查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Cancer is the subject of a report. According to news reporting originating in Quebec, Canada, by NewsR x journalists, research stated, "To compare postoperative stay in octogenarians and younger patients undergoing gynecologic oncology robot-assisted surgery. A r etrospective review of robot-assisted surgery in Gynecological Oncology division during 2019-2022." The news reporters obtained a quote from the research from McGill University, "W e included all consecutive cases. Octogenarians (age 80 years) and younger patie nts were investigated by univariable analysis for characteristics and outcome. A total of 816 robot-assisted surgeries were performed, 426 (52.2 %) endometrial cancer, 159 (19.5%) ovarian cancer, 27 (3.3% ) cervical cancer, 35 (4.3%) endometrial intraepithelial neoplasia, and in 169 (20.7%) the final pathology was benign. There were 60 ( 7.4%) octogenarians and 756 (92.6%) younger patients. The proportion of patients with an American Society of Anesthesiology score grea ter than 2 was higher among octogenarians (66.7% vs 32.0% , P<0.001). The median console time, surgical time, and to tal operation theater time were similar between groups (P = 0.303, P = 0.643 and P = 0.688, respectively). Conversion rate did not differ between groups (0.4% among younger patients vs 0% in octogenarians, P> 0.99). The median length of stay in the recovery room was similar in both group s (median 170 min, interquartile range [IQR] 125-225 min vs 170 min, IQR 128-240 min in octogenarians, P = 0.731). Length of hospital stay was similar in both age groups; median 1 day (IQR 1-1) among octog enarians versus 1 (0-1) in younger patients (P = 0.136)."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Drugs and Therapies-Personalized Medicine is the subject of a report. According to news reporting fr om Kaifeng, People's Republic of China, by NewsRx journalists, research stated, "This study aims to enhance the prognosis prediction of Head and Neck Squamous C ell Carcinoma (HNSCC) by employing artificial intelligence (AI) to analyse CDKN2 A gene expression from pathology images, directly correlating with patient outco mes. Our approach introduces a novel AI-driven pathomics framework, delineating a more precise relationship between CDKN2A expression and survival rates compare d to previous studies." The news correspondents obtained a quote from the research from Henan University , "Utilizing 475 HNSCC cases from the TCGA database, we stratified patients into high-risk and low-risk groups based on CDKN2A expression thresholds. Through pa thomics analysis of 271 cases with available slides, we extracted 465 distinctiv e features to construct a Gradient Boosting Machine (GBM) model. This model was then employed to compute Pathomics scores (PS), predicting CDKN2A expression lev els with validation for accuracy and pathway association analysis. Our study dem onstrates a significant correlation between higher CDKN2A expression and improve d median overall survival (66.73 months for high expression vs. 42.97 months for low expression, p = 0.013), establishing CDKN2A's prognostic value. The pathomi c model exhibited exceptional predictive accuracy (training AUC: 0.806; validati on AUC: 0.710) and identified a strong link between higher Pathomics scores and cell cycle activation pathways. Validation through tissue microarray corroborate d the predictive capacity of our model. Confirming CDKN2A as a crucial prognosti c marker in HNSCC, this study advances the existing literature by implementing a n AI-driven pathomics analysis for gene expression evaluation."
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 originating from Hamilton, Canada, by NewsRx correspondents, research stated, "Cement-based foam has emerged as a s trong contender in sustainable construction owing to its superior thermal and so und insulation properties, fire resistance, and cost-effectiveness. To effective ly use cement-based foam as a thermal insulation material, it is important to ac curately predict its thermal conductivity." Our news editors obtained a quote from the research from McMaster University, "T he current study aims at coining an accurate methodology for predicting the ther mal conductivity of cement-based foam using state-ofthe-art machine learning tec hniques. A comprehensive experimental dataset of 504 data points was developed a nd used for training ensemble learning models including XGBoost, CatBoost, Light GBM and Random Forest. The independent variables of this dataset affecting the t hermal conductivity are the cast density, percentage of pozzolan, porosity, perc entage of moisture, and duration of hydration in days. Using the Isolation Fores t algorithm proved effective in detecting and eliminating outliers in the datase t. All the ensemble learning techniques explored in this study achieved superior predictive accuracy with a coefficient of determination greater than 0.98 on th e test dataset. The influence of the input features on the thermal conductivity was visualized using the SHapley Additive exPlanations (SHAP) approach and indiv idual conditional expectation (ICE) plots. The cast density had the greatest eff ect on thermal conductivity."
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 Harbin, People's Repub lic of China, by NewsRx editors, research stated, "Previous studies have shown t he inconsistent performance of various daylighting glare prediction metrics in o ffice daylitdominated environments. This lack of consensus may stem from a limi ted understanding of how their component variables contribute to the prediction power reflected on these discomfort glare models." Funders for this research include National Natural Science Foundation of China ( NSFC), National Natural Science Foundation of China (NSFC), Assistant Professor Research Initiation Project at Harbin Institute of Technology, Fundamental Resea rch Funds for the Central Universities.
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 the Universit y of Maine by NewsRx correspondents, research stated, "We propose a novel framew ork that combines state-of-the-art deep learning approaches with pre- and post-p rocessing algorithms for particle detection in complex/heterogeneous backgrounds common in the manufacturing domain. Traditional methods, like size analyzers an d those based on dilution, image processing, or deep learning, typically excel w ith homogeneous backgrounds." Financial supporters for this research include National Science Foundation; Main e Technology Institute.
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 from Liaon ing, People's Republic of China, by NewsRx correspondents, research stated, "Nov el coronaviruses constitute a significant health threat, prompting the adoption of vaccination as the primary preventive measure. However, current evaluations o f immune response and vaccine efficacy are deemed inadequate." Our news editors obtained a quote from the research from the Shengjing Hospital of China Medical University, "The study sought to explore the evolving dynamics of immune response at various vaccination time points and during breakthrough in fections. It aimed to elucidate the synergistic effects of epidemiological facto rs, humoral immunity, and cellular immunity. Additionally, regression curves wer e used to determine the correlation between the protective efficacy of the vacci ne and the stimulated immune response. Employing LASSO for high-dimensional data analysis, the study utilised four machine learning algorithms-logistical regres sion, random forest, LGBM classifier, and AdaBoost classifier-to comprehensively assess the immune response following booster vaccination. Neutralising antibody levels exhibited a rapid surge post-booster, escalating to 102.38 AU/mL at one week and peaking at 298.02 AU/mL at two weeks. Influential factors such as sex, age, disease history, and smoking status significantly impacted post-booster ant ibody levels. The study further constructed regression curves for neutralising a ntibodies, non-switched memory B cells, CD4T cells, and CD8T cells using LASSO c ombined with the random forest algorithm. The establishment of an artificial int elligence evaluation system emerges as pivotal for predicting breakthrough infec tion prognosis after the COVID-19 booster vaccination. This research underscores the intricate interplay between various components of immunity and external fac tors, elucidating key insights to enhance vaccine effectiveness. 3D modelling di scerned distinctive interactions between humoral and cellular immunity within pr ognostic groups (Class 0-2)."
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 originating from Kazan State Power Engi neering University by NewsRx editors, the research stated, "This article discuss es forecasting consumer activity, and in particular forecasting household energy consumption using machine learning." Our news reporters obtained a quote from the research from Kazan State Power Eng ineering University: "Forecasting household energy consumption using machine lea rning is a topic that addresses various aspects of efficient and environmentally friendly use of electricity. The article discusses various machine learning met hods and models that can be applied to solve the forecasting problem. The consid eration of a neural network model such as LTSM is highlighted in a separate cate gory, its description, the learning and use process are given, as well as the ad vantages and disadvantages of this model are given."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Heart Disorders and Diseases-He art Disease is the subject of a report. According to news reporting from Dhaka, Bangladesh, by NewsRx journalists, research stated, "This paper presents a compr ehensive exploration of machine learning algorithms (MLAs) and feature selection techniques for accurate heart disease prediction (HDP) in modern healthcare. By focusing on diverse datasets encompassing various challenges, the research shed s light on optimal strategies for early detection." The news correspondents obtained a quote from the research from the University o f Dhaka, "MLAs such as Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), Gaussian Naive Bayes (NB), and others were studied, with precisi on and recall metrics emphasized for robust predictions. Our study addresses cha llenges in real-world data through data cleaning and one-hot encoding, enhancing the integrity of our predictive models. Feature extraction techniques-Recursive Feature Extraction (RFE), Principal Component Analysis (PCA), and univariate fe ature selection-play a crucial role in identifying relevant features and reducin g data dimensionality. Our findings showcase the impact of these techniques on i mproving prediction accuracy. Optimized models for each dataset have been achiev ed through grid search hyperparameter tuning, with configurations meticulously o utlined. Notably, a remarkable 99.12 % accuracy was achieved on th e first Kaggle dataset, showcasing the potential for accurate HDP. Model robustn ess across diverse datasets was highlighted, with caution against overfitting. T he study emphasizes the need for validation of unseen data and encourages ongoin g research for generalizability. Serving as a practical guide, this research aid s researchers and practitioners in HDP model development, influencing clinical d ecisions and healthcare resource allocation."
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 Eskisehir, T urkey, by NewsRx correspondents, research stated, "Machine learning techniques ( MLT) build models to detect complex patterns and solve new problems using big da ta. The present study aims to create a prediction interface for mothers breastfe eding exclusively for the first 6 months using MLT." Our news editors obtained a quote from the research from Eskisehir Osmangazi Uni versity, "All mothers who had babies aged 6-24 months between 15.09.2021 and 15. 12.2021 and to whom the surveys could be delivered were included. 'Personal Info rmation Form' created by the researchers was used as a data collection tool. Dat a from 514 mothers participating in the study were used for MLT. Data from 70% of mothers were used for educational purposes, and a prediction model was create d. The data obtained from the remaining 30% of the mothers were us ed for testing. The best MLT algorithm for predicting exclusive breastfeeding fo r the first 6 months was determined to be the Random Forest Classifier. The top five variables affecting the possibility of mothers breastfeeding exclusively fo r the first 6 months were as follows: 'the mother not having any health problems during pregnancy,' 'there were no people who negatively affected the mother's m orale about breastfeeding,' 'the amount of water the mother drinks in a day,' 't hinking that her milk supply is insufficient,' 'having no problems breastfeeding the baby'. Using created prediction model may allow early identification of mot hers with a risk of not breastfeeding their babies exclusively for the first 6 m onths."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-A new study on artificial intelligence is now ava ilable. According to news originating from Yangzhou, People's Republic of China, by NewsRx correspondents, research stated, "Ancient glass artifacts were suscep tible to weathering from the environment, causing changes in their chemical comp osition, which pose significant obstacles to the identification of glass product s."Financial supporters for this research include National Natural Science Foundati on of China; China Postdoctoral Science Foundation; "chunhui Plan" Cooperative S cientific Research Project of Ministry of Education of China; Priority Academic Program Development of Jiangsu Higher Education Institutions.
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