Ischemic and non-ischemic heartbeat classifier for portable automatic detection systems

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
Keyword(s):  
Automatic Detection ◽  
Detection Systems

scholarly journals Automatic Identification of Harmful, Aggressive, Abusive, and Offensive Language on the Web: A Survey of Technical Biases Informed by Psychology Literature

2021 ◽  
Vol 4 (3) ◽  
pp. 1-56
Author(s):  
Agathe Balayn ◽  
Jie Yang ◽  
Zoltan Szlavik ◽  
Alessandro Bozzon
Keyword(s):  
Computer Science ◽  
State Of The Art ◽  
Automatic Detection ◽  
Automatic Identification ◽  
Machine Learning Classification ◽  
Detection Systems ◽  
Science Community ◽  
Psychology Literature ◽  
Offensive Language ◽  
The Web

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.

scholarly journals Techniques for reducing false alarms in infrared forest-fire automatic detection systems

1999 ◽  
Vol 7 (1) ◽  
pp. 123-131 ◽  
Author(s):  
A. Ollero ◽  
B.C. Arrue ◽  
J.R. Martinez ◽  
J.J. Murillo
Keyword(s):  
Forest Fire ◽  
Automatic Detection ◽  
False Alarms ◽  
Detection Systems

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 ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Automatic Detection ◽  
Diagnostic Value ◽  
Detection Systems

A study on forest fire automatic detection systems

1993 ◽  
Vol 16 (1) ◽  
pp. 35-50 ◽  
Author(s):  
F. Andreucci ◽  
M. V. Arbolino
Keyword(s):  
Forest Fire ◽  
Automatic Detection ◽  
Detection Systems

A study of forest fire automatic detection systems

1993 ◽  
Vol 16 (1) ◽  
pp. 51-65 ◽  
Author(s):  
F. Andreucci ◽  
M. V. Arbolino
Keyword(s):  
Forest Fire ◽  
Automatic Detection ◽  
Detection Systems

scholarly journals Automatic Detection of Osteosarcoma Based on Integrated Features and Feature Selection Using Binary Arithmetic Optimization Algorithm

2021 ◽  
Author(s):  
Priti Bansal ◽  
Kshitiz Gehlot ◽  
Abhishek Singhal
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Optimization Algorithm ◽  
New Technologies ◽  
Detection System ◽  
Computation Time ◽  
Automatic Detection ◽  
Detection Systems ◽  
Viable Tumor ◽  
Automatic Detection System

Abstract Osteosarcoma is one of the most common malignant bone tumor mostly found in children and teenagers. Manual detection of osteosarcoma requires expertise and is a labour-intensive process. If detected on time, the mortality rate can be reduced. With the advent of new technologies, automatic detection systems are used to analyse and classify images obtained from different sources. Here, we propose an automatic detection system Integrated Features-Feature Selection Model for Classification (IF-FSM-C) that detect osteosarcoma from the high-resolution whole slide images (WSIs). The novelty of the proposed approach is the use of integrated features obtained by fusion of features extracted using traditional handcrafted feature extraction techniques and deep learning models. It is quite possible that the integrated features may contain some redundant and irrelevant features which may unnecessarily increases the computation time and leads to wastage of resources. To avoid this, we perform feature selection (FS) before giving the integrated features to the classifier. To perform feature selection, we propose two binary variants of recently proposed Arithmetic Optimization Algorithm (AOA) known as BAOA-S and BAOA-V. The selected features are given to a classifier that classifies the WSIs into Viable tumor (VT), Non-viable tumor (NVT) and non-tumor (NT). Experiments are performed and the results prove the superiority of the proposed IF-FSM-C that uses integrated features and feature selection in classifying WSIs as compared to the classifiers which use handcrafted or deep learning features alone as well as state-of-the-art methods for osteosarcoma detection.

The Potentials of Scanning Microscopy

1968 ◽  
Vol 26 ◽  
pp. 352-353
Author(s):  
A. V. Crewe
Keyword(s):  
Salient Feature ◽  
Secondary Emission ◽  
Resolving Power ◽  
X Rays ◽  
Scanning Microscope ◽  
Forming Process ◽  
Additional Tool ◽  
Detection Systems ◽  
Conventional Microscope ◽  
Present Information

If the resolving power of a scanning electron microscope can be improved until it is comparable to that of a conventional microscope, it would serve as a valuable additional tool in many investigations.The salient feature of scanning microscopes is that the image-forming process takes place before the electrons strike the specimen. This means that several different detection systems can be employed in order to present information about the specimen. In our own particular work we have concentrated on the use of energy loss information in the beam which is transmitted through the specimen, but there are also numerous other possibilities (such as secondary emission, generation of X-rays, and cathode luminescence).Another difference between the pictures one would obtain from the scanning microscope and those obtained from a conventional microscope is that the diffraction phenomena are totally different. The only diffraction phenomena which would be seen in the scanning microscope are those which exist in the beam itself, and not those produced by the specimen.

Universal ESEM

1993 ◽  
Vol 51 ◽  
pp. 786-787
Author(s):  
G.D. Danilatos
Keyword(s):  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
High Vacuum ◽  
Natural Extension ◽  
Necessary Condition ◽  
Environmental Scanning ◽  
Detection Systems ◽  
Small Gain ◽  
Detection Medium ◽  
Scanning Electron

The environmental scanning electron microscope (ESEM) has evolved as the natural extension of the scanning electron microscope (SEM), both historically and technologically. ESEM allows the introduction of a gaseous environment in the specimen chamber, whereas SEM operates in vacuum. One of the detection systems in ESEM, namely, the gaseous detection device (GDD) is based on the presence of gas as a detection medium. This might be interpreted as a necessary condition for the ESEM to remain operational and, hence, one might have to change instruments for operation at low or high vacuum. Initially, we may maintain the presence of a conventional secondary electron (E-T) detector in a "stand-by" position to switch on when the vacuum becomes satisfactory for its operation. However, the "rough" or "low vacuum" range of pressure may still be considered as inaccessible by both the GDD and the E-T detector, because the former has presumably very small gain and the latter still breaks down.

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