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

    New Biomarkers Findings Has Been Reported by Investigators at University of Melb ourne (Suppressed Activity of the Rostral Anterior Cingulate Cortex As a Biomark er for Depression Remission)

    17-18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 Diagnostics and S creening - Biomarkers are discussed in a new report. According to news reporting originating from Melbourne, Australia, by NewsRx correspondents, research state d, “Suppression of the rostral anterior cingulate cortex (rACC) has shown promis e as a prognostic biomarker for depression. We aimed to use machine learning to characterise its ability to predict depression remission.” Funders for this research include National Health & Medical Resear ch Council (NHMRC) of Australia, National Health & Medical Researc h Council (NHMRC) of Australia.
      原文链接:
    • NETL
    • NSTL

    New Machine Learning Findings from University of New Hampshire Described (Multi- criteria Evaluation of Health News Stories)

    19-20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 Machine Learn ing have been published. According to news reporting originating from Durham, Ne w Hampshire, by NewsRx correspondents, research stated, “The proliferation of di gital and social media technologies has enabled quick and wide dissemination of news stories and press releases about new medical treatments. Evaluating these s tories is difficult for two reasons.” Our news editors obtained a quote from the research from the University of New H ampshire, “First, these stories are often not completely true or false. A nuance d approach that considers different aspects of these stories (e.g., the presence of inflated claims, suppression of risks associated with the treatment or withh olding other essential information) is more appropriate for evaluation. Second, evaluating the quality and completeness of the arguments in the stories is costl y and requires expertise in the relevant medical field, which laypeople do not h ave. To address this problem, in this study, we train different machine learning models on multi-criteria expert evaluations for health news stories about new m edical treatments and compare their performance. We then compare the machine lea rning model evaluations to laypeople evaluations. We find that machine learning models overall outperform laypeople, who have a propensity to overestimate the c omprehensiveness of the claims. Our machine learning models employ multi-criteri a evaluation, which is different from most previous studies that evaluate news s tories on whether they are true or false.”
      原文链接:
    • NETL
    • NSTL

    Shanghai Jiao Tong University School of Medicine Reports Findings in Artificial Intelligence (Leveraging Large Language Models for Improved Patient Access and S elf-Management: Assessor-Blinded Comparison Between Expert- and AI-Generated Con tent)

    23-24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 report. According to news reporting out of Shanghai, Peopl e’s Republic of China, by NewsRx editors, research stated, “While large language models (LLMs) such as ChatGPT and Google Bard have shown significant promise in various fields, their broader impact on enhancing patient health care access an d quality, particularly in specialized domains such as oral health, requires com prehensive evaluation. This study aims to assess the effectiveness of Google Bar d, ChatGPT-3.5, and ChatGPT-4 in offering recommendations for common oral health issues, benchmarked against responses from human dental experts.” Our news journalists obtained a quote from the research from the Shanghai Jiao T ong University School of Medicine, “This comparative analysis used 40 questions derived from patient surveys on prevalent oral diseases, which were executed in a simulated clinical environment. Responses, obtained from both human experts an d LLMs, were subject to a blinded evaluation process by experienced dentists and lay users, focusing on readability, appropriateness, harmlessness, comprehensiv eness, intent capture, and helpfulness. Additionally, the stability of artificia l intelligence responses was also assessed by submitting each question 3 times u nder consistent conditions. Google Bard excelled in readability but lagged in ap propriateness when compared to human experts (mean 8.51, SD 0.37 vs mean 9.60, S D 0.33; P=.03). ChatGPT-3.5 and ChatGPT-4, however, performed comparably with hu man experts in terms of appropriateness (mean 8.96, SD 0.35 and mean 9.34, SD 0. 47, respectively), with ChatGPT-4 demonstrating the highest stability and reliab ility. Furthermore, all 3 LLMs received superior harmlessness scores comparable to human experts, with lay users finding minimal differences in helpfulness and intent capture between the artificial intelligence models and human responses. L LMs, particularly ChatGPT-4, show potential in oral health care, providing patie nt-centric information for enhancing patient education and clinical care. The ob served performance variations underscore the need for ongoing refinement and eth ical considerations in health care settings.”
      原文链接:
    • NETL
    • NSTL

    New Artificial Intelligence Findings from University of South Carolina Reported [Neurosymbolic Value-inspired Artificial Intelligence (Why, W hat, and How)]

    24-25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 originating from Columbia, So uth Carolina, by NewsRx correspondents, research stated, “The rapid progression of artificial intelligence (AI) systems, facilitated by the advent of large lang uage models (LLMs), has resulted in their widespread application to provide huma n assistance across diverse industries. This trend has sparked significant disco urse centered around the ever-increasing need for LLM-based AI systems to functi on among humans as a part of human society.” Financial support for this research came from National Science Foundation (NSF).
      原文链接:
    • NETL
    • NSTL

    Chongqing University Reports Findings in Machine Learning (Functional near-infra red spectroscopy-based diagnosis support system for distinguishing between mild and severe depression using machine learning approaches)

    25-26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 from Chongqing, P eople’s Republic of China, by NewsRx correspondents, research stated, “Early dia gnosis of depression is crucial for effective treatment. Our study utilizes func tional near-infrared spectroscopy (fNIRS) and machine learning to accurately cla ssify mild and severe depression, providing an objective auxiliary diagnostic to ol for mental health workers.” Our news editors obtained a quote from the research from Chongqing University, “ Develop prediction models to distinguish between severe and mild depression usin g fNIRS data. We collected the fNIRS data from 140 subjects and applied a comple te ensemble empirical mode decomposition with an adaptive noise-wavelet threshol d combined denoising method (CEEMDAN-WPT) to remove noise during the verbal flue ncy task. The temporal features (TF) and correlation features (CF) from 18 prefr ontal lobe channels of subjects were extracted as predictors. Using recursive fe ature elimination with cross-validation, we identified optimal TF or CF and exam ined their role in distinguishing between severe and mild depression. Machine le arning algorithms were used for classification. The combination of TF and CF as inputs for the prediction model yielded higher classification accuracy than usin g either TF or CF alone. Among the prediction models, the SVM-based model demons trates excellent performance in nested cross-validation, achieving an accuracy r ate of 92.8%.”
      原文链接:
    • NETL
    • NSTL

    Stanford University Reports Findings in Machine Learning (Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance)

    26-27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 Stanford, California, by NewsRx journalists, research stated, “Computationally predicting the performanc e of catalysts under reaction conditions is a challenging task due to the comple xity of catalytic surfaces and their evolution in situ, different reaction paths , and the presence of solid-liquid interfaces in the case of electrochemistry. W e demonstrate here how relatively simple machine learning models can be found th at enable prediction of experimentally observed onset potentials.” The news correspondents obtained a quote from the research from Stanford Univers ity, “Inputs to our model are comprised of data from the oxygen reduction reacti on on non-precious transition-metal antimony oxide nanoparticulate catalysts wit h a combination of experimental conditions and computationally affordable bulk a tomic and electronic structural descriptors from density functional theory simul ations. From human-interpretable genetic programming models, we identify key exp erimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. We finally v alidate these machine learning predictions by experimentally confirming that sca ndium as a dopant in nickel antimony oxide leads to a desired onset potential in crease.”
      原文链接:
    • NETL
    • NSTL

    Zhejiang University Researchers Highlight Research in Robotics (CORMAND2: A Dece ption Attack Against Industrial Robots)

    29-29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 robotics have been pr esented. According to news reporting from Hangzhou, People’s Republic of China, by NewsRx journalists, research stated, “Industrial robots are becoming increasi ngly vulnerable to cyber incidents and attacks, particularly with the dawn of th e Industrial Internet-of-Things (IIoT).” The news correspondents obtained a quote from the research from Zhejiang Univers ity: “To gain a comprehensive understanding of these cyber risks, vulnerabilitie s of industrial robots were analyzed empirically, using more than three million communication packets collected with testbeds of two ABB IRB120 robots and five other robots from various original equipment manufacturers (OEMs). This analysis , guided by the confidentiality-integrity-availability (CIA) triad, uncovers rob ot vulnerabilities in three dimensions: confidentiality, integrity, and availabi lity. These vulnerabilities were used to design Covering Robot Manipulation via Data Deception (CORMAND2), an automated cyber-physical attack against industrial robots. CORMAND2 manipulates robot operation while deceiving the Supervisory Co ntrol and Data Acquisition (SCADA) system that the robot is operating normally b y modifying the robot’s movement data and data deception. CORMAND2 and its capab ility of degrading the manufacturing was validated experimentally using the afor ementioned seven robots from six different OEMs.”
      原文链接:
    • NETL
    • NSTL

    Ankara University Reports Findings in Artificial Intelligence (Accuracy and usab ility of artificial intelligence chatbot generated chemotherapy protocols)

    31-31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 report. According to news reporting from Ankara, Turkey, b y NewsRx journalists, research stated, “Medical practitioners are increasingly u sing artificial intelligence (AI) chatbots for easier and faster access to infor mation. To our knowledge, the accuracy and availability of AI-generated chemothe rapy protocols has not yet been studied.” The news correspondents obtained a quote from the research from Ankara Universit y, “Nine simulated cancer patient cases were designed and AI chatbots, ChatGPT v ersion 3.5 (OpenAI) and Bing (Microsoft), were used to generate chemotherapy pro tocols for each case. Generated chemotherapy protocols were compared with the or iginal protocols for nine simulated cancer patients. ChatGPT’s overall performan ce was 5 out of 9 on protocol generation, and Bing’s was 4 out of 9; this was st atistically nonsignificant (p = 1). AI chatbots show both potential and limitati ons in generating chemotherapy protocols.”
      原文链接:
    • NETL
    • NSTL

    Studies from Wenzhou Medical University Provide New Data on Machine Learning (En hancing Vault Prediction and Icl Sizing Through Advanced Machine Learning Models )

    32-32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 Machine Learning. According to news reporting from Wenzhou, People’s Republic of China, by NewsRx journalists, research stated, “To use artificial intelligence (AI) technology to accurately predict vault and Implantable Collamer Lens (ICL) size. The methodology focused on enhancing predictive capabilities through the f usion of machine -learning algorithms.” Funders for this research include State Administration of Traditional Chinese Me dicine Science and Technology Department, Zhejiang Provincial Administration of Traditional Chinese Medicine Coconstruction Science and Technology Plan, Scienc e and Technology Program of Traditional Chinese Medicine in Zhejiang Province, S cience and Technology Plan Project of Wenzhou Science and Technology Bureau.
      原文链接:
    • NETL
    • NSTL

    Reports Summarize Machine Translation Study Results from Minzu University of Chi na (Information Dropping Data Augmentation for Machine Translation Quality Estim ation)

    33-33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 Machine Translation. According to news reporting originating from Beijing, Peopl e’s Republic of China, by NewsRx correspondents, research stated, “Machine trans lation quality estimation (QE) refers to the quality assessment of machine trans lations without a given reference translation. Supervised QE models based on neu ral networks have achieved state-of-the-art results.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC).
      原文链接:
    • NETL
    • NSTL