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Advancements in Barrett's esophagus detection:The role of artificial intelligence and its implications
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作者 Sara Massironi 《World Journal of Gastroenterology》 SCIE CAS 2024年第11期1494-1496,共3页
Artificial intelligence(AI)is making significant strides in revolutionizing the detection of Barrett's esophagus(BE),a precursor to esophageal adenocarcinoma.In the research article by Tsai et al,researchers utili... Artificial intelligence(AI)is making significant strides in revolutionizing the detection of Barrett's esophagus(BE),a precursor to esophageal adenocarcinoma.In the research article by Tsai et al,researchers utilized endoscopic images to train an AI model,challenging the traditional distinction between endoscopic and histological BE.This approach yielded remarkable results,with the AI system achieving an accuracy of 94.37%,sensitivity of 94.29%,and specificity of 94.44%.The study's extensive dataset enhances the AI model's practicality,offering valuable support to endoscopists by minimizing unnecessary biopsies.However,questions about the applicability to different endoscopic systems remain.The study underscores the potential of AI in BE detection while highlighting the need for further research to assess its adaptability to diverse clinical settings. 展开更多
关键词 Barrett's esophagus artificial intelligence Endoscopic images artificial intelligence model Early cancer detection ENDOSCOPY
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Prediction of primary energy demand in China based on AGAEDE optimal model 被引量:1
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作者 Lu Liu Junbing Huang Shiwei Yu 《Chinese Journal of Population,Resources and Environment》 2016年第1期16-29,共14页
In this article,we present an application of Adaptive Genetic Algorithm Energy Demand Estimation(AGAEDE) optimal model to improve the efficiency of energy demand prediction.The coefficients of the two forms of the mod... In this article,we present an application of Adaptive Genetic Algorithm Energy Demand Estimation(AGAEDE) optimal model to improve the efficiency of energy demand prediction.The coefficients of the two forms of the model(both linear and quadratic) are optimized by AGA using factors,such as GDP,population,urbanization rate,and R&D inputs together with energy consumption structure,that affect demand.Since the spurious regression phenomenon occurs for a wide range of time series analysis in econometrics,we also discuss this problem for the current artificial intelligence model.The simulation results show that the proposed model is more accurate and reliable compared with other existing methods and the China's energy demand will be 5.23 billion TCE in 2020 according to the average results of the AGAEDE optimal model.Further discussion illustrates that there will be great pressure for China to fulfill the planned goal of controlling energy demand set in the National Energy Demand Project(2014—2020). 展开更多
关键词 AGAEDE optimal model spurious regression artificial intelligence model energy demand
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Automated robot and artificial intelligence-powered wastewater surveillance for proactive mpox outbreak prediction
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作者 Guanyong Ou Yuxuan Tang +11 位作者 Jiexiang Liu Yabin Hao Zhi Chen Ting Huang Shaxi Li a Shiyu Niu Yun Peng Jiaqi Feng Hongwei Tu Yang Yang Han Zhang Yingxia Liu 《Biosafety and Health》 CAS CSCD 2024年第4期225-234,共10页
In the wake of the largest‐ever recorded outbreak of mpox in terms of magnitude and geographical spread in human history since May 2022,we innovatively developed an automated online sewage virus enrichment and concen... In the wake of the largest‐ever recorded outbreak of mpox in terms of magnitude and geographical spread in human history since May 2022,we innovatively developed an automated online sewage virus enrichment and concentration robot for disease tracking.Coupled with an artificial intelligence(AI)model,our research aims to estimate mpox cases based on the concentration of the monkeypox virus(MPXV)in wastewater.Our research has revealed a compelling link between the levels of MPXV in wastewater and the number of clinically confirmed mpox infections,a finding that is reinforced by the ability of our AI prediction model to forecast cases with remarkable precision,capturing 87%of the data’s variability.However,it is worth noting that this high precision in predictions may be related to the relatively high frequency of data acquisition and the relatively non‐mobile isolated environment of the hospital itself.In conclusion,this study represents a significant step forward in our ability to track and respond to mpox outbreaks.It has the potential to revolutionize public health surveillance by utilizing innovative technologies for disease surveillance and prediction。 展开更多
关键词 Automated online sewage virus enrichment robot artificial intelligence(AI)model Early warning system Mpox Monkeypox virus(MPXV)
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Artificial intelligence-based predictive model of nanoscale friction using experimental data 被引量:5
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作者 Marko PERČIĆ Saša ZELENIKA Igor MEZIĆ 《Friction》 SCIE EI CAS CSCD 2021年第6期1726-1748,共23页
A recent systematic experimental characterisation of technological thin films,based on elaborated design of experiments as well as probe calibration and correction procedures,allowed for the first time the determinati... A recent systematic experimental characterisation of technological thin films,based on elaborated design of experiments as well as probe calibration and correction procedures,allowed for the first time the determination of nanoscale friction under the concurrent influence of several process parameters,comprising normal forces,sliding velocities,and temperature,thus providing an indication of the intricate correlations induced by their interactions and mutual effects.This created the preconditions to undertake in this work an effort to model friction in the nanometric domain with the goal of overcoming the limitations of currently available models in ascertaining the effects of the physicochemical processes and phenomena involved in nanoscale contacts.Due to the stochastic nature of nanoscale friction and the relatively sparse available experimental data,meta-modelling tools fail,however,at predicting the factual behaviour.Based on the acquired experimental data,data mining,incorporating various state-of-the-art machine learning(ML)numerical regression algorithms,is therefore used.The results of the numerical analyses are assessed on an unseen test dataset via a comparative statistical validation.It is therefore shown that the black box ML methods provide effective predictions of the studied correlations with rather good accuracy levels,but the intrinsic nature of such algorithms prevents their usage in most practical applications.Genetic programming-based artificial intelligence(AI)methods are consequently finally used.Despite the marked complexity of the analysed phenomena and the inherent dispersion of the measurements,the developed AI-based symbolic regression models allow attaining an excellent predictive performance with the respective prediction accuracy,depending on the sample type,between 72%and 91%,allowing also to attain an extremely simple functional description of the multidimensional dependence of nanoscale friction on the studied variable process parameters.An effective tool for nanoscale friction prediction,adaptive control purposes,and further scientific and technological nanotribological analyses is thus obtained. 展开更多
关键词 nanoscale friction thin films data mining machine learning(ML) predictive artificial intelligence(AI)-based model
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Visual interpretability for deep learning:a survey 被引量:49
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作者 Quan-shi ZHANG Song-chun ZHU 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2018年第1期27-39,共13页
This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations.Although deep neural networks have exhibited ... This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations.Although deep neural networks have exhibited superior performance in various tasks,interpretability is always Achilles' heel of deep neural networks.At present,deep neural networks obtain high discrimination power at the cost of a low interpretability of their black-box representations.We believe that high model interpretability may help people break several bottlenecks of deep learning,e.g.,learning from a few annotations,learning via human–computer communications at the semantic level,and semantically debugging network representations.We focus on convolutional neural networks(CNNs),and revisit the visualization of CNN representations,methods of diagnosing representations of pre-trained CNNs,approaches for disentangling pre-trained CNN representations,learning of CNNs with disentangled representations,and middle-to-end learning based on model interpretability.Finally,we discuss prospective trends in explainable artificial intelligence. 展开更多
关键词 artificial intelligence Deep learning Interpretable model
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