In deep learning, model quality is extremely important. Consequently, the quality and the sufficiency of the datasets for training models have attracted considerable attention from both industry and academia. Automatic data augmentation, which provides a means of using image processing operators to generate data from existing datasets, is quite effective in searching for mutants of the images and expanding the training datasets. However, existing automatic data augmentation techniques often fail to fully exploit the potential of the data, failing to balance the search efficiency and the model accuracy. This paper presents CAugment, a novel approach to diversifying image datasets by combining image processing operators. Given a training image dataset, CAugment is composed of: 1) the three-level evolutionary algorithm (TLEA) that employs three levels of atomic operations for augmenting the dataset and an adaptive strategy for decreasing granularity and 2) a design that uses the three-dimensional evaluation method (TDEM) and a dHash algorithm to measure the diversity of the dataset. The search space can be expanded, which further improves model accuracy during training. We use CAugment to augment the CIFAR-10/100 and SVHN datasets and use the augmented datasets to train the WideResNet and Shake-Shake models. Our results show that the amount of data increases linearly along with the training epochs; in addition, the models trained by the CAugment-augmented datasets outperform those trained by the datasets augmented by the other techniques by up to 17.9% in accuracy on the SVHN dataset.

This paper proposes a novel PVF-YOLO model to extract the multi-scale traffic sign features more effectively during car driving. Firstly, the original convolution module is replaced with the Omni-Dimensional convolution (ODconv) and the feature information obtained from the shallow feature layer is incorporated into the network. Secondly, this paper proposes a parallel structure block module for capturing multi-scale features. This module uses the Large Kernel Attention (LKA) and Visual Multilayer Perceptron (Visual MLP) to capture the information generated by the network model. It enhances the representation ability of feature maps. Next, in the process of training, the gradient concentration algorithm is used to optimize the initial Stochastic Gradient Descent (SGD). Under the condition of real-time detection, it improves the detection accuracy. Finally, to improve the robustness of the model, this paper conducts extensive experiments. Tsinghua-Tencent 100K (TT100K), Changsha University of Science and Technology CCTSDB (CSUST Chinese Traffic Sign Detection Benchmark) are used as the training data set. It verifies that the PVF-YOLO method proposed in this paper enhances the detection ability of traffic signs of different scales, and the detection speed and accuracy are better than the original model.

Landslides significantly impact economic development and public safety. Aiming at the problem of insufficient prediction accuracy of the displacement data series of the traditional grey Verhulst model, this paper proposes a fractional Verhulst model optimized using the beetle tentacle search algorithm. First, based on the grey Verhulst model, a fractional order operator is introduced to accurately adjust the magnitude between cumulative values, constructing a fractional order-based grey Verhulst model. Expanding the accumulative order range improves prediction performance. Second, the fractional operator is optimized. The beetle antennae search algorithm finds the optimal fractional order between 0 and 1 in the grey Verhulst model, minimizing average relative error. Finally, using Heifangtai landslide group displacement data from Gansu Province, simulation experiments verified that the model has higher fitting accuracy and prediction effect than the traditional grey Verhulst model, Huang's improved Verhulst model, GM (1,1) model, cubic exponential smoothing model, and DGM (2,1) model. The average relative error is 2.949 %. Results show that the beetle antennae search algorithm optimized fractional order grey prediction model significantly improves fitting and prediction effect on data. The optimized fractional Verhulst model is more suitable for predicting landslide displacement deformation.

Traditional detect and count strategy can’t well handle the extremely crowded footage in computer vision-based counting task. In recent years, deep learning approaches have been widely explored to tackle this challenge. By regressing visual features to density map, the total crowd number can be predicted while avoids the detection of their actual positions. Efforts of improving performance distribute at various phases of the detecting pipeline, such as feature extraction and eliminating deviation of regressed density map etc. In this article, we conduct a thorough review on the most representative and state-of-the-art techniques. The efforts are systematically categorized into three topics: the evolving of front-end network, the handling of unbalanced density map prediction, and the selection of loss function. After the evaluation of most significant techniques, innovations of the state-of-the-art are inspected in detail to analyze specific reasons to achieve high performances. As conclusion, possible directions of enhancement are discussed to provide insights of future research.

Protest Event Analysis is important for government officials and social scientists. Here we present a new method for predicting protest events and identifying indicators of protests and violence by monitoring the content generated on Twitter. By identifying these indicators, protests and the possibility of violence can be predicted and controlled more accurately. Twitter user behaviors such as opinion share and event log share are used as indicators and this study presents a new method based on a Bayesian logistic regression algorithm for predicting protests and violence using Twitter user behaviors. According to the proposed method, users’ event log share behaviors which include the rate of tweets containing date and time information is the reliable indicator for identifying protests. Users’ opinion share behaviors which include hate-anger tweet rates is also best for identifying violence in protests. A dataset which consists of tweets that are generated on protests in the Black Lives Matter (BLM) movement after the death of George Floyd is used in the evaluation of the proposed method. According to information published on acleddata.com, protests and violence have been reported in various cities on specific dates. The dataset contains 1414 protest events and 3078 non-protest events from 460 cities in 37 U.S. states. Protest events in the BLM movement between May 28 and June 30 among which 285 were violent and 1129 were peaceful. Our proposed method is tested on this dataset and the occurrence of protests is predicted with 85% precision. It is also possible to predict violence in protests with 85% precision with our method on this dataset. This study provides a successful method to predict small and large-scale protests, different from the existing literature focusing on large-scale protests.

Figure skating video jumping action is a complex combination action, which is difficult to recognize, and the recognition of jumping action can correct athletes’ technical errors, which is of great significance to improve athletes’ performance. Due to the recognition effect of figure skating video jumping action recognition algorithm is poor, we propose a figure skating video jumping action recognition algorithm using improved deep reinforcement learning in Internet of things (IoT). First, IoT technology is used to collect the figure skating video, the figure skating video target is detected, the human bone point features through the feature extraction network is obtained, and centralized processing is performed to complete the optimization of the extraction results. Second, the shallow STGCN network is improved to the DSTG dense connection network structure, based on which an improved deep reinforcement learning action recognition model is constructed, and the actionrecognition results are output through the deep network structure. Finally, a confidence fusion scheme is established to determine the final jumping action recognition result through the confidence is established. The results show that this paper effectively improves the accuracy of figure skating video jumping action recognition results, and the recognition quality is higher. It can be widely used in the field of figure skating action recognition, to improve the training effect of athletes.

Due to the vast amounts of textual data available in various forms such as online content, social media comments, corporate data, public e-services and media data, text clustering has been experiencing rapid development. Text clustering involves categorizing and grouping similar content. It is a process of identifying significant patterns from unstructured textual data. Algorithms are being developed globally to extract useful and relevant information from large amounts of text data. Measuring the significance of content in documents to partition the collection of text data is one of the most important obstacles in text clustering. This study suggests utilizing an improved metaheuristics algorithm to fine-tune the K-means approach for text clustering task. The suggested technique is evaluated using the first 30 unconstrained test functions from the CEC2017 test-suite and six standard criterion text datasets. The simulation results and comparison with existing techniques demonstrate the robustness and supremacy of the suggested method.

Lane detection problem has been considered as an important computer vision task in autonomous driving. While it has received massive research attention in the literature, the problem is not yet fully solved. In this paper, we present a comprehensive literature review for lane detection, especially those with deep learning models. The latest collection of lane detection datasets is presented. We further fill the research gap by proposing a novel lane detection dataset named MudLane, which focuses on the lane detection task on suburban roads.

In recent years, heart disease has superseded several other contributory death factors. It is challenging to predict an individual’s risk of acquiring heart disease since it requires both expert knowledge and real-world experience. Developing an effective method for the prognosis of heart disease using Internet of Medical Things (IoMT) technology in healthcare organizations by collecting sensor data from patients’ bodies, utilizing robust expert systems, and incorporating vast healthcare data on cardiac disorders to alert physicians in critical situations is a challenging task. Several machine learning-based techniques for predicting and diagnosing cardiac disease have recently been demonstrated. However, these algorithms cannot effectively handle high-dimensionalinformation due to the need for an intelligent framework incorporating multiple sources to predict cardiac illness. This work proposes a unique model for heart disease prediction based on deep learning, Deep Gated Recurrent Units (D-GRU), which combines with Stacked Auto Encoders. A novel optimization algorithm, the Logistic Chaos Honey Badger Algorithm, is proposed for optimal feature selection. Publicly available heart disease-related datasets collected from UCI Repository, Cleveland Database, are used for training the proposed D-GRU model. The trained model is further tested on the data gathered from the sensors in the IoMT framework. The performance of the proposed model is compared against several deep learning models and existing works in the literature. The proposed D-GRU model outperforms the other models taken for comparison andexhibits performance supremacy with an accuracy of 95.15% in predicting heart diseases.

Video summarization based on generative adversarial networks (GANs) has been shown to easily produce more realistic results. However, most summary videos are composed of multiple key components. If the selection of some video frames changes during the training process, the information carried by these frames may not be reasonably reflected in the identification results. In this paper, we propose a video summarization method based on selecting keyframes over GANs. The novelty of the proposed method is the discriminator not only identifies the completeness of the video, but also takes into account the value judgment of the candidate keyframes, thus enabling the influence of keyframes on the result value. Given GANs are mainly designed to generate continuous real values, it is generally challenging to generate discrete symbol sequences during the summarization process directly. However, if the generated sample is based on discrete symbols, the slight guidance change of the discrimination network may be meaningless. To better use the advantages of GANs, the study also adopts the video summarization optimization method of GANs under a collaborative reinforcement learning strategy. Experimental results show the proposed method gets a significant summarization effect and character compared with the existing cutting-edge methods.