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    Research Conducted at Zhengzhou University Has Provided New Information about Ma chine Learning (Advance and Prospect of Machine Learning Based Fault Detection a nd Diagnosis In Air Conditioning Systems)

    58-58页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Research findings on Machine Learning are discussed in a new report. Accordingto news reporting originating from Zhen gzhou, People's Republic of China, by NewsRx correspondents,research stated, "F ault detection and diagnosis (FDD) are crucial aspects of maintaining efficient andenergy-saving heating ventilation and air conditioning (HVAC) systems. Condi tions such as inadequatemaintenance, poor equipment performance, improper insta llation and defective control mechanisms canall contribute to a reduction in th e operational efficiency of HVAC systems, resulting in unnecessary energywastag e." Funders for this research include National Natural Science Foundation of China ( NSFC), Henan provincial key science and technology research projects.
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    Reports Outline Support Vector Machines Study Findings from Academy Science & Innovation Research (Prediction of Confined Compressive Strength of Concrete Col umn Strengthened With Frcm Composites)

    59-59页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Researchers detail new data in Machine Learning - Support Vector Machines. Accordingto news originating from Ghaziaba d, India, by NewsRx correspondents, research stated, "Nowadays,retrofitting and rehabilitation of deteriorated reinforced concrete structures are becoming a gr owing needof the construction industry instead of demolishing aged structures. The application of fabric-reinforcedcementitious matrix (FRCM) on the existing concrete structures is one of the sustainable solutions toretrofit the concrete structures."Our news journalists obtained a quote from the research from Academy Science & Innovation Research,"This study used machine learning (ML) models such as linea r regression (LR), support vector machines(SVM), and adaptive neuro-fuzzy infer ence systems (ANFIS) to estimate the compressive strength (CS) ofcolumns wrappe d with FRCM. The experimental dataset of 301 column specimens was collected incl udinginput parameters such as cross-sectional properties, mechanical properties of concrete and steel, and characteristicsof FRCM material. Apart from ML mode ls, seven analytical models were also used to comparethe accuracy and precision of ML models. The results illustrate that the ANFIS model outperformed otherML models and established itself as a dependable and precise model. The R-value of the ANFIS modelwas 0.9816, whereas R-values of 0.9269 and 0.9572 were achieved by LR and SVM models, respectively.In addition, the MAPE value acquired by the ANFIS model was 1.52% which was lower than those of theLR model by 73.24%, and the SVM model by 60.60%, respectively. As the precision of the ANFIS modelwas higher as compared with SVM and LR model s, so, the developed ANFIS-based mathematical modelcan be easily used to predic t the CS of FRCM-strengthened concrete columns."
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    University of East Anglia Reports Findings in Artificial Intelligence [Cost-Effectiveness of GaitSmart and an Artificial Intelligence Solution for Reha bilitation of Patients Undergoing Total Hip Arthroplasty (THA) and Total Knee Ar throplasty ...]

    60-60页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on Artificial Intelligenc e is the subject of a report. According to newsreporting originating in Norwich , United Kingdom, by NewsRx journalists, research stated, "GaitSmart(GS) is a s ensor-based digital medical device that can be used with the integrated app vGym to providea personalised rehabilitation programme for older people undergoing total hip arthroplasty (THA) or totalknee arthroplasty (TKA). This study aimed to determine whether the GS intervention used in the rehabilitationof older peo ple undergoing THA or TKA is potentially cost-effective compared to the currentstandard of care (SoC)."Financial support for this research came from Digital health technology catalyst round 4: collaborativeR&D project: Modelling and artificial intel ligence using sensor data to personalise rehabilitation followingjoint replacem ent.
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    Findings from Linyi University Has Provided New Data on Nanoplastics (Algae-base d Self-driven Microrobot for Efficient Removal of Nanoplastics From Water Enviro nment)

    61-61页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews-Researchers detail new data in Nanotechnology - N anoplastics. According to news originatingfrom Linyi, People's Republic of Chin a, by NewsRx correspondents, research stated, "Nanoplastics (NPs)have the chara cteristics of various species, wide distribution, low concentration and difficul t degradation.In recent years, the researches of NPs have mainly focused on its toxicity, origin and migration, and thequantitative detection and removal tech nology of NPs is an urgent technical problem to be solved."Financial supporters for this research include National Natural Science Foundati on of China (NSFC),Natural Science Foundation of Shandong Province.Our news journalists obtained a quote from the research from Linyi University, " Here, an activebiohybrid microrobots (DPA-algae robots, diphenolic acids-alage robots) was designed and developed fordynamic removal of NPs in water environme nt. Firstly, diphenolic acids were functionalized on algaeto realize the specif ic adsorption of nanoplastics through hydrophobic force, electrostatic attractio nand van der Waals force between diphenolic acids and nanoplastic particles. Se condly, to realize therapid identification of NPs, the functionalized algae wer e assembled into microrobots through rapid clickchemistry reaction, thus the se lfpropulsion ability of algae can be utilized to accelerate the identificationa nd enrichment of target objects. The removal rate of nanoplastics by microalgae robots has increased to83.1 % at the concentration of 0.125 mg/ m L within 2 h compared with the unmodified algae, which is avery significant imp rovement. In addition, the removal rate was 84.1 % to 87.7 % in different media, sothe dynamic removal of NPs is expected to be applied into the filtration process of waterworks by preparinga large number of algae-based microrobots."
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    Studies from Durban University of Technology Yield New Data on Machine Learning (Prediction of Wastewater Quality Parameters Using Adaptive and Machine Learning Models: a South African Case Study)

    62-62页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Investigators discuss new findings in Machine Learning. According to news reportingoriginating from Durban, South Afr ica, by NewsRx correspondents, research stated, "The wastewatertreatment proces s often faces challenges in monitoring water quality parameters (WQ), to overcom e thisthere is a need for developing innovative modeling approaches. Hence, the present study is motivatedby the potential application of adaptive and machine learning (ML) models as soft sensors to predict theWQ in one of the largest Mu nicipal Wastewater Treatment Plants (MWWTP) in KwaZulu-Natal, SouthAfrica."Financial support for this research came from National Research Foundation - Sou th Africa.Our news editors obtained a quote from the research from the Durban University o f Technology, "Sevendifferent adaptive and ML algorithms were examined and comp ared, varying from adaptive strategies to MLarchitectures such as Long Short-Te rm Memory (LSTM), Bidirectional LSTM (BiLSTM), Time Difference(TD), Just in Tim e Learning (JIT), Moving Window (MW), and fusion of adaptive strategies (JITTD,and JITTDMW), Support Vector Regression (SVR), and Artificial Neural Network (AN N). Based on theresults, BiLSTM consistently provided the most accurate estimat ion of effluent parameters, with an errorrate ranging from 3.12 to 9.75 % for all variables. For Chemical Oxygen Demand (COD), ammonia, pH,and Total Susp ended Solids (TSS), BiLSTM model yielded low errors (Mean Absolute Error (MAE) values of 1.54, 0.1, 0.22, and 1.14) with lower correlation coefficient values (<0.7) compared to the sixother models proposed. As for conductivity, COD, TSS, p H, ammonia, LSTM, and JITTDMW, JITTDperformed well with MAE values between 1 an d 8 but had difficulty estimating soluble reactive phosphate(SRP). From a futur e perspective, these models could be applied to other MWWTPs facing similar challenges, potentially helping to improve their performance and effectiveness."
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    Findings in Machine Learning Reported from Shanghai Jiao Tong University (Machin e Learning-based In-process Monitoring for Laser Deep Penetration Welding: a Sur vey)

    63-63页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Investigators publish new report on Ma chine Learning. According to news reportingoriginating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, "Inprocessmonito ring (IPM) of laser deep penetration welding (LDPW) has witnessed a rapid growth inapproaches that embrace machine learning algorithms, utilizing raw sensor in put to generate various weldquality evaluations, instead of concentrating on th ermomechanical modeling that is hypotheses-driven andhence biased by it. Benefi tting from the capability to unravel hidden interactions in the complex laser welding process, numerous data-driven IPM methods have been proposed to address di fferent problems inthis area."Financial supporters for this research include National Natural Science Foundati on of China (NSFC),State Key Laboratory of Mechanical System and Vibration.
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    New Intelligence Technology Findings from Harbin University Described (Local Sal iency Consistency-based Label Inference for Weakly Supervised Salient Object Det ection Using Scribble Annotations)

    64-64页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Fresh data on Machine Learning - Intel ligence Technology are presented in a newreport. According to news reporting fr om Harbin, People's Republic of China, by NewsRx journalists,research stated, " Recently, weak supervision has received growing attention in the field of salien t objectdetection due to the convenience of labelling. However, there is a larg e performance gap between weaklysupervised and fully supervised salient object detectors because the scribble annotation can only providevery limited foregrou nd/background information."The news correspondents obtained a quote from the research from Harbin Universit y, "Therefore, anintuitive idea is to infer annotations that cover more complet e object and background regions for training.To this end, a label inference str ategy is proposed based on the assumption that pixels with similar coloursand c lose positions should have consistent labels. Specifically, k-means clustering a lgorithm was firstperformed on both colours and coordinates of original annotat ions, and then assigned the same labels topoints having similar colours with co lour cluster centres and near coordinate cluster centres. Next, thesame annotat ions for pixels with similar colours within each kernel neighbourhood was set fu rther."
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    Study Data from Huazhong University of Science and Technology Update Knowledge o f Robotics (Adaptive Global Graph Optimization for Lidar-inertial Slam)

    64-65页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Robotics have been publi shed. According to news reporting from Wuhan,People's Republic of China, by New sRx journalists, research stated, "A complete SLAM system comprisesa front-end odometry module and a back-end optimization module. The front-end utilizes senso r data(such as from cameras or LiDAR) to estimate the robot's pose and construc t a map of the surroundingenvironment."
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    Researchers at Imperial College London Release New Data on Machine Learning (Int erval Abstractions for Robust Counterfactual Explanations)

    65-66页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-A new study on Machine Learning is now available. According to news reportingout of London, United Kingdom, by NewsRx editors, research stated, "Counterfactual Explanations (CEs)have emerged as a major paradigm in explainable AI research, providing recourse recommendations fo rusers affected by the decisions of machine learning models. However, CEs found by existing methods oftenbecome invalid when slight changes occur in the param eters of the model they were generated for." Funders for this research include J.P. Morgan, Royal Academy of Engineering unde r the Research Chairs and Senior Research Fellowships scheme, Imperial College L ondon through under the ImperialCollege Research Fellowship scheme, European Re search Council (ERC).
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    Data on Machine Learning Reported by Jannatul Mawa Misti and Colleagues (Feature Contributions and Predictive Accuracy in Modeling Adolescent Daytime Sleepiness Using Machine Learning: The MeLiSA Study)

    66-67页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on Machine Learning is th e subject of a report. According to newsreporting out of Dhaka, Bangladesh, by NewsRx editors, research stated, "Excessive daytime sleepiness(EDS) among adole scents poses significant risks to academic performance, mental health, and overa llwell-being. This study examines the prevalence and risk factors of EDS in ado lescents in Bangladesh andutilizes machine learning approaches to predict the r isk of EDS."Funders for this research include University of South Asia, Princess Nourah bint Abdulrahman University.Our news journalists obtained a quote from the research, "A cross-sectional stud y was conductedamong 1496 adolescents using a structured questionnaire. Data we re collected through a two-stage stratified cluster sampling method. Chi-square tests and logistic regression analyses were performed usingSPSS. Machine learni ng models, including Categorical Boosting (CatBoost), Extreme Gradient Boosting(XGBoost), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), andGradient Boosting Machine (GBM), were employed to identify and predi ct EDS risk factors using Pythonand Google Colab. The prevalence of EDS in the cohort was 11.6%. SHAP values from the CatBoostmodel identified se lf-rated health status, gender, and depression as the most significant predictor s ofEDS. Among the models, GBM achieved the highest accuracy (90.15% ) and precision (88.81%), whileCatBoost had comparable accuracy (8 9.48%) and the lowest log loss (0.25). ROC-AUC analysis showedthat CatBoost and GBM performed robustly in distinguishing between EDS and non-EDS c ases, with AUCscores of 0.86. Both models demonstrated the superior predictive performance for EDS compared to others.The study emphasizes the role of health and demographic factors in predicting EDS among adolescentsin Bangladesh. Machi ne learning techniques offer valuable insights into the relative contribution of thesefactors, and can guide targeted interventions."
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