首页期刊导航|Robotics & Machine Learning Daily News
期刊信息/Journal information
Robotics & Machine Learning Daily News
NewsRx
Robotics & Machine Learning Daily News

NewsRx

Robotics & Machine Learning Daily News/Journal Robotics & Machine Learning Daily News
正式出版
收录年代

    Data from Ton Duc Thang University Advance Knowledge in Robotics (Complete Cover age Planning Using Deep Reinforcement Learning for Polyiamonds-based Reconfigura ble Robot)

    30-31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 Robotics have been published. According to news reportingfrom Ho Chi Minh City, Vietnam, by NewsRx journalists, research stated, “Achieving complete coveragein complex are as is a critical objective for tilling tasks such as cleaning, painting, mainten ance, andinspection. However, existing robots in the market, with their fixed m orphologies, face limitations whenit comes to accessing confined spaces.”

    Recent Findings in Support Vector Machines Described by Researchers from Guizhou Normal University [Novel Imbalanced Multiclass Fault Diagno sis Method Using Transfer Learning and Oversampling Strategies-based Multi-layer Support Vector Machines …]

    31-32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 Support Vector Machines is now available. According to news reportingfrom Guizhou, People’s Republic o f China, by NewsRx journalists, research stated, “For health monitoringand faul t diagnosis of critical mechanical system components, historical data related to equipment failuresare often limited and exhibit varying imbalanced multi-class characteristics (e.g., with noisy and time-seriesdata). Moreover, fault diagno sis frameworks based on traditional resampling algorithms (e.g., SMOTE)mostly h eavily rely on manual feature extraction, making them difficult to adapt to dive rse workingconditions or objects.”

    University of Michigan Researcher Details New Studies and Findings in the Area o f Artificial Intelligence (Artificial Intelligence and Strategic Decision-Making : Evidence from Entrepreneurs and Investors)

    32-33页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators discuss new findings in artificial intelligence. According to news originatingfrom the University of Mi chigan by NewsRx editors, the research stated, “This paper explores how artificial intelligence (AI) may impact the strategic decision-making (SDM) process in f irms.”

    Investigators at Lulea University of Technology Report Findings in Machine Learn ing (Real-time In-situ Coatings Corrosion Monitoring Using Machine Learning-enha nced Triboelectric Nanogenerator)

    33-34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 tonews originating from Lulea, Sweden, by Ne wsRx correspondents, research stated, “Current methods formonitoring coating co rrosion are limited by their inability to provide real-time data and dependence onexternal power sources. This study presents a novel in-situ corrosion monitor ing system using a solid-liquidtriboelectric nanogenerator (TENG) that converts mechanical energy into electrical signals for selfpoweredsensing.”

    Researchers from Norwegian University of Science and Technology (NTNU) Describe Research in Intelligent Systems (Enhancing misogyny detection in bilingual texts using explainable AI and multilingual fine-tuned transformers)

    34-35页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Data detailed on intelligent systems h ave been presented. According to news reportingfrom Norwegian University of Sci ence and Technology (NTNU) by NewsRx journalists, researchstated, “Gendered dis information undermines women’s rights, democratic principles, and national security by worsening societal divisions through authoritarian regimes’ intentional w eaponization of social media.Online misogyny represents a harmful societal issu e, threatening to transform digital platforms intoenvironments that are hostile and inhospitable to women.”

    Report Summarizes Machine Learning Study Findings from Department of Geography ( Assessing Critical Flood-prone Districts and Optimal Shelter Zones In the Brahma putra Valley: Strategies for Effective Flood Risk Management)

    35-36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 Gauhati, Indi a, by NewsRx journalists, research stated, “Frequent flooding hasbecome a persi stent issue in floodplain regions, causing significant disasters during each rai ny seasondue to insufficient disaster management planning. This study proposes a methodology to prioritize floodsusceptibility areas at the district level and identify suitable sites for flood shelters using a combination ofmachine learn ing algorithms and multi-criteria analysis, supported by geospatial technology.”

    New Machine Learning Study Findings Have Been Reported by Researchers at Univers ity of Georgia (Development and Validation of Machine-learning Models for Monito ring Individual Behaviors In Group-housed Broiler Chickens)

    36-37页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on Ma chine Learning. According to news reportingoriginating in Athens, Georgia, by N ewsRx journalists, research stated, “Animals’ individual behavioris commonly mo nitored by live or video observation by a person. This can be labor intensive an dinconsistent.”

    Uppsala University Researcher Provides New Study Findings on Artificial Intellig ence (A comparison of artificial intelligence-enhanced electrocardiography appro aches for prediction of time-to-mortality using electrocardiogram images)

    37-38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 from Uppsala Uni versity by NewsRx correspondents, research stated, “Most artificialintelligence -enhanced ECG (AI-ECG) models used to predict adverse events including death req uire thatthe ECGs be stored digitally. However, the majority of clinical facili ties worldwide store ECGs as images.”

    Report Summarizes Machine Learning Study Findings from Tecnalia Research & Innovation (On the Use of Machine Learning for Predicting Femtosecond Laser Groo ves In Tribological Applications)

    38-39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 newsoriginating from Donostia San Sebasti an, Spain, by NewsRx correspondents, research stated, “Femtosecondlaser surface texturing is gaining increased interest for optimizing tribological behaviour. However, the lasersurface texturing parameter selection is often conducted thro ugh time-consuming and inefficient trial-anderrorprocesses.”

    Studies Conducted at Southwest Jiaotong University on Machine Learning Recently Reported (Reliable Simulation Analysis for Hightemperature Inrush Water Hazard Based On the Digital Twin Model of Tunnel Geological Environment)

    39-40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 reportingoriginating in Sichuan, People’s Republi c of China, by NewsRx journalists, research stated, “In complexmountainous terr ains, tunnel construction faces unique challenges from high-temperature water in rushhazards, a systemic risk arising from the interplay of stress, seepage, and temperature fields. Traditionalsimulation methods, focusing on isolated disast er scenarios, fall short in addressing the multifaceted natureof these risks du e to geological ambiguity and data incompleteness.”