ISee project, Engineering & Phys ical Sciences Research Council (EPSRC), Irish Research Council for Science, Engi neering and Technology, Science Foundation Ireland, European Union (EU), MCIN/AE I, Marie Curie Actions. Our news journalists obtained a quote from the research from Complutense Univers ity Madrid, "This paper presents the iSee project, which aims to address some of the XAI challenges by providing a unifying platform where personalized explanat ion experiences are generated using Case-Based Reasoning. An explanation experie nce includes the proposed solution to a particular explainability problem and it s corresponding evaluation, provided by the end user."
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    New Machine Learning Study Findings Reported from Chinese Academy of Sciences [High-resolution ocean color reconstruction and analysis focusing on Kd490 via ma chine learning model integration of MODIS and Sentinel-2 (MSI)]

    64-65页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting out of Shanghai, People's R epublic of China, by NewsRx editors, research stated, "Oceanic water quality mon itoring is essential for environmental protection, resource management, and ecos ystem vitality. Optical remote sensing from space plays a pivotal role in global surveillance of oceanic water quality." Funders for this research include National Natural Science Foundation of China; Youth Innovation Promotion Association of The Chinese Academy of Sciences; Shang hai Rising-star Program. Our news journalists obtained a quote from the research from Chinese Academy of Sciences: "However, the spatial resolution of current ocean color data products falls short of scrutinizing intricate small-scale marine features. This study in troduces a hybrid model that fuses MODIS (Moderate Resolution lmaging Spectrorad iometer) ocean color products with Sentinel-2 ‘s remote sensing reflectance data to generate high-resolution ocean color imagery, specifically investigating the diffuse attenuation coefficient at a wavelength of 490 nm (Kd490). To address t he intricacies of coastal environments, we propose two complementary strategies to improve the accuracy of inversion. The first strategy leverages MODIS ocean c olor products alongside a geographic segmentation model to perform distinct inve rsions for separate marine zones, enhancing spatial resolution and specificity i n coastal regions. The second strategy bolsters model interpretability during tr aining by integrating predictions from conventional physical models into a Rando m Forest-based Regression Ensemble (RFRE) model. This study focuses on the coast al regions surrounding the Beibu Gulf, near Hainan Island in China. Our findings exhibit a strong concordance with MODIS products, achieving a monthly average c oefficient of determination (R²) of 0.90, peaking at 0.97, and sustaining a mont hly average root-mean-square error (RMSE) of less than 0.02."
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    University of Guadalajara Researcher Describes Recent Advances in Robotics (Inve rse Kinematics of Robotic Manipulators Based on Hybrid Differential Evolution an d Jacobian Pseudoinverse Approach)

    65-66页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on robotics have bee n published. According to news originating from Jalisco, Mexico, by NewsRx corre spondents, research stated, "Robot manipulators play a critical role in several industrial applications by providing high precision and accuracy." The news correspondents obtained a quote from the research from University of Gu adalajara: "To perform these tasks, manipulator robots require the effective com putation of inverse kinematics. Conventional methods to solve IK often encounter significant challenges, such as singularities, non-linear equations, and poor g eneralization across different robotic configurations. In this work, we propose a novel approach to solve the inverse kinematics (IK) problem in robotic manipul ators using a metaheuristic algorithm enhanced with a Jacobian step."
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    King Saud University Researchers Add New Study Findings to Research in Artificia l Intelligence (Artificial intelligence literacy among university students-a com parative transnational survey)

    66-66页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news reporting from Riyadh, Saudi Arabi a, by NewsRx journalists, research stated, "Artificial intelligence (AI) literac y is a crucial aspect of media and information literacy (MIL), regarded not only as a human right but also as a fundamental requirement for societal advancement and stability." Our news journalists obtained a quote from the research from King Saud Universit y: "This study aimed to provide a comprehensive, cross-border perspective on AI literacy levels by surveying 1,800 university students from four Asian and Afric an nations. The findings revealed significant disparities in AI literacy levels based on nationality, scientific specialization, and academic degrees, while age and gender did not show notable impacts. Malaysian participants scored signific antly higher on the AI literacy scale than individuals from other countries. The results indicated that various demographic and academic factors influenced resp ondents' perceptions of AI and their inclination to utilize it. Nationality and academic degree were identified as the most influential factors, followed by sci entific specialization, with age and gender exerting a lesser influence. The stu dy highlights the necessity of focusing research efforts on the detailed dimensi ons of the AI literacy scale and examining the effects of previously untested in tervening variables."
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