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Robotics & Machine Learning Daily News

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    University of Guadalajara Reports Findings in Machine Learning (Application of the performance of machine learning techniques as support in the prediction of school dropout)

    57-57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 Machine Learning is the subject of a report. According to news reporting originating from Jalisco, Mexico, by NewsRx correspondents, research stated, "This article presents a study, intending to design a model with 90% reliability, which helps in the prediction of school dropouts in higher and secondary education institutions, implementing machine learning techniques. The collection of information was carried out with open data from the 2015 Intercensal Survey and the 2010 and 2020 Population and Housing censuses carried out by the National Institute of Statistics and Geography, which contain information about the inhabitants and homes. in the 32 federal entities of Mexico." Our news editors obtained a quote from the research from the University of Guadalajara, "The data were homologated and twenty variables were selected, based on the correlation. After cleaning the data, there was a sample of 1,080,782 records in total." According to the news editors, the research concluded: "Supervised learning was used to create the model, automating data processing with training and testing, applying the following techniques, Artificial Neural Networks, Support Vector Machines, Linear Ridge and Lasso Regression, Bayesian Optimization, Random Forest, the first two with a reliability greater than 99% and the last with 91%."
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    Researchers from University of Illinois Discuss Findings in Escherichia coli O157:H7 (Machine Learning and Taguchi Doe Combined Approach for Modeling Dynamic Ultrasound-assisted Freshcut Leafy Green Sanitation)

    58-58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 Gram-Negative Bacteria-Escherichia coli O157:H7. According to news reporting originating in Urbana, Illinois, by NewsRx journalists, research stated, "Chlorinebased fresh produce sanitation is a dynamic process, and sanitation efficiency is limited due to chlorine degradation. Here, ultrasound was coupled with a benchtop sanitation system to enhance chlorine sanitizer efficiency in fresh-cut leafy green sanitation." Financial supporters for this research include National Institute of Food and Agriculture, AFRI Competitive Grant, United States Department of Agriculture (USDA). The news reporters obtained a quote from the research from the University of Illinois, "Taguchi design of experiments (DOE) and machine learning (ML) were combined to model the relationship between sanitation condition parameters and sanitation outcomes. Multiple ML algorithms were fitted, tuned, and compared for performance using 127 experimental trials (training-to-validation ratio = 3:1). Gaussian process regression (GPR) models showed the best performance in predicting sanitation outcomes of chemical oxygen demand (COD, R-2 = 0.73), remaining Escherichia coli O157:H7 on the leaf surface (‘Surface Microbe', R-2 = 0.88), and E. coli O157:H7 concentration in sanitation water (‘Water Microbe', R-2 = 1.00). Cut size and agitation speed were identified as the most critical input parameters. An initial free chlorine concentration over 20 mg/L was recommended to minimize the E. coli O157:H7 concentration in sanitation water. This work showcases the combined approach of ML and DOE in optimizing fresh-cut produce sanitation."
      原文链接:
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    Findings on Machine Learning Reported by Investigators at University of Illinois (Explainable, Interpretable, and Trustworthy Ai for an Intelligent Digital Twin: a Case Study On Remaining Useful Life)

    59-59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 Learning have been published. According to news originating from Urbana, Illinois, by NewsRx correspondents, research stated, "Artificial intelligence (AI) and Machine learning (ML) are increasingly used for digital twin development in energy and engineering systems, but these models must be fair, unbiased, interpretable, and explainable. It is critical to have confidence in AI's trustworthiness." Financial support for this research came from National Science Foundation (NSF). Our news journalists obtained a quote from the research from the University of Illinois, "ML techniques have been useful in predicting important parameters and improving model performance. However, for these AI techniques to be useful in making decisions, they need to be audited, accounted for, and easy to understand. Therefore, the use of explainable AI (XAI) and interpretable machine learning (IML) is crucial for the accurate prediction of prognostics, such as remaining useful life (RUL), in a digital twin system to make it intelligent while ensuring that the AI model is transparent in its decision-making processes and that the predictions it generates can be understood and trusted by users. By using an explainable, interpretable, and trustworthy AI, intelligent digital twin systems can make more accurate predictions of RUL, leading to better maintenance and repair planning and, ultimately, improved system performance. This paper aims to explain the ideas of XAI and IML and justify the important role of AI/ML for the digital twin components, which requires XAI to understand the prediction better."
      原文链接:
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    Wuchang Shouyi University Researcher Publishes Findings in Artificial Intelligence (Strategic Research on English Teaching Reform in Colleges and Universities Supported by Artificial Intelligence Technology)

    60-60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 artificial intelligence. According to news originating from Hubei, People's Republic of China, by NewsRx editors, the research stated, "The emergence of new technologies and new means has brought unprecedented opportunities for college teaching, and the traditional college teaching mode can no longer meet the current needs of English teaching." The news journalists obtained a quote from the research from Wuchang Shouyi University: "This paper analyzes the current English teaching mode in colleges and universities by using the S-T teaching analysis method and constructs the S-T behavioral sequence of English teaching in colleges and universities by coding the samples in classroom teaching videos. Utilizing Markov chain to evaluate the drawbacks of traditional college English classroom teaching enables reflection and modification of English classroom teaching methods and specific teaching links. Classify English classroom teaching behaviors according to S-T teaching behavior coding, establish a link between teachers' comprehensive control level in the classroom and the area of the teaching behavior cloud, and judge teachers' comprehensive control level according to the area of the cloud. The proposed teaching reform strategies are based on an analysis of English teaching problems in colleges and universities."
      原文链接:
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    Reports from University for Development Studies Describe Recent Advances in Machine Learning (Provenance Studies of Aubearing Stream Sediments and Performance Assessment of Machine Learning-based Models: Insight From Whole-rock Geochemistry ...)

    61-61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 Learning have been published. According to news originating from Tamale, Ghana, by NewsRx correspondents, research stated, "The source of clastic sediments generally, can be traced to their source through provenance studies using the whole rock geochemistry of clastic sediments. However, the provenance of the Au-bearing stream sediments within the central parts of Tanzania is yet to be deciphered." Our news journalists obtained a quote from the research from University for Development Studies, "Hence, in this study, to enhance exploration targeting, the source of the Au-bearing stream sediments was characterized using whole-rock geochemistry. The performance of linear regression (LR), decision tree (DT), and polynomial regression (PR) models as prediction models for the Au mineralization in the area, were also compared as additional Au exploration techniques worth exploring in the area. The weathering condition proxies, CIA, ICV, CIW, and PIA as well as discriminant diagrams suggest weakly to intensely weathered sediments. The values of SiO2/Al2O3 and K2O/Al2O3 are indicative of felsic source rocks rather than compositional maturity due to sediments reworking. From Th/Cr, Cr/Th, Th/U, La/Sc, and Th/Sc proxies, the Au-bearing stream sediments are sourced from felsic igneous rocks. These indications are corroborated by the correlation matrix assessment. However, Au is not sourced from the same source rocks as the host sediments due probably, to a prior depositional mixing of the sediments before subsequent transportation to their current depositional environment. With R2 (0.62), MAE (0.6035), MSE (0.6546), and RMSE (0.8091) for LR, R2 (1.0), MAE (0.7500), MSE (1.6273), and RMSE (1.2752) for DT, and R2 (1.0), MAE (2.6608), MSE (12.7840), and RMSE (3.5755), for PR."
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    Researcher from University of Sindh Reports Details of New Studies and Findings in the Area of Artificial Intelligence (The Rise of Artificial Intelligence and Its Influence on Employee Performance and Work)

    62-62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 artificial intelligence. According to news reporting out of Sindh, Pakistan, by NewsRx editors, research stated, "The objective of this paper is to investigate the impact of artificial intelligence (AI) on employee performance and work commitment within the workplace, while also acknowledging its limitations." The news reporters obtained a quote from the research from University of Sindh: "The study employs a qualitative research approach and utilizes a simple random sampling method. Data collection is conducted through online questionnaires using Google Forms. The majority of the respondents (91.8%) fall within the age range of 20-30 years, with a total of 100 participants consisting of 58% females and 42% males. The findings reveal that AI can positively influence employee performance and work engagement. AI refers to the use of computers to simulate intelligent behavior with minimal human intervention." According to the news editors, the research concluded: "However, there are concerns raised by academics regarding potential job losses and an increase in unemployment rates due to AI. Consequently, this may pose challenges in terms of infrastructure reconstruction, ensuring vehicle safety, and adapting laws and regulations."
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    Researchers from Boston University Report Recent Findings in Robotics (Safe Supervisory Control of Soft Robot Actuators)

    62-63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 in a new report. According to news reporting originating in Boston, Massachusetts, by NewsRx journalists, research stated, "Although soft robots show safer interactions with their environment than traditional robots, soft mechanisms and actuators still have significant potential for damage or degradation particularly during unmodeled contact. This article introduces a feedback strategy for safe soft actuator operation during control of a soft robot." Financial supporters for this research include Office of Naval Research, National Oceanographic Partnership Program (NOPP), Intelligence Community Postdoctoral Research Fellowship through the Oak Ridge Institute for Science and Education.
      原文链接:
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    Findings from Huazhong Agricultural University Provides New Data on Machine Learning (Multi-omics Assists Genomic Prediction of Maize Yield With Machine Learning Approaches)

    63-64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 Machine Learning. According to news reporting originating from Wuhan, People's Republic of China, by NewsRx correspondents, research stated, "With the improvement of high-throughput technologies in recent years, large multi-dimensional plant omics data have been produced, and big-data-driven yield prediction research has received increasing attention. Machine learning offers promising computational and analytical solutions to interpret the biological meaning of large amounts of data in crops." Financial support for this research came from National Natural Science Foundation of China (NSFC).
      原文链接:
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    New Robotics Data Have Been Reported by Investigators at Fuzhou University (Review: Application of 3d Printing Technology In Soft Robots)

    64-65页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Robotics have been presented. According to news originating from Fuzhou, People's Republic of China, by NewsRx correspondents, research stated, "Soft robots, inspired by living organisms in nature, are primarily made of soft materials, and can be used to perform delicate tasks due to their high flexibility, such as grasping and locomotion. However, it is a challenge to efficiently manufacture soft robots with complex functions." Funders for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Fujian Province.
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    Data on Robotics Detailed by Researchers at Huazhong University of Science and Technology (Robot-assisted Laser Additive Manufacturing for High-strength/low-porosity Continuous Fiber-reinforced Thermoplastic Composites)

    65-66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 Robotics is the subject of a report. According to news reporting originating in Wuhan, People's Republic of China, by NewsRx journalists, research stated, "Additive manufacturing (AM) of continuous fiber-reinforced thermoplastic composites (CFRTPCs) has become a hot area for both academia and industry. In this paper, a robot-assisted laser additive manufacturing (RLAM) technique is proposed, which involves utilizing a laser beam to heat the filament to a semi-molten state, followed by compacting it with a roller and bonding it layer by layer to create densely structured components." Financial supporters for this research include Science and Technology Department of Hubei Province, Major Program (JD) of Hubei Province.
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