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    New Machine Learning Findings Reported from University of Wis- consin Madison (Machine Learning for Interpreting Coherent X-ray Speckle Patterns)

    20-20页
    查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news originating from Madison, Wisconsin, by NewsRx correspondents, research stated, "Speckle patterns produced by coherent X-ray have a close relationship with the internal structure of materials but quantitative inversion of the relationship to determine structure from speckle patterns is challenging." Funders for this research include Laboratory Directed Research and Development (LDRD), United States Department of Energy (DOE), United States Department of Energy (DOE), United States Department of Energy (DOE), United States Department of Energy (DOE). Our news journalists obtained a quote from the research from the University of Wisconsin Madison, "Here, we investigate the link between coherent X-ray speckle patterns and sample structures using a model 2D disk system and explore the ability of machine learning to learn aspects of the relationship. Specifically, we train a deep neural network to classify the coherent Xray speckle patterns according to the disk number density in the corresponding structure."
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Data from Universitas Muhammadiyah Yogyakarta Advance Knowl- edge in Artificial Intelligence (A Review on the Application of In- ternet of Medical Things in Wearable Personal Health Monitoring: A Cloud-Edge Artificial Intelligence Approach)

    20-21页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting out of the Universitas Muhammadiyah Yogyakarta by NewsRx editors, research stated, "The advent of the fifth-generation mobile communication technology (5G) era has catalyzed significant advancements in medical diagnosis delivery, primarily driven by the surge in medical data from wearable Internet of Medical Things (IoMT) devices." Funders for this research include Universitas Muhammadiyah Yogyakarta, Indonesia Through The Cen- ter of Artificial Intelligence And Robotic Studies, The Research And Innovation Centre; Cooperation And International Affairs; Ministry of Education, Culture, Research, And Technology, Indonesia; Chelpis Quan- tum Tech Co., Ltd.; Asia University, Taiwan, And China Medical University Hospital, China Medical University, Taiwan.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Xi'an Medical University Reports Findings in Artificial Intelligence (A study on the improvement in the ability of endoscopists to diag- nose gastric neoplasms using an artificial intelligence system)

    21-22页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news re- porting out of Xi'an, People's Republic of China, by NewsRx editors, research stated, "Artificial intelligence- assisted gastroscopy (AIAG) based on deep learning has been validated in various scenarios, but there is a lack of studies regarding diagnosing neoplasms under white light endoscopy. This study explored the potential role of AIAG systems in enhancing the ability of endoscopists to diagnose gastric tumor lesions under white light." Our news journalists obtained a quote from the research from Xi'an Medical University, "A total of 251 patients with complete pathological information regarding electronic gastroscopy, biopsy, or ESD surgery in Xi'an Gaoxin Hospital were retrospectively collected and comprised 64 patients with neoplasm lesions (excluding advanced cancer) and 187 patients with non-neoplasm lesions. The diagnosis competence of endoscopists with intermediate experience and experts was compared for gastric neoplasms with or without the assistance of AIAG, which was developed based on ResNet-50. For the 251 patients with difficult clinical diagnoses included in the study, compared with endoscopists with intermediate experience, AIAG's diagnostic competence was much higher, with a sensitivity of 79.69% (79.69% vs. 72.50%, = 0.012) and a specificity of 73.26% (73.26% vs. 52.62%, ＜0.001). With the help of AIAG, the endoscopists with intermediate experience (＜8 years) demonstrated a relatively higher specificity (59.79% vs. 52.62%, ＜0.001). Experts ( 8 years) had similar results with or without AI assistance (with AI vs. without AI; sensitivities, 70.31% vs. 67.81%, = 0.358; specificities, 83.85% vs. 85.88%, = 0.116)."
      原文链接:
    • NETL
    • NSTL
    • Frontiers Media Sa

    Study Results from Cardiff University Provide New Insights into Ma- chine Learning (Microcapsule Triggering Mechanics in Cementitious Materials: A Modelling and Machine Learning Approach)

    22-23页
    查看更多>>摘要：Researchers detail new data in artificial intelligence. According to news originating from Cardiff, United Kingdom, by NewsRx correspondents, research stated, "Self-healing cementitious materials containing microcapsules filled with healing agents can autonomously seal cracks and restore structural integrity." Financial supporters for this research include Epscr. Our news editors obtained a quote from the research from Cardiff University: "However, optimising the microcapsule mechanical properties to survive concrete mixing whilst still rupturing at the cracked interface to release the healing agent remains challenging. This study develops an integrated numerical modelling and machine learning approach for tailoring acrylate-based microcapsules for triggering within cementitious matrices. Microfluidics is first utilised to produce microcapsules with systematically varied shell thickness, strength, and cement compatibility. The capsules are characterised and simulated using a continuum damage mechanics model that is able to simulate cracking. A parametric study investigates the key microcapsule and interfacial properties governing shell rupture versus matrix failure. The simulation results are used to train an artificial neural network to rapidly predict the triggering behaviour based on capsule properties."
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Southern Medical University Reports Findings in Stroke (Machine learning decision support model for discharge planning in stroke patients)

    23-24页
    查看更多>>摘要：New research on Cerebrovascular Diseases and Conditions - Stroke is the subject of a report. According to news reporting from Guangzhou, People's Republic of China, by NewsRx journalists, research stated, "Efficient discharge for stroke patients is crucial but challenging. The study aimed to develop early predictive models to explore which patient characteristics and variables significantly influence the discharge planning of patients, based on the data available within 24 h of admission. Prospective observational study." The news correspondents obtained a quote from the research from Southern Medical University, "A prospective cohort was conducted at a university hospital with 523 patients hospitalised for stroke. We built and trained six different machine learning (ML) models, followed by testing and tuning those models to find the best-suited predictor for discharge disposition, dichotomized into home and non-home. To evaluate the accuracy, reliability and interpretability of the best-performing models, we identified and analysed the features that had the greatest impact on the predictions. In total, 523 patients met the inclusion criteria, with a mean age of 61 years. Of the patients with stroke, 30.01% had non-home discharge. Our model predicting non-home discharge achieved an area under the receiver operating characteristic curve of 0.95 and a precision of 0.776. After threshold was moved, the model had a recall of 0.809. Top 10 variables by importance were National Institutes of Health Stroke Scale (NIHSS) score, family income, Barthel index (BI) score, FRAIL score, fall risk, pressure injury risk, feeding method, depression, age and dysphagia. The ML model identified higher NIHSS, BI, and FRAIL, family income, higher fall risk, pressure injury risk, older age, tube feeding, depression and dysphagia as the top 10 strongest risk predictors in identifying patients who required non-home discharge to higher levels of care. Modern ML techniques can support timely and appropriate clinical decision-making."
      原文链接:
    • NETL
    • NSTL

    New Artificial Intelligence Study Results Reported from Bucharest University of Economic Studies (Architecture to Transform Clas- sic Academic Courses into Adaptive Learning Flows with Artificial Intelligence)

    24-25页
    查看更多>>摘要：Data detailed on artificial intelligence have been presented. According to news reporting out of Bucharest, Romania, by NewsRx editors, research stated, "The literature on adaptive learning suggests that it can provide significant improvements to the educational process and numerous studies have found a necessity for personalised learning, which is one of the strong suits of adaptive learning." Our news reporters obtained a quote from the research from Bucharest University of Economic Studies: "Adaptive learning platforms require that content be effective, and lack thereof has hindered large-scale adoption by adding the cost of content creation to the upfront implementation cost and creating a 'critical mass' type problem where a platform without content is ineffective and unattractive, leading to lack of interest from users and lack of funding for developing new content. Artificial intelligence (AI) technology has the potential to aid in content creation by taking on a significant part of the workload. This paper aims to explore this possibility and propose an architecture based on current artificial intelligence technologies that will help teachers and experts transform classic course materials into adaptive learning flows. The system is not autonomous and will not replace a human expert but rather will take on some of the more straightforward, but timeconsuming, work."
      原文链接:
    • NETL
    • NSTL

    University of Auckland Reports Findings in Biomimetics (Biomimetic leaves with immobilized catalase for machine learning- enabled validating fresh produce sanitation processes)

    25-26页
    查看更多>>摘要：New research on Nanotechnology - Biomimetics is the subject of a report. According to news reporting out of Auckland, New Zealand, by NewsRx editors, research stated, "Washing and sanitation are vital steps during the postharvest processing of fresh produce to reduce the microbial load on the produce surface. Although current process control and validation tools effectively predict sanitizer concentrations in wash water, they have significant limitations in assessing sanitizer effectiveness for reducing microbial counts on produce surfaces." Our news journalists obtained a quote from the research from the University of Auckland, "These challenges highlight the urgent need to improve the validation of sanitation processes, especially con- sidering the presence of dynamic organic contaminants and complex surface topographies. This study aims to provide the fresh produce industry with a novel, reliable, and highly accurate method for vali- dating the sanitation efficacy on the produce surface. Our results demonstrate the feasibility of using a food-grade, catalase (CAT)-immobilized biomimetic leaf in combination with vibrational spectroscopy and machine learning to predict microbial inactivation on microgreen surfaces. This was tested using two sanitizers: sodium hypochlorite (NaClO) and hydrogen peroxide (HO). The developed CAT-immobilized leaf-replicated PDMS (CAT@L-PDMS) effectively mimics the microscale topographies and bacterial distri- bution on the leaf surface. Alterations in the FTIR spectra of CAT@L-PDMS, following simulated sanitation processes, indicate chemical changes due to CAT oxidation induced by NaClO or HO treatments, facilitat- ing the subsequent machine learning modeling. Among the five algorithms tested, the competitive adaptive reweighted sampling partial least squares discriminant analysis (CARS-PLSDA) algorithm was the most effective for classifying the inactivation efficacy of E. coli on microgreen leaf surfaces. It predicted bacterial reduction on microgreen surfaces with 100% accuracy in both training and prediction sets for NaClO, and 95% in the training set and 86% in the prediction set for HO."
      原文链接:
    • NETL
    • NSTL

    Research from University of Edinburgh Edinburgh in the Area of Machine Learning Described (Machine Intelligence in Metamateri- als Design: A Review)

    26-27页
    查看更多>>摘要：New research on artificial intelligence is the subject of a new report. According to news originating from the University of Edinburgh Edinburgh by NewsRx correspondents, research stated, "Machine intelligence continues to rise in popularity as an aid to the design and discovery of novel metamaterials." The news journalists obtained a quote from the research from University of Edinburgh Edinburgh: "The properties of metamaterials are essentially controllable via their architectures and until recently, the design process has relied on a combination of trial-and-error and physics-based methods for optimization. These processes can be time-consuming and challenging, especially if the design space for metamaterial optimization is explored thoroughly. Artificial intelligence (AI) and machine learning (ML) can be used to overcome challenges like these as pre-processed massive metamaterial datasets can be used to very accu- rately train appropriate models. The models can be broad, describing properties, structure, and function at numerous levels of hierarchy, using relevant inputted knowledge. Here, we present a comprehensive review of the literature where state-of-the-art machine intelligence is used for the design, discovery and development of metamaterials. In this review, individual approaches are categorized based on methodology and application."
      原文链接:
    • NETL
    • NSTL

    Recent Findings from Baylor University Has Provided New Informa- tion about Machine Learning (Fairness Issues, Current Approaches, and Challenges In Machine Learning Models)

    27-28页
    查看更多>>摘要：Investigators discuss new findings in Machine Learning. According to news reporting out of Waco, Texas, by NewsRx editors, research stated, "With the increasing influence of machine learning algorithms in decision-making processes, concerns about fairness have gained significant attention. This area now offers significant literature that is complex and hard to penetrate for newcomers to the domain." Financial support for this research came from National Foundation for Science and Technology Devel- opment. Our news journalists obtained a quote from the research from Baylor University, "Thus, a mapping study of articles exploring fairness issues is a valuable tool to provide a general introduction to this field. Our paper presents a systematic approach for exploring existing literature by aligning their discoveries with predetermined inquiries and a comprehensive overview of diverse bias dimensions, encompassing training data bias, model bias, conflicting fairness concepts, and the absence of prediction transparency, as ob- served across several influential articles. To establish connections between fairness issues and various issue mitigation approaches, we propose a taxonomy of machine learning fairness issues and map the diverse range of approaches scholars developed to address issues. We briefly explain the responsible critical factors behind these issues in a graphical view with a discussion and also highlight the limitations of each approach analyzed in the reviewed articles."
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Study Data from Bucharest University of Economic Studies Up- date Understanding of Machine Learning (Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research)

    28-29页
    查看更多>>摘要：Current study results on artificial intelligence have been published. According to news originating from Bucharest, Romania, by NewsRx correspondents, research stated, "Fake news is an explosive subject, being undoubtedly among the most controversial and difficult challenges facing society in the present-day environment of technology and information, which greatly affects the individuals who are vulnerable and easily influenced, shaping their decisions, actions, and even beliefs." Financial supporters for this research include Romanian Ministry of Research And Innovation; Bucharest University of Economic Studies During The Phd Program. The news editors obtained a quote from the research from Bucharest University of Economic Studies: "In the course of discussing the gravity and dissemination of the fake news phenomenon, this article aims to clarify the distinctions between fake news, misinformation, and disinformation, along with conducting a thorough analysis of the most widely read academic papers that have tackled the topic of fake news research using various machine learning techniques. Utilizing specific keywords for dataset extraction from Clarivate Analytics' Web of Science Core Collection, the bibliometric analysis spans six years, offering valuable insights aimed at identifying key trends, methodologies, and notable strategies within this multidisciplinary field. The analysis encompasses the examination of prolific authors, prominent journals, collaborative efforts, prior publications, covered subjects, keywords, bigrams, trigrams, theme maps, co-occurrence networks, and various other relevant topics. One noteworthy aspect related to the extracted dataset is the remarkable growth rate observed in association with the analyzed subject, indicating an impressive increase of 179.31%."
      原文链接:
    • NETL
    • NSTL
    • Mdpi