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    Data from Tianjin Chengjian University Provide New Insights into Machine Learnin g (Machine Learning-based Prediction of Outdoor Thermal Comfort: Combining Bayes ian Optimization and the Shap Model)

    19-20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 Machine Learning. According to news reporting out of Tianjin, People's Republic of Chin a, by NewsRx editors, research stated, "Rising global temperatures have resulted in urban heat waves in recent years, endangering residents' health and even the ir lives. As a result, accurate outdoor thermal comfort prediction is critical." Financial support for this research came from National Natural Science Foundatio n of China (NSFC).
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    University of Sevilla Researchers Advance Knowledge in Machine Learning (Tacklin g unbalanced datasets for yellow and brown rust detection in wheat)

    21-22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 artificial intelligenc e is the subject of a new report. According to news reporting from Seville, Spai n, by NewsRx journalists, research stated, "This study evaluates the efficacy of hyperspectral data for detecting yellow and brown rust in wheat, employing mach ine learning models and the SMOTE (Synthetic Minority Oversampling Technique) au gmentation technique to tackle unbalanced datasets." Our news editors obtained a quote from the research from University of Sevilla: "Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (R F), and Gaussian Naive Bayes (GNB) models were assessed. Overall, SVM and RF mod els showed higher accuracies, particularly when utilizing SMOTE-enhanced dataset s. The RF model achieved 70% accuracy in detecting yellow rust wit hout data alteration. Conversely, for brown rust, the SVM model outperformed oth ers, reaching 63% accuracy with SMOTE applied to the training set. This study highlights the potential of spectral data and machine learning (ML) techniques in plant disease detection."
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    China Jiliang University Researchers Discuss Research in Machine Learning (Gas-L iquid Two-Phase Flow Measurement Based on Optical Flow Method with Machine Learn ing Optimization Model)

    21-21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 artificial in telligence have been published. According to news originating from Hangzhou, Peo ple's Republic of China, by NewsRx correspondents, research stated, "Gas-Liquid two-phase flows are a common flow in industrial production processes." Financial supporters for this research include Science And Technology Department of Zhejiang Province. The news reporters obtained a quote from the research from China Jiliang Univers ity: "Since these flows inherently consist of discrete phases, it is challenging to accurately measure the flow parameters. In this context, a novel approach is proposed that combines the pyramidal Lucas-Kanade (L-K) optical flow method wit h the Split Comparison (SC) model measurement method. In the proposed approach, videos of gas-liquid two-phase flows are captured using a camera, and optical fl ow data are acquired from the flow videos using the pyramid L-K optical flow det ection method. To address the issue of data clutter in optical flow extraction, a dynamic median value screening method is introduced to optimize the corner poi nt for optical flow calculations. Machine learning algorithms are employed for t he prediction model, yielding high flow prediction accuracy in experimental test s. Results demonstrate that the gradient boosted regression (GBR) model is the m ost effective among the five preset models, and the optimized SC model significa ntly improves measurement accuracy compared to the GBR model, achieving an * * R * * 2 value of 0.97, RMSE of 0.74 m3/h, MAE of 0.52 m3/h, and MAPE of 8.0%."
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    New Robotics Study Findings Have Been Reported from Wenzhou University of Techno logy (Design and Performance Analysis of Soft Crawling Robot Based on Photoelect ric Sensor/STM32)

    22-23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 robotic s. According to news reporting from Zhejiang, People's Republic of China, by New sRx journalists, research stated, "In this study, a control system based on STM3 2/photoelectric sensor is designed for soft crawling robot." The news editors obtained a quote from the research from Wenzhou University of T echnology: "The system consists of a front-end camera installed on soft robot, w ireless routing, STM32 single-chip microcomputer and several photoelectric senso rs. In this design, STM32 single-chip microcomputer is used as the main controll er to send instructions to the four-limb drive steering gear through RS-485 modu le, and the feedback incentive mechanism is adopted to improve the accuracy and stability of the digital steering gear system. In order to better perceive the w orking environment, this design is also equipped with a variety of photoelectric level sensors, and photoelectric pressure sensors are installed to monitor the perceived pressure of the robot to make it move better on the ground. The photoe lectric sensor is used to realize the soft crawling robot walking according to t he planned route. The photoelectric tilt robot is used to change the walking dir ection during the moving process. Through the overall cooperation of the above c omponents, the movement angle of limbs can be changed to make them crawl under n arrow conditions."
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    Swansea University Reports Findings in Artificial Intelligence (Using Artificial Intelligence to Improve the Accuracy of a Wrist-Worn, Noninvasive Glucose Monit or: A Pilot Study)

    23-24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 Artificial Intelligenc e is the subject of a report. According to news originating from Swansea, United Kingdom, by NewsRx correspondents, research stated, "Self-monitoring of glucose is important to the successful management of diabetes; however, existing monito ring methods require a degree of invasive measurement which can be unpleasant fo r users. This study investigates the accuracy of a noninvasive glucose monitorin g system that analyses spectral variations in microwave signals." Our news journalists obtained a quote from the research from Swansea University, "An open-label, pilot design study was conducted with four cohorts (N = 5/cohor t). In each session, a dial-resonating sensor (DRS) attached to the wrist automa tically collected data every 60 seconds, with a novel artificial intelligence (A I) model converting signal resonance output to a glucose prediction. Plasma gluc ose was measured in venous blood samples every 5 minutes for Cohorts 1 to 3 and every 10 minutes for Cohort 4. Accuracy was evaluated by calculating the mean ab solute relative difference (MARD) between the DRS and plasma glucose values. Acc urate plasma glucose predictions were obtained across all four cohorts using a r andom sampling procedure applied to the full four-cohort data set, with an avera ge MARD of 10.3%. A statistical analysis demonstrates the quality o f these predictions, with a surveillance error grid (SEG) plot indicating no dat a pairs falling into the high-risk zones. These findings show that MARD values a pproaching accuracies comparable to current commercial alternatives can be obtai ned from a multiparticipant pilot study with the application of AI."
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    University of Strasbourg Researchers Update Current Study Findings on Robotics ( Exploration of the creative processes in animals, robots, and AI: who holds the authorship?)

    24-25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 robotics have been published. According to news reporting originating from the University of Strasbourg by NewsRx correspondents, research stated, "Picture a simple scenario : a worm, in its modest way, traces a trail of paint as it moves across a sheet of paper. Now shift your imagination to a more complex scene, where a chimpanzee paints on another sheet of paper." The news reporters obtained a quote from the research from University of Strasbo urg: "A simple question arises: Do you perceive an identical creative process in these two animals? Can both of these animals be designated as authors of their creation? If only one, which one? This paper delves into the complexities of aut horship, consciousness, and agency, unpacking the nuanced distinctions between s uch scenarios and exploring the underlying principles that define creative autho rship across different forms of life. It becomes evident that attributing author ship to an animal hinges on its intention to create, an aspect intertwined with its agency and awareness of the creative act. These concepts are far from straig htforward, as they traverse the complex landscapes of animal ethics and law. But our exploration does not stop there. Now imagine a robot, endowed with artifici al intelligence, producing music. This prompts us to question how we should eval uate and perceive such creations. Is the creative process of a machine fundament ally different from that of an animal or a human? As we venture further into thi s realm of human-made intelligence, we confront an array of ethical, philosophic al, and legal quandaries."
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    German Institute of Human Nutrition Reports Findings in Chronic Disease (Biomark er signatures associated with ageing free of major chronic diseases: results fro m a population-based sample of the EPIC-Potsdam cohort)

    25-26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 Disease Attributes-C hronic Disease is the subject of a report. According to news reporting originati ng from Nuthetal, Germany, by NewsRx correspondents, research stated, "A number of biomarkers denoting various pathophysiological pathways have been implicated in the aetiology and risk of age-related diseases. Hence, the combined impact of multiple biomarkers in relation to ageing free of major chronic diseases, such as cancer, cardiovascular disease and type 2 diabetes, has not been sufficiently explored." Funders for this research include European Community, German Cancer Aid, Europea n Union, Bundesministerium fur Bildung und Forschung, Germany.
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    Westlake University Reports Findings in Liver Cancer (Development of a machine l earning-based model to predict prognosis of alphafetoprotein-positive hepatocel lular carcinoma)

    26-27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 Oncology-Liver Cance r is the subject of a report. According to news reporting originating in Hangzho u, People's Republic of China, by NewsRx journalists, research stated, "Patients with alpha-fetoprotein (AFP)-positive hepatocellular carcinoma (HCC) have aggre ssive biological behavior and poor prognosis. Therefore, survival time is one of the greatest concerns for patients with AFP-positive HCC." The news reporters obtained a quote from the research from Westlake University, "This study aimed to demonstrate the utilization of six machine learning (ML)-ba sed prognostic models to predict overall survival of patients with AFP-positive HCC. Data on patients with AFP-positive HCC were extracted from the Surveillance , Epidemiology, and End Results database. Six ML algorithms (extreme gradient bo osting [XGBoost], logistic regression [LR], support vector machine [SVM] , random forest [RF], K-nearest neighbor [KNN], and decision tree [ID3] ) were used to develop the prognostic models of patients with AFP-positive HCC a t one year, three years, and five years. Area under the receiver operating chara cteristic curve (AUC), confusion matrix, calibration curves, and decision curve analysis (DCA) were used to evaluate the model. A total of 2,038 patients with A FP-positive HCC were included for analysis. The 1-, 3-, and 5-year overall survi val rates were 60.7%, 28.9%, and 14.3%, r espectively. Seventeen features regarding demographics and clinicopathology were included in six ML algorithms to generate a prognostic model. The XGBoost model showed the best performance in predicting survival at 1-year (train set: AUC = 0.771; test set: AUC = 0.782), 3-year (train set: AUC = 0.763; test set: AUC = 0 .749) and 5-year (train set: AUC = 0.807; test set: AUC = 0.740). Furthermore, f or 1-, 3-, and 5-year survival prediction, the accuracy in the training and test sets was 0.709 and 0.726, 0.721 and 0.726, and 0.778 and 0.784 for the XGBoost model, respectively. Calibration curves and DCA exhibited good predictive perfor mance as well."
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    China University of Mining and Technology Researchers Detail New Studies and Fin dings in the Area of Machine Learning (Aboveground Biomass Inversion Based on Ob ject-Oriented Classification and Pearson-mRMR-Machine Learning Model)

    27-28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 artificial intelligence are presented in a new report. According to news reporting out of Beijing, Peopl e's Republic of China, by NewsRx editors, research stated, "Cities play a crucia l role in the carbon cycle. Measuring urban aboveground biomass (AGB) is essenti al for evaluating carbon sequestration." Funders for this research include Science & Technology Fundamental Resources Investigation Program; Research Project of Huaibei Mining Co. Ltd.; N ational Natural Science Foundation of China.
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    Researchers at Chinese Academy of Sciences Release New Data on Robotics (The Par ameters Optimization of Robotic Polishing With Force Controlled for Mold Steel B ased On Taguchi Method)

    28-29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 pr esented. According to news reporting out of Quanzhou, People's Republic of China , by NewsRx editors, research stated, "Aimed to explore the effect of robotic po lishing parameters on workpiece machining quality and improve polishing efficien cy, the polishing parameters of mold steel which contain polishing pressure, fee d speed and rotational speed of tool are optimized. A robotic polishing platform with constant force control is constructed based on a six-axis industrial robot and an axial force position compensator." Funders for this research include Laboratory of Robotics and Intelligent Systems (CASQuanzhou), Scientific and Technological Project of Quanzhou, Laboratory of Robotics and Intelligent Systems (CASQuanzhou).
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