Land suitability map and ecological carrying capacity for the recognition of touristic zones in the Kalat region, Iran: a multi-criteria analysis based on AHP and GIS

Ebrahimi, Majid; Nejadsoleymani, Hamid; Daneshvar, Mohammad Reza Mansouri

doi:10.1007/s41685-019-00123-w

Cited by 21 publications

(14 citation statements)

References 41 publications

(47 reference statements)

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“…In Tensor flow, nodes are used to represent different function operations, and each edge in the structure represents the data interaction between the operation nodes. Tensor represents the data transferred Complexity between different nodes, usually a multidimensional matrix or vector; Flow is the information flow, which can be understood as data information in a form of flow into each node through the whole operation graph [19].…”

Section: English Phrase Speech Recognition System Implementationmentioning

confidence: 99%

English Phrase Speech Recognition Based on Continuous Speech Recognition Algorithm and Word Tree Constraints

Du¹,

Duan

2021

Complexity

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This paper combines domestic and international research results to analyze and study the difference between the attribute features of English phrase speech and noise to enhance the short-time energy, which is used to improve the threshold judgment sensitivity; noise addition to the discrepancy data set is used to enhance the recognition robustness. The backpropagation algorithm is improved to constrain the range of weight variation, avoid oscillation phenomenon, and shorten the training time. In the real English phrase sound recognition system, there are problems such as massive training data and low training efficiency caused by the super large-scale model parameters of the convolutional neural network. To address these problems, the NWBP algorithm is based on the oscillation phenomenon that tends to occur when searching for the minimum error value in the late training period of the network parameters, using the K-MEANS algorithm to obtain the seed nodes that approach the minimal error value, and using the boundary value rule to reduce the range of weight change to reduce the oscillation phenomenon so that the network error converges as soon as possible and improve the training efficiency. Through simulation experiments, the NWBP algorithm improves the degree of fitting and convergence speed in the training of complex convolutional neural networks compared with other algorithms, reduces the redundant computation, and shortens the training time to a certain extent, and the algorithm has the advantage of accelerating the convergence of the network compared with simple networks. The word tree constraint and its efficient storage structure are introduced, which improves the storage efficiency of the word tree constraint and the retrieval efficiency in the English phrase recognition search.

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Section: English Phrase Speech Recognition System Implementationmentioning

confidence: 99%

English Phrase Speech Recognition Based on Continuous Speech Recognition Algorithm and Word Tree Constraints

Du¹,

Duan

2021

Complexity

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“…To eliminate domain differences, DDC employs the AlexNet model for source and target domain data, while a separate layer is introduced, and the MMD distance is added at layer 7 (the upper layer of softmax) to reduce the differences between the source and target domains. Subsequently, the literature [16] improved the previous work by introducing multicore MMD distances instead of MMD distances and proposed the DAN (deep adaptation networks) model to solve the domain adaptation problem. DAN maps the source and target domains into an RKHS (reproducing kernel Hilbert space) and then finds the mean difference between the mapped source domain data and the target domain data and then applies multilayer adaptation to the higher-level part of DCNN.…”

Section: Related Workmentioning

confidence: 99%

“…DAN maps the source and target domains into an RKHS (reproducing kernel Hilbert space) and then finds the mean difference between the mapped source domain data and the target domain data and then applies multilayer adaptation to the higher-level part of DCNN. After that, the literature [16] proposes a joint maximum mean difference method to measure the relationship of joint distributions, which is used to improve the generalization ability of DCNN to perform migration learning and thus adapt the data distribution between different domains. The literature [17] points out that deep neural network models are more powerful in feature learning and can be initialized layer by layer to alleviate the complexity of deep models in training, and this paper marks the beginning of a new wave of deep learning.…”

Section: Related Workmentioning

confidence: 99%

A Study on the Intelligent Translation Model for English Incorporating Neural Network Migration Learning

Zhang

2021

Wireless Communications and Mobile Computing

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Under the current artificial intelligence boom, machine translation is a research direction of natural language processing, which has important scientific research value and practical value. In practical applications, the variability of language, the limited capability of representing semantic information, and the scarcity of parallel corpus resources all constrain machine translation towards practicality and popularization. In this paper, we conduct deep mining of source language text data to express complex, high-level, and abstract semantic information using an appropriate text data representation model; then, for machine translation tasks with a large amount of parallel corpus, I use the capability of annotated datasets to build a more effective migration learning-based end-to-end neural network machine translation model on a supervised algorithm; then, for machine translation tasks with parallel corpus data resource-poor language machine translation tasks, migration learning techniques are used to prevent the overfitting problem of neural networks during training and to improve the generalization ability of end-to-end neural network machine translation models under low-resource conditions. Finally, for language translation tasks where the parallel corpus is extremely scarce but monolingual corpus is sufficient, the research focuses on unsupervised machine translation techniques, which will be a future research trend.

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“…As the computation continues, many nodes may be functionally idle, wasting supercomputer service nodes and monopolizing resources. This parallel algorithm proposed in the literature [14] has the above significant advantages, but the current parallel implementation is only for two algorithms, depression filling and single flow direction-based sink accumulation calculation, which makes this parallel framework not yet complete for the entire set of algorithms for ultra-large-scale DEM hydrological information extraction, thus making this parallel framework less effective in practical applications. The literature [15] proposed a multiattribute decision-making method with uncertainty in attribute values and attribute weight values by combining evidential reasoning methods and stochastic multicriteria acceptability analysis.…”

Section: Related Studiesmentioning

confidence: 99%

Identification of Fuzzy Information in English Interpretation Based on the Digital Elevation Model

Zhang¹,

Liu

2021

Advances in Mathematical Physics

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With the rapid development of information society, a large amount of vague or uncertain English interpretation information appears in daily life. Uncertain information processing is an important research content in the field of artificial intelligence. In this paper, we combine the three-branch concept lattice and linguistic values with the digital elevation model and propose the three-branch fuzzy linguistic concept lattice as well as the attribute approximation method. In this paper, an improved serial algorithm for sink accumulation is proposed. The improved algorithm changes the order of cell calculation; after the cumulative amount of a “sub-basin” is calculated, all cells of the next “sub-basin” are calculated until all cells are calculated. The improved algorithm reduces the overhead space in the calculation process, reduces the pressure of cells entering and leaving the queue, and improves the calculation efficiency. The improved cumulant algorithm is compared with the commonly used recursive cumulant algorithm and the nonrecursive cumulant algorithm, and the improved algorithm improves by about 17% compared with the nonrecursive algorithm at 106 cell level, and the computation time of the recursive algorithm is about 3 times of the improved algorithm. Because the sink accumulation serial algorithm is an important part of the parallel calculation of sink accumulation, and the execution time is shorter by using the improved algorithm, this paper applies the proposed improved accumulation serial algorithm to the process of parallel calculation of accumulation.

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Land suitability map and ecological carrying capacity for the recognition of touristic zones in the Kalat region, Iran: a multi-criteria analysis based on AHP and GIS

Cited by 21 publications

References 41 publications

English Phrase Speech Recognition Based on Continuous Speech Recognition Algorithm and Word Tree Constraints

English Phrase Speech Recognition Based on Continuous Speech Recognition Algorithm and Word Tree Constraints

A Study on the Intelligent Translation Model for English Incorporating Neural Network Migration Learning

Identification of Fuzzy Information in English Interpretation Based on the Digital Elevation Model

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