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    Study Data from University of Kerala Provide New Insights into Human-Centric Int elligent Systems (A Local Explainability Technique for Graph Neural Topic Models )

    62-63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 human-centric intellige nt systems is now available. According to news reporting from the University of Kerala by NewsRx journalists, research stated, “Topic modelling is a Natural Lan guage Processing (NLP) technique that has gained popularity in the recent past. It identifies word co-occurrence patterns inside a document corpus to reveal hid den topics.” The news correspondents obtained a quote from the research from University of Ke rala: “Graph Neural Topic Model (GNTM) is a topic modelling technique that uses Graph Neural Networks (GNNs) to learn document representations effectively. It p rovides high-precision documents-topics and topics-words probability distributio ns. Such models find immense application in many sectors, including healthcare, financial services, and safety-critical systems like autonomous cars. This model is not explainable. As a matter of fact, the user cannot comprehend the underly ing decision-making process. The paper introduces a technique to explain the doc uments-topics probability distributions output of GNTM. The explanation is achie ved by building a local explainable model such as a probabilistic Naive Bayes cl assifier.”
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    University of Agriculture and Forestry Reports Findings in Machine Learning (Int egrating machine learning models with crossvalidation and bootstrapping for eva luating groundwater quality in Kanchanaburi Province, Thailand)

    66-66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 Machine Learning is th e subject of a report. According to news reporting originating in Hue City, Viet nam, by NewsRx journalists, research stated, “Exploring the potential of new mod els for mapping groundwater quality presents a major challenge in water resource management, particularly in Kanchanaburi Province, Thailand, where groundwater faces contamination risks. This study aimed to explore the applicability of rand om forest (RF) and artificial neural networks (ANN) models to predict groundwate r quality.” The news reporters obtained a quote from the research from the University of Agr iculture and Forestry, “Particularly, these two models were integrated into cros s-validation (CV) and bootstrapping (B) techniques to build predictive models, i ncluding RF-CV, RF-B, ANN-CV, and ANN-B. Entropy groundwater quality index (EWQI ) was converted to normalized EWQI which was then classified into five levels fr om very poor to very good. A total of twelve physicochemical parameters from 180 groundwater wells, including potassium, sodium, calcium, magnesium, chloride, s ulfate, bicarbonate, nitrate, pH, electrical conductivity, total dissolved solid s, and total hardness, were investigated to decipher groundwater quality in the eastern part of Kanchanaburi Province, Thailand. Our results indicated that grou ndwater quality in the study area was primarily polluted by calcium, magnesium, and bicarbonate and that the RF-CV model (RMSE = 0.06, R = 0.87, MAE = 0.04) out performed the RF-B (RMSE = 0.07, R = 0.80, MAE = 0.04), ANN-CV (RMSE = 0.09, R = 0.70, MAE = 0.06), and ANN-B (RMSE = 0.10, R = 0.67, MAE = 0.06). Our findings highlight the superiority of the RF models over the ANN models based on the CV a nd B techniques. In addition, the role of groundwater parameters to the normaliz ed EWQI in various machine learning models was found.”
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    New Artificial Intelligence Findings from North Carolina State University (NC St ate) Outlined (Evaluation of a New Artificial Intelligence-based Textile Digitiz ation Using Fabric Drape)

    67-67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 Artificial Intelligence is now available. According to news reporting originating from Raleigh, North C arolina, by NewsRx correspondents, research stated, “Three-dimensional (3D) text ile-based garment prototyping, widely adopted in the apparel and textile industr y, enhances cost efficiency, work productivity, and seamless communication via v isual prototyping. Neural network-based 3D textile digitization has the potentia l to streamline manufacturing processes by negating the need for traditional phy sical property (PT) measurements.” Financial support for this research came from Department of Defense (DoD) throug h the North Carolina Defense Manufacturing Community Support Program (DMCSP).
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    Research on Machine Learning Published by Researchers at Drexel University (Brai n-age estimation with a low-cost EEG-headset: effectiveness and implications for large-scale screening and brain optimization)

    69-69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 reporting out of Philadelphia, Pennsylv ania, by NewsRx editors, research stated, “Over time, pathological, genetic, env ironmental, and lifestyle factors can age the brain and diminish its functional capabilities.” Our news correspondents obtained a quote from the research from Drexel Universit y: “While these factors can lead to disorders that can be diagnosed and treated once they become symptomatic, often treatment is difficult or ineffective by the time significant overt symptoms appear. One approach to this problem is to deve lop a method for assessing general age-related brain health and function that ca n be implemented widely and inexpensively. To this end, we trained a machine-lea rning algorithm on restingstate EEG (RS-EEG) recordings obtained from healthy i ndividuals as the core of a brain-age estimation technique that takes an individ ual’s RS-EEG recorded with the low-cost, user-friendly EMOTIV EPOC X headset and returns that person’s estimated brain age. We tested the current version of our machinelearning model against an independent test-set of healthy participants and obtained a correlation coefficient of 0.582 between the chronological and es timated brain ages (r = 0.963 after statistical bias-correction). The test-retes t correlation was 0.750 (0.939 after bias-correction) over a period of 1 week.”
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    Research from Chiba Institute of Technology Has Provided New Data on Robotics an d Mechatronics (Dynamic Visualization of Construction Sites with Machine-Borne S ensors Toward Automated Earth Moving)

    69-70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 ro botics and mechatronics. According to news reporting out of Chiba, Japan, by New sRx editors, research stated, “The digitization of the construction site environ ment has progressed rapidly.” Funders for this research include Japan Society For The Promotion of Science.
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    Data from Tianjin University of Technology and Education Provide New Insights in to Robotic Systems (An explicit solution of forward and inverse kinematics for a serial manipulator with insufficient intersection joints based on finite and .. .)

    71-72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 robotic systems a re discussed in a new report. According to news reporting from Tianjin, People’s Republic of China, by NewsRx journalists, research stated, “To satisfy the requ irements of large workspace, high capability, and low inertia demanded by the hi gh precision and high stiffness aerospace equipment, serial manipulator with ins ufficient intersection joints is proposed in this article based on finite and in stantaneous screw theory.” Our news journalists obtained a quote from the research from Tianjin University of Technology and Education: “Due to the specific topology structure with no int ersecting joints at both ends, the explicit solution of forward and inverse kine matics for the serial manipulator with insufficient intersection joints proposed in this article is extremely difficult. Therefore, an analytical method based o n finite and instantaneous screw theory is proposed for this serial manipulator, and the explicit kinematic solution is derived in this article. According to pa rticular calculation principle of finite and instantaneous screw theory, the fin ite motion of this serial manipulator is described. Based on this description, t he product of exponentials formula of the kinematics of the serial manipulator w ith insufficient intersection joints is established on the basic of finite and i nstantaneous screw theory for the first time, and a novel subproblem is arose du ring the decomposition of product of exponentials formula.”
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    Reports on Robotics Findings from Verona Provide New Insights (Robot-assisted st aged bilateral reno-lymphatic disconnection for massive idiopathic chyluria: A c ase report)

    72-73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 Verona, Italy, by NewsRx correspondents, research stated, “Chyluria, an abnormal lymphatic disorde r, results in excessive abdominal lymph drainage into the urinary system, causin g protein loss, nutritional deficiencies, and immune issues.” Our news editors obtained a quote from the research from Department of Urology: “Mainly linked to parasitic infections in developed countries, non-parasitic cau ses like trauma or tumors are rare. Typically appearing in adults with bilateral involvement, management options include conservative or surgical approaches. We present the case of a 13-year-old with congenital chyluria, treated with robot- assisted staged reno-lymphatic disconnection after failed interventional radiolo gy. Bilateral scleroangiography followed, leading to persistently milky urine fo r a month.”
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    Findings on Robotics Detailed by Investigators at Hanoi University of Science an d Technology (The Development of Fleet Management System for Mobile Robots Deliv ering Medicine In Healthcare Environments)

    73-73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 Ro botics. According to news reporting originating in Hanoi, Vietnam, by NewsRx jou rnalists, research stated, “This paper focuses on implementing a Fleet Managemen t System (FMS) in hospitals, using autonomous mobile delivery robots. The primar y aim is to enhance efficiency and alleviate the workload of healthcare staff.” Financial support for this research came from Hanoi University of Science and Te chnology (HUST).
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    Researchers at Middle Technical University Target Robotics (Antidisturbance con trol design of Exoskeleton Knee robotic system for rehabilitative care)

    74-75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 originating from Baghdad,Iraq, by NewsRx editors, the rese arch stated, “In this study, Active Disturbance Rejection Control (ADRC) has bee n designed for motion control of knee-joint based on exoskeleton medical robot.” The news correspondents obtained a quote from the research from Middle Technical University: “The extended state observer (ESO) is the main part of ADRC structu re, which is responsible for estimating both actual states and system uncertaint ies. The proposed control scheme has adopted two versions of observers as distur bance estimators: linear extended state observer (LESO) and nonlinear extended s tate observer (NESO). The efficacy of proposed ADRC is strongly related to the p erformance of used ESO. As such, a comparison study has been conducted to evalua te the performance of two ADRCs in terms of disturbance-rejection capability and robustness to variation in system parameters under two version of ESO (LSO and NLESO). In order to enhance the dynamic performance of ADRC, Particle Swarm Opti mization (PSO) algorithm has been used to optimally tune the design parameters o f control scheme. To show the effectiveness of proposed LESO-based ADRC and NLES O-based ADRC, numerical simulation have been conducted.”
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    China University of Geosciences Reports Findings in Machine Learning (Thermograv imetric experiments based prediction of biomass pyrolysis behavior: A comparison of typical machine learning regression models in Scikit-learn)

    75-76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 th e subject of a report. According to news reporting out of Wuhan, People’s Republ ic of China, by NewsRx editors, research stated, “A variety of machine learning (ML) models have been extensively utilized in predicting biomass pyrolysis owing to their prowess in deciphering complex non-linear relationships between inputs and outputs, but there is still a lack of consensus on the optimal methods. Thi s study elaborates on the development, optimization, and evaluation of three ML methodologies, namely, artificial neural networks, random forest (RF), and suppo rt vector machines, aimed to determine the optimal model for accurate prediction of biomass pyrolysis behavior using thermogravimetric data.” Our news journalists obtained a quote from the research from the China Universit y of Geosciences, “This work assesses the utility of thermal data derived from t hese models in the computation of kinetic and thermodynamic parameters, alongsid e an analysis of their statistical performance. Eventually, the RF model exhibit s superior physical interpretability and the least discrepancy in predicting kin etic and thermodynamic parameters.”
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