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    Netherlands Cancer Institute Reports Findings in Artificial Intelligence (Artifi cial intelligence and explanation: How, why, and when to explain black boxes)

    65-66页
    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 originating from Amsterdam, Nethe rlands, by NewsRx correspondents, research stated, "Artificial intelligence (AI) is infiltrating nearly all fields of science by storm. One notorious property t hat AI algorithms bring is their so-called black box character." Our news editors obtained a quote from the research from Netherlands Cancer Inst itute, "In particular, they are said to be inherently unexplainable algorithms. Of course, such characteristics would pose a problem for the medical world, incl uding radiology. The patient journey is filled with explanations along the way, from diagnoses to treatment, follow-up, and more. If we were to replace part of these steps with non-explanatory algorithms, we could lose grip on vital aspects such as finding mistakes, patient trust, and even the creation of new knowledge . In this article, we argue that, even for the darkest of black boxes, there is hope of understanding them. In particular, we compare the situation of understan ding black box models to that of understanding the laws of nature in physics. In the case of physics, we are given a ‘black box' law of nature, about which ther e is no upfront explanation. However, as current physical theories show, we can learn plenty about them. During this discussion, we present the process by which we make such explanations and the human role therein, keeping a solid focus on radiological AI situations. We will outline the AI developers' roles in this pro cess, but also the critical role fulfilled by the practitioners, the radiologist s, in providing a healthy system of continuous improvement of AI models."
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    Study Results from Georgia Institute of Technology Update Understanding of Robot ics (The Rhizodynamics Robot: Automated Imaging System for Studying Long-term Dy namic Root Growth)

    66-67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 Robotics is now availab le. According to news reporting originating in Atlanta, Georgia, by NewsRx journ alists, research stated, "The study of plant root growth in real time has been d ifficult to achieve in an automated, high-throughput, and systematic fashion. Dy namic imaging of plant roots is important in order to discover novel root growth behaviors and to deepen our understanding of how roots interact with their envi ronments." Funders for this research include National Science Foundation, Gordon and Betty Moore Foundation, John S. Dunn Foundation. The news reporters obtained a quote from the research from the Georgia Institute of Technology, "We designed and implemented the Generating Rhizodynamic Observa tions Over Time (GROOT) robot, an automated, high-throughput imaging system that enables time-lapse imaging of 90 containers of plants and their roots growing i n a clear gel medium over the duration of weeks to months. The system uses low-c ost, widely available materials. As a proof of concept, we employed GROOT to col lect images of root growth of Oryza sativa, Hudsonia montana, and multiple speci es of orchids including Platanthera integrilabia over six months."
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    Researchers from Wuhan University Discuss Research in Machine Learning (Comparis on of Random Forest and XGBoost Classifiers Using Integrated Optical and SAR Fea tures for Mapping Urban Impervious Surface)

    67-68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 originating from Wuhan, People 's Republic of China, by NewsRx correspondents, research stated, "The integratio n of optical and SAR datasets through ensemble machine learning models shows pro mising results in urban remote sensing applications. The integration of multi-se nsor datasets enhances the accuracy of information extraction." Funders for this research include National Key Research And Development Program of China; Guangxi Science And Technology Program Guangxi Key R&D Pl an; Sichuan Science And Technology Program; Hubei Key R&D Plan. The news editors obtained a quote from the research from Wuhan University: "This research presents a comparison of two ensemble machine learning classifiers (ra ndom forest and extreme gradient boost (XGBoost)) classifiers using an integrati on of optical and SAR features and simple layer stacking (SLS) techniques. There fore, Sentinel-1 (SAR) and Landsat 8 (optical) datasets were used with SAR textu res and enhanced modified indices to extract features for the year 2023. The cla ssification process utilized two machine learning algorithms, random forest and XGBoost, for urban impervious surface extraction. The study focused on three sig nificant East Asian cities with diverse urban dynamics: Jakarta, Manila, and Seo ul. This research proposed a novel index called the Normalized Blue Water Index (NBWI), which distinguishes water from other features and was utilized as an opt ical feature. Results showed an overall accuracy of 81% for UIS cl assification using XGBoost and 77% with RF while classifying land use land cover into four major classes (water, vegetation, bare soil, and urban impervious). However, the proposed framework with the XGBoost classifier outperf ormed the RF algorithm and Dynamic World (DW) data product and comparatively sho wed higher classification accuracy."
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    Findings from Chongqing University in the Area of Artificial Intelligence Report ed (A Hybrid Approach of Process Reasoning and Artificial Intelligence-based Int elligent Decision System Framework for Fatigue Life of Belt Grinding)

    68-69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 Artificial Intelligenc e is the subject of a report. According to news reporting originating in Chongqi ng, People's Republic of China, by NewsRx journalists, research stated, "Belt gr inding is widely used as the final step in the fabrication of fatigue-resistant surfaces of nickel-based superalloy components, and fatigue life after grinding is one of the most concerning issues. However, the response mechanism of fatigue life under different grinding parameter excitation conditions is not well under stood for a long time." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Natural Science Foundation of Chongqing, Innovation Group Sc ience Fund of Chongqing Natural Science Foundation, Basic Research Funds for Cen tral Universities. The news reporters obtained a quote from the research from Chongqing University, "In this study, a system framework of fatigue life prediction for nickel-based superalloy abrasive belt based on process reasoning and artificial intelligence algorithm is proposed. Based on the process reasoning method, the mathematical r elationship between grinding parameters and fatigue life is established. The equ ation is solved by RNN and LSMT algorithms embedded in the system, and the excit ation response model of process parameters to fatigue life is obtained. The resu lts show that the prediction accuracy of the system is high. The mean squared er ror (MSE) of the LSTM algorithm is below 0.0441, and the R-squared can be above 0.9956. In addition, experimental verification has been carried out, the observa tion of the specimen section shows that the process parameters have an effect on the initiation position, distribution, and crack length of the fatigue crack so urce, which are related to the stress concentration and residual stress distribu tion at the depth of the grinding scratches."
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    South China University of Technology Reports Findings in Machine Learning (Predi ction of heavy metal removal performance of sulfate reducing bacteria using mach ine learning)

    69-69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 originating from Guangzhou, People's Re public of China, by NewsRx correspondents, research stated, "A robust modeling a pproach for predicting heavy metal removal by sulfate-reducing bacteria (SRB) is currently missing. In this study, four machine learning models were constructed and compared to predict the removal of Cd, Cu, Pb, and Zn as individual ions by SRB." Our news journalists obtained a quote from the research from the South China Uni versity of Technology, "The CatBoost model exhibited the best predictive perform ance across the four subsets, achieving R values of 0.83, 0.91, 0.92, and 0.83 f or the Cd, Cu, Pb, and Zn models, respectively. Feature analysis revealed that t emperature, pH, sulfate concentration, and C/S (the mass ratio of chemical oxyge n demand to sulfate) had significant impacts on the outcomes. These features exh ibited the most effective metal removal at 35 °C and sulfate concentrations of 1 000-1200 mg/L, with variations observed in pH and C/S ratios."
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    Laboratory for Advanced Materials Researchers Describe New Findings in Machine L earning (Classification of Progressive Wear on a Multi-Directional Pin-on-Disc T ribometer Simulating Conditions in Human Joints-UHMWPE against CoCrMo Using Acou stic ...)

    70-70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 from Thun, Switzerland, by NewsRx journalists, research stated, "Human joint prostheses experience wear failure d ue to the complex interactions between Ultra-High-Molecular-Weight Polyethylene (UHMWPE) and Cobalt-Chromium-Molybdenum (CoCrMo)." Funders for this research include Empa Internal; Robert Mathys Foundation. Our news reporters obtained a quote from the research from Laboratory for Advanc ed Materials: "This study uses the wear classification to investigate the gradua l and progressive abrasive wear mechanisms in UHMWPE. Pin-on-disc tests were con ducted under simulated in vivo conditions, monitoring wear using Acoustic Emissi on (AE). Two Machine Learning (ML) frameworks were employed for wear classificat ion: manual feature extraction with ML classifiers and a contrastive learning-ba sed Convolutional Neural Network (CNN) with ML classifiers. The CNN-based featur e extraction approach achieved superior classification performance (94% to 96%) compared to manual feature extraction (81% to 89%). The ML techniques enable accurate wear classification, aidin g in understanding surface states and early failure detection."
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    Changi General Hospital Researcher Furthers Understanding of Machine Learning (A Novel Ear Impression-Taking Method Using Structured Light Imaging and Machine L earning: A Pilot Proof of Concept Study with Patients' Feedback on Prototype)

    71-71页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on artificial in telligence. According to news reporting from Singapore, Singapore, by NewsRx jou rnalists, research stated, "Taking an ear impression is a minimally invasive pro cedure. A review of existing literature suggests that contactless methods of sca nning the ear have not been developed." Funders for this research include Cgh-sutd Health Technology Innovation Fund. The news correspondents obtained a quote from the research from Changi General H ospital: "We proposed to establish a correlation between external ear features w ith the ear canal and with this proof of concept to develop a prototype and an a lgorithm for capturing and predicting ear canal information. We developed a nove l prototype using structured light imaging to capture external images of the ear . Using a large database of existing ear impression images obtained by tradition al methods, correlation analyses were carried out and established. A deep neural network was devised to build a predictive algorithm. Patients undergoing hearin g aid evaluation undertook both methods of ear impression-taking. We evaluated t heir subjective feedback and determined if there was a close enough objective ma tch between the images obtained from the impression techniques. A prototype was developed and deployed for trial, and most participants were comfortable with th is novel method of ear impression-taking. Partial matching of the ear canal coul d be obtained from the images taken, and the predictive algorithm applied for a few sample images was within good standard of error with proof of concept establ ished."
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    Findings from State University of New York (SUNY) Stony Brook Provides New Data on Machine Learning (Utilizing Machine Learning To Model Interdependency of Bulk Molecular Weight, Solution Concentration, and Thickness of Spin Coated Polystyr ene ...)

    72-73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 from Stony Brook, New York, by NewsRx correspondents, research stated, "Spin coating is a quick a nd inexpensive method to create nanometer-thick thin films of various polymers o n solid substrates." Our news editors obtained a quote from the research from the State University of New York (SUNY) Stony Brook, "Since the film thickness determines the mechanica l, optical, and degradation properties of the coating, it is essential to develo p a simple method to predict thickness based on other manipulatable factors. In this study, a three-dimensional manifold simultaneously relating initial solutio n concentration, film thickness, and monodisperse bulk molecular weight is devel oped utilizing curve-fit machine learning on a dataset of spin coated polystyren e samples."
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    Jeonbuk National University Researcher Reports Research in Machine Learning (Sma rt Strategic Management for the Cold Plasma Process Using ORP Monitoring and Tot al Organic Carbon Correlation)

    72-72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 originating from Jeonju, South Korea, by Ne wsRx correspondents, research stated, "Assessing oxidation-reduction potential ( ORP) is of paramount importance in the efficient management of wastewater within both chemical and biological treatment processes." Our news journalists obtained a quote from the research from Jeonbuk National Un iversity: "However, despite its critical role, insufficient information exists a bout how reactive chemical species generated by cold plasma (CP) in chemical tre atment are associated with ORP and air flow rate. Therefore, we aim to identify the correlation between ORP and the removal of organic pollutants when using CP treatment. Additionally, we introduce a machine-learning-based operation to pred ict removal efficiency in the CP process. Results reveal a significant correlati on of over 0.9 between real-time ORP and total organic carbon (TOC), which under scores the efficacy of ORP as a key parameter." According to the news reporters, the research concluded: "This approach made it possible to control OH radical generation by regulating the air flow rate of the CP. This study posits that smart management facilitated by machine learning has the potential to enhance the economic viability of CP feasibility while maintai ning overall treatment performance."
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    Department of Orthodontics and Dentofacial Orthopedics Reports Findings in Artif icial Intelligence (Artificial intelligence in oral health surveillance among un der-served communities)

    73-74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 Artificial Intelligenc e is the subject of a report. According to news originating from Bhopal, India, by NewsRx correspondents, research stated, "A sizable percentage of the populati on in India still does not have easy access to dental facilities. Therefore, it is of interest to document the role of artificial intelligence (AI) in oral surv eillance of underserved communities." Our news journalists obtained a quote from the research from the Department of O rthodontics and Dentofacial Orthopedics, "Available data shows that AI makes it possible to screen, diagnose, track, prioritize, and monitor dental patients rem otely via smart devices. As a result, dentists won't have to deal with simple si tuations that only require standard treatments; freeing them up to focus on more complicated cases. Additionally, this would allow dentists to reach a broader, more underprivileged population in difficultto- reach places. AI fracture recogn ition and categorization performance has shown promise in preliminary testing."
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