scholarly journals Implementing Hierarchical Indoor Semantic Location Identity Classification: A Case Study for COVID-19 Proximity Tracking in the Philippines

2021 ◽  
Author(s):  
Irvin Kean Paulus Paderes ◽  
Ligayah Leah Figueroa ◽  
Rommel Feria
Keyword(s):  
Indoor Localization ◽  
Hierarchical Classification ◽  
The Philippines ◽  
Activity Monitoring ◽  
Model Complexity ◽  
Classification Framework ◽  
Localization Theory ◽  
The Hierarchical Structure ◽  
Meaningful Context ◽  
Semantic Location

Efforts toward COVID-19 proximity tracking in closed environments focus on efficient proximity identification by combining it with indoor localization theory for location activity monitoring and proximity detection. But these are met with concerns based on existing considerations of the localization theory like costly infrastructure, multi-story support, and over-reliance on sensor networks. Semantic location identities (SLI), or location data stored with additional meaningful context, has become a feasible localizing factor especially in locations that have multiple spaces with different usage from each other. There is also a novel method of classification framework, called hierarchical classification, that leverages the hierarchical structure of the labels to reduce model complexity. The research aims to provide a solution to proximity analysis and location activity monitoring considering guidelines released in a Philippine context that addresses concerns of indoor localization and handling of geospatial data by implementing a hybrid hierarchical indoor semantic location identity classification that focuses on observable events within context-unique locations.

scholarly journals Received Signal Strength-Based Indoor Localization Using Hierarchical Classification

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1067 ◽  
Author(s):  
Chenbin Zhang ◽  
Ningning Qin ◽  
Yanbo Xue ◽  
Le Yang
Keyword(s):  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Hierarchical Classification ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Position Information ◽  
Area Of Interest

Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. Wi-Fi received signal strength (RSS)-based indoor localization techniques have attracted extensive attentions because Wi-Fi access points (APs) are widely deployed and we can obtain the Wi-Fi RSS measurements without extra hardware cost. In this paper, we propose a hierarchical classification-based method as a new solution to the indoor localization problem. Within the developed approach, we first adopt an improved K-Means clustering algorithm to divide the area of interest into several zones and they are allowed to overlap with one another to improve the generalization capability of the following indoor positioning process. To find the localization result, the K-Nearest Neighbor (KNN) algorithm and support vector machine (SVM) with the one-versus-one strategy are employed. The proposed method is implemented on a tablet, and its performance is evaluated in real-world environments. Experiment results reveal that the proposed method offers an improvement of 1.4% to 3.2% in terms of position classification accuracy and a reduction of 10% to 22% in terms of average positioning error compared with several benchmark methods.

scholarly journals Neural Relation Classification Using Selective Attention and Symmetrical Directional Instances

Symmetry ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 357 ◽  
Author(s):  
Zhen Tan ◽  
Bo Li ◽  
Peixin Huang ◽  
Bin Ge ◽  
Weidong Xiao
Keyword(s):  
Neural Network ◽  
Selective Attention ◽  
Information Fusion ◽  
Simple Structure ◽  
Superior Performance ◽  
Model Complexity ◽  
Network Architectures ◽  
Unstructured Text ◽  
Classification Framework ◽  
Relation Classification

Relation classification (RC) is an important task in information extraction from unstructured text. Recently, several neural methods based on various network architectures have been adopted for the task of RC. Among them, convolution neural network (CNN)-based models stand out due to their simple structure, low model complexity and “good” performance. Nevertheless, there are still at least two limitations associated with existing CNN-based RC models. First, when handling samples with long distances between entities, they fail to extract effective features, even obtaining disturbing ones from the clauses, which results in decreased accuracy. Second, existing RC models tend to produce inconsistent results when fed with forward and backward instances of an identical sample. Therefore, we present a novel CNN-based sentence encoder with selective attention by leveraging the shortest dependency paths, and devise a classification framework using symmetrical directional—forward and backward—instances via information fusion. Comprehensive experiments verify the superior performance of the proposed RC model over mainstream competitors without additional artificial features.

scholarly journals A Hierarchical Classification Framework of Satellite Multispectral/Hyperspectral Images for Mapping Coastal Wetlands

2019 ◽  
Vol 11 (19) ◽  
pp. 2238 ◽  
Author(s):  
Leilei Jiao ◽  
Weiwei Sun ◽  
Gang Yang ◽  
Guangbo Ren ◽  
Yinnian Liu
Keyword(s):  
Land Cover ◽  
Coastal Wetlands ◽  
Hierarchical Classification ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Landsat 8 ◽  
Land Cover Types ◽  
Multispectral Data ◽  
Classification Framework ◽  
Land Covers

Mapping different land cover types with satellite remote sensing data is significant for restoring and protecting natural resources and ecological services in coastal wetlands. In this paper, we propose a hierarchical classification framework (HCF) that implements two levels of classification scheme to identify different land cover types of coastal wetlands. The first level utilizes the designed decision tree to roughly group land covers into four rough classes and the second level combines multiple features (i.e., spectral feature, texture feature and geometric feature) of each class to distinguish different subtypes of land covers in each rough class. Two groups of classification experiments on Landsat and Sentinel multispectral data and China Gaofen (GF)-5 hyperspectral data are carried out in order to testify the classification behaviors of two famous coastal wetlands of China, that is, Yellow River Estuary and Yancheng coastal wetland. Experimental results on Landsat data show that the proposed HCF performs better than support vector machine and random forest in classifying land covers of coastal wetlands. Moreover, HCF is suitable for both multispectral data and hyperspectral data and the GF-5 data is superior to Landsat-8 and Sentinel-2 multispectral data in obtaining fine classification results of coastal wetlands.

scholarly journals scClassify: hierarchical classification of cells

2019 ◽  
Author(s):  
Yingxin Lin ◽  
Yue Cao ◽  
Hani J Kim ◽  
Agus Salim ◽  
Terence P. Speed ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Hierarchical Classification ◽  
Cell Number ◽  
Cell Type ◽  
Classification Framework ◽  
Single Cell Rna Sequencing ◽  
A Cell ◽  
Type Hierarchy

AbstractCell type identification is a key computational challenge in single-cell RNA-sequencing (scRNA-seq) data. To capitalize on the large collections of well-annotated scRNA-seq datasets, we present scClassify, a hierarchical classification framework based on ensemble learning. scClassify can identify cells from published scRNA-seq datasets more accurately and more finely than in the original publications. We also estimate the cell number needed for accurate classification anywhere in a cell type hierarchy.

scholarly journals Hierarchical Classification of Geographical Systems

2021 ◽  
Vol 35 ◽  
pp. 125-153
Author(s):  
A. K. Cherkashin ◽  
Keyword(s):  
Hierarchical Structure ◽  
Chemical Elements ◽  
Hierarchical Classification ◽  
Southern Taiga ◽  
Hierarchy Formation ◽  
Local Coordinates ◽  
The Hierarchical Structure ◽  
Upper Level ◽  
Different Levels

A hierarchical system is the result of dividing a set of objects into subordinate groups in order from highest to lowest, where each lower level reveals and clarifies the properties of objects at a higher level. There is a difference between the natural hierarchy of geosystems-geochors and the hierarchy of geomers, which leads to taxonomic classification. Theoretical basis for creating a hierarchical classification of geosystems are developed using a conceptual model of geographical cycles of accumulation and removal of factor load on territorial objects of various scales. The cone of chorological and typological connections is considered as the basic metamodel of hierarchical structure. For its research, we use descriptive geometry tools to represent the cone in the vertical and horizontal (plan) projections. The surface and unfolding structures of the cone with sections at different levels reflect the hierarchy. The planned projection in the form of concentric structures is considered as model of the archetype of hierarchy formation. The horological and typological classifications converge in the position “natural zone” as the “parent core” of the type of natural environment, which represents the zonal norm. The concentric model has various interpretations, in particular, it is described as a system of local coordinates, where each coordinate corresponds to the categories of seriality of geosystems, i.e. the degree of their factoral-dynamic variability relatively to zonal geosystems. In the coordinate approach, the classification looks like a ranked set of merons and taxa, where the meron categories are represented by quantum numbers of the coordinate series, and the taxon is a sequence of such numbers of different series (numeric code). The formation of hierarchical classification is based on the triad principle, when the taxon of the upper level is divided into three lower level gradations, which are arranged in a homological series according to the degree of seriality. There is an analogy between the hierarchical structure of the periodic system of chemical elements and the typological classification of geosystems, when the periods of the system of elements correspond to the high-altitude layers and latitudinal zones of geochor placement or hierarchical levels of geomer classification. An unfolding and plan projection of the classification cone of facies for the Prichunsky landscape of the southern taiga of Central Siberia in three basic categories of variability of different levels geomers are presented.

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