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    Recent Findings in Machine Learning Described by Researchers from Saveetha Schoo l of Engineering (Model Forecasting of Hydrogen Yield and Lower Heating Value In Waste Mahua Wood Gasification With Machine Learning)

    10-11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 Ma chine Learning. According to news reporting from Tamil Nadu, India, by NewsRx jo urnalists, research stated, “Biomass is an excellent source of green energy with numerous benefits such as abundant availability, net carbon zero, and renewable nature. However, the conventional methods of biomass combustion are polluting a nd poor efficiency processes.” Financial support for this research came from Deanship of Scientific Research at Shaqra University. The news correspondents obtained a quote from the research from the Saveetha Sch ool of Engineering, “Biomass gasification overcomes these challenges and provide s a sustainable method for the supply of greener fuel in the form of producer ga s. The producer gas can be employed as a gaseous fuel in compression ignition en gines in dual-fuel systems. The biomass gasification process is a complex as wel l as a nonlinear process that is highly dependent on the ambient environment, ty pe of biomass, and biomass composition as well as the gasification medium. This makes the modeling of such systems quite difficult and time-consuming. Modern ma chine learning (ML) techniques offer the use of experimental data as a convenien t approach to modeling and forecasting such systems. In the present study, two m odern and highly efficient ML techniques, random forest (RF) and AdaBoost, were employed for this purpose. The outcomes were employed with results of a baseline method, i.e., linear regression. The RF could forecast the hydrogen yield with R2 as 0.978 during model training and 0.998 during the model test phase. AdaBoos t ML was close behind with R2 at 0.948 during model training and 0.842 during th e model test phase. The mean squared error was as low as 0.17 and 0.181 during m odel training and testing, respectively. In the case of the low heating value mo del, during model testing, the R2 was 0.971 and RF and AdaBoost, respectively, d uring model training and 0.842 during the model test phase.”
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    University of Duisburg-Essen Reports Findings in Cancer (Machine Learning-Based Prediction of 1-Year Survival Using Subjective and Objective Parameters in Patie nts With Cancer)

    11-12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 Cancer is the subject of a report. According to news reporting originating from Essen, Germany, by New sRx correspondents, research stated, “Palliative care is recommended for patient s with cancer with a life expectancy of <12 months. Machine learning (ML) techniques can help in predicting survival outcomes among patient s with cancer and may help distinguish who benefits the most from palliative car e support.” Our news editors obtained a quote from the research from the University of Duisb urg-Essen, “We aim to explore the importance of several objective and subjective self-reported variables. Subjective variables were collected through electronic psycho-oncologic and palliative care self-assessment screenings. We used these variables to predict 1-year mortality. Between April 1, 2020, and March 31, 2021 , a total of 265 patients with advanced cancer completed a patient-reported outc ome tool. We documented objective and subjective variables collected from electr onic health records, self-reported subjective variables, and all clinical variab les combined. We used logistic regression (LR), 20-fold cross-validation, decisi on trees, and random forests to predict 1-year mortality. We analyzed the receiv er operating characteristic (ROC) curve-AUC, the precision-recall curve-AUC (PR- AUC)-and the feature importance of the ML models. The performance of clinical no npatient variables in predictions (LR reaches 0.81 [ROC-AUC] and 0.72 [F1 score]) are much more predict ive than that of subjective patient-reported variables (LR reaches 0.55 [ROCAUC] and 0.52 [F1 score] ). The results show that objective variables used in this study are much more pr edictive than subjective patient-reported variables, which measure subjective bu rden. These findings indicate that subjective burden cannot be reliably used to predict survival.”
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    Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Cente r) Reports Findings in Stroke (Effect of robotic exoskeleton training on lower l imb function, activity and participation in stroke patients: a systematic review and ...)

    12-13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 Cerebrovascular Diseas es and Conditions - Stroke is the subject of a report. According to news reporti ng out of Shanghai, People’s Republic of China, by NewsRx editors, research stat ed, “The current lower limb robotic exoskeleton training (LRET) for treating and managing stroke patients remains a huge challenge. Comprehensive ICF analysis a nd informative treatment options are needed.” Our news journalists obtained a quote from the research from Shanghai YangZhi Re habilitation Hospital (Shanghai Sunshine Rehabilitation Center), “This review ai ms to analyze LRET’ s efficacy for stroke patients, based on ICF, and explore th e impact of intervention intensities, devices, and stroke phases. We searched We b of Science, PubMed, and The Cochrane Library for RCTs on LRET for stroke patie nts. Two authors reviewed studies, extracted data, and assessed quality and bias . Standardized protocols were used. PEDro and ROB2 were employed for quality ass essment. All analyses were done with RevMan 5.4. Thirty-four randomized controll ed trials (1,166 participants) were included. For function, LRET significantly i mproved motor control (MD = 1.15, 95%CI = 0.29-2.01, = 0.009, FMA-L E), and gait parameters (MD = 0.09, 95%CI = 0.03-0.16, = 0.004, Ins trumented Gait Velocity; MD = 0.06, 95%CI = 0.02-0.09, = 0.002, Ste p length; MD = 4.48, 95%CI = 0.32-8.65, = 0.04, Cadence) compared w ith conventional rehabilitation. For activity, LRET significantly improved walki ng independence (MD = 0.25, 95%CI = 0.02-0.48, = 0.03, FAC), Gait V elocity (MD = 0.07, 95%CI = 0.03-0.11, = 0.001) and balance (MD = 2 .34, 95%CI = 0.21-4.47, = 0.03, BBS). For participation, social par ticipation (MD = 0.12, 95%CI = 0.03-0.21, = 0.01, EQ-5D) was superi or to conventional rehabilitation. Based on subgroup analyses, LRET improved mot or control (MD = 1.37, 95%CI = 0.47-2.27, = 0.003, FMA-LE), gait pa rameters (MD = 0.08, 95%CI = 0.02-0.14, = 0.006, Step length), Gait Velocity (MD = 0.11, 95%CI = 0.03-0.19, = 0.005) and activities of daily living (MD = 2.77, 95%CI = 1.37-4.16, = 0.0001, BI) for the subacute patients, while no significant improvement for the chronic patients. Fo r exoskeleton devices, treadmill-based exoskeletons showed significant superiori ty for balance (MD = 4.81, 95%CI = 3.10-6.52, <0.00001, BBS) and activities of daily living (MD = 2.67, 95%CI = 1 .25-4.09, = 0.00002, BI), while Over-ground exoskeletons was more effective for gait parameters (MD = 0.05, 95%CI = 0.02-0.08, = 0.0009, Step lengt h; MD = 6.60, 95%CI = 2.06-11.15, = 0.004, Cadence) and walking ind ependence (MD = 0.29, 95%CI = 0.14-0.44, = 0.0002, FAC). Depending on the training regimen, better results may be achieved with daily training inte nsities of 45-60 min and weekly training intensities of 3 h or more. These findi ngs offer insights for healthcare professionals to make effective LRET choices b ased on stroke patient needs though uncertainties remain.”
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    New Machine Learning Data Have Been Reported by Researchers at Charles Darwin Un iversity (Leak detection and localization in underground water supply system usi ng thermal imaging and geophone signals through machine learning)

    13-14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 Darwin, Austra lia, by NewsRx editors, research stated, “The underground water pipeline system is a crucial infrastructure that largely remains out of sight. However, it is th e source of a clean and uninterrupted flow of water for our everyday lives.” Our news editors obtained a quote from the research from Charles Darwin Universi ty: “Various factors, including corrosion, material degradation, ground movement , and improper maintenance, cause pipe leaks, a silent crisis that causes an est imated 39 billion dollars of loss every year. Prompt leakage detection and local ization can help reduce the loss. This research investigates the potential of tw o machine learning models as supporting tools for surveying extensive areas to i dentify and pinpoint the location of underground leaks. The presented combined a pproach ensures the speed and accuracy of the leakage survey. The first machine learning model is a hybrid ML model that employs thermal imaging to identify sub terranean water leakage. It relies on detecting thermal anomalies and distinctiv e signatures associated with water leakage to identify and locate underground wa ter leakage. The developed model can detect up to 750 mm underground leakage wit h 95.20 % accuracy. The second model uses binaural audio from geop hones to localize the leakage position.”
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    New Machine Learning Study Findings Recently Were Published by Researchers at Ch ina University of Geosciences Beijing [Characterizing Malyshe va Emeralds (Urals, Russia) by Microscopy, Spectroscopy, Trace Element Chemistry , and Machine Learning]

    14-15页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news originating from Beijing , People’s Republic of China, by NewsRx correspondents, research stated, “The Ma lysheva emerald mine (Urals, Russia) boasts a long history and extraordinary eme rald output. However, recent studies indicate that Malysheva emeralds share high ly similar inclusion varieties, UV-visible-near infrared (UV-Vis-NIR) spectra, a nd compositional characteristics with other tectonicmagmatic- related (type I) e meralds from Zambia, Brazil, and Ethiopia.” Funders for this research include Beijing College Students Innovation And Entrep reneurship Plan.
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    Nanjing University of Aeronautics and Astronautics Reports Findings in Robotics (Design and Motion Control of Master-Slave Control Endotracheal Intubation Robot )

    15-16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 subjec t of a report. According to news reporting out of Nanjing, People’s Republic of China, by NewsRx editors, research stated, “Master-slave remote control technolo gy allows patients to be treated promptly during transport and also reduces the risk of contagious infections. Endotracheal intubation, guided by endoscopy and a master-slave system, enables doctors to perform the procedure efficiently and accurately.” Financial support for this research came from Fundamental Research Funds for the Central Universities. Our news journalists obtained a quote from the research from the Nanjing Univers ity of Aeronautics and Astronautics, “In this paper, we present the development of a master-slave controlled endotracheal intubation robot (EIR). It is based on operation incremental mapping, a weighted recursive average filtering method to reduce vibration, and a virtual fixture designed to reduce mishandling in minim ally invasive surgery. Simulation analysis of the master-slave control demonstra tes that the weighted recursive average filtering method effectively reduces vib ration, while the virtual fixture assists in confining the operator’s movement w ithin a delimited area. Experimental validation confirms the validity of the rob ot’s structural design and control method.”
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    Study Data from University of Technology Update Knowledge of Machine Learning (P redicting the Compressive Strength of Engineered Geopolymer Composites Using Aut omated Machine Learning)

    16-17页
    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 reporting out of Seri Iskandar, Malaysia, by NewsR x editors, research stated, “Engineered Geopolymer Composites (EGC) offer a sust ainable and high-performance alternative to traditional concrete and Engineered Cementitious Composites, boasting reduced environmental impact and enhanced mech anical properties. However, optimising EGC mix design for specific applications requires an accurate prediction of its compressive strength.” Financial support for this research came from Universiti Teknologi PETRONAS Mala ysia. Our news journalists obtained a quote from the research from the University of T echnology, “This study investigates the application of Automated Machine Learnin g using the PyCaret library to develop reliable predictive models for EGC compre ssive strength. A comprehensive experimental program was conducted, testing 132 EGC specimens with varying mix design parameters, including binder ratio, silica fume content, activator ratio, water content, superplasticizer dosage, and curi ng method. The collected data was used to train and evaluate twenty different ma chine learning models. Model performance was assessed using various metrics. The top six models were shortlisted and optimised using Random Search algorithm. Th e models were assessed through a detailed analysis of their residual plots and l earning curves. Additionally, Feature importance and SHAP analysis were employed to understand the influence of each input parameter on the predicted compressiv e strength. The results demonstrate the effectiveness of AML in accurately predi cting EGC compressive strength, with the Gradient Boosting Regressor and CatBoos t Regressor models exhibiting superior performance, achieving Mean Absolute Erro r (MAE) below 1.2 MPa and R2 exceeding 0.96.”
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    Research Conducted at Brigham and Women’s Hospital Has Updated Our Knowledge abo ut Machine Learning (A Machine Learning Technology for Addressing Medication-rel ated Risk In Older, Multimorbid Patients)

    17-18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 Ma chine Learning. According to news reporting originating from Boston, Massachuset ts, by NewsRx correspondents, research stated, “To evaluate the FeelBetter machi ne learning system’s ability to accurately identify older patients with multimor bidity at Brigham and Women’s Hospital at highest risk of medication-associated emergency department (ED) visits and hospitalizations, and to assess the system’ s ability to provide accurate medication recommendations these patients. Retrosp ective cohort study.” Financial support for this research came from FeelBetter Inc.
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    New Data from Harbin Engineering University Illuminate Findings in Artificial In telligence (Artificial Intelligence Adversity Event, Interorganisational Trust, and Firm Resilience: the Moderating Effect of Responsible Innovation)

    18-18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 reporting from Harbin, People’ s Republic of China, by NewsRx journalists, research stated, “Research on the im pact mechanism of the adverse event set of artificial intelligence (AI) on firm resilience (FR) is still limited. This study explores the relationship between t he intensity of AI adversity events (AIEI), inter-organisational trust (IOT), an d firm resilience in 217 Chinese emerging AI-related firms.” Financial support for this research came from National Office of Philosophy and Social Sciences. The news correspondents obtained a quote from the research from Harbin Engineeri ng University, “We find that greater AIEI negatively impacts FR. Additionally, I OT partially mediates the connection between AIEI and FR. Notably, responsible i nnovation (RI) significantly moderates this relationship.”
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    South Carolina State University Researchers Have Published New Study Findings on Machine Learning (Spatial instability of crash prediction models: A case of sco oter crashes)

    19-19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 ar tificial intelligence. According to news originating from South Carolina State U niversity by NewsRx correspondents, research stated, “Scooters have gained wides pread popularity in recent years due to their accessibility and affordability, b ut safety concerns persist due to the vulnerability of riders. Researchers are a ctively investigating the safety implications associated with scooters, given th eir relatively new status as transportation options.” The news reporters obtained a quote from the research from South Carolina State University: “However, analyzing scooter safety presents a unique challenge due t o the complexity of determining safe riding environments. This study presents a comprehensive analysis of scooter crash risk within various buffer zones, utiliz ing the Extreme Gradient Boosting (XGBoost) machine learning algorithm. The core objective was to unravel the multifaceted factors influencing scooter crashes a nd assess the predictive model’s performance across different buffers or spatial proximity to crash sites. After evaluating the model’s accuracy, sensitivity, a nd specificity across buffer distances ranging from 5 ft to 250 ft with the scoo ter crash as a reference point, a discernible trend emerged: as the buffer dista nce decreases, the model’s sensitivity increases, although at the expense of acc uracy and specificity, which exhibit a gradual decline. Notably, at the widest b uffer of 250 ft, the model achieved a high accuracy of 97% and spe cificity of 99 %, but with a lower sensitivity of 31%. Contrastingly, at the closest buffer of 5 ft, sensitivity peaked at 95 % , albeit with slightly reduced accuracy and specificity. Feature importance anal ysis highlighted the most significant predictor across all buffer distances, emp hasizing the impact of vehicle interactions on scooter crash likelihood. Explain able Artificial Intelligence through SHAP value analysis provided deeper insight s into each feature’s contribution to the predictive model, revealing passenger vehicle types of significantly escalated crash risks. Intriguingly, specific veh icular maneuvers, notably stopping in traffic lanes, alongside the absence of Tr affic Control Devices (TCDs), were identified as the major contributors to incre ased crash occurrences. Road conditions, particularly wet and dry, also emerged as substantial risk factors.”
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