期刊,Robotics & Machine Learning Daily News 2024年卷Mar.12期_国家学术搜索

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New Machine Learning Study Findings Have Been Reported by Investigators at Jilin University (Near-infrared Off-axis Cavityenhanced Optical Frequency Comb Spect roscopy for Co2/co Dualgas Detection Assisted By Machine Learning)

97-98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 Changchun,People's Re public of China,by NewsRx journalists,research stated,"Cavityenhanced direct frequency comb spectroscopy (CE-DFCS) is widely used as a highly sensitive gas sensing technology in various gas detection fields.For the on-axis coupling inc idence scheme,the detection accuracy and stability are seriously affected by th e cavity-mode noise,and therefore,stable operation inevitably requires externa l electronic mode-locking and sweeping devices,substantially increasing system complexity." Financial supporters for this research include National Natural Science Foundati on of China (NSFC),National Natural Science Foundation of China (NSFC),Science and Technology Development Program of Jilin Province,China,Science and Techno logy Research Project of the Department of Education,Jilin Province,China,Key R&D Program of Changchun.

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Department of Biotechnology Reports Findings in Machine Learning (Accurate Machi ne Learning for Predicting the Viscosities of Deep Eutectic Solvents)

98-99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 out of Andhra Pradesh,India,by NewsRx editors,research stated,"Deep eutectic solvents (DESs) are emerging as environmentally friendly designer solvents for mass transport and heat trans fer processes in industrial applications; however,the lack of accurate tools to predict and thus control their viscosities under both a range of environmental factors and formulations hinders their general application.While DESs may serve as designer solvents,with nearly unlimited combinations,this unfortunately ma kes it experimentally infeasible to comprehensively measure the viscosities of a ll DESs of potential industrial interest." Our news journalists obtained a quote from the research from the Department of B iotechnology,"To assist in the design of DESs,we have developed several new ma chine learning (ML) models that accurately and rapidly predict the viscosities o f a diverse group of DESs at different temperatures and molar ratios using,to d ate,one of the most comprehensive data sets containing the properties of over 6 70 DESs over a wide range of temperatures (278.15-385.25 K).Three ML models,in cluding support vector regression (SVR),feed forward neural networks (FFNNs),a nd categorical boosting (CatBoost),were developed to predict DES viscosity as a function of temperature and molar ratio and contrasted with multilinear and two -factor polynomial regression baselines.Quantum chemistry-based,COSMO-RS-deriv ed sigma profile (s-profile) features were used as inputs for the ML models.The CatBoost model is excellent at externally predicting DES viscosity,as indicate d by high (0.99) and low root-mean-square-error (RMSE) and average absolute rela tive deviations (AARD) (5.22%) values for the testing data sets,an d 98% of the data points lie within the 15% of AARD deviations.Furthermore,SHapley additive explanation (SHAP) analysis was employ ed to interpret the ML results and rationalize the viscosity predictions."

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Investigators from National Nanotechnology Center Zero in on Machine Learning (E xpanding the Applicability Domain of Machine Learning Model for Advancements In Electrochemical Material Discovery)

99-100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 reporting originating in Pathum Thani,Thailan d,by NewsRx journalists,research stated,"Machine learning has gained consider able attention in the material science domain and helped discover advanced mater ials for electrochemical applications.Numerous studies have demonstrated its po tential to reduce the resources required for material screening." Financial supporters for this research include VISTEC-NSTDA collaborative resear ch and education scholarship,VISTEC-NSTDA collaborative research and education scholarship,National Research Council of Thailand (NRCT),Program Management Un it for Human Resources & Institutional Development,Research and I nnovation.The news reporters obtained a quote from the research from National Nanotechnolo gy Center,"However,a significant proportion of these studies have adopted a su pervised learning approach,which entails the laborious task of constructing ran dom training databases and does not always ensure the model's reliability while screening unseen materials.Herein,we evaluate the limitations of supervised ma chine learning from the perspective of the applicability domain.The applicabili ty domain of a model is the region in chemical space where the structure-propert y relationship is covered by the training set so that the model can give reliabl e predictions.We review methods that have been developed to overcome such limit ations,such as the active learning framework and self-supervised learning.The effort required for material discovery has decreased thanks to machine learning.However,the quality of the dataset,which should be vast and diverse,determin es the model's effectiveness and reliability.Therefore,the trustworthiness of prediction should be evaluated based on the concept of applicability domain."

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University of Canberra Reports Findings in Machine Learning (Empirical compariso n of deep learning models for fNIRS pain decoding)

100-101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 Canberra,Australia,by NewsRx journalists,research stated,"Pain assessment is extremely important in patients unable to communicate and it is often done by clinical judgement.Howev er,assessing pain using observable indicators can be challenging for clinicians due to the subjective perceptions,individual differences in pain expression,a nd potential confounding factors." The news correspondents obtained a quote from the research from the University o f Canberra,"Therefore,the need for an objective pain assessment method that ca n assist medical practitioners.Functional near-infrared spectroscopy (fNIRS) ha s shown promising results to assess the neural function in response of nocicepti on and pain.Previous studies have explored the use of machine learning with han d-crafted features in the assessment of pain.In this study,we aim to expand pr evious studies by exploring the use of deep learning models Convolutional Neural Network (CNN),Long Short-Term Memory (LSTM),and (CNN-LSTM) to automatically e xtract features from fNIRS data and by comparing these with classical machine le arning models using hand-crafted features.The results showed that the deep lear ning models exhibited favourable results in the identification of different type s of pain in our experiment using only fNIRS input data.The combination of CNN and LSTM in a hybrid model (CNN-LSTM) exhibited the highest performance (accurac y = 91.2%) in our problem setting.Statistical analysis using one-w ay ANOVA with Tukey's () test performed on accuracies showed that the deep learn ing models significantly improved accuracy performance as compared to the baseli ne models.Overall,deep learning models showed their potential to learn feature s automatically without relying on manually-extracted features and the CNN-LSTM model could be used as a possible method of assessment of pain in non-verbal pat ients."

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Studies from FEMTO-ST Institute in the Area of Machine Learning Published (Machi ne Learning Algorithms for Solar Irradiance Prediction:A Recent Comparative Stu dy)

101-102页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on artificial in telligence.According to news reporting from the FEMTO-ST Institute by NewsRx jo urnalists,research stated,"Photovoltaic panels have emerged as a promising tec hnology to generate renewable energy and reduce the dependence on fossil fuels b y harnessing solar radiation and converting it into electricity." The news journalists obtained a quote from the research from FEMTO-ST Institute:"The efficiency of this conversion process relies on various factors,including solar panel quality and region-specific intake of solar radiation.Therefore,a ccurate solar irradiance forecasting is crucial for making informed decisions ab out designing and managing efficient solar power systems.One way to address thi s issue is through leveraging artificial intelligence algorithms that can predic t precise amounts of irradiance in specific locations.To this end,this paper e xplores solar irradiance forecasting as a machine-learning problem.We utilize d ata from Izmir,Turkey,over a period spanning three years while testing several deep learning algorithms against traditional machine learning models,with resu lts detailed within our comparative analysis."

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Studies Conducted at Tianjin University on Robotics Recently Published (Reconfig urable Thick-Panel Structures Based on a Stacked Origami Tube)

101-101页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on robotics are presented i n a new report.According to news reporting from Tianjin University by NewsRx jo urnalists,research stated,"Variable crease origami that exhibits crease topolo gical morphing allows a given crease pattern to be folded into multiple shapes,greatly extending the reconfigurability of origami structures." Funders for this research include National Natural Science Foundation of China.The news reporters obtained a quote from the research from Tianjin University:" However,it is a challenge to enable the thick-panel forms of such crease patter ns to bifurcate uniquely and reliably into desired modes.Here,thick-panel theo ry combined with cuts is applied to a stacked origami tube with multiple bifurca tion paths.The thick-panel form corresponding to the stacked origami tube is co nstructed,which can bifurcate exactly between two desired modes without falling into other bifurcation paths.Then,kinematic analysis is carried out and the r esults exhibit that the thick-panel origami tube is kinematically equivalent to its zero-thickness form with one degree of freedom (DOF)."

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Reports Outline Machine Learning Research from College of Health Science (Classi fication of chronic ankle instability using machine learning technique based on ankle kinematics during heel rise in delivery workers)

102-103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 artificial intelligen ce have been presented.According to news reporting out of Wonju,South Korea,b y NewsRx editors,research stated,"Ankle injuries in delivery workers (DWs) are often caused by trips,and high recurrence rates of ankle sprains are related t o chronic ankle instability (CAI).Heel rise requires joint angles and moments s imilar to those of the terminal stance phase of walking that the foot supinates." The news correspondents obtained a quote from the research from College of Healt h Science:"Thus,our study aimed to develop,determine,and compare the predict ive performance of statistical machine learning models to classify DWs with and without CAI using ankle kinematics during heel rise.In total,203 DWs were scre ened for eligibility.Seven predictors were included in our study (age,work dur ation,body mass index,calcaneal stance position angle [CSPA ] in the initial and terminal positions during heel rise,cal caneal movement during heel rise [CM HR ],and plantar flexion angle during heel rise).Six machine learning algorithms,i ncluding logistic regression,decision tree,AdaBoost,Extreme Gradient boosting machines,random forest,and support vector machine,were trained.The random f orest model (area under the curve [AUC],0 .967 [excellent]; F1,0.889; accuracy,0.9 25) confirmed the best predictive performance in the test datasets among the six machine learning models.For Shapley Additive Explanations,old age,low CMHR,high CSPA in the initial position,high PFA,long work duration,low CSPA in the terminal position,and high body mass index were the most important predictors of CAI in the random forest model."

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Patent Application Titled "Hybrid Metaverse Using Edge Nodes To Support A Soft R epository For Event Processing" Published Online (USPTO 20240061890)

103-106页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-According to news reporting originatin g from Washington,D.C.,by NewsRx journalists,a patent application by the inve ntors Arora,Saurabh (Gurugram,IN); Chauhan,Sandeep (Hyderabad,IN),filed on August 18,2022,was made available online on February 22,2024.No assignee for this patent application has been made.Reporters obtained the following quote from the background information supplied by the inventors:"In some instances,individuals may make requests over the pho ne (e.g.,with contact centers,or the like),such as to change an address or to update other information.In some instances,however,such requests might not b e fulfilled unless certain documentation is provided (e.g.,to verify a user's i dentity).It may be difficult,in some instances (such as virtual interactions b etween individuals communicating in a virtual environment such as a metaverse en vironment),to convey to the individuals exactly what documentation is needed fo r a specific request.This may result in delayed and/or incorrect request proces sing.Accordingly,it may be important to provide an improved method for request ing and providing requested specific documentation."

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Researchers Submit Patent Application,"Device For Shredding Fibrous Plant Mater ial",for Approval (USPTO 20240058824)

107-110页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-From Washington,D.C.,NewsRx journali sts report that a patent application by the inventor LIET,Robert Jan (Oldenzaal,NL),filed on August 20,2023,was made available online on February 22,2024.No assignee for this patent application has been made.News editors obtained the following quote from the background information suppli ed by the inventors:"Devices for shredding fibrous plant material,in particula r hay,straw or silage,are increasingly employed in agriculture.Exemplary devi ces here comprise a chamber into which food can be introduced via a filling open ing,wherein in the chamber,bar-shaped elements move past the filling opening i n an endless path.The bar-shaped elements can move through below the food where by the food is loosened up and/or mixed.A cutting operation is performed when t he food is supplied to a cutting element which is arranged near the filling open ing.In this manner,the food,in particular the food ingredients consisting of relatively long fibers,such as e.g.grass,is cut.Such a device is known,for example,from EP 0 838 147 A1."While known devices permit a good shredding of fibrous plant material,shreddin g is sometimes not satisfactory,in particular with highly compressed food,for example in the form of bales."

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Patent Application Titled "Granular Taxonomy For Customer Support Augmented With Ai" Published Online (USPTO 20240062219)

111-114页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-According to news reporting originatin g from Washington,D.C.,by NewsRx journalists,a patent application by the inve ntors Carter,Nick (San Francisco,CA,US); Chamoun,Jad (San Francisco,CA,US) ; Galada,Sambhav (Fremont,CA,US); Gan,Lewin (San Francisco,CA,US); Goche,Sami (San Francisco,CA,US); Khouja,Jude (San Francisco,CA,US); Kim,Andrew (Fremont,CA,US); Kiselbach,Dustin (Astoria,NY,US); Kong,Sunny (Portland,O R,US); Laird,Andrew (Santa Clara,CA,US); Liao,EJ (Huntington Beach,CA,US) ; Lu,Yi (Bellevue,WA,US); Man,James (Brisbane,CA,US); Nasr,Antoine (San F rancisco,CA,US); Nicholas,Deon (Millbrae,CA,US); Sharma,Dev (San Francisco,CA,US); Sun,Carolyn (Saratoga,CA,US); Wu,Madeline (San Francisco,CA,US) ; Wu,Salina (Los Angeles,CA,US); Xing,Weitian (Ottawa,CA); Yuen,Holman (Ch ino Hills,CA,US),filed on September 1,2023,was made available online on Feb ruary 22,2024.No assignee for this patent application has been made.Reporters obtained the following quote from the background information supplied by the inventors:"Customer support service is an important aspect of many busin esses.For example,there are a variety of customer support applications to addr ess customer service support issues.As one illustration,a customer service hel pdesk may have a set of human agents who use text messages to service customer s upport issues.There are a variety of Customer Relationship Management (CRM) and helpdesk-related software tools,such as SalesForce® or Zendesk®.

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