GaitDONet: Gait Recognition Using Deep Features Optimization and Neural Network

Human gait recognition(HGR)is the process of identifying a sub-ject(human)based on their walking pattern.Each subject is a unique walking pattern and cannot be simulated by other subjects.But,gait recognition is not easy and makes the system difficult if any object is carried by a subject,such as a bag or coat.This article proposes an automated architecture based on deep features optimization for HGR.To our knowledge,it is the first architecture in which features are fused using multiset canonical correlation analysis(MCCA).In the proposed method,original video frames are processed for all 11 selected angles of the CASIA B dataset and utilized to train two fine-tuned deep learning models such as Squeezenet and Efficientnet.Deep transfer learning was used to train both fine-tuned models on selected angles,yielding two new targeted models that were later used for feature engineering.Features are extracted from the deep layer of both fine-tuned models and fused into one vector using MCCA.An improved manta ray foraging optimization algorithm is also proposed to select the best features from the fused feature matrix and classified using a narrow neural network classifier.The experimental process was conducted on all 11 angles of the large multi-view gait dataset(CASIA B)dataset and obtained improved accuracy than the state-of-the-art techniques.Moreover,a detailed confidence interval based analysis also shows the effectiveness of the proposed architecture for HGR.

Leaf recognition using BP-RBF hybrid neural network

Plant recognition has great potential in forestry research and management.A new method combined back propagation neural network and radial basis function neural network to identify tree species using a few features and samples.The process was carried out in three steps:image pretreatment,feature extraction,and leaf recognition.In the image pretreatment processing,an image segmentation method based on hue,saturation and value color space and connected component labeling was presented,which can obtain the complete leaf image without veins and back-ground.The BP-RBF hybrid neural network was used to test the influence of shape and texture on species recogni-tion.The recognition accuracy of different classifiers was used to compare classification performance.The accuracy of the BP-RBF hybrid neural network using nine dimensional features was 96.2%,highest among all the classifiers.

An improved micro-expression recognition algorithm of 3D convolutional neural network

The micro-expression lasts for a very short time and the intensity is very subtle.Aiming at the problem of its low recognition rate,this paper proposes a new micro-expression recognition algorithm based on a three-dimensional convolutional neural network(3D-CNN),which can extract two-di-mensional features in spatial domain and one-dimensional features in time domain,simultaneously.The network structure design is based on the deep learning framework Keras,and the discarding method and batch normalization(BN)algorithm are effectively combined with three-dimensional vis-ual geometry group block(3D-VGG-Block)to reduce the risk of overfitting while improving training speed.Aiming at the problem of the lack of samples in the data set,two methods of image flipping and small amplitude flipping are used for data amplification.Finally,the recognition rate on the data set is as high as 69.11%.Compared with the current international average micro-expression recog-nition rate of about 67%,the proposed algorithm has obvious advantages in recognition rate.

Method of neural network modulation recognition based on clustering and Polak-Ribiere algorithm

To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is extracted by using a clustering algorithm, the neural network is trained by using the algorithm of variable gradient correction （Polak-Ribiere） so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram. Simulation results show that the recognition rate based on this algorithm is enhanced over 30% compared with the methods that adopt clustering algorithm or neural network based on the back propagation algorithm alone under the low SNR. The recognition rate can reach 90% when the SNR is 4 dB, and the method is easy to be achieved so that it has a broad application prospect in the modulating recognition.

Research on Rice Leaf Disease Recognition Based on BP Neural Network

To solve the problem of mistake recognition among rice diseases, automatic recognition methods based on BP(back propagation) neural network were studied in this paper for blast, sheath blight and bacterial blight. Chose mobile terminal equipment as image collecting tool and built database of rice leaf images with diseases under threshold segmentation method. Characteristic parameters were extracted from color, shape and texture. Furthermore, parameters were optimized using the single-factor variance analysis and the effects of BP neural network model. The optimization would simplify BP neural network model without reducing the recognition accuracy. The finally model could successfully recognize 98%, 96% and 98% of rice blast, sheath blight and white leaf blight, respectively.

Parameter Self - Learning of Generalized Predictive Control Using BP Neural Network

This paper describes the self—adjustment of some tuning-knobs of the generalized predictive controller(GPC).A three feedforward neural network was utilized to on line learn two key tuning-knobs of GPC,and BP algorithm was used for the training of the linking-weights of the neural network.Hence it gets rid of the difficulty of choosing these tuning-knobs manually and provides easier condition for the wide applications of GPC on industrial plants.Simulation results illustrated the effectiveness of the method.

Sub-pixel mapping method based on BP neural network

A new sub-pixel mapping method based on BP neural network is proposed in order to determine the spatial distribution of class components in each mixed pixel.The network was used to train a model that describes the relationship between spatial distribution of target components in mixed pixel and its neighboring information.Then the sub-pixel scaled target could be predicted by the trained model.In order to improve the performance of BP network,BP learning algorithm with momentum was employed.The experiments were conducted both on synthetic images and on hyperspectral imagery(HSI).The results prove that this method is capable of estimating land covers fairly accurately and has a great superiority over some other sub-pixel mapping methods in terms of computational complexity.

Enhanced Marathi Speech Recognition Facilitated by Grasshopper Optimisation-Based Recurrent Neural Network

Communication is a significant part of being human and living in the world.Diverse kinds of languages and their variations are there;thus,one person can speak any language and cannot effectively communicate with one who speaks that language in a different accent.Numerous application fields such as education,mobility,smart systems,security,and health care systems utilize the speech or voice recognition models abundantly.Though,various studies are focused on the Arabic or Asian and English languages by ignoring other significant languages like Marathi that leads to the broader research motivations in regional languages.It is necessary to understand the speech recognition field,in which the major concentrated stages are feature extraction and classification.This paper emphasis developing a Speech Recognition model for the Marathi language by optimizing Recurrent Neural Network(RNN).Here,the preprocessing of the input signal is performed by smoothing and median filtering.After preprocessing the feature extraction is carried out using MFCC and Spectral features to get precise features from the input Marathi Speech corpus.The optimized RNN classifier is used for speech recognition after completing the feature extraction task,where the optimization of hidden neurons in RNN is performed by the Grasshopper Optimization Algorithm(GOA).Finally,the comparison with the conventional techniques has shown that the proposed model outperforms most competing models on a benchmark dataset.

A Modified Algorithm for Feedforward Neural Networks

As a most popular learning algorithm for the feedforward neural networks, the classic BP algorithm has its many shortages. To overcome some of the shortages, a modified learning algorithm is proposed in the article. And the simulation result illustrate the modified algorithm is more effective and practicable.

A CASCADED MODEL OF NEURAL NETWORK FOR PATTERN RECOGNITION

A cascaded model of neural network and its learning algorithm suitable for opticalimplementation are proposed.Computer simulations have shown that this model may successfullybe applied to an error-tolerance pattern recognitions of multiple 3-D targets with arbitrary spatialorientations.

A Novel Combinational Convolutional Neural Network for Automatic Food-Ingredient Classification

With the development of deep learning and Convolutional Neural Networks(CNNs),the accuracy of automatic food recognition based on visual data have significantly improved.Some research studies have shown that the deeper the model is,the higher the accuracy is.However,very deep neural networks would be affected by the overfitting problem and also consume huge computing resources.In this paper,a new classification scheme is proposed for automatic food-ingredient recognition based on deep learning.We construct an up-to-date combinational convolutional neural network(CBNet)with a subnet merging technique.Firstly,two different neural networks are utilized for learning interested features.Then,a well-designed feature fusion component aggregates the features from subnetworks,further extracting richer and more precise features for image classification.In order to learn more complementary features,the corresponding fusion strategies are also proposed,including auxiliary classifiers and hyperparameters setting.Finally,CBNet based on the well-known VGGNet,ResNet and DenseNet is evaluated on a dataset including 41 major categories of food ingredients and 100 images for each category.Theoretical analysis and experimental results demonstrate that CBNet achieves promising accuracy for multi-class classification and improves the performance of convolutional neural networks.

Gesture Recognition Based on BP Neural Network Improved by Chaotic Genetic Algorithm

Aim at the defects of easy to fall into the local minimum point and the low convergence speed of back propagation（BP）neural network in the gesture recognition, a new method that combines the chaos algorithm with the genetic algorithm（CGA） is proposed. According to the ergodicity of chaos algorithm and global convergence of genetic algorithm, the basic idea of this paper is to encode the weights and thresholds of BP neural network and obtain a general optimal solution with genetic algorithm, and then the general optimal solution is optimized to the accurate optimal solution by adding chaotic disturbance. The optimal results of the chaotic genetic algorithm are used as the initial weights and thresholds of the BP neural network to recognize the gesture. Simulation and experimental results show that the real-time performance and accuracy of the gesture recognition are greatly improved with CGA.

Feature Fusion-Based Deep Learning Network to Recognize Table Tennis Actions

A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study.The wearable device consisted of a six-axis sensor,Raspberry Pi 3,and a power bank.Multiple kernel sizes were used in convolutional neural network(CNN)to evaluate their performance for extracting features.Moreover,a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner.The CNN achieved recognition of the four table tennis strokes.Experimental data were obtained from20 research participants who wore sensors on the back of their hands while performing the four table tennis strokes in a laboratory environment.The data were collected to verify the performance of the proposed models for wearable devices.Finally,the sensor and multi-scale CNN designed in this study achieved accuracy and F1 scores of 99.58%and 99.16%,respectively,for the four strokes.The accuracy for five-fold cross validation was 99.87%.This result also shows that the multi-scale convolutional neural network has better robustness after fivefold cross validation.

A novel compensation-based recurrent fuzzy neural network and its learning algorithm

Based on detailed study on several kinds of fuzzy neural networks, we propose a novel compensationbased recurrent fuzzy neural network （CRFNN） by adding recurrent element and compensatory element to the conventional fuzzy neural network. Then, we propose a sequential learning method for the structure identification of the CRFNN in order to confirm the fuzzy rules and their correlative parameters effectively. Furthermore, we improve the BP algorithm based on the characteristics of the proposed CRFNN to train the network. By modeling the typical nonlinear systems, we draw the conclusion that the proposed CRFNN has excellent dynamic response and strong learning ability.

Multiple Feature Fusion in Convolutional Neural Networks for Action Recognition

Action recognition is important for understanding the human behaviors in the video,and the video representation is the basis for action recognition.This paper provides a new video representation based on convolution neural networks（CNN）.For capturing human motion information in one CNN,we take both the optical flow maps and gray images as input,and combine multiple convolutional features by max pooling across frames.In another CNN,we input single color frame to capture context information.Finally,we take the top full connected layer vectors as video representation and train the classifiers by linear support vector machine.The experimental results show that the representation which integrates the optical flow maps and gray images obtains more discriminative properties than those which depend on only one element.On the most challenging data sets HMDB51 and UCF101,this video representation obtains competitive performance.

Recognition of weeds at asparagus fields using multi-feature fusion and backpropagation neural network

In order to solve the problem of low recognition rates of weeds by a single feature,a method was proposed in this study to identify weeds in Asparagus(Asparagus officinalis L.)field using multi-feature fusion and backpropagation neural network(BPNN).A total of 382 images of weeds competing with asparagus growth were collected,including 135 of Cirsium arvense(L.)Scop.,138 of Conyza sumatrensis(Retz.)E.Walker,and 109 of Calystegia hederacea Wall.The grayscale images were extracted from the RGB images of weeds using the 2G-R-B factor.Threshold segmentation of the grayscale image of weeds was applied using Otsu method.Then the internal holes of the leaves were filled through the expansion and corrosion morphological operations,and other interference targets were removed to obtain the binary image.The foreground image was obtained by masking the binary image and the RGB image.Then,the color moment algorithm was used to extract weeds color feature,the gray level co-occurrence matrix and the Local Binary Pattern(LBP)algorithm was used to extract weeds texture features,and seven Hu invariant moment features and the roundness and slenderness ratio of weeds were extracted as their shape features.According to the shape,color,texture,and fusion features of the test samples,a weed identification model was built.The test results showed that the recognition rate of Cirsium arvense(L.)Scop.,Calystegia hederacea Wall.and Conyza sumatrensis(Retz.)E.Walker were 82.72%(color feature),72.41%(shape feature),86.73%(texture feature)and 93.51%(fusion feature),respectively.Therefore,this method can provide a reference for the study of weeds identification in the asparagus field.

AWeb Application Fingerprint Recognition Method Based on Machine Learning

Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web applications.In response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering techniques.The method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction.The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated labels.By transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint recognition.The experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad coverage.This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.

Memetic algorithms-based neural network learning for basic oxygen furnace endpoint prediction

Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development of a novel memetic algorithm （MA） for neural network （NN） lcarnmg. Included in this is the integration of extremal optimization （EO） and Levenberg-Marquardt （LM） pradicnt search, and its application in BOF endpoint quality prediction. The fundamental analysis reveals that the proposed EO-LM algorithm may provide superior performance in generalization, computation efficiency, and avoid local minima, compared to traditional NN learning methods. Experimental results with production-scale BOF data show that the proposed method can effectively improve the NN model for BOF endpoint quality prediction.

Optimizing Deep Learning Parameters Using Genetic Algorithm for Object Recognition and Robot Grasping

The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.