An important use of environmental indicators is oriented to know their individual impact on the whole environment quality. Nevertheless, most of the important causes of environment affectations are derived from multip...An important use of environmental indicators is oriented to know their individual impact on the whole environment quality. Nevertheless, most of the important causes of environment affectations are derived from multiple interactions between indicators which correspond more specifically to the environmental reality. The affectations derived from interactions should be analyzed and interpreted through numerical expressions representing a relevant challenge for developers of environmental indicators. To cope with the analysis and interpretation problem, we propose in this work a methodology in two senses: in a bottom-up sense a directed graph is built representing interactions between environmental indicators as behavioral relations, which exert an effect on the state of an environmental issue of a site over time (10 years);in a top-down sense to assist users in the analysis and interpretation of interactions through a computer interface that provides users with the capacity of knowing how and what relational behaviors between indicators are affecting, the most or the least, the performance of the environmental issue being studied. This methodology was applied to the analysis an interpretation of interactions between environmental variables that affect the state of an environmental quality issue related with the State of Morelos in Mexico. The results showed the adequate expressivity of a directed graph to represent interactions allowed to verify the coherence of the numerical values associated with their behaviors during a period of time and with their effects on the environmental issue under study.展开更多
Background Oral cancer is one of the most common types of cancer in men causing mortality if not diagnosedearly.In recent years,computer-aided diagnosis(CAD)using artificial intelligence techniques,in particular,deepn...Background Oral cancer is one of the most common types of cancer in men causing mortality if not diagnosedearly.In recent years,computer-aided diagnosis(CAD)using artificial intelligence techniques,in particular,deepneural networks have been investigated and several approaches have been proposed to deal with the automateddetection of various pathologies using digital images.Recent studies indicate that the fusion of images with thepatient’s clinical information is important for the final clinical diagnosis.As such dataset does not yet exist fororal cancer,as far as the authors are aware,a new dataset was collected consisting of histopathological images,demographic and clinical data.This study evaluated the importance of complementary data to histopathologicalimage analysis of oral leukoplakia and carcinoma for CAD.Methods A new dataset(NDB-UFES)was collected from 2011 to 2021 consisting of histopathological imagesand information.The 237 samples were curated and analyzed by oral pathologists generating the gold standardfor classification.State-of-the-art image fusion architectures and complementary data(Concatenation,MutualAttention,MetaBlock and MetaNet)using the latest deep learning backbones were investigated for 4 distincttasks to identify oral squamous cell carcinoma,leukoplakia with dysplasia and leukoplakia without dysplasia.We evaluate them using balanced accuracy,precision,recall and area under the ROC curve metrics.Results Experimental results indicate that the best models present balanced accuracy of 83.24%using images,demographic and clinical information with MetaBlock fusion and ResNetV2 backbone.It represents an improvement in performance of 30.68%(19.54 pp)in the task to differentiate samples diagnosed with oral squamous cellcarcinoma and leukoplakia with or without dysplasia.Conclusion This study indicates that cured demographic and clinical data may positively influence the performance of artificial intelligence models in automated classification of oral cancer.展开更多
In this paper, we present an approach that can handle Z-numbers in the context of multi-criteria decision-making problems. The concept of Z-number as an ordered pair Z=(A, B) of fuzzy numbers A and B is used, where A ...In this paper, we present an approach that can handle Z-numbers in the context of multi-criteria decision-making problems. The concept of Z-number as an ordered pair Z=(A, B) of fuzzy numbers A and B is used, where A is a linguistic value of a variable of interest and B is a linguistic value of the probability measure of A. As human beings, we communicate with each other by means of natural language using sentences like "the journey from home to university most likely takes about half an hour." The Z-numbers are converted to fuzzy numbers. Then the Z-TODIM and Z-TOPSIS are presented as a direct extension of the fuzzy TODIM and fuzzy TOPSIS, respectively. The proposed methods are applied to two case studies and compared with the standard approach using crisp values. The results obtained show the feasibility of the approach.展开更多
文摘An important use of environmental indicators is oriented to know their individual impact on the whole environment quality. Nevertheless, most of the important causes of environment affectations are derived from multiple interactions between indicators which correspond more specifically to the environmental reality. The affectations derived from interactions should be analyzed and interpreted through numerical expressions representing a relevant challenge for developers of environmental indicators. To cope with the analysis and interpretation problem, we propose in this work a methodology in two senses: in a bottom-up sense a directed graph is built representing interactions between environmental indicators as behavioral relations, which exert an effect on the state of an environmental issue of a site over time (10 years);in a top-down sense to assist users in the analysis and interpretation of interactions through a computer interface that provides users with the capacity of knowing how and what relational behaviors between indicators are affecting, the most or the least, the performance of the environmental issue being studied. This methodology was applied to the analysis an interpretation of interactions between environmental variables that affect the state of an environmental quality issue related with the State of Morelos in Mexico. The results showed the adequate expressivity of a directed graph to represent interactions allowed to verify the coherence of the numerical values associated with their behaviors during a period of time and with their effects on the environmental issue under study.
基金The research were supported by Grant Nos.304688/2021-5 and21/2022.
文摘Background Oral cancer is one of the most common types of cancer in men causing mortality if not diagnosedearly.In recent years,computer-aided diagnosis(CAD)using artificial intelligence techniques,in particular,deepneural networks have been investigated and several approaches have been proposed to deal with the automateddetection of various pathologies using digital images.Recent studies indicate that the fusion of images with thepatient’s clinical information is important for the final clinical diagnosis.As such dataset does not yet exist fororal cancer,as far as the authors are aware,a new dataset was collected consisting of histopathological images,demographic and clinical data.This study evaluated the importance of complementary data to histopathologicalimage analysis of oral leukoplakia and carcinoma for CAD.Methods A new dataset(NDB-UFES)was collected from 2011 to 2021 consisting of histopathological imagesand information.The 237 samples were curated and analyzed by oral pathologists generating the gold standardfor classification.State-of-the-art image fusion architectures and complementary data(Concatenation,MutualAttention,MetaBlock and MetaNet)using the latest deep learning backbones were investigated for 4 distincttasks to identify oral squamous cell carcinoma,leukoplakia with dysplasia and leukoplakia without dysplasia.We evaluate them using balanced accuracy,precision,recall and area under the ROC curve metrics.Results Experimental results indicate that the best models present balanced accuracy of 83.24%using images,demographic and clinical information with MetaBlock fusion and ResNetV2 backbone.It represents an improvement in performance of 30.68%(19.54 pp)in the task to differentiate samples diagnosed with oral squamous cellcarcinoma and leukoplakia with or without dysplasia.Conclusion This study indicates that cured demographic and clinical data may positively influence the performance of artificial intelligence models in automated classification of oral cancer.
基金Project supported by the Brazilian Agency CNPq(No.309161/2015-0)the Local Agency of the State of Espirito Santo FAPES(No.039/2016)
文摘In this paper, we present an approach that can handle Z-numbers in the context of multi-criteria decision-making problems. The concept of Z-number as an ordered pair Z=(A, B) of fuzzy numbers A and B is used, where A is a linguistic value of a variable of interest and B is a linguistic value of the probability measure of A. As human beings, we communicate with each other by means of natural language using sentences like "the journey from home to university most likely takes about half an hour." The Z-numbers are converted to fuzzy numbers. Then the Z-TODIM and Z-TOPSIS are presented as a direct extension of the fuzzy TODIM and fuzzy TOPSIS, respectively. The proposed methods are applied to two case studies and compared with the standard approach using crisp values. The results obtained show the feasibility of the approach.
