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    Heilongjiang University of Chinese Medicine Reports Findings in Ovarian Cancer (Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis)

    1-2页
    查看更多>>摘要：2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Ovarian Cancer is the subject of a report. According to news reporting out of Harbin, People’s Republic of China, by NewsRx editors, research stated, “Machine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate.” Our news journalists obtained a quote from the research from the Heilongjiang University of Chinese Medicine, “This study aims to systematically review relevant literature on the predictive value of ma- chine learning for platinum-based chemotherapy responses in patients with ovarian cancer. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we system- atically searched the PubMed, Embase, Web of Science, and Cochrane databases for relevant studies on predictive models for platinum-based therapies for the treatment of ovarian cancer published before April 26, 2023. The Prediction Model Risk of Bias Assessment tool was used to evaluate the risk of bias in the included articles. Concordance index (C-index), sensitivity, and specificity were used to evaluate the performance of the prediction models to investigate the predictive value of machine learning for platinum chemotherapy responses in patients with ovarian cancer. A total of 1749 articles were examined, and 19 of them involving 39 models were eligible for this study. The most commonly used modeling methods were logistic regression (16/39, 41%), Extreme Gradient Boosting (4/39, 10%), and support vector machine (4/39, 10%). The training cohort reported C-index in 39 predictive models, with a pooled value of 0.806; the validation cohort reported C-index in 12 predictive models, with a pooled value of 0.831. Support vector machine performed well in both the training and validation cohorts, with a C-index of 0.942 and 0.879, respectively. The pooled sensitivity was 0.890, and the pooled specificity was 0.790 in the training cohort.”
      原文链接:
    • NETL
    • NSTL
    • Jmir Publications, Inc

    Keio University Researchers Release New Study Findings on Ma- chine Learning (Toward Building Trust in Machine Learning Models: Quantifying the Explainability by SHAP and References to Human Strategy)

    2-2页
    查看更多>>摘要：2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on artificial intelligence. According to news reporting out of Yokohama, Japan, by NewsRx editors, research stated, “Local model-agnostic Explainable Artificial Intelligence (XAI), such as LIME or SHAP, has recently gained popularity among researchers and data scientists for explaining black box Machine Learning (ML) models.” The news editors obtained a quote from the research from Keio University: “In the industry, practitioners focus not only on how these explanations can validate their models but also on how they can help maintain trust from end-users. Some studies attempted to measure this ability by quantifying what they refer to as the explainability or interpretability of ML models. In this paper, we introduce a new method for measuring explainability with reference to an approximated human model. We develop a human-friendly interface to strategically collect human decision-making and translate it into a set of logical rules and intuitions, or simply annotations. These annotations are then compared with the local explanations derived from common XAI tools. Through a human survey, we demonstrate that it is possible to quantify human intuition and empirically compare it to a given explanation, enabling a practical quantification of explainability.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Chinese Academy of Sciences Reports Findings in Machine Learn- ing (Identifying stress scores from gait biometrics captured using a camera: A cross-sectional study)

    3-3页
    查看更多>>摘要：2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “Stress is a critical risk factor for various health issues, but an objective, non-intrusive and effective measurement approach for stress has not yet been established. Gait, the pattern of movements in human locomotion, has been proven to be a valid behavioral indicator for recognizing various mental states in a convenient manner.” Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, “This study aims to identify the severity of stress by assessing human gait recorded through an objective, non- intrusive measurement approach. One hundred and fifty-two participants with an average age of 23 years old (SD = 1.07) were recruited. The Chinese version of the Perceived Stress Scale with 10 items (PSS- 10) was used to assess participants’ stress levels. The participants were then required to walk naturally while being recorded with a regular camera. A total of 1320 time-domain and 1152 frequency-domain gait features were extracted from the videos. The top 40 contributing features, confirmed by dimensionality reduction, were input into models consisting of four machine-learning regression algorithms (i.e., Gaussian Process Regressor, Linear Regression, Random Forest Regressor, and Support Vector regression), to assess stress levels. The models that combined time- and frequency-domain features performed best, with the lowest RMSE (4.972) and highest validation (r = 0.533). The Gaussian Process Regressor and Linear Regression outperformed the others. The greatest contribution to model performance was derived from gait features of the waist, hands, and legs. The severity of stress can be accurately detected by machine learning models using two-dimensional (2D) video-based gait data. The machine learning models used for assessing perceived stress were reliable.”
      原文链接:
    • NETL
    • NSTL

    Findings from University of Michigan Broaden Understanding of Cholesterol (Cross-sectional Association Between Blood Cholesterol and Calcium Levels In Genetically Diverse Strains of Mice)

    4-4页
    查看更多>>摘要：2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Cholesterol. According to news originating from Ann Arbor, Michigan, by NewsRx correspondents, research stated, “Genetically diverse outbred mice allow for the study of genetic variation in the context of high dietary and environmental control. Using a machine learning approach, we investigated clinical and morphometric factors that associate with serum cholesterol levels in 840 genetically unique Diversity Outbred mice of both sexes (n = 417 male and 423 female), and on both a control chow (% kcals in diet: protein 22%, carbohydrate 62%, fat 16%, no cholesterol) and high fat high sucrose (% kcals in diet: protein 15%, carbohydrate 41%, fat 45%, 0.05% cholesterol).” Financial supporters for this research include NIH National Institute of General Medical Sciences (NIGMS), National Institutes of Diabetes and Digestive Kidney Diseases (NIDDK), NIH National Institute of General Medical Sciences (NIGMS).
      原文链接:
    • NETL
    • NSTL
    • Wiley

    New Machine Learning Study Findings Have Been Reported by In- vestigators at University of Liverpool (Machine Learning-driven Op- timization of Ni-based Catalysts for Catalytic Steam Reforming of Biomass Tar)

    5-6页
    查看更多>>摘要：2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Machine Learning. According to news originating from Liverpool, United Kingdom, by NewsRx correspondents, research stated, “Biomass gasification is a promising process for producing syngas, which is widely used in various industrial processes. However, the presence of tar in syngas poses a significant challenge to biomass gasification due to the difficulties in its removal and potential downstream issues, such as clogging, slagging, and corrosion.” Funders for this research include European Union (EU), University of Liverpool, China Scholarship Council. Our news journalists obtained a quote from the research from the University of Liverpool, “Extensive efforts have been made to address this challenge through catalytic tar removal using various catalysts, generating a vast amount of experimental data. Processing this large dataset and gaining new insights into process optimization requires the development of efficient data analysis methods. In this study, a comprehensive database was built, encompassing a total of 584 data points and 14 input parameters collected from literature published between 2005 and 2020. Machine learning algorithms were then trained using this dataset to predict and optimize the catalytic steam reforming of biomass tar. The predicted results were found to agree well with the experimental data. The results show that the reaction temperature is the most important process parameter, with the highest relative importance of 0.24, followed by the support (0.16), additive (0.12), nickel (Ni) loading (0.08), and calcination temperature (0.07), among the 14 input parameters. This work has proposed optimal ranges for the reaction temperature (600-700 degrees C), Ni loading (5-15 wt%), and calcination temperature (500-650 degrees C). Furthermore, it was found that a larger specific surface area and higher Ni dispersion are two critical factors for selecting additives and supports.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Iran University of Science and Technology Researchers Publish New Data on Machine Learning (A General Study for the Complex Re- fractive Index Extraction Including Noise Effect Using a Machine Learning-Aided Method)

    6-6页
    查看更多>>摘要：2024 FEB 02 (NewsRx) – 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 Tehran, Iran, by NewsRx editors, research stated, “This article investigates the extraction of complex refractive indices from the amplitude and phase of the transmitted electric field. In the first step, an incident plane wave has been assumed and the amplitude and phase of the transmitted plane wave is calculated analytically.” The news journalists obtained a quote from the research from Iran University of Science and Technology: “In this calculation, different values of the complex refractive index have been assumed for the non-magnetic material under test. In fact, the real part and imaginary part of the refractive index are assumed in the range of [1-10] and [0-1], respectively. Furthermore, a general study is made by an assumption of the material thickness to simulation wavelength ratio in the range of [0.01-20]. Due to examining the measurement noise, noisy data are produced for different values of signal-to-noise ratio in the range of [25-40] dB. Due to the difficulties of estimating the refractive index confronted in the theoretical or iterative methods, a Long short-term memory (LSTM) network is proposed and used for the estimation of complex refractive index based on the amplitude and phase of the transmitted electric field. It is shown that the estimation accuracy of about 97% can be achieved in the trained network. Furthermore, the estimation accuracy as a function of thickness-to-wavelength ratio, signal-to-noise ratio, and the values of real and imaginary parts of the refractive index are studied in detail and shown that higher estimation accuracy can be achieved.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    University of Pisa Reports Findings in Central Nervous System Agents (Analgesic Strategies for Urologic Videolaparoscopic or Robotic Surgery in the Context of an Enhanced Recovery after Surgery Protocol: A Prospective Study Comparing Erector ...)

    7-7页
    查看更多>>摘要：2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Drugs and Therapies - Central Nervous System Agents is the subject of a report. According to news reporting from Pisa, Italy, by NewsRx journalists, research stated, “Regional anesthesia in postoperative pain management has developed in recent years, especially with the advent of fascial plane blocks. This study aims to compare the ultrasound-guided bilateral erector spinae plane block (ESPB) versus the ultrasound-guided bilateral transversus abdominis plane block (TAPB) on postoperative analgesia after laparoscopic or robotic urologic surgery.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Data on Machine Learning Described by Researchers at Shanghai University (Discovery and verification of two-dimensional organic- inorganic hybrid perovskites via diagrammatic machine learning model)

    8-8页
    查看更多>>摘要：2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artificial intelligence. According to news reporting out of Shanghai, People’s Republic of China, by NewsRx editors, research stated, “Two-dimensional (2D) organic-inorganic hybrid perovskites (OIHPs) have drawn increased attention due to rich physical properties such as ferroelectricity and photovoltaic properties.” The news journalists obtained a quote from the research from Shanghai University: “Nevertheless, it is challenging to discover novel 2D OIHPs within the vast chemical composition space. Herein, a diagrammatic machine learning model was employed to improve this issue. We collected 179 OIHPs with a variety of organic cations and screened out 6 features from 10,622 descriptors. Subsequently, a decision tree model was created to predict the dimensionality of OIHPs, achieving a LOOCV accuracy of 0.94 and a test accuracy of 0.89, respectively. Then, one candidate from a virtual space with 8256 samples was successfully synthesized, which was consistent with the prediction of the model. Finally, three rules were produced by visualization of the tree structure to generally discriminate 2D from non-2D OIHPs.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Research on Machine Learning Reported by Researchers at Korea Institute of Ocean Science and Technology (Predicting rapid in- tensification of tropical cyclones in the western North Pacific: a machine learning and net energy gain rate approach)

    8-9页
    查看更多>>摘要：2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on artificial intelligence. According to news reporting from Busan, South Korea, by NewsRx journalists, research stated, “In this study, a machine learning (ML)- based Tropical Cyclones (TCs) Rapid Intensification (RI) prediction model has been developed by using the Net Energy Gain Rate Index (NGR). This index realistically captures the energy exchanges between the ocean and the atmosphere during the intensification of TCs.” Our news correspondents obtained a quote from the research from Korea Institute of Ocean Science and Technology: “It does so by incorporating the thermal conditions of the upper ocean and using an accurate parameterization for sea surface roughness. To evaluate the effectiveness of NGR in enhancing prediction accuracy, five distinct ML algorithms were utilized: Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Feed-forward Neural Network. Two sets of experiments were performed for each algorithm. The first set used only traditional predictors, while the second set incorporated NGR. The outcomes revealed that models trained with the inclusion of NGR exhibited superior performance compared to those that only used traditional predictors. Additionally, an ensemble model was developed by utilizing a hard-voting method, combining the predictions of all five individual algorithms.”
      原文链接:
    • NETL
    • NSTL
    • Frontiers Media Sa

    Data on Robotics Described by Researchers at Georgia Institute of Technology (Asc: Adaptive Skill Coordination for Robotic Mobile Manipulation)

    9-10页
    查看更多>>摘要：2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Robotics. According to news reporting from Atlanta, Georgia, by NewsRx journalists, research stated, “We present Adaptive Skill Coordination (ASC) - an approach for accomplishing long-horizon tasks like mobile pick-and-place (i.e., navigating to an object, picking it, navigating to another location, and placing it). ASC consists of three components - (1) a library of basic visuomotor skills (navigation, pick, place), (2) a skill coordination policy that chooses which skill to use when, and (3) a corrective policy that adapts pre-trained skills in out-of-distribution states.” Financial support for this research came from Apple Scholars.
      原文链接:
    • NETL
    • NSTL
    • IEEE