scholarly journals Diagnostic value of artificial intelligence automatic detection systems for breast BI-RADS 4 nodules

2022 ◽  
Vol 10 (2) ◽  
pp. 518-527
Author(s):  
Shu-Yi Lyu ◽  
Yan Zhang ◽  
Mei-Wu Zhang ◽  
Bai-Song Zhang ◽  
Li-Bo Gao ◽  
...  
2021 ◽  
Vol 93 (6) ◽  
pp. AB190-AB191
Author(s):  
João Afonso ◽  
Miguel M. Saraiva ◽  
Helder Cardoso ◽  
João Ferreira ◽  
Patrícia Andrade ◽  
...  

2021 ◽  
Vol 19 (6) ◽  
pp. 952-960
Author(s):  
Gisela De La Fuente Cortes ◽  
Jose Alejandro Diaz-Mendez ◽  
Guillermo Espinosa Flores-Verdad ◽  
Victor Rodolfo Gonzalez-Diaz

2021 ◽  
Vol 4 (3) ◽  
pp. 1-56
Author(s):  
Agathe Balayn ◽  
Jie Yang ◽  
Zoltan Szlavik ◽  
Alessandro Bozzon

The automatic detection of conflictual languages (harmful, aggressive, abusive, and offensive languages) is essential to provide a healthy conversation environment on the Web. To design and develop detection systems that are capable of achieving satisfactory performance, a thorough understanding of the nature and properties of the targeted type of conflictual language is of great importance. The scientific communities investigating human psychology and social behavior have studied these languages in details, but their insights have only partially reached the computer science community. In this survey, we aim both at systematically characterizing the conceptual properties of online conflictual languages, and at investigating the extent to which they are reflected in state-of-the-art automatic detection systems. Through an analysis of psychology literature, we provide a reconciled taxonomy that denotes the ensemble of conflictual languages typically studied in computer science. We then characterize the conceptual mismatches that can be observed in the main semantic and contextual properties of these languages and their treatment in computer science works; and systematically uncover resulting technical biases in the design of machine learning classification models and the dataset created for their training. Finally, we discuss diverse research opportunities for the computer science community and reflect on broader technical and structural issues.


2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Gulshan Kumar ◽  
Krishan Kumar

In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI-) based techniques play prominent role in development of ensemble for intrusion detection (ID) and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their current research status to solve the ID problem. Here, an updated review of ensembles and their taxonomies has been presented in general. The paper also presents the updated review of various AI-based ensembles for ID (in particular) during last decade. The related studies of AI-based ensembles are compared by set of evaluation metrics driven from (1) architecture & approach followed; (2) different methods utilized in different phases of ensemble learning; (3) other measures used to evaluate classification performance of the ensembles. The paper also provides the future directions of the research in this area. The paper will help the better understanding of different directions in which research of ensembles has been done in general and specifically: field of intrusion detection systems (IDSs).


2020 ◽  
Vol 32 (3) ◽  
pp. 382-390 ◽  
Author(s):  
Akiyoshi Tsuboi ◽  
Shiro Oka ◽  
Kazuharu Aoyama ◽  
Hiroaki Saito ◽  
Tomonori Aoki ◽  
...  

2011 ◽  
Vol 26 (S2) ◽  
pp. 1712-1712
Author(s):  
V.V. Enatescu ◽  
V.R. Enatescu ◽  
I. Enatescu

Background and aimsBeside the interpretation and processing of content of communication, an important part of psychiatric diagnosis is made on behavioral signs and symptoms. While the semantic assessment of the content of thinking through communication was enriched by the development of several psychopathological scales, schedule and structured or semi-structured interviews the assessment of non verbal parameters remains uncovered. Our aims was to analyses the non verbal parameters, by an automatic system conceived by Dr. Enatescu et colab., in patients with mood disorders.MethodsThe instrument we used are: original traductors, systems of calculation and programming belonging to the artificial intelligence which create new pattern of representation of the gait, gesture, sonorous background of the speech, the dynamic of the writing which can be represented or through a matrix or in a n-dimensional space on specific clusters or to some human typology or to some psychical disorders.ResultsThe non verbal parameters processed by computer were sensible altered along with switching in the depressive states of subjects. The informatics data has had both diagnostic value and screening value for the course of unipolar depression.ConclusionsWe demonstrate that there is the chances for a new semiology which have objective paraclinic value for psychiatry field of automate analyses, nonverbal behavior parameters having the name “Extraverbale Analysis”.


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