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    New Findings from National Center for Scientific Research (CNRS) in the Area of Machine Learning Described (Machine Learning- Based Modeling of Hot Carrier Injec tion in 40 nm CMOS Transistors)

    57-57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 reporting originating from Montpel lier, France, by NewsRx correspondents, research stated, “This paper presents a machine-learning-based approach for the degradation modeling of hot carrier inje ction in metaloxide- semiconductor field-effect transistors (MOSFETs).” Financial supporters for this research include Technological Research Council of Turkey Through Project Tub itak 1001. The news reporters obtained a quote from the research from National Center for S cientific Research (CNRS): “Stress measurement data have been employed at variou s stress conditions of both n- and p- MOSFETs with different channel geometries. Gaussian process regression algorithm is preferred to model the post-stress char acteristics of the drain-source current, the threshold voltage, and the drain-so urce conductance. The model outcomes have been compared with the actual measurem ents, and the accuracy of the generated models has been demonstrated across the test data by providing the appropriate statistics metrics. Finally, case studies of degradation estimation have been considered involving the usage of machine-l earning-based models on transistors with different channel geometries or subject ed to distinct stress conditions.”
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    Researchers from Beihang University Report Findings in Machine Learning (Develop ment of a Novel Continuum Damage Mechanicsbased Machine Learning Approach for V ibration Fatigue Assessment of Fastener Clip Subjected To High-frequency Vibrati on)

    59-59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 in Beijing, Peo ple’s Republic of China, by NewsRx journalists, research stated, “This paper pro poses a novel method based on continuum damage mechanics (CDM) and machine learn ing (ML) models to evaluate the vibration fatigue behavior of W1-type railway fa stener clips subjected to high-frequency vibration. Firstly, static and fatigue tests are conducted on 60Si2Mn spring steel to acquire elastic modulus, tensile strength, and P-S-N curves.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), China Academy of Railway Sciences Co., Ltd..
      原文链接:
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    Findings from Suez University Provide New Insights into Machine Learning (Improv ing Energy Efficiency In Ammonia Production Plants Using Machine Learning)

    60-60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 from Suez, Egypt, by NewsRx journalists, research stated, “Energy efficiency is becoming increasingly impor tant nowadays due to the need for energy conservation and environmental sustaina bility. An existing ammonia plant was simulated using Aspen Hysys software, the simulated plant was used to produce large volumes of data to train and test our machine-learning model.” The news correspondents obtained a quote from the research from Suez University, “In this work a benchmark methodology is proposed through machine-learning (ML) techniques to identify patterns and anomalies in energy consumption. Our ML mod el was developed in Python programming language using a multiple linear regressi on algorithm. Microsoft Power BI was used to build interactive visualizations to illustrate insights to users. The ML model was able to predict energy consumpti on by developing equations that relate the energy consumption and the operating variables for each significant energy user in the ammonia plant. In this study, actual versus optimum energy consumption was analyzed for four ammonia productio n plants. The ML model identified the ammonia plant operating costs and potentia l savings by adjusting operating conditions. An annual saving of up to 3.9 milli on dollars was reached in one of the ammonia production plants operating costs.”
      原文链接:
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    Santa Maria Nuova Hospital Reports Findings in Atrial Fibrillation (Machine lear ning approach for prediction of outcomes in anticoagulated patients with atrial fibrillation)

    60-61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 Heart Disorders and Di seases - Atrial Fibrillation is the subject of a report. According to news repor ting originating from Florence, Italy, by NewsRx correspondents, research stated , “The accuracy of available prediction tools for clinical outcomes in patients with atrial fibrillation (AF) remains modest. Machine Learning (ML) has been use d to predict outcomes in the AF population, but not in a population entirely on anticoagulant therapy.” Our news editors obtained a quote from the research from Santa Maria Nuova Hospi tal, “Different supervised ML models were applied to predict all-cause death, ca rdiovascular (CV) death, major bleeding and stroke in anticoagulated patients wi th AF, processing data from the multicenter START-2 Register. 11078 AF patients (male n = 6029, 54.3%) were enrolled with a median follow-up period of 1.5 years [IQR 1.0-2.6]. Patients on V itamin K Antagonists (VKA) were 5135 (46.4%) and 5943 (53.6% ) were on Direct Oral Anticoagulants (DOAC). Using Multi-Gate Mixture of Experts , a cross-validated AUC of 0.779 ± 0.016 and 0.745 ± 0.022 were obtained, respec tively, for the prediction of all-cause death and CV-death in the overall popula tion. The best ML model outperformed CHADSVASC and HAS-BLED for all-cause death prediction (p <0.001 for both). When compared to HAS-BLED, Gradient Boosting improved major bleeding prediction in DOACs patients (0.711 v s. 0.586, p<0.001). A very low number of events during fol low-up (52) resulted in a suboptimal ischemic stroke prediction (best AUC of 0.6 06 ± 0.117 in overall population). Body mass index, age, renal function, platele t count and hemoglobin levels resulted the most important variables for ML predi ction.”
      原文链接:
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    Harbin Engineering University Researcher Publishes New Studies and Findings in t he Area of Robotics (Study on the Adsorption Performance of a Vortex Suction Cup under Varying Diameters of Underwater Structure Tubes)

    61-62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 robotics have bee n published. According to news reporting out of Harbin, People’s Republic of Chi na, by NewsRx editors, research stated, “In certain precision work scenarios, un derwater robots require the ability to adhere to surfaces in order to perform ta sks effectively.” Funders for this research include National Key Research And Development Program of China; National Natural Science Foundation of Heilongjiang Province.
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    Study Data from University of Kerala Provide New Insights into Human-Centric Int elligent Systems (A Local Explainability Technique for Graph Neural Topic Models )

    62-63页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on human-centric intellige nt systems is now available. According to news reporting from the University of Kerala by NewsRx journalists, research stated, “Topic modelling is a Natural Lan guage Processing (NLP) technique that has gained popularity in the recent past. It identifies word co-occurrence patterns inside a document corpus to reveal hid den topics.” The news correspondents obtained a quote from the research from University of Ke rala: “Graph Neural Topic Model (GNTM) is a topic modelling technique that uses Graph Neural Networks (GNNs) to learn document representations effectively. It p rovides high-precision documents-topics and topics-words probability distributio ns. Such models find immense application in many sectors, including healthcare, financial services, and safety-critical systems like autonomous cars. This model is not explainable. As a matter of fact, the user cannot comprehend the underly ing decision-making process. The paper introduces a technique to explain the doc uments-topics probability distributions output of GNTM. The explanation is achie ved by building a local explainable model such as a probabilistic Naive Bayes cl assifier.”
      原文链接:
    • NETL
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    University of St Andrews Reports Findings in Artificial Intelligence (Automated reporting of cervical biopsies using artificial intelligence)

    63-64页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Artificial Intelligence is the su bject of a report. According to news originating from St. Andrews, United Kingdo m, by NewsRx correspondents, research stated, “When detected at an early stage, the 5-year survival rate for people with invasive cervical cancer is 92% . Being aware of signs and symptoms of cervical cancer and early detection great ly improve the chances of successful treatment.” Financial support for this research came from Innovate UK.
      原文链接:
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    Data from Mohammed V University Provide New Insights into Machine Learning (Glob al and Local Interpretability Techniques of Supervised Machine Learning Black Bo x Models for Numerical Medical Data)

    64-65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 originating from Rabat, Morocco, by NewsRx cor respondents, research stated, “The most effective machine learning classificatio n techniques, such as artificial neural networks, are not easily interpretable, which limits their usefulness in critical areas, such as medicine, where errors can have severe consequences. Researchers have been working to balance the trade -off between the model performance and interpretability.” Our news journalists obtained a quote from the research from Mohammed V Universi ty, “In this study, seven interpretability techniques (global surrogate, accumul ated local effects, local interpretable model-agnostic explanations (LIME), Shap ley additive explanations (SHAP), model agnostic post hoc local explanations (MA PLE), local rule-based explanation (LORE), and Contextual Importance and Utility (CIU)) were evaluated to interpret five medical classifiers (multilayer percept ron, support vector machines, random forests, extreme gradient boosting, and nai ve bayes) using six model performance metrics and three interpretability techniq ue metrics across six medical numerical datasets. The results confirmed the effe ctiveness of integrating global and local interpretability techniques, and highl ighted the superior performance of global SHAP explainer and local CIU explanati ons.”
      原文链接:
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    Recent Studies from Indian Institute of Technology (IIT) Madras Add New Data to Robotics (A Modular Computational Framework for the Dynamic Analyses of Cable-dr iven Parallel Robots With Different Types of Actuation Including the Effects of ...)

    65-65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 Tamil Nadu, India, by NewsRx editor s, research stated, “Dynamic simulations of the cable-driven parallel robots (CD PRs) with cable models closer to reality can predict the motions of moving platf orms more accurately than those with idealisations. Hence, the present work prop oses an efficient and modular computational framework for this purpose.” Our news journalists obtained a quote from the research from the Indian Institut e of Technology (IIT) Madras, “The primary focus is on the developments required in the context of CDPRs actuated by moving the exit points of cables while the lengths are held constant. Subsequently, the framework is extended to those case s where simultaneous changes in the lengths of cables are employed. Also, the ef fects due to the inertia, stiffness and damping properties of the cables undergo ing 3D motions are included in their dynamic models. The efficient recursive for ward dynamics algorithms from the prior works are utilised to minimise the compu tational effort.”
      原文链接:
    • NETL
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    University of Agriculture and Forestry Reports Findings in Machine Learning (Int egrating machine learning models with crossvalidation and bootstrapping for eva luating groundwater quality in Kanchanaburi Province, Thailand)

    66-66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 Hue City, Viet nam, by NewsRx journalists, research stated, “Exploring the potential of new mod els for mapping groundwater quality presents a major challenge in water resource management, particularly in Kanchanaburi Province, Thailand, where groundwater faces contamination risks. This study aimed to explore the applicability of rand om forest (RF) and artificial neural networks (ANN) models to predict groundwate r quality.” The news reporters obtained a quote from the research from the University of Agr iculture and Forestry, “Particularly, these two models were integrated into cros s-validation (CV) and bootstrapping (B) techniques to build predictive models, i ncluding RF-CV, RF-B, ANN-CV, and ANN-B. Entropy groundwater quality index (EWQI ) was converted to normalized EWQI which was then classified into five levels fr om very poor to very good. A total of twelve physicochemical parameters from 180 groundwater wells, including potassium, sodium, calcium, magnesium, chloride, s ulfate, bicarbonate, nitrate, pH, electrical conductivity, total dissolved solid s, and total hardness, were investigated to decipher groundwater quality in the eastern part of Kanchanaburi Province, Thailand. Our results indicated that grou ndwater quality in the study area was primarily polluted by calcium, magnesium, and bicarbonate and that the RF-CV model (RMSE = 0.06, R = 0.87, MAE = 0.04) out performed the RF-B (RMSE = 0.07, R = 0.80, MAE = 0.04), ANN-CV (RMSE = 0.09, R = 0.70, MAE = 0.06), and ANN-B (RMSE = 0.10, R = 0.67, MAE = 0.06). Our findings highlight the superiority of the RF models over the ANN models based on the CV a nd B techniques. In addition, the role of groundwater parameters to the normaliz ed EWQI in various machine learning models was found.”
      原文链接:
    • NETL
    • NSTL