scholarly journals Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Benedikt Lorch ◽  
Ghislain Vaillant ◽  
Christian Baumgartner ◽  
Wenjia Bai ◽  
Daniel Rueckert ◽  
...  
Keyword(s):  
Respiratory Motion ◽  
Diagnostic Value ◽  
Learning Approach ◽  
Motion Artefacts ◽  
Patient Motion ◽  
Random Decision Forests ◽  
Machine Learning Approach ◽  
Image Artefacts ◽  
The Subject ◽  
Space Data

The acquisition of a Magnetic Resonance (MR) scan usually takes longer than subjects can remain still. Movement of the subject such as bulk patient motion or respiratory motion degrades the image quality and its diagnostic value by producing image artefacts like ghosting, blurring, and smearing. This work focuses on the effect of motion on the reconstructed slices and the detection of motion artefacts in the reconstruction by using a supervised learning approach based on random decision forests. Both the effects of bulk patient motion occurring at various time points in the acquisition on head scans and the effects of respiratory motion on cardiac scans are studied. Evaluation is performed on synthetic images where motion artefacts have been introduced by altering the k-space data according to a motion trajectory, using the three common k-space sampling patterns: Cartesian, radial, and spiral. The results suggest that a machine learning approach is well capable of learning the characteristics of motion artefacts and subsequently detecting motion artefacts with a confidence that depends on the sampling pattern.

scholarly journals PENINGKATAN KEMAMPUAN KONEKSI MATEMATIK DAN SIKAP POSITIF SISWA DENGAN PEMBELAJARAN KONTEKSTUAL PADA SISWA SMK PELAYARAN SAMUDERA INDONESIA MEDAN

2018 ◽  
Vol 4 (1) ◽  
pp. 113-123
Author(s):  
Taruli Marito Silalahi
Keyword(s):  
High School ◽  
Data Analysis ◽  
Student Attitudes ◽  
Contextual Learning ◽  
Learning Approach ◽  
Positive Thinking ◽  
Ability Level ◽  
The Subject ◽  
Space Data ◽  
Mathematical Connection

Abstract. This research aimed study to determine: 1) the increasing ability of mathematic connection and student’s positive thingking by using Contextual Learning are higher than students or usual learning approach. 2) there was the interaction between learning by students mathematic ability toward the increasing ability of mathematic connection and student’s positive thinking. 3) to determine how the answering process are made by the students in problem solving by using contextual learning and usual learning approach. This kind of research is the quasi experiment. The populations of this research are all of the students in X grade of Tehnical High School with acreditation B where is in Medan and the sample is chosen random sample. Where SMK Medan consist of X Nautic as experiment class and X Tehnical as control class am each consist of 34 students. The instrument used consist of: (1) test students initial mathematic ability, (2) test for mathematic connection and (3) scale for positive thingking, the subject up space. Data analysis are done by using ANAVA two ways. The result of this research shown that (1) the increasing ability in mathematic connection and student’s positive thinking by using Contextual Learning is higher than using student’s usual thinking approach, (2) there were no interaction between learning and student’s ability level to the increasing ability of mathematics and student’s positive thinking. The researcher suggests to use the Contextual Learning as the alternative way for teachers to increase the ability of mathematic connection and student’s positive thingking.Keyword: Contextual Learning, Mathematical Connection Ability, and Positive Student Attitudes

A Machine Learning Approach for One-Stop Learning

2008 ◽  
pp. 333-357 ◽  
Author(s):  
Marco A. Alvarez ◽  
SeungJin Lim
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approach ◽  
Internet Users ◽  
Machine Learning Approach ◽  
Supervised Learning Algorithms ◽  
One Stop ◽  
The Subject

Current search engines impose an overhead to motivated students and Internet users who employ the Web as a valuable resource for education. The user, searching for good educational materials for a technical subject, often spends extra time to filter irrelevant pages or ends up with commercial advertisements. It would be ideal if, given a technical subject by user who is educationally motivated, suitable materials with respect to the given subject are automatically identified by an affordable machine processing of the recommendation set returned by a search engine for the subject. In this scenario, the user can save a significant amount of time in filtering out less useful Web pages, and subsequently the user’s learning goal on the subject can be achieved more efficiently without clicking through numerous pages. This type of convenient learning is called One-Stop Learning (OSL). In this paper, the contributions made by Lim and Ko in (Lim and Ko, 2006) for OSL are redefined and modeled using machine learning algorithms. Four selected supervised learning algorithms: Support Vector Machine (SVM), AdaBoost, Naive Bayes and Neural Networks are evaluated using the same data used in (Lim and Ko, 2006). The results presented in this paper are promising, where the highest precision (98.9%) and overall accuracy (96.7%) obtained by using SVM is superior to the results presented by Lim and Ko. Furthermore, the machine learning approach presented here, demonstrates that the small set of features used to represent each Web page yields a good solution for the OSL problem.

scholarly journals Peak-Load Forecasting for Small Industries: A Machine Learning Approach

2020 ◽  
Vol 12 (16) ◽  
pp. 6539 ◽  
Author(s):  
Dong-Hoon Kim ◽  
Eun-Kyu Lee ◽  
Naik Bakht Sania Qureshi
Keyword(s):  
Peak Load ◽  
Load Forecasting ◽  
Environmental Issues ◽  
Predictive Performance ◽  
Learning Approach ◽  
Industrial Facilities ◽  
Machine Learning Approach ◽  
The Subject ◽  
Feature Information ◽  
Small Industries

Peak-load forecasting prevents energy waste and helps with environmental issues by establishing plans for the use of renewable energy. For that reason, the subject is still actively studied. Most of these studies are focused on improving predictive performance by using varying feature information, but most small industrial facilities cannot provide such information because of a lack of infrastructure. Therefore, we introduce a series of studies to implement a generalized prediction model that is applicable to these small industrial facilities. On the basis of the pattern of load information of most industrial facilities, new features were selected, and a generalized model was developed through the aggregation of ensemble models. In addition, a new method is proposed to improve prediction performance by providing additional compensation to the prediction results by reflecting the fewest opinions among the prediction results of each model. Actual data from two small industrial facilities were applied to our process, and the results proved the effectiveness of our proposed method.

New CNF Features and Formula Classification

10.29007/b8t1 ◽  
2018 ◽  
Author(s):  
Enrique Alfonso ◽  
Norbert Manthey
Keyword(s):  
Machine Learning ◽  
Structural Information ◽  
Computation Time ◽  
The Other ◽  
Learning Approach ◽  
Sat Solvers ◽  
Random Decision Forests ◽  
Machine Learning Approach ◽  
Sat Solver ◽  
Cnf Formulas

In this paper we first present three new features for classifying CNF formulas. These features are based on the structural information of the formula and consider AND-gates as well as exactly-one constraints. Next, we use these features to construct a machine learning approach to select a SAT solver configuration for CNF formulas with random decision forests. Based on this classification task we can show that our new features are useful compared to existing features. Since the computation time for these features is small, the constructed classifier improves the performance of the SAT solvers on application and hand crafted benchmarks. On the other hand, the comparison shows that the set of new features also results in a better classification.

scholarly journals Twitter Sentiment Analysis and Visualization Using Apache Spark and Elasticsearch

2018 ◽  
Vol 7 (3.12) ◽  
pp. 314 ◽  
Author(s):  
Maragatham G ◽  
Shobana Devi A
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Learning Approach ◽  
Internet Users ◽  
Machine Learning Approach ◽  
Novel Method ◽  
Rich Information ◽  
Space Data ◽  
User Friendly ◽  
The Impact

Sentiment analysis on Twitter data has paying more attention recently. The system’s key feature, is the immediate communication with other users in an easy, fast way and user-friendly too. Sentiment analysis is the process of identifying and classifying opinions or sentiments expressed in source text. There is a huge volume of data present in the web for internet users and a lot of data is generated per second due to the growth and advancement of web technology. Nowadays, Internet has become best platform to share everyone's opinion, to exchange ideas and to learn online. People are using social network sites like facebook, twitter and it has gained more popularity among them to share their views and pass messages about some topics around the world. As tweets, notices and blog entries, the online networking is producing a tremendous measure of conclusion rich information. This client produced assumption examination information is extremely helpful in knowing the supposition of the general population swarm. At the point when contrasted with general supposition investigation the twitter assumption examination is much troublesome because of its slang words and incorrect spellings. Twitter permits 140 as the most extreme cutoff of characters per message. The two procedures that are mostly utilized for content examination is information base approach and machine learning approach. In this paper, we investigated the twitter created posts utilizing Machine Learning approach. Performing assumption examination in a particular area, is to distinguish the impact of space data in notion grouping. we ordered the tweets as constructive, pessimistic and separate diverse people groups' data about that specific space. In this paper, we developed a novel method for sentiment learning using the Spark coreNLP framework. Our method exploits the hashtags and emoticons inside a tweet, as sentiment labels, and proceeds to a classification procedure of diverse sentiment types in a parallel and distributed manner.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use
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