Multiresolution wavelet analysis based feature extraction for neural network classification

Purpose In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Design/methodology/approach In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Findings In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Originality/value In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

How to solve multi-category image recognition and meet a certain accuracy is a key issue in the research of high-resolution remote sensing images, and it is of great significance. This article mainly studies artificial neural network in the classification of multi-source remote sensing images. This paper improves the efficiency and accuracy of image segmentation by studying the principle and implementation process of image segmentation algorithm from the two aspects of initial segmentation and region merging; secondly, it studies the method of object feature quantization and the image of different object features on the classification results; and finally, it selects BP neural network. The network classification method classifies the image objects and realizes the extraction and classification of high-resolution remote sensing images. Experiments in this paper show that for multi-source remote sensing image data, the overall accuracy of the two parallel classification algorithms is very similar, and both are close to 85%, which has a good classification effect. When performing large-scale image classification, the terrain types in the image will be more complicated. In this case, the extraction accuracy relative to the artificial neural network classification method will decrease, and the classification time will also become longer. This paper proves through experiments that the classification method of multi-source remote sensing images based on artificial neural network is feasible and has certain advantages.

Some features of cognitive modeling are presented, including the prerequisites for a cognitive approach to solving complex problems. Cognitive modeling involves the use of various artificial neural networks, including convolutional neural networks. The classification of artificial neural networks according to various characteristics is given. The features of self-organizing neural networks and networks using deep learning methods are considered. The artificial neural network, which is a three-layer unidirectional direct propagation network, the interface of a computer program used to approximate functions using the specified neural network, as well as the solution of the image recognition problem using an artificial convolutional neural network, in which the neural network parameters are adjusted for each recognizable image fragment in order to adaptively filter the image, are considered in detail. The analysis of images in video surveillance systems in order to detect fires allows them to be detected at an early stage and, thus, prevent the fire propogation.

Forecasting and alert of atmospheric bioaerosol concentration profile based on adaptive genetic algorithm back propagation neural network, atmospheric parameter and fluorescence lidar

Cross-cultural learning competence, a critical skill in our globally interconnected world, is advanced through the application of the Backpropagation (BP) neural network algorithm. This innovative approach involves leveraging neural network techniques to model and enhance individuals' abilities to navigate and understand diverse cultural contexts. The BP neural network algorithm facilitates personalized learning experiences by adapting to individuals' cultural backgrounds and preferences. This research explores a comprehensive approach for assessing and enhancing cross-cultural learning competence among Chinese college students, integrating the Word Embedding Multilingual Model with the Back Propagation Neural Network (WEMM-BPNN) algorithm. Recognizing the importance of global competencies in higher education, our study focuses on leveraging advanced neural network techniques to evaluate and elevate students' cross-cultural learning abilities. The WEMM-BPNN model combines the power of word embedding and multilingual considerations, tailoring the learning experience to individual cultural backgrounds. Through a meticulous analysis of cross-cultural data and linguistic patterns, the algorithm refines its recommendations for personalized learning strategies. The research aims not only to assess the current state of cross-cultural learning competence but also to provide targeted interventions to enhance students' intercultural understanding and adaptability. By merging linguistic models with neural network algorithms, this study offers a pioneering approach to cultivating cross-cultural competencies, contributing valuable insights to the ongoing discourse on globalized education.

The Sixth International Symposium on Neural Networks (ISNN 2009)

Discusses the effects of the sample size on the estimates of the error rate of the artificial neural network (ANN) classifiers. Experimental results show that the standard deviation of the estimated error rate of ANN classifiers is independent of the hidden unit size. In addition, it is shown that nevertheless the class distributions are Gaussian, ANN classifiers outperform the quadratic discriminant function when sizes of samples per class are much unequal.

Traffic Sign Recognition (TSR) system is a significant component of Intelligent Transport System (ITS) as traffic signs assist the drivers to drive more safely and efficiently. This paper represents a new approach for TSR system where detection of traffic sign is carried out using fuzzy rules based color segmentation method and recognition is accomplished using Speeded Up Robust Features(SURF) descriptor, trained by artificial neural network (ANN) classifier. In the detection step, the region of interest (sign area) is segmented using a set of fuzzy rules depending on the hue and saturation values of each pixel in the HSV color space, post processed to filter unwanted region. Finally the recognition of the traffic sign is implemented using ANN classifier upon the training of SURF features descriptor. The proposed system simulated on offline road scene images captured under different illumination conditions. The detection algorithm shows a high robustness and the recognition rate is quite satisfactory. The performance of the ANN model is illustrated in terms of cross entropy, confusion matrix and receiver operating characteristic (ROC) curves. Also, performances of some classifier such as Support Vector Machine (SVM), Decision Trees, Ensembles Learners (Adaboost) and K-Nearest Neighbor (KNN) classifier are assessed with ANN approach. The simulation results illustrate that recognition using ANN model is higher than classifiers stated above.

While many BSN applications require that sensor nodes be able to operate for extended periods of time, they also often require the wireless transmission of copious amounts of sensor data to a data aggregator or base station, where the raw data is processed into application-relevant information. The energy requirements of such streaming can be prohibitive, given the competing considerations of form factor and battery life requirements. Making intelligent decisions on the node about which data to store or transmit, and which to ignore, is a promising method of reducing energy consumption. Artificial neural network (ANN) classifiers are among several competitive techniques for such data selection. However, no systematic metrics exist for determining if an ANN classifier is suited for a particular resource constrained computing environment of a typical BSN node. An especially difficult task is assessing, at the design stage, which classifier architectures are feasible on a given resource-constrained node, what computational resources are required to execute a given classifier, and what classification performance might be achieved by a particular classifier on a given set of resources. This paper describes techniques for quantifying and predicting the performance of ANN classifiers on wearable sensor nodes using scalable synthetic test data. Additionally, the paper shows a comparison of synthetic data with gait data collected using an inertial BSN node, and classification results of the gait data using a cerebellar model arithmetic computer (CMAC) architecture show excellent agreement with theoretical predictions.

Surface electromyogram (sEMG) signals are widely used to control the myoelectric prosthetic arm for amputees. In this study, the authors investigated the usefulness of discrete wavelet transform (DWT) features from multiple levels of approximation and detail coefficients obtained from sEMG signals. DWT is used for de-noising as well as feature extraction in this study and further tested using Support Vector Machine (SVM) classifier and Artificial Neural Network (ANN) Classifier. The performance of SVM classifier is compared with ANN classifier. The classification accuracy of SVM classifiers was found better as compared to ANN in term of speed and robustness. In the first section of paper the authors presented the introduction related to the study. The experimental set-up for recording surface EMG signal is presented in section 2. In section 3 the experimental results are depicted and in section 4 the results are concluded.

Icing condition prediction of wind turbine blade by using artificial neural network based on modal frequency

For the localization of epileptic seizure, it has a guiding significance to detect high frequency oscillations (HFOs) which are novel biomarkers of the seizure onset zone. In this paper, a new method including Fuzzy entropy (FuzzyEn) and Fuzzy neural network (FNN) for automatic HFOs detection is proposed. FuzzyEn and short time energy which is calculated by mean square method are used as the inputs of the FNN classifier. The T-S interference model is applied to the consequent part of the fuzzy rules. The least squares estimation and error back propagation algorithms are used to adjust the consequent and antecedent parameters, respectively. The output of the FNN classifier is utilized to classify the signal as HFOs or as normal activity. The simulation results indicate that FuzzyEn can extract the HFOs features more effectively compared with approximate entropy. Besides, the sensitivity and specificity of the FNN classifier proposed in this paper are obviously higher than those of the artificial neural network (ANN) classifier which has the same number of hidden layers, neurons and epochs with the FNN classifier. It is clear that the classification performance of the FNN classifier is better compared with the ANN classifier.

In this current research the aim of study is to predict changes happening in land by comparing an Innovative Artificial Neural Network (ANN) classifier and Mahalanobis Classifier (MC) by digital image processing and also comparing which algorithm gives more accuracy. For the years 2001 and 2011 Landsat7 ETM+(Enhanced Thematic Mapper plus) is used and Landsat 8 is used for 2021 of study region. These satellite images were classified into two groups which are ANN classifier and Mahalanobis classifier, each group contains 3 samples with a total of N=6 samples. The pretest power is assumed to be 80% and with alpha value of 0.05 and Confidence Interval of 95%. The land use and land cover changes have been analyzed with supervised classifiers and percentages of different types of region has been noted. An independent samples-t test from SPSS statistical analysis it was observed that from a single tail test <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{p} &gt; 0.05$</tex> hence there is no significance difference between two groups of classifiers, namely, ANN and MC. The mean and standard deviation of overall classification accuracy is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$98.69\pm 1.24$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$91.13\pm 6.47$</tex> respectively. The mean and standard deviation for kappa coefficient is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.97\pm 0.016$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.87\pm 0.076$</tex> for ANN and MC respectively. From this research, within the limits of the study, it can be concluded that Innovative Artificial Neural Network has performed better than Mahalanobis based classifier.

Classification is the process of recognition, differentiation and categorizing objects into groups. Breast abnormalities are calcifications which are tumor markers that indicate the presence of cancer in the breast. The aims of this research are to classify the types of breast abnormalities using artificial neural network (ANN) classifier and to evaluate the accuracy performance using receiver operating characteristics (ROC) curve. The methods used in this research are ANN for breast abnormalities classifications and Canny edge detector as a feature extraction method. Previously the ANN classifier provides only the number of benign and malignant cases without providing information for specific cases. However in this research, the type of abnormality for each image can be obtained. The existing MIAS MiniMammographic database classified the mammogram images into three features only namely characteristic of background tissues, class of abnormality and radius of abnormality. However, in this research three other features are added-in. These three features are number of spots, area and shape of abnormalities. Lastly the performance of the ANN classifier is evaluated using ROC curve. It is found that ANN has an accuracy of 97.9% which is considered acceptable.

Artificial neural networks (ANNs) especially Backpropagation Neural Network (BPNN) was used largely in image processing. The backpropagation neural network algorithm (BP) was used for training the BPNN for image compression/decompression. The BP requires long time to train the BPNN with small error. Therefore, in this research, a three layered BPNN was designed for building image compression system. The Fast backpropagation neural network algorithm (FBP) was used for training the designed BPNN to reduce the training time (convergence time) of BPNN as possible as. Many techniques were used to improve the use of FBP for BPNN training. This is done by using different architecture of BPNN by changing the number of input layer neurons and number of hidden layer neurons. Also we trained the BPNN with different FBP parameters. Finally, FBP results such as compression ratio (CR) and peak signal to noise ratio (PSNR) are computed and compared with BP results. From the results, we noticed that the use of FBP improve the BPNN training by reducing the convergence time of image compression learning process.