156 results
Search Results
2. Prediction of heart conditions by consensus K-nearest neighbor algorithm and convolution neural network.
- Author
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Waris, Saiyed Faiayaz and Koteeswaran, S.
- Subjects
CONVOLUTIONAL neural networks ,ARTIFICIAL neural networks ,K-nearest neighbor classification ,BLOOD sugar ,ALGORITHMS - Abstract
In recent days, health problems are becoming more prevalent because of changes in lifestyles and inherited factors. Heart disease (HD), in particular, has become increasingly widespread in recent years, putting people's lives in jeopardy. Blood pressure, cholesterol, and pulse rate are all varied for each person. Normal blood pressure should be 120/90, cholesterol should be 100–129 mg/dL, pulse rate should be 72, fasting blood sugar level should be 100 mg/dL, heart rate should be 60–100 bpm, and ECG should be normal, according to medically established data. The aorta is 25 mm (1 inch) wide, whereas capillaries are only 8 μ m wide, which signifies HD. This paper is based on a public health dataset that also contains a cardiac dataset. In our previous work, a new conventional neural network (CNN) architecture was used to extract and categorize histopathological images using the K -means consensus clustering. We achieved good results with the cardiac dataset compared to the existing results. The outcome of the proposed work achieves a precision rate of 97%. In this paper, a novel conventional neural network (CNN) architecture was utilized to identify and characterize histopathology pictures with the help of using the consensus K -nearest neighbor algorithm (CKNN). The usage of a deep neural network, as well as the selection and recovery of functionality, is an essential step until the dataset is classified. As a result, the training process has pre-formed the dataset. The system evaluates the person's symptoms as input and provides the disease's possibility as an output. The suggested approach is linked to temporal data modeling and makes use of a prior HD CNN prediction. In comparison to previous outcomes, we had good outcomes with the current cardiac dataset. The proposed model's conclusion has brought out accuracy of 99.1%. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
3. Classification of Acute Pathology for Vocal Cord Using Advanced Multi-Resolution Algorithm.
- Author
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Sophia, N Antony and Jiji, G. Wiselin
- Subjects
DEEP learning ,VOCAL cords ,CONVOLUTIONAL neural networks ,ARTIFICIAL neural networks ,VOICE disorders ,ALGORITHMS ,K-means clustering - Abstract
Vocal fold, a significant body structure, is accountable for phonation, which regulates air motion within and out of the lungs. The disorders in the vocal fold influence the quality of life. Thus, diagnosis of vocal fold disorders has a significant need, and CT of the neck is employed for an effective imaging scheme. Accordingly, this paper proposes an advanced multi-resolution algorithm (MRA) that optimally identifies and classifies pathologies. The vocal regions are acquired using the genetic k-means algorithm. The pathology features are generated using the local directional pattern (LDP) fed to pathology classification using moth search-rider optimization-based deep convolutional neural networks (MRA-based DCNN). The hybrid optimization (MRA), integrates the standard rider optimization algorithm (ROA) and moth search algorithm (MS) that trains deep learning classifier (DCNN). The analysis using the real databases regarding the performance metrics divulge that the proposed pathology detection module obtained the accuracy, specificity, and sensitivity of 97.020%, 91.698%, and 96.624%. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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- View/download PDF
4. Feature Projection and SVR-Based Model Update for Object Tracking.
- Author
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Qiu, Shoumeng, Gu, Yuzhang, and Zhang, Xiaolin
- Subjects
TRACKING algorithms ,OBJECT tracking (Computer vision) ,DEEP learning ,ARTIFICIAL neural networks ,ALGORITHMS ,TRACKING radar ,POPULARITY - Abstract
Siamese network-based object tracking algorithms have recently gained popularity due to continuous improvement in tracking accuracy and robustness. However, these trackers are often limited by inefficient initialization and unstable online update. The model drift accumulates as more frames are processed. To address the above issues, this paper introduced a method that combines both deep learning and conventional regression algorithms (SVR, Support Vector Regression). Specifically, we propose a feature projection algorithm, which can effectively reduce the dimension of the features extracted from deep neural networks and improve the distinguishability of them at the same time. To make the features more robust for SVR training, we further propose two feature aggregation methods at both the channel level and the spatial level. We update the SVR model online instead of the deep neural network to make the tracking process more robust. Extensive experiments on four challenging benchmarks indicate that our proposed tracker is superior to baseline methods both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
5. User Authentication System Based on Speech and Cascade Hybrid Facial Feature.
- Author
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Debnath, Saswati and Roy, Pinki
- Subjects
AUTOMATIC speech recognition ,SPEECH perception ,ARTIFICIAL neural networks ,FACE ,ALGORITHMS ,FEATURE extraction - Abstract
With the increasing demand for security in many fastest growing applications, biometric recognition is the most prominent authentication system. User authentication through speech and face recognition is the important biometric technique to enhance the security. This paper proposes a speech and facial feature-based multi-modal biometric recognition technique to improve the authentication of any system. Mel Frequency Cepstral Coefficients (MFCC) is extracted from audio as speech features. In visual recognition, this paper proposes cascade hybrid facial (visual) feature extraction method based on static, dynamic and key-point salient features of the face and it proves that the proposed feature extraction method is more efficient than the existing method. In this proposed method, Viola–Jones algorithm is used to detect static and dynamic features of eye, nose, lip, Scale Invariant Feature Transform (SIFT) algorithm is used to detect some stable key-point features of face. In this paper, a research on the audio-visual integration method using AND logic is also made. Furthermore, all the experiments are carried out using Artificial Neural Network (ANN) and Support Vector Machine (SVM). An accuracy of 94.90% is achieved using proposed feature extraction method. The main objective of this work is to improve the authenticity of any application using multi-modal biometric features. Adding facial features to the speech recognition improve system security because biometric features are unique and combining evidence from two modalities increases the authenticity as well as integrity of the system. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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6. Calibration Algorithm for Error Screening Based on Line Structured Light.
- Author
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Liu, Baolong, Wu, Ruixia, and Liu, Yu
- Subjects
ARTIFICIAL neural networks ,CAMERA calibration ,CALIBRATION ,CONSTRAINT algorithms ,ALGORITHMS - Abstract
The 3D measurement system based on line-structured light uses a camera to capture laser stripes due to changing in the shape of an object, and uses the acquired pixel coordinates for 3D reconstruction. System calibration is an important step in 3D measurement. The current camera calibration algorithm research mainly focuses on improving the algorithm itself, and there is less research on the influence of external factors. This paper proposes a coplanar hybrid calibration algorithm based on the error screening model by combining the error screening model, mathematical model and neural network model. It is mainly divided into two steps. The first step is to use the radial array constraint calibration algorithm based on the error screening model to solve the camera's internal and external parameters. The second step uses the camera internal and external parameters obtained in the first step to convert the pixel coordinates into real three-dimensional coordinates, and compares the calculated three-dimensional coordinates with the actual coordinates. Using machine learning to establish a compensation network, get a compensation function, and use the resulting 3D world coordinates to perform point cloud stitching. Experiments show that compared with the traditional calibration algorithm, the calibration algorithm has a small error and reduces the calibration error by about 6.5%. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
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7. A Deep Learning Artificial Neural Network Algorithm for Instance-based Arabic Language Authorship Attribution.
- Author
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Al-Sarem, Mohammad, Alsaeedi, Abdullah, and Saeed, Faisal
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ARTIFICIAL neural networks ,DEEP learning ,ATTRIBUTION of authorship ,INTELLECTUAL property theft ,ARABIC language ,ALGORITHMS - Abstract
One of the common examples of cybercrime are identity theft and violating of intellectual property that commonly occur in social media. Authorship attribution (AA) techniques are used to extract and use several features of the text in order to identify the original author. These features are used to differentiate the writing style of one author from others. Several machine learning methods have been used to identify the AA using different languages. Few studies were conducted for Arabic AA. This paper aims to investigate the performance of deep learning-based artificial neural network (ANN) for identifying the attribution of authors using Arabic text. The applied model helps protect users in social media from identity theft and violating of their intellectual property. The experiments of this study used a dataset that includes 4,686 Arabic texts for 15 different authors. The performance of the deep learning method was compared with several machine learning methods. The experimental results showed the superior performance of deep learning for AA in Arabic language using different evaluation criteria such as F-score, accuracy, precision, and recall measures. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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8. An Improved Artificial Neural Network Model for Flights Delay Prediction.
- Author
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Shi, Tongyu, Lai, Jinghan, Gu, Runping, and Wei, Zhiqiang
- Subjects
ARTIFICIAL neural networks ,COMMERCIAL aeronautics ,GREY relational analysis ,BACK propagation ,ALGORITHMS ,INTELLIGENT transportation systems - Abstract
With the limitation of air traffic and the rapid increase in the number of flights, flight delay is becoming more frequent. Flight delay leads to financial and time losses for passengers and increases operating costs for airlines. Therefore, the establishment of an accurate prediction model for flight delay becomes vital to build an efficient airline transportation system. The air transportation system has a huge amount of data and complex operation modes, which is suitable for analysis by using machine learning methods. This paper discusses the factors that may affect the flight delay, and presents a new flight delay prediction model. The five warning levels are defined based on flight delay database by using K-means clustering algorithm. After extracting the key factors related to flight operation by the grey relational analysis (GRA) algorithm, an improved machine learning algorithm called GRA — Genetic algorithm (GA) — back propagation neural network, GRA-GA-BP, is introduced, which is optimized by GA. The calculation results show that, compared with models before optimization and other two algorithms in previous papers, the proposed prediction model based on GRA-GA-BP algorithm shows a higher prediction accuracy and more stability. In terms of operation efficiency and memory consumption, it also has good performance. The analysis presented in this paper indicates that this model can provide effective early warnings for flight delay, and can help airlines to intervene in flights with abnormal trend in advance. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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9. Quasinormal modes of dS and AdS black holes: Feedforward neural network method.
- Author
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Övgün, Ali, Sakallı, İzzet, and Mutuk, Halil
- Subjects
BLACK holes ,SCHWARZSCHILD black holes ,ARTIFICIAL neural networks ,FEEDFORWARD neural networks ,SCALAR field theory ,RELIABILITY in engineering ,ALGORITHMS - Abstract
In this paper, we show how the quasinormal modes (QNMs) arise from the perturbations of massive scalar fields propagating in the curved background by using the artificial neural networks. To this end, we architect a special algorithm for the feedforward neural network method (FNNM) to compute the QNMs complying with the certain types of boundary conditions. To test the reliability of the method, we consider two black hole spacetimes whose QNMs are well known: 4 D pure de Sitter (dS) and five-dimensional Schwarzschild anti-de Sitter (AdS) black holes. Using the FNNM, the QNMs of are computed numerically. It is shown that the obtained QNMs via the FNNM are in good agreement with their former QNM results resulting from the other methods. Therefore, our method of finding the QNMs can be used for other curved spacetimes that obey the same boundary conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
10. A study on the estimations of (n, t) reaction cross-sections at 14.5 MeV by using artificial neural network.
- Author
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Özdoğan, Hasan, Üncü, Yiğit Ali, Şekerci, Mert, and Kaplan, Abdullah
- Subjects
ARTIFICIAL neural networks ,NON-equilibrium reactions ,ALGORITHMS - Abstract
In this paper, calculations of the (n , t) reaction cross-sections at 14.5 MeV have been presented by utilizing artificial neural network algorithms (ANNs). The systematics are based on the account for the non-equilibrium reaction mechanism and the corresponding analytical formulas of the pre-equilibrium exciton model. Experimental results, obtained from the EXFOR database, have been used to train the ANN with the Levenberg–Marquardt (LM) algorithm which is a feed-forward algorithm and is considered one of the well-known and most effective methods in neural networks. The Regression (R) values for the ANN estimation have been determined as 0.9998, 0.9927 and 0.9895 for training, testing and for all process. The (n , t) reaction cross-sections have been reproduced with the TALYS 1.95 and the EMPIRE 3.2 codes. In summary, it has been demonstrated that the ANN algorithms can be used to calculate the (n , t) reaction cross-section with the semi-empirical systematics. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
11. CPMAN: Change Point Detection Approach in Time Series Based on the Prediction of Multi-stage Attention Networks.
- Author
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Du, Haizhou, Duan, Ziyi, and Zheng, Yang
- Subjects
TIME series analysis ,CHANGE-point problems ,ALGORITHMS ,FORECASTING ,ARTIFICIAL neural networks ,WAVELETS (Mathematics) - Abstract
Time series change point detection can identify the locations of abrupt points in many dynamic processes. It can help us to find anomaly data in an early stage. At the same time, detecting change points for long, periodic, and multiple input series data has received a lot of attention recently, and is universally applicable in many fields including power, environment, finance, and medicine. However, the performance of classical methods typically scales poorly for such time series. In this paper, we propose CPMAN, a novel prediction-based change point detection approach via multi-stage attention networks. Our model consists of two key modules. First, in the time series prediction module, we employ the multi-stage attention-based networks and integrate the multi-series fusion mechanism. This module can adaptively extract features from the relevant input series and capture the long-term temporal dependencies. Secondly, in the change point detection module, we use the wavelet analysis-based algorithm to detect change points efficiently and identify the change points and outliers. Extensive experiments are conducted on various real-world datasets and synthetic datasets, proving the superiority and effectiveness of CPMAN. Our approach outperforms the state-of-the-art methods by up to 12.1% on the F1 Score. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
12. Developing an optimized artificial intelligence model for S&P 500 option pricing: A hybrid GARCH model.
- Author
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Hajizadeh, Ehsan
- Subjects
ARTIFICIAL intelligence ,PARTICLE swarm optimization ,ALGORITHMS ,FUZZY sets ,ARTIFICIAL neural networks - Abstract
In this paper, we propose two hybrid models to release some limitations and enhancement of the results. In this regard, three popular GARCH-type models are utilized for more accurate estimating of volatility, as the most important parameter for option pricing. Furthermore, the two non-parametric models based on Artificial Neural Networks and Neuro-Fuzzy Networks tuned by Particle Swarm Optimization algorithm are proposed to price call options for the S&P 500 index. By comparing the results obtained using these models, we conclude that both Neural Network and Neuro-Fuzzy Network models outperform the Black–Scholes model. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
13. PPG-BASED AUTOMATED ESTIMATION OF BLOOD PRESSURE USING PATIENT-SPECIFIC NEURAL NETWORK MODELING.
- Author
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CHAKRABORTY, ABHISHEK, SADHUKHAN, DEBOLEENA, PAL, SAURABH, and MITRA, MADHUCHHANDA
- Subjects
SYSTOLIC blood pressure ,BLOOD pressure ,ARTIFICIAL neural networks ,PHOTOPLETHYSMOGRAPHY ,ALGORITHMS ,CORRECTION factors ,INTENSIVE care units - Abstract
Recently, photoplethysmography (PPG)-based techniques have been extensively used for cuff-less, automated estimation of blood pressure because of their inexpensive and effortless acquisition technology compared to other conventional approaches. However, most of the reported PPG-based, generalized BP estimation methods often lack the desired accuracy due to pathophysiological diversity. Moreover, some methods rely on several correction factors, which are not globalized yet and require further investigation. In this paper, a simple and automated systolic (SBP) and diastolic (DBP) blood pressure estimation method is proposed based on patient-specific neural network (NN) modeling. Initially, 15 time-plane PPG features are extracted and after feature selection, only four selected features are used in the NN model for beat-to-beat estimation of SBP and DBP, respectively. The proposed technique also presents reasonable accuracy while used for generalized estimation of BP. Performance of the algorithm is evaluated on 670 records of 50 intensive care unit (ICU) patients taken from MIMIC, MIMIC II and MIMIC Challenge databases. The proposed algorithm exhibits high average accuracy with (mean ± SD) of the estimated SBP as (0. 4 6 1 ± 2. 6 2 6) mmHg and DBP as (0. 1 5 0 ± 1. 4 8 2) mmHg. Compared to the other generalized models, the use of patient-specific approach eliminates the necessity of individual correction factors, thus increasing the robustness, accuracy and potential of the method to be implemented in personal healthcare applications. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
14. Experiment Improvement of Restricted Boltzmann Machine Methods for Image Classification.
- Author
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Dewi, Christine, Chen, Rung-Ching, Hendry, and Hung, Hsiu-Te
- Subjects
BOLTZMANN machine ,IMAGE analysis ,ALGORITHMS ,ARTIFICIAL neural networks ,DEEP learning - Abstract
Restricted Boltzmann machine (RBM) plays an important role in current deep learning techniques, as most of the existing deep networks are based on or related to generative models and image classification. Many applications for RBMs have been developed for a large variety of learning problems. Recent developments have demonstrated the capacity of RBM to be powerful generative models, able to extract useful features from input data or construct deep artificial neural networks. In this work, we propose a learning algorithm to find the optimal model complexity for the RBM by improving the hidden layer (50–750 layers). Then, we compare and analyze the classification performance in depth of regular RBM use RBM () function, classification RBM use stackRBM() function, and Deep Belief Network (DBN) use DBN() function with the different hidden layer. As a result, Stacking RBM and DBN could improve our classification performance compared to regular RBM. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
15. A FEATURE SELECTION-BASED ALGORITHM FOR DETECTION OF ATRIAL FIBRILLATION USING SHORT-TERM ECG.
- Author
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ZHU, JUNJIANG, PU, YU, HUANG, HAO, WANG, YUXUAN, LI, XIAOLU, and YAN, TIANHONG
- Subjects
BUNDLE-branch block ,ARTIFICIAL neural networks ,ARRHYTHMIA ,ATRIAL fibrillation ,FEATURE selection ,ELECTROCARDIOGRAPHY ,ALGORITHMS - Abstract
In the presence of premature atrial contraction (PAC), premature ventricular contraction (PVC) or other ectopic beats, RR intervals (RRIs) may be disturbed, which results in other types of heart disease being misdiagnosed as atrial fibrillation (AF). In this study, a low-complexity AF detection method based on short ECG is proposed, which includes RRIs modification and feature selection. The extracted RRIs are used to determine whether the potential RRI interference exists and to modify it. Next, based on the modified RRIs, the features are evaluated and selected by the methods of correlation criterion, Fisher criterion, and minimum redundancy maximum relevance criterion. Finally, filtered features are classified by the artificial neural network (ANN). The algorithm is validated in a test set including 2332 AF, 313 normal (NOR), 239 atrioventricular block (IAVB), 81 left bundle branch block (LBBB), 624 right bundle branch block (RBBB), 426 PAC and 564 PVC. Compared with the previous detection method of AF based on the RRIs, the proposed method achieved an overall sensitivity of 94.04% and an overall specificity of 86.74%. The specificity of the test set containing only AF and NOR is up to 99.04%. Meanwhile, the overall false-positive rate (FPR) of PAC and PVC can be reduced by 9.19%. While ensuring accuracy, this method effectively reduces the probability of misdiagnosis of PVC and PAC as AF. It is an automatic detection method of AF suitable for inter-patient clinical short-term ECG. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
16. The Principle of Homology Continuity and Geometrical Covering Learning for Pattern Recognition.
- Author
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Ning, Xin, Li, Weijun, and Xu, Jiang
- Subjects
PATTERN perception ,HOMOLOGY theory ,ARTIFICIAL neural networks ,ALGORITHMS ,GEOMETRY - Abstract
Homology Continuity is a fundamental property of the nature, but few of the traditional pattern recognition algorithms were aware of it. Firstly, this paper gives a brief description to the Principle of Homology Continuity (PHC), and tries to mathematically redefine it. Then, we introduce a PHC-based pattern learning method — Geometrical Covering Learning (GCL), following the Hyper sausage neural network as an instance of GCL. Lastly, we propose a GCL solution to the “two-spirals” pattern recognition problem. The final experimental results show that the new method is feasible and efficient. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
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17. Effective optimal dismantling strategy for interdependent networks based on residual theory.
- Author
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Song, Wenjing, Liu, Sanyang, and Bai, Yiguang
- Subjects
ENTROPY ,CENTRALITY ,PARALYSIS ,ALGORITHMS ,ARTIFICIAL neural networks - Abstract
Because of interdependence between different network layers, interdependent networks are more fragile than single-layer networks, and large-scale iterative paralysis occurs easily. How to seek nodes whose removal can effectively dismantle networks has attracted great research attention. In this paper, a novel optimal dismantling strategy Maximum Entropy Centrality (EC) and overlapping betweenness (OB) based on residual theory (ECOB) is proposed. In the ECOB, the residual theory is used to detect the highest influence nodes according to the quality of the residual networks. In addition, to make sorting more accurate, EC and OB parameters are both considered in the node selection mechanism. Simulation shows that the ECOB strategy performs much better than existing methods both in artificial interdependent networks and real-world interdependent networks. This is thanks to the introduced ECOB node selection algorithm with proper parameter criterions. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
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18. COLORECTAL POLYP DETECTION USING IMAGE ENHANCEMENT AND SCALED YOLOv4 ALGORITHM.
- Author
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Nisha, J. S., Gopi, Varun P., and Palanisamy, P.
- Subjects
COLON polyps ,DEEP learning ,IMAGE intensifiers ,ARTIFICIAL neural networks ,ALGORITHMS ,THRESHOLDING algorithms ,ARTIFICIAL intelligence - Published
- 2022
- Full Text
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19. Deep distributed convolutional neural networks: Universality.
- Author
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Zhou, Ding-Xuan
- Subjects
DEEP learning ,ALGORITHMS ,ARTIFICIAL neural networks ,COMPUTATIONAL complexity ,APPROXIMATION theory ,WAVELETS (Mathematics) - Abstract
Deep learning based on structured deep neural networks has provided powerful applications in various fields. The structures imposed on the deep neural networks are crucial, which makes deep learning essentially different from classical schemes based on fully connected neural networks. One of the commonly used deep neural network structures is generated by convolutions. The produced deep learning algorithms form the family of deep convolutional neural networks. Despite of their power in some practical domains, little is known about the mathematical foundation of deep convolutional neural networks such as universality of approximation. In this paper, we propose a family of new structured deep neural networks: deep distributed convolutional neural networks. We show that these deep neural networks have the same order of computational complexity as the deep convolutional neural networks, and we prove their universality of approximation. Some ideas of our analysis are from ridge approximation, wavelets, and learning theory. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
20. An Integrated Hybrid Algorithm Based on Nature Inspired Evolutionary for Radial Basis Function Neural Network Learning.
- Author
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Chen, Zhen-Yao, Kuo, R. J., and Hu, Tung-Lai
- Subjects
EVOLUTIONARY computation ,ALGORITHMS ,RADIAL basis functions ,ARTIFICIAL neural networks ,MACHINE learning ,PARTICLE swarm optimization ,GENETIC algorithms - Abstract
This paper intends to propose an integrated hybrid algorithm for training radial basis function neural network (RBFNN) learning. The proposed integrated of particle swarm and genetic algorithm based optimization (IPGO) algorithm is composed of two approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) for gathering both their virtues to improve the learning performance of RBFNN. The diversity of individuals results in higher chance to search in the direction of global optimal instead of being confined to local optimal particularly in problem with higher complexity. The IPGO algorithm with PSO-based and GA-based approaches has shown promising results in some benchmark problems with three continuous test functions. After proposing the algorithm for these problems with result providing its outperforming performance, this paper supplements a practical application case for the papaya milk sales forecasting to expound the superiority of the IPGO algorithm. In addition, model evaluation results of the case have showed that the IPGO algorithm outperforms other algorithms and auto-regressive moving average (ARMA) models in terms of forecasting accuracy and execution time. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
21. RESEARCH ON MULTI-SENSOR DATA FUSION TECHNOLOGY BASED ON BP NEURAL NETWORK.
- Author
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Hai-Xia Chen
- Subjects
MULTISENSOR data fusion ,BACK propagation ,NEURAL circuitry ,PRESSURE measurement ,ARTIFICIAL neural networks ,ALGORITHMS ,PRESSURE sensors - Published
- 2015
22. Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks.
- Author
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Duda, Piotr, Jaworski, Maciej, and Rutkowski, Leszek
- Subjects
DATA mining ,CONVERGENT boundary (Plate tectonics) ,RECURSIVE functions ,ALGORITHMS ,ARTIFICIAL neural networks - Abstract
One of the greatest challenges in data mining is related to processing and analysis of massive data streams. Contrary to traditional static data mining problems, data streams require that each element is processed only once, the amount of allocated memory is constant and the models incorporate changes of investigated streams. A vast majority of available methods have been developed for data stream classification and only a few of them attempted to solve regression problems, using various heuristic approaches. In this paper, we develop mathematically justified regression models working in a time-varying environment. More specifically, we study incremental versions of generalized regression neural networks, called IGRNNs, and we prove their tracking properties - weak (in probability) and strong (with probability one) convergence assuming various concept drift scenarios. First, we present the IGRNNs, based on the Parzen kernels, for modeling stationary systems under nonstationary noise. Next, we extend our approach to modeling time-varying systems under nonstationary noise. We present several types of concept drifts to be handled by our approach in such a way that weak and strong convergence holds under certain conditions. Finally, in the series of simulations, we compare our method with commonly used heuristic approaches, based on forgetting mechanism or sliding windows, to deal with concept drift. Finally, we apply our concept in a real life scenario solving the problem of currency exchange rates prediction. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
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23. Bayes Classification for Nonstationary Patterns.
- Author
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Kulczycki, Piotr and Kowalski, Piotr Andrzej
- Subjects
BAYES' estimation ,PATTERN recognition systems ,PROBABILITY theory ,ALGORITHMS ,ERROR analysis in mathematics - Abstract
The paper's subject is classification with nonstationary patterns. The attribute space is finite-dimensional, while its coordinates in particular may be continuous, binary, discrete, categorical in character, or also a combination of these. The number of patterns is not methodologically limited. Use of the Bayes approach minimizes the expected value of misclassifications, allowing additionally for an influence in the proportions of probability of errors when assigning to specific classes. In turn, the statistical kernel estimators method makes the algorithm independent of patterns' shapes. The investigated procedure also eliminates elements of patterns which have insignificant or even negative influence on the results' accuracy. Appropriate modifications follow the classifier parameters, which increases the effectiveness of procedure adaptation for nonstationary patterns. The algorithm concept is based on the sensitivity method, used with artificial neural networks. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
24. WASD Algorithm with Pruning-While-Growing and Twice-Pruning Techniques for Multi-Input Euler Polynomial Neural Network.
- Author
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Zhang, Yunong, Wang, Ying, Li, Weibing, Chou, Yao, and Zhang, Zhijun
- Subjects
BACK propagation ,ALGORITHMS ,ARTIFICIAL intelligence ,EULER polynomials ,ARTIFICIAL neural networks ,NUMERICAL analysis - Abstract
Differing from the conventional back-propagation (BP) neural networks, a novel multi-input Euler polynomial neural network, in short, MIEPNN (specifically, 4-input Euler polynomial neural network, 4IEPNN) is established and investigated in this paper. In order to achieve satisfactory performance of the established MIEPNN, a weights and structure determination (WASD) algorithm with pruning-while-growing (PWG) and twice-pruning (TP) techniques is built up for the established MIEPNN. By employing the weights direct determination (WDD) method, the WASD algorithm not only determines the optimal connecting weights between hidden layer and output layer directly, but also obtains the optimal number of hidden-layer neurons. Specifically, a sub-optimal structure is obtained via the PWG technique, then the redundant hidden-layer neurons are further pruned via the TP technique. Consequently, the optimal structure of the MIEPNN is obtained. To provide a reasonable choice in practice, several different MATLAB computing routines related to the WDD method are studied. Comparative numerical-experiment results of the 4IEPNN using these different MATLAB computing routines and the standard multi-layer perceptron (MLP) neural network further verify the superior performance and efficacy of the proposed MIEPNN equipped with the WASD algorithm including PWG and TP techniques in terms of training, testing and predicting. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
25. Enhancing performance of the back-propagation algorithm based on a novel regularization method of preserving inter-object-distance of data.
- Author
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Hu, Junying, Sun, Kai, and Zhang, Hai
- Subjects
ALGORITHMS ,ARTIFICIAL neural networks ,SUPERVISED learning ,MATHEMATICAL regularization ,CLASSIFICATION algorithms - Abstract
Artificial neural networks, consisting of many levels of nonlinearities, have been widely used to deal with various supervised learning tasks. At present, the most popular and effective training method is back-propagation algorithm (BP). Inspired by manifold regularization framework, we introduce a novel regularization framework, which aims at preserving the inter-object-distance of the data. Then a refined BP algorithm (IOD-BP) is proposed by imposing the proposed regularization framework into the objective function of BP algorithm. Comparative experiments on various benchmark classification tasks show that the new regularization BP method significantly improves the performance of BP algorithm in terms of classification accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
26. INTRUSION DETECTION SYSTEM BASED ON SELF-ORGANIZATION MAP AND ON LEARNING VECTOR QUANTIZATION.
- Author
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DENG YIGUI, XIAO SHUCHENG, WANG KANG, and TU GUANGYOU
- Subjects
INTRUSION detection systems (Computer security) ,COMPUTER network security ,VECTOR quantization ,ARTIFICIAL neural networks ,ALGORITHMS - Published
- 2006
27. A VECTOR MATRIX REAL TIME RECURSIVE BACKPROPAGATION ALGORITHM FOR RECURRENT NEURAL NETWORKS THAT APPROXIMATE MULTI-VALUED PERIODIC FUNCTIONS.
- Author
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STUBBERUD, PETER
- Subjects
ARTIFICIAL intelligence ,ALGORITHMS ,ARTIFICIAL neural networks ,PERIODIC functions ,MACHINE theory - Abstract
Unlike feedforward neural networks (FFNN) which can act as universal function approximators, recursive, or recurrent, neural networks can act as universal approximators for multi-valued functions. In this paper, a real time recursive backpropagation (RTRBP) algorithm in a vector matrix form is developed for a two-layer globally recursive neural network that has multiple delays in its feedback path. This algorithm has been evaluated on two GRNNs that approximate both an analytic and nonanalytic periodic multi-valued function that a feedforward neural network is not capable of approximating. [ABSTRACT FROM AUTHOR]
- Published
- 2009
- Full Text
- View/download PDF
28. ENSEMBLE OF NOVEL NEURAL NETWORK BASED ON CLONAL SELECTION ALGORITHM FOR SNEAK CIRCUIT ANALYSIS.
- Author
-
XINZHAN, QI, BINGJIE, LIU, and XINGLIANG, JIA
- Subjects
ARTIFICIAL neural networks ,ALGORITHMS ,NEURAL circuitry ,COMPUTER architecture ,NUMERICAL analysis ,MATHEMATICAL models - Abstract
Neural network was introduced to sneak circuit analysis (SCA) in previous works. However, it may generate suspect results which were hard to explain. To overcome the shortcomings, this paper proposed a novel neural network model based on circuit architecture, named CArNN, which is used as an individual of an ensemble. In CArNN, neurons represented system components, and weights represented the joints between components. Models of neurons are sigmoid functions. Clone selection algorithm was used to train CArNNs population. The trained antibodies were used as individuals of an ensemble. The inputs of CArNN are states of switches, and the outputs are states of functional components. Ensemble predicted all possible functions of circuit. The sneak circuits can be discovered by comparing the predicted and designed functions. The results revealed that CArNNs can exactly discover sneak circuits. [ABSTRACT FROM AUTHOR]
- Published
- 2009
- Full Text
- View/download PDF
29. CENTROID NEURAL NETWORK WITH WEIGHTED FEATURES.
- Author
-
PARK, DONG-CHUL
- Subjects
ARTIFICIAL neural networks ,SELF-organizing systems ,IMAGE compression ,ALGORITHMS ,NUMERICAL analysis ,MATHEMATICAL models - Abstract
A Centroid Neural Network with Weighted Features (CNN-WF) is proposed and presented in this paper. The proposed CNN-WF is based on a Centroid Neural Network (CNN), an effective clustering tool that has been successfully applied to various problems. In order to evaluate the importance of each feature in a set of data, a feature weighting concept is introduced to the Centroid Neural Network in the proposed algorithm. The weight update equations for CNN-WF are derived by applying the Lagrange multiplier procedure to the objective function constructed for CNN-WF in this paper. The use of weighted features makes it possible to assess the importance of each feature and to reject features that can be considered as noise in data. Experiments on a synthetic data set and a typical image compression problem show that the proposed CNN-WF can assess the importance of each feature and the proposed CNN-WF outperforms conventional algorithms including the Self-Organizing Map (SOM) and CNN in terms of clustering accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2009
- Full Text
- View/download PDF
30. CONSTRAINED FORMULATIONS AND ALGORITHMS FOR PREDICTING STOCK PRICES BY RECURRENT FIR NEURAL NETWORKS.
- Author
-
WAH, BENJAMIN W. and QIAN, MING-LUN
- Subjects
ARTIFICIAL neural networks ,STOCK prices ,ALGORITHMS ,FILTERING software ,TIME series analysis ,WAVELETS (Mathematics) ,MATHEMATICAL decomposition ,MATHEMATICAL optimization - Abstract
In this paper, we develop a new constrained artificial-neural-network (ANN) formulation and the associated learning algorithm for predicting stock prices, a difficult time-series prediction problem. We characterize daily stock prices as a noisy non-stationary time series and identify its predictable low-frequency components. Using a recurrent finite-impulse-response ANN, we formulate the learning problem as a constrained optimization problem, develop constraints for incorporating cross validations, and solve the learning problem using algorithms based on the theory of extended saddle points for nonlinear constrained optimization. Finally, we illustrate our prediction results on ten stock-price time series. Our main contributions in this paper are the channel-specific low-pass filtering of noisy time series obtained by wavelet decomposition, the transformation of the low-pass signals to improve their stationarity, and the incorporation of constraints on cross validation that can improve the accuracy of predictions. Our experimental results demonstrate good prediction accuracy and annual returns. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
31. FUSION OF EXTREME LEARNING MACHINE WITH FUZZY INTEGRAL.
- Author
-
JUNHAI ZHAI, HONGYU XU, and YAN LI
- Subjects
DATA fusion (Statistics) ,MACHINE learning ,FUZZY integrals ,ARTIFICIAL neural networks ,SET theory ,ALGORITHMS - Abstract
Extreme learning machine (ELM) is an efficient and practical learning algorithm used for training single hidden layer feed-forward neural networks (SLFNs). ELM can provide good generalization performance at extremely fast learning speed. However, ELM suffers from instability and over-fitting, especially on relatively large datasets. Based on probabilistic SLFNs, an approach of fusion of extreme learning machine (F-ELM) with fuzzy integral is proposed in this paper. The proposed algorithm consists of three stages. Firstly, the bootstrap technique is employed to generate several subsets of original dataset. Secondly, probabilistic SLFNs are trained with ELM algorithm on each subset. Finally, the trained probabilistic SLFNs are fused with fuzzy integral. The experimental results show that the proposed approach can alleviate to some extent the problems mentioned above, and can increase the prediction accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
32. ARTIFICIAL METAPLASTICITY NEURAL NETWORK APPLIED TO CREDIT SCORING.
- Author
-
MARCANO-CEDEÑO, ALEXIS, MARIN-DE-LA-BARCENA, A., JIMENEZ-TRILLO, J., PIÑUELA, J. A., and ANDINA, D.
- Subjects
ARTIFICIAL neural networks ,CREDIT scoring systems ,MACHINE learning ,ALGORITHMS ,DEFAULT (Finance) ,FINANCIAL risk ,ERROR rates - Abstract
The assessment of the risk of default on credit is important for financial institutions. Different Artificial Neural Networks (ANN) have been suggested to tackle the credit scoring problem, however, the obtained error rates are often high. In the search for the best ANN algorithm for credit scoring, this paper contributes with the application of an ANN Training Algorithm inspired by the neurons' biological property of metaplasticity. This algorithm is especially efficient when few patterns of a class are available, or when information inherent to low probability events is crucial for a successful application, as weight updating is overemphasized in the less frequent activations than in the more frequent ones. Two well-known and readily available such as: Australia and German data sets has been used to test the algorithm. The results obtained by AMMLP shown have been superior to state-of-the-art classification algorithms in credit scoring. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
33. SIMULATION AND DESIGN OF A CONSTANT-CURRENT-CONTROLLED SPOT WELDING INVERTER WITH THE FUZZY NEURAL NETWORK.
- Author
-
ZHANG, YONG, BAI, HUA, CAI, YONGQING, MA, TIEJUN, and XIE, HONGXIA
- Subjects
ARTIFICIAL neural networks ,ELECTRIC inverters ,ELECTRIC welding ,ENGINEERING design ,SIMULATION methods & models ,FUZZY systems ,MATHEMATICAL models ,SWITCHING theory ,ALGORITHMS ,BACK propagation - Abstract
Resistance spot welding is a major metal connecting method in vehicle and other domestic electronic domains. Among all the welding techniques, the spot welding inverter is an important direction at the present time. The high nonlinearity and strongly coupled multiple parameters in the resistance spot welding process challenge the classical control theory based on some specific conditions and ideal assumptions, which in real practice obstacle the high-quality welding. This paper put the fuzzy neural network into a constant-current-controlled spot welding inverter, where the welding current peak and its variation are adopted as the input parameters and the duty ratio of the switches is regarded as the output. Eventually a five-layer feed-forward network was constructed, back propagation (BP) algorithm was applied to revise the adjustable parameters in the network, and a mathematical model was established to obtain the training samples serving for the network. The ultimate precision could reach 1.75%, the relative control error is 2.28% with strong external disturbances, the overmodulation is 3.35%, and the total modulating period is seven switching period, which indicated that the proposed algorithm has good performance. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
34. OBJECT CLASSIFICATION AND OCCLUSION HANDLING USING QUADRATIC FEATURE CORRELATION MODEL AND NEURAL NETWORKS.
- Author
-
FAN, NA
- Subjects
CLASSIFICATION ,ARTIFICIAL neural networks ,STATISTICAL correlation ,COMPUTER vision ,PATTERN perception ,ALGORITHMS ,ROBUST control ,PERFORMANCE evaluation - Abstract
Occlusion handling is an old but important problem for the computer vision and pattern recognition community. Features from different objects may twist with each other, and any matched feature points may belong to different objects for many traditional object recognition algorithms. To recognize occlusions, we should not only match objects from different view points but also match features extracted from the same object. In this paper, we propose a method to consider these two perspectives simultaneously by encoding various types of features, such as geometry, color and texture relationships among feature points into a matrix and find the best quadratic feature correlation model to fit them. Experiments on our own built dataset and the publicly available PASCAL VOC dataset shows that, our method can robustly classify objects and handle occluded objects under large occlusions, and the performance is among the state-of-the-art. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
35. ON THE PROBABILISTIC OPTIMIZATION OF SPIKING NEURAL NETWORKS.
- Author
-
SCHLIEBS, STEFAN, KASABOV, NIKOLA, and DEFOIN-PLATEL, MICHAËL
- Subjects
ARTIFICIAL neural networks ,ALGORITHMS ,EVOLUTIONARY computation ,MATHEMATICAL optimization ,PROBABILITY theory ,ELECTROENCEPHALOGRAPHY - Abstract
The construction of a Spiking Neural Network (SNN), i.e. the choice of an appropriate topology and the configuration of its internal parameters, represents a great challenge for SNN based applications. Evolutionary Algorithms (EAs) offer an elegant solution for these challenges and methods capable of exploring both types of search spaces simultaneously appear to be the most promising ones. A variety of such heterogeneous optimization algorithms have emerged recently, in particular in the field of probabilistic optimization. In this paper, a literature review on heterogeneous optimization algorithms is presented and an example of probabilistic optimization of SNN is discussed in detail. The paper provides an experimental analysis of a novel Heterogeneous Multi-Model Estimation of Distribution Algorithm (hMM-EDA). First, practical guidelines for configuring the method are derived and then the performance of hMM-EDA is compared to state-of-the-art optimization algorithms. Results show hMM-EDA as a light-weight, fast and reliable optimization method that requires the configuration of only very few parameters. Its performance on a synthetic heterogeneous benchmark problem is highly competitive and suggests its suitability for the optimization of SNN. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
36. ONE-TO-MANY NODE MATCHING BETWEEN COMPLEX NETWORKS.
- Author
-
DU, FANG, XUAN, QI, and WU, TIE-JUN
- Subjects
ARTIFICIAL neural networks ,COMPLEXITY (Philosophy) ,SYMMETRY ,MATCHING theory ,ALGORITHMS ,ANALYTIC mappings ,PROBLEM solving - Abstract
Revealing the corresponding identities of the same individual in different systems is a common task in various areas, e.g., criminals inter-network tracking, homologous proteins revealing, ancient words translating, and so on. With the reason that, recently, more and more complex systems are described by networks, this task can also be accomplished by solving a node matching problem among these networks. Revealing one-to-one matching between networks is for sure the best if we can, however, when the target networks are highly symmetric, or an individual has different identities (corresponds to several nodes) in the same network, the exact one-to-one node matching algorithms always lose their effects to obtain acceptable results. In such situations, one-to-many (or many-to-many) node matching algorithms may be more useful. In this paper, we propose two one-to-many node matching algorithms based on local mapping and ensembling, respectively. Although such algorithms may not tell us the exact correspondence of the identities in different systems, they can indeed help us to narrow down the inter-network searching range, and thus are of significance in practical applications. These results have been verified by the matching experiments on pairwise artificial networks and real-world networks. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
37. A REPAIR ALGORITHM FOR RADIAL BASIS FUNCTION NEURAL NETWORK AND ITS APPLICATION TO CHEMICAL OXYGEN DEMAND MODELING.
- Author
-
QIAO, JUN-FEI and HAN, HONG-GUI
- Subjects
ARTIFICIAL neural networks ,ARTIFICIAL intelligence ,COMPUTER architecture ,CHEMICAL oxygen demand ,ALGORITHMS - Abstract
This paper presents a repair algorithm for the design of a Radial Basis Function (RBF) neural network. The proposed repair RBF (RRBF) algorithm starts from a single prototype randomly initialized in the feature space. The algorithm has two main phases: an architecture learning phase and a parameter adjustment phase. The architecture learning phase uses a repair strategy based on a sensitivity analysis (SA) of the network's output to judge when and where hidden nodes should be added to the network. New nodes are added to repair the architecture when the prototype does not meet the requirements. The parameter adjustment phase uses an adjustment strategy where the capabilities of the network are improved by modifying all the weights. The algorithm is applied to two application areas: approximating a non-linear function, and modeling the key parameter, chemical oxygen demand (COD) used in the waste water treatment process. The results of simulation show that the algorithm provides an efficient solution to both problems. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
38. 3D NOSE FEATURE IDENTIFICATION AND LOCALIZATION THROUGH SELF-ORGANIZING MAP AND GRAPH MATCHING.
- Author
-
BEVILACQUA, VITOANTONIO, MASTRONARDI, GIUSEPPE, SANTARCANGELO, VITO, and SCARAMUZZI, ROCCO
- Subjects
METHODOLOGY ,MAPS ,ARTIFICIAL neural networks ,ALGORITHMS ,DATA ,CAMERAS - Abstract
In this paper, two different methodologies respectively based on an unsupervised self-organizing (SOM) neural network and on a graph matching are shown and discussed to validate the performance of a new 3D facial feature identification and localization algorithm. Experiments are performed on a dataset of 23 3D faces acquired by a 3D laser camera at eBIS lab with pose and expression variations. In particular results referred to five nose landmarks are encouraging and reveal the validity of this approach that although low computational complexity and the small number of landmarks guarantees an average face recognition performance greater than 80%. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
39. COMPARISON BETWEEN PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM IN ARTIFICIAL NEURAL NETWORK FOR LIFE PREDICTION OF NC TOOLS.
- Author
-
WANG, SHILONG, ZHENG, FEI, and XU, LING
- Subjects
NUMERICAL control of machine tools ,GENETIC algorithms ,ALGORITHMS ,COMBINATORIAL optimization ,ARTIFICIAL neural networks - Abstract
Accurate life prediction of NC (Numeric Control) tools is very essential in an advanced manufacturing system. In this paper, tool life prediction in a drilling process was researched. An Artificial Neural Network (ANN) has been established for prediction, with drill diameter, cutting speed and feed rate as input parameters and tool life as an output parameter. To improve the performance of the network, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) were applied independently to train the network instead of standard Backward Propagation (BP) algorithm, which has drawbacks of low convergence rate and weak generalization capacity. And the two methods were compared in terms of algorithm complexity, convergence rate and prediction accuracy, with reference to standard BP method. [ABSTRACT FROM AUTHOR]
- Published
- 2008
- Full Text
- View/download PDF
40. AN IMPROVED ALGORITHM OF OPTICAL FORMULA EXTRACTION WITH FUZZY CLASSIFICATION.
- Author
-
Ming-Hu Ha and Xue-Dong Tian
- Subjects
ALGORITHMS ,FUZZY systems ,KERNEL functions ,ARTIFICIAL intelligence ,ARTIFICIAL neural networks - Abstract
Formula extraction is the first stage of optical formula recognition which converts printed scientific documents into their corresponding electronic format. So far, little research has been done in this area. In this paper, an improved method using fuzzy classification and irregularity rate feature is proposed to separate formulas from texts in the printed documents. Firstly, according to a statistical threshold of distance, connected components are extracted and merged to form the areas of characters and lines. Secondly, the isolated formulas are extracted based on the line features. Finally, the formula symbols in the rest lines are labeled using irregularity degree, and the embedded formulas are located by extending kernel symbols using the propagation of context. In these steps, fuzzy classification algorithm and irregularity degree feature are introduced to solve the problems existing in traditional methods and improve the extracting accuracy. The experimental results show that the method is of great significance in both theory and practice. [ABSTRACT FROM AUTHOR]
- Published
- 2008
- Full Text
- View/download PDF
41. CONVERGENCE ANALYSIS OF A DISCRETE HOPFIELD NEURAL NETWORK WITH DELAY AND ITS APPLICATION TO KNOWLEDGE REFINEMENT.
- Author
-
TSANG, ERIC C. C., QIU, S. S., and YEUNG, DANIEL S.
- Subjects
STOCHASTIC convergence ,ARTIFICIAL neural networks ,ALGORITHMS ,SYMMETRIC matrices ,MATHEMATICAL optimization - Abstract
This paper investigates the convergence theorems that are associated with a Discrete Hopfield Neural Network (DHNN) with delay. We present two updating rules, one for serial mode and the other for parallel mode. The speed of convergence of these proposed updating rules is faster than all of the existing updating rules. It has been proved in this paper that a DHNN with delay will converge to a stable state when operating in a serial mode if the matrix of weights of the no-delay term is symmetric. In addition, it has been proved that they will converge to a stable state when operating in a parallel mode if the matrix of weights of the no-delay term is a symmetric and non-negative definite matrix. The condition for convergence of a DHNN without delay can been relaxed from the need to have a symmetric matrix to an even weaker condition of having a quasi-symmetric matrix. The results in this paper extend both the existing results concerning the convergence of a DHNN without delay and our previous findings. By means of the new network structure and its convergence theorems, we propose a local searching algorithm for combinatorial optimization. We also relate the maximum value of a bivariate energy function to the stable states of a DHNN with delay, which generalizes Hopfield's energy function. Moreover, for the serial model we give the relationship between the convergence of the energy function and the convergence of the corresponding network. One application is presented to demonstrate the higher rate of convergence and the accuracy of the classification using our algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2007
- Full Text
- View/download PDF
42. HANDWRITTEN CHARACTER RECOGNITION USING NONSYMMETRICAL PERCEPTUAL ZONING.
- Author
-
FREITAS, CINTHIA O. A., OLIVEIRA, LUIZ S., BORTOLOZZI, FLÁVIO, and AIRES, SIMONE B. K.
- Subjects
VISUAL perception ,ALPHABET ,ARTIFICIAL intelligence ,ARTIFICIAL neural networks ,GRAPHOLOGY ,ALGORITHMS - Abstract
In this paper we present an alternative strategy to define zoning for handwriting recognition, which is based on nonsymmetrical perceptual zoning. The idea is to extract some knowledge from the confusion matrices in order to make the zoning process less empirical. The feature set considered in this work is based on concavities/convexities deficiencies, which are obtained by labeling the background pixels of the input image. To better assess the nonsymmetrical zoning we carried out experiments using four different zonings strategies. Experiments show that the nonsymmetrical zoning could be considered as a tool to build more reliable handwriting recognition systems. [ABSTRACT FROM AUTHOR]
- Published
- 2007
- Full Text
- View/download PDF
43. NEURAL NETWORKS AS A TOOL FOR NONLINEAR PREDICTIVE CONTROL:: APPLICATION TO SOME BENCHMARK SYSTEMS.
- Author
-
JALILI, MAHDI, ATASHBARI, SAEID, MOMENBELLAH, SAMAD, and ROUDSARI, FARZAD HABIBIPOUR
- Subjects
ARTIFICIAL neural networks ,NONLINEAR control theory ,PREDICTIVE control systems ,LINEAR systems ,ALGORITHMS - Abstract
This paper deals with the application of neural networks to design intelligent nonlinear predictive controllers. Predictive controllers are now widely used in many industrial applications. They have been used for linear systems in early applications and then some methods based on predictive control theory were proposed to govern the dynamics of nonlinear systems. In this paper, we will make use of multi-layer perceptron neurofuzzy models with Locally Linear Model Tree (LoLiMoT) learning algorithm as a part of intelligent predictive control system, which has shown excellent performance in identifying of nonlinear systems. The nonlinear dynamics of the system is identified using the neural network based method and then the identified model is used as a part of predictive control algorithm. The proposed method is used to solve the control problems in some benchmark systems. As a first study, the viscosity control in a Continuous Stirred Tank Reactor (CSTR) plant is considered. The mathematical model of the plant is used to generate the input output data set and then the dynamic behavior of the system is identified using a proper multi-layer perceptron neural network, which is used in the predictive control loop. Also, the predictive control based on the locally linear neurofuzzy model is applied to temperature control of an electrically heated micro heat exchanger. The dynamic behavior of the heat exchanger is identified based on some experimental data of the real plant. Comparing the identification results obtained by the neurofuzzy model with those of some linear models such as ARX and BJ, confirms the superior performance for the locally linear neurofuzzy model. Then, the predictive control is applied to the identified model to obtain a satisfactory performance in the output temperature that should track a desired reference signal. As another application, the algorithm is applied to temperature control of a solution polymerization methyl methacrylate in a batch reactor. The results show also somehow satisfactory performance for this highly nonlinear system. All the simulation results reveal the effectiveness of the proposed intelligent control strategy. [ABSTRACT FROM AUTHOR]
- Published
- 2007
- Full Text
- View/download PDF
44. A DELAYED NEURAL NETWORK METHOD FOR SOLVING CONVEX OPTIMIZATION PROBLEMS.
- Author
-
YANG, YONGQING and CAO, JINDE
- Subjects
CONVEX programming ,ALGORITHMS ,MATHEMATICAL optimization ,ARTIFICIAL neural networks ,LAGRANGE equations - Abstract
In this paper, the delayed projection neural network for a class of solving convex programming problem is proposed. The existence of solution and global exponential stability of the proposed network are proved, which can guarantee to converge at an exact optimal solution of the convex programming problems. Several examples are given to show the effectiveness of the proposed network. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
45. A HYBRID NEURAL LEARNING ALGORITHM USING EVOLUTIONARY LEARNING AND DERIVATIVE FREE LOCAL SEARCH METHOD.
- Author
-
GHOSH, RANADHIR, YEARWOOD, JOHN, GHOSH, MOUMITA, and BAGIROV, ADIL
- Subjects
PHYSICAL & theoretical chemistry ,ARTIFICIAL neural networks ,MATHEMATICAL optimization ,LINEAR statistical models ,ALGORITHMS ,MAXIMA & minima - Abstract
In this paper we investigate a hybrid model based on the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. Also we discuss different variants for hybrid models using the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. The Discrete Gradient method has the advantage of being able to jump over many local minima and find very deep local minima. However, earlier research has shown that a good starting point for the discrete gradient method can improve the quality of the solution point. Evolutionary algorithms are best suited for global optimisation problems. Nevertheless they are cursed with longer training times and often unsuitable for real world application. For optimisation problems such as weight optimisation for ANNs in real world applications the dimensions are large and time complexity is critical. Hence the idea of a hybrid model can be a suitable option. In this paper we propose different fusion strategies for hybrid models combining the evolutionary strategy with the discrete gradient method to obtain an optimal solution much quicker. Three different fusion strategies are discussed: a linear hybrid model, an iterative hybrid model and a restricted local search hybrid model. Comparative results on a range of standard datasets are provided for different fusion hybrid models. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
46. SYSTEM ORIENTED NEURAL NETWORKS — PROBLEM FORMULATION, METHODOLOGY AND APPLICATION.
- Author
-
LI, KANG and PENG, JIAN-XUN
- Subjects
ARTIFICIAL neural networks ,ENGINEERING systems ,NONLINEAR theories ,GENETIC algorithms ,ALGORITHMS ,COMBINATORIAL optimization - Abstract
A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental nonlinear functions from a priori engineering knowledge, which are then constructed and coded into appropriate chromosome representations. Given a suitable fitness function, using evolutionary approaches such as genetic algorithms, a population of chromosomes evolves for a certain number of generations to finally produce a neural network model best fitting the system data. The objective is to improve the transparency of the neural networks, i.e. to produce physically meaningful "white box" neural network model with better generalization performance. In this paper, the problem formulation, the neural network configuration, and the associated optimization software are discussed in detail. This methodology is then applied to a practical real-world system to illustrate its effectiveness. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
47. Application of Multi-Sensor Information Fusion Method Based on Rough Sets and Support Vector Machine.
- Author
-
Jinxue XUE, Guohu WANG, Xiaoqiang WANG, and Fengkui CUI
- Subjects
DETECTORS ,ROUGH sets ,SUPPORT vector machines ,ARTIFICIAL neural networks ,FUZZY sets ,ALGORITHMS - Published
- 2016
48. A FULLY AUTOMATED OFFLINE HANDWRITING RECOGNITION SYSTEM INCORPORATING RULE BASED NEURAL NETWORK VALIDATED SEGMENTATION AND HYBRID NEURAL NETWORK CLASSIFIER.
- Author
-
Ghosh, Moumita, Ghosh, Ranadhir, and Verma, Brijesh
- Subjects
WRITING ,PATTERN recognition systems ,ARTIFICIAL neural networks ,ALGORITHMS ,PATTERN perception ,COMPUTER vision ,METHODOLOGY - Abstract
In this paper we propose a fully automated offline handwriting recognition system that incorporates rule based segmentation, contour based feature extraction, neural network validation, a hybrid neural network classifier and a hamming neural network lexicon. The work is based on our earlier promising results in this area using heuristic segmentation and contour based feature extraction. The segmentation is done using many heuristic based set of rules in an iterative manner and finally followed by a neural network validation system. The extraction of feature is performed using both contour and structure based feature extraction algorithm. The classification is performed by a hybrid neural network that incorporates a hybrid combination of evolutionary algorithm and matrix based solution method. Finally a hamming neural network is used as a lexicon. A benchmark dataset from CEDAR has been used for training and testing. [ABSTRACT FROM AUTHOR]
- Published
- 2004
- Full Text
- View/download PDF
49. PDE-BASED HISTOGRAM MODIFICATION WITH EMBEDDED MORPHOLOGICAL PROCESSING OF THE LEVEL-SETS.
- Author
-
Cserey, György, Reiceczicy, Csaba, and Földesy, Péter
- Subjects
ARTIFICIAL neural networks ,ALGORITHMS ,PARTIAL differential equations ,ECHOCARDIOGRAPHY ,COMPUTER software - Abstract
This paper describes parallel histogram modification techniques with embedded morphological preprocessing methods within the CNN-UM framework. The procedure is formulated in terms of nonlinear partial differential equations (PDE) and approximated through finite differences in space, resulting in coupled nonlinear ordinary differential equations (ODE). The I/O mapping of the system (containing both local and global couplings) can be calculated by a complex analogic (analog and logic) algorithm executed on a stored program nonlinear array processor, called the cellular nonlinear network universal machine (CNN-UM[sup 3]). We describe and illustrate how the implementation of the algorithm results in an adaptive multi-thresholding scheme when histogram modification is combined with embedded morphological processing at a finite (low) number of gray-scale levels. This has obvious advantages if the further processing steps are segmentation and/or recognition. Experimental results processing real-life and echocardiography images are measured on different hardware/software platforms, including a 64 × 64 CNN-UM chip (ACE4k[sup 6,17]). [ABSTRACT FROM AUTHOR]
- Published
- 2003
- Full Text
- View/download PDF
50. Associative Memory Design Using Overlapping Decomposition and Generalized Brain-State-in-a-Box Neural Networks.
- Author
-
Oh, Cheolhwan and Żak, Stanislaw H.
- Subjects
ARTIFICIAL neural networks ,COMPUTER storage devices ,ALGORITHMS ,ARTIFICIAL intelligence - Abstract
This paper is concerned with large scale associative memory design. A serious problem with neural associative memories is the quadratic growth of the number of interconnections with the problem size. An overlapping decomposition algorithm is proposed to attack this problem. Specifically, a pattern to be processed is decomposed into overlapping sub-patterns. Then, neural sub-networks are constructed that process the sub-patterns. An error correction algorithm operates on the outputs of each sub-network in order to correct the mismatches between sub-patterns that are obtained from the independent recall processes of individual sub-networks. The performance of the proposed large scale associative memory is illustrated using two-dimensional images. It is shown that the proposed method reduces the computing cost of the design of the associative memories compared with non-interconnected associative memories. [ABSTRACT FROM AUTHOR]
- Published
- 2003
- Full Text
- View/download PDF
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