Automatic Alignment of Medical Terminologies with General Dictionaries for an Efficient Information Retrieval

2010 ◽  
pp. 78-105
Author(s):  
Laura Diosan ◽  
Alexandrina Rogozan ◽  
Jean-Pierre Pécuchet
Keyword(s):  
Medical Information ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Nearest Neighbour ◽  
Automatic Alignment ◽  
Alignment Task ◽  
Efficient Information ◽  
Different Levels

The automatic alignment between a specialized terminology used by librarians in order to index concepts and a general vocabulary employed by a neophyte user in order to retrieve medical information will certainly improve the performances of the search process, this being one of the purposes of the ANR VODEL project. The authors propose an original automatic alignment of definitions taken from different dictionaries that could be associated to the same concept although they may have different labels. The definitions are represented at different levels (lexical, semantic and syntactic), by using an original and shorter representation, which concatenates more similarities measures between definitions, instead of the classical one (as a vector of word occurrence, whose length equals the number of different words from all the dictionaries). The automatic alignment task is considered as a classification problem and three Machine Learning algorithms are utilised in order to solve it: a k Nearest Neighbour algorithm, an Evolutionary Algorithm and a Support Vector Machine algorithm. Numerical results indicate that the syntactic level of nouns seems to be the most important, determining the best performances of the SVM classifier.

TREMO: A dataset for emotion analysis in Turkish

2018 ◽  
Vol 44 (6) ◽  
pp. 848-860 ◽  
Author(s):  
Mansur Alp Tocoglu ◽  
Adil Alpkocak
Keyword(s):  
Vector Space Model ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Training Process ◽  
Validation Process ◽  
Space Model ◽  
Vector Machines ◽  
F Measure

This study presents a new dataset to be used in emotion extraction studies in Turkish text. We consider emotion extraction as a supervised text classification problem, which thereby requires a dataset for the training process. To satisfy this requirement, we aim to create a new dataset containing data for the six emotion categories: happiness, fear, anger, sadness, disgust and surprise. To gather this dataset, we conducted a survey and collected 27,350 entries from 4709 individuals. In the next step, we performed a validation process in which annotators validated each entry one by one by assigning a related emotion category. As a result of this process, we obtained two datasets, one raw and the other validated. Subsequently, we generated four versions of these two datasets using two different stemming methods and then modelled them using a vector space model. Then, we ran machine learning algorithms, including complement naive Bayes (CNB), random forest (RF), decision tree C4.5 (J48) and an updated version of support vector machines (SVMs), on the models to calculate the accuracy, precision, recall and F-measure values. Based on the results we obtained, we concluded that the SVM classifier yielded the highest performance value and that the models trained with a validated dataset provide more accurate results than the models trained with a non-validated dataset.

Classification of Diffusion Tensor Metrics for the Diagnosis of a Myelopathic Cord Using Machine Learning

2018 ◽  
Vol 28 (02) ◽  
pp. 1750036 ◽  
Author(s):  
Shuqiang Wang ◽  
Yong Hu ◽  
Yanyan Shen ◽  
Hanxiong Li
Keyword(s):  
Machine Learning ◽  
Surgical Planning ◽  
Diffusion Tensor ◽  
Mean Value ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Control Groups ◽  
Diffusion Tensor Imaging Dti

In this study, we propose an automated framework that combines diffusion tensor imaging (DTI) metrics with machine learning algorithms to accurately classify control groups and groups with cervical spondylotic myelopathy (CSM) in the spinal cord. The comparison between selected voxel-based classification and mean value-based classification were performed. A support vector machine (SVM) classifier using a selected voxel-based dataset produced an accuracy of 95.73%, sensitivity of 93.41% and specificity of 98.64%. The efficacy of each index of diffusion for classification was also evaluated. Using the proposed approach, myelopathic areas in CSM are detected to provide an accurate reference to assist spine surgeons in surgical planning in complicated cases.

scholarly journals A Novel Fast Training Method for SVM and Its Application in Fault Diagnosis of Service Robot

2015 ◽  
Vol 11 (6) ◽  
pp. 4 ◽  
Author(s):  
Xianfeng Yuan ◽  
Mumin Song ◽  
Fengyu Zhou ◽  
Yugang Wang ◽  
Zhumin Chen
Keyword(s):  
Fault Diagnosis ◽  
Classification Accuracy ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Service Robot ◽  
Support Vector ◽  
Svm Classifier ◽  
Processing Unit ◽  
Training Method ◽  
Fast Training

Support Vector Machines (SVM) is a set of popular machine learning algorithms which have been successfully applied in diverse aspects, but for large training data sets the processing time and computational costs are prohibitive. This paper presents a novel fast training method for SVM, which is applied in the fault diagnosis of service robot. Firstly, sensor data are sampled under different running conditions of the robot and those samples are divided as training sets and testing sets. Secondly, the sampled data are preprocessed and the principal component analysis (PCA) model is established for fault feature extraction. Thirdly, the feature vectors are used to train the SVM classifier, which achieves the fault diagnosis of the robot. To speed up the training process of SVM, on the one hand, sample reduction is done using the proposed support vectors selection (SVS) algorithm, which can ensure good classification accuracy and generalization capability. On the other hand, we take advantage of the excellent parallel computing abilities of Graphics Processing Unit (GPU) to pre-calculate the kernel matrix, which avoids the recalculation during the cross validation process. Experimental results illustrate that the proposed method can significantly reduce the training time without decreasing the classification accuracy.

scholarly journals Classification of Emotional States Inparkinson’s Disease Patients Using Time,Frequency and Time-Frequency Analysis

2021 ◽  
Author(s):  
Rejith K.N ◽  
Kamalraj Subramaniam ◽  
Ayyem Pillai Vasudevan Pillai ◽  
Roshini T V ◽  
Renjith V. Ravi ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Classification Accuracy ◽  
Machine Learning Algorithms ◽  
Signal Frequency ◽  
Spectral Energy ◽  
Support Vector ◽  
Svm Classifier ◽  
Time Frequency ◽  
Energy Entropy ◽  
Teager Energy

Abstract In this work, PD patients and healthy individuals were categorized with machine-learning algorithms. EEG signals associated with six different emotions, (Happiness(E1), Sadness(E2), Fear(E3), Anger(E4), Surprise,(E5) and disgust(E6)) were used for the study. EEG data were collected from 20 PD patients and 20 normal controls using multimodal stimuli. Different features were used to categorize emotional data. Emotional recognition in Parkinson’s disease (PD) has been investigated in three domains namely, time, frequency and time frequency using Entropy, Energy-Entropy and Teager Energy-Entropy features. Three classifiers namely, K-Nearest Neighbor Algorithm, Support Vector Machine and Probabilistic Neural Network were used to observethe classification results. Emotional EEG stimuli such as anger, surprise, happiness, sadness, fear, and disgust were used to categorize PD patients and healthy controls (HC). For each EEG signal, frequency features corresponding to alpha, beta and gamma bands were obtained for nine feature extraction methods (Entropy, Energy Entropy, Teager Energy Entropy, Spectral Entropy, Spectral Energy-Entropy, Spectral Teager Energy-Entropy, STFT Entropy, STFT Energy-Entropy and STFT Teager Energy-Entropy). From the analysis, it is observed that the entropy feature in frequency domain performs evenly well (above 80 %) for all six emotions with KNN. Classification results shows that using the selected energy entropy combination feature in frequency domain provides highest accuracy for all emotions except E1 and E2 for KNN and SVM classifier, whereas other features give accuracy values of above 60% for most emotions.It is also observed that emotion E1 gives above 90 % classification accuracy for all classifiers in time domain.In frequency domain also, emotion E1 gives above 90% classification accuracy using PNN classifier.

scholarly journals A Comparative Study of Indian Food Image Classification Using K-Nearest-Neighbour and Support-Vector-Machines

2018 ◽  
Vol 7 (3.12) ◽  
pp. 521 ◽  
Author(s):  
Pathanjali C ◽  
Vimuktha E Salis ◽  
Jalaja G ◽  
Latha A
Keyword(s):  
Comparative Study ◽  
Detection System ◽  
Experimental Result ◽  
Support Vector ◽  
Svm Classifier ◽  
Nearest Neighbour ◽  
Classification Models ◽  
Food Detection ◽  
Knn Classifier ◽  
Indian Food

Food being the vital part of everyone’s lives, food detection and recognition becomes an interesting and challenging problem in computer vision and image processing. In this paper we mainly propose an automatic food detection system that detects and recognises varieties of Indian food. This paper uses a combined colour and shape features. The K-Nearest-Neighbour (KNN) and Support-Vector -Machine (SVM) classification models are used to classify the features. A comparative study on the performance of both the classification models is performed. The experimental result shows the higher efficiency of SVM classifier over KNN classifier. 

scholarly journals Smart Face Detection and Recognition in Low Resolution Images Using Alexnet CNN Compare Accuracy with SVM

2021 ◽  
Vol 36 (1) ◽  
pp. 721-726
Author(s):  
S. Mahesh ◽  
Dr.G. Ramkumar
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Low Resolution ◽  
Accuracy Ratio ◽  
Low Resolution Images

Aim: Machine learning algorithm plays a vital role in various biometric applications due to its admirable result in detection, recognition and classification. The main objective of this work is to perform comparative analysis on two different machine learning algorithms to recognize the person from low resolution images with high accuracy. Materials & Methods: AlexNet Convolutional Neural Network (ACNN) and Support Vector Machine (SVM) classifiers are implemented to recognize the face in a low resolution image dataset with 20 samples each. Results: Simulation result shows that ACNN achieves a significant recognition rate with 98% accuracy over SVM (89%). Attained significant accuracy ratio (p=0.002) in SPSS statistical analysis as well. Conclusion: For the considered low resolution images ACNN classifier provides better accuracy than SVM Classifier.

scholarly journals An Arabic Mispronunciation Detection System Based on the Frequency of Mistakes for Asian Speakers

2021 ◽  
Vol 40 (2) ◽  
pp. 279-297
Author(s):  
Faria Nazir ◽  
Muhammad Nadeem Majeed ◽  
Mustansar Ali Ghazanfar ◽  
Muazzam Maqsood
Keyword(s):  
Language Learning ◽  
Second Language Learners ◽  
Positive Impact ◽  
Detection System ◽  
Confusion Matrix ◽  
Classification Problem ◽  
Support Vector ◽  
Svm Classifier ◽  
Computer Assisted ◽  
Detection Model

Over the last few decades, the field of artificial intelligence and machine learning has evolved. Due to the advancement in these fields, much work has been done to assist language learning with the help of computers called Computer-Assisted Language Learning (CALL). Mispronunciation detection is one of the significant tasks of the CALL system. An efficient mispronunciation detection model has a positive impact on the life of second language learners by providing phoneme level feedback. In this paper, we introduce the phone grouping technique for mispronunciation detection that is based on mistakes probability. We consider mispronunciation detection as a classification problem, traditionally for this purpose, a separate classifier is trained for each phoneme mistake that requires a lot of memory and time. Instead of training a separate classifier, we group the phoneme based on their mistakes probability that helps in reducing the number of the classifiers to be trained and also saves memory and time. We use the Support Vector Machine (SVM) classifier and test the results on the Arabic dataset (28 Phonemes). The performance of our proposed method is evaluated by using accuracy. The results of the model are evaluated using the confusion matrix and gives an accuracy of 88%. Our approach outperforms the existing systems developed for Arabic phonemes in terms of accuracy and is also time/memory efficient.

scholarly journals Soil Classification and Harvest Proposal Implemented using Machine Learning Techniques

2020 ◽  
Vol 9 (12) ◽  
pp. 19-22
Keyword(s):  
Machine Learning ◽  
Soil Type ◽  
Soil Classification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
The People ◽  
Learning Techniques ◽  
Crop Type

The major source of living for the people of India is agriculture. It is considered as important economy for the country. India is one of the country that suffer from natural calamities like drought and flood that may destroy the crops which may lead to heavy loss for the people doing agriculture. Predicting the crop type can help them to cultivate the suitable crop that can be cultivated in that particular soil type. Soil is one major factor or agriculture. There are several types of soil available in our county. In order to classify the soil type we need to understand the characteristics of the soil. Data mining and machine learning is one of the emerging technology in the field of agriculture and horticulture. In order to classify the soil type and Provide suggestion of fertilizers that can improve the growth of the crop cultivated in that particular soil type plays major role in agriculture. For that here exploring Several machine learning algorithms such as Support vector machine(SVM),k-Nearest Neighbour(k-NN) and logistic regression are used to classify the soil type.

Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies

2017 ◽  
Vol 11 (4) ◽  
pp. 80-92
Author(s):  
Junjie Bai ◽  
Kan Luo ◽  
Jun Peng ◽  
Jinliang Shi ◽  
Ying Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Affective Computing ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Cognitive Computing ◽  
Bayes Classifier ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Neuro Fuzzy

Music emotions recognition (MER) is a challenging field of studies addressed in multiple disciplines such as musicology, cognitive science, physiology, psychology, arts and affective computing. In this article, music emotions are classified into four types known as those of pleasing, angry, sad and relaxing. MER is formulated as a classification problem in cognitive computing where 548 dimensions of music features are extracted and modeled. A set of classifications and machine learning algorithms are explored and comparatively studied for MER, which includes Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Neuro-Fuzzy Networks Classification (NFNC), Fuzzy KNN (FKNN), Bayes classifier and Linear Discriminant Analysis (LDA). Experimental results show that the SVM, FKNN and LDA algorithms are the most effective methodologies that obtain more than 80% accuracy for MER.

Support Vector Machine Classifier with WHM Offset for Unbalanced Data

2008 ◽  
Vol 12 (1) ◽  
pp. 94-101 ◽  
Author(s):  
Boyang Li ◽  
◽  
Jinglu Hu ◽  
Kotaro Hirasawa
Keyword(s):  
Support Vector Machine ◽  
Real World ◽  
Support Vector Machine Classifier ◽  
Classification Problem ◽  
Training Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Real World Data ◽  
Unbalanced Classification ◽  
Improved Support Vector Machine

We propose an improved support vector machine (SVM) classifier by introducing a new offset, for solving the real-world unbalanced classification problem. The new offset is calculated based on the unbalanced support vectors resulting from the unbalanced training data. We developed a weighted harmonic mean (WHM) algorithm to further reduce the effects of noise on offset calculation. We apply the proposed approach to classify real-world data. Results of simulation demonstrate the effectiveness of our proposed approach.

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