查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Nutritional and Metabo lic Diseases and Conditions - Diabetic Retinopathy is the subject of a report. A ccording to news reporting originating in Ganzhou, People’s Republic of China, b y NewsRx journalists, research stated, “This study aims to analyze the applicati on and clinical translation value of the self-evolving machine learning methods in predicting diabetic retinopathy and visualizing clinical outcomes. A retrospe ctive study was conducted on 300 diabetic patients admitted to our hospital betw een January 2022 and October 2023.” The news reporters obtained a quote from the research from the Department of Oph thalmology, “The patients were divided into a diabetic retinopathy group (n=150) and a non-diabetic retinopathy group (n=150). The improved Beetle Antennae Sear ch (IBAS) was used for hyperparameter optimization in machine learning, and a se lf-evolving machine learning model based on XGBoost was developed. Value analysi s was performed on the predictive features for diabetic retinopathy selected thr ough multifactor logistic regression analysis, followed by the construction of a visualization system to calculate the risk of diabetic retinopathy occurrence. Multifactor logistic regression analysis revealed that being male, having a long er disease duration, higher systolic blood pressure, fasting blood glucose, glyc osylated hemoglobin, low-density lipoprotein cholesterol, and urine albumin-to-c reatinine ratio were risk factors for the development of diabetic retinopathy, w hile non-pharmacological treatment was a protective factor. The self-evolving ma chine learning model demonstrated significant performance advantages in early di agnosis and prediction of diabetic retinopathy occurrence.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting out of Xi’an, People’s Republic of Chi na, by NewsRx editors, research stated, “Based on the principles of biomimicry, evolutionary algorithms (EAs) have been widely applied across diverse domains to tackle practical challenges. However, the inherent limitations of these algorit hms call for further refinement to strike a delicate balance between global expl oration and local exploitation.” Financial supporters for this research include Key Project of Ningxia Natural Sc ience Foundation “several Swarm Intelligence Algorithms And Their Application”; National Natural Science Foundation of China; Basic Discipline Research Projects Supported By Nanjing Securities; Construction Project of Firstclass Subjects i n Ningxia Higher Education.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting from Lanzhou, People’s R epublic of China, by NewsRx journalists, research stated, “The source region of the Yellow River (SRYR), known as the ‘Chinese Water Tower’, is currently grappl ing with severe soil erosion, which jeopardizes the sustainability of its alpine grasslands. Large-scale soil erosion monitoring poses a significant challenge, complicating global efforts to study soil erosion and land cover changes.” Financial supporters for this research include National Natural Science Foundati on of China; The Key Consulting Project of The Chinese Academy of Engineering.
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 originating from Poca tello, Idaho, by NewsRx correspondents, research stated, “Combustion involves th e study of multiphysics phenomena that includes fluid and chemical kinetics, che mical reactions and complex nonlinear processes across various time and space sc ales.” Funders for this research include Internal Grant of Idaho State University. Our news correspondents obtained a quote from the research from Idaho State Univ ersity: “Accurate simulation of combustion is essential for designing energy con version systems. Nonetheless, due to its multiscale, multiphysics nature, simula ting these systems at full resolution is typically difficult. The massive and co mplex data generated from experiments and simulations, particularly in turbulent combustion, presents both a challenge and a research opportunity for advancing combustion studies. Machine learning facilitates data-driven techniques to manag e the substantial amount of combustion data that is either obtained through expe riments or simulations, and thereby can find the hidden patterns underlying thes e data. Alternatively, machine learning models can be useful to make predictions with comparable accuracy to existing models, while reducing computational costs significantly. In this era of big data, machine learning is rapidly evolving, o ffering promising opportunities to explore its integration with combustion resea rch.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news reporting originating from Dhaka, Bang ladesh, by NewsRx correspondents, research stated, “In today’s educational insti tutions, student performance can vary widely due to differences in cognition, mo tivation, and environmental factors. These variations create challenges in achie ving optimal learning outcomes.” Our news reporters obtained a quote from the research from Southeast University: “To address these challenges, Optimal Group Formation (OGF) has emerged as a pr omising research area. Optimal Group Formation (OGF) aims to form student groups that maximize learning efficiency based on past academic performance. Group for mation problems are inherently complex and time-consuming, but their application s are extensive, spanning from manufacturing systems to educational contexts. Th is paper introduces a machine learning-based model designed to create optimal st udent groups using academic records as the primary input. The goal is to enhance overall group performance and reduce error rates by organizing students into co hesive, efficient teams. What sets this research apart is its focus on education al group formation, leveraging machine learning to improve collaborative learnin g outcomes.”
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 York, United Kingdom, by NewsRx editors, research stated, “Electric motors find widespread application across various industrial fields. The pursuit of enhanced comprehensive electri c motors performance has consistently drawn significant attention, prompting ext ensive research in this domain.” Financial supporters for this research include Royal Society, Engineering & Physical Sciences Research Council (EPSRC).
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 Learn ing have been published. According to news reporting out of Athens, Greece, by N ewsRx editors, research stated, “Human-robot collaboration (HRC) in manufacturin g allows advantageous distribution of tasks, e.g. exploiting robot accuracy and human dexterity, safety being of paramount importance. Safety is mostly linked t o avoiding collisions between the human and the robot but the pertinent measures adopted should prolong task duration as little as possible.” Our news journalists obtained a quote from the research from the National Techni cal University of Athens, “In order to test such measures in HRC pertinent algor ithms need to be applied, which is made possible without jeopardising human safe ty only in an Extended Reality environment. In order to implement path planning algorithms and human-robot interaction rules freely the environment must be open . In this work, the development of such an environment is presented and demonstr ated by example of laying up carbon fibre fabric sheets in a mould. An existing open platform was substantially extended by embedding robot control functionalit y concerning motion, path and trajectory planning emphasizing static and dynamic obstacle detection, interactive input and manipulation and real-time path plann ing, whereas trajectory planning focused on ensuring acceptability of joint moti on solutions using inverse kinematics. Two different real-time path planning met hods are embedded in the environment as representative examples. The first one i s the established ‘Rapidly exploring Random Tree’ (RRT) algorithm followed by pa th optimization. The second one is ‘Machine-Learned Path Planning’ (MLPP) a prot otype machine learning model trained using linear regression with Gaussian noise based on safe path planning data generated by users. The evaluation criteria of these methods were the number and severity of collisions as well as the total c ompletion time of the manufacturing task. In the particular case examined, the m achine learning technique proved much faster than RRT but not as safe, despite i ts potential.”
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 Changchun, People’s Republic of China, by Ne wsRx journalists, research stated, “In engineering practice, the nonlinear vibra tion effect can easily lead to chaos in the system, which will not only reduce t he performance of the system but also lead to premature fatigue of components, c ontrol failure, and increased safety risks.” Financial supporters for this research include Jilin Province Science And Techno logy Development Plan Project; Beijing Enterprise Horizontal Project.
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 out of Richmond, Vir ginia, by NewsRx editors, research stated, “There is growing interest in leverag ing advanced analytics, including artificial intelligence (AI) and machine learn ing (ML), for disaster risk analysis (RA) applications. These emerging methods o ffer unprecedented abilities to assess risk in settings where threats can emerge and transform quickly by relying on ‘learning’ through datasets.” Our news journalists obtained a quote from the research from the University of R ichmond, “There is a need to understand these emerging methods in comparison to the more established set of risk assessment methods commonly used in practice. T hese existing methods are generally accepted by the risk community and are groun ded in use across various risk application areas. The next frontier in RA with e merging methods is to develop insights for evaluating the compatibility of those risk methods with more recent advancements in AI/ML, particularly with consider ation of usefulness, trust, explainability, and other factors. This article leve rages inputs from RA and AI experts to investigate the compatibility of various risk assessment methods, including both established methods and an example of a commonly used AI-based method for disaster RA applications. This article utilize s empirical evidence from expert perspectives to support key insights on those m ethods and the compatibility of those methods.”
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 originating from Prince Sattam Bin Abdulaziz University by NewsRx correspondents, research stated, “This work e valuates the performance of four machine learning models (MLMs): support vector machine (SVM), K-nearest neighbor (KNN), discriminant analysis (DA), and logisti c regression (LR) in predicting the biodegradability of chemicals, a critical fa ctor for assessing environmental risks. For this purpose, the RDKit library was initially employed to extract nine fingerprints from a dataset consisting of 171 7 chemical compounds.” Financial supporters for this research include Prince Sattam Bin Abdulaziz Unive rsity.
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