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    Researcher from Federico Ⅱ University Hospital Publishes New Studies and Findings in the Area of Machine Learning (Machine learning improves the accuracy of graft weight prediction in living donor liver transplantation)

    38-39页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting from Naples, Italy, by NewsRx journalists, research stated, “Precise graft weight (GW) estimation is essential for planning living donor liver transplantation to select grafts of adequate size for the recipient. This study aimed to investigate whether a machine-learning model can improve the accuracy of GW estimation.” Our news journalists obtained a quote from the research from Federico Ⅱ University Hospital: “Data from 872 consecutive living donors of a left lateral sector, left lobe, or right lobe to adults or children for living-related liver transplantation were collected from January 2011 to December 2019. Supervised machine-learning models were trained (80% of observations) to predict GW using the following information: donor’s age, sex, height, weight, and body mass index; graft type (left, right, or left lateral lobe); computed tomography estimated graft volume and total liver volume. Model performance was measured in a random independent set (20% of observations) and in an external validation cohort using the mean absolute error (MAE) and the mean absolute percentage error and compared with methods currently available for GW estimation. The best-performing machine-learning model showed an MAE value of 50 ± 62 g in predicting GW, with a mean error of 10.3%. These errors were significantly lower than those observed with alternative methods. In addition, 62% of predictions had errors <10%, whereas errors >15% were observed in only 18.4% of the cases compared with the 34.6% of the predictions obtained with the best alternative method (p <0.001).”
      原文链接:
    • NETL
    • NSTL
    • Lippincott Williams & Wilkins

    Panzhihua University Researcher Has Published New Data on Artificial Intelligence (Research on the Innovation of Business English Teaching Mode in Colleges and Universities under the Background of Artificial Intelligence)

    39-39页
    查看更多>>摘要：Research findings on artificial intelligence are discussed in a new report. According to news reporting from Sichuan, People’s Republic of China, by NewsRx journalists, research stated, “This paper examines the application of artificial intelligence technology in business English teaching and artificial intelligence in natural language processing and recognition to receive good results.” The news reporters obtained a quote from the research from Panzhihua University: “These two technologies are suitable for business English teaching innovation. At this time, the introduction of the concept of ADDIE is the process of integrating artificial intelligence technology into the business English teaching innovation model. The business English teaching model is designed to utilize the conditional random field expansion model to address the label bias limitations of the entropy model. Add the LSTM model to this basis to handle speech recognition and translation tasks to complete the construction of a business English teaching model based on LSTM-CRF. After speech recognition, detect the existence of grammatical errors in the sentence through the multi-task model, deform the LSTM model, and use the deformed Bi-LSTM to construct the multi-task learning model framework. The objective of controlled experiments and empirical analyses is to investigate the students’ willingness to learn AI Business English and their learning effects.”
      原文链接:
    • NETL
    • NSTL

    Research from Department of Mechanical Design Engineering Has Provided New Study Findings on Machine Learning (Optimization of Occupant Restraint System Using Machine Learning for THORM50 and Euro NCAP)

    40-40页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting from the Department of Mechanical Design Engineering by NewsRx journalists, research stated, “In this study, we propose an optimization method for occupant protection systems using a machine learning technique.” Funders for this research include Ministry of Land, Infrastructure And Transport. Our news journalists obtained a quote from the research from Department of Mechanical Design Engineering: “First, a crash simulation model was developed for a Euro NCAP MPDB frontal crash test condition. Second, a series of parametric simulations were performed using a THOR dummy model with varying occupant safety system design parameters, such as belt attachment locations, belt load limits, crash pulse, and so on. Third, metamodels were developed using neural networks to predict injury criteria for a given occupant safety system design. Fourth, the occupant safety system was optimized using metamodels, and the optimal design was verified using a subsequent crash simulation. Lastly, the effects of design variables on injury criteria were investigated using the Shapely method. The Euro NCAP score of the THOR dummy model was improved from 14.3 to 16 points.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Studies Conducted at Chinese Academy of Sciences on Machine Learning Recently Reported (Expert-augmented Machine Learning To Accelerate the Discovery of Copolymers for Anion Exchange Membrane)

    41-42页
    查看更多>>摘要：Investigators discuss new findings in Machine Learning. According to news reporting from Changchun, People’s Republic of China, by NewsRx journalists, research stated, “We constructed an automated machine learning pipeline to screen the broad chemical space of candidate co-polymers for anion exchange membranes (AEMs). The pipeline utilizes a genetic algorithm integrated twelve machine learning algorithms, to screen copolymers that are potentially capable to improve AEM performance through the evolution of polymer hydrophobic and hydrophilic backbones and cation groups.” Funders for this research include National Natural Science Foundation of China (NSFC), Major Science and Technology Projects for Independent Innovation (Research and Development of Key Technologies) of China FAW Group Co., Ltd.), National S & T Major Project, Guizhou University Talents Fund.
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    New Robotics Data Has Been Reported by a Researcher at University of Catania (Stability and Safety Learning Methods for Legged Robots)

    41-41页
    查看更多>>摘要：New research on robotics is the subject of a new report. According to news reporting from Catania, Italy, by NewsRx journalists, research stated, “Learning-based control systems have shown impressive empirical performance on challenging problems in all aspects of robot control and, in particular, in walking robots such as bipeds and quadrupeds.” Funders for this research include Mur Pnrr-mission 4-COMP.2-INV:1.3. Our news correspondents obtained a quote from the research from University of Catania: “Unfortunately, these methods have a major critical drawback: a reduced lack of guarantees for safety and stability. In recent years, new techniques have emerged to obtain these guarantees thanks to data-driven methods that allow learning certificates together with control strategies. These techniques allow the user to verify the safety of a trained controller while providing supervision during training so that safety and stability requirements can directly influence the training process.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    University of Florence Reports Findings in Machine Learning (Machine Learning and Deep Learning for Safety Applications: Investigating the Intellectual Structure and the Temporal Evolution)

    42-43页
    查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting originating from Florence, Italy, by NewsRx correspondents, research stated, “Over the last decades, safety requirements have become of primary concern. In the context of safety, several strategies could be pursued in many engineering fields.” Our news editors obtained a quote from the research from the University of Florence, “Moreover, many techniques have been proposed to deal with safety, risk, and reliability matters, such as Machine Learning (ML) and Deep Learning (DL). ML and DL are characterised by a high variety of algorithms, adaptable for different purposes. This generated wide and fragmented literature on ML and DL for safety purposes, moreover, literature review and bibliometric studies of the past years mainly focus on a single research area or application field. Thus, this paper aims to provide a holistic understanding of the research on this topic through a Systematic Bibliometric Analysis (SBA), along with proposing a viable option to conduct SBAs. The focus is on investigating the main research areas, application fields, relevant authors and studies, and temporal evolution. It emerged that rotating equipment, structural health monitoring, batteries, aeroengines, and turbines are popular fields. Moreover, the results depicted an increase in popularity of DL, along with new approaches such as deep reinforcement learning through the past four years.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    New Machine Learning Study Findings Has Been Reported by a Researcher at University of Applied Sciences and Arts (Enhancing Tree Species Identification in Forestry and Urban Forests through Light Detection and Ranging Point Cloud Structural ...)

    43-44页
    查看更多>>摘要：Research findings on artificial intelligence are discussed in a new report. According to news reporting originating from Gottingen, Germany, by NewsRx correspondents, research stated, “As remote sensing transforms forest and urban tree management, automating tree species classification is now a major challenge to harness these advances for forestry and urban management.” The news editors obtained a quote from the research from University of Applied Sciences and Arts: “This study investigated the use of structural bark features from terrestrial laser scanner point cloud data for tree species identification. It presents a novel mathematical approach for describing bark characteristics, which have traditionally been used by experts for the visual identification of tree species. These features were used to train four machine learning algorithms (decision trees, random forests, XGBoost, and support vector machines). These methods achieved high classification accuracies between 83% (decision tree) and 96% (XGBoost) with a data set of 85 trees of four species collected near Krakow, Poland.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    New Robotics Findings from School of Applied Technology Described (Path Planning of Fruit And Vegetable Picking Robots Based on Improved A* Algorithm And Particle Swarm Optimization Algorithm)

    44-45页
    查看更多>>摘要：Investigators publish new report on robotics. According to news reporting from Henan, People’s Republic of China, by NewsRx journalists, research stated, “Aiming at the suboptimal local path, slow convergence speed, and many inflection points in the path planning of fruit and vegetable picking robots in complex environments, a global planning method combining particle swarm optimization (PSO) algorithm and A* algorithm was proposed.” Our news editors obtained a quote from the research from School of Applied Technology: “Firstly, Manhattan distance was taken as a heuristic function of global programming based on the A* algorithm. Secondly, the step size of PSO was adjusted to optimize the path, shorten the path length, and reduce the number of inflection points. Finally, the planned path of the fruit and vegetable picking robot was smoothed so that it could steadily move along a smoother driving path in real scenarios. The experimental results show that compared with the traditional PSO algorithm, the hybrid algorithm based on the improved A* algorithm and PSO algorithm achieves a smoother path and fewer folding points.”
      原文链接:
    • NETL
    • NSTL
    • Inst Natl Cercetare

    University of Strasbourg Reports Findings in Machine Learning (Kinetic solubility: Experimental and machine-learning modeling perspectives)

    45-46页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting out of Strasbourg, France, by NewsRx editors, research stated, “Kinetic aqueous or buffer solubility is important parameter measuring suitability of compounds for high throughput assays in early drug discovery while thermodynamic solubility is reserved for later stages of drug discovery and development. Kinetic solubility is also considered to have low inter-laboratory reproducibility because of its sensitivity to protocol parameters [1].” Our news journalists obtained a quote from the research from the University of Strasbourg, “Presumably, this is why little efforts have been put to build QSPR models for kinetic in comparison to thermodynamic aqueous solubility. Here, we investigate the reproducibility and modelability of kinetic solubility assays. We first analyzed the relationship between kinetic and thermodynamic solubility data, and then examined the consistency of data from different kinetic assays. In this contribution, we report differences between kinetic and thermodynamic solubility data that are consistent with those reported by others [1, 2] and good agreement between data from different kinetic solubility campaigns in contrast to general expectations. The latter is confirmed by achieving high performing QSPR models trained on merged kinetic solubility datasets. The poor performance of QSPR model trained on thermodynamic solubility when applied to kinetic solubility dataset reinforces the conclusion that kinetic and thermodynamic solubilities do not correlate: one cannot be used as an ersatz for the other. This encourages for building predictive models for kinetic solubility.”
      原文链接:
    • NETL
    • NSTL
    • Wiley

    New Machine Learning Findings from Blekinge Institute of Technology Described (Bibliometric Mining of Research Trends in Machine Learning)

    46-47页
    查看更多>>摘要：New study results on artificial intelligence have been published. According to news reporting from Karlskrona, Sweden, by NewsRx journalists, research stated, “We present a method, including tool support, for bibliometric mining of trends in large and dynamic research areas. The method is applied to the machine learning research area for the years 2013 to 2022.” Financial supporters for this research include Knowledge Foundation in Sweden Through The Project “green Clouds-load Prediction And Optimization in Private Cloud Systems”. The news editors obtained a quote from the research from Blekinge Institute of Technology: “A total number of 398,782 documents from Scopus were analyzed. A taxonomy containing 26 research directions within machine learning was defined by four experts with the help of a Python program and existing taxonomies. The trends in terms of productivity, growth rate, and citations were analyzed for the research directions in the taxonomy. Our results show that the two directions, Applications and Algorithms, are the largest, and that the direction Convolutional Neural Networks is the one that grows the fastest and has the highest average number of citations per document. It also turns out that there is a clear correlation between the growth rate and the average number of citations per document, i.e., documents in fast-growing research directions have more citations. The trends for machine learning research in four geographic regions (North America, Europe, the BRICS countries, and The Rest of the World) were also analyzed.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi