查看更多>>摘要：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 new report. According to news reporting from Jeddah, Saudi Arabia, by NewsRx journalists, research stated, "The rise of the e-commerce ind ustry has markedly changed the global economy, providing customers with unparall eled access to goods and services." The news correspondents obtained a quote from the research from University of Je ddah: "This study empirically examines online shoppers' perceptions and preferen ces, focusing on their experiences with last-mile delivery (LMD) services and it s impact on their shopping behaviour. This research employs machine learning cla ssification and regression models for a large-scale analysis of customers' respo nses, collected using an online survey in the main cities in Saudi Arabia, which is experiencing rapid e-commerce growth amidst a broader digital transformation . The findings highlight a strong consumer preference for timely LMD services, t ypically within a day of purchase, while noting dissatisfaction with exceedingly early delivery windows. The research emphasises the need to address customer di ssatisfaction with delivery services to retain clientele, as many may switch ret ailers without informing the retailers. Additionally, a considerable trend towar ds preferring digital over cash-on-delivery payment methods was observed among o nline shoppers."
查看更多>>摘要：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 new report. According to news reporting from Sidi Bel Abbe s, Algeria, by NewsRx journalists, research stated, "This paper introduces a num erical methodology for classifying and identifying types of bio-based materials through experimental thermal characterization." Our news reporters obtained a quote from the research from University of Djillal i Liabes: "In contrast to prevailing approaches that primarily focus on thermal conductivity, our characterization methodology encompasses several thermal param eters. In this paper, the physical characteristics of seven types of biobased c oncrete were analyzed, focusing on the thermal properties of palm- and esparto-f iber-reinforced concrete. The proposed method uses artificial intelligence techn iques, specifically the k-means clustering approach, to segregate data into homo geneous groups with shared thermal characteristics. This enables the elucidation of insights and recommendations regarding the utilization of bio-based insulati on in building applications. The results show that the k-means algorithm is able to efficiently classify the reference concrete (RC) with a performance of up to 71%."
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 originating from Nanning, Peop le's Republic of China, by NewsRx correspondents, research stated, "The accurate estimation of forest above-ground biomass (AGB) is crucial for sustainable fore st management and tracking the carbon cycle of forest ecosystem." Financial supporters for this research include Science And Technology Base And T alent Project of Guangxi; Guangxi Young And Middle-aged University Teachers' Sci entific Research Ability Enhancement Project; Ecosystem Soil And Water Conservat ion Function Assessment Project in Beibu Gulf, Guangxi Province; Mnr-cn Key Labo ratory of China-asean Satellite Remote Sensing Applications.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Robotics is the subject of a repo rt. According to news originating from Hefei, People's Republic of China, by New sRx correspondents, research stated, "The Large Sky Area Multi- Object Fiber Spec troscopy Telescope (LAMOST) is one of the most effective multiobject spectroscop ic instruments. Its survey efficiency is guaranteed by simultaneously positionin g multiple fibers via 4000 robotic fiber positioners (RFPs)." Funders for this research include the Scientific Research Project of Universitie s in Anhui Province, Scientific Research Project of Universities in Anhui Provin ce, Anhui Medical University.
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 Harbin, People's Repub lic of China, by NewsRx journalists, research stated, "High strength steel has b een attracting attention in the building industry due to its superior mechanical properties. The accurate design of high strength steel structures is crucial to boost its wide application." The news correspondents obtained a quote from the research from the Harbin Insti tute of Technology, "In this paper, an accurate and unified design approach for high strength steel I -section columns with different material grades, boundary conditions, geometric dimensions (including cross-section sizes and member lengt hs) and failure modes is proposed based on machine learning. Firstly, 871 experi mental and numerical data were collected from the literature to establish a data base. Then, seven machine learning algorithms, including Decision Tree, Random F orest, Support Vector Machine, K -Nearest Neighbour, Adaptive Boosting, Extreme Gradient Boosting and Categorical Boosting, were applied to establish machine le arning regression models to predict buckling resistances of high strength steel I -section columns. The model performance was then evaluated through statistic i ndices, with the evaluation results indicating that the Categorical Boosting tra ined model yields the highest level of accuracy. Based on the data in the collec ted database, the regression model trained by Categorical Boosting and existing codified design provisions, as given in the European code and American specifica tion, were assessed and compared."
查看更多>>摘要：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 from Semarang, Indone sia, by NewsRx journalists, research stated, "This paper endeavors to present an in-depth investigation into the corrosion inhibition efficiency (CIE) of novel triazole derivatives serving as corrosion inhibitors. Among the array of models considered, the extreme gradient boosting (XGBoost) model emerged as the most ad ept predictor in forecasting the CIE of N-heterocyclic organic compounds." The news correspondents obtained a quote from the research from the Faculty of C omputer Science, "This resolute preference for the XGBoost model was consistentl y upheld when employed in the prediction of the CIE for three newly synthesized triazole derivatives, namely, 2-[5-Phenyl-1-(2 ‘-furanylmethy lene)imino-(1,3,4)homotriazole]thio-N-(2 ‘-furanyl)hypomethyl acetylhydrazine, 2-[5-(3 ‘-Methyl)Phenyl- 1(2 ‘-furanylidine)i mino-(1,3,4)homotriazole]thio-N-(2 ‘-furanylidine) acetylhydr azine, and 2-[5-(4 ‘- Methyl) Phenyl-1-(2 ‘-furanylidine)imino -(1,3,4)homotriazole]thio-N-(2 ‘-furanylidine) acetylhydrazin e. Remarkably, this application of the XGBoost model yielded notably elevated CI E values, spanning from 88.35 % to 93.41 %. Supplemen tary density functional theory (DFT) calculations for these derivative compounds further substantiated the predictive trends observed through machine learning a nd experimental predictions."
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 Support Vector Machines. According to news reporting originating from Delhi, Ind ia, by NewsRx correspondents, research stated, "This paper proposes Parametric n on-parallel support vector machines for binary pattern classification." Our news editors obtained a quote from the research from South Asian University, "Through an intelligent redesigning of the Support vector machine optimisation, not only do we bring noise resilience into the model, but also retain its spars ity. Our model exhibits properties similar to Support vector machines, hence man y SVM related learning algorithms can be extended to make it scalable for large scale problems." According to the news editors, the research concluded: "Experimental results on several benchmark UCI datasets validate our claims." This research has been peer-reviewed.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Robotics is the subjec t of a report. According to news reporting out of Valencia, Spain, by NewsRx edi tors, research stated, "This paper introduces a novel resource-efficient control structure for remote path-following control of autonomous vehicles based on a c omprehensive combination of Kalman filtering, non-uniform dual-rate sampling, pe riodic event-triggered communication, and prediction-based and packet-based cont rol techniques. An essential component of the control solution is a non-uniform dual-rate extended Kalman filter (NUDREKF), which includes an h-step ahead predi ction stage." Our news journalists obtained a quote from the research from Technical Universit y Valencia (TU Valencia), "The prediction error of the NUDREKF is ensured to be exponentially mean-square bounded. The algorithmic implementation of the filter is straightforward and triggered by periodic event conditions. The main goal of the approach is to achieve efficient usage of resources in a wireless networked control system (WNCS), while maintaining satisfactory path-following behavior fo r the vehicle (a holonomic Mecanumwheeled robot). The proposal is additionally capable of coping with typical drawbacks of WNCS such as time-varying delays, an d packet dropouts and disorder. A Simscape Multibody simulation application reve als reductions of up to 93% in resource usage compared to a nomina l time-triggered control solution."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Research findings on artificial intelligence are discussed in a new report. According to news reporting out of Ottawa, Canada, by NewsRx editors, research stated, "Lifelong machine learning concerns the develo pment of systems that continuously learn from diverse tasks, incorporating new k nowledge without forgetting the knowledge they have previously acquired." Financial supporters for this research include Natural Sciences And Engineering Research Council of Canada. Our news editors obtained a quote from the research from University of Ottawa: " Multi-label classification is a supervised learning process in which each instan ce is assigned multiple non-exclusive labels, with each label denoted as a binar y value. One of the main challenges within the lifelong learning paradigm is the stability-plasticity dilemma, which entails balancing a model's adaptability in terms of incorporating new knowledge with its stability in terms of retaining p reviously acquired knowledge. When faced with multi-label data, the lifelong lea rning challenge becomes even more pronounced, as it becomes essential to preserv e relations between multiple labels across sequential tasks. This scoping review explores the intersection of lifelong learning and multi-label classification, an emerging domain that integrates continual adaptation with intricate multi-lab el datasets. By analyzing the existing literature, we establish connections, ide ntify gaps in the existing research, and propose new directions for research to improve the efficacy of multi-label lifelong learning algorithms. Our review une arths a growing number of algorithms and underscores the need for specialized ev aluation metrics and methodologies for the accurate assessment of their performa nce."
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 from Chapel Hill, North Carolina, by NewsRx correspondents, research stated, "Barrier islands are highly dynamic coastal landforms that are economically, ecologically, and socie tally important. Woody vegetation located within barrier island interiors can al ter patterns of overwash, leading to periods of periodic-barrier island retreat. " Funders for this research include National Science Foundation (NSF), Preston Jon es and Mary Elizabeth Frances Dean Martin Fellowship Fund from the Department of Earth, Marine and Environmental Sciences at the University of North Carolina at Chapel Hill.
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