Search

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Object detection is currently a research hotspot in the field of computer vision. With the development of deep learning, object detection algorithms based on deep learning are increasingly applied and their performance is constantly improved. This paper summarizes the latest research progress of object detection methods based on deep learning by summarizing common problems encountered in the process of object detection and corresponding improvement methods. This paper focuses on two types of object detection algorithms based on deep learning. In addition, the latest improvement ideas of target detection algorithms are summarized from the aspects of attention mechanism, lightweight network, multiscale detection. Finally, in view of the current problems in the field of target detection, the future development trend is prospected. And the feasible solution is put forward in order to provide reference ideas and directions for the follow-up research work in this field. [ABSTRACT FROM AUTHOR]

Aiming at the problems of multi-scale, multi angle and occlusion in mask wearing detection in complex scenes, a mask wearing detection algorithm in complex scenes based on improved FCOS is proposed in this paper. Firstly, in order to improve the feature extraction ability of the network for masks with different scales, the packet residual connection structure of Res2Net is introduced into the backbone network of the algorithm, and the deformable convolution is integrated to expand its modeling ability for objects with unknown shapes. Then, a feature pyramid integrating attention mechanism is designed to give different weights to feature channels and suppress useless feature information. Finally, according to the relevant statistical characteristics of the target mask, the positive and negative samples are automatically divided to improve the sample quality of masks with different scales, and Generalized Focal Loss is introduced to jointly train the classification score and positioning quality score of samples so as to improve the performance of the algorithm. The experimental results show that the mAP of improved algorithm in this paper improves 6.7 percentage points compared with the original FCOS in the detection of mask wearing in complex scenes. Meanwhile, compared with some mainstream target detection algorithms, the improved algorithm in this paper also has better effect and robustness. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the process of moving target detection, the background is complex and the target is easily occluded. This paper proposes an autonomous detection algorithm for moving targets based on unsupervised missing value prediction. The missed targets are regarded as missing values in the tag data. According to the prior knowledge of the category and number of objects to be detected, the unsupervised generative adversarial imputation networks (GAIN) are used to predict the missing values through the acquired tag data, which greatly improves the recall rate at the expense of less accuracy. The experimental results on the small sample dataset of the characteristic parts of cattle show when the missing rate of tag data is less than 40%, the accuracy of missing value prediction is about 95%, and the average F1 score of detection is 0.92 for different degrees of occluded targets. This method has good detection performance for moving targets under the condition of small samples, which can reduce the uncertainty in practical application and the dependence of the algorithm on sample data, and improve the problem of missing detection in the process of moving target detection. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recommendation system can help users quickly find useful information and improve the retrieval efficiency of users effectively. However, the recommendation system has problems such as data sparsity and cold start, most of the existing recommendation algorithms that integrate social relations ignore the sparsity of social relations data, and there are few recommendation algorithms that integrate social relations and item attribute data at the same time. This paper proposes a recommendation model that is multi-task feature learning approach for social relationship and knowledge graph enhanced recommendation (MSAKR) in response to solve the above problems. Firstly, the algorithm extracts the user's social relations through the graph convolutional neural network to get the user's feature vector, then selects the neighbor by the graph centrality, and generates the virtual neighbor by the word2vec model, so as to alleviate the sparsity of the social data. This paper uses the attention mechanism to gather the neighbors. Secondly, multi-task learning and semantic-based matching model are used to extract the information of attribute knowledge graph to obtain the feature vector of the item. Finally, comprehensive recommendation is made to the user based on the obtained user and item feature vectors. In order to assess the performance of the recommendation algorithm, experiments are carried out on real datasets Douban and Yelp. Click-through rate prediction and Top-K recommendation are used to evaluate the performance of the model respectively. Experimental results show that the proposed model is superior to other benchmark models. [ABSTRACT FROM AUTHOR]

Aiming at the problem of incomplete location and fuzzy edge of salient region, this paper proposes a method of RGB-D salient target detection. Firstly, a cross-modal feature fusion module is designed to integrate RGB and depth information layer by layer. Six modal fusion feature outputs are obtained. This module reduces the discrepancy between RGB and depth information, providing more common and complementary deep features for the subsequent advanced semantic repair. Multiple levels of information is obtained based on the above modules. This paper uses the last three layer features to jointly extract richer high-level semantic information, and the initial salient map is obtained. After that, the network structure of U-Net is used to fuse from top to the bottom of the network. After upsampling, each layer is fused with the next layer in channel dimension. The first three layers of bottom features are guided by advanced semantic features before and after fusion, to complete the repair of the lowlevel features. Finally, the final salient map is obtained. The proposed cross-modal feature fusion module can adaptively fuse multi-modal features, highlight the commonness and complementarity of fusion features, and reduce the ambiguity of fusion. The proposed advanced semantic repair strategy is helpful to accurately detect the salient region and improve the edge clarity. Experimental results show that the proposed algorithm outperforms most excellent methods on five datasets, including NJU2K, NLPR, STERE, DES and SIP, which achieves relatively advanced performance. [ABSTRACT FROM AUTHOR]

TVM (tensor virtual machine) is a deep learning compiler, that translates the deep learning operators described by tensor expression to TVM IR (TVM intermediate representation) programs. After a series of operatorlevel optimizations on TVM IR, TVM generates the target code across diverse hardware back-ends. Tensor expression, a domain- specific language for tensor computation, performs loop transformation to operators. The result of loop transformation is a number of complicated expressions emerging in nested loop statements, which contain loop invariant code. However, in the context of deep learning applications, the traditional loop invariant code motion algorithm has severe limitations. Firstly, it's difficult to determine the extra-benefit of moving certain invariant code out of loops. Secondly, it's difficult to detect loop invariant code which has different orders of operands. Thirdly, it cannot process nested condition expressions well. Furthermore, there are conflicts with target hardware compiler optimizations. The application of loop invariant code motion technique is constrained by the aforementioned problems. In this paper, a new loop invariant code motion algorithm is proposed, which takes deep learning application characteristics into consideration in a heuristics way. The algorithm normalizes the program by manipulating the expression operands and simplifying the nested condition expression. This paper introduces a new cost model, which evaluates the cost of moving certain loop invariant code while the characteristics of TVM IR and target hardware back-ends are fully considered. The algorithm is implemented as a registered TVM pass on opensource compiler TVM version 0.7. To testify the effectiveness and correctness of this algorithm, experiments are conducted on TVM TOPI benchmark with 27 operators and 511 test cases under different input. Experimental results show that this algorithm improves 47.6% of operators'performance, and achieves speedups up to 40.0%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the nodes in the internet of vehicles (IoV), they are vulnerable to malicious node attacks during the consensus process, resulting in low transaction throughput and long transaction delays of the blockchain consensus algorithm. Therefore, the paper proposes a node consistency consensus algorithm for malicious node attacks in the IoV (NCCA). Firstly, NCCA divides the network of the IoV into several cellular single regions. It determines the list of trusted nodes based on the credit score of nodes and updates the verification nodes periodically. Secondly, NCCA proposes a voting mechanism with different weights of voting types to calculate the voting number of transactions and select the set of transactions requiring consensus. It also improves the node selection of block verification in the process of consensus and selects high- credit value verification nodes to complete consensus. Finally, NCCA proposes a two-round detection mechanism of malicious nodes for the active detection of malicious node attacks, which combines multi-area cooperative detection and local detection in a single area, and implements the operations of empowering and stopping the detected nodes. Experimental results show that NCCA can efficiently detect malicious nodes and reduce the impact of poor performance nodes and malicious nodes on the efficiency of block consensus. NCCA can improve transaction throughput, reduce the average transaction delay and average node communication overhead, and realize efficient consensus under malicious node attacks. [ABSTRACT FROM AUTHOR]

In the field of autonomous driving, existing semantic segmentation algorithms for road scenes have huge overhead and cannot meet the real-time performance of autonomous driving. Based on the overall structure of DeepLabV3+, this paper proposes a lightweight image semantic segmentation model with parallel feature processing, which takes into account both high accuracy and real-time performance. Firstly, MobileNetV2 is used as the backbone network to streamline the upsampling process and reduce the number of network parameters for network migration and training. Then, a dual-attention mechanism is introduced to combine with the null convolutional space pyramid module to form a parallel feature processing structure to improve segmentation accuracy. Finally, the parallel feature processing structure is then combined with MobileNetV2 to complete the extraction of image features. The experimental results show that the proposed model can guarantee efficient and accurate image segmentation with less system overhead and number of network parameters than the traditional model. The model achieves 73.61% mIoU in the Cityscapes dataset and processes a 512 x 512 image in 25 ms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to defects of relatively slow convergence and being easily trapped in local optimal of flower pollination algorithm (FPA), an enhanced lens learning and traction mutation based flower pollination algorithm (LMFPA) is proposed in this paper. First, LMFPA applies a modified lens learning mechanism to improve the distribution of population. Second, LMFPA uses the observation factor based traction mutation strategy to further accelerate the convergence and increase the probability to jumping out of local optimal. 12 benchmark functions from CEC2013 are selected as testbed to evaluate performance of LMFPA with original FPA algorithm and another 3 improved FPA algorithms, including TMFPA (T-distribution mutation-based flower pollination algorithm), t-GSSA(improved sparrow search algorithm based on adaptive t-distribution and golden sine and its application) and PCSPSO (particle compaction and scheduling based particle swarm optimization). Experimental result shows that LMFPA can achieve superior performance both in convergence speed and accuracy. At last, LMFPA is also used to solve 3D path planning for UAVs. Experimental result illustrates that LMFPA can also find better 3D paths for UAVs. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems of the existing citrus detection algorithms, such as low accuracy, large amount of model parameters, poor real-time detection, and unsuitability for mobile picking equipment, a citrus detection method based on the improved lightweight model YOLO-DoC is proposed. This paper introduces the ShuffleNetV2 network of the Bottleneck structure as the YOLOv5 backbone network model to construct a lightweight network. At the same time, the nonparametric SimAM attention mechanism is added to improve the recognition accuracy of targets in complex environments. In order to improve the positioning accuracy of the bounding box of the target fruit by the detection network, the bounding box of the target is obtained by introducing the method of Alpha-IoU bounding box regression loss function. Experiments show that the P (precision) value and mAP (mean average precision) value of the YOLO-DoC model are 98.8% and 99.1%, respectively, and the number of parameters is reduced to 1/7 that of the YOLOv5 network, and the size of the model is 2.8 MB. Compared with the original network model, the improved model has the advantages of fast recognition speed, high positioning accuracy and less memory usage. It can improve the picking efficiency under the premise of meeting the requirements of precise picking work. [ABSTRACT FROM AUTHOR]

In view of the low classification accuracy of traditional K-nearest neighbor method for data classification, this paper combines feature selection with KNN classification method and uses improved marine predator algorithm to optimize data features. Firstly, domain learning is used to provide rich neighborhood location information to expand the search range of marine predators, the dimensional variation mechanism is introduced to increase population diversity and avoid falling into local optimum too early, and the sines and cosines disturbance operator and jump step control factor are used to update the location of predators, so as to strengthen the global search and local search capabilities. Secondly, the feature selection object is taken as the optimization object to obtain the selected optimal feature subset. Finally, through to the 14 classic test function optimization test and 14 groups of classic data set classification study, in optimizing the performance, the average number of feature subset and the average classification accuracy comparison research, experimental results show that the proposed algorithm can effectively reduce the redundant features, realize the characteristics of purification, has a broad application prospect in data mining. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)