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    Researchers from Nanjing University of Information Science and Technology (NUIST ) Report New Studies and Findings in the Area of Machine Learning (Uncertainty F orecasting System for Tropical Cyclone Tracks Based On Conformal Prediction)

    87-87页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learn ing have been published. According to newsreporting out of Nanjing, People’s Re public of China, by NewsRx editors, research stated, “Tropicalcyclone tracks ar e one of the most critical factors in tropical cyclone forecasting, but there is an inherentuncertainty in their forecasts, but there are no relevant machine l earning methods to carry out uncertaintystudies. This study proposes an uncerta inty forecasting system using machine learning models within aconformal forecas ting framework, aiming to provide reliable forecast regions to improve the abili ty ofdecision makers to prepare for and respond to potential hazards.”

    Researchers from National Atmospheric Research Laboratory Report on Findings in Machine Learning (Nowcasting of Storms Using Predicted Integrated Water Vapor Wi th a Machine Learning Technique and Satellite Brightness Temperature)

    88-88页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Fresh data on Machine Learning are pre sented in a new report. According to newsreporting originating in Gadanki, Indi a, by NewsRx journalists, research stated, “A hybrid model fornowcasting of sto rms has been developed by employing predicted integrated water vapor (IWV) with lightgradient boosting machine (GBM) on Global Navigation Satellite System (GNS S) receiver data collectedat Gadanki (13.46 degrees N, 79.17 degrees E) and est imated thresholds for three storm predictors. Theutilization of predicted IWV a llows more lead time for disaster preparedness.”

    Studies from Harbin Institute of Technology in the Area of Machine Learning Desc ribed (The Structural Evolution of Sibcnzr Amorphous Ceramics Analyzed By Machin e Learning Potential)

    89-89页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – A new study on Machine Learning is now available. According to news originating fromHeilongjiang, People’s Republic o f China, by NewsRx correspondents, research stated, “In the presentstudy, a sui table machine learning potential for SiBCNZr amorphous materials was used in cla ssicalmolecular dynamics (CMD) for creating the large-scale SiBCNZr amorphous m aterials with 3375 atoms.This study focused on the effects of Zr on the amorpho us structure and atomic diffusion of SiBCNmaterials.”

    Researchers from Hebei Normal University Report Findings in Support Vector Machi nes (Workpiece Classification Based On Transfer Component Analysis)

    90-90页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Support Vector Machine s is the subject of a report. According to newsreporting originating from Hebei , People’s Republic of China, by NewsRx correspondents, research stated,“Classi fication algorithm is the crucial technique for machine vision system. The Trans fer ComponentAnalysis has been introduced for workpiece classification system.”

    Investigators from Johns Hopkins University Report New Data on Artificial Intell igence (Neurodynamical Computing At the Information Boundaries of Intelligent Sy stems)

    91-91页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Data detailed on Artificial Intelligen ce have been presented. According to newsreporting originating in Baltimore, Ma ryland, by NewsRx journalists, research stated, “Artificial intelligencehas not achieved defining features of biological intelligence despite models boasting m ore parameters thanneurons in the human brain. In this perspective article, we synthesize historical approaches to understandingintelligent systems and argue that methodological and epistemic biases in these fields can be resolved byshif ting away from cognitivist brain-as-computer theories and recognizing that brain s exist within large,interdependent living systems.”

    Yunnan Cancer Hospital Reports Findings in Hypertrophic Cardiomyopathy (Machine learning and experimental validation of novel biomarkers for hypertrophic cardio myopathy and cancers)

    92-93页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Heart Disorders and Di seases - Hypertrophic Cardiomyopathy is thesubject of a report. According to ne ws reporting from Yunnan, People’s Republic of China, by NewsRxjournalists, res earch stated, “Hypertrophic cardiomyopathy (HCM) is a hereditary cardiac disorde r markedby anomalous thickening of the myocardium, representing a significant c ontributor to mortality. Whilethe involvement of immune inflammation in the dev elopment of cardiac ailments is well-documented, itsspecific impact on HCM path ogenesis remains uncertain.”

    Data on Artificial Intelligence Detailed by Researchers at Macquarie University (Navigating Career Stages In the Age of Artificial Intelligence: a Systematic In terdisciplinary Review and Agenda for Future Research)

    93-93页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Data detailed on Artificial Intelligen ce have been presented. According to newsreporting out of Macquarie Park, Austr alia, by NewsRx editors, research stated, “As artificial intelligence(AI) use e xpands within organizations, its influence is increasingly permeating careers an d vocationaldomains. However, there is a notable lack of structured insights re garding AI’s role in shaping individualcareer paths across career stages.”

    Reports Outline Robotics Study Results from University of Putra Malaysia (Nsga-I ii Algorithm for Optimizing Robot Collaborative Task Allocation In the Internet of Things Environment)

    94-94页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – A new study on Robotics is now available. Accordi ng to news reporting originating from Serdang,Malaysia, by NewsRx correspondent s, research stated, “To improve the performance of intelligentproducts and reas onably distribute the load of the loading robot, a multi-objective, and multi-ob jective(Traveling-salesman-problem, TSP) mathematical model was established. A genetic algorithm based onspeed invariant and the elite algorithm is proposed t o solve the multi-TSP assignment problem.”

    Guizhou Provincial People’s Hospital Reports Findings in Breast Cancer (Decipher ing breast cancer prognosis: a novel machine learning-driven model for vascular mimicry signature prediction)

    95-96页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Oncology - Breast Canc er is the subject of a report. Accordingto news reporting out of Guizhou, Peopl e’s Republic of China, by NewsRx editors, research stated, “Inthe ongoing battl e against breast cancer, a leading cause of cancer-related mortality among womenglobally, the urgent need for innovative prognostic markers and therapeutic tar gets is undeniable. Thisstudy pioneers an advanced methodology by integrating m achine learning techniques to unveil a vascularmimicry signature, offering pred ictive insights into breast cancer outcomes.”

    Researchers at Tsinghua University Have Reported New Data on Robotics (Two-stage Task Allocation for Multiple Construction Robots Using an Improved Genetic Algo rithm)

    96-96页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – Researchers detail new data in Robotics. Accordin g to news reporting from Beijing, People’sRepublic of China, by NewsRx journali sts, research stated, “The construction industry is facing seriouschallenges, s uch as labor shortage and safety hazards. Construction robots can perform danger ous andrepetitive tasks instead of workers.”