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This paper presents a new methodology for classification of local climate zones based on ensemble learning techniques. Landsat-8 data and open street map data are used to extract spectral-spatial features, including spectral reflectance, spectral indexes, and morphological profiles fed to subsequent classification methods as inputs. Canonical correlation forests and rotation forests are used for the...
Built-up area has been one of the most important objects to be extracted in remote sensing images. Several factors such as complex structure, diverse texture and varied background, bring the challenges for the task of built-up area extraction. In this paper, a multiple input structure of deep convolution neural network (CNN) is proposed to extract built-up area automatically, which can fuse the information...
This paper proposes a new approach for contextual feature extraction from superpixels in aerial urban scenes. Our method extracts features with many levels of context from superpixels by exploiting different layers of a pre-trained convolutional neural network. Experimental results show the effectiveness of the proposed approach, which outperforms traditional methods based on handcrafted feature extraction...
This paper deals with the acoustic event detection (AED) to improve the detection accuracy of acoustic events. Acoustic event detection task is performed by a regression via classification (RvC) based approach along with the random forest technique. A discretization process is used to convert the continuous frame positions within acoustic events into event duration class labels. Outputs of the category-specific...
Despite continued sustainability as an academic field of study, researchers of technical communication have struggled with employing appropriate research methods in their studies. In this panel, the panelists will each discuss an aspect of this struggle framed within their own experiences and expertise. Topics will include the quality of evidence in research studies; quality of methodology in workplace...
Different data mining techniques are employed in stylometry domain for performing authorship attribution tasks. Sometimes to improve the decision system the discretization of input data can be applied. In many cases such approach allows to obtain better classification results. On the other hand, there were situations in which discretization decreased overall performance of the system. Therefore, the...
Electric power SDH network is a comprehensive communication network, which takes advantages of SDH technology, and is a widely used technology. How to protect the network more effectively is the concern of the operators. So the main purpose of this paper is to find a way to analysis risk in advance and enhance reliability of electric power SDH network. Firstly, we set up an evaluation index system...
In this paper, we put forward deep neural network ensemble to model and predict Chinese stock market index (including Shanghai composite index and SZSE component index), based on the input indices of recent days. A set of component networks are trained by historical data for this task, where Backpropagation and Adam algorithm are used to train each network efficiently. Bagging approach combines these...
This paper deals with random forest regression based acoustic event detection (AED) by combining acoustic features with bottleneck features (BN). The bottleneck features have a good reputation of being inherently discriminative in acoustic signal processing. To deal with the unstructured and complex real-world acoustic events, an acoustic event detection system is constructed using bottleneck features...
The emergence of Deep neural networks has seen human-level performance on large scale computer vision tasks such as image classification. However these deep networks typically contain large amount of parameters due to dense matrix multiplications and convolutions. As a result, these architectures are highly memory intensive, making them less suitable for embedded vision applications. Sparse Computations...
Convolutional Neural Network (CNN) has recently achieved significant performances for visual computing, and a number of researches are made to explore advanced model structures to solve the problem of over-fitting. In this paper, a regularization technique named ShuffleNode is proposed, which shuffles feature map elements to achieve regularization functions during model training. Specifically, there...
With the development of social media technology, users often register accounts, post messages and create friend links on several different platforms. Performing user identity mapping on multi-platform based on the behavior patterns of users is considerable for network supervision and personalization service. The existing methods focus on utilizing either text information or structure information alone...
Security issues in the IoT based CPS are exacerbated with human participation in CPHS due to the vulnerabilities in both the technologies and the human involvement. A holistic framework to mitigate security threats in the IoT-based CPHS environment is presented to mitigate these issues. We have developed threat model involving human elements in the CPHS environment. Research questions, directions,...
We consider the problem of time-series prediction with missing observations. We consider the autoregressive model (AR model) and cast the problem as a regression problem. On the basic of sampling methods and the online gradient descent (OGD), we propose efficient any-time methods to solve this problem. We show that our algorithm can learn the underlying model efficiently, meanwhile, is robust to the...
Virtual Reality applications for integrated cognitive and motor stroke rehabilitation show promise for providing more comprehensive rehabilitation programs. However, we are still missing evidence on its impact in comparison with standard rehabilitation, particularly in patients with cognitive impairment. Additionally, little is known on how specific stimuli in the virtual environment affect task performance...
The aim of the current study was to examine the effectiveness of a meta-cognitive intervention program that uses virtual reality training (VAP-S) to improve the implementation of a shopping task among adolescents with Autism Spectrum Disorder (ASD). The study included 56 adolescents aged 11–19 with ASD, of which 37 performed the intervention program while the remaining 25 formed a control group who...
This work presents the development and evaluation of a new scheme based on Artificial Neural Network (ANN) for fault detection and fault location in distribution systems with distributed generator. Two different ANNs are used, where the first one is able to detect which part of the distribution system the fault occurred and the second one is able to precisely locate the fault along the faulty line...
Learning Management System, such as Moodle, has been utilized extensively as part of e-learning implementation for higher institutions. The flexibility of LMS to convey the learning materials in many ways and approaches enable the instructor to implement blended learning. The student's interaction and activities while learning are captured by Moodle in the log data file and are useful to identify...
In the process of establishing evaluation index system of physical education, the traditional methods setting weights for each indicator mainly include analytic hierarchy process, fuzzy comprehensive evaluation method, and Delphi method, etc. These methods mostly rely on experience, which is strongly influenced by artificial factors and cannot be avoided. Because artificial neural network model has...
At present, the researches on credit risk analysis mainly focus on commercial bank loan or consumer credit risk, and there is little research about the credit risk of rural credit cooperatives. The purpose of this paper is to evaluate credit risk for the rural credit cooperatives using artificial neural network model. We establish credit risk assessment index system for rural credit cooperatives....
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