查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-A new study on symbolic computation is now availa ble. According to news reporting from Islamabad,Pakistan,by NewsRx journalists ,research stated,"This study shows the link between computer science and appli ed mathematics." The news editors obtained a quote from the research from Riphah International Un iversity I-14: "It conducts a dynamics investigation of new root solvers using c omputer tools and develops a new family of single-step simple root-finding metho ds. The convergence order of the proposed family of iterative methods is two,ac cording to the convergence analysis carried out using symbolic computation in th e computer algebra system CAS-Maple 18. Without further evaluations of a given n onlinear function and its derivatives,a very rapid convergence rate is achieved ,demonstrating the remarkable computing efficiency of the novel technique. To d etermine the simple roots of nonlinear equations,this paper discusses the dynam ic analysis of one-parameter families using symbolic computation,computer anima tion,and multiprecision arithmetic. To choose the best parametric value used i n iterative schemes,it implements the parametric and dynamical plane technique using CAS-MATLAB$ ∧{@} R2011b. $ The dynamic evaluation of the methods is also presented utilizing basins of attraction to analyze their convergence behavior."
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 - Intelligent Systems. According to news reporting originating fr om Chengdu,People's Republic of China,by NewsRx correspondents,research state d,"Network embedding is a technique used to generate low-dimensional vectors re presenting each node in a network while maintaining the original topology and pr operties of the network. This technology enables a wide range of learning tasks,including node classification and link prediction." Funders for this research include National Natural Science Foundation of China ( NSFC),Science and Technology Program of Sichuan Province,National Natural Scie nce Foundation of China (NSFC),Foundation of Cyberspace Security Key Laboratory of Sichuan Higher Education Institutions. Our news editors obtained a quote from the research from Xihua University,"Howe ver,the current landscape of network embedding approaches predominantly revolve s around static networks,neglecting the dynamic nature that characterizes real social networks. Dynamics at both the micro- and macrolevels are fundamental dri vers of network evolution. Microlevel dynamics provide a detailed account of the network topology formation process,while macrolevel dynamics reveal the evolut ionary trends of the network. Despite recent dynamic network embedding efforts,a few approaches accurately capture the evolution patterns of nodes at the micro level or effectively preserve the crucial dynamics of both layers. Our study int roduces a novel method for embedding networks,i.e.,bilayer evolutionary patter n-preserving embedding for dynamic networks (Bi-DNE),that preserves the evoluti onary patterns at both the microand macrolevels. The model utilizes strengthen ed triadic closure to represent the network structure formation process at the m icrolevel,while a dynamic equation constrains the network structure to adhere t o the densification power-law evolution pattern at the macrolevel. The proposed Bi-DNE model exhibits significant performance improvements across a range of tas ks,including link prediction,reconstruction,and temporal link analysis. These improvements are demonstrated through comprehensive experiments carried out on both simulated and real-world dynamic network datasets. The consistently superio r results to those of the state-of-the-art methods provide empirical evidence fo r the effectiveness of Bi-DNE in capturing complex evolutionary patterns and lea rning high-quality node representations. These findings validate the methodologi cal innovations presented in this work and mark valuable progress in the emergin g field of dynamic network representation learning."
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 in Kunming,People's Republic of China,by NewsRx journalists,research stated,"Accurate prediction of the n orthward shift of the South Asian High (SAH) in June is crucial for improving th e flood and drought management of Asian countries during the summer. This study investigates the ability of three supervised machine learning (ML) models in pre dicting the meridional index of the SAH (SAHI) in June." Funders for this research include National Natural Science Foundation of China ( NSFC),Natural Science Foundation of Yunnan Province. The news reporters obtained a quote from the research from Yunnan University,"T he ML models include the extreme gradient boosting (XGBoost),the support vector machine (SVM),and the multi-layer perceptron (MLP) neural network. The trainin g data is derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) model data that is significantly correlated with the reference data at a 99 % confidence level. The hyperparameter optimization (HPO) is performed for each ML model using the particle swarm optimization (PSO). Six objective functions are defined for the HPO based on the conventional root-mean-square error (RMSE),the interannual variability skill score,and the temporal correlation coefficient ( TCC). The performance of optimized ML models is evaluated with the TCC and the s ame sign rate (SSR). The top two models are the PSOXGBoost model tuned with RMSE + IVS and the PSO-SVM model tuned with log(RMSE+TCC). Their stacked ensemble mo del has the TCC of 0.54 and the SSR of 72%. The average of the best model hindcasts has a higher TCC of 0.61 but a lower SSR of 67% t han the ensemble model. Further investigation suggests that the ensemble model o nly preserves the predictor-predictand relationships for two predictors. To impr ove the representation of the predictor-predictand relationship,we divided the predictors into two groups and trained the ML models separately with interannual increments of predictors in Group 1 and standardized anomalies of predictors in Group 2. The average of the best model hindcasts from the two groups have the T CC of 0.63 and the SSR of 72%. The improvement in the SAHI hindcast is associated with a more realistic predictor-predictand relationship in the ML models."
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 Ar tificial Intelligence. According to news reporting from Castellon de la Plana,S pain,by NewsRx journalists,research stated,"Advanced Machine Learning (ML) al gorithms can be applied using Edge Computing (EC) to detect anomalies,which is the basis of Artificial Intelligence of Things (AIoT). EC has emerged as a solut ion for processing and analysing information on IoT devices." Funders for this research include MCIN/AEI,ERDF,a way of making Europe,Europe an Union (EU),European Union-Next GenerationEU/PRTR,Juan de la Cierva-Incorpor acion postdoctoral programme of the Ministerio de Ciencia e Innovacion-Spanish g overnment - MCIN/AEI,ValgrAI-Valencian Graduate School and Research Network for Artificial Intelligence (Generalitat Valenciana). The news correspondents obtained a quote from the research from Jaume I Universi ty,"This field aims to allow the implementation of Machine/Deep Learning (DL) m odels on MicroController Units (MCUs). Integrating anomaly detection analysis on Internet of Things (IoT) devices produces clear benefits as it ensures the use of accurate data from the initial stage. However,this process poses a challenge due to the unique characteristics of IoT. This article presents a Systematic Li terature Mapping of scientific research on the application of anomaly detection techniques in EC using MCUs. A total of 18 papers published over the period 2021 -2023 were selected from a total of 162 in four databases of scientific papers."
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 artificial intelligence. According to news reporting from Beijing,People's Repu blic of China,by NewsRx journalists,research stated,"The severe wildfires tha t have ravaged Guangdong province,China,present a significant threat to the lo cal ecosystem,socioeconomics,and public health." Funders for this research include National Natural Science Foundation of China; Guangdong Provincial Department of Science And Technology; Shenzhen Science And Technology Innovation Committee. The news correspondents obtained a quote from the research from Tsinghua Univers ity: "Effective risk assessment is essential for early warning and timely preven tion in wildfire management,thereby mitigating disaster losses. In this study,we compiled a dataset comprising 11,507 historical wildfire incidents in Guangdo ng Province spanning a decade (2011-2021) and developed a deep learning-based mo del to predict the likelihood of wildfire occurrence in the region. In addition to analyzing risk characteristics throughout the year,we also trained separate models for different seasons and analyzed the discrepancies in the contribution of driven factors to wildfire occurrence across seasons. Furthermore,the perfor mance of our deep learning-based model was compared with that of traditional mac hine learning algorithms. The experimental results revealed that: (1) Factors su ch as relative humidity,temperature,NDVI,and precipitation exerted significan t influence on wildfire occurrence. (2) The impact of wildfire driving factors v aried across different seasons."
查看更多>>摘要：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 originating from Aachen,German y,by NewsRx correspondents,research stated,"3D modeling is a major challenge in computer-assisted surgery (CAS). Manual segmentation,as the gold standard,i s tedious,time consuming,and particularly challenging for the mandible,while artificial intelligence (AI)- based segmentation is a promising and time-saving a lternative." Financial support for this research came from Medical Faculty of RWTH Aachen Uni versity as part of the Clinician Scientist Program.
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 Dalian,People's Rep ublic of China,by NewsRx editors,research stated,"The innovative combination of additive manufacturing (AM) and continuous fiber-reinforced polymer composite s (CFRPCs) confers products with the dual advantages of integrated manufacturing and designability of properties,but lack an efficient and reliable method for property prediction. This study presents a materials informatics framework using reduced-order models and machine learning (ML) to extract the structurepropert y (SP) linkages between microstructures and macroscopic elastic properties of AM -CFRPCs." Funders for this research include Project of Liaoning Provincial Department of E ducation,National Natural Science Foundation of China (NSFC),Dalian Excellent Young Science and Technology Talent Program,Research Project of Dalian Science and Technology Innovation Fund.
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 from Reno,Neva da,by NewsRx correspondents,research stated,"Study region: The Mississippi Al luvial Plain (MAP) in the United States (US). Study focus: Understanding local-s cale groundwater use,a critical component of the water budget,is necessary for implementing sustainable water management practices." Funders for this research include United States Department of Agriculture (USDA) ,University of Colorado Boulder,University of Colorado Anschutz,Colorado Stat e University,National Science Foundation (NSF). Our news editors obtained a quote from the research from Desert Research Institu te,"The MAP is one of the most productive agricultural regions in the US and ex tracts more than 11 km3/year for irrigation activities. Consequently,groundwate r-level declines in the MAP region pose a substantial challenge to water sustain ability,and hence,we need reliable groundwater pumping monitoring solutions to manage this resource appropriately. New hydrological insights for the region: W e incorporate remote sensing datasets and machine learning to improve an existin g lookup table-based model of groundwater use previously developed by the U.S. G eological Survey (USGS). Here,we employ Distributed Random Forests,an ensemble machine learning algorithm to predict annual and monthly groundwater use (2014- 2020) throughout this region at 1-km resolution,using pumping data from existin g flowmeters in the Mississippi Delta. Our model compares favorably with the exi sting USGS model,with higher R2 (0.51 compared to 0.42 in the previous model),and lower root mean square error (RMSE) and mean absolute error (MAE)- 0.14 m an d 0.09 m,respectively in our model,compared to 0.15 m and 0.1 m in the previou s model."
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 . According to news originating from Maharashtra,India,by NewsRx correspondents,research stated,"Speckle noise (SN) is one of the major types of noise that fr equently occurs in different coherent imaging systems such as medical imaging,S ynthetic Aperture Radar (SAR) and active Radar images. SAR is a powerful imaging technology that generates fine-resolution images and monitors the earth's surfa ce in order to identify its physical properties." The news editors obtained a quote from the research from Dr. Vishwanath Karad MI T World Peace University: "The satellite images captured by SAR are mainly affec ted by SN,which reduces the quality of images and complicates the image represe ntation. Therefore,removing SN from SAR images is one of the major challenges a nd needs significant attention. The proposed study introduces an optimal Machine Learning (ML) classifier named Kernel Support Vector Machine-Improved Aquila Op timization (KSVMIAO) for reducing SN in SAR images. This study uses a two-step process called filtering and enhanced despeckling to minimize the consequence of speckle suppression. In the first step,different imaging filters,namely Impro ved Lee Filter (ILF),Improved Frost Filter (IFF),Improved Kuan Filter (IKF) an d Improved Boxcar Filter (IBF),are utilized to remove the SN in SAR images. Nex t,the denoised image is fed to the second stage,which makes use of an optimize d KSVM-IAO classifier to obtain an enhanced despeckle image."
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 originating from Caceres,Spain,by New sRx correspondents,research stated,"Industries transition to the Industry 4.0 paradigm requires solutions based on devices attached to machines that allow mon itoring and control of industrial equipment. Monitoring is essential to ensure d evices' proper operation against different aggressions." Financial supporters for this research include State Research Agency,Government of Extremadura (Spain). Our news journalists obtained a quote from the research from the University of E xtremadura,"We propose an approach to detect and classify faults that are typic al in these devices,based on machine learning techniques that use energy,proce ssing,and main application use as features. The proposal was validated using a dataset collected from a testbed executing a typical equipment monitoring applic ation. The proposed machine learning pipeline uses a decision tree-based model f or fault detection (with 99.4% accuracy,99.7% preci sion,99.6% recall,75.2% specificity,and 99.7% F1) followed by a Semi-Supervised Graph-Based model (with 99.3% ac curacy,96.4% precision,96.1% recall,99.6% specificity,and 96.2% F1) for further fault classification."
NETL
NSTL