A Study on the Development of an Artificial Intelligence Healthcare Robot System Based on UX Design for Non-Face-to-Face Diagnosis and Treatment -Centering the Development of UX Design for Non-Face-to-Face Diagnosis-

2021 ◽  
Vol 27 (4) ◽  
pp. 141-152
Author(s):  
Ji Young Kim ◽  
Byung Ju Yi ◽  
Ji Sung Song
Keyword(s):  
Artificial Intelligence ◽  
Diagnosis And Treatment ◽  
Robot System ◽  
Face To Face ◽  
Ux Design

Investigating the Impact of Design Thinking, Content Strategy, and Artificial Intelligence: A “Streams” Approach for Technical Communication and User Experience

2021 ◽  
pp. 004728162110419
Author(s):  
Gustav Verhulsdonck ◽  
Tharon Howard ◽  
Jason Tham
Keyword(s):  
Artificial Intelligence ◽  
User Experience ◽  
Technical Communication ◽  
Design Thinking ◽  
Professional Communication ◽  
Ux Design ◽  
Content Strategy ◽  
The Impact ◽  
Technical And Professional Communication ◽  
User Centered

Technical and professional communication (TPC) and user experience (UX) design are often seen as intertwined due to being user-centered. Yet, as widening industry positions combine TPC and UX, new streams enrich our understanding. This article looks at three such streams, namely, design thinking, content strategy, and artificial intelligence to uncover specific industry practices, skills, and ways to advocate for users. These streams foster a multistage user-centered methodology focused on a continuous designing process, strategic ways for developing content across different platforms and channels, and for developing in smart contexts where agentive products act for users. In this article, we synthesize these developments and draw out how these impact TPC.

X3D-Based Robot Kinematics Simulation

2015 ◽  
Vol 7 (1) ◽  
pp. 46-56
Author(s):  
Meng XianHui ◽  
Yuan Chong
Keyword(s):  
Artificial Intelligence ◽  
Mechanism Design ◽  
Industrial Robot ◽  
Virtual Prototype ◽  
Practical Significance ◽  
Robot Kinematics ◽  
Prototype Model ◽  
Control Logic ◽  
Robot System ◽  
Kinematics Simulation

This paper introduces the related technology in the design of robot virtual prototype. Research is mainly focused on the virtual prototype of the mechanism design, kinematics simulation, control logic and main problems of prototype performance analysis, and try to use X3D technology to realize virtual prototype model of the robot. It is verifies the effectiveness of X3D technology in robot virtual prototype design. The key to realize the robot mechanism design, kinematics simulation, several aspects such as the logic control. But the design of the robot system is a comprehensive mechanical mechanisms, kinematics, dynamics, graphics, artificial intelligence, concurrent engineering, and simulation project of multiple disciplines such as advanced manufacturing technology. The design of the robot system includes dynamic analysis, static analysis, speed, trajectory control, sensor fusion, artificial intelligence analysis, and other technology. The comprehensive realization of multidisciplinary various restrictive factors is to achieve a feasible, effective and ideal robot virtual prototype model of the key problems. Further use X3D technology to add various related techniques to achieve X3D virtual prototype model, the design of robot system, the development of industrial robot has important practical significance.

How will artificial intelligence affect diagnosis and treatment of liver disease?

2019 ◽  
Vol 51 (9) ◽  
pp. 1350-1352
Author(s):  
Christopher A. Lovejoy ◽  
Bruce Keogh ◽  
Mahiben Maruthappu
Keyword(s):  
Artificial Intelligence ◽  
Liver Disease ◽  
Diagnosis And Treatment

scholarly journals Effective Diagnosis and Treatment through Content-Based Medical Image Retrieval (CBMIR) by Using Artificial Intelligence

2019 ◽  
Vol 8 (4) ◽  
pp. 462 ◽  
Author(s):  
Muhammad Owais ◽  
Muhammad Arsalan ◽  
Jiho Choi ◽  
Kang Ryoung Park
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Image Retrieval ◽  
Medical Image ◽  
Medical Images ◽  
Diagnosis And Treatment ◽  
Imaging Modalities ◽  
Imaging Data ◽  
Medical Database ◽  
Medical Image Retrieval

Medical-image-based diagnosis is a tedious task‚ and small lesions in various medical images can be overlooked by medical experts due to the limited attention span of the human visual system, which can adversely affect medical treatment. However, this problem can be resolved by exploring similar cases in the previous medical database through an efficient content-based medical image retrieval (CBMIR) system. In the past few years, heterogeneous medical imaging databases have been growing rapidly with the advent of different types of medical imaging modalities. Recently, a medical doctor usually refers to various types of imaging modalities all together such as computed tomography (CT), magnetic resonance imaging (MRI), X-ray, and ultrasound, etc of various organs in order for the diagnosis and treatment of specific disease. Accurate classification and retrieval of multimodal medical imaging data is the key challenge for the CBMIR system. Most previous attempts use handcrafted features for medical image classification and retrieval, which show low performance for a massive collection of multimodal databases. Although there are a few previous studies on the use of deep features for classification, the number of classes is very small. To solve this problem, we propose the classification-based retrieval system of the multimodal medical images from various types of imaging modalities by using the technique of artificial intelligence, named as an enhanced residual network (ResNet). Experimental results with 12 databases including 50 classes demonstrate that the accuracy and F1.score by our method are respectively 81.51% and 82.42% which are higher than those by the previous method of CBMIR (the accuracy of 69.71% and F1.score of 69.63%).

scholarly journals A Vision to Face Covid-19 pandemic and Future Risks Through Artificial Intelligence

2020 ◽  
Vol 6 (1) ◽  
pp. 15-20
Author(s):  
Malek Albzeirat ◽  
Nik Zulkepli ◽  
Haitham Qaralleh
Keyword(s):  
Artificial Intelligence ◽  
Health Economic ◽  
International Relations ◽  
Political Stability ◽  
Diagnosis And Treatment ◽  
Economic Losses ◽  
Social Stability ◽  
Short Period ◽  
The World

Coved-19 pandemic is spreading fear among the world in several aspects such as health, economic, international relations, political stability, and social stability. It emerged suddenly and attacked the world in a short period without warning. Details about the virus such as the source, symptoms, transmission, diagnosis and treatment are still incomplete.  Subsequently, more than one million people have died and huge economic losses. In order to avoid this issue in future, this paper aims to focus on artificial intelligence in predicting and tracking viral pandemic Disease and to control similar future risks using artificial intelligence, algorithms and cognitive fission theory.

scholarly journals Artificial Intelligence (AI) and Translation Teaching: A Critical Perspective on the Transformation of Education

2021 ◽  
Vol 33 (1-3) ◽  
Author(s):  
Liu Kanglang
Keyword(s):  
Artificial Intelligence ◽  
Teaching Method ◽  
Computer Assisted ◽  
Private Institutions ◽  
Critical Perspective ◽  
Face To Face ◽  
Recent Advancement ◽  
Motivational Mechanism ◽  
Teaching In Higher Education ◽  
Self Learning

The majority of the universities and private institutions have initiated the use of artificial intelligence (AI) and machine translation (MT) in teaching translation. Translators have been trained by a systematic teaching method with newly designed curriculum with the addition of computer-assisted technology. However, the learner’s face-to-face experience is relating them to advance self-learning of languages through AI machine, which lack the motivational mechanism. This review paper presents the recent advancement in the use of AI and MT in the teaching translations to translators. The aim of the study is to investigate the pedagogical implications of AI for teaching translation studies. The study concludes that there is lack of critical reflection of challenges and jeopardies of AI in translation teaching, there is a weak connection to academic instructive perceptions, and that there is a need for further exploration of principled and enlightening approaches in the application of AI in translation teaching in higher education.

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