scholarly journals Machine learning for learner English

2020 ◽  
Vol 6 (1) ◽  
pp. 72-103 ◽  
Author(s):  
Nicolas Ballier ◽  
Stéphane Canu ◽  
Caroline Petitjean ◽  
Gilles Gasso ◽  
Carlos Balhana ◽  
...  
Keyword(s):  
Machine Learning ◽  
Foreign Language ◽  
Linguistic Competence ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Classification Task ◽  
Reference Levels ◽  
Learner Corpus ◽  
Learning Techniques ◽  
Lexical Bias

Abstract This paper discusses machine learning techniques for the prediction of Common European Framework of Reference (CEFR) levels in a learner corpus. We summarise the CAp 2018 Machine Learning (ML) competition, a classification task of the six CEFR levels, which map linguistic competence in a foreign language onto six reference levels. The goal of this competition was to produce a machine learning system to predict learners’ competence levels from written productions comprising between 20 and 300 words and a set of characteristics computed for each text extracted from the French component of the EFCAMDAT data (Geertzen et al., 2013). Together with the description of the competition, we provide an analysis of the results and methods proposed by the participants and discuss the benefits of this kind of competition for the learner corpus research (LCR) community. The main findings address the methods used and lexical bias introduced by the task.

Extraction of drug–drug interaction using neural embedding

2018 ◽  
Vol 16 (06) ◽  
pp. 1840027 ◽  
Author(s):  
Wen Juan Hou ◽  
Bamfa Ceesay
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Interaction ◽  
Relevant Information ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Learning Approach ◽  
Learning Techniques ◽  
Drug Drug Interaction

Information on changes in a drug’s effect when taken in combination with a second drug, known as drug–drug interaction (DDI), is relevant in the pharmaceutical industry. DDIs can delay, decrease, or enhance absorption of either drug and thus decrease or increase their action or cause adverse effects. Information Extraction (IE) can be of great benefit in allowing identification and extraction of relevant information on DDIs. We here propose an approach for the extraction of DDI from text using neural word embedding to train a machine learning system. Results show that our system is competitive against other systems for the task of extracting DDIs, and that significant improvements can be achieved by learning from word features and using a deep-learning approach. Our study demonstrates that machine learning techniques such as neural networks and deep learning methods can efficiently aid in IE from text. Our proposed approach is well suited to play a significant role in future research.

scholarly journals Fundus Image Classification Using Convolutional Neural Network

2021 ◽  
Author(s):  
U. Savitha ◽  
Kodali Lahari Chandana ◽  
A. Cathrin Sagayam ◽  
S. Bhuvaneswari
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Eye Disease ◽  
Clinical Use ◽  
Fundus Image ◽  
Learning Techniques ◽  
Eye Pattern

Different eye disease has clinical use in defining of the actual status of eye, in the outcome of the medication and other alternatives in the curative phase. Mainly simplicity, clinical nature are the most important requirements for any classification system. In the existing they used different machine learning techniques to detect only single disease. Whereas deep learning system, which is named as Convolutional neural networks (CNNs) can show hierarchical representing of images between disease eye and normal eye pattern.

scholarly journals Hear and See: End-to-end sound classification and visualization of classified sounds

2018 ◽  
Author(s):  
Thomas Miano
Keyword(s):  
Machine Learning ◽  
Digital Media ◽  
Digital Art ◽  
Machine Learning Techniques ◽  
Classification Task ◽  
Scientific Instrument ◽  
Sound Classification ◽  
Art Form ◽  
Learning Techniques ◽  
End To End

Machine learning is a field of study that uses computational and statistical techniques to enable computers to learn. When machine learning is applied, it functions as an instrument that can solve problems or expand knowledge about the surrounding world. Increasingly, machine learning is also an instrument for artistic expression in digital and non-digital media. While painted art has existed for thousands of years, the oldest digital art is less than a century old. Digital media as an art form is a relatively nascent, and the practice of machine learning in digital art is even more recent. Across all artistic media, a piece is powerful when it can captivate its consumer. Such captivation can be elicited through through a wide variety of methods including but not limited to distinct technique, emotionally evocative communication, and aesthetically pleasing combinations of textures. This work aims to explore how machine learning can be used simultaneously as a scientific instrument for understanding the world and as an artistic instrument for inspiring awe. Specifically, our goal is to build an end-to-end system that uses modern machine learning techniques to accurately recognize sounds in the natural environment and to communicate via visualization those sounds that it has recognized. We validate existing research by finding that convolutional neural networks, when paired with transfer learning using out-of-domain data, can be successful in mapping an image classification task to a sound classification task. Our work offers a novel application where the model used for performant sound classification is also used for visualization in an end-to-end, sound-to-image system.

scholarly journals Hear and See: End-to-end sound classification and visualization of classified sounds

2018 ◽  
Author(s):  
Thomas Miano
Keyword(s):  
Machine Learning ◽  
Digital Media ◽  
Digital Art ◽  
Machine Learning Techniques ◽  
Classification Task ◽  
Scientific Instrument ◽  
Sound Classification ◽  
Art Form ◽  
Learning Techniques ◽  
End To End

Machine learning is a field of study that uses computational and statistical techniques to enable computers to learn. When machine learning is applied, it functions as an instrument that can solve problems or expand knowledge about the surrounding world. Increasingly, machine learning is also an instrument for artistic expression in digital and non-digital media. While painted art has existed for thousands of years, the oldest digital art is less than a century old. Digital media as an art form is a relatively nascent, and the practice of machine learning in digital art is even more recent. Across all artistic media, a piece is powerful when it can captivate its consumer. Such captivation can be elicited through through a wide variety of methods including but not limited to distinct technique, emotionally evocative communication, and aesthetically pleasing combinations of textures. This work aims to explore how machine learning can be used simultaneously as a scientific instrument for understanding the world and as an artistic instrument for inspiring awe. Specifically, our goal is to build an end-to-end system that uses modern machine learning techniques to accurately recognize sounds in the natural environment and to communicate via visualization those sounds that it has recognized. We validate existing research by finding that convolutional neural networks, when paired with transfer learning using out-of-domain data, can be successful in mapping an image classification task to a sound classification task. Our work offers a novel application where the model used for performant sound classification is also used for visualization in an end-to-end, sound-to-image system.

scholarly journals Experimental Evidence of the Speed Variation Effect on SVM Accuracy for Diagnostics of Ball Bearings

Machines ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 48
Author(s):  
Jacopo Cavalaglio Camargo Molano ◽  
Riccardo Rubini ◽  
Marco Cocconcelli
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ball Bearings ◽  
Variable Speed ◽  
Learning Techniques ◽  
Stationary Conditions ◽  
Mechanical Components

In recent years, we have witnessed a considerable increase in scientific papers concerning the condition monitoring of mechanical components by means of machine learning. These techniques are oriented towards the diagnostics of mechanical components. In the same years, the interest of the scientific community in machine diagnostics has moved to the condition monitoring of machinery in non-stationary conditions (i.e., machines working with variable speed profiles or variable loads). Non-stationarity implies more complex signal processing techniques, and a natural consequence is the use of machine learning techniques for data analysis in non-stationary applications. Several papers have studied the machine learning system, but they focus on specific machine learning systems and the selection of the best input array. No paper has considered the dynamics of the system, that is, the influence of how much the speed profile changes during the training and testing steps of a machine learning technique. The aim of this paper is to show the importance of considering the dynamic conditions, taking the condition monitoring of ball bearings in variable speed applications as an example. A commercial support vector machine tool is used, tuning it in constant speed applications and testing it in variable speed conditions. The results show critical issues of machine learning techniques in non-stationary conditions.

scholarly journals AndroAnalyzer: android malicious software detection based on deep learning

2021 ◽  
Vol 7 ◽  
pp. e533
Author(s):  
Recep Sinan Arslan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Smart Devices ◽  
Android Malware ◽  
Original Dataset ◽  
Learning Techniques ◽  
Technological Developments

Background Technological developments have a significant effect on the development of smart devices. The use of smart devices has become widespread due to their extensive capabilities. The Android operating system is preferred in smart devices due to its open-source structure. This is the reason for its being the target of malware. The advancements in Android malware hiding and detection avoidance methods have overridden traditional malware detection methods. Methods In this study, a model employing AndroAnalyzer that uses static analysis and deep learning system is proposed. Tests were carried out with an original dataset consisting of 7,622 applications. Additional tests were conducted with machine learning techniques to compare it with the deep learning method using the obtained feature vector. Results Accuracy of 98.16% was achieved by presenting a better performance compared to traditional machine learning techniques. Values of recall, precision, and F-measure were 98.78, 99.24 and 98.90, respectively. The results showed that deep learning models using trace-based feature vectors outperform current cutting-edge technology approaches.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques
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