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A hybrid feature selection strategy for image defining features: towards interpretation of optic nerve images

Yu, Abidi, Artes
2005 2005 International Conference on Machine Learning and Cybernetics  
We present our approach that involves the analysis of CSLT images using moment methods to derive abstract image defining features, and then the use of these features to train classifiers for automatically  ...  distinguishing CSLT images of healthy and diseased optic nerves.  ...  The rationale for feature subset selection is based on the observation that a large number of abstract moments tend to compromise the accuracy of supervised learning classifiers, the classification rules  ... 
doi:10.1109/icmlc.2005.1527847 fatcat:vh6lsuhzgned5l3jmjw6mtlxx4

The Relationship between English Language Learners' Perceptions towards Classroom Oral Error Corrections and Their Pronunciation Accuracy

Afshin Peerdadeh Beiranvand, Ali Entezamara
2016 International Journal of English Linguistics  
Needless to say, language teachers' awareness of language learners' perceptions towards error and error correction strategies can heighten the quality and the quantity of language teaching and learning  ...  and their pronunciation accuracy; 3) if there is a relationship between ILI learners' gender and their attitudes towards classroom oral error corrections.  ...  ijel.ccsenet.org International Journal of English Linguistics Vol. 6, No. 7; 2016  ... 
doi:10.5539/ijel.v6n7p1 fatcat:4wxiauvx6vd5lcfshjcl2b55rq

Experimental Force-Torque Dataset for Robot Learning of Multi-Shape Insertion [article]

Giovanni De Magistris, Asim Munawar, Tu-Hoa Pham, Tadanobu Inoue, Phongtharin Vinayavekhin, Ryuki Tachibana
2018 arXiv   pre-print
The accurate modeling of real-world systems and physical interactions is a common challenge towards the resolution of robotics tasks.  ...  However, a common bottleneck in machine learning techniques resides in the availability of suitable data.  ...  Using this dataset, we conducted several analysis and we trained a MLP network able to select the right action based on forces and moments. The learned motion was tested on the UR5 robot.  ... 
arXiv:1807.06749v2 fatcat:f747inymsnbyzhtmyracljmkbi

Emotional Monitoring of Learners Based on EEG Signal Recognition

Ludi Bai, Junqi Guo, Tianyou Xu, Minghui Yang
2020 Procedia Computer Science  
The importance of education determines learners' learning attributes. In the process of learning, the attitude of learning greatly affects the efficiency and direction of their learning.  ...  Abstract The importance of education determines learners' learning attributes. In the process of learning, the attitude of learning greatly affects the efficiency and direction of their learning.  ...  Introduction Students' emotions in receiving education, to a large extent, will affect students' interest in learning, affect their attitude towards learning, and thus affect their understanding and absorption  ... 
doi:10.1016/j.procs.2020.06.100 fatcat:7bqt4zhmd5cohg3a2nlbababwm

Criminal Detection: Study of Activation Functions and Optimizers

2021 International Journal of Advanced Trends in Computer Science and Engineering  
In learning to achieve the highest test accuracy, CNN was more reliable than the SNN, which was 8% better than the SNN's test accuracy.  ...  In addition to the recent specialisation of deep learning models, the exponential output and memory space growth of computer machines has greatly increased the role of images in recognising semantic patterns  ...  There were no major variations in classification accuracies or learning accuracy, implying that the classifier was not biased towards women.  ... 
doi:10.30534/ijatcse/2021/931022021 fatcat:pos45iz5bnagjppvunob7tbscq

An Empirical Study of Shape Recognition in Ensemble Learning Context

Weili Ding, Xinming Wang, Han Liu, Bo Hu
2018 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)  
Shape recognition has been a popular application of machine learning, where each shape is defined as a class for training classifiers that recognize the shapes of new instances.  ...  Since training of classifiers is essentially achieved through learning from features, it is crucial to extract and select a set of relevant features that can effectively distinguish one class from other  ...  Acknowledgements This paper is supported by the Natural Science Foundation of Hebei Province (No.  ... 
doi:10.1109/icwapr.2018.8521305 fatcat:voscefgmsbapvffyusa6q3hrzm

Delusions and the Role of Beliefs in Perceptual Inference

K. Schmack, A. Gomez-Carrillo de Castro, M. Rothkirch, M. Sekutowicz, H. Rossler, J.-D. Haynes, A. Heinz, P. Petrovic, P. Sterzer
2013 Journal of Neuroscience  
In a combined behavioral and functional neuroimaging study in healthy participants, we used ambiguous visual stimulation to probe the relationship between delusion-proneness and the effect of learned predictions  ...  Delusions are unfounded yet tenacious beliefs and a symptom of psychotic disorder. Varying degrees of delusional ideation are also found in the healthy population.  ...  The arithmetic mean of the two classspecific accuracies gave the balanced prediction accuracy.  ... 
doi:10.1523/jneurosci.1778-13.2013 pmid:23966692 pmcid:PMC6618656 fatcat:6pgrpagbdbbsxdjymqczbdu5b4

Deep-learning based single object tracker for night surveillance

Zulaikha Kadim, Mohd Asyraf Zulkifley, Nabilah Hamzah
2020 International Journal of Electrical and Computer Engineering (IJECE)  
The results show that the best accuracy is obtained by using Adam optimizer with learning rate of 0.00075 and sampling ratio of 2:1 for positive and negative training data.  ...  Various learning hyperparameters for the optimization function, learning rate and ratio of training samples are tested to find optimal setup for tracking in night scenarios.  ...  ACKNOWLEDGEMENTS This work was supported by the Nvidia Corporation through the Titan V Grant (KK-2019-005) and Ministry of Education through FRGS/1/2019/ICT02/UKM/02/1.  ... 
doi:10.11591/ijece.v10i4.pp3576-3587 fatcat:rwxhipirtvbfvkl34hqsekgytq

sEMG Based Human Motion Intention Recognition

Li Zhang, Geng Liu, Bing Han, Zhe Wang, Tong Zhang
2019 Journal of Robotics  
According to the method adopted, motion intention recognition is divided into two groups: sEMG-driven musculoskeletal (MS) model based motion intention recognition and machine learning (ML) model based  ...  In this paper, we review and discuss the state of the art of the sEMG based motion intention recognition that is mainly used in detail.  ...  Deep learning based methods have an important adverse impact on enhancing recognition accuracy and will become the trend of future development.  ... 
doi:10.1155/2019/3679174 fatcat:siyz5eydurdjddpeoqq7shhjji

Intelligent Plant Disease Identification System Using Machine Learning

Paramasivam Alagumariappan, Najumnissa Jamal Dewan, Gughan Narasimhan Muthukrishnan, Bhaskar K. Bojji Raju, Ramzan Ali Arshad Bilal, Vijayalakshmi Sankaran
2020 Engineering Proceedings  
Furthermore, the performance of three machine learning algorithms, such as Extreme Learning Machine (ELM) and Support Vector Machine (SVM) with linear and polynomial kernels was analyzed.  ...  Results demonstrate that the performance of the extreme learning machine is better when compared to the adopted support vector machine classifier.  ...  Further, it is observed that the accuracy of extreme learning machine is higher when compared to the other two classifiers. Figure 3 . 3 Accuracy of the different classifiers.  ... 
doi:10.3390/ecsa-7-08160 fatcat:jxaurxij3nbkviynphn3zh47me

Levenberg-marquardt backpropagation neural network with techebycheve moments for face detection

Ali Nadhim Razzaq, Rozaida Ghazali, Nidhal Khdhair El Abbadi, Hussein Ali Hussein Al Naffakh
2021 Bulletin of Electrical Engineering and Informatics  
Model performance was measured based on its accuracy and the best result from the newly proposed method was 98.9%.  ...  This work presents a new method which composes of a neural network and Techebycheve transforms for face detection.  ...  ACKNOWLEDGEMENTS The authors would like to thank the Universiti Tun Hussein Onn Malaysia (UTHM) and the University of Kufa, Iraq for supporting this research work.  ... 
doi:10.11591/eei.v10i5.2364 fatcat:33cbokx7tvb6jdkewsfyvpjfu4

Adadb: Adaptive diff-batch optimization technique for gradient descent

Muhammad U. S. Khan, Muhammad Jawad, Samee U. Khan
2021 IEEE Access  
The diffGrad algorithm claims that Adam has ignored the impact of 1st moment to control the learning rate over the entire optimization landscape and the inclusion of DFC provides high learning rate in  ...  All the variants of gradient descent (Batch, mini-batch, and stochastic) inherit the disadvantage of slow convergence towards global minima.  ... 
doi:10.1109/access.2021.3096976 fatcat:zb2lhpib75hmrh5qs7xh5omb4q

A Survey on Evaluating Handwritten Iterative Mathematical Expressions

Tina Vaz
2018 International Journal for Research in Applied Science and Engineering Technology  
Handwritten Mathematical Expression Recognition have become one of the most challenging, fascinating and growing research area in the field of Pattern Recognition.  ...  An attempt is made to analyze different stages of recognition process.  ...  Performance of developed system is been measured and systems accuracy and learning rate is been calculated. Nicolas D.  ... 
doi:10.22214/ijraset.2018.4652 fatcat:3hhisdmeg5hqnhshpnlgs7pb5u

Transferable kriging machine learning models for the multipolar electrostatics of helical deca-alanine

Timothy L. Fletcher, Paul L. A. Popelier
2015 Theoretical Chemistry accounts  
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution  ...  It is a common theme in machine learning that the inclusion of too many features can lead to lower prediction accuracy, known as 'over fitting'.  ...  this accuracy.  ... 
doi:10.1007/s00214-015-1739-y fatcat:2x3wabrpcbdijlu6j2om3quhom

Deep similarity network fusion for 3D shape classification

Lorenzo Luciano, A. Ben Hamza
2019 The Visual Computer  
This geometric shape descriptor is then fed into a graph convolutional neural network to learn a deep feature representation of a 3D shape.  ...  The proposed approach coalesces the geometrical discriminative power of geodesic moments and similarity network fusion in an effort to design a simple, yet discriminative shape descriptor.  ...  of geodesic moments.  ... 
doi:10.1007/s00371-019-01668-9 fatcat:vc4pcy7urvcmbd5td3iy5cppim
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