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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]

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Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition 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.)

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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]

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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.)

By studying the user's preferences, the recommendation algorithm recommends the content of interest to users from massive data resources. Collaborative filtering algorithm is the most widely used recommendation algorithm. In order to improve the recommendation accuracy, many scholars have studied the similarity. This paper summarizes the most widely used collaborative filtering algorithm in recommendation system and the common similarity calculation methods in recommendation system. The similarity based on Pearson correlation coefficient, cosine similarity, modified cosine similarity and Jaccard similarity coefficient are compared and analyzed. The paper combs the research status of similarity from the aspects of cold start problem and data sparsity. The research shows that using mixed similarity to calculate the similarity between users improves the recommendation quality. It summarizes the problems of low recommendation efficiency and high complexity in the improved literature, and looks forward to the improvement of similarity in improving recommendation accuracy and efficiency. [ABSTRACT FROM AUTHOR]

In order to further improve the accuracy of text generated by image description, this paper proposes an image description method that combines global-local features and attention mechanism. This method is an improvement on the traditional encoder-decoder model. From an overall perspective, the encoder stage uses the residual network ResNet101 to extract the global and local features of the image to avoid object loss or object prediction errors. In the processor stage, a two-way GRU embedded with an improved attention mechanism is used to generate text sequences. From a local point of view, the attention mechanism proposed by this model is an independent loop structure. The attention weight is obtained by calculating the similarity between the image local feature vector and the semantic vector, and the mapping relationship between image features and semantic information is enhanced. The experimental results on the MSCOCO dataset show that the algorithm in this paper has achieved varying degrees of improvement in evaluation indicators such as BLEU, CIDEr, and METEOR, indicating that the description text generated by this model is highly accurate and rich in details. [ABSTRACT FROM AUTHOR]

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