Journal articles on the topic 'Minimum Classification Error algorithm'
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CHEN, LIANG-HUA, SHAO-HUA DENG, and HONG-YUAN LIAO. "MCE-BASED FACE RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 08 (December 2001): 1311–27. http://dx.doi.org/10.1142/s0218001401001477.
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This paper proposes a complete procedure for the extraction and recognition of human faces in complex scenes. The morphology-based face detection algorithm can locate multiple faces oriented in any direction. The recognition algorithm is based on the minimum classification error (MCE) criterion. In our work, the minimum classification error formulation is incorporated into a multilayer perceptron neural network. Experimental results show that our system is robust to noisy images and complex background.
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Xu, Yong, Xiaozhao Fang, Qi Zhu, Yan Chen, Jane You, and Hong Liu. "Modified minimum squared error algorithm for robust classification and face recognition experiments." Neurocomputing 135 (July 2014): 253–61. http://dx.doi.org/10.1016/j.neucom.2013.11.025.
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Toh, Kar-Ann. "Deterministic Neural Classification." Neural Computation 20, no. 6 (June 2008): 1565–95. http://dx.doi.org/10.1162/neco.2007.04-07-508.
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This letter presents a minimum classification error learning formulation for a single-layer feedforward network (SLFN). By approximating the nonlinear counting step function using a quadratic function, the classification error rate is shown to be deterministically solvable. Essentially the derived solution is related to an existing weighted least-squares method with class-specific weights set according to the size of data set. By considering the class-specific weights as adjustable parameters, the learning formulation extends the classification robustness of the SLFN without sacrificing its intrinsic advantage of being a closed-form algorithm. While the method is applicable to other linear formulations, our empirical results indicate SLFN's effectiveness on classification generalization.
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Fan, Yu. "Study on Cooperative Multipoint Communication Precoding Algorithm under SLNR-MMSE Framework." Advances in Multimedia 2022 (July 31, 2022): 1–10. http://dx.doi.org/10.1155/2022/9457248.
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With the rapid growth of demand for wireless data services and the continuous introduction of new air interface technologies, mobile communication systems continue to face new challenges in supporting high-speed multimedia service transmission and achieving seamless coverage. In order to meet the requirements of the IMS system in terms of bandwidth, peak rate, communication throughput, etc., multiantenna enhancement technology and cooperative multipoint transmission technology have become research hotspots as key technologies. In the study of multiuser system, this paper focuses on the precoding technology based on noncode book, based on the minimum mean square error criterion and the maximum letter leakage noise ratio criterion, studies the precoding technology of different multiuser systems, expounds the collaborative multipoint transmission system, and makes a basic classification. The signal leakage-to-noise ratio precoding algorithm and the minimum mean square error precoding algorithm are analyzed in detail. In view of the shortcomings of these two algorithms, this paper takes the minimum sum of the total mean square error of the system as the optimization goal of the combinations of precoding and power allocation. The precoding algorithm of SLNR-MMSE is proposed. The simulation analysis shows that the proposed algorithm has certain advantages over other algorithms in terms of bit error rate and system capacity. It shows that this study is important for optimizing collaborative multipoint communication system.
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Huang, Wei, Xiaohui Wang, Yuzhen Jiang, and Yinghui Zhu. "Two-Directional Minimum Squared Error Algorithm and Classification Experiments on Face and Building Images." Journal of Computational and Theoretical Nanoscience 12, no. 11 (November 1, 2015): 4654–60. http://dx.doi.org/10.1166/jctn.2015.4414.
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Liu, Zhonghua, Shan Xue, Lin Zhang, Jiexin Pu, and Haijun Wang. "An Improved Kernel Minimum Square Error Classification Algorithm Based on $L_{2,1}$ -Norm Regularization." IEEE Access 5 (2017): 14133–40. http://dx.doi.org/10.1109/access.2017.2730218.
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Hu, Zheng-ping, Yan Peng, and Shuhuan Zhao. "A new sparse representation algorithm based on kernel spatial non-minimum residual error for classification." Optik 126, no. 23 (December 2015): 4665–70. http://dx.doi.org/10.1016/j.ijleo.2015.08.088.
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ZHANG, RUI, XIAO QING DING, and HAI LONG LIU. "DISCRIMINATIVE TRAINING BASED QUADRATIC CLASSIFIER FOR HANDWRITTEN CHARACTER RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 06 (September 2007): 1035–46. http://dx.doi.org/10.1142/s0218001407005776.
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In offline handwritten character recognition, the classifier with modified quadratic discriminant function (MQDF) has achieved good performance. The parameters of MQDF classifier are commonly estimated by the maximum likelihood (ML) estimator, which maximizes the within-class likelihood instead of directly minimizing the classification errors. To improve the performance of MQDF classifier, in this paper, the MQDF parameters are revised by discriminative training using a minimum classification error (MCE) criterion. The proposed algorithm is applied to recognizing handwritten numerals and handwritten Chinese characters, the recognition rates obtained are among the highest that have ever been reported.
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Zhang, Mingmei, Yongan Xue, Yonghui Ge, and Jinling Zhao. "Watershed Segmentation Algorithm Based on Luv Color Space Region Merging for Extracting Slope Hazard Boundaries." ISPRS International Journal of Geo-Information 9, no. 4 (April 17, 2020): 246. http://dx.doi.org/10.3390/ijgi9040246.
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To accurately identify slope hazards based on high-resolution remote sensing imagery, an improved watershed segmentation algorithm is proposed. The color difference of the Luv color space was used as the regional similarity measure for region merging. Furthermore, the area relative error for evaluating the image segmentation accuracy was improved and supplemented with the pixel quantity error to evaluate the segmentation accuracy. An unstable slope was identified to validate the algorithm on Chinese Gaofen-2 (GF-2) remote sensing imagery by a multiscale segmentation extraction experiment. The results show the following: (1) the optimal segmentation and merging scale parameters were, respectively, minimum threshold constant C for minimum area Amin of 500 and optimal threshold D for a color difference of 400. (2) The total processing time for segmentation and merging of unstable slopes was 39.702 s, much lower than the maximum likelihood classification method and a little more than the object-oriented classification method. The relative error of the slope hazard area was 4.92% and the pixel quantity error was 1.60%, which were superior to the two classification methods. (3) The evaluation criteria of segmentation accuracy were consistent with the results of visual interpretation and the confusion matrix, indicating that the criteria established in this study are reliable. By comparing the time efficiency, visual effect and classification accuracies, the proposed method has a good comprehensive extraction effect. It can provide a technical reference for promoting the rapid extraction of slope hazards based on remote sensing imagery. Meanwhile, it also provides a theoretical and practical experience reference for improving the watershed segmentation algorithm.
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Koloko, R. J. Koloko, P. Ele, R. Wamkeue, and A. Melingui. "Fault Detection and Classification of a Photovoltaic Generator Using the BES Optimization Algorithm Associated with SVM." International Journal of Photoenergy 2022 (November 8, 2022): 1–14. http://dx.doi.org/10.1155/2022/6841861.
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In this work, an innovative approach based on the estimation of the photovoltaic generator (GPV) parameters from the Bald Eagle Search (BES) optimization algorithm, associated with a support vector machine (SVM) classification algorithm, allowed to highlight a new tool for the classification of the signatures of shading and moisture PV defects. It recognizes signatures generated by the GPV in healthy and erroneous operation using the optimized parametric vector and classifies defects using the same optimized vector. The technique emphasizes the resilience of parameter estimate in terms of error on all parameters. The classification accuracy is 93%. The residuals between the estimated curve in healthy operation with a minimum error of the order of 10-4 and the one at fault are used as an indicator of faults.
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Song, Yongchao, Jieru Yao, Yongfeng Ju, Yahong Jiang, and Kai Du. "Automatic Detection and Classification of Road, Car, and Pedestrian Using Binocular Cameras in Traffic Scenes with a Common Framework." Complexity 2020 (May 14, 2020): 1–17. http://dx.doi.org/10.1155/2020/2435793.
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In order to solve the problems of traffic object detection, fuzzification, and simplification in real traffic environment, an automatic detection and classification algorithm for roads, vehicles, and pedestrians with multiple traffic objects under the same framework is proposed. We construct the final V view through a considerate U-V view method, which determines the location of the horizon and the initial contour of the road. Road detection results are obtained through error label reclassification, omitting point reassignment, and so an. We propose a peripheral envelope algorithm to determine sources of vehicles and pedestrians on the road. The initial segmentation results are determined by the regional growth of the source point through the minimum neighbor similarity algorithm. Vehicle detection results on the road are confirmed by combining disparity and color energy minimum algorithms with the object window aspect ratio threshold method. A method of multifeature fusion is presented to obtain the pedestrian target area, and the pedestrian detection results on the road are accurately segmented by combining the disparity neighbor similarity and the minimum energy algorithm. The algorithm is tested in three datasets of Enpeda, KITTI, and Daimler; then, the corresponding results prove the efficiency and accuracy of the proposed approach. Meanwhile, the real-time analysis of the algorithm is performed, and the average time efficiency is 13 pfs, which can realize the real-time performance of the detection process.
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GARRIDO, LLUIS, and SERGIO GÓMEZ. "ANALYTICAL INTERPRETATION OF FEED-FORWARD NETS OUTPUTS AFTER TRAINING." International Journal of Neural Systems 07, no. 01 (March 1996): 19–27. http://dx.doi.org/10.1142/s0129065796000038.
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The minimization quadratic error criterion which gives rise to the back-propagation algorithm is studied using functional analysis techniques. With them, we recover easily the well-known statistical result which states that the searched global minimum is a function which assigns, to each input pattern, the expected value of its corresponding output patterns. Its application to classification tasks shows that only certain output class representations can be used to obtain the optimal Bayesian decision rule. Finally, our method permits the study of other error criterions, finding out, for instance, that absolute value errors lead to medians instead of mean values.
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Buisson, Lise du, Navin Sivanandam, Bruce A. Bassett, and Mathew Smith. "Machine Classification of Transient Images." Proceedings of the International Astronomical Union 10, S306 (May 2014): 288–91. http://dx.doi.org/10.1017/s1743921314013842.
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AbstractUsing transient imaging data from the 2nd and 3rd years of the SDSS supernova survey, we apply various machine learning techniques to the problem of classifying transients (e.g. SNe) from artefacts, one of the first steps in any transient detection pipeline, and one that is often still carried out by human scanners. Using features mostly obtained from PCA, we show that we can match human levels of classification success, and find that a K-nearest neighbours algorithm and SkyNet perform best, while the Naive Bayes, SVM and minimum error classifier have performances varying from slightly to significantly worse.
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Wang, Wei. "Machine Learning-Based Intelligent Scoring of College English Teaching in the Field of Natural Language Processing." Computational Intelligence and Neuroscience 2022 (August 4, 2022): 1–9. http://dx.doi.org/10.1155/2022/2754626.
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The current education evaluation is limited not only to the mode of simplification, indexing, and datafication, but also to the scientific nature of college teaching evaluation. This work firstly conducts a theoretical analysis of natural language processing technology, analyzes the related technologies of intelligent scoring, designs a systematic process for intelligent scoring of college English teaching, and finally conducts theoretical research on the Naive Bayesian algorithm in machine learning. In addition, the error of intelligent scoring of English teaching in colleges and universities and the accuracy of scoring and classification are analyzed and researched. The results show that the error between manual scoring and machine scoring is basically about 2 points and the minimum error of intelligent scoring in college English teaching under machine scoring can reach 0 points. There is a certain bias in manual scoring, and scoring on the machine can reduce the generation of this error. The Naive Bayes algorithm has the highest classification accuracy on the college intelligent scoring dataset, which is 76.43%. The weighted Naive Bayes algorithm has been improved in the classification accuracy of college English teaching intelligent scoring, with an average accuracy rate of 74.87%. To sum up, the weighted Naive Bayes algorithm has better performance in the classification accuracy of college English intelligent scoring. This work has a significant effect on the scoring of the college intelligent teaching scoring system under natural language processing and the classification of college teaching intelligence scoring under the Naive Bayes algorithm, which can improve the efficiency of college teaching scoring.
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Pertami, Diah, I. Wayan Nuarsa, and I. Dewa Nyoman Nurweda Putra. "Pemetaan Perubahan Penggunaan Lahan Wilayah Pesisir Kecamatan Rungkut, Kota Surabaya, Tahun 2013 dan 2019." Journal of Marine Research and Technology 5, no. 1 (February 28, 2022): 10. http://dx.doi.org/10.24843/jmrt.2022.v05.i01.p03.
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The coastal area is a meeting space between land and sea that is easy to change temporally and spatially. The changes were triggered due to an increase in population and community activities such as industry, housing, ports, cultivation, transportation, farms, agriculture, tourism, and so on centered in the coastal area and become the center of Indonesia's economy. Remote sensing technology is one of the right ways for monitoring activities in the near term. This research aims to map the change of coastal land use in Rungkut district, Surabaya, in 2013 and 2019 using high-resolution satellite imagery of SPOT imagery. The method of classification of coastal land use two types of supervised classification, namely Minimum Distance and Maximum Likelihood. Land use class obtained in this study as many as six classes, namely mangrove, settlement, pond, green open space, the body of water, and industry. The results showed that using two different algorithms gave a difference in classification results. The largest land-use change from classification with Minimum Distance method is in mangrove and body of water with +231,80 and –230,89 ha, while the classification result with the method of Maximum Likelihood the change of the largest land use is in mangrove class and ponds respectively +202,41 and –210,89 ha. Accuracy test using error matrix obtained by 85,50% with kappa coefficient 0,78 on the classification result of coastal land use using Minimum Distance algorithm and for Maximum Likelihood algorithm obtained accuracy of 89% with Kappa coefficient is 0,84. It is demonstrated that by using the algorithm, Maximum Likelihood accuracy on the land use map is very good.
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Wang, Mei, Ke Zhai, Chi Harold Liu, and Yujie Li. "A Mobile Computing Method Using CNN and SR for Signature Authentication with Contour Damage and Light Distortion." Wireless Communications and Mobile Computing 2018 (June 25, 2018): 1–10. http://dx.doi.org/10.1155/2018/5412925.
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A signature is a useful human feature in our society, and determining the genuineness of a signature is very important. A signature image is typically analyzed for its genuineness classification; however, increasing classification accuracy while decreasing computation time is difficult. Many factors affect image quality and the genuineness classification, such as contour damage and light distortion or the classification algorithm. To this end, we propose a mobile computing method of signature image authentication (SIA) with improved recognition accuracy and reduced computation time. We demonstrate theoretically and experimentally that the proposed golden global-local (G-L) algorithm has the best filtering result compared with the methods of mean filtering, medium filtering, and Gaussian filtering. The developed minimum probability threshold (MPT) algorithm produces the best segmentation result with minimum error compared with methods of maximum entropy and iterative segmentation. In addition, the designed convolutional neural network (CNN) solves the light distortion problem for detailed frame feature extraction of a signature image. Finally, the proposed SIA algorithm achieves the best signature authentication accuracy compared with CNN and sparse representation, and computation times are competitive. Thus, the proposed SIA algorithm can be easily implemented in a mobile phone.
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Nawi, Nazri Mohd, Abdullah Khan, M. Z. Rehman, Haruna Chiroma, and Tutut Herawan. "Weight Optimization in Recurrent Neural Networks with Hybrid Metaheuristic Cuckoo Search Techniques for Data Classification." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/868375.
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Recurrent neural network (RNN) has been widely used as a tool in the data classification. This network can be educated with gradient descent back propagation. However, traditional training algorithms have some drawbacks such as slow speed of convergence being not definite to find the global minimum of the error function since gradient descent may get stuck in local minima. As a solution, nature inspired metaheuristic algorithms provide derivative-free solution to optimize complex problems. This paper proposes a new metaheuristic search algorithm called Cuckoo Search (CS) based on Cuckoo bird’s behavior to train Elman recurrent network (ERN) and back propagation Elman recurrent network (BPERN) in achieving fast convergence rate and to avoid local minima problem. The proposed CSERN and CSBPERN algorithms are compared with artificial bee colony using BP algorithm and other hybrid variants algorithms. Specifically, some selected benchmark classification problems are used. The simulation results show that the computational efficiency of ERN and BPERN training process is highly enhanced when coupled with the proposed hybrid method.
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Kang, Mingu, Siho Shin, Gengjia Zhang, Jaehyo Jung, and Youn Tae Kim. "Mental Stress Classification Based on a Support Vector Machine and Naive Bayes Using Electrocardiogram Signals." Sensors 21, no. 23 (November 27, 2021): 7916. http://dx.doi.org/10.3390/s21237916.
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Examining mental health is crucial for preventing mental illnesses such as depression. This study presents a method for classifying electrocardiogram (ECG) data into four emotional states according to the stress levels using one-against-all and naive Bayes algorithms of a support vector machine. The stress classification criteria were determined by calculating the average values of the R-S peak, R-R interval, and Q-T interval of the ECG data to improve the stress classification accuracy. For the performance evaluation of the stress classification model, confusion matrix, receiver operating characteristic (ROC) curve, and minimum classification error were used. The average accuracy of the stress classification was 97.6%. The proposed model improved the accuracy by 8.7% compared to the previous stress classification algorithm. Quantifying the stress signals experienced by people can facilitate a more effective management of their mental state.
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Leema N., Khanna H. Nehemiah, Elgin Christo V. R., and Kannan A. "Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms." International Journal of Operations Research and Information Systems 11, no. 4 (October 2020): 62–85. http://dx.doi.org/10.4018/ijoris.2020100104.
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Artificial neural networks (ANN) are widely used for classification, and the training algorithm commonly used is the backpropagation (BP) algorithm. The major bottleneck faced in the backpropagation neural network training is in fixing the appropriate values for network parameters. The network parameters are initial weights, biases, activation function, number of hidden layers and the number of neurons per hidden layer, number of training epochs, learning rate, minimum error, and momentum term for the classification task. The objective of this work is to investigate the performance of 12 different BP algorithms with the impact of variations in network parameter values for the neural network training. The algorithms were evaluated with different training and testing samples taken from the three benchmark clinical datasets, namely, Pima Indian Diabetes (PID), Hepatitis, and Wisconsin Breast Cancer (WBC) dataset obtained from the University of California Irvine (UCI) machine learning repository.
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Huang, D., Y. Zhang, and W. Q. Yu. "TRACEABILITY OF OIL SPILL FROM BAYESIAN CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 133–40. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-133-2020.
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Abstract. Oil spills over the sea resulted in the destruction of the marine environment. Therefore, the Bayesian algorithm was developed to determine the source of spilled oil for reducing the damage to the marine environment. Based on the flow data of the offshore waters of the Bohai Sea, the influencing factors of oil spill drift were used for algorithm. The main factors affecting the traceability of marine oil spills are considered: the distance from the oil point to the source point, the flow direction of water, the type of the spilled oil source, and the scale of the source. The algorithm is completed on the basis of a feasible simulation database. We not consider the relationship between the attributes by naive Bayesian classification. In this paper, we determine the result by the minimum error rates of each source. Then the performance evaluation of the model was done by cross-validation method. The experimental result shows that the Bayesian algorithm can be used to determine the source of spilled oil. It is easier and faster to determine the source by the method raised.
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Zhang, Xiaochun, and Zhiyu Zhou. "Classifying colour differences in dyed fabrics using an improved hunger games search optimised random vector functional link." Journal of Engineered Fibers and Fabrics 17 (January 2022): 155892502211115. http://dx.doi.org/10.1177/15589250221111508.
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This study proposes an algorithm for classifying colour differences in dyed fabrics using random vector functional link (RVFL) optimised using an improved hunger games search (HGS) algorithm to replace the inefficient traditional classification methods. First, to prevent the HGS algorithm from easily arriving at the local optimal solution, we used the grey wolf optimiser (GWO) to generate the solution set of the HGS algorithm. Subsequently, to reduce the impact of the randomness of the input weight and hidden layer offset on the classification accuracy of RVFL, we used the improved HGS to optimise these two parameters of RVFL. Finally, the RVFL optimised using the improved HGS algorithm is used for classifying the colour differences of dyed fabrics. The performance of the proposed classification algorithm is compared with HGS algorithms improved using the whale optimiser, sine cosine algorithm, and Harris hawks optimiser. The results revealed that the proposed algorithm possesses several advantages, including the maximum, minimum, and average classification errors; good stability; and fast convergence.
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Kaplun, Dmitry, Alexander Voznesenskiy, Sergei Romanov, Erivelton Nepomuceno, and Denis Butusov. "Optimal Estimation of Wavelet Decomposition Level for a Matching Pursuit Algorithm." Entropy 21, no. 9 (August 29, 2019): 843. http://dx.doi.org/10.3390/e21090843.
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In this paper, we consider the application of the matching pursuit algorithm (MPA) for spectral analysis of non-stationary signals. First, we estimate the approximation error and the performance time for various MPA modifications and parameters using central processor unit and graphics processing unit (GPU) to identify possible ways to improve the algorithm. Next, we propose the modifications of discrete wavelet transform (DWT) and package wavelet decomposition (PWD) for further use in MPA. We explicitly show that the optimal decomposition level, defined as a level with minimum entropy, in DWT and PWD provides the minimum approximation error and the smallest execution time when applied in MPA as a rough estimate in the case of using wavelets as basis functions (atoms). We provide an example of entropy-based estimation for optimal decomposition level in spectral analysis of seismic signals. The proposed modification of the algorithm significantly reduces its computational costs. Results of spectral analysis obtained with MPA can be used for various signal processing applications, including denoising, clustering, classification, and parameter estimation.
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Safaralizade, Esmaeel, Robab Husseinzade, Gholamhussein Pashazade, and Bakhtiar Khosravi. "Assessing the Accuracy of the Pixel-Based Algorithms in Classifying the Urban Land Use, Using the Multi Spectral Image of the IKONOS Satellite (Case Study, Uromia City)." International Letters of Natural Sciences 11 (February 2014): 40–56. http://dx.doi.org/10.18052/www.scipress.com/ilns.11.40.
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With the development of urbanization and expansion of urban land use, the need to up to date maps, has drawn the attention of the urban planners. With the advancement of the remote sensing technology and accessibility to images with high resolution powers, the classification of these land uses could be executed in different ways. In the current research, different algorithms for classifying the pixel-based were tested on the land use of the city of Urmia, using the multi spectral images of the IKONOS satellite. Here, in this method, the algorithms of the supervised classification of the maximum likelihood, minimum distance to mean and parallel piped were executed on seven land use classes. Results obtained using the error matrix indicated that the algorithm for classifying the maximum likelihood has an overall accuracy of 88/93 % and the Kappa coefficient of 0/86 while for the algorithms of minimum distance to mean and parallel piped , the overall accuracy are 05/79 % and 40/70 % respectively. Also, the accuracy of the producer and that of the user in most land use classes in the method of maximum likelihood are higher compared to the other algorithms.
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Safaralizade, Esmaeel, Robab Husseinzade, Gholamhussein Pashazade, and Bakhtiar Khosravi. "Assessing the Accuracy of the Pixel-Based Algorithms in Classifying the Urban Land Use, Using the Multi Spectral Image of the IKONOS Satellite (Case Study, Uromia City)." International Letters of Natural Sciences 11 (February 27, 2014): 40–56. http://dx.doi.org/10.56431/p-62vje4.
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With the development of urbanization and expansion of urban land use, the need to up to date maps, has drawn the attention of the urban planners. With the advancement of the remote sensing technology and accessibility to images with high resolution powers, the classification of these land uses could be executed in different ways. In the current research, different algorithms for classifying the pixel-based were tested on the land use of the city of Urmia, using the multi spectral images of the IKONOS satellite. Here, in this method, the algorithms of the supervised classification of the maximum likelihood, minimum distance to mean and parallel piped were executed on seven land use classes. Results obtained using the error matrix indicated that the algorithm for classifying the maximum likelihood has an overall accuracy of 88/93 % and the Kappa coefficient of 0/86 while for the algorithms of minimum distance to mean and parallel piped , the overall accuracy are 05/79 % and 40/70 % respectively. Also, the accuracy of the producer and that of the user in most land use classes in the method of maximum likelihood are higher compared to the other algorithms.
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Guan, Shan, Kai Zhao, and Shuning Yang. "Motor Imagery EEG Classification Based on Decision Tree Framework and Riemannian Geometry." Computational Intelligence and Neuroscience 2019 (January 21, 2019): 1–13. http://dx.doi.org/10.1155/2019/5627156.
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This paper proposes a novel classification framework and a novel data reduction method to distinguish multiclass motor imagery (MI) electroencephalography (EEG) for brain computer interface (BCI) based on the manifold of covariance matrices in a Riemannian perspective. For method 1, a subject-specific decision tree (SSDT) framework with filter geodesic minimum distance to Riemannian mean (FGMDRM) is designed to identify MI tasks and reduce the classification error in the nonseparable region of FGMDRM. Method 2 includes a feature extraction algorithm and a classification algorithm. The feature extraction algorithm combines semisupervised joint mutual information (semi-JMI) with general discriminate analysis (GDA), namely, SJGDA, to reduce the dimension of vectors in the Riemannian tangent plane. And the classification algorithm replaces the FGMDRM in method 1 with k-nearest neighbor (KNN), named SSDT-KNN. By applying method 2 on BCI competition IV dataset 2a, the kappa value has been improved from 0.57 to 0.607 compared to the winner of dataset 2a. And method 2 also obtains high recognition rate on the other two datasets.
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Gupta, Niharika, and Priya Khobragade. "Muti-class Image Classification using Transfer Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (January 31, 2023): 700–704. http://dx.doi.org/10.22214/ijraset.2023.48665.
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Abstract: Humans are very proficient at perceiving natural scenes and understanding their contents. Everyday image content across the globe is rapidly increasing and there is a need for classifying these images for further research. Scene classification is a challenging task, because in some natural scenes there will be common features in images and some images may contain half indoor and half outdoor scene features. In this project we are going to classify natural scenery in images using Artificial Intelligence. Based on the analysis of the error backpropagation algorithm, we propose an innovative training criterion of depth neural network for maximum interval minimum classification error. At the same time, the cross entropy and M3CE are analyzed and combined to obtain better results. Finally, we tested our proposed M3 CE-CEc on two deep learning standard databases, MNIST and CIFAR-10. The experimental results show that M3 CE can enhance the cross-entropy, and it is an effective supplement to the cross-entropy criterion. M3 CE-CEc has obtained good results in both databases.
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Chesalin, A. N., S. Ya Grodzenskiy, M. Yu Nilov, and A. N. Agafonov. "Modification of the WaldBoost algorithm to improve the efficiency of solving pattern recognition problems in real-time." Russian Technological Journal 7, no. 5 (October 15, 2019): 20–29. http://dx.doi.org/10.32362/2500-316x-2019-7-5-20-29.
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The implementation of the WaldBoost algorithm is considered, and its modification is proposed, which allows to significantly reduce the number of weak classifiers to achieve a given classification accuracy. The efficiency of the proposed algorithm is shown by specific examples. The paper studies modifications of compositions (ensembles) of algorithms for solving real-time pattern recognition problems. The aim of the study is to improve the known machine learning algorithms for pattern recognition using a minimum amount of time (the minimum number of used classifiers) and with a given accuracy of the results. We consider the implementation of the WaldBoost algorithm, which combines two algorithms: adaptive boosting of weak classifiers – AdaBoost (adaptive boosting), which has a high generalizing ability, and the sequential probability ratio test – SPRT (Wald test), which is the optimal rule of decision-making when distinguishing two hypotheses. It is noted that when using the WaldBoost, the values of the actual probability of classification errors, as a rule, are less than given because of the approximate boundaries of the SPRT, so that the classification process uses an excessive series of weak classifiers. In this regard, we propose a modification of the WaldBoost based on iterative refinement of the decision boundaries, which can significantly reduce the number of used weak classifiers required for pattern recognition with a given accuracy. The efficiency of the proposed algorithm is shown by specific examples. The results are confirmed by statistical modeling on several data sets. It is noted that the results can be applied in the refinement of other cascade classification algorithms.
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Ibraheem, Kais I., and Hisham M. Khudhur. "Optimization algorithm based on the Euler method for solving fuzzy nonlinear equations." Eastern-European Journal of Enterprise Technologies 1, no. 4 (115) (February 25, 2022): 13–19. http://dx.doi.org/10.15587/1729-4061.2022.252014.
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In a variety of engineering, scientific challenges, mathematics, chemistry, physics, biology, machine learning, deep learning, regression classification, computer science, programming, artificial intelligence, in the military, medical and engineering industries, robotics and smart cars, fuzzy nonlinear equations play a critical role. As a result, in this paper, an Optimization Algorithm based on the Euler Method approach for solving fuzzy nonlinear equations is proposed. In mathematics and computer science, the Euler approach (sometimes called the forward Euler method) is a first-order numerical strategy for solving ordinary differential equations (ODEs) with a specified initial value. The local error is proportional to the square of the step size, while the general error is proportional to the step size, according to the Euler technique. The Euler method is frequently used to create more complicated algorithms. The Optimization Algorithm Based on the Euler Method (OBE) uses the logic of slope differences, which is computed by the Euler approach for global optimizations as a search mechanism for promising logic. Furthermore, the mechanism of the proposed work takes advantage of two active phases: exploration and exploitation to find the most important promising areas within the distinct space and the best solutions globally based on a positive movement towards it. In order to avoid the solution of local optimal and increase the rate of convergence, we use the ESQ mechanism. The optimization algorithm based on the Euler method (OBE) is very efficient in solving fuzzy nonlinear equations and approaches the global minimum and avoids the local minimum. In comparison with the GWO algorithm, we notice a clear superiority of the OBE algorithm in reaching the solution with higher accuracy. We note from the numerical results that the new algorithm is 50 % superior to the GWO algorithm in Example 1, 51 % in Example 2 and 55 % in Example 3.
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Shi, Yong, Wenzhong Shi, Xintao Liu, and Xianjian Xiao. "An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning." Sensors 20, no. 15 (July 30, 2020): 4244. http://dx.doi.org/10.3390/s20154244.
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Received signal strength indicator (RSSI)-based positioning is suitable for large-scale applications due to its advantages of low cost and high accuracy. However, it suffers from low stability because RSSI is easily blocked and easily interfered with by objects and environmental effects. Therefore, this paper proposed a tri-partition RSSI classification and its tracing algorithm as an RSSI filter. The proposed filter shows an available feature, where small test RSSI samples gain a low deviation of less than 1 dBm from a large RSSI sample collected about 10 min, and the sub-classification RSSIs conform to normal distribution when the minimum sample count is greater than 20. The proposed filter also offers several advantages compared to the mean filter, including lower variance range with an overall range of around 1 dBm, 25.9% decreased sample variance, and 65% probability of mitigating RSSI left-skewness. We experimentally confirmed the proposed filter worked in the path-loss exponent fitting and location computing, and a 4.45-fold improvement in positioning stability based on the sample standard variance, and positioning accuracy improved by 20.5% with an overall error of less than 1.46 m.
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Liu, Zhu, Xuesong Qiu, Yonggui Wang, Shuai Zhang, and Zhi Li. "Improved firefly algorithm–extended Kalman filter–least-square support-vector machine voltage sag monitoring and classification method based on edge computing." International Journal of Distributed Sensor Networks 18, no. 3 (March 2022): 155013292210870. http://dx.doi.org/10.1177/15501329221087055.
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Aiming at the hardware reusability, multi-service carrying capacity, and computing resource limitations of edge devices, a light-weight voltage sag monitoring and classification method based on improved firefly algorithm optimization, extended Kalman filter, and least-square support-vector machine is proposed. The strategy of linearly decreasing inertia weight is introduced to optimize the state error of the extended Kalman filter algorithm and the measurement noise covariance matrix to achieve accurate monitoring of voltage sags. Extract characteristic quantities such as average value, duration of sag, minimum sag dispersion characteristics, number of sag phases, and flow direction of disturbance energy. As a model training data set, the least-square support-vector machine method optimized based on the improved firefly algorithm is used to create a multi-level classification model of voltage sag source to realize the classification of voltage sag sources. This method fully considers the influence of the limited resources of edge computing equipment on the algorithm, and effectively improves the use of computing resources by improving the optimization algorithm. Simulation and experimental results show that this method is suitable for edge computing equipment to monitor and distinguish voltage sags.
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Mohd Salleh, Mohd Radhie, Muhammad Zulkarnain Abd Rahman, Zamri Ismail, Mohd Faisal Abdul Khanan, and Mohd Asraff Asmadi. "REVISED PROGRESSIVE MORPHOLOGICAL METHOD FOR GROUND POINT CLASSIFICATION OF AIRBORNE LIDAR DATA." International Journal of Built Environment and Sustainability 6, no. 1-2 (April 1, 2019): 31–38. http://dx.doi.org/10.11113/ijbes.v6.n1-2.380.
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Airborne Light Detection and Ranging (LiDAR) has been very effectively used in collecting terrain information over different scales of area. Inevitably, filtering the non-ground returns is the major step of digital terrain model (DTM) generation and this step poses the greatest challenge especially for tropical forest environment which consists of steep undulating terrain and mostly covered by a relatively thick canopy density. The aim of this research is to assess the performance of the Progressive Morphological (PM) algorithm after the implementation of local slope value in the ground filtering process. The improvement on the PM filtering method was done by employing local slope values obtained either using initial filtering of airborne LiDAR data or ground survey data. The filtering process has been performed with recursive mode and it stops after the results of the filtering does not show any improvement and the DTM error larger than the previous iteration. The revised PM filtering method has decreasing pattern of DTM error with increasing filtering iterations with minimum ±0.520 m of RMSE value. The results also suggest that spatially distributed slope value applied in PM filtering algorithm either from LiDAR ground points or ground survey data is capable in preserving discontinuities of terrain and correctly remove non-terrain points especially in steep area.
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Pradeepika, Aluru, and Sabitha R. "Examination of Diabetes Mellitus for Early Forecast Using Decision Tree Classifier and an Innovative Dependent Feature Vector Based Naive Bayes Classifier." ECS Transactions 107, no. 1 (April 24, 2022): 12937–52. http://dx.doi.org/10.1149/10701.12937ecst.
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The proposed study aims to evaluate the accuracy and precision in earlier diabetes mellitus detection using Decision Tree and Naive Bayes classification algorithm. Materials and methods: Naive Bayes classifier is applied on a Pima Indian diabetes dataset that consist of 769 records. A machine learning technique for earlier prediction of diabetes disease which compares Decision Tree and Naive Bayes classification algorithms has been proposed and developed. The sample size was measured as 27 per group. The accuracy and precision of the classifiers was evaluated and recorded. Results: The accuracy was maximum in predicting diabetes using Naive Bayesian classifier (76.46%) with minimum mean error when compared with Decision Tree Classifier (70.09%). There is a significant difference of 0.006 between the groups. Hence, Naive Bayes appears to be better than Decision Tree Classifier. Conclusion: The study proves that Naive Bayesian Classifier exhibits better accuracy than Decision Tree Classifier in predicting diabetes.
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Alazab, Moutaz, Ruba Abu Khurma, Albara Awajan, and Mohammad Wedyan. "Digital Forensics Classification Based on a Hybrid Neural Network and the Salp Swarm Algorithm." Electronics 11, no. 12 (June 17, 2022): 1903. http://dx.doi.org/10.3390/electronics11121903.
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In recent times, cybercrime has increased significantly and dramatically. This made the need for Digital Forensics (DF) urgent. The main objective of DF is to keep proof in its original state by identifying, collecting, analyzing, and evaluating digital data to rebuild past acts. The proof of cybercrime can be found inside a computer’s system files. This paper investigates the viability of Multilayer perceptron (MLP) in DF application. The proposed method relies on analyzing the file system in a computer to determine if it is tampered by a specific computer program. A dataset describes a set of features of file system activities in a given period. These data are used to train the MLP and build a training model for classification purposes. Identifying the optimal set of MLP parameters (weights and biases) is a challenging matter in training MLPs. Using traditional training algorithms causes stagnation in local minima and slow convergence. This paper proposes a Salp Swarm Algorithm (SSA) as a trainer for MLP using an optimized set of MLP parameters. SSA has proved its applicability in different applications and obtained promising optimization results. This motivated us to apply SSA in the context of DF to train MLP as it was never used for this purpose before. The results are validated by comparisons with other meta-heuristic algorithms. The SSAMLP-DF is the best algorithm because it achieves the highest accuracy results, minimum error rate, and best convergence scale.
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Selvakumari Jeya, I. Jasmine, and S. N. Deepa. "Lung Cancer Classification Employing Proposed Real Coded Genetic Algorithm Based Radial Basis Function Neural Network Classifier." Computational and Mathematical Methods in Medicine 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/7493535.
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A proposed real coded genetic algorithm based radial basis function neural network classifier is employed to perform effective classification of healthy and cancer affected lung images. Real Coded Genetic Algorithm (RCGA) is proposed to overcome the Hamming Cliff problem encountered with the Binary Coded Genetic Algorithm (BCGA). Radial Basis Function Neural Network (RBFNN) classifier is chosen as a classifier model because of its Gaussian Kernel function and its effective learning process to avoid local and global minima problem and enable faster convergence. This paper specifically focused on tuning the weights and bias of RBFNN classifier employing the proposed RCGA. The operators used in RCGA enable the algorithm flow to compute weights and bias value so that minimum Mean Square Error (MSE) is obtained. With both the lung healthy and cancer images from Lung Image Database Consortium (LIDC) database and Real time database, it is noted that the proposed RCGA based RBFNN classifier has performed effective classification of the healthy lung tissues and that of the cancer affected lung nodules. The classification accuracy computed using the proposed approach is noted to be higher in comparison with that of the classifiers proposed earlier in the literatures.
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Pan, Lina. "Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation." Journal of Mathematics 2022 (March 7, 2022): 1–10. http://dx.doi.org/10.1155/2022/8752217.
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Aiming at the problems of poor classification effect, low accuracy, and long time in the current automatic classification methods of music genres, an automatic classification method of music genres based on deep belief network and sparse representation is proposed. The music signal is preprocessed by framing, pre-emphasis, and windowing, and the characteristic parameters of the music signal are extracted by Mel frequency cepstrum coefficient analysis. The restricted Boltzmann machine is trained layer by layer to obtain the connection weights between layers of the depth belief network model. According to the output classification, the connection weights in the model are fine-tuned by using the error back-propagation algorithm. Based on the deep belief network model after fine-tuning training, the structure of the music genre classification network model is designed. Combined with the classification algorithm of sparse representation, for the training samples of sparse representation music genre, the sparse solution is obtained by using the minimum norm, the sparse representation of test vector is calculated, the category of training samples is judged, and the automatic classification of music genre is realized. The experimental results show that the music genre automatic classification effect of the proposed method is better, the classification accuracy rate is higher, and the classification time can be effectively shortened.
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Devi, S. Sathiya, and Prithiviraj K. "Breast Cancer Classification With Microarray Gene Expression Data Based on Improved Whale Optimization Algorithm." International Journal of Swarm Intelligence Research 14, no. 1 (February 3, 2023): 1–21. http://dx.doi.org/10.4018/ijsir.317091.
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Breast cancer is one of the most common and dangerous cancer types in women worldwide. Since it is generally a genetic disease, microarray technology-based cancer prediction is technically significant among lot of diagnosis methods. The microarray gene expression data contains fewer samples with many redundant and noisy genes. It leads to inaccurate diagnose and low prediction accuracy. To overcome these difficulties, this paper proposes an Improved Whale Optimization Algorithm (IWOA) for wrapper based feature selection in gene expression data. The proposed IWOA incorporates modified cross over and mutation operations to enhance the exploration and exploitation of classical WOA. The proposed IWOA adapts multiobjective fitness function, which simultaneously balance between minimization of error rate and feature selection. The experimental analysis demonstrated that, the proposed IWOA with Gradient Boost Classifier (GBC) achieves high classification accuracy of 97.7% with minimum subset of features and also converges quickly for the breast cancer dataset.
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Xue, Jianxia, Xiaojing Chen, Zhonghao Xie, Shujat Ali, Leiming Yuan, Xi Chen, Wen Shi, and Guangzao Huang. "Recognition of Continuous Face Occlusion Based on Block Permutation by Using Linear Regression Classification." Applied Sciences 12, no. 23 (November 22, 2022): 11885. http://dx.doi.org/10.3390/app122311885.
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Face occlusion is still a key issue in the study of face recognition. Continuous occlusion affects the overall features and contour structure of a face, which brings significant challenges to face recognition. In previous studies, although the Representation-Based Classification Method (RBCM) can better capture the differences in different categories of faces and accurately identify human face images with changes in light and facial expressions, it is easily affected by continuous occlusion. For face recognition, there is a situation where face error recognition occurs. The RBCM method frequently learns to cover the characteristics of face recognition and then handle face error recognition. Therefore, the elimination of occlusion information from the image is necessary to improve the robustness of such models. The Block Permutation Linear Regression Classification (BPLRC) method proposed in this paper includes image block permutation and Linear Regression Classification (LRC). The LRC algorithm belongs to the category of nearest subspace classification and uses the Euclidean distance as a metric to classify images. The LRC algorithm is based on one of the classification methods that is susceptible to outliers. Therefore, block permutation was used with the aim of establishing an image set that does not contain much occlusion information and constructing a robust linear regression model. The BPLRC method first modulates all the images and then lists the schemes that arrange all segments, enters the image features of various schemes into linear models, and classifies the result according to the minimum residual of the person’s face image and reconstruction image. Compared to several state-of-the-art algorithms, the proposed method effectively solves the continuous occlusion problem for the Extended Yale B, ORL, and AR datasets. The proposed method recognizes the AR data concentration scarf to cover the accuracy of human face images to 93.67%. The dataset recognition speed is 0.094 s/piece. The arrangement method can be combined not only with the LRC algorithm, but also other algorithms with weak robustness. Due to the increase in the number of blocks and the increase in the calculation index of block arrangement methods, it is necessary to explore reasonable iteration methods in the future, quickly find the optimal or sub-best arrangement scheme, and reduce the calculation of the proposed method.
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Paul, Okuwobi Idowu, and Yong Hua Lu. "Vibration Monitoring Using Wavelets Transform Feature Extraction Algorithm and Technique." Applied Mechanics and Materials 666 (October 2014): 256–66. http://dx.doi.org/10.4028/www.scientific.net/amm.666.256.
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Vibration is a mechanical phenomenon whereby oscillations occur about an equilibrium point. The oscillation may be periods such as the motion of a pendulum or random such as the movement of tire on a gravel road. Vibration causes waste of energy and creates unwanted sound-noise. Monitoring such process generally possess a big problem especially for a system. The present traditional single resolution techniques could not solve this problem, coupled with the Fourier transform which seems to be one of the best method in analyzing and monitoring vibration in machineries or machinery components.This paper present a new algorithm using wavelet- packet based feature in classification of vibration signals. This study explores the feasibility of the wavelet packet transform as a tool in search for features that may be used in the detection and classification of machinery vibration signals. By formulating a systematic method of determining wavelet packet based features that exploit class specific differences among interested signals, which avoid human interaction. This new algorithm provide more effective method to achieve robust classification than traditional single resolution techniques. The new algorithm in wavelet transform techniques proved to be more efficient, better analysis, and provides better results with minimum error than any existing method.
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Wu, Jie, Xiaojuan Chen, and Zhaohua Zhang. "Potential Fault Diagnosis Method and Classification Accuracy Detection of IGBT Device Based on Improved Single Hidden Layer Feedforward Neural Network." Computational Intelligence and Neuroscience 2021 (September 30, 2021): 1–11. http://dx.doi.org/10.1155/2021/6036118.
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Insulated Gate Bipolar Transistor (IGBT) is a high-power switch in the field of power electronics. Its reliability is closely related to system stability. Once failure occurs, it may cause irreparable loss. Therefore, potential fault diagnosis methods of IGBT devices are studied in this paper, and their classification accuracy is tested. Due to the shortcomings of incomplete data application in the traditional method of characterizing the device state based on point frequency parameters, a fault diagnosis method based on full frequency threshold screening was proposed in this paper, and its classification accuracy was detected by the Extreme Learning Machine (ELM) algorithm. The randomly generated input layer weight ω and hidden layer deviation lead to the change of output layer weight β and then affect the overall output result. In view of the errors and instability caused by this randomness, an improved Finite Impulse Response Filter ELM (FIR-ELM) training algorithm is proposed. The filtering technique is used to initialize the input weights of the Single Hidden Layer Feedforward Neural Network (SLFN). The hidden layer of SLFN is used as the preprocessor to achieve the minimum output error. To reduce the structural risk and empirical risk of SLFN, the simulation results of 300 groups of spectral data show that the improved FIR-ELM algorithm significantly improves the training accuracy and has good robustness compared with the traditional extreme learning machine algorithm.
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Hu, Haozhong. "Segmentation of Breast Mass and Diagnosis of Benign and Malignant Breast Tumors Based on Edge Constraint in Pulse Coupled Neural Network." Journal of Medical Imaging and Health Informatics 10, no. 7 (July 1, 2020): 1597–602. http://dx.doi.org/10.1166/jmihi.2020.3086.
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In order to segment breast tumor accurately, an improved Unit-Linking Pulse-Coupled Neural Networks based mammography image segmentation method is proposed. Firstly, the link input and coupled parameter in the original model are improved according to the relationship between this neuron and its neighbors. Then, the improved model is used to segment the breast tumor image to obtain multiple output images. Finally, the gradient algorithm is used to calculate the edges of the original image and each output image respectively, and the minimum mean square error (MMSE) of the two edge images is calculated to find the best output image. The final experimental results indicate that the improved method can accurately segment breast tumor images in different environments. In addition, based on the segmentation results, we use the SVM method to diagnose the type of tumor, and its classification accuracy is much higher than the existing deep classification algorithm.
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Fong, Simon, Yan Zhuang, Rui Tang, Xin-She Yang, and Suash Deb. "Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search." Journal of Applied Mathematics 2013 (2013): 1–18. http://dx.doi.org/10.1155/2013/590614.
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Selecting the right set of features from data of high dimensionality for inducing an accurate classification model is a tough computational challenge. It is almost a NP-hard problem as the combinations of features escalate exponentially as the number of features increases. Unfortunately in data mining, as well as other engineering applications and bioinformatics, some data are described by a long array of features. Many feature subset selection algorithms have been proposed in the past, but not all of them are effective. Since it takes seemingly forever to use brute force in exhaustively trying every possible combination of features, stochastic optimization may be a solution. In this paper, we propose a new feature selection scheme called Swarm Search to find an optimal feature set by using metaheuristics. The advantage of Swarm Search is its flexibility in integrating any classifier into its fitness function and plugging in any metaheuristic algorithm to facilitate heuristic search. Simulation experiments are carried out by testing the Swarm Search over some high-dimensional datasets, with different classification algorithms and various metaheuristic algorithms. The comparative experiment results show that Swarm Search is able to attain relatively low error rates in classification without shrinking the size of the feature subset to its minimum.
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Li, Yuxia, and Zhinan Zhou. "RFID Anti-Collision Detection Algorithm Based on Improved Adaptive N-Tree." International Journal of Online Engineering (iJOE) 14, no. 10 (October 26, 2018): 129. http://dx.doi.org/10.3991/ijoe.v14i10.9310.
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<p style="margin: 1em 0px;"><span style="font-family: Times New Roman; font-size: medium;">This paper proposes a type of improved adaptive N-tree anti-collision algorithm based on the traditional one for RFID system by combination with maximum likelihood estimation and probe pre-detection. This algorithm inherits some features from Alpha- and tree-based anti-collision algorithms and effectively restrain the star-vation of the two algorithms. It has also filled in the gaps of tag collision with higher probability. The study turns out that the improved adaptive N-tree anti-collision algorithm as proposed can feature adaptive choice of the value N of the tree, length breaks of free timeslots, restraints on defects such as more tag classification and higher collision probability just as what the traditional tree-based algorithm has. N-tree built by level-to-level frame identification eliminates the free timeslots, and improves the tag identification precision for the RFID system. The results from simulation experiment reveal that the algorithm proposed in this paper has lower Error Sampling Reckon (ESR) and Throughput Rate Deviation (TRD), and features large throughput rate (87%), low delay of tag recognition and minimum timeslots, and etc. hence to be better applied in large-scale logistics and other fields where fast information recognition is involved.</span></p>
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Ducinskas, Kestutis, and Lina Dreiziene. "Performance Evaluations of Gaussian Spatial Data Classifiers Based on Hybrid Actual Error Rate Estimators." Austrian Journal of Statistics 49, no. 4 (April 13, 2020): 27–34. http://dx.doi.org/10.17713/ajs.v49i4.1122.
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Discrimination and classification of spatial data has been widely mentioned in the scientific literature, but lacks full mathematical treatment and easily available algorithms and software. This paper fills this gap by introducing the method of statistical classification based on Bayes discriminant function (BDF) and by providing original approach for the classifier performance evaluation. Supervised classification of spatial data with response variable modelled by Gaussian random fields (GRF) with continuous or discrete spatial index is studied. Populations are assumed to be with different regression parameters vectors. Classification rule based on BDF with inserted ML estimators of regression and scale parameters is studied. We focus on the derived actual error rate (AER) and the approximation of the expected error rate (AEER) for both types of models. These are used in the construction of hybrid actual error rate estimators that are spatial modifications of widely applicable D and O estimators applied in cases of independent observations. Simulation experiments are used for comparison of proposed AER estimators by the minimum of unconditional mean squared error criterion for both types of GRF models.
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Long, Zhili, Ronghua He, Yuxiang He, Haoyao Chen, and Zuohua Li. "Feature Extraction and Mapping Construction for Mobile Robot via Ultrasonic MDP and Fuzzy Model." Sensors 18, no. 11 (October 29, 2018): 3673. http://dx.doi.org/10.3390/s18113673.
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This paper presents a modeling approach to feature classification and environment mapping for indoor mobile robotics via a rotary ultrasonic array and fuzzy modeling. To compensate for the distance error detected by the ultrasonic sensor, a novel feature extraction approach termed “minimum distance of point” (MDP) is proposed to determine the accurate distance and location of target objects. A fuzzy model is established to recognize and classify the features of objects such as flat surfaces, corner, and cylinder. An environmental map is constructed for automated robot navigation based on this fuzzy classification, combined with a cluster algorithm and least-squares fitting. Firstly, the platform of the rotary ultrasonic array is established by using four low-cost ultrasonic sensors and a motor. Fundamental measurements, such as the distance of objects at different rotary angles and with different object materials, are carried out. Secondly, the MDP feature extraction algorithm is proposed to extract precise object locations. Compared with the conventional range of constant distance (RCD) method, the MDP method can compensate for errors in feature location and feature matching. With the data clustering algorithm, a range of ultrasonic distances is attained and used as the input dataset. The fuzzy classification model—including rules regarding data fuzzification, reasoning, and defuzzification—is established to effectively recognize and classify the object feature types. Finally, accurate environment mapping of a service robot, based on MDP and fuzzy modeling of the measurements from the ultrasonic array, is demonstrated. Experimentally, our present approach can realize environment mapping for mobile robotics with the advantages of acceptable accuracy and low cost.
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Kurdi, Sarah Zuhair, Mohammed Hasan Ali, Mustafa Musa Jaber, Tanzila Saba, Amjad Rehman, and Robertas Damaševičius. "Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks." Journal of Personalized Medicine 13, no. 2 (January 20, 2023): 181. http://dx.doi.org/10.3390/jpm13020181.
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The field of medical image processing plays a significant role in brain tumor classification. The survival rate of patients can be increased by diagnosing the tumor at an early stage. Several automatic systems have been developed to perform the tumor recognition process. However, the existing systems could be more efficient in identifying the exact tumor region and hidden edge details with minimum computation complexity. The Harris Hawks optimized convolution network (HHOCNN) is used in this work to resolve these issues. The brain magnetic resonance (MR) images are pre-processed, and the noisy pixels are eliminated to minimize the false tumor recognition rate. Then, the candidate region process is applied to identify the tumor region. The candidate region method investigates the boundary regions with the help of the line segments concept, which reduces the loss of hidden edge details. Various features are extracted from the segmented region, which is classified by applying a convolutional neural network (CNN). The CNN computes the exact region of the tumor with fault tolerance. The proposed HHOCNN system was implemented using MATLAB, and performance was evaluated using pixel accuracy, error rate, accuracy, specificity, and sensitivity metrics. The nature-inspired Harris Hawks optimization algorithm minimizes the misclassification error rate and improves the overall tumor recognition accuracy to 98% achieved on the Kaggle dataset.
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Djelloul, Imene, Zaki Sari, and Ibrahima dit Bouran Sidibe. "Fault diagnosis based on the quality effect of learning algorithm for manufacturing systems." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 233, no. 7 (January 16, 2019): 801–14. http://dx.doi.org/10.1177/0959651818823097.
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Fault diagnosis is becoming an important issue in industrial environment, and the accurate diagnosis is the most significant part in fault handling. This article discusses a fault detection and diagnosis problem for manufacturing systems taking into account rapid detection and speed performances of fault isolation with minimum ambiguity. However, in many complex real plants, it may not be possible to discover accurately the causes of probable faults. The accuracy of fault or fault detection by the traditional approaches is not adequate. Considering the quality effect of the learning algorithm, a new hybrid neural network approach is developed using the integration of a regression task for classification accuracy. Two models of neural networks: gradient descent and momentum & adaptive learning rate and Levenberg–Marquardt are investigated and compared. The performance of the proposed approach is evaluated using mean square error, convergence speed, and classification accuracy. The case study and experimental results are presented and discussed. A comparison with the Levenberg–Marquardt regression approach shows the importance of considering the proposed learning algorithm quality in the fault detection and diagnosis problem compared with those reported in the literature.
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Tao, Jianfeng, Chengjin Qin, Weixing Li, and Chengliang Liu. "Intelligent Fault Diagnosis of Diesel Engines via Extreme Gradient Boosting and High-Accuracy Time–Frequency Information of Vibration Signals." Sensors 19, no. 15 (July 25, 2019): 3280. http://dx.doi.org/10.3390/s19153280.
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Accurate and timely misfire fault diagnosis is of vital significance for diesel engines. However, existing algorithms are prone to fall into model over-fitting and adopt low energy-concentrated features. This paper presents a novel extreme gradient boosting-based misfire fault diagnosis approach utilizing the high-accuracy time–frequency information of vibration signals. First, diesel engine misfire tests were conducted under different spindle speeds, and the corresponding vibration signals were acquired via a triaxial accelerometer. The time-domain features of signals were extracted by using a time-domain statistics method, while the high-accuracy time–frequency domain features were obtained via the high-resolution multisynchrosqueezing transform. Thereafter, considering the nonlinearity and high dimensionality of the original characteristic data sets, the locally linear embedding method was employed for feature dimensionality reduction. Eventually, to avoid model overfitting, the extreme gradient boosting algorithm was utilized for diesel engine misfire fault diagnosis. Experiments under different spindle speeds and comprehensive comparisons with other evaluation methods were conducted to demonstrate the effectiveness of the proposed extreme gradient boosting-based misfire diagnosis method. The results verify that the highest classification accuracy of the proposed extreme gradient boosting-based algorithm is up to 99.93%. Simultaneously, the classification accuracy of the presented approach is approximately 24.63% higher on average than those of algorithms that use wavelet packet-based features. Moreover, it is shown that it obtains the minimum root mean squared error and can effectively prevent the model from falling into overfitting.
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Xue, Yun, Zhiwen Liu, Jie Luo, Zhihao Ma, Meizhen Zhang, Xiaohui Hu, and Qiuhua Kuang. "Stock Market Trading Rules Discovery Based on Biclustering Method." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/849286.
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The prediction of stock market’s trend has become a challenging task for a long time, which is affected by a variety of deterministic and stochastic factors. In this paper, a biclustering algorithm is introduced to find the local patterns in the quantized historical data. The local patterns obtained are regarded as the trading rules. Then the trading rules are applied in the short term prediction of the stock price, combined with the minimum-error-rate classification of the Bayes decision theory under the assumption of multivariate normal probability model. In addition, this paper also makes use of the idea of the stream mining to weaken the impact of historical data on the model and update the trading rules dynamically. The experiment is implemented on real datasets and the results prove the effectiveness of the proposed algorithm.
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Kacur, Juraj. "Algebraic Zero Error Training Method for Neural Networks Achieving Least Upper Bounds on Neurons and Layers." Computers 11, no. 5 (May 4, 2022): 74. http://dx.doi.org/10.3390/computers11050074.
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In the domain of artificial neural networks, it is important to know what their representation, classification and generalization capabilities are. There is also a need for time and resource-efficient training algorithms. Here, a new zero-error training method is derived for digital computers and single hidden layer networks. This method is the least upper bound on the number of hidden neurons as well. The bound states that if there are N input vectors expressed as rational numbers, a network having N − 1 neurons in the hidden layer and M neurons at the output represents a bounded function F: RD→RM for all input vectors. Such a network has massively shared weights calculated by 1 + M regular systems of linear equations. Compared to similar approaches, this new method achieves a theoretical least upper bound, is fast, robust, adapted to floating-point data, and uses few free parameters. This is documented by theoretical analyses and comparative tests. In theory, this method provides a new constructional proof of the least upper bound on the number of hidden neurons, extends the classes of supported activation functions, and relaxes conditions for mapping functions. Practically, it is a non-iterative zero-error training algorithm providing a minimum number of neurons and layers.
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Kittisuwan, Pichid. "Low-complexity image denoising based on mixture model and simple form of MMSE estimation." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 06 (October 10, 2018): 1850052. http://dx.doi.org/10.1142/s0219691318500522.
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In order to enhance efficiency of artificial intelligence (AI) tools such as classification or pattern recognition, it is important to have noise-free data to be processed with AI tools. Therefore, the study of algorithms used for reducing noise is also very significant. In thermal condition, Gaussian noise is important problem in analog circuit and image processing. Therefore, this paper focuses on the study of an algorithm for Gaussian noise reduction. In recent year, Bayesian with wavelet-based methods provides good efficiency in noise reduction and spends short time in processing. In Bayesian method, mixture density is more flexible than non-mixture density. Therefore, we proposed novel form of minimum mean square error (MMSE) estimation for mixture model, Pearson type VII and logistic densities, in Gaussian noise. The expectation-maximization (EM) algorithm is most deeply used for computing statistical parameters of mixture model. However, the EM estimator for the proposed method does not have the closed-form. Numerical methods are required to implement an EM algorithm. Therefore, we employ maximum a posteriori (MAP) estimation to compute local noisy variances with half-normal distribution prior for local noisy variances and Gaussian density for noisy wavelet coefficients. Here, the proposed method is expressed in closed-form. The denoising results present that our proposed algorithm outperforms the state-of-the-art method qualitatively and quantitatively.