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    Data from Chinese Academy of Sciences Update Knowledge in Machine Learning (Enha ncing the streamflow simulation of a processbased hydrological model using mach ine learning and multi-source data)

    86-86页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on artificial intelligence have been published. According to news originating from Lanzhou, People’s Republ ic of China, by NewsRx editors, the research stated, “Streamflow simulation is c rucial for flood mitigation, ecological protection, and water resource planning. Process-based hydrological models and machine learning algorithms are the mains tream tools for streamflow simulation.” The news correspondents obtained a quote from the research from Chinese Academy of Sciences: “However, their inherent limitations, such as time-consuming and la rge data requirements, make achieving high-precision simulations challenging. Th is study developed a hybrid approach to simultaneously improve the accuracy and computational efficiency of streamflow simulation, which integrates Block-wise u se of the TOPMODEL (BTOP) model into the eXtreme Gradient Boosting (XGBoost), i. e., BTOP_XGB. In this approach, BTOP generates simulated streamflow using the Latin hypercube sampling algorithm instead of the time-consuming cali bration algorithms to reduce computational costs. Then, XGBoost combines BTOP si mulated streamflow with multi-source data to reduce simulation errors. In which, serval input variable selection algorithms are employed to choose relevant inpu ts and remove redundant information for model. The hybrid approach is validated and compared with a standalone model at three hydrological stations in the Jiali ng River basin, China. The results show that the performance of BTOP_ XGB is significantly better than the BTOP and XGBoost models. The NSE of BTOP_ XGB at Beibei, Xiaoheba, and Luoduxi stations increases by 54%, 21% , and 83%, respectively. Meanwhile, the computational time of BTOP_ XGB is saved by >90% compared to the origi nal calibrated BTOP. BTOP_XGB is less affected by parameter sample sizes and data amounts, demonstrating the robustness of the hybrid model.”
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    Research Data from Central China Normal University Update Understanding of Machi ne Learning (Capital Market Consequences of Information about Individual Auditor s)

    87-88页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news reporting from Wuhan, People’s Republic of C hina, by NewsRx journalists, research stated, “Synopsis: The research problem: T his study examines the capital market consequences of the voluntary disclosure o f individual auditor information. Specifically, we examined whether and how the disclosure of auditor qualification certificates is associated with information asymmetry and investors’ responses to earnings.” Funders for this research include National Natural Science Foundation of China.
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    University of Sadat City Researcher Adds New Data to Research in Artificial Inte lligence (Catalyzing Green Work Engagement in Hotel Businesses: Leveraging Artif icial Intelligence)

    88-89页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting originating from Sadat C ity, Egypt, by NewsRx correspondents, research stated, “This study explores gree n work engagement in response to the global demand for sustainability in busines ses and the shift toward green-oriented agendas.” Financial supporters for this research include Deanship of Research And Graduate Studies At King Khalid University For Supporting This Work Through Large Resear ch Project. Our news journalists obtained a quote from the research from University of Sadat City: “Specifically, this study aims to examine how green work engagement (GWE) is affected by artificial intelligence awareness (AIA) through job stress (JS) as a mediator. It also explores the moderating roles of technological self-effic acy (TSE) in the AIA->JS relationship and trust in leade rship (TIL) in the GWE->JS relationship. A PLS-SEM analy sis was conducted on 392 valid replies from full-time employees of five-star hot els in Egypt using WarpPLS 7.0. The findings indicated that artificial intellige nce awareness (AIA) negatively affects employees’ green work engagement (GWE) an d positively affects job stress (JS). In addition, GWE is negatively affected by JS. Moreover, TSE negatively moderates the AIA->JS rela tionship, while TIL negatively moderates the JS->GWE rel ationship.”
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    Researchers at Semmelweis Egyetem Target Machine Learning (The role of machine l earning in the modern management of heart failure)

    89-90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 Semmelweis Egyete m by NewsRx correspondents, research stated, “The use of machine learning is exp loding in all areas of healthcare, including the diagnosis and treatment of hear t failure. Supervised machine learning can help predict the onset of heart failu re, establish the diagnosis, and even predict decompensations. Conversely, unsup ervised machine learning is chiefly used for phenotyping of the heart failure po pulation.” Our news editors obtained a quote from the research from Semmelweis Egyetem: “Se veral studies have identified distinctive groups of heart failure patients, but the widespread clinical implementation is still lacking. Our study aims to ident ify groups with similar characteristics among patients cared for HFrEF at the Ci ty Major Heart and Vascular Clinic of Semmelweis University using unsupervised m achine learning and to describe the characteristic features of the resulting gro ups. We then examine the differences in outcome between the resulting groups. Me thods: data from outpatients with reduced left ventricular ejection fraction hea rt failure were collected in a prospective registry. A total of 27 parameters in cluded anamnestic data, laboratory tests, echocardiographic parameters and EQ5D quality of life questionnaire scores. The composite of hospitalization for heart failure and all-cause mortality was considered as the endpoint of the study. Sp ectral clustering was used to divide the population into three groups. The group s were plotted spatially using principal component analysis. Finally, we compare d the groups in terms of parameters and endpoint occurrence. Three characteristi c groups were identified in the analysis of 259 patients. The first group consis ted of 89 patients with ischemic etiology, more complaining, renal failure, and requiring duck diuretic therapy. The second group of 99 patients consisted of pr edominantly younger patients with atrial fibrillation, non-ischemic cardiomyopat hy, dilated left ventricle, and a lower ejection fraction, almost exclusively on ARNI therapy. The third group of 71 patients included patients with the best ej ection fraction, frequently taking ACE inhibitors and MRAs, and not requiring lo op diuretics. Group 1 had significantly worse prognosis than group 2 (p=0.013) w ith a trend to worse prognosis compared to group 3.”
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    Queensland University of Technology Researchers Detail Research in Intelligent S ystems (Generalisable deep Learning framework to overcome catastrophic forgettin g)

    90-90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 intelligent s ystems have been published. According to news reporting out of Queensland Univer sity of Technology by NewsRx editors, research stated, “Generalisation across mu ltiple tasks is a major challenge in deep learning for medical imaging applicati ons, as it can cause a catastrophic forgetting problem. One commonly adopted app roach to address these challenges is to train the model from scratch, incorporat ing old and new data, classes, and tasks.” Funders for this research include Australian Research Council.
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    Research and Innovation Department Reports Findings in Antibiotics (Retrospectiv e validation study of a machine learning-based software for empirical and organi sm-targeted antibiotic therapy selection)

    91-92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 Drugs and Therapies - Antibiotics is the subject of a report. According to news reporting originating in Oviedo, Spain, by NewsRx journalists, research stated, “Errors in antibiotic prescriptions are frequent, often resulting from the inadequate coverage of the infection-causative microorganism. The efficacy of iAST, a machine-learning-base d software offering empirical and organism-targeted antibiotic recommendations, was assessed.” The news reporters obtained a quote from the research from Research and Innovati on Department, “The study was conducted in a 12-hospital Spanish institution. Af ter model fine-tuning with 27,531 historical antibiograms, 325 consecutive patie nts with acute infections were selected for retrospective validation. The primar y endpoint was comparing each of the top three of iAST’s antibiotic recommendati ons’ success rates (confirmed by antibiogram results) with the antibiotic prescr ibed by the physicians. Secondary endpoints included examining the same hypothes is within specific study population subgroups and assessing antibiotic stewardsh ip by comparing the percentage of antibiotics recommended that belonged to diffe rent World Health Organization AWaRe groups within each arm of the study. All of iAST first three recommendations were non-inferior to doctor prescription in th e primary endpoint analysis population as well as the secondary endpoint. The ov erall success rate of doctors’ empirical treatment was 68.93 %, whil e that of the first three iAST options was 91.06% (<0.001), 90.63% (<0.001), and 91.06% ( <001), respectively. For organism-targeted therapy, the d octor’s overall success rate was 84.16%, and that of the first thre e ranked iAST options was 97.83% (<0.001), 9 4.09% (<0.001), and 91.30% ( <0.001), respectively. In empirical therapy, compared to physician prescriptions , iAST demonstrated a greater propensity to recommend access antibiotics, fewer watch antibiotics, and higher reserve antibiotics. In organism-targeted therapy, iAST advised a higher utilization of access antibiotics. The present study demo nstrates iAST accuracy in predicting antibiotic susceptibility, showcasing its p otential to promote effective antibiotic stewardship.”
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    Mazandaran University of Medical Sciences Researchers Discuss Findings in Artifi cial Intelligence (The Future and Application of Artificial Intelligence in Toxi cology)

    92-93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 originating from Sari, Iran, by N ewsRx correspondents, research stated, “Toxicology is a critical field that is o f significant importance to various industries, including pharmaceuticals, envir onmental protection, and consumer product safety. It’s a multidisciplinary scien ce that often involves time-consuming and expensive toxicity tests, which can de lay the development of new products and pose significant risks to public health and the environment.” Our news journalists obtained a quote from the research from Mazandaran Universi ty of Medical Sciences: “Therefore, there is an ever-growing demand for faster a nd more efficient toxicity evaluations. Artificial Intelligence (AI) has emerged as a promising solution to address these pressing challenges. By enabling the d evelopment of machine learning models that can analyze vast amounts of data. Thi s review article focuses on the potential impact of AI in toxicology and its app lications in different areas, such as predictive toxicology, development of toxi city screening assays, assessment of chemical mixtures, interpretation of toxico logical data, and forensic toxicology. This review was done a comprehensive lite rature search across multiple scientific databases. Searches were conducted in M edline/PubMed, Google Scholar and Web of Science to identify relevant publicatio ns. The search terms used included combinations of ‘artificial intelligence’, ‘t oxicology’, ‘toxicity’, and related keywords. The final set of articles selected provided a comprehensive overview of the current state of research on the appli cations of AI techniques in toxicology and chemical risk assessment. The review highlighted a growing body of research exploring the potential role of AI in acc elerating and enhancing various aspects of toxicity assessment and chemical risk evaluation. The reviewed studies demonstrate how AI models can be trained on la rge datasets of chemical structures, in vitro assay results, and toxicological o utcomes to predict the toxicity of novel compounds and other fields such as fore nsic toxicology. On the other hand, legal and ethical aspects of using AI was in vestigated.”
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    Investigators from Indian Institute of Management Bangalore Have Reported New Da ta on Artificial Intelligence (Artificial Intelligencebased Virtual Assistant a nd Employee Engagement: an Empirical Investigation)

    93-94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 from Bangalore, India, by NewsRx journalists, research stated, “PurposeScholars have highlighted perso nal interactions between employees and their leaders in an increasingly distribu ted and hybrid work environment as an essential mechanism that engages employees toward organizational goals. Enhanced employee engagement significantly contrib utes to sustained organizational performance and growth.”
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    Study Data from Debre Markos University Update Understanding of Intelligent Syst ems (Enhancing Word Sense Disambiguation for Amharic homophone words using Bidir ectional Long Short-Term Memory network)

    94-94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 intelligent systems is now available. According to news reporting originating from Debre Markos Univers ity by NewsRx correspondents, research stated, “Given the Amharic language has a lot of perplexing terminology since it features duplicate homophone letters, fi del’s ha, , and xe (three of which are pronounced as HA), sze and se (both prono unced as SE), and (both pronounced as AE), and tse and (both pronounced as TSE). ” Our news reporters obtained a quote from the research from Debre Markos Universi ty: “The WSD (Word Sense Disambiguation) model, which tackles the issue of lexic al ambiguity in the context of the Amharic language, is developed using a deep l earning technique. Due to the unavailability of the Amharic wordnet, a total of 1756 examples of paired Amharic ambiguous homophonic words were collected. These words were dihi ti(dhnet) and dixi ti(dhnet), ri(m’hur) and hhuri(m’hur), be (b e’al) and bezhi (be’al), biyi (abiy) and biyi(abiy), with a total of 1756 exampl es. Following word preprocessing, word2vec, fasttext, Term Frequency-Inverse Doc ument Frequency (TFIDF), and bag of words (BoW) were used to vectorize the text. The vectorized text was divided into train and test data. The train data was th en analysed using Naive Bayes (NB), K-nearest neighbour (KNN), logistic regressi on (LG), decision trees (DT), random forests (RF), and random oversampling techn ique.”
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    New Machine Learning Findings Reported from Indian Institute for Technology (Mac hine Learning In Experimental Neutrino Physics)

    95-95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 Machine Learn ing. According to news reporting originating from Uttar Pradesh, India, by NewsR x editors, the research stated, “Neutrino physics has entered into the era of pr ecision measurements. Over the last two decades, significant efforts have been m ade to measure precise parameters of the PMNS matrix, which describes neutrino o scillation phenomena.” Our news editors obtained a quote from the research from Indian Institute for Te chnology, “The next generation neutrino experiment will prioritize measuring lep tonic CP-violation, potentially revealing the matter-antimatter asymmetry of the universe. Technological advancements will enable faster and more precise measur ements. This article describes how neutrino experiments, will utilize machine le arning techniques to identify and reconstruct different neutrino event topology in detectors.”
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