Artificial Intelligence and Computer-Assisted Evaluation of Chest Pathology

2019 ◽  
pp. 145-166
Author(s):  
Edwin J. R. van Beek ◽  
John T. Murchison
Keyword(s):  
Artificial Intelligence ◽  
Computer Assisted

A Comprehensive Overview of AI-Enabled Music Classification and Its Influence in Games

2021 ◽  
Author(s):  
Tiancheng Yang ◽  
Shah Nazir
Keyword(s):  
Artificial Intelligence ◽  
Computer Assisted ◽  
Comprehensive Overview ◽  
Effective Learning ◽  
Music Classification ◽  
Interactive Tools ◽  
Visually Impaired People ◽  
Interactive Environment ◽  
Assisted Technologies

Abstract With the development and advancement of information technology, artificial intelligence (AI) and machine learning (ML) are applied in every sector of life. Among these applications, music is one of them which has gained attention in the last couple of years. The music industry is revolutionized with AIbased innovative and intelligent techniques. It is very convenient for composers to compose music of high quality using these technologies. Artificial intelligence and Music (AIM) is one of the emerging fields used to generate and manage sounds for different media like the Internet, games, etc. Sounds in the games are very effective and can be made more attractive by implementing AI approaches. The quality of sounds in the game directly impacts the productivity and experience of the player. With computer-assisted technologies, the game designers can create sounds for different scenarios or situations like horror and suspense and provide gamer information. The practical and productive audio of a game can guide visually impaired people during other events in the game. For the better creation and composition of music, good quality of knowledge about musicology is essential. Due to AIM, there are a lot of intelligent and interactive tools available for the efficiency and effective learning of music. The learners can be provided with a very reliable and interactive environment based on artificial intelligence. The current study has considered presenting a detailed overview of the literature available in the area of research. The study has demonstrated literature analysis from various perspectives, which will become evidence for researchers to devise novel solutions in the field.

scholarly journals Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation

2019 ◽  
Vol 8 (9) ◽  
pp. 1446 ◽  
Author(s):  
Arsalan ◽  
Owais ◽  
Mahmood ◽  
Cho ◽  
Park
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Medical Image Analysis ◽  
Semantic Segmentation ◽  
Vessel Segmentation ◽  
Superior Performance ◽  
Retinal Images ◽  
Computer Assisted ◽  
Hypertensive Retinopathy ◽  
Retinal Vessels

Automatic segmentation of retinal images is an important task in computer-assisted medical image analysis for the diagnosis of diseases such as hypertension, diabetic and hypertensive retinopathy, and arteriosclerosis. Among the diseases, diabetic retinopathy, which is the leading cause of vision detachment, can be diagnosed early through the detection of retinal vessels. The manual detection of these retinal vessels is a time-consuming process that can be automated with the help of artificial intelligence with deep learning. The detection of vessels is difficult due to intensity variation and noise from non-ideal imaging. Although there are deep learning approaches for vessel segmentation, these methods require many trainable parameters, which increase the network complexity. To address these issues, this paper presents a dual-residual-stream-based vessel segmentation network (Vess-Net), which is not as deep as conventional semantic segmentation networks, but provides good segmentation with few trainable parameters and layers. The method takes advantage of artificial intelligence for semantic segmentation to aid the diagnosis of retinopathy. To evaluate the proposed Vess-Net method, experiments were conducted with three publicly available datasets for vessel segmentation: digital retinal images for vessel extraction (DRIVE), the Child Heart Health Study in England (CHASE-DB1), and structured analysis of retina (STARE). Experimental results show that Vess-Net achieved superior performance for all datasets with sensitivity (Se), specificity (Sp), area under the curve (AUC), and accuracy (Acc) of 80.22%, 98.1%, 98.2%, and 96.55% for DRVIE; 82.06%, 98.41%, 98.0%, and 97.26% for CHASE-DB1; and 85.26%, 97.91%, 98.83%, and 96.97% for STARE dataset.

Design of the Intelligent Computer Assisted Instruction System

2014 ◽  
Vol 687-691 ◽  
pp. 2565-2568
Author(s):  
Shao Hua Nie
Keyword(s):  
Artificial Intelligence ◽  
Teaching Quality ◽  
Computer Assisted Instruction ◽  
Computer Assisted ◽  
Instruction System ◽  
Artificial Intelligence Technology ◽  
Assisted Instruction ◽  
Intelligent Computer ◽  
Analysis And Study

With the introduction of artificial intelligence technology, Intelligent Computer Assisted Instruction (ICAI) not only overcomes many weaknesses of the traditional CAI. But also greatly enhance and improve the teaching quality and efficiency. In this paper, it firstly analysis and study the characteristics and structure of the ICAI, then proposed the method to achieve ICAI system.

The Research on Tactics Application of Table Tennis Matches Based on Artificial Intelligence

2014 ◽  
Vol 926-930 ◽  
pp. 2755-2758 ◽  
Author(s):  
Zhao Wei Cao
Keyword(s):  
Artificial Intelligence ◽  
Computer Simulation ◽  
Decision Support ◽  
Quantitative Research ◽  
System Analysis ◽  
Simulation Analysis ◽  
General Nature ◽  
Computer Assisted ◽  
Table Tennis ◽  
Manual Methods

Table tennis technical and tactical game research has been achieved by the qualitative to quantitative research and then to the interact and change, methods have changed from the past rely solely on the development of statistics to rely on manual methods of modern computing and computer-assisted technology. Computer simulation analysis of ball games provides a new idea for the game of table tennis technical and tactical analysis, part of the study attempts to artificial neural networks and computer simulation of ball games. However, technical and tactical indicators restrict its general nature training and competition practice applications. As an advanced artificial intelligence, machine learning methods has been widely used in data mining, decision support and other areas. In the analysis of the technical and tactical aspects of the game, it has emerged recently, but few people have a comprehensive system analysis, assessment and prediction research. This paper intends to conduct in-depth discussion on this research in order to provide decision support for table tennis.

The Relevance of AI Research to CAI

1978 ◽  
Vol 6 (3) ◽  
pp. 229-250 ◽  
Author(s):  
Greg P. Kearsley
Keyword(s):  
Artificial Intelligence ◽  
Natural Language ◽  
Programming Languages ◽  
Rule Induction ◽  
Computer Assisted Instruction ◽  
Current Work ◽  
Computer Assisted ◽  
Language Understanding ◽  
General Theme ◽  
Assisted Instruction

This article provides a tutorial introduction to Artificial Intelligence (AI) research for those involved in Computer Assisted Instruction (CAI). The general theme espoused is that much of the current work in AI, particularly in the areas of natural language understanding systems, rule induction, programming languages, and socratic systems, has important applications to CAI. It is hoped that this tutorial will stimulate or catalyze more intensive interaction between AI and CAI.

scholarly journals Application of Machine Learning in Auditing Teaching: A Case Study of Predicting the Audit Report Type of China ST Listed Companies

2020 ◽  
Vol 2 (1) ◽  
pp. 023-040
Author(s):  
Shi-Ming Huang Shi-Ming Huang ◽  
Chang-ping Chen Shi-Ming Huang ◽  
Tzu-ching Wong Chang-ping Chen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Listed Company ◽  
Accounting Students ◽  
Computer Assisted ◽  
Research Database ◽  
Audit Report ◽  
Professional Fields ◽  
Future Work

<p>Artificial intelligence is an important emerging technology in the accounting industry. Fear and hype associated with artificial intelligence and its impact on accounting and auditing jobs have pervaded the professional fields of accounting and auditing. It is important to develop AI competency in accountants and auditors. This paper presents a teaching case for a professor or lecturer to use for teaching machine learning to accounting students. The case is based on openly available data from the China Stock Market & Accounting Research database and aims to teach students how to predict the future audit report type of a China ST listed company. Through case teaching, students can learn skills related to computer-assisted auditing tools and machine learning (such as ACL) develop the confidence to apply artificial intelligence in their education and future work.</p> <p>&nbsp;</p>

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