Metrics for User Assessment in Simulators Based on VR

Implementation of Augmented Reality for Earth Layer Structure on Android Based as A Learning Media isa research that aims to help in presenting material to Elementary School children. The research methodchosen in the completion of this study uses the 4D method (Define, Design, Develop and Disseminate) witha data collecting method using Technology Acceptance Model (TAM) built one construct with threedimensions of user assessment level of technology acceptance to support the basis of questionnaire design.AR design supported by 3D models, in order to be able to support the details of each explanation of thematerial contained, thus helping users to understand the material and ease of interaction on the media.The final result obtained in this research is that the application is stated to be able to help the school, it is used as a media display in the classroom so students do not need to imagine themselves, but simply byusing learning media is able to present the material to students.Keywords: Learning Media, 4D Method, User Acceptance Test, Augmented reality, Android.

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Sentiment Analysis using Deep Belief Network for User Rating Classification

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.h9233.0610821 ◽

2021 ◽

Vol 10 (8) ◽

pp. 87-91

Author(s):

Ravi Chandra ◽

Basavaraj Vaddatti

Keyword(s):

Sentiment Analysis ◽

Feature Vector ◽

Deep Belief Network ◽

Deep Belief Networks ◽

User Reviews ◽

Belief Network ◽

Precision And Accuracy ◽

People’S Attitudes ◽

User Assessment ◽

Different Levels

People’s attitudes, opinions, feelings and sentiments which are usually expressed in the written languages are studied by using a well known concept called the sentiment analysis. The emotions are expressed at various different levels like document, sentence and phrase level are studied by using the sentiment analysis approach. The sentiment analysis combined with the Deep learning methodologies achieves the greater classification in a larger dataset. The proposed approach and methods are Sentiment Analysis and deep belief networks, these are used to process the user reviews and to give rise to a possible classification for recommendations system for the user. The user assessment classification can be progressed by applying noise reduction or pre-processing to the system dataset. Further by the input nodes the system uses an exploration of user’s sentiments to build a feature vector. Finally, the data learning is achieved for the suggestions; by using deep belief network. The prototypical achieves superior precision and accuracy when compared with the LSTM and SVM algorithms.

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Skyline

International Journal of Mobile Human Computer Interaction ◽

10.4018/ijmhci.2017070103 ◽

2017 ◽

Vol 9 (3) ◽

pp. 34-53 ◽

Cited By ~ 1

Author(s):

Ignacio Alvarez ◽

Laura Rumbel

Keyword(s):

Research And Development ◽

Rapid Prototyping ◽

User Experience ◽

Development Process ◽

User Feedback ◽

High Fidelity ◽

Use Case ◽

Software Components ◽

User Assessment

This paper describes the research and development process of an in-vehicle user experience using Skyline, an automotive prototyping platform created in Intel Labs to empower interaction designers and user experience researches to rapidly and iteratively develop and test in-vehicle user experience concepts. The paper describes the hardware and software components of Skyline in depth and how to configure them to suit individual researcher needs. The paper also presents a case study to exemplify the design making process that Skyline enables. From ideation to use-case creation, prototyping and validation through user assessment, the paper showcases the benefits of capturing early qualitative user feedback as support for rapid prototyping walking through a study titled Agency vs. Control and the associated interactions inside the cockpit. Ten defined use-cases are developed and integrated into a hero scenario in Skyline. High fidelity HMI concepts are tested and validated over the course of six months with feedback from a total of fifty users.

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Perception of nurses about the risk assessment of the low complexity non-referred user

Acta Scientiarum Health Sciences ◽

10.4025/actascihealthsci.v42i1.47205 ◽

2020 ◽

Vol 42 ◽

pp. e47205

Author(s):

Aline Buriola ◽

Camilla Passarela Silva ◽

Eduardo Fuzetto Cazañas ◽

Tayomara Ferreira Nascimento

Keyword(s):

Risk Assessment ◽

Data Analysis ◽

Low Complexity ◽

Risk Classification ◽

Data Organization ◽

Healthcare Network ◽

Nurses Perceptions ◽

The Relationship ◽

User Assessment

The goal of this study was to assess the perceptions and behaviors of nurses who provide triage with risk assessment to low complexity non-referred patients. The participants of the study were nurses who were performing patients’ triage with risk assessment, and the sample consisted of thirteen participants. The instruments used for the interviews were semi-structured questionnaires related to the characterization of the topic under study. Content analysis, i.e., the method proposed by Bardin, was used for data analysis. For data organization, we used MAXQDA Analytics Pro 2018, a software program that favored the identification between the similarities of the elements and ideas, thus making it possible to reach the cores of meanings. The identified categories were: (a) understanding about the healthcare provided by the emergency/urgency care Network; (b) evaluation of patient triage with risk classification; and (c) difficulties/challenges observed at the institution when providing user assessment with risk classification. It is concluded that nurses’ perceptions regarding the topic under study were linked to the disarticulation of the healthcare Network, the fragility of the relationship between physicians and nurses, and the lack of use of institutional protocols.

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