期刊,Robotics & Machine Learning Daily News 2024年卷Oct.3期_国家学术搜索

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Robotics & Machine Learning Daily News

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Data on Machine Learning Reported by Jhon Faber Marulanda Lopez and Colleagues ( Machine Learning Approach to Support Taxonomic Discrimination of Mayflies Specie s Based on Morphologic Data)

134-135页

查看更多>>摘要：2024 OCT 03 (NewsRx)-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 Vicosa, Brazil, by NewsR x journalists, research stated, "Artificial intelligence (AI) and machine learni ng (ML) offer objective solutions in the elaboration of taxonomic keys, such as the processing of large numbers of samples, aiding in the species identification , and optimizing the time required for this process. We utilized ML to study the morphological data of eight species of Americabaetis Kluge 1992, a diverse genu s in South American freshwater environments." The news correspondents obtained a quote from the research, "Decision trees were employed, examining specimens from the Museu de Entomologia da Universidade Fed eral de Vicosa (UFVB/Brazil) and literature data. Eleven morphological traits of taxonomic importance from the literature, including frontal keel, shape of the mouthparts, and abdominal color pattern, were analyzed. The decision tree obtain ed with the Gini algorithm effectively differentiates eight species (40% of the known species), using only eight morphological characters. Our analysis r evealed distinct groups within Americabaetis alphus Lugo-Ortiz and McCafferty 19 96a, based on variations in abdominal tracheae pigmentation. This study introduc es a novel approach,integrating AI techniques, biological collections, and lite rature data for aid in the Americabaetis species identification."

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Beihang University Reports Findings in Artificial Intelligence (Automatic Segmen tation of Ultrasound-Guided Quadratus Lumborum Blocks Based on Artificial Intell igence)

135-136页

查看更多>>摘要：2024 OCT 03 (NewsRx)-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 Beijing, People's Republic of China, by NewsRx journalists, research stated, "Ultrasoundguided q uadratus lumborum block (QLB) technology has become a widely used perioperative analgesia method during abdominal and pelvic surgeries. Due to the anatomical co mplexity and individual variability of the quadratus lumborum muscle (QLM) on ul trasound images, nerve blocks heavily rely on anesthesiologist experience." The news correspondents obtained a quote from the research from Beihang Universi ty, "Therefore, using artificial intelligence (AI) to identify different tissue regions in ultrasound images is crucial. In our study, we retrospectively collec ted 112 patients (3162 images) and developed a deep learning model named Q-VUM, which is a U-shaped network based on the Visual Geometry Group 16 (VGG16) networ k. Q-VUM precisely segments various tissues, including the QLM, the external obl ique muscle, the internal oblique muscle, the transversus abdominis muscle (coll ectively referred to as the EIT), and the bones. Furthermore, we evaluated Q-VUM . Our model demonstrated robust performance, achieving mean intersection over un ion (mIoU), mean pixel accuracy, dice coefficient, and accuracy values of 0.734, 0.829, 0.841, and 0.944, respectively. The IoU, recall, precision, and dice coe fficient achieved for the QLM were 0.711, 0.813, 0.850, and 0.831, respectively. Additionally, the Q-VUM predictions showed that 85% of the pixels in the blocked area fell within the actual blocked area. Finally, our model exh ibited stronger segmentation performance than did the common deep learning segme ntation networks (0.734 vs. 0.720 and 0.720, respectively). In summary, we propo sed a model named Q-VUM that can accurately identify the anatomical structure of the quadratus lumborum in real time."

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Reports Outline Robotics Findings from Zhejiang University (A Skeleton-based Ass embly Action Recognition Method With Feature Fusion for Human-robot Collaborativ e Assembly)

136-137页

查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Robotic s. According to news reporting from Hangzhou, People's Republic of China, by New sRx journalists, research stated, "Human-robot collaborative assembly (HRCA) is one of the current trends of intelligent manufacturing, and assembly action reco gnition is the basis of and the key to HRCA. A multi-scale and multi-stream grap h convolutional network (2MSGCN) for assembly action recognition is proposed in this paper. 2MSGCN takes the temporal skeleton sample as input and outputs the c lass of the assembly action to which the sample belongs." Funders for this research include National Natural Science Foundation of China ( NSFC), Key Research and Development Program of Zhejiang Province.

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University of California Researchers Reveal New Findings on Machine Learning (Sy nergizing physics and machine learning for advanced battery management)

137-138页

查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on artificial intelligen ce have been presented. According to news reporting out of the University of Cal ifornia by NewsRx editors, research stated, "Improving battery health and safety motivates the synergy of a powerful duo: physics and machine learning." The news correspondents obtained a quote from the research from University of Ca lifornia: "Through seamless integration of these disciplines, the efficacy of ma thematical battery models can be significantly enhanced. This paper delves into the challenges and potentials of managing battery health and safety, highlightin g the transformative impact of integrating physics and machine learning to addre ss those challenges. Based on our systematic review in this context, we outline several future directions and perspectives, offering a comprehensive exploration of efficient and reliable approaches."

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New Findings on Intelligent Systems Described by Investigators at Shanxi Univers ity (A Spherical Z-number Multi-attribute Group Decision Making Model Based On t he Prospect Theory and Glds Method)

138-139页

查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning - Intelligent Systems. According to news reporting out of Taiyu an, People's Republic of China, by NewsRx editors, research stated, "Multi-attri bute group decision-making is an important research field in decision science, a nd its theories and methods have been widely applied to engineering, economics a nd management. However, as the information embedded volume and complexity of dec ision-making expand, the diversity and heterogeneity of decision-making groups p resent significant challenges to the decision-making process." Our news journalists obtained a quote from the research from Shanxi University, "In order to effectively address these challenges, this paper defines the concep t of spherical Z-number, which is a fuzzy number that takes into account a wide range of evaluation and its reliability. Additionally, a group decision-making m odel in a spherical Z-number environment is proposed. First, an objective phased tracking method is used to determine expert weights, maintain the consistency i n decision-making group evaluations. The gained and lost dominance score method is combined with prospect theory to integrate expert psychological behavior when facing risks. The proposed method considers both group utility and individual r egret, and balances the gains and losses of various options in the decision-maki ng process. Finally, in response to the 3R principle, the model is employed to a ddress the shared e-bike recycling supplier selection problem and to assess the viability of the decision-making outcomes. The results demonstrate that the mode l is robust in the context of varying parameter configurations. Moreover, the co rrelation coefficients between its ranking outcomes and those of alternative met hodologies are all above 0.77, and its average superiority degree is 1.121, whic h is considerably higher than that of other methods."

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Studies from Ibaraki University Yield New Data on Neural Computation (l 1 -Regul arized ICA: A Novel Method for Analysis of Task-Related fMRI Data)

139-139页

查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on neural computation is n ow available. According to news reporting originating from Ibaraki, Japan, by Ne wsRx correspondents, research stated, "We propose a new method of independent co mponent analysis (ICA) in order to extract appropriate features from high-dimens ional data." The news reporters obtained a quote from the research from Ibaraki University: " In general, matrix factorization methods including ICA have a problem regarding the interpretability of extracted features. For the improvement of interpretabil ity, sparse constraint on a factorized matrix is helpful. With this background, we construct a new ICA method with sparsity."

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Reports Outline Androids Research from Indian Institute of Technology (Gait Gene ration of a 10-Degree-of-Freedom Humanoid Robot on Deformable Terrain Based on S pherical Inverted Pendulum Model)

139-140页

查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in androids. According to news reporting originating from Uttar Pradesh, India, by NewsRx correspondents, research stated, "Gait generation of a humanoid robot on a deformable terrain is a complex problem as the foot and terrain interaction an d terrain deformation have to be included in the dynamics."Our news journalists obtained a quote from the research from Indian Institute of Technology: "To simplify the dynamics of walk on deformable terrain, we used a spherical inverted pendulum (SIP) to represent the single support phase, in whic h the effect of terrain deformation is represented by a spring and damper contac t model. The impact model for leg transition is derived from angular momentum co nservation. In order to minimize the energy loss due to impact, the double suppo rt phase is modeled as a suspended pendulum. Based on the motion of the SIP mode l, the hip and leg trajectories of a 10-degreeof- freedom (DOF) humanoid robot a re generated. The joint trajectories of the robot are obtained from inverse kine matics. The motion of the center of mass is analyzed by inverse dynamics of a fl oating-base robot."

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Affiliated Hospital of Qingdao University Reports Findings in Acute Kidney Injur y (Personalized Prediction of Long-Term Renal Function Prognosis Following Nephr ectomy Using Interpretable Machine Learning Algorithms: Case-Control Study)

140-141页

查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Kidney Diseases and Conditions - Acute Kidney Injury is the subject of a report. According to news reporting out of Qingdao, People's Republic of China, by NewsRx editors, research stated, "Acu te kidney injury (AKI) is a common adverse outcome following nephrectomy. The pr ogression from AKI to acute kidney disease (AKD) and subsequently to chronic kid ney disease (CKD) remains a concern; yet, the predictive mechanisms for these tr ansitions are not fully understood." Our news journalists obtained a quote from the research from the Affiliated Hosp ital of Qingdao University, "Interpretable machine learning (ML) models offer in sights into how clinical features influence long-term renal function outcomes af ter nephrectomy, providing a more precise framework for identifying patients at risk and supporting improved clinical decision-making processes. This study aime d to (1) evaluate postnephrectomy rates of AKI, AKD, and CKD, analyzing long-ter m renal outcomes along different trajectories; (2) interpret AKD and CKD models using Shapley Additive Explanations values and Local Interpretable Model-Agnosti c Explanations algorithm; and (3) develop a web-based tool for estimating AKD or CKD risk after nephrectomy. We conducted a retrospective cohort study involving patients who underwent nephrectomy between July 2012 and June 2019. Patient dat a were randomly split into training, validation, and test sets, maintaining a ra tio of 76.5:8.5:15. Eight ML algorithms were used to construct predictive models for postoperative AKD and CKD. The performance of the best-performing models wa s assessed using various metrics. We used various Shapley Additive Explanations plots and Local Interpretable Model-Agnostic Explanations bar plots to interpret the model and generated directed acyclic graphs to explore the potential causal relationships between features. Additionally, we developed a web-based predicti on tool using the top 10 features for AKD prediction and the top 5 features for CKD prediction. The study cohort comprised 1559 patients. Incidence rates for AK I, AKD, and CKD were 21.7 % (n=330), 15.3% (n=238), a nd 10.6% (n=165), respectively. Among the evaluated ML models, the Light Gradient-Boosting Machine (LightGBM) model demonstrated superior performa nce, with an area under the receiver operating characteristic curve of 0.97 for AKD prediction and 0.96 for CKD prediction. Performance metrics and plots highli ghted the model's competence in discrimination, calibration, and clinical applic ability. Operative duration, hemoglobin, blood loss, urine protein, and hematocr it were identified as the top 5 features associated with predicted AKD. Baseline estimated glomerular filtration rate, pathology, trajectories of renal function , age, and total bilirubin were the top 5 features associated with predicted CKD . Additionally, we developed a web application using the LightGBM model to estim ate AKD and CKD risks. An interpretable ML model effectively elucidated its deci sion-making process in identifying patients at risk of AKD and CKD following nep hrectomy by enumerating critical features."

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Researchers from Chengdu University of Technology Describe Findings in Machine L earning (Prediction of Total Organic Carbon Content In Deep Marine Shale Reservo irs Based On a Super Hybrid Machine Learning Model)

141-142页

查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news reporting out of Chengdu, Peopl e's Republic of China, by NewsRx editors, research stated, "The total organic ca rbon (TOC) content is crucial for assessing the gas-bearing potential of shale r eservoirs. Thus,quantitative characterization and intelligent prediction of TOC content play important roles in determining geological sweet spots and the deve lopment of shale reservoirs."

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Findings from Dalhousie University Has Provided New Data on Machine Learning (Da s: a Drl-based Scheme for Workload Allocation and Worker Selection In Distribute d Coded Machine Learning)

142-143页

查看更多>>摘要：2024 OCT 03 (NewsRx)-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 Halifax, Can ada, by NewsRx journalists, research stated, "Machine learning (ML) has been wid ely applied to successfully address a variety of different problems across diver se domains,such as robotics, healthcare, and finance. However, high-complexity ML algorithms often require overlong computation time, which significantly impac ts their feasibility." Financial support for this research came from Natural Sciences and Engineering R esearch Council of Canada (NSERC).

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