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 the Tokyo Institute of T echnology by NewsRx journalists, research stated, “Various machine learning mode ls have been proposed to predict injuries to vehicle occupants from crash condit ions, and optimized and evaluated for model accuracy using binary classification performance metrics.” Our news correspondents obtained a quote from the research from Tokyo Institute of Technology: “However, performance metrics for injury probability prediction h ave not been utilized to develop injury prediction models. Therefore, this paper developed injury probability prediction models using evaluation metrics to eval uate the probability prediction performance of injury prediction models and to v erify the validity of injury probability predictions.”
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 San Diego, Ca lifornia, by NewsRx journalists, research stated, “This paper investigates the f easibility of downscaling within high-dimensional Lorenz models through the use of machine learning (ML) techniques.” The news journalists obtained a quote from the research from San Diego State Uni versity: “This study integrates atmospheric sciences, nonlinear dynamics, and ma chine learning, focusing on using large-scale atmospheric data to predict small- scale phenomena through ML-based empirical models. The highdimensional generali zed Lorenz model (GLM) was utilized to generate chaotic data across multiple sca les, which was subsequently used to train three types of machine learning models : a linear regression model, a feedforward neural network (FFNN)-based model, an d a transformer-based model. The linear regression model uses large-scale variab les to predict small-scale variables, serving as a foundational approach. The FF NN and transformer-based models add complexity, incorporating multiple hidden la yers and selfattention mechanisms, respectively, to enhance prediction accuracy . All three models demonstrated robust performance, with correlation coefficient s between the predicted and actual small-scale variables exceeding 0.9.”
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 from Norrkoping, Sweden, by NewsRx correspondents, research stated, “In ML4VIS investigates how to use m achine learning (ML) techniques to generate visualizations, and the field is rap idly growing with high societal impact. However, as with any computational pipel ine that employs ML processes, ML4VIS approaches are susceptible to a range of M L-specific adversarial attacks.”
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 from Beijing, People’s Republic of Ch ina, by NewsRx journalists, research stated, “Tactile sensing, especially pressu re and temperature recognition, is crucial for both humans and robots in identif ying objects. The general solutions, which use piezoresistive, capacitive, and t hermal resistance effects, are usually subject to single-mode sensing and an ene rgy supply.” The news correspondents obtained a quote from the research from the Minzu Univer sity of China, “Here, we propose a multimode self-powered sensor. The sensor can respond to pressure and temperature stimuli using triboelectric and thermoelect ric effects. Furthermore, we developed a sensing system comprising sensors, a de ep learning block, and a smart board. The deep learning model can fuse features of triboelectric and thermoelectric signals, enabling a high accuracy of 99.8% in recognizing ten objects.”
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 out of Pittsburgh, Pennsylvania, by NewsRx e ditors, research stated, “Representing the environment is a central challenge in robotics, and is essential for effective decision-making. Traditionally, before capturing images with a manipulator-mounted camera, users need to calibrate the camera using a specific external marker, such as a checkerboard or AprilTag.” Our news journalists obtained a quote from the research from Carnegie Mellon Uni versity, “However, recent advances in computer vision have led to the developmen t of 3D foundation models. These are large, pre-trained neural networks that can establish fast and accurate multi-view correspondences with very few images, ev en in the absence of rich visual features. This paper advocates for the integrat ion of 3D foundation models into scene representation approaches for robotic sys tems equipped with manipulator-mounted RGB cameras. Specifically, we propose the Joint Calibration and Representation (JCR) method. JCR uses RGB images, capture d by a manipulator-mounted camera, to simultaneously construct an environmental representation and calibrate the camera relative to the robot’s end-effector, in the absence of specific calibration markers. The resulting 3D environment repre sentation is aligned with the robot’s coordinate frame and maintains physically accurate scales.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news originating from Fuzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Fractional vegetation cove r (FVC) is an essential metric forvaluating ecosystem health and soil erosion. T raditional groundmeasuring methods are inadequate for large-scale FVC monitorin g, while remote sensing-based estimation approaches face issues such as spatial scale discrepancies between ground truth data and image pixels, as well as limit ed sample representativeness.” Financial supporters for this research include National Key Research And Develop ment Program of China; National Natural Science Foundation of China; Natural Sci ence Foundation of Fujian Province; Special Fund Project For Science And Technol ogy Innovation of Fujian Agriculture And Forestry University.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Drugs and Therapies - Personalized Medicine is the subject of a report. According to news reporting or iginating from New York City, New York, by NewsRx correspondents, research state d, “Accurate sample classification using transcriptomics data is crucial for adv ancing personalized medicine. Achieving this goal necessitates determining a sui table sample size that ensures adequate statistical power without undue resource allocation.” Our news editors obtained a quote from the research from Memorial Sloan-Ketterin g Cancer Center, “Current sample size calculation methods rely on assumptions an d algorithms that may not align with supervised machine learning techniques for sample classification. Addressing this critical methodological gap, we present a novel computational approach that establishes the power-versus-sample-size rela tionship by employing a data augmentation strategy followed by fitting a learnin g curve. We comprehensively evaluated its performance for microRNA and RNA seque ncing data, considering diverse data characteristics and algorithm configuration s, based on a spectrum of evaluation metrics. To foster accessibility and reprod ucibility, the Python and R code for implementing our approach is available on G itHub.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Implant Technology - C ochlear Implants is the subject of a report. According to news originating from Bern, Switzerland, by NewsRx correspondents, research stated, “As an increasing number of cochlear implant candidates exhibit residual inner ear function, heari ng preservation strategies during implant insertion are gaining importance. Manu al implantation is known to induce traumatic force and pressure peaks.” Financial support for this research came from University of Bern.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news originating from Coimbatore, India, by NewsRx corr espondents, research stated, “Forest cover type prediction is used for the fores t management organizations. It also get the insight on area of the forest cover up to date and development lack in present time.” Our news journalists obtained a quote from the research from the Karpagam Colleg e of Engineering, “Classification of the forest area and type of trees could eve ntually help in maintaining the eco system and to get inference on deforestation . In present scenario this problem gains more attention hence to retain the clim ate change impact forest cover type and area prediction would help a lot. This p aper proposes a novel ensemble machine learning based random de-correlated extra decision tree model for the forest cover type prediction. The tree based classi fiers perform well in prediction of the forest cover data. Many researchers use tree based classifiers for the problem. Even though the enhancement of the accur acy seems to be lower in the multi-class classification problem. So, this resear ch proposes the Extra random de-correlated decision tree method for the predicti on of the forest cover. The results the multiple de-correlated decision trees ar e aggregated for the final classification. This proposed method is the ensemble based method. In ensemble machine learning method combines several base optimal results in order to produce one final optimal result. A decision tree follows a simple predictive outcomes based on the series of the cause and effect values. W hile adopting the decision tree models the user has to follow the factors includ ing the variable on which the decision to be taken and threshold for deciding th e class. Instead of depending on one tree for decision making, multiple tree spl it criteria can be considered. Also these ensemble based machine learning allow to fine tune the predictor variable based on the feature to use and split criter ia. The random forest based methods follows the bagging strategy. It has a major role in the split aspect and decision-making aspect in significant manner. This machine learning model decides where to split based on random selection of feat ures. Random forest tree methods have a uniqueness where each split can be done through scrutiny of different features. This paper proposes the ensemble machine learning based random de-correlated extra decision tree model for the forest co ver type prediction. This algorithm especially suits the problem for the multicl ass classification nature. Forest cover type prediction helps in identifying the wilderness type and total area of the forest predicted and available. The datas et considered for the paper is from the UCI Machine Learning repository. It cont ains various features including elevation, slop, aspect, vertical and horizontal distance to hydrology, fire points and roadways, hill shade, wilderness area, s oil type and cover type. Initially the preprocessing is done in the data set by identifying the missing values, outlier detection and formatting data. Later the exploratory analysis is carried out using the Pearson correlation coefficients aspect. Then three machine learning techniques: Multiclass SVM, Boosting and pro posed EMLARDE were deployed. The accuracy of the proposed EMLARDE method outperf orms the other two algorithms.”
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 from London, United King dom, by NewsRx correspondents, research stated, “Solid-state sodium batteries re quire effective electrolytes that conduct at room temperature. The Na(3)PnCh(4) (Pn = P, Sb; Ch = S, Se) family has been studied for their high Na ion conductiv ity.” Financial supporters for this research include Swedish Research Council, Swedish Research Council, Engineering & Physical Sciences Research Counci l (EPSRC).
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