Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

"Graefe's Archive" is a distinguished international journal that presents original clinical reports and clinically relevant experimental studies. Founded in 1854 by Albrecht von Graefe to serve as a source of useful clinical information and a stimulus for discussion, the journal has published articles by leading ophthalmologists and vision research scientists for more than a century. With peer review by an international Editorial Board and prompt English-language publication, "Graefe's Archive" provides rapid dissemination of clinical and clinically related experimental information.

Graefe's archive for clinical and experimental ophthalmology

Echocardiography (echo) is a skilled technical procedure that depends on the experience of the operator. The aim of this paper is to reduce user variability in data acquisition by automatically computing a score of echo quality for operator feedback. To do this, a deep convolutional neural network model, trained on a large set of samples, was developed for scoring apical four-chamber (A4C) echo. In this paper, 6,916 end-systolic echo images were manually studied by an expert cardiologist and were assigned a score between 0 (not acceptable) and 5 (excellent). The images were divided into two independent training-validation and test sets. The network architecture and its parameters were based on the stochastic approach of the particle swarm optimization on the training-validation data. The mean absolute error between the scores from the ultimately trained model and the expert’s manual scores was 0.71 ± 0.58. The reported error was comparable to the measured intra-rater reliability. The learned features of the network were visually interpretable and could be mapped to the anatomy of the heart in the A4C echo, giving confidence in the training result. The computation time for the proposed network architecture, running on a graphics processing unit, was less than 10 ms per frame, sufficient for real-time deployment. The proposed approach has the potential to facilitate the widespread use of echo at the point-of-care and enable early and timely diagnosis and treatment. Finally, the approach did not use any specific assumptions about the A4C echo, so it could be generalizable to other standard echo views.

Correction to “Automatic Quality Assessment of Echocardiograms Using Convolutional Neural Networks: Feasibility on the Apical Four-Chamber View”

BackgroundArtificial-intelligence algorithms derive rules and patterns from large amounts of data to calculate the probabilities of various outcomes using new sets of similar data. In medicine, artificial intelligence (AI) has been applied primarily to image-recognition diagnostic tasks and

What Are the Applications and Limitations of Artificial Intelligence for Fracture Detection and Classification in Orthopaedic Trauma Imaging? A Systematic Review

As one of the major problems is that the time taken for executing the traditional algorithm is larger and that it is very difficult for processing large amount of data. Clusters possess high degree of similarity among each cluster and have low degree of similarity among other clusters. Optimization algorithm for clustering is the art of allocating scarce resources to the best possible effect. The traditional optimization algorithm is not suitable for processing high dimensional data. The main objective of proposed Artificial Bee Colony (ABC) approach is to minimize the execution time and to optimize the best cluster for the various sizes of the dataset. To deal with this, we are normalizing to distributed environment for time efficiency and accuracy. The proposed ABC algorithm simulates the behavior of real bees for solving numerical optimization problems particularly in clustering. The dataset size is varied for the algorithm and is mapped with its appropriate timings. The result is observed for various fitness and probability value which is obtained from the employed and the onlooker phase of ABC algorithm from which the further calibrations of classification error percentage is done. The proposed ABC Algorithm is implemented in Hadoop environment using mapper and reducer programming. An experimental result reveals that the proposed ABC scheme reduces the execution time and classification error for selecting optimal clusters. The results show that the proposed ABC scheme gives a better performance than PSO and DE in terms of time efficiency.

Optimization using Artificial Bee Colony based clustering approach for big data

The pandemic has being fought with passion over the world, and India has already in the critical phases. They must be proactive now more than ever, and governments all around the globe have realized the value of emerging technologies such as AI (Artificial Intelligence), ML (Machine Learning), and other similar technologies to combat the epidemic. Since wearing a mask and avoiding crowded places is the only option even a reliable vaccination is launched into the general market. Face mask detection can be a rejuvenating element in restoring normalcy to people’s life. In nations with huge populations, such as ours, real-time mask detection can also help with monitoring. This paper shows a variety of deep learning models for detecting whether or not a person is wearing a mask. Entry and exit access systems may be combined with such technology to assist reduce the transmission of the virus, not just for the Covid19 pandemic, but wherever and whenever facemasks are enforced to minimize any air-borne infections. This paper also shows a variety of deep learning models for social distancing monitoring.

Using Computer Vision to enhance Safety in a Post COVID World

Advances in the fields of image processing and information technology have led to the use of computers for the diagnosis of diseases. This has led to the emergence of Computer-Aided Diagnosis (CAD) systems for disease diagnosis. This research work focuses on improving the performance of CAD systems that use Computed Tomography (CT) of the chest for diagnosis of lung disorders. This improvement has been achieved by developing techniques for determining the significance of image features for discrimination among different diseases of the lung and a technique for segmentation of lung parenchyma in chest CT irrespective of the presence or absence of peripherally placed Pathology Bearing Regions (PBRs). Another major challenge in CAD of lung disorders based on analysis of chest CTs is accurate segmentation of lungs especially in the presence of peripherally placed PBRs. In this research work, a segmentation algorithm has been developed to extract the complete lung parenchyma even in the presence of severe peripherally placed PBR in chest CT. The proposed system has been found to improve the diagnostic performance of CAD systems for diagnosis of lung disorders based on analysis of chest CT slices. This would aid the physicians to perform better diagnosis, which would result in choosing the appropriate treatment strategy, thereby reducing the mortality rate.

G. N. Balaji

Papers

Using Computer Vision to enhance Safety in a Post COVID World

Computer-Aided Lung Parenchyma Segmentation Using Supervised Learning