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

    Studies Conducted at University of Colorado on Machine Learning Recently Reporte d (Impacts of Increased Prediction Accuracy In Management Decisions: a Study of Full-depth Reclamation Pavements)

    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 out of Boulder, Colorado, b y NewsRx editors, research stated, "Given the abundance of condition data regula rly collected for major roadways, machine learning has the potential to enhance pavement deterioration modeling. This is particularly important for recycling-ba sed rehabilitation techniques, such as full-depth reclamation (FDR), which lack accurate models of deterioration." Funders for this research include Colorado Department of Transportation, United States Department of Education Graduate Assistance in Areas of National Need Gra nt. Our news journalists obtained a quote from the research from the University of C olorado, "Previous studies have demonstrated the effectiveness of machine learni ng (ML) to predict pavement deterioration. However, the increased accuracy of th ese models often is reported using statistical metrics that pavement managers ca nnot easily relate to asset management decision-making. This paper quantifies th e impacts that increased accuracies in deterioration modeling have on relevant m etrics used in the management of pavement assets. The study analyzed the perform ance of full-depth-reclamation pavements and developed random forest models to e stimate roughness, rutting, and fatigue cracking. These random forest models wer e compared with mechanistic-empirical (M-E) models tuned to the same sites to qu antify differences in prediction accuracy, useful life, life-cycle costs, and lo ng-term performance. The tuned random forest deterioration models reduced errors by 90%-97% compared with the tuned M-E models. The r esults suggest that M-E predicts that FDR reaches the end of service life 8 year s sooner than do the random forest predictions. The long-term performance of FDR was found to be 28%-73% higher in a 10-year design l ife than M-E models predict."
      原文链接:
    • NETL
    • NSTL

    Researchers at University of California San Diego (UCSD) Release New Data on Rob otics (Materials Consideration for the Design, Fabrication and Operation of Micr oscale Robots)

    20-21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 originating from La Jolla, Californi a, by NewsRx correspondents, research stated, "Microscale robots have been the f ocus of extensive research efforts resulting in various innovative developments that broaden their capabilities and applications. The rational design of materia ls has been the cornerstone for developing and innovating microscale robots, and breakthroughs in materials science are expected to further push the boundaries of this field." Financial support for this research came from UCSD Contextual Robotics Institute . Our news editors obtained a quote from the research from the University of Calif ornia San Diego (UCSD), "This Review provides an overview of the design principl es and material selection for the propulsion and operation of microrobots. The f undamental mechanisms governing the motion of microrobots are first introduced, followed by the material design strategies enabling efficient and controllable p ropulsion. We highlight the use of diverse reactive and responsive materials in realizing the advanced functionalities and capabilities of microrobots, cover re presentative biomedical and environmental applications and discuss how future ma terial innovations will impact the development of next-generation microscale rob ots. Microscale robots have unique advantages for biomedical and environmental a pplications."
      原文链接:
    • NETL
    • NSTL

    Studies from Pangasinan State University in the Area of Machine Learning Publish ed (Sentiment Analysis of Students' Feedback on Faculty Online Teaching Performa nce Using Machine Learning Techniques)

    21-22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 artificial in telligence have been published. According to news reporting out of Pangasinan St ate University by NewsRx editors, research stated, "The pandemic has given rise to challenges across different sectors, particularly in educational institutions . The mode of instruction has shifted from in-person to flexible learning, leadi ng to increased stress and concerns for key stakeholders such as teachers, paren ts, and students. The ongoing spread of diseases has made in-person classes unfe asible." Our news journalists obtained a quote from the research from Pangasinan State Un iversity: "Even if limited face to face classes will be allowed, online teaching is deemed to remain a practice to support instructional delivery to students. T herefore, it is essential to understand the challenges and issues encountered in online teaching, particularly from the perspective of students. This knowledge is crucial for supervisors and administrators, as it provides insights to aid in planning intervention measures. These interventions can support teachers in enh ancing their online teaching performance for the benefit of their students. A pr ocess that can be applied to achieve this goal is sentiment analysis. In the fie ld of education, one of the applications of sentiment analysis is in the evaluat ion of faculty teaching performance. It has been a practice in educational insti tutions to periodically assess their teachers' performance. However, it has not been easy to take into account the students' comments due to the lack of methods for automated text analytics. In line with this, techniques in sentiment analys is are presented in this study. Base models such as Naive Bayes, Support Vector Machines, Logistic Regression, and Random Forest were explored in experiments an d compared to a combination of the four called ensemble. Outcomes indicate that the ensemble of the four outperformed the base models. The utilization of Ngram vectorization in conjunction with ensemble techniques resulted in the highest F1 score compared to Count and TF-IDF methods. Additionally, this approach achieve d the highest Cohen's Kappa and Matthews Correlation Coefficient (MCC), along wi th the lowest Cross-entropy, signifying its preference as the model of choice fo r sentiment classification."
      原文链接:
    • NETL
    • NSTL

    Studies from Western New England University Yield New Data on Machine Learning [Bidirectional Long Short-term Memory (Bilstm) - Support Vector Machine: a New Ma chine Learning Model for Predicting Water Quality Parameters]

    22-23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 Springfield, Massachusetts, b y NewsRx journalists, research stated, "Water pollution threatens human health, agriculture, and ecosystems. Accurate prediction of water quality pa-rameters is crucial for effective protection." The news correspondents obtained a quote from the research from Western New Engl and University, "We suggest a novel hybrid deep learning model that enhances the efficiency of Support Vector Machines (SVMs) in predicting Electrical Conductiv ity (EC) and Total Dissolved Solids (TDS). Our model combines Bidirectional Long Short-Term Memory (BILSTM) and SVMs to extract essential features and predict o utput variables. We evaluated the models using input parameters (PH, Ca++, Mg++, Na+, K+, HCO3, SO4, and Cl) for one, two, and three-day predictions. Employing the Ali Baba and Forty Thieves (AFT) optimization algorithm, we identified optim al input combinations. The BILSTM-SVM model accurately estimated TDS values, wit h MAPE values of 2%, outperforming other models. Similarly, it succ essfully predicted EC values, exhibiting an R2 value of 0.94."
      原文链接:
    • NETL
    • NSTL

    New Findings from Beijing Institute of Technology Update Understanding of Roboti cs (Motion Transition Under Urgent Change of Target Step-stone During Three-dime nsional Biped Walking)

    23-24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 Ro botics. According to news reporting originating in Beijing, People's Republic of China, by NewsRx journalists, research stated, "Controlling a biped robot to wa lk through rough terrains is crucial to the robot's field application. For a hum an in the workplace, the ability to flexibly transfer motion while walking in so me urgent circumstances is necessary." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news reporters obtained a quote from the research from the Beijing Institute of Technology, "Explicitly, the according scenario can be dodging an approachin g object or instantly modifying the target place to step on. The function is als o important for humanoid robot workers. Therefore, we proposed a walking control framework that achieves three-dimensional (3-D) walking and transfers the whole body motion when the target stepping location is urgently changed. The proposed framework contains a motion planner which outputs the desired center of mass (C oM) and center of pressure (CoP) trajectories in 3-D space and a hierarchical wh ole body controller (WBC) that outputs corresponding whole body joints' trajecto ries. In the motion planner, the CoM jerk for each loop is calculated by the Lin ear-Quadratic- Tracker (LQT), a variation of the Linear-Quadratic-Regulator (LQR) . The LQT coefficients adapt to the adjusted step length, making the desired CoM and CoP trajectories respond flexibly to the change of target step-stone. In WB C, three levels of tasks are defined, which meet dynamic, kinematic, and viable contact constraints, respectively. The optimal joints' angular accelerations are obtained by exploiting the nullspace of the first two levels tasks and by quadr atic programming (QP) for the third-level task." According to the news reporters, the research concluded: "In the simulations, ou r method is demonstrated to be effective for the robot to transfer the motion un der urgent change of the target step-stone." This research has been peer-reviewed.
      原文链接:
    • NETL
    • NSTL

    Researchers at Shanghai Jiao Tong University Release New Data on Machine Learnin g (A Machine Learning Framework for Geodesics Under Spherical Wasserstein-fisher -rao Metric and Its Application for Weighted Sample Generation)

    24-25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 Shanghai, People's Rep ublic of China, by NewsRx journalists, research stated, "Wasserstein- Fisher-Rao (WFR) distance is a family of metrics to gauge the discrepancy of two Radon meas ures, which takes into account both transportation and weight change. Spherical WFR distance is a projected version of WFR distance for probability measures so that the space of Radon measures equipped with WFR can be viewed as metric cone over the space of probability measures with spherical WFR." Financial support for this research came from National Key R &D Pro gram of China. The news correspondents obtained a quote from the research from Shanghai Jiao To ng University, "Compared to the case for Wasserstein distance, the understanding of geodesics under the spherical WFR is less clear and still an ongoing researc h focus. In this paper, we develop a deep learning framework to compute the geod esics under the spherical WFR metric, and the learned geodesics can be adopted t o generate weighted samples. Our approach is based on a Benamou-Brenier type dyn amic formulation for spherical WFR. To overcome the difficulty in enforcing the boundary constraint brought by the weight change, a Kullback-Leibler divergence term based on the inverse map is introduced into the cost function. Moreover, a new regularization term using the particle velocity is introduced as a substitut e for the Hamilton-Jacobi equation for the potential in dynamic formula." According to the news reporters, the research concluded: "When used for sample g eneration, our framework can be beneficial for applications with given weighted samples, especially in the Bayesian inference, compared to sample generation wit h previous flow models."
      原文链接:
    • NETL
    • NSTL

    Study Results from Chulalongkorn University Broaden Understanding of Machine Lea rning (An Efficient Lightgbm-based Differential Evolution Method for Nonlinear I nelastic Truss Optimization)

    25-26页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting originating from Bangkok, Thailand, by Ne wsRx correspondents, research stated, "A metaheuristic-based structural optimiza tion method, whilst being popularly adopted due to its advantages in by-passing gradient function calculations, requires the use of time-consuming advanced anal yses for constraint evaluation. To overcome this drawback, the present paper pro poses a robust (machine learning-based) optimization method that combines the li ght gradient boosting machine (LightGBM) with the efficient p-best differential evolution (EpDE) method." Financial supporters for this research include Thailand Science Research and Inn ovation Fund Chulalongkorn University, Ratchadapisek Somphot Fund for Postdoctor al Fellowship, Chulalongkorn University. Our news editors obtained a quote from the research from Chulalongkorn Universit y, "In essence, the LightGBM classification model is constructed to assess the c onstraint (safety and integrity) satisfaction of structures. An efficient framew ork using a so-called safety parameter is proposed to prevent inaccurate predict ions of the LightGBM model. The EpDE processes the optimization procedures on th e constructed classification LightGBM model. This enables an enhanced machine le arning-based optimization technique that not only maintains the sufficiently acc urate optimal design of structures but also significantly reduces the required c omputing efforts, as compared to standard optimization schemes. Various examples of steel structure designs (i.e., two of which have been provided herein) have been successfully performed by the proposed approach. These illustrate the accur acy and robustness of the proposed method, where good comparisons with reference algorithms (including standard DE with ‘DE/rand/1' mutational strategy, Jaya, R ao-1 and CaDE) are evidenced."
      原文链接:
    • NETL
    • NSTL

    Barts Heart Centre Reports Findings in Hypertrophic Cardiomyopathy (Electrophysi ological Characterization of Subclinical and Overt Hypertrophic Cardiomyopathy b y Magnetic Resonance Imaging-Guided Electrocardiography)

    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 Heart Disorders and Di seases - Hypertrophic Cardiomyopathy is the subject of a report. According to ne ws reporting originating from London, United Kingdom, by NewsRx correspondents, research stated, "Ventricular arrhythmia in hypertrophic cardiomyopathy (HCM) re lates to adverse structural change and genetic status. Cardiovascular magnetic r esonance (CMR)-guided electrocardiographic imaging (ECGI) noninvasively maps car diac structural and electrophysiological (EP) properties." Our news editors obtained a quote from the research from Barts Heart Centre, "Th e purpose of this study was to establish whether in subclinical HCM (genotype [G]+ left ventricular hypertrophy [LVH] -), ECGI detects early EP abnormality, and in overt HCM, whether the EP substrat e relates to genetic status (G+/G-LVH+) and structural phenotype. This was a pro spective 211-participant CMR-ECGI multicenter study of 70 G+LVH-, 104 LVH+ (51 G +/53 G-), and 37 healthy volunteers (HVs). Local activation time (AT), corrected repolarization time, corrected activation-recovery interval, spatial gradients (G/G), and signal fractionation were derived from 1,000 epicardial sites per par ticipant. Maximal wall thickness and scar burden were derived from CMR. A suppor t vector machine was built to discriminate G+LVH- from HV and low-risk HCM from those with intermediate/high-risk score or nonsustained ventricular tachycardia. Compared with HV, subclinical HCM showed mean AT prolongation (P = 0.008) even with normal 12-lead electrocardiograms (ECGs) (P = 0.009), and repolarization wa s more spatially heterogenous (G: P = 0.005) (23% had normal ECGs) . Corrected activation-recovery interval was prolonged in overt vs subclinical H CM (P <0.001). Mean AT was associated with maximal wall th ickness; spatial conduction heterogeneity (G) and fractionation were associated with scar (all P<0.05), and G+LVH+ had more fractionation than G-LVH+ (P = 0.002). The support vector machine discriminated subclinical HC M from HV (10-fold cross-validation accuracy 80% [95% CI: 73%-85%]) and identified patients at higher risk of sudden cardiac death (accuracy 82% [95% CI: 78%-86%] ). In the absence of LVH or 12-lead ECG abnormalities, HCM sarcomere gene mutati on carriers express an aberrant EP phenotype detected by ECGI."Keywords for this news article include: London, United Kingdom, Europe, Cardiolo gy, Cardiomyopathies, Cardiovascular, Cardiovascular Diseases and Conditions, Di agnosis, Diagnostic Techniques and Procedures, Electrocardiography, Emerging Tec hnologies, Genetics, Health and Medicine, Heart Disease, Heart Disorders and Dis eases, Heart Function Tests, Hypertrophic Cardiomyopathy, Machine Learning, Magn etic Resonance, Risk and Prevention, Sudden Cardiac Death, Support Vector Machin es, Vector Machines
      原文链接:
    • NETL
    • NSTL

    Study Data from Indian Institute of Astrophysics Update Understanding of Machine Learning (Aerosol Classification By Application of Machine Learning Spectral Cl ustering Algorithm)

    27-28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 Machine Learn ing. According to news reporting originating in Bangalore, India, by NewsRx jour nalists, research stated, "Precise understanding of aerosol classification is cr ucial for accurately quantifying the effects of aerosols on the Earth's energy b udget, improving remote sensing retrieval algorithms, formulating climate change related policies, and more. In this study, we used aerosol measurements from the quality assured AERosol Robotic NETwork (AERONET) and utilized a multivariate s pectral clustering algorithm, a machine learning tool, to classify global aeroso ls." Financial support for this research came from Ministry of Earth Science (MoES), Government of India. The news reporters obtained a quote from the research from the Indian Institute of Astrophysics, "The spectral clustering algorithm is a variant of the clusteri ng algorithm that employs eigenvalues and eigenvectors of the data matrix to pro ject the data into a lower -dimensional space of a similar cluster. To accomplis h this, we considered five aerosol optical parameters: fine -mode Aerosol Optica l Depth, Extinction Angstrom Exponent, Absorption Angstrom Exponent, Single Scat tering Albedo, and Refractive Index from 150 AERONET sites distributed in six co ntinents (Africa, Asia, Australia, Europe, North and South America) during 1993 to 2022. Using the clustering analysis, we identified four primary aerosol types : dust, urban, biomass burning, and mixed aerosols. Among the continents, the Af rican and Asian sites exhibited the highest contribution of dust aerosols, as th e region has significant global dust sources." According to the news reporters, the research concluded: "Conversely, Australia, Europe, North, and South America are predominantly influenced by fine -mode aer osols, given their considerable distance from major dust source regions."
      原文链接:
    • NETL
    • NSTL

    Study Results from Donghua University Broaden Understanding of Machine Learning (Handling Missing Values and Imbalanced Classes In Machine Learning To Predict C onsumer Preference: Demonstrations and Comparisons To Prominent Methods)

    28-29页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-A new study on Machine Learning is now available. According to news reporting originating in Shanghai, People's Republic of China , by NewsRx journalists, research stated, "Consumer preference prediction aims t o predict consumers' future purchases based on their historical behavior-level d ata. Using machine learning algorithms, the prediction results provide evidence to conduct commercial activities and further improve consumer experiences." Funders for this research include National Natural Science Foundation of China ( NSFC), Fundamental Research Funds for the Central Universities and Graduate Stud ent Innovation Fund of Donghua University, Grants-in-Aid for Scientific Research (KAKENHI). The news reporters obtained a quote from the research from Donghua University, " However, missing values and imbalanced class problems of consumer behavioral dat a always make machine learning algorithms ineffective. While several methods hav e been proposed to address missing data or imbalanced class problems, few works have considered the relation-ships among missing mechanisms, imputation algorith ms, imbalanced class methods, and the effectiveness of classification algorithms that use impute data. In this study, we aim to propose an adaptive process for selecting the optimal combination of amputation, imputation, imbalance treatment , and classification based on classifi-cation performance. Our research extends the literature by showing significant interaction effects between 1) the amputat ion mechanism and imputation algorithms, 2) imputation and imbalance treatments, and 3) imbalance treatments and classification algorithms. Using three consumer behavioral datasets from the UCI Machine Learning Repository, we empirically sh ow that, among different classification methods, the overall performance of Rand om Forest is better than that of Logit, SVM, or Decision Tree. Moreover, Logit, as the most widely used classification method, suffers most from imbalance issue s in real-world datasets."
      原文链接:
    • NETL
    • NSTL