Image Classification Algorithm Based on Big Data and Multilabel Learning of Improved Convolutional Neural Network

More and more image materials are used in various industries these days. Therefore, how to collect useful images from a large set has become an urgent priority. Convolutional neural networks (CNN) have achieved good results in certain image classification tasks, but there are still problems such as poor classification ability, low accuracy, and slow convergence speed. This article mainly introduces the image classification algorithm (ICA) research based on the multilabel learning of the improved convolutional neural network and some improvement ideas for the research of the ICA based on the multilabel learning of the convolutional neural network. This paper proposes an ICA research method based on multilabel learning of improved convolutional neural networks, including the image classification process, convolutional network algorithm, and multilabel learning algorithm. The conclusions show that the average maximum classification accuracy of the improved CNN in this paper is 90.63%, and the performance is better, which is beneficial to improving the efficiency of image classification. The improved CNN network structure has reached the highest accuracy rate of 91.47% on the CIFAR-10 data set, which is much higher than the traditional CNN algorithm.

Generation of Digital Art Composition Using a Multilabel Learning Algorithm

The traditional methods for generating digital art composition have the disadvantage of capturing incomplete geometric information, which leads to obvious defects in the generation results. Therefore, a digital art composition generation method based on the multilabel learning algorithm is proposed in this research. Firstly, a preset series of grids are prepared to generate sampling and fractal pixels on the drawing base. Then, the preset grid construction is constructed by the interactive program of the preset grid library. After the stroke is drawn by the user, the actual motion trajectory of the pen is sampled by the digital panel, and the stroke information in the motion trajectory is obtained by the multilabel learning algorithm. Next, the steps of generating art composition are designed, including generating the skeleton of art composition, generating the geometric network structure of the skeleton, generating the sampling pixel and connecting the fractal pixel, and initializing other attributes of the mesh. Experimental results show that the proposed method has higher sampling rate and geometric information capture rate and has better application performance and prospect.

Multilabel Classification Based on Graph Neural Networks

Typical Laplacian embedding focuses on building Laplacian matrices prior to minimizing weights of connected graph components. However, for multilabel problems, it is difficult to determine such Laplacian graphs owing to multiple relations between vertices. Unlike typical approaches that require precomputed Laplacian matrices, this chapter presents a new method for automatically constructing Laplacian graphs during Laplacian embedding. By using trace minimization techniques, the topology of the Laplacian graph can be learned from input data, subsequently creating robust Laplacian embedding and influencing graph convolutional networks. Experiments on different open datasets with clean data and Gaussian noise were carried out. The noise level ranged from 6% to 12% of the maximum value of each dataset. Eleven different multilabel classification algorithms were used as the baselines for comparison. To verify the performance, three evaluation metrics specific to multilabel learning are proposed because multilabel learning is much more complicated than traditional single-label settings; each sample can be associated with multiple labels. The experimental results show that the proposed method performed better than the baselines, even when the data were contaminated by noise. The findings indicate that the proposed method is reliably robust against noise.

Multilabel CNN-Based Hybrid Learning Metric for Pedestrian Reidentification

Pedestrian reidentification has recently emerged as a hot topic that attains considerable attention since it can be applied to many potential applications in the surveillance system. However, high-accuracy pedestrian reidentification is a stimulating research problem because of variations in viewpoints, color, light, and other reasons. This work addresses the interferences and improves pedestrian reidentification accuracy by proposing two novel algorithms, pedestrian multilabel learning, and investigating hybrid learning metrics. First, unlike the existing models, we construct the identification framework using two subnetworks, namely, part detection subnetwork and feature extraction subnetwork, to obtain pedestrian attributes and low-level feature scores, respectively. Then, a hybrid learning metric that combines pedestrian attributes and low-level feature scores is proposed. Both low-level features and pedestrian attributes are utilized, thus enhancing the identification rate. Our simulation results on both datasets, i.e., CUHK03 and VIPeR, reveal that the identification rate is improved compared to the existing pedestrian reidentification methods.

Employing machine learning techniques to assess requirement change volatility

Lack of planning when changing requirements to reflect stakeholders’ expectations can lead to propagated changes that can cause project failures. Existing tools cannot provide the formal reasoning required to manage requirement change and minimize unanticipated change propagation. This research explores machine learning techniques to predict requirement change volatility (RCV) using complex network metrics based on the premise that requirement networks can be utilized to study change propagation. Three research questions (RQs) are addressed: (1) Can RCV be measured through four classes namely, multiplier, absorber, transmitter, and robust, during every instance of change? (2) Can complex network metrics be explored and computed for each requirement during every instance of change? (3) Can machine learning techniques, specifically, multilabel learning (MLL) methods be employed to predict RCV using complex network metrics? RCV in this paper quantifies volatility for change propagation, that is, how requirements behave in response to the initial change. A multiplier is a requirement that is changed by an initial change and propagates change to other requirements. An absorber is a requirement that is changed by an initial change, but does not propagate change to other requirements. A transmitter is a requirement that is not changed by an initial change, but propagates change to other requirements. A robust requirement is a requirement that is not changed by an initial change and does not propagate change to other requirements. RCV is determined using industrial data and requirement network relationships obtained from previously developed Refined Automated Requirement Change Propagation Prediction (R-ARCPP) tool. Useful complex network metrics in highest performing machine learning models are discussed along with the limitations and future directions of this research.

A Microcosmic Syndrome Differentiation Model for Metabolic Syndrome with Multilabel Learning

Background. Metabolic syndrome (MS) is a complex multisystem disease. Traditional Chinese medicine (TCM) is effective in preventing and treating MS. Syndrome differentiation is the basis of TCM treatment, which is composed of location and/or nature syndrome elements. At present, there are still some problems for objective and comprehensive syndrome differentiation in MS. This study mainly proposes a solution to two problems. Firstly, TCM syndromes are concurrent, that is, multiple TCM syndromes may develop in the same patient. Secondly, there is a lack of holistic exploration of the relationship between microscopic indexes, and TCM syndromes. In regard to these two problems, multilabel learning (MLL) method in machine learning can be used to solve them, and a microcosmic syndrome differentiation model can also be built innovatively, which can provide a foundation for the establishment of the next model of multidimensional syndrome differentiation in MS. Methods. The standardization scale of TCM four diagnostic information for MS was designed, which was used to obtain the results of TCM diagnosis. The model of microcosmic syndrome differentiation was constructed based on 39 physicochemical indexes by MLL techniques, called ML-kNN. Firstly, the multilabel learning method was compared with three commonly used single learning algorithms. Then, the results from ML-kNN were compared between physicochemical indexes and TCM information. Finally, the influence of the parameter k on the diagnostic model was investigated and the best k value was chosen for TCM diagnosis. Results. A total of 698 cases were collected for the modeling of the microcosmic diagnosis of MS. The comprehensive performance of the ML-kNN model worked obviously better than the others, where the average precision of diagnosis was 71.4%. The results from ML-kNN based on physicochemical indexes were similar to the results based on TCM information. On the other hand, the k value had less influence on the prediction results from ML-kNN. Conclusions. In the present study, the microcosmic syndrome differentiation model of MS with MLL techniques was good at predicting syndrome elements and could be used to solve the diagnosis problems of multiple labels. Besides, it was suggested that there was a complex correlation between TCM syndrome elements and physicochemical indexes, which worth future investigation to promote the development of objective differentiation of MS.