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    Study Results from University of Cadi Ayyad Provide New Insights into Machine Le arning (Assessing the influence of different Synthetic Aperture Radar parameters and Digital Elevation Model layers combined with optical data on the identifica tion ...)

    95-96页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting out of the University of Cadi Ayyad by NewsRx editors, research stated, “Forest resource conservation necessi tates a deeper understanding of forest ecosystem processes and how future manage ment decisions and climate change may affect these processes. Argania spinosa (L .) Skeels is one of the most popular species in Morocco.” Our news correspondents obtained a quote from the research from University of Ca di Ayyad: “Despite its ability to survive under harsh drought, it is endangered due to soil land removal and a lack of natural regeneration. Remote sensing offe rs a powerful resource for mapping, assessing, and monitoring the forest tree sp ecies at high spatio-temporal resolution. Multi-spectral Sentinel-2 and Syntheti c Aperture Radar (SAR) time series combined with Digital Elevation Model (DEM) o ver the Argan forest in Essaouira province, Morocco, were subjected to pixel-bas ed machine learning classification and analysis. We investigated the influence o f different SAR data parameters and DEM layers on the performance of machine lea rning algorithms. In addition, we evaluated the synergistic effects of integrati ng remote sensing data, including optical, SAR, and DEM data, for identifying ar gan trees in the Smimou area. We collected data from Sentinel-2, Sentinel-1, SRT M DEM, and ground truth sources to achieve our goal. Testing different SAR param eters and integrating DEM layers of different resolutions with other remote sens ing data showed that the Lee Sigma filter with a size of 11 x 11 and a DEM layer of 30 m resolution gave the best results using the Support Vector Machine algor ithm. Significant improvements in overall accuracy (OA) and kappa index (K) were observed in the following phase. After applying a smoothing technique, the comb ined use of two Sentinel constellation products improved map accuracy and qualit y.”
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    Data from Yellow River Conservancy Technical Institute Advance Knowledge in Mach ine Learning (Research on Damage Detection of Civil Structures Based on Machine Learning of Multiple Vegetation Index Time Series)

    96-96页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on artificial in telligence have been published. According to news reporting from Kaifeng, People ’s Republic of China, by NewsRx journalists, research stated, “On the basis of a nalyzing the natural frequency of the structure, the identification quantity of each process is constructed with modal parameters and input into the machine lea rning as characteristic parameters to realize the damage identification.” The news journalists obtained a quote from the research from Yellow River Conser vancy Technical Institute: “By extracting the median curve of vegetation index t ime series after 5G filtering in the damaged area of typical civil structures, a nd comparing it with the actual growth curve of crops in the area, the vegetatio n index time series monitoring model was constructed, and 10 was selected as the best threshold. The accuracy of the result is verified, and the iteration time is 0.18 hours. A damage detection method based on machine learning is proposed.”
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    Findings from Swiss Federal Institute of Technology Lausanne Has Provided New Da ta on Robotics (A Lunar Reconnaissance Drone for Cooperative Exploration and Hig h-resolution Mapping of Extreme Locations)

    97-97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 Robotics. According to news reporting out of Neuchatel, Switzerland, by NewsRx e ditors, research stated, “An efficient characterization of scientifically signif icant locations is essential prior to the return of humans to the Moon. The high est resolution imagery acquired from orbit of south-polar shadowed regions and o ther relevant locations remains, at best, an order of magnitude larger than the characteristic length of most of the robotic systems to be deployed.” Our news journalists obtained a quote from the research from the Swiss Federal I nstitute of Technology Lausanne, “This hinders the planning and successful imple mentation of prospecting missions and poses a high risk for the traverse of robo ts and humans, diminishing the potential overall scientific and commercial retur n of any mission. We herein present the design of a lightweight, compact, autono mous, and reusable lunar reconnaissance drone capable of assisting other ground- based robotic assets, and eventually humans, in the characterization and high-re solution mapping (similar to 0.1 m/px) of particularly challenging and hardto-ac cess locations on the lunar surface. The proposed concept consists of two main s ubsystems: the drone and its service station. With a total combined wet mass of 100 kg, the system is capable of 11 flights without refueling the service statio n, enabling almost 9 km of accumulated flight distance.”
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    Investigators from Wake Forest University Release New Data on Machine Learning ( Employing Machine Learning To Enhance Fracture Recovery Insights Through Gait An alysis)

    98-99页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting out of Winston-Salem, North Carolina , by NewsRx editors, research stated, “This study aimed to explore the potential of gait analysis coupled with supervised machine learning models as a predictiv e tool for assessing post-injury complications such as infection, malunion, or h ardware irritation among individuals with lower extremity fractures. We prospect ively identified participants with lower extremity fractures at a tertiary acade mic center.” Funders for this research include AO North America, AO North America via the AO Strategy Fund.
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    Researcher from University of South Florida Provides Details of New Studies and Findings in the Area of Artificial Intelligence (Clinical Applications of Machin e Learning)

    99-99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 originating from Tampa, Florida, b y NewsRx editors, the research stated, “This review introduces interpretable pre dictive machine learning approaches, natural language processing, image recognit ion, and reinforcement learning methodologies to familiarize end users.” Our news editors obtained a quote from the research from University of South Flo rida: “As machine learning, artificial intelligence, and generative artificial i ntelligence become increasingly utilized in clinical medicine, it is imperative that end users understand the underlying methodologies. This review describes pu blicly available datasets that can be used with interpretable predictive approac hes, natural language processing, image recognition, and reinforcement learning models, outlines result interpretation, and provides references for in-depth inf ormation about each analytical framework. This review introduces interpretable p redictive machine learning models, natural language processing, image recognitio n, and reinforcement learning methodologies.”
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    Reports Outline Robotics Study Findings from University of Manchester (Decentral ized Deconfliction of Aerial Robots In High Intensity Traffic Structures)

    100-101页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Robotics have been published. According to news reporting from Manchester, United Kingdom, by NewsRx journalists, research stated, “Projections for future air mobility envis age intensely utilized airspace that does not simply scale up from existing syst ems with centralized air traffic control. This paper considers the implementatio n and test of a software and hardware framework for decentralized control of aer ial vehicles within intensely used airspace.”Funders for this research include Engineering & Physical Sciences Research Council (EPSRC), Engineering & Physical Sciences Research Council (EPSRC).
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    Aalto University Reports Findings in Machine Learning (Automated Structure Disco very for Scanning Tunneling Microscopy)

    100-100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 Machine Learning is th e subject of a report. According to news reporting from Espoo, Finland, by NewsR x journalists, research stated, “Scanning tunneling microscopy (STM) with a func tionalized tip apex reveals the geometric and electronic structures of a sample within the same experiment. However, the complex nature of the signal makes imag es difficult to interpret and has so far limited most research to planar samples with a known chemical composition.” The news correspondents obtained a quote from the research from Aalto University , “Here, we present automated structure discovery for STM (ASD-STM), a machine l earning tool for predicting the atomic structure directly from an STM image, by building upon successful methods for structure discovery in noncontact atomic fo rce microscopy (nc-AFM). We apply the method on various organic molecules and ac hieve good accuracy on structure predictions and chemical identification on a qu alitative level while highlighting future development requirements for ASD-STM. This method is directly applicable to experimental STM images of organic molecul es, making structure discovery available for a wider scanning probe microscopy a udience outside of nc-AFM.”
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    Ministry of Agriculture and Rural Affairs Reports Findings in Machine Learning ( Explainable machine learning for predicting the geographical origin of Chinese O ysters via mineral elements analysis)

    101-102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 Machine Learning is th e subject of a report. According to news reporting originating in Qingdao, Peopl e’s Republic of China, by NewsRx journalists, research stated, “The traceability of geographic origin is essential for guaranteeing the quality, safety, and pro tection of oyster brands. However, the current outcomes of traceability lack cre dibility as they do not adequately explain the model’s predictions.” The news reporters obtained a quote from the research from the Ministry of Agric ulture and Rural Affairs, “Consequently, we conducted a study to evaluate the ef ficacy of utilizing explainable machine learning combined with mineral elements analysis. The study findings revealed that 18 elements have the ability to deter mine regional orientation. Simultaneously, individuals should pay closer attenti on to the potential risks associated with oyster consumption due to the regional differences in essential and toxic elements they contain. Light gradient boosti ng machine (LightGBM) model exhibited indistinguishable performance, achieving f lawless accuracy, precision, recall, F1 score and AUC, with values of 96.77% , 96.43%, 98.53%, 97.32% and 0.998, resp ectively. The SHapley Additive exPlanations (SHAP) method was used to evaluate t he output of the LightGBM model, revealing differences in feature interactions a mong oysters from different provinces. Specifically, the features Na, Zn, V, Mg, and K were found to have a significant impact on the predictive process of the model.”
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    Findings on Machine Learning Detailed by Investigators at Southwestern Universit y of Finance and Economics (Mean-variance Efficient Large Portfolios: a Simple M achine Learning Heuristic Technique Based On the Two-fund Separation Theorem)

    102-103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 Machine Learning is th e subject of a report. According to news reporting originating in Chengdu, Peopl e’s Republic of China, by NewsRx journalists, research stated, “We revisit in th is article the Two-Fund Separation Theorem as a simple technique for the Mean-Va riance optimization of large portfolios.” The news reporters obtained a quote from the research from the Southwestern Univ ersity of Finance and Economics, “The proposed approach is fast and scalable and provides equivalent results of commonly used ML techniques but, with computing time differences counted in hours (1 min vs. several hours). In the empirical ap plication, we consider three geographic areas (China, US, and French stock marke ts) and show that the Two-Fund Separation Theorem holds exactly when no constrai nts are imposed and is approximately true with (realistic) positive constraints on weights.”
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    Reports Summarize Machine Learning Study Results from University of British Colu mbia [Hybrid Machine Learning Model and Predictive Equations for Compressive Stress-strain Constitutive Modelling of Confined Ultra-high-perf ormance Concrete (Uhpc) ...]

    103-104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 from Kelowna, Canada, by NewsRx jou rnalists, research stated, “Stress-strain constitutive model for confined ultra- high-performance concrete (UHPC) plays a pivotal role in the design and modellin g of UHPC structures. However, while extensive research exists on the stress-str ain responses of unconfined UHPC, a notable dearth of studies focuses on the str ess-strain constitutive model for confined UHPC.” Funders for this research include Kon Kast Concrete Products Inc, Mitacs through the Accelerate grant.
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