(2019) introduced the deep learning system for occupancy classification (DeepEOC). The system studies the impact of various feature extraction algorithms. Such algorithms include the principal component analysis (PCA) and SHapley Additive exPlanation (SHAP). For evaluating and comparing the algorithms, ...
The next phase of s2s2p involves occupancy classification through s2p learning using deep classifiers. We matched the smart meter, device, and occupancy data by their timestamps. Supplementary Table A1 contains the number of matched samples (in terms of days) for each device and each house after...
All spatial data is house in two foldersspatial/andlandUseClassification/within thedata/folder. Thespatial/folder contains shapefiles corresponding to the outline of our study area (the Nilgiris and the Anamalai hills), an Open Street Map Roads shapefile and an elevation raster (obtained from SRTM...
(2021) used the law of total probability (LTP), naïve Bayes classifier (NB), and classification and regression tree (CART) to predict the mean occupancy, and compared the performance of the three models. The third statistical model subtype is characterized by the statistical modeling of ...
UIV fracture was defined as a change of shape above grade 2 in the Genant classification17. Figure 5 Parameters related to instrument installation at upper instrumented vertebra (UIV), TAD (Tip-apex and distance of the Pedicle screw [PS] [a + b + c + d]), APD (...
The cerebellar cortex template excludes the vermis as well as lobules IX and X (uvula and nodulus) and a tissue-classification process (described in Rusjan, et al. 2006) removes all voxels with the cerebellar ROI that contain white matter (that is, voxels with a probability of gray matter ...
section over union (IoU) of occupied voxels, ignoring their tropy loss to improve point classification accuracy and avoid semantic class, for the scene completion (SC) task and the semantic ambiguity. For 3D semantic occupancy predic- mIoU of all semantic classe...
Full size table In addition, as hedgehogs have been shown to avoid crossing major roads26, and hedgehog presence may be influenced by road density46, we incorporated five measures of road “availability”. These were the total length of: (i) all roads in the survey square (ALLROADS); (ii...
compare different classification approaches. The models of Chapters14and15were developed specifically for multi-species occupancy surveys, where interest is on species-specific occupancies and/or evidence of spatial segregation or aggregation among species. The models permit inferences about these issues ...
Decision tree: The classification and regression tree (CART), a type of the DT method, was selected to predict occupancy status using indoor/outdoor environment and energy consumption data [51]. The CART can construct binary trees, so each internal node has two edges. A notable advantage of ...