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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 Nanchang University (Engineering & Technology) is the property of Nanchang University 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.)

The deformation of open-pit mine slopes is influenced by various factors such as geological structure, hydrogeological conditions, mining activities, etc., making the prediction model complex. It is difficult to accurately capture all influencing factors. At present, a large number of monitoring devices are deployed around the slope of open-pit mines to record real-time displacement data of open-pit mine slopes. The data has the features of high-dimensional, temporal correlation, and nonlinear. Traditional slope stability analysis methods cannot effectively predict slope deformation without knowing other conditions and only data, it is necessary to use a data-based model to predict the displacement data of open-pit mine slopes in advance for slope stability analysis. In order to solve the above problems, a deformation prediction model for open-pit mine slopes based on the golden jackal optimized multilayer perception machine (GJO-MLP) is proposed. Each agent in GJO is independent of each other and can accelerate the training process of optimizing MLP through parallel computing. GJO can combine the nonlinear modeling and feature extraction capabilities of MLP, making the optimized MLP more advantageous in dealing with complex problems. To test the feasibility and effectiveness of GJO-MLP, GJO-MLP is compared and analyzed with ant colony algorithm optimization based MLP (ACO-MLP), gravity search algorithm optimization based MLP (GSA-MLP), and differential evolution algorithm optimization based MLP (DE-MLP). The simulation results on six datasets show that under the same experimental conditions, GJO-MLP shows better optimization performance compared to the other three algorithms. The slope deformation prediction model based on GJO-MLP is applied to the slope deformation prediction of Baorixile open-pit mine and Huapingzi slope deformation prediction. The results show that under the same conditions, compared to the other three algorithms, the slope deformation prediction model based on GJO-MLP not only show better predictive performance in predicting slope deformation data, but also has better feasibility and robustness.

Copyright of Journal of Shenzhen University Science & Engineering is the property of Editorial Department of Journal of Shenzhen University Science & Engineering 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 Mine Automation is the property of Industry & Mine Automation Editorial Department 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-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui 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.)

Molecular property prediction is a hot issue in the field of material chemistry. Although the calculation method based on the first principle can clearly describe the electron distribution in the system, the calculation process is too complex, and the calculation complexity increases exponentially with the increase of atoms in the molecule. In recent years, with the deepening of related research, a variety of deep learning algorithms have emerged. The algorithms were divided into two categories based on multi-layer perceptron ( MLP) and graph neural network ( GNN) and six sub categories, and the characteristics of different algorithms were studied. The analysis shows that MLP algorithm has simple structure, limited expansibility and low correlation with the internal structure of molecules. On the contrary, GNN class algorithms integrate the message passing mechanism, and transform the interaction between molecules into the feature transfer between nodes and edges, which is superior in the evaluation indexes of all directions. At present, the molecular property prediction algorithms based on deep learning is developing from MLP algorithm to GNN algorithm. Based on the above analysis, this paper puts forward the development direction of molecular property prediction algorithm based on deep learning in data set, anisotropic feature transfer, guiding the practical application in material science and Life Science in the future. [ABSTRACT FROM AUTHOR]

In order to solve the problem of poor consistency between the traditional super-resolution image quality assessment (SRIQA) index and human subjective perception, this paper presents a reference free super-resolution image quality evaluation method by using multi-layer perceptron (MLP). This method used the pre trained VGG16 network to extract the perceptual quality features of the super-resolution image, and established the regression model between the perceptual quality features of the super-resolution image and the corresponding subjective quality score through MLP. Experimental results show that the Pearson linear correlation coefficient (PLCC) and Spearman rank order correlation coefficient (SROCC) of the proposed algorithm on the public super-resolution image data set exceed 0.95, which is significantly superior to other existing image quality evaluation methods, and has higher consistency with human subjective perception.

Copyright of Journal of Xi'an Polytechnic University is the property of Editorial Department of Journal of Xi'an Polytechnic University 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.)

Antenna azimuth was seen as a key factor in the quality of mobile communications, and its accuracy will directly affect the quality of network optimization.An antenna azimuth diagnosis method was proposed based on multi-layer perceptron.The azimuth was divided into 12 interval classes, each class covered a range of 30°, that was,[0, 30°) was recorded as class 0, …,[330°, 360°) was recorded as class 11.The multi-layer perceptron algorithm was used to identify the range of the antenna azimuth angle and automatically identify the angle range of the antenna azimuth angle, which provided effective data support for the network optimization engineer to determine the actual network coverage problem, and greatly reduced workload and labor cost in verifying antenna performance.Experimental results show that the method can effectively and quickly discriminate the antenna azimuth interval class, and the recognition accuracy reaches 92.6%, which is higher than the classification accuracy of random forest and logistic regression classification algorithms.

Copyright of Information & Control is the property of Gai Kan Bian Wei Hui 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 view of the problems of sparse user rating matrix data set in personalized recommendation and insufficient utilization of user and item description information, this paper proposed a DeepRec that combined the rating matrix and comment text. Firstly, it obtained a text collection of user preference features and item attribute features through data preprocessing and then input the text collections into the convolutional neural network for training to obtain deep nonlinear features of users and items. At the same time, it input rating matrix into the multi-layer perceptron to get the user implicit representation, and integrated the user implicit representations. Secondly, it used multi-layer perceptron to model users and items implicit representations to personalize recommendations for users. Finally, this paper conducted a comparative experiment based on three datasets with RMSE as the evaluation indexes. The results show that the prediction error of DeepRec is lower, which effectively improves the recommendation accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media 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 recommendation system. the inner product interaction of a single learning matrix de composition or the use of deep neural networks to capture user interaction with the project is not sufficient to effectively learn the potential characteristics of users and projects. In view of this problem. this paper proposes a collaborative filtering algorithm based on deep learning feature representation (DLFeaCF) based on display feedback and implicit feedback. The model first learns the inner product and outer product interaction between the user and the project. Then, based on the inner product, it uses the nonlinear interactive learning ability of the multi-layer perceptron (MLP) to obtain the two aspects from implicit mapping and feature mapping: on the basis of the outer product, CNN learning is used to capture the local features of the user and the project finally, combine the features in the fusion layer and obtain the prediction score, The experiments on the real MovieLens dataset show that the DLFeaCF model can achieve better recommendation performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology 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 the traditional end-to-end memory network model had insufficient feature representation ability, it couldn't represent the relationship between each memory well, which led to the low accuracy of location reasoning and path finding in the bAbI dataset. This paper proposed a new memory network combining density connectivity and multi-layer perceptron to solve this problem. This model used density connectivity and full connected layer to capture relationship features, which enhanced the capability of feature representation. The proposed model evaluated the accuracy of text reasoning using bAbI dataset. The experimental results show that compare with traditional me-mory network and the existing end-to-end memory network, the model can effectively improve the reasoning accuracy. [ABSTRACT FROM AUTHOR]

Puerariae Lobatae Radix, the dried root of Pueraria lobata, is a traditional Chinese medicine with a long history. Puerariae Lobatae Caulis as an adulterant is always mixed into Puerariae Lobatae Radix for sales in the market. This study employed hyperspectral imaging(HSI) to distinguish between the two products. VNIR lens(spectral scope of 410-990 nm) and SWIR lens(spectral scope of 950-2 500 nm) were used for image acquiring. Multi-layer perceptron(MLP), partial least squares discriminant analysis(PLS-DA), and support vector machine(SVM) were employed to establish the full-waveband models and select the effective wavelengths for the distinguishing between Puerariae Lobatae Caulis and Puerariae Lobatae Radix, which provided technical and data support for the development of quick inspection equipment based on HSI. The results showed that MLP model outperformed PLS-DA and SVM models in the accuracy of discrimination with full wavebands in VNIR, SWIR, and VNIR+SWIR lens, which were 95.26%, 99.11%, and 99.05%, respectively. The discriminative band selection(DBS) algorithm was employed to select the effective wavelengths, and the discrimination accuracy was 93.05%, 98.05%, and 98.74% in the three different spectral scopes, respectively. On this basis, the MLP model combined with the effective wavelengths within the range of 2 100-2 400 nm can achieve the accuracy of 97.74%, which was close to that obtained with the full waveband. This waveband can be used to develop quick inspection devices based on HSI for the rapid and non-destructive distinguishing between Puerariae Lobatae Radix and Puerariae Lobatae Caulis.