Feature engineering using shallow parsing in argument classification of Persian verbs

2012 ◽  
Author(s):  
Parisa Saeedi ◽  
Hesham Faili
Keyword(s):  
Feature Engineering ◽  
Shallow Parsing

scholarly journals Classification of fetal compromise during labour: signal processing and feature engineering of the cardiotocograph

2021 ◽  
Author(s):  
M. O'Sullivan ◽  
T. Gabruseva ◽  
GB. Boylan ◽  
M. O'Riordan ◽  
G. Lightbody ◽  
...  
Keyword(s):  
Signal Processing ◽  
Feature Engineering ◽  
Fetal Compromise

scholarly journals Classification of Medical Thermograms Belonging Neonates by Using Segmentation, Feature Engineering and Machine Learning Algorithms

2020 ◽  
Vol 37 (4) ◽  
pp. 611-617
Author(s):  
Ahmet H. Ornek ◽  
Saim Ervural ◽  
Murat Ceylan ◽  
Murat Konak ◽  
Hanifi Soylu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Intensive Care Unit ◽  
Neonatal Intensive Care ◽  
Local Binary Pattern ◽  
Intelligent System ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Harmful Radiation

Monitoring and evaluating the skin temperature value are considerably important for neonates. A system detecting diseases without any harmful radiation in early stages could be developed thanks to thermography. This study is aimed at detecting healthy/unhealthy neonates in neonatal intensive care unit (NICU). We used 40 different thermograms belonging 20 healthy and 20 unhealthy neonates. Thermograms were exported to thermal maps, and subsequently, the thermal maps were converted to a segmented thermal map. Local binary pattern and fast correlation-based filter (FCBF) were applied to extract salient features from thermal maps and to select significant features, respectively. Finally, the obtained features are classified as healthy and unhealthy with decision tree, artificial neural networks (ANN), logistic regression, and random forest algorithms. The best result was obtained as 92.5% accuracy (100% sensitivity and 85% specificity). This study proposes fast and reliable intelligent system for the detection of healthy/unhealthy neonates in NICU.

scholarly journals RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

2021 ◽  
Author(s):  
Shubo Yang ◽  
Yang Luo ◽  
Wang Miao ◽  
Changhao Ge ◽  
Wenjian Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Deep Neural Network ◽  
Feature Engineering ◽  
Network Approach ◽  
Neural Network Approach ◽  
Aerial Vehicles ◽  
Accurate Detection ◽  
Rf Signal

With the proliferation of Unmanned Aerial Vehicles (UAVs) to provide diverse critical services, the accurate detection of these small devices and the efficient classification of their flight modes are of paramount importance. In this paper, we propose a joint Feature Engineering Generator (FEG) and Multi-Channel Deep Neural Network (MC-DNN) approach.

scholarly journals Convolutional Neural Networks for Recognition of Lymphoblast Cell Images

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Tatdow Pansombut ◽  
Siripen Wikaisuksakul ◽  
Kittiya Khongkraphan ◽  
Aniruth Phon-on
Keyword(s):  
Deep Learning ◽  
Lymphoblastic Leukaemia ◽  
Specific Problem ◽  
Support Vector ◽  
Feature Engineering ◽  
Learning Approach ◽  
Domain Specific ◽  
Vector Machines ◽  
Multilayer Architecture

This paper presents the recognition for WHO classification of acute lymphoblastic leukaemia (ALL) subtypes. The two ALL subtypes considered are T-lymphoblastic leukaemia (pre-T) and B-lymphoblastic leukaemia (pre-B). They exhibit various characteristics which make it difficult to distinguish between subtypes from their mature cells, lymphocytes. In a common approach, handcrafted features must be well designed for this complex domain-specific problem. With deep learning approach, handcrafted feature engineering can be eliminated because a deep learning method can automate this task through the multilayer architecture of a convolutional neural network (CNN). In this work, we implement a CNN classifier to explore the feasibility of deep learning approach to identify lymphocytes and ALL subtypes, and this approach is benchmarked against a dominant approach of support vector machines (SVMs) applying handcrafted feature engineering. Additionally, two traditional machine learning classifiers, multilayer perceptron (MLP), and random forest are also applied for the comparison. The experiments show that our CNN classifier delivers better performance to identify normal lymphocytes and pre-B cells. This shows a great potential for image classification with no requirement of multiple preprocessing steps from feature engineering.

scholarly journals Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning

2021 ◽  
Vol 14 (1) ◽  
pp. 16
Author(s):  
Chandrashekar Jatoth ◽  
Rishabh Jain ◽  
Ugo Fiore ◽  
Subrahmanyam Chatharasupalli
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Ensemble Learning ◽  
Feature Engineering ◽  
Machine Learning Classifiers ◽  
Blockchain Technology ◽  
Widespread Adoption ◽  
Learning Classifiers ◽  
Correlation Based Feature Selection

Although the blockchain technology is gaining a widespread adoption across multiple sectors, its most popular application is in cryptocurrency. The decentralized and anonymous nature of transactions in a cryptocurrency blockchain has attracted a multitude of participants, and now significant amounts of money are being exchanged by the day. This raises the need of analyzing the blockchain to discover information related to the nature of participants in transactions. This study focuses on the identification for risky and non-risky blocks in a blockchain. In this paper, the proposed approach is to use ensemble learning with or without feature selection using correlation-based feature selection. Ensemble learning yielded good results in the experiments, but class-wise analysis reveals that ensemble learning with feature selection improves even further. After training Machine Learning classifiers on the dataset, we observe an improvement in accuracy of 2–3% and in F-score of 7–8%.

scholarly journals RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

2021 ◽  
Author(s):  
Shubo Yang ◽  
Yang Luo ◽  
Wang Miao ◽  
Changhao Ge ◽  
Wenjian Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Deep Neural Network ◽  
Feature Engineering ◽  
Network Approach ◽  
Neural Network Approach ◽  
Aerial Vehicles ◽  
Accurate Detection ◽  
Rf Signal

With the proliferation of Unmanned Aerial Vehicles (UAVs) to provide diverse critical services, the accurate detection of these small devices and the efficient classification of their flight modes are of paramount importance. In this paper, we propose a joint Feature Engineering Generator (FEG) and Multi-Channel Deep Neural Network (MC-DNN) approach.

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