41 results on '"Neural network classifier"'
Search Results
2. Neural Network Model for Quality Indicators Assessment: Case of Paper Manufacturing Industry
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Rudakova, Irina, Peshekhonov, Alexey, Chernikova, Anna, Kuzmina, Svetlana, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, and Mottaeva, Angela, editor
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- 2022
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3. Signature Recognition and Verification Using Zonewise Statistical Features
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Lakkannavar, Banashankaramma F., Kodabagi, M. M., Naik, Susen P., Xhafa, Fatos, Series Editor, Pandian, A. Pasumpon, editor, Palanisamy, Ram, editor, and Ntalianis, Klimis, editor
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- 2020
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4. Extraction of the Beam Elastic Shape from Uncertain FBG Strain Measurement Points
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Pinto, Manuel, Roveri, Nicola, Pepe, Gianluca, Nicoletti, Andrea, Balconi, Gabriele, Carcaterra, Antonio, Ceccarelli, Marco, Series Editor, Hernandez, Alfonso, Editorial Board Member, Huang, Tian, Editorial Board Member, Velinsky, Steven A., Editorial Board Member, Takeda, Yukio, Editorial Board Member, Corves, Burkhard, Editorial Board Member, Carbone, Giuseppe, editor, and Gasparetto, Alessandro, editor
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- 2019
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5. Improving Neural Network Classifier Using Gradient-Based Floating Centroid Method
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Islam, Mazharul, Liu, Shuangrong, Zhang, Xiaojing, Wang, Lin, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Gedeon, Tom, editor, Wong, Kok Wai, editor, and Lee, Minho, editor
- Published
- 2019
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6. GanDef: A GAN Based Adversarial Training Defense for Neural Network Classifier
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Liu, Guanxiong, Khalil, Issa, Khreishah, Abdallah, Rannenberg, Kai, Editor-in-Chief, Sakarovitch, Jacques, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Pras, Aiko, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Furbach, Ulrich, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Dhillon, Gurpreet, editor, Karlsson, Fredrik, editor, Hedström, Karin, editor, and Zúquete, André, editor
- Published
- 2019
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7. Gesture Recognition Using an EEG Sensor and an ANN Classifier for Control of a Robotic Manipulator
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Alba-Flores, Rocio, Rios, Fernando, Triplett, Stephanie, Casas, Antonio, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Arai, Kohei, editor, Bhatia, Rahul, editor, and Kapoor, Supriya, editor
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- 2019
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8. Performance analyses of five neural network classifiers on nodule classification in lung CT images using WEKA: a comparative study
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Hussain, Md. Anwar and Gogoi, Lakshipriya
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- 2022
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9. ECG Based Myocardial Infarction Detection Using Different Classification Techniques
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Kora, Padmavathi, Annavarapu, Ambika, Borra, Surekha, Tavares, João Manuel R.S., Series editor, Jorge, Renato Natal, Series editor, Dey, Nilanjan, editor, Ashour, Amira S., editor, and Borra, Surekha, editor
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- 2018
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10. Automatic Text Classification Using Neural Network and Statistical Approaches
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ElGhazaly, Tarek, Kacprzyk, Janusz, Series editor, Shaalan, Khaled, editor, Hassanien, Aboul Ella, editor, and Tolba, Fahmy, editor
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- 2018
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11. Early Stage Detection of Diabetic Retinopathy Using an Optimal Feature Set
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Shirbahadurkar, S. D., Mane, Vijay M., Jadhav, D. V., Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Thampi, Sabu M., editor, Krishnan, Sri, editor, Corchado Rodriguez, Juan Manuel, editor, Das, Swagatam, editor, Wozniak, Michal, editor, and Al-Jumeily, Dhiya, editor
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- 2018
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12. A Histogram of Oriented Gradients for Broken Bars Diagnosis in Squirrel Cage Induction Motors
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Silva, Luiz C., Dias, Cleber G., Alves, Wonder A. L., Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Kůrková, Věra, editor, Manolopoulos, Yannis, editor, Hammer, Barbara, editor, Iliadis, Lazaros, editor, and Maglogiannis, Ilias, editor
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- 2018
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13. Identification of Diabetes Disease Using Committees of Neural Network-Based Classifiers
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Hassan El-Baz, Ali, Ella Hassanien, Aboul, Schaefer, Gerald, Kacprzyk, Janusz, Series editor, Ryżko, Dominik, editor, Gawrysiak, Piotr, editor, Kryszkiewicz, Marzena, editor, and Rybiński, Henryk, editor
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- 2016
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14. Mel Frequency Cepstral Coefficients Based Similar Albanian Phonemes Recognition
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Karahoda, Bertan, Pireva, Krenare, Imran, Ali Shariq, and Yamamoto, Sakae, editor
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- 2016
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15. HIST: HyperIntensity Segmentation Tool
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Manjón, Jose V., Coupé, Pierrick, Raniga, Parnesh, Xia, Ying, Fripp, Jurgen, Salvado, Olivier, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Wu, Guorong, editor, Coupé, Pierrick, editor, Zhan, Yiqiang, editor, Munsell, Brent C., editor, and Rueckert, Daniel, editor
- Published
- 2016
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16. Location and Activity Detection for Indoor Environments
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Reyes, Fernando Martínez, Gurrola, Luis C. González, Estrada, Hector Valenzuela, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Pichardo Lagunas, Obdulia, editor, Herrera Alcántara, Oscar, editor, and Arroyo Figueroa, Gustavo, editor
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- 2015
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17. Patient Classification Using the Hybrid AHP-CNN Approach
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Samira Achki and Layla Aziz
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2019-20 coronavirus outbreak ,Hierarchy ,business.industry ,Process (engineering) ,Computer science ,Analytic hierarchy process ,Machine learning ,computer.software_genre ,Convolutional neural network ,Neural network classifier ,Patient classification ,Artificial intelligence ,business ,computer ,Classifier (UML) - Abstract
Covid19 is a horrible disease, which upset our life everywhere. The main complexity of this disease lies in its rapid evolution and through people’s contact and gaps in our understanding. Moreover, it represents critical cases when the immune system has not presented any symptoms. Hence, the design of an effective classifier is necessary. This paper aims to hybrid the multi-criteria Analytic Hierarchy Pprocess (AHP) tool and the process of the convolutional neural network (CNN), for making the classification of a category of patients. Our novel method is divided into two main phases: the first one focuses on the generation of the priorities of the essential criteria using the AHP model, while the second phase aims to classify the patients using the neural network classifier. In the present study, we considered three important criteria: fever, patient, localiztion, and the age of the patient. From the obtained results, the proposed model has proved its efficiency even if we consider different cases.
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- 2021
18. Medical Image Retrieval Using Efficient Texture and Color Patterns with Neural Network Classifier
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C. Ashok Kumar and S. Sathiamoorthy
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Maxima and minima ,Artificial neural network ,Computer science ,business.industry ,Deep learning ,Search engine indexing ,Feature extraction ,Pattern recognition ,Artificial intelligence ,business ,Neural network classifier ,Image retrieval ,Classifier (UML) - Abstract
Presently, the usage of medicinal images has drastically increased and it provides extensive details related to the patient’s health status. It shows the applicability of diagnosing the disease and stored in a memory for examination purposes. For the retrieval of medical images in a real world environment, significant need is present in the designing of an effective medical image indexing and retrieval technique. This paper offers an efficient medical image retrieval (MIR) model through feature extraction based classification model. Here, Directional local ternary quantized extrema patterns (DLTerQEPs) and autocorrelogram (AC) based feature extraction process takes place to extract texture and color features. Next, neural network (NN) based classification process takes place. The investigation of the simulation results takes place to showcase the betterment of the presented mode. During experimentation process, it is noticed that the presented model is superior to compared methods.
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- 2020
19. Introducing Domain Knowledge
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Akka Zemmari and Jenny Benois-Pineau
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Information fusion ,business.industry ,Computer science ,Deep learning ,Medical imaging ,Domain knowledge ,Artificial intelligence ,business ,Machine learning ,computer.software_genre ,Neural network classifier ,computer ,Domain (software engineering) - Abstract
In this chapter we will consider another application case of Deep learning: classification of brain images for detection of Alzheimer’s disease. In this particular application of medical imaging domain, Deep NNs have become the mandatory tool. In this chapter we give some highlights on how the usual steps in design of a Deep Neural Network classifier are implemented in the case when domain knowledge has to be considered. But more than that: faithful to our principle of showing new aspects of Deep Learning even in application cases, we will show how information fusion with siamese CNNs helps in increasing of performances of these classifiers.
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- 2020
20. Improving Alcoholism Diagnosis: Comparing Instance-Based Classifiers Against Neural Networks for Classifying EEG Signal
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Tanusree Sharma, Shelia Rahman, and Mufti Mahmud
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Discrete wavelet transform ,Artificial neural network ,medicine.diagnostic_test ,business.industry ,Computer science ,SIGNAL (programming language) ,Pattern recognition ,Electroencephalography ,Neural network classifier ,Independent component analysis ,Principal component analysis ,medicine ,Artificial intelligence ,Data pre-processing ,business - Abstract
Alcoholism involves psychological and biological components where multiple risk factors come into play. Assessment of the psychiatric emergency is a challenging issue for clinicians working with alcohol-dependent patients. Identifying alcoholics from healthy controls from their EEG signals can be effective in this scenario. In this research, we have applied two instance-based classifiers and three neural network classifier to classify Electroencephalogram data of alcoholics and normal person. For data preprocessing, we have applied discrete wavelet transform, Principal component analysis and Independent component analysis. After successful implementation of the classifiers, an accuracy of 95% is received with Bidirectional Long Short-Term Memory. Finally, comparing the performance of the two categories of algorithms, we have found that neural networks have higher potentiality against instance-based classifiers in the classification of EEG signals of alcoholics.
- Published
- 2020
21. Predicting Adverse Drug-Drug Interactions via Semi-supervised Variational Autoencoders
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Wei Guo, Lizhen Cui, Meihao Hou, and Fan Yang
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Drug ,050101 languages & linguistics ,Computer science ,business.industry ,Drug discovery ,Core component ,media_common.quotation_subject ,05 social sciences ,Drug target ,02 engineering and technology ,Machine learning ,computer.software_genre ,Neural network classifier ,Drug development ,0202 electrical engineering, electronic engineering, information engineering ,Labeled data ,020201 artificial intelligence & image processing ,0501 psychology and cognitive sciences ,Artificial intelligence ,business ,computer ,Classifier (UML) ,media_common - Abstract
Adverse Drug-Drug Interactions (DDIs) are a very important risk factor in the medical process, which may lead to readmission or death. Although a part of DDIs can be obtained through in vitro or in vivo experiments in the drug development stage, a large number of new DDIs still appear after the market, more and more researchers begin to pay attention to the research related to drug molecules, such as drug discovery, drug target prediction, DDIs prediction, etc. In recent years, many computational methods for predicting DDIs have been proposed. However, most of them only used labeled data and neglect a lot of information hidden in unlabeled data. Moreover, they always focus on binary prediction instead of multiclass prediction, although the exact DDI type is very helpful for our reasonable choice of medication. In this paper, a Semi-Surpervised Variational Autoencoders (SPRAT) method for predicting DDIs is proposed, which is composed of a neural network classifier and a Variational autoencoders (VAE). Classifier is the core components, VAE plays a role of calibration. In the end, the predicted label is a multi-hot vector which indicates specific DDI types between drug pairs. Finally, the experiments on real world dataset demonstrate the effectiveness of the proposed method in this paper.
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- 2020
22. Improving Nearest Neighbor Partitioning Neural Network Classifier Using Multi-layer Particle Swarm Optimization
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Jin Zhou, He Zhang, Zhenxiang Chen, Lin Wang, Xuehui Zhu, Bo Yang, and Ajith Abraham
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Artificial neural network ,Computer science ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Effective method ,Particle swarm optimization ,020201 artificial intelligence & image processing ,02 engineering and technology ,Neural network classifier ,Classifier (UML) ,Algorithm ,Multi layer ,k-nearest neighbors algorithm - Abstract
Nearest neighbor partitioning (NNP) method has been proved to be an effective method to enhance the quality of neural network classifiers. However, there are many cluster shapes in NNP, which results in a large number of local optimal solutions in the searching space by the traditional particle swarm optimization (PSO) algorithm. Therefore, the multi-layer particle swarm optimization (MLPSO) is introduced to increase the diversity of searching groups through increasing the number of layers, thereby improving the performance when facing with large scale problems. In this study, we adopt the combination of multi-layer particle swarm optimization and nearest neighbor partitioning to solve the local optimal problem caused by multi-cluster shapes in the optimization of NNP. Experimental results show that this method improves the performance of classifier.
- Published
- 2019
23. Improving Neural Network Classifier Using Gradient-Based Floating Centroid Method
- Author
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Shuangrong Liu, Lin Wang, Xiaojing Zhang, and Mazharul Islam
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Artificial neural network ,Computational complexity theory ,business.industry ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Centroid ,Pattern recognition ,02 engineering and technology ,Function (mathematics) ,010501 environmental sciences ,01 natural sciences ,Neural network classifier ,Evolutionary computation ,ComputingMethodologies_PATTERNRECOGNITION ,Gradient based algorithm ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Benchmark (computing) ,Artificial intelligence ,business ,0105 earth and related environmental sciences - Abstract
Floating centroid method (FCM) offers an efficient way to solve a fixed-centroid problem for the neural network classifiers. However, evolutionary computation as its optimization method restrains the FCM to achieve satisfactory performance for different neural network structures, because of the high computational complexity and inefficiency. Traditional gradient-based methods have been extensively adopted to optimize the neural network classifiers. In this study, a gradient-based floating centroid (GDFC) method is introduced to address the fixed centroid problem for the neural network classifiers optimized by gradient-based methods. Furthermore, a new loss function for optimizing GDFC is introduced. The experimental results display that GDFC obtains promising classification performance than the comparison methods on the benchmark datasets.
- Published
- 2019
24. C4.5 Decision Tree Enhanced with AdaBoost Versus Multilayer Perceptron for Credit Scoring Modeling
- Author
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Thitimanan Damrongsakmethee and Victor-Emil Neagoe
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ComputingMethodologies_PATTERNRECOGNITION ,Binary classification ,Computer science ,business.industry ,Multilayer perceptron ,Decision tree ,Pattern recognition ,Artificial intelligence ,AdaBoost ,business ,Classifier (UML) ,Neural network classifier - Abstract
Within this paper, we evaluate two main machine learning techniques for credit scoring. The first algorithm consists of a cascade with two steps: (a) C4.5 decision tree; (b) AdaBoost for binary classification (credit accepted or rejected). The second technique corresponds to choosing a neural network classifier implemented by Multilayer Perceptron (MLP). For evaluation of the proposed models, we have used the German credit dataset and the Australian credit dataset. For the German dataset, MLP leads to the best result corresponding to an accuracy of 81.0%, versus C4.5 enhanced with AdaBoost that leads to an accuracy of 78.67%. For the Australian credit dataset, we found that MLP is also the best classifier with an accuracy of 90.85%, versus C4.5 followed by AdaBoost obtaining an accuracy of 89.00%. At the same time, one can remark that C4.5 enhanced by AdaBoost has led to a better performance than a simple C4.5 .
- Published
- 2019
25. GanDef: A GAN Based Adversarial Training Defense for Neural Network Classifier
- Author
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Abdallah Khreishah, Issa Khalil, and Guanxiong Liu
- Subjects
050101 languages & linguistics ,Discriminator ,Artificial neural network ,Computer science ,business.industry ,05 social sciences ,Feature selection ,02 engineering and technology ,Machine learning ,computer.software_genre ,Neural network classifier ,Adversarial system ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,0501 psychology and cognitive sciences ,Artificial intelligence ,business ,Classifier (UML) ,computer - Abstract
Machine learning models, especially neural network (NN) classifiers, are widely used in many applications including natural language processing, computer vision and cybersecurity. They provide high accuracy under the assumption of attack-free scenarios. However, this assumption has been defied by the introduction of adversarial examples – carefully perturbed samples of input that are usually misclassified. Many researchers have tried to develop a defense against adversarial examples; however, we are still far from achieving that goal. In this paper, we design a Generative Adversarial Net (GAN) based adversarial training defense, dubbed GanDef, which utilizes a competition game to regulate the feature selection during the training. We analytically show that GanDef can train a classifier so it can defend against adversarial examples. Through extensive evaluation on different white-box adversarial examples, the classifier trained by GanDef shows the same level of test accuracy as those trained by state-of-the-art adversarial training defenses. More importantly, GanDef-Comb, a variant of GanDef, could utilize the discriminator to achieve a dynamic trade-off between correctly classifying original and adversarial examples. As a result, it achieves the highest overall test accuracy when the ratio of adversarial examples exceeds 41.7%.
- Published
- 2019
26. BatchNorm Decomposition for Deep Neural Network Interpretation
- Author
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Lucas Y. W. Hui and Alexander Binder
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Normalization (statistics) ,Artificial neural network ,Computer science ,02 engineering and technology ,010501 environmental sciences ,01 natural sciences ,Neural network classifier ,Convolutional neural network ,Residual neural network ,Initial training ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Algorithm ,0105 earth and related environmental sciences - Abstract
Layer-wise relevance propagation (LRP) has shown potential for explaining neural network classifier decisions. In this paper, we investigate how LRP is to be applied to deep neural network which makes use of batch normalization (BatchNorm), and show that despite the functional simplicity of BatchNorm, several intuitive choices of published LRP rules perform poorly for a number of frequently used state of the art networks. Also, we show that by using the \(\varepsilon \)-rule for BatchNorm layers we are able to detect training artifacts for MobileNet and layer design artifacts for ResNet. The causes for such failures are analyzed deeply and thoroughly. We observe that some assumptions on the LRP decomposition rules are broken given specific networks, and propose a novel LRP rule tailored for BatchNorm layers. Our quantitatively evaluated results show advantage of our novel LRP rule for BatchNorm layers and its wide applicability to common deep neural network architectures. As an aside, we demonstrate that one observation made by LRP analysis serves to modify a ResNet for faster initial training convergence.
- Published
- 2019
27. Classification of Regional Accent Using Speech Rhythm Metrics
- Author
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Ghania Droua-Hamdani
- Subjects
Speech rhythm ,Rhythm ,Arabic ,Computer science ,Speech recognition ,0202 electrical engineering, electronic engineering, information engineering ,language ,Modern Standard Arabic ,020201 artificial intelligence & image processing ,02 engineering and technology ,Classifier (UML) ,Neural network classifier ,language.human_language - Abstract
In this paper, MSA speech rhythm metrics were used to classify two regional accent (northern vs. southern regions) using an MLP - neural network classifier. Seven rhythm metrics vectors were computed from a speech dataset taken from ALGerian Arabic Speech Database (ALGASD) using both Interval Measures (IM) and Control/Compensation Index (CCI) algorithms. The classifier was trained and tested using different input vectors of speech rhythm measurements. The best accuracy of the NN-classifier was achieved when a combination of all metrics was used (88.6%).
- Published
- 2019
28. Improved Bat algorithm for the detection of myocardial infarction
- Author
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Kora, Padmavathi and Kalva, Sri Ramakrishna
- Published
- 2015
- Full Text
- View/download PDF
29. Extraction of the Beam Elastic Shape from Uncertain FBG Strain Measurement Points
- Author
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Gianluca Pepe, Manuel Pinto, Andrea Nicoletti, Antonio Carcaterra, N. Roveri, and Gabriele Balconi
- Subjects
Optical fiber ,Materials science ,structural health monitoring ,Acoustics ,FBG ,Glass fiber ,Strain measurement ,Physics::Optics ,Context (language use) ,Fibre-reinforced plastic ,law.invention ,strain measurements ,GFRP ,neural network classifier ,Fiber Bragg grating ,law ,Structural health monitoring ,Beam (structure) - Abstract
Aim of the present paper is the analysis of the strain along the beam that is equipped with Glass Fibers Reinforced Polymers (GFRP) with an embedded set of optical Fiber Bragg Grating sensors (FBG), in the context of a project to equip with these new structural elements an Italian train bridge.
- Published
- 2018
30. Intent Detection System Based on Word Embeddings
- Author
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Kaspars Balodis and Daiga Deksne
- Subjects
Computer science ,business.industry ,0102 computer and information sciences ,02 engineering and technology ,computer.software_genre ,01 natural sciences ,Neural network classifier ,010201 computation theory & mathematics ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Image tracing ,Artificial intelligence ,Dialog system ,business ,computer ,Word (computer architecture) ,Natural language processing ,Sentence - Abstract
Intent detection is one of the main tasks of a dialogue system. In this paper we present our intent detection system that is based on FastText word embeddings and neural network classifier. We find a significant improvement in the FastText sentence vectorization. The results show that our intent detection system provides state-of-the-art results on three English datasets outperforming many popular services.
- Published
- 2018
31. Analysis on Hybrid Dominance-Based Rough Set Parameterization Using Private Financial Initiative Unitary Charges Data
- Author
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Masurah Mohamad and Ali Selamat
- Subjects
Complex data type ,Finance ,021103 operations research ,Computer science ,business.industry ,0211 other engineering and technologies ,02 engineering and technology ,Hybrid approach ,Slicing ,Neural network classifier ,Unitary state ,Parameter reduction ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Rough set ,business ,Classifier (UML) - Abstract
This paper evaluates the capability of the hybrid parameter reduction approach in handling private financial initiative (PFI) unitary charges data to increase the classification performance. The objective of this study is to analyse the performance of the proposed hybrid parameter reduction approach in assisting the neural network classifier to classify complex data sets that might contain uncertain and inconsistent problems. The proposed hybrid parameter reduction approach consists of several methods that will be executed during the data analysis process. Slicing technique and dominance-based rough set approach (DRSA) are the two techniques that play important roles in the proposed parameter reduction process. In order, to analyse the performance of the proposed work, the PFI data that covers all regions in Malaysia is applied in the experimental works. Besides, several standard data sets have also been used to validate the obtained results. The results reveal that the hybrid approach has successfully assisted the classifier in the classification process.
- Published
- 2018
32. A Vision Inspection System for the Defects of Resistance Spot Welding Based on Neural Network
- Author
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Peng Zheng, Lei Wang, Shaofeng Ye, Chun Lin, and Zhiye Guo
- Subjects
010302 applied physics ,Vision inspection ,0209 industrial biotechnology ,Artificial neural network ,Computer science ,business.industry ,Machine vision ,02 engineering and technology ,Welding ,01 natural sciences ,Neural network classifier ,law.invention ,020901 industrial engineering & automation ,law ,visual_art ,0103 physical sciences ,Electronic component ,visual_art.visual_art_medium ,Computer vision ,Artificial intelligence ,business ,Spot welding - Abstract
The appearance of spot welding reflects the quality of welding to a large extent. In this study, we developed a vision inspection system, which recognizes the defects of weld in electronic components based on neural network. First, the images of weld are acquired by color camera. Then, we extracted 15 features from the welding images that had been corrected and enhanced. Finally, we used 1800 training samples to train the neural network. And then we got a accuracy of 95.82% under 407 testing samples by the neural network classifier, which had 15 input nodes, 4 hidden nodes and 2 output nodes.
- Published
- 2017
33. Speaker Discrimination Based on a Fusion Between Neural and Statistical Classifiers
- Author
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Siham Ouamour and Halim Sayoud
- Subjects
Random subspace method ,Fusion ,Computer science ,Speech recognition ,0202 electrical engineering, electronic engineering, information engineering ,020206 networking & telecommunications ,02 engineering and technology ,Some confidence ,Speech processing ,Perceptron ,Neural network classifier ,Hybrid model ,Statistical classifier - Abstract
Speaker discrimination consists in checking whether two (or more) speech segments belong to the same speaker or not. In this framework, we propose a new approach developed for the task of speaker discrimination, this approach results from the fusion between a neural network classifier (NN) and a statistical classifier, this fusion is obtained once by combining the scores of the simple classifiers weighted by some confidence coefficients and another time, by using the scores of the statistical classifier as an additional input of the Multi-Layer Perceptron (MLP), in order to optimize the NN training (Hybrid model).
- Published
- 2016
34. Understanding and Predicting Bonding in Conversations Using Thin Slices of Facial Expressions and Body Language
- Author
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Natasha Jaques, Rosalind W. Picard, Yoo Lim Kim, and Daniel McDuff
- Subjects
Communication ,Facial expression ,business.industry ,Computer science ,Speech recognition ,020206 networking & telecommunications ,020207 software engineering ,02 engineering and technology ,computer.software_genre ,Neural network classifier ,Body language ,Intelligent agent ,ComputerApplications_MISCELLANEOUS ,0202 electrical engineering, electronic engineering, information engineering ,business ,computer - Abstract
This paper investigates how an intelligent agent could be designed to both predict whether it is bonding with its user, and convey appropriate facial expression and body language responses to foster bonding. Video and Kinect recordings are collected from a series of naturalistic conversations, and a reliable measure of bonding is adapted and verified. A qualitative and quantitative analysis is conducted to determine the non-verbal cues that characterize both high and low bonding conversations. We then train a deep neural network classifier using one minute segments of facial expression and body language data, and show that it is able to accurately predict bonding in novel conversations.
- Published
- 2016
35. Mel Frequency Cepstral Coefficients Based Similar Albanian Phonemes Recognition
- Author
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Krenare Pireva, Ali Shariq Imran, and Bertan Karahoda
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Human ear ,business.industry ,Computer science ,020209 energy ,Pattern recognition ,02 engineering and technology ,Albanian language ,Pronunciation ,Neural network classifier ,language.human_language ,Back propagation neural network ,Task (computing) ,0202 electrical engineering, electronic engineering, information engineering ,language ,020201 artificial intelligence & image processing ,Mel-frequency cepstrum ,Artificial intelligence ,business - Abstract
In Albanian language there are several phonemes that are similar in pronunciation like /q/ - /c/, /rr/ - /r/, /th/ - /dh/ and /gj/ - /xh/. These phonemes are difficult to distinguish by human ear even for native speaking Albanians from different regions. The task becomes more challenging for automated speech systems, recognizing and classifying Albanian words and language due to the similar sounding phonemes. This paper proposes to use Mel Frequency Cepstral Coefficients (MFCC) based features to distinguish these phonemes correctly. The three layers back propagation neural network is used for classification. The experiments are performed on speech signals that are collected from different male and female native speakers. The speaker independent tests are performed for analyzing the performance of the classification. The obtained results show that the serial MFCC features can be used to classify the very similar speech phonemes with higher accuracy.
- Published
- 2016
36. HIST: HyperIntensity Segmentation Tool
- Author
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José V. Manjón, Olivier Salvado, Jurgen Fripp, Parnesh Raniga, Pierrick Coupé, Ying Xia, ITACA, Universitat Politècnica de València (UPV), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), CSIRO Information and Commuciation Technologies (CSIRO ICT Centre), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), IdEx Bordeaux (ANR-10- IDEX-03-02, HL-MRI Project), Cluster of excellence CPU, TRAIL (HR-DTI ANR-10-LABX-57), CNRS multidisciplinary project 'Défi imag'In', Universidad Politécnica de Valencia, and Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université Sciences et Technologies - Bordeaux 1-Université Bordeaux Segalen - Bordeaux 2
- Subjects
Lesion segmentation ,Artificial neural network ,business.industry ,Pattern recognition ,Neural network classifier ,Hyperintensity ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Geography ,[INFO.INFO-IM]Computer Science [cs]/Medical Imaging ,Segmentation ,Artificial intelligence ,business ,Cartography ,030217 neurology & neurosurgery - Abstract
International audience; Accurate quantification of white matter hyperintensities (WMH) from MRI is a valuable tool for studies on ageing and neurodegeneration. Reliable automatic extraction of WMH biomarkers is challenging, primarily due to their heterogeneous spatial occurrence, their small size and their diffuse nature. In this paper, we present an automatic and accurate method to segment these le-sions that is based on the use of neural networks and an overcomplete strategy. The proposed method was compared to other related methods showing competitive and reliable results in two different neurodegenerative datasets.
- Published
- 2016
37. A Real Time Fast Non-soft Computing Approach towards Leaf Identification
- Author
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Ajit Kumar Pasayat, Pratikshya Mohanty, Avinash Kranti Pradhan, and Shreetam Behera
- Subjects
Soft computing ,education.field_of_study ,Computer science ,business.industry ,Population ,Machine learning ,computer.software_genre ,Neural network classifier ,Automation ,Backpropagation ,Artificial intelligence ,business ,education ,computer ,Classifier (UML) - Abstract
In an agricultural country like India, majority of population depend on plant produce for their survival. Plants occupy a large portion of our ecosystem. In order to derive different benefits from plants in an optimum manner, one needs to be aware of the properties being possessed by plants. For that purpose, one needs to have proper source carrying significant information about plants and an expert so as to respond to ones queries. However, both these are not available in adequate which drives the need to create automation in the process of recognition of leaves for plant classification. Thus, a novel algorithm has been developed which helps in recognizing different varieties of leaves without human interference. The system uses real time images of leaves and extracts physiological as well as morphological features of the leaves, which are then fed as input to a classifier. The same has been implemented on a Back propagation based neural network classifier and a comparative study has been made. The study shows that the recognition rates of the proposed method are more accurate than that of BPNN and the proposed algorithm is found to be an efficient one.
- Published
- 2015
38. Vehicle Classification Using Neural Networks with a Single Magnetic Detector
- Author
-
Peter Sarcevic
- Subjects
Microcontroller ,Artificial neural network ,business.industry ,Computer science ,Vehicle detection ,Detector ,Classification methods ,Pattern recognition ,Artificial intelligence ,Magnetic detector ,business ,Neural network classifier - Abstract
In this work, principles of operation, advantages and disadvantages are presented for different detector technologies. An idea of a new detection and classification method for a single magnetic sensor based system is also discussed. It is important that the detection algorithm and the neural network classifier needs to be easily implementable in a microcontroller based system.
- Published
- 2014
39. Towards an Intelligent Framework for Pressure-Based 3D Curve Drawing
- Author
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Chan-Yet Lai and Nordin Zakaria
- Subjects
Signal processing ,Computer science ,business.industry ,Pressure control ,Pattern recognition ,Interaction technique ,Artificial intelligence ,business ,Neural network classifier ,Classifier (UML) ,Simulation - Abstract
The act of controlling pressure through pencil and brush appears effortless, but to mimic this natural ability in the realm of electronic medium using tablet pen device is difficult. Previous pressure based interaction work have explored various signal processing techniques to improve the accuracy in pressure control, but a one-for-all signal processing solutions tend not to work for different curve types. We propose instead a framework which applies signal processing techniques tuned to individual curve type. A neural network classifier is used as a curve classifier. Based on the classification, a custom combination of signal processing techniques is then applied. Results obtained point to the feasibility and advantage of the approach. The results are generally applicable to the design of pressure based interaction technique and possibly unlock the potential of pressure based system for richer interactions.
- Published
- 2014
40. A Fused Feature Extraction Approach to OCR: MLP vs. RBF
- Author
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Amit Choudhary and Rahul Rishi
- Subjects
Engineering ,Artificial neural network ,business.industry ,Feature extraction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Pattern recognition ,Neural network classifier ,ComputingMethodologies_PATTERNRECOGNITION ,Preprocessor ,Artificial intelligence ,business ,Classifier (UML) ,Character recognition ,Hierarchical RBF - Abstract
This paper is focused on evaluating the capability of MLP and RBF neural network classifier algorithms for performing handwritten character recognition task. Projection profile features for the character images are extracted and merged with the binarization features obtained after preprocessing every character image. The fused features thus obtained are used to train both the classifiers i.e. MLP and RBF Neural Networks. Simulation studies are examined extensively and the proposed fused features are found to deliver better recognition accuracy when used with RBF Network as a classifier.
- Published
- 2014
41. Fault Diagnosis of a Corrugator Cut-off Using Neural Network Classifier
- Author
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Stanisław K. Musielak and Jerzy Kasprzyk
- Subjects
Artificial neural network ,Computer science ,Kernel density estimation ,Cutoff ,Feedforward neural network ,Probability density function ,Data mining ,Cut-off ,computer.software_genre ,Neural network classifier ,computer ,Classifier (UML) - Abstract
In this paper a proposal for solving the problem of diagnostics of cutting errors in a rotary cutoff in a corrugated board machine processing line is presented. There are many different reasons for errors, and their identification requires a sound knowledge and experience of the service staff. The authors, using their many years’ experience and a huge database, have found that many sources of errors can be characterized using a probability density function (pdf). They proposed a diagnostics method based on classification of sources of disturbances using the analysis of pdf determined with a kernel density estimator. Multilayer feedforward neural network is proposed as a classifier. Classification procedure is discussed, together with research results based on data from real industrial processes.
- Published
- 2014
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