查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Artificial Intelligence is the su bject of a report. According to news reporting out of Reno, Nevada, by NewsRx ed itors, research stated, “Diabetic retinopathy (DR) is one of the most common com plications of diabetes mellitus. The global burden is immense with a worldwide p revalence of 8.5%.” Our news journalists obtained a quote from the research from the University of N evada, “Recent advancements in artificial intelligence (AI) have demonstrated th e potential to transform the landscape of ophthalmology with earlier detection a nd management of DR. This study seeks to provide an update and evaluate the accu racy and current diagnostic ability of AI in detecting DR versus ophthalmologist s. Additionally, this review will highlight the potential of AI integration to e nhance DR screening, management, and disease progression. A systematic review of the current landscape of AI’s role in DR will be undertaken, guided by the PRIS MA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) model. R elevant peer-reviewed papers published in English will be identified by searchin g 4 international databases: PubMed, Embase, CINAHL, and the Cochrane Central Re gister of Controlled Trials. Eligible studies will include randomized controlled trials, observational studies, and cohort studies published on or after 2022 th at evaluate AI’s performance in retinal imaging detection of DR in diverse adult populations. Studies that focus on specific comorbid conditions, nonimage-based applications of AI, or those lacking a direct comparison group or clear methodo logy will be excluded. Selected papers will be independently assessed for bias b y 2 review authors (JS and DM) using the Quality Assessment of Diagnostic Accura cy Studies tool for systematic reviews. Upon systematic review completion, if it is determined that there are sufficient data, a meta-analysis will be performed . Data synthesis will use a quantitative model. Statistical software such as Rev Man and STATA will be used to produce a random-effects meta-regression model to pool data from selected studies. Using selected search queries across multiple d atabases, we accumulated 3494 studies regarding our topic of interest, of which 1588 were duplicates, leaving 1906 unique research papers to review and analyze. This systematic review and meta-analysis protocol outlines a comprehensive eval uation of AI for DR detection.”
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 from Xi’an, People’s Republ ic of China, by NewsRx journalists, research stated, “Failure assessment is one of the fundamental tasks for optimization of composite pressure vessels (CPVs). However, the extensive design space of composites usually leads to costly and re petitive work of failure assessment that hinders the design and optimization of CPVs.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Scientific Research Program of Shaanxi province, China Schol arship Council.
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 Santa Cruz de Tenerife, Spain, by NewsRx correspondents, research stated, “Some materials, su ch as reinforced and prestressed concrete, involve non-linear constitutive relat ionships in elasticity problems defined on them. In particular, the shear streng th of a reinforced concrete beam may be calculated by considering a diagonal str uts field in the context of the so-called ‘Compression Field Theories’ (CFTs).” Funders for this research include Ministerio De Ciencia E Innovacion; Gobierno D e Espana Ministerio De Ciencia E Innovacion.
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 Rutgers Univer sity - The State University of New Jersey by NewsRx correspondents, research sta ted, “Melt pool dynamics in metal additive manufacturing (AM) is critical to pro cess stability, microstructure formation, and final properties of the printed ma terials.” Funders for this research include Division of Civil, Mechanical And Manufacturin g Innovation. Our news journalists obtained a quote from the research from Rutgers University - The State University of New Jersey: “Physics-based simulation, including compu tational fluid dynamics (CFD), is the dominant approach to predict melt pool dyn amics. However, the physics-based simulation approaches suffer from the inherent issue of very high computational cost. This paper provides a physics-informed m achine learning method by integrating the conventional neural networks with the governing physical laws to predict the melt pool dynamics, such as temperature, velocity, and pressure, without using any training data on velocity and pressure . This approach avoids solving the nonlinear Navier-Stokes equation numerically, which significantly reduces the computational cost (if including the cost of ve locity data generation). The difficult-to-determine parameters’ values of the go verning equations can also be inferred through data-driven discovery.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Researchers detail new data in robotics. Accordin g to news reporting out of Beijing, People’s Republic of China, by NewsRx editor s, research stated, “In the field of pipeline inner wall inspection, the snake r obot demonstrates significant advantages over other inspection methods. While a simple traveling wave or meandering motion will suffice for inspecting the inner wall of small-diameter pipes, comprehensively and meticulously inspecting the i nner wall of large-diameter pipes requires the snake robot to adopt a helical ga it that closely adheres to the inner wall.” Financial supporters for this research include Beijing University of Civil Engin eering And Architecture.
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 from Athens, Greece, by NewsR x journalists, research stated, “Motivated by the successful usage of machine le arning around computer science and its wide acceptance from the finance literatu re, we utilize monthly data spanning the period 2008-2018 for the Euro area peri pheral countries, in order to embark on a two-fold mission. First, to construct short-term prediction models for bank deposit flows in the Euro area peripheral countries, employing machine learning techniques.” The news correspondents obtained a quote from the research from the Athens Unive rsity of Economics and Business, “Second, to examine whether textual features en hance the predictive ability of our models. From the variety of models tested, w e find that Random Forest models including both textual features and macroeconom ic variables outperform models including only macro factors or textual features. ”
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 new report. According to news originating from Innsbruck, Austria, by New sRx correspondents, research stated, “Modular self-reconfigurable robots hold th e promise of being capable of performing a wide variety of tasks.” Funders for this research include Publication Fund of The University of Innsbruc k.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting from Taiyuan, People’s Repu blic of China, by NewsRx journalists, research stated, “Owing to the hexagonal c lose-packed (HCP) crystal structure inherent in Mg alloys, strong basal texture can readily be induced through the processes of rolling or extrusion. The anisot ropy of the texture of Mg alloys impacts their stamping and forming capabilities , limiting their use in certain applications.” Our news editors obtained a quote from the research from Taiyuan University of T echnology: “Microalloying and shear deformation are currently the most common me thods of weakening the texture of Mg alloys. Many shearing processes have been e xtensively studied, and given that they require complex equipment and make it di fficult to achieve mass production, major attention has turned to studying the d esign of microalloys. Traditional trial-and-error approaches for developing micr o-alloying confront many challenges, including longer test cycles and increasing expenses. The rapid advancement of big data and artificial intelligence opens u p a new channel for the efficient advancement of metallic materials, specificall y the application of machine learning to aid in the design of Mg alloys. ML mode ling can be used to find correlations between features and attributes in data, a llowing for the development and design of high-performance Mg alloys. The articl e provides an extensive overview of machine learning applications in Mg alloys.”
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 Kocaeli Univer sity by NewsRx editors, research stated, “Automatic detection of tire defects ha s become an important issue for tire production companies since these defects ca use road accidents and loss of human lives.” Funders for this research include The Scientific And Technological Research Coun cil of Turkiye. The news reporters obtained a quote from the research from Kocaeli University: “ Defects in the inner structure of the tire cannot be detected with the naked eye ; thus, a radiographic image of the tire is gathered using X-ray cameras. This i mage is then examined by a quality control operator, and a decision is made on w hether it is a defective tire or not. Among all defect types, the foreign object type is the most common and may occur anywhere in the tire. This study proposes an explainable deep learning model based on Xception and Grad-CAM approaches. T his model was fine-tuned and trained on a novel real tire dataset consisting of 2303 defective tires and 49,198 non-defective. The defective tire class was augm ented using a custom augmentation technique to solve the imbalance problem of th e dataset.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting out of Tianjin, People’s Republic o f China, by NewsRx editors, research stated, “The glacio-hydrological process is essential in the global water cycle but is complex and poorly understood. In th is study, we couple the deep Shapley additive explanation (SHAP) with a long sho rt-term memory (LSTM) model to construct a machine-learning (XAI) framework that describes the glacio-hydrological process in Urumqi Glacier No. 1, China.”Funders for this research include National Natural Science Foundation of China ( NSFC), Tianjin Normal University Research Innovation Project for Postgraduate gr ant, Second Qinghai-Tibet Scientific Expedition Program, Third Xinjiang Scientif ic Expedition Program.
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