Apache Spark is a distributed memory-based computing framework which is natural suitable for machinelearning. Compared to Hadoop, Spark has a better ability of computing. In this paper, we analyze Spark's primary...
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ISBN:
(纸本)9781509035755
Apache Spark is a distributed memory-based computing framework which is natural suitable for machinelearning. Compared to Hadoop, Spark has a better ability of computing. In this paper, we analyze Spark's primary framework, core technologies, and run a machinelearning instance on it. Finally, we will analyze the results and introduce our hardware equipment.
This research offers an in-depth comparison of emotion detection models developed using real-world and synthetic datasets in the field of artificial intelligence andmachinelearning. The research rigorously analyses ...
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In order to improve the accuracy of test sample data of distribution communication network fusion control, an improved machinelearning technology for distribution communication network fusion control was established....
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The aim of this paper is to propose a new strategy adapted to the semantic segmentation of document images in order to extract baselines. Inspired by the work of Gruning [7], we used a convolutional model with residua...
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ISBN:
(纸本)9781728150543
The aim of this paper is to propose a new strategy adapted to the semantic segmentation of document images in order to extract baselines. Inspired by the work of Gruning [7], we used a convolutional model with residual layers enriched by an attention mechanism, called ARU-Net, a post-processing for the agglomeration of predictions and a data augmentation to enrich the database. Then, to consolidate the ARU-Net and help explicitly model dependencies between feature maps, we added a module of "Squeeze and Excitation" as proposed by Hu et al. [9]. Finally, to exploit the amount of unrated data available, we used a semi-supervised learning, based on ARU-Net, through the use of adversary networks. This approach has shown some interesting predictive qualities, compared to Gruning's work, with easier processing and less task-specific error correction. The resulting performance improvement is a success.
Due to the diversity and heterogeneity of electrical equipment, the operation monitoring systems for power transformers are undergoing diversified development. By utilizing machinelearning methods, this study focuses...
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Data mining is an important part of strategic planning for the development of modern urban settlement with capacities to accommodate population explosion. Developing countries arefast becoming urbanized giving the dev...
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ISBN:
(纸本)9781728107134
Data mining is an important part of strategic planning for the development of modern urban settlement with capacities to accommodate population explosion. Developing countries arefast becoming urbanized giving the developments and opportunities that are lacking in rural areas. Data regarding urban development such as satellite image need to be analysed to ascertain the possibilities for further development or opening up of new settlements. This work presents a binary sub-pixel and feature based method of classification to detect water bodies and vegetation in earth observatory images. In this work, the images were subjected data pre-processing, feature extraction, and analysed the data using machinelearning classification method to detection regions that support urban expansion or development of new settlement The proposed method achieved 88.93% accuracy and 0.06% RAISE
Optimum-path forest (OPF) is a novel supervised graph-based classifier which reduces the classification problem into partitioning of vertices in a graph derived from the data samples. One of the main processes in OPF ...
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ISBN:
(纸本)9781509058204
Optimum-path forest (OPF) is a novel supervised graph-based classifier which reduces the classification problem into partitioning of vertices in a graph derived from the data samples. One of the main processes in OPF is identifying the optimum set of key samples named prototypes. This process is based on creating a minimum spanning tree on a complete weighted graph which is derived from the training samples;hence, it is much time-consuming for large-scale problems. In this study, for overcoming this limitation, the process of finding the prototypes in traditional OPF is modified by using Markov cluster (MCL) algorithm. The graph partitioning in MCL is based on finding key samples named attractors, which attract other related samples;so the obtained attractors can be selected as prototypes for generating optimum-path trees. Experiments on public benchmark datasets show that the speed of proposed modified OPF is improved considerably as compared to the traditional OPF.
According to existing multiple choice of detonating fuse, the setting, initiating the shortage of the existing control direction, adopt multi-function control system design scheme based on DSP, with concise, highly au...
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Crop recommendation is the crucial aspect of modern agriculture, aiming to assist farmers in selecting the most suitable crops for their land and maximizing yield. In this study, the effectiveness of various preproces...
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With the unprecedented availability of computing and data resources, there has been a widespread trend in using machinelearning techniques in the field of communication as well. In the existing communication system, ...
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