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    First Affiliated Hospital of Guangxi Medical University Reports Findings in Head and Neck Cancer (Machine learning-based identification of a consensus immune-de rived gene signature to improve head and neck squamous cell carcinoma therapy an d ...)

    76-77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 Oncology - Head and Ne ck Cancer is the subject of a report. According to news originating from Guangxi , People’s Republic of China, by NewsRx correspondents, research stated, “Head a nd neck squamous cell carcinoma (HNSCC), an extremely aggressive tumor, is often associated with poor outcomes. The standard anatomy-based tumor-node-metastasis staging system does not satisfy the requirements for screening treatment-sensit ive patients.” Our news journalists obtained a quote from the research from the First Affiliate d Hospital of Guangxi Medical University, “Thus, an ideal biomarker leading to p recise screening and treatment of HNSCC is urgently needed. Ten machine learning algorithms-Lasso, Ridge, stepwise Cox, CoxBoost, elastic network (Enet), partia l least squares regression for Cox (plsRcox), random survival forest (RSF), gene ralized boosted regression modelling (GBM), supervised principal components (Sup erPC), and survival support vector machine (survival-SVM)-as well as 85 algorith m combinations were applied to construct and identify a consensus immune-derived gene signature (CIDGS). Based on the expression profiles of three cohorts compr ising 719 patients with HNSCC, we identified 236 consensus prognostic genes, whi ch were then filtered into a CIDGS, using the 10 machine learning algorithms and 85 algorithm combinations. The results of a study involving a training cohort, two testing cohorts, and a meta-cohort consistently demonstrated that CIDGS was capable of accurately predicting prognoses for HNSCC. Incorporation of several c ore clinical features and 51 previously reported signatures, enhanced the predic tive capacity of the CIDGS to a level which was markedly superior to that of oth er signatures. Notably, patients with low CIDGS displayed fewer genomic alterati ons and higher immune cell infiltrate levels, as well as increased sensitivity t o immunotherapy and other therapeutic agents, in addition to receiving better pr ognoses. The survival times of HNSCC patients with high CIDGS, in particular, we re shorter. Moreover, CIDGS enabled accurate stratification of the response to i mmunotherapy and prognoses for bladder cancer. Niclosamide and ruxolitinib showe d potential as therapeutic agents in HNSCC patients with high CIDGS.”
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    New Machine Learning Data Have Been Reported by Investigators at Changchun Unive rsity of Science and Technology (Enhanced Short-term Load Forecasting With Hybri d Machine Learning Models: Catboost and Xgboost Approaches)

    77-78页
    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 Machine Learning. According to news originating from Changchun, People’s Republi c of China, by NewsRx correspondents, research stated, “The focus of this paper is to improve short-term load forecasting for electric power. To achieve this go al, the study explores and evaluates hybrid models, specifically using the CatBo ost and XGBoost algorithms, which are optimized with different optimizers.” Financial support for this research came from Scientific Research Project of Edu cation Department of Jilin Province.
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    Researchers at Faculty of Engineering Release New Data on Machine Learning (Mach ine-Learning-Assisted Transmission Power Control for LoRaWAN Considering Environ ments With High Signalto -Noise Variation)

    78-78页
    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 originating from the Faculty of Engine ering by NewsRx editors, the research stated, “To achieve an adequate tradeoff b etween range and energy efficiency, LoRaWAN End Nodes (ENs) choose their transmi ssion parameters using an Adaptive Data Rate (ADR) scheme based on the maximum v alue of previous Signal-to-Noise (SNR) values. However, the ADR only performs we ll in favorable channel conditions.” Financial supporters for this research include Universidad De Medellin.
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    Federal University Pernambuco Reports Findings in Machine Learning (Machine lear ning classification based on k-Nearest Neighbors for PolSAR data)

    79-79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 Recife, Brazil, by New sRx editors, research stated, “In this work, we focus on obtaining insights of t he performances of some well-known machine learning image classification techniq ues (k-NN, Support Vector Machine, randomized decision tree and one based on sto chastic distances) for PolSAR (Polarimetric Synthetic Aperture Radar) imagery. W e test the classifiers methods on a set of actual PolSAR data and provide some c onclusions.” Our news journalists obtained a quote from the research from Federal University Pernambuco, “The aim of this work is to show that suitable adapted standard mach ine learning methods offer excellent performances vs. computational complexity t rade-off for PolSAR image classification. In this work, we evaluate well-known m achine learning techniques for PolSAR (Polarimetric Synthetic Aperture Radar) im age classification, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), randomized decision tree, and a method based on the Kullback-Leibler stoc hastic distance. Our experiments with real PolSAR data show that standard machin e learning methods, when adapted appropriately, offer a favourable trade-off bet ween performance and computational complexity. The KNN and SVM perform poorly on these data, likely due to their failure to account for the inherent speckle pre sence and properties of the studied reliefs.”
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    Studies from Sichuan University Provide New Data on Support Vector Machines (Res earch On Pso-svm Base Wine Grade Recognition Based On Max-relevance and Min-redu ndancy Feature Selection)

    79-80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 Support Vecto r Machines have been published. According to news reporting from Zigong, People’ s Republic of China, by NewsRx journalists, research stated, “In response to the challenges inherent in Baijiu classification, characterized by ambiguity and li mited methodologies, this study introduces a novel framework for comprehensive B aijiu brewing process management. By integrating mathematical models and machine learning algorithms, our aim is to standardize and enhance the accuracy of the Baijiu brewing process.” Financial support for this research came from Sichuan Provincial Transfer Paymen t Key RD Project.
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    Researchers’ Work from University of Central Florida Focuses on Machine Learning (A Machine Learning Approach To Study Plant Functional Trait Divergence)

    80-81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 Machine Learn ing have been published. According to news reporting from Orlando, Florida, by N ewsRx journalists, research stated, “PremisePlant functional traits are often us ed to describe the spectra of ecological strategies used by different species.”Financial support for this research came from University of Central Florida Coll ege of Graduate Studies Open Access Publishing Fund.
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    New York University (NYU) Reports Findings in Artificial Intelligence (Artificia l intelligence/machine learning for epilepsy and seizure diagnosis)

    81-82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 out of New York City, New York, by NewsRx editors, research stated, “Accurate seizure and epilepsy dia gnosis remains a challenging task due to the complexity and variability of manif estations, which can lead to delayed or missed diagnosis. Machine learning (ML) and artificial intelligence (AI) is a rapidly developing field, with growing int erest in integrating and applying these tools to aid clinicians facing diagnosti c uncertainties.” Our news journalists obtained a quote from the research from New York University (NYU), “ML algorithms, particularly deep neural networks, are increasingly empl oyed in interpreting electroencephalograms (EEG), neuroimaging, wearable data, a nd seizure videos. This review discusses the development and testing phases of A I/ML tools, emphasizing the importance of generalizability and interpretability in medical applications, and highlights recent publications that demonstrate the current and potential utility of AI to aid clinicians in diagnosing epilepsy. C urrent barriers of AI integration in patient care include dataset availability a nd heterogeneity, which limit studies’ quality, interpretability, comparability, and generalizability. ML and AI offer substantial promise in improving the accu racy and efficiency of epilepsy diagnosis.”
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    New Research on Machine Learning from Arizona State University Summarized (The I mpact of Teachable Machine on Middle School Teachers’ Perceptions of Science Les sons after Professional Development)

    82-83页
    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 Tempe, Arizona , by NewsRx editors, research stated, “Technological advances in computer vision and machine learning image and audio classification will continue to improve an d evolve.” Financial supporters for this research include National Science Foundation. Our news reporters obtained a quote from the research from Arizona State Univers ity: “Despite their prevalence, teachers feel ill-prepared to use these technolo gies to support their students’ learning. To address this, in-service middle sch ool teachers participated in professional development, and middle school student s participated in summer camp experiences that included the use of Google’s Teac hable Machine, an easy-to-use interface for training machine learning classifica tion models. An overview of Teachable Machine is provided. As well, lessons that highlight the use of Teachable Machine in middle school science are explained.”
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    Chengdu University of Information Technology Reports Findings in Machine Learnin g (Utilising intraoperative respiratory dynamic features for developing and vali dating an explainable machine learning model for postoperative pulmonary ...)

    83-84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 Sichuan, People’s Repu blic of China, by NewsRx correspondents, research stated, “Timely detection of m odifiable risk factors for postoperative pulmonary complications (PPCs) could in form ventilation strategies that attenuate lung injury. We sought to develop, va lidate, and internally test machine learning models that use intraoperative resp iratory features to predict PPCs.” Our news journalists obtained a quote from the research from the Chengdu Univers ity of Information Technology, “We analysed perioperative data from a cohort com prising patients aged 65 yr and older at an academic medical centre from 2019 to 2023. Two linear and four nonlinear learning models were developed and compared with the current gold-standard risk assessment tool ARISCAT (Assess Respiratory Risk in Surgical Patients in Catalonia Tool). The Shapley additive explanation of artificial intelligence was utilised to interpret feature importance and inte ractions. Perioperative data were obtained from 10 284 patients who underwent 10 484 operations (mean age [range] 71 [65-98] yr; 42% female). An optimised XGBoost mo del that used preoperative variables and intraoperative respiratory variables ha d area under the receiver operating characteristic curves (AUROCs) of 0.878 (0.8 66-0.891) and 0.881 (0.879-0.883) in the validation and prospective cohorts, res pectively. These models outperformed ARISCAT (AUROC: 0.496-0.533). The intraoper ative dynamic features of respiratory dynamic system compliance, mechanical powe r, and driving pressure were identified as key modifiable contributors to PPCs. A simplified model based on XGBoost including 20 variables generated an AUROC of 0.864 (0.852-0.875) in an internal testing cohort.
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    Researchers from Delft University of Technology Describe Findings in Machine Lea rning (Inverse Designing Surface Curvatures By Deep Learning)

    84-85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 Machine Learn ing have been published. According to news reporting originating in Delft, Nethe rlands, by NewsRx journalists, research stated, “Smooth and curved microstructur al topologies found in nature-from soap films to trabecular bone-have inspired s everal mimetic design spaces for architected metamaterials and bio-scaffolds. Ho wever, the design approaches so far are ad hoc, raising the challenge: how to sy stematically and efficiently inverse design such artificial microstructures with targeted topological features? Herein, surface curvature is explored as a desig n modality and a deep learning framework is presented to produce topologies with as-desired curvature profiles.” Financial support for this research came from China Scholarship Council.
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