Automatic Extraction of Conceptual Maps From Design Team Documents

2010 ◽  
Author(s):  
Sharad Oberoi ◽  
Dong Nguyen ◽  
Susan Finger ◽  
Carolyn Penstein Rose´
Keyword(s):  
Latent Dirichlet Allocation ◽  
Machine Learning Techniques ◽  
Design Team ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Engineering Project ◽  
Web Based ◽  
Learning Techniques ◽  
Conceptual Maps ◽  
Big Picture

Most engineering project classes expect teams of students to collaborate and to build on existing knowledge to accomplish their project goals. As the project evolves, the team is expected to develop a shared understanding. However, students often become overwhelmed by the amount of information available and lose sight of the big picture. Instructors may also find it difficult to keep track of individual and team activities and are often forced to evaluate the product instead of the learning process. This paper presents preliminary results from a tool that supports effective knowledge management for engineering design projects. This framework, called DesignWebs, automatically extracts conceptual maps from the team’s evolving set of documents and discussions about an engineering artifact. It uses Latent Dirichlet Allocation, hierarchical clustering, and other machine learning techniques to generate a navigable web-based graph. Both instructors and students can browse this graph interactively to explore the concepts embedded inside design team documents and the connections between them. An experiment performed on documents obtained from a project course shows the effectiveness of DesignWebs in synthesizing the design knowledge from multiple sources of information in engineering project teams.

scholarly journals Design-Based Research: Definition, Characteristics, Application and Challenges

2019 ◽  
Vol 5 (1) ◽  
pp. 26-35
Author(s):  
Hamed Vaezi ◽  
Hossein Karimi Moonaghi ◽  
Reyhaneh Golbaf
Keyword(s):  
Medical Education ◽  
Practical Knowledge ◽  
Educational Innovation ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Short Term ◽  
Web Based ◽  
Design Based Research ◽  
Definition Of ◽  
New Research

In recent years medical education has developed dramatically, but lecturers often cite the existence of a gap between theoretical and practical knowledge. In the first decade of the present century, new research methodology named “design-based research (DBR)” was developed, which most experts and journals refer to as a fundamental way to make changes in the quality and applicability of studies and educational research as well as to enhance and improve the practice of instruction. The aim of the present study was introducing design-based research and its concepts, features, applications, and challenges. A narrative review was conducted in 2018. For this purpose, authorized English academic database including Web of Science, Science Direct, Google Scholar, international database and library in medical research filed with keywords including “design-based research, definition of DBR, DBR applications, medical education, and DBR challenges” without date limitation until 2018.11.21 were screened. Overall, 68 articles were selected and after careful reading, 21 article with related subjects were selected for material extraction. The conclusion was made that DBR that combines empirical research with design-based theories could be considered as an effective method for understanding quality, time and the cause of the phenomenon of educational innovation in practice. Usually DBR is formed by initial evaluation of a problem that occurs in a particular context, and this assessment continues throughout design and implementation. One of the characteristics of DBR is the guiding team, which includes researchers, professionals, designers, managers, teachers, trainers and others whose expertise and knowledge may in some way help. The application of DBR in web-based training programs is quite evident. The probability of non-returns in short-term projects is one of the main challenges of DBR. Medical education has developed dramatically in recent years, but it has made little progress in promoting innovative research methodologies. DBR can be used as a bridge between theories and practice and provide the basis for close communication between researchers, designers, and participants. By applying sophisticated methods and multiple sources of information, the success rate of an intervention in a particular environment is assessed, which ultimately leads to improved theories.

scholarly journals Leveraging Business Data Analytics and Machine Learning Techniques for Competitive Advantage: Case Study Evidence from Small Businesses

2021 ◽  
Vol 2 (1) ◽  
pp. 73-87
Author(s):  
Rathimala Kannan ◽  
Intan Soraya Rosdi ◽  
Kannan Ramakrishna ◽  
Haziq Riza Abdul Rasid ◽  
Mohamed Haryz Izzudin Mohamed Rafy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Competitive Advantage ◽  
Small Businesses ◽  
Data Analytics ◽  
Research Question ◽  
Machine Learning Techniques ◽  
Multiple Sources ◽  
Learning Techniques ◽  
Business Data

Data analytics is the essential component in deriving insights from data obtained from multiple sources. It represents the technology, methods and techniques used to obtain insights from massive datasets. As data increases, companies are looking for ways to gain relevant business insights underneath layers of data and information, to help them better understand new business ventures, opportunities, business trends and complex challenges. However, to date, while the extensive benefits of business data analytics to large organizations are widely published, micro, small, and medium sized organisations have not fully grasped the potential benefits to be gained from data analytics using machine learning techniques. This study is guided by the research question of how data analytics using machine learning techniques can benefit small businesses. Using the case study method, this paper outlines how small businesses in two different industries i.e. healthcare and retail can leverage data analytics and machine learning techniques to gain competitive advantage from the data. Details on the respective benefits gained by the small business owners featured in the two case studies provide important answers to the research question.

scholarly journals A Study of University Website Content Classification Using Machine Learning

2021 ◽  
Vol 2129 (1) ◽  
pp. 012043
Author(s):  
H R Mohd Sharul ◽  
I Nor Azman ◽  
M Mohd Su Elya
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Web Pages ◽  
Web Content ◽  
Web Based ◽  
Content Classification ◽  
Learning Techniques ◽  
Information Products ◽  
Website Content ◽  
New Framework

Abstract A university website is a gateway to the institution’s information, products, and services. As websites grow into millions in numbers, it is essential to ensure that the content reflects the needs of its students, staff, and other academic institution as their primary users. This research investigates the development of a new framework that uses machine learning techniques based on webometrics and web usability to classify the web pages of academic websites automatically. The framework briefly introduced how it can help classify web content and eliminate unrelated content and reduce storage space. The findings can also be used to analyse other web-based data to give additional insights that may be beneficial for webometrics studies and identify university website’ characteristics.

scholarly journals Public Opinions and Concerns Regarding the Canadian Prime Minister’s Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques

10.2196/23957 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e23957
Author(s):  
Chengda Zheng ◽  
Jia Xue ◽  
Yumin Sun ◽  
Tingshao Zhu
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Public Discourse ◽  
Latent Dirichlet Allocation ◽  
Prime Minister ◽  
Machine Learning Techniques ◽  
Future Health ◽  
The Public ◽  
Learning Techniques ◽  
The Government

Background During the COVID-19 pandemic in Canada, Prime Minister Justin Trudeau provided updates on the novel coronavirus and the government’s responses to the pandemic in his daily briefings from March 13 to May 22, 2020, delivered on the official Canadian Broadcasting Corporation (CBC) YouTube channel. Objective The aim of this study was to examine comments on Canadian Prime Minister Trudeau’s COVID-19 daily briefings by YouTube users and track these comments to extract the changing dynamics of the opinions and concerns of the public over time. Methods We used machine learning techniques to longitudinally analyze a total of 46,732 English YouTube comments that were retrieved from 57 videos of Prime Minister Trudeau’s COVID-19 daily briefings from March 13 to May 22, 2020. A natural language processing model, latent Dirichlet allocation, was used to choose salient topics among the sampled comments for each of the 57 videos. Thematic analysis was used to classify and summarize these salient topics into different prominent themes. Results We found 11 prominent themes, including strict border measures, public responses to Prime Minister Trudeau’s policies, essential work and frontline workers, individuals’ financial challenges, rental and mortgage subsidies, quarantine, government financial aid for enterprises and individuals, personal protective equipment, Canada and China’s relationship, vaccines, and reopening. Conclusions This study is the first to longitudinally investigate public discourse and concerns related to Prime Minister Trudeau’s daily COVID-19 briefings in Canada. This study contributes to establishing a real-time feedback loop between the public and public health officials on social media. Hearing and reacting to real concerns from the public can enhance trust between the government and the public to prepare for future health emergencies.

Interactive Design Navigational Tools

2009 ◽  
Author(s):  
Sharad Oberoi ◽  
Susan Finger
Keyword(s):  
Information Management ◽  
Development Process ◽  
Knowledge Building ◽  
Design Knowledge ◽  
Design Teams ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Web Based ◽  
Management Technology ◽  
Building Activities

For both student and professional design teams, the design and development process requires that collaborators build and retain knowledge through discussions, creating documents and sharing artifacts. Key to supporting these knowledge building activities is the development of an infrastructure that supports effective knowledge management. This paper presents the framework for an information management technology called DesignWebs, which assimilates the product structures from the evolving set of documents and discussions about an engineering artifact. A DesignWeb enables users to see evolving connections between concepts by using a navigable web-based interface that synthesizes the design knowledge from multiple sources of information.

Feature Evaluation and Classification for Content-Based Medical Image Retrieval System

2010 ◽  
pp. 509-531 ◽  
Author(s):  
Ivica Dimitrovski ◽  
Suzana Loskovska
Keyword(s):  
Image Retrieval ◽  
Information Management ◽  
Machine Learning Techniques ◽  
Web Based ◽  
Feature Evaluation ◽  
Research Fields ◽  
Learning Techniques ◽  
Image Retrieval System ◽  
Similar Images ◽  
Web Based System

Image retrieval in general and content-based image retrieval (CBIR) in particular are well-known research fields in information management. A large number of methods have been proposed and investigated in both areas but satisfactory general solution has not still been developed. The aim of this research is to develop highly flexible web-based system for storage, organization and retrieval of medical images. The system besides text and metadata retrieval also supports querying by image to find visually similar images to presented query. Several algorithms and techniques were implemented in the system to support content-based retrieval. For efficient and reliable search machine learning techniques were included in the system.

scholarly journals A three-stage quality diagnosis platform for laser-based manufacturing processes

2020 ◽  
Vol 110 (11-12) ◽  
pp. 2991-3003
Author(s):  
Panagiotis Stavropoulos ◽  
Alexios Papacharalampopoulos ◽  
John Stavridis ◽  
Kyriakos Sampatakakis
Keyword(s):  
Technical Assistance ◽  
Image Data ◽  
Real Data ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Web Based ◽  
Learning Techniques ◽  
Weld Defect ◽  
Readiness Level ◽  
Quality Diagnosis

Abstract Diagnosis systems for laser processing are being integrated into industry. However, their readiness level is still questionable under the prism of the Industry’s 4.0 design principles for interoperability and intuitive technical assistance. This paper presents a novel multifunctional, web-based, real-time quality diagnosis platform, in the context of a laser welding application, fused with decision support, data visualization, storing, and post-processing functionalities. The platform’s core considers a quality assessment module, based upon a three-stage method which utilizes feature extraction and machine learning techniques for weld defect detection and quality prediction. A multisensorial configuration streams image data from the weld pool to the module in which a statistical and geometrical method is applied for selecting the input features for the classification model. A Hidden Markov Model is then used to fuse this information with earlier results for a decision to be made on the basis of maximum likelihood. The outcome is fed through web services in a tailored User Interface. The platform’s operation has been validated with real data.

scholarly journals KymoButler, a deep learning software for automated kymograph analysis

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Maximilian AH Jakobs ◽  
Andrea Dimitracopoulos ◽  
Kristian Franze
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Biological Data ◽  
Machine Learning Techniques ◽  
Unconscious Bias ◽  
Web Based ◽  
Complex Particle ◽  
Learning Techniques ◽  
Biological Data Analysis ◽  
Learning Software

Kymographs are graphical representations of spatial position over time, which are often used in biology to visualise the motion of fluorescent particles, molecules, vesicles, or organelles moving along a predictable path. Although in kymographs tracks of individual particles are qualitatively easily distinguished, their automated quantitative analysis is much more challenging. Kymographs often exhibit low signal-to-noise-ratios (SNRs), and available tools that automate their analysis usually require manual supervision. Here we developed KymoButler, a Deep Learning-based software to automatically track dynamic processes in kymographs. We demonstrate that KymoButler performs as well as expert manual data analysis on kymographs with complex particle trajectories from a variety of different biological systems. The software was packaged in a web-based ‘one-click’ application for use by the wider scientific community (http://kymobutler.deepmirror.ai). Our approach significantly speeds up data analysis, avoids unconscious bias, and represents another step towards the widespread adaptation of Machine Learning techniques in biological data analysis.

scholarly journals The use of Machine Learning Techniques in a Web-Based Learning Diagnosis System Program

2020 ◽  
Vol 8 (6) ◽  
pp. 4861-4865
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Analysis Framework ◽  
Web Based ◽  
Web Based Learning ◽  
Learning Techniques ◽  
Insightful Analysis ◽  
System Program

This work proposes a canny learning finding framework that bolsters a Web-based topical learning model, which expects to develop students' capacity of information incorporation by giving the students the chances to choose the learning themes that they are intrigued, and gain information on the particular subjects by surfing on the Internet to look through related adapting course-product and examining what they have realized with their associates. In view of the log documents that record the students' past web-based learning conduct, an insightful analysis framework is utilized to give fitting learning direction to help the students in improving their investigation practices and grade online class interest for the teacher. The accomplishment of the students' last reports can likewise be anticipated by the conclusion framework precisely. Our trial results uncover that the proposed learning finding framework can proficiently assist students with expanding their insight while surfing in the internet Web-based "topic based learning" model.

scholarly journals ONLINE NEWS CLASSIFICATION USING MACHINE LEARNING TECHNIQUES

2021 ◽  
Vol 22 (2) ◽  
pp. 210-225
Author(s):  
Jeelani Ahmed ◽  
Muqeem Ahmed
Keyword(s):  
Online News ◽  
Structured Data ◽  
Machine Learning Techniques ◽  
Web Based ◽  
Learning Techniques ◽  
Learning Technique ◽  
Automatic Text Classification ◽  
Machine Readable ◽  
Automatic Text

A massive rise in web-based online content today pushes businesses to implement new approaches and resources that might support better navigation, processing, and handling of high-dimensional data. Over the Internet, 90% of the data is unstructured, and there are several approaches through which this data can translate into useful, structured data—classification is one such approach. Classification of knowledge into a good collection of groups is significant and necessary. As the number of machine-readable documents proliferates, automatic text classification is badly needed to classify these documents. Unlabeled documents are categorized into predefined classes of labeled documents using text labeling, a supervised learning technique. This paper reviewed some existing approaches for classifying online news articles and discusses a framework for the automatic classification of online news articles. For achieving high accuracy, different classifiers were tried. Our experimental method achieved 93% accuracy using a Bayesian classifier and present in terms of confusion metrics. ABSTRAK: Peningkatan tinggi pada masa kini pada maklumat dalam talian berasaskan web menyebabkan kaedah baru dalam bisnes telah diguna pakai dan sumber sokongan seperti navigasi, proses, dan pengurusan data berdimensi-tinggi adalah perlu. 90% data di internet adalah data tidak berstruktur, dan terdapat pelbagai kaedah data ini dapat diterjemahkan kepada data berguna, lebih berstruktur — iaitu melalui kaedah klasifikasi. Klasifikasi ilmu kepada koleksi kumpulan baik adalah penting dan perlu. Seperti mana mesin-boleh baca dokumen berkembang pesat, teks klasifikasi automatik juga sangat diperlukan bagi mengklasifikasi dokumen-dokumen ini. Dokumen yang tidak dilabel dikategori sebagai pengelasan pratakrif dokumen berlabel melalui teks label, iaitu teknik pembelajaran berpenyelia. Kajian ini mengkaji semula pendekatan sedia ada bagi artikel berita dalam talian dan membincangkan rangka kerja bagi pengelasan automatik artikel berita dalam talian. Bagi menghasilkan ketepatan yang tinggi, kami menggunakan pelbagai alat klasifikasi. Kaedah eksperimen ini mempunyai ketepatan 93% menggunakan pengelas Bayesian dan data dibentangkan berdasarkan matriks kekeliruan.

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