Adaptive Gaussian Kernel Research Articles

Retraction Note: Saliency detection in stereoscopic images using adaptive Gaussian Kernel and Gabor filter

Retraction Note: Saliency detection in stereoscopic images using adaptive Gaussian Kernel and Gabor filter

Penerapan Model Geographically Weighted Logistic Regression dengan Fungsi Pembobot Adaptive Gaussian Kernel pada Data Kemiskinan

Regression analysis is one statistical method used to determine the relationship between a dependent variable and one or more independent variables. Dependent variables that are categorical are analyzed using logistic regression analysis. Geographically Weighted Logistic Regression (GWLR) is a method that is a local version of logistic regression, where location factors are considered. This method assumes that the dependent variable data are distributed binomially. In this study, the GWLR method is used to determine the factors influencing the poverty percentage in West Java Province in 2022 using an adaptive Gaussian kernel weighting function. The variables used are per capita expenditure, average length of schooling, Gross Regional Domestic Product (GRDP) per capita, and population density. The results of this study indicate that the variables of per capita expenditure, Gross Regional Domestic Product (GRDP) per capita, and population density significantly influence the poverty percentage in West Java Province in 2022.

PEMODELAN GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) MENGGUNAKAN PEMBOBOT KERNEL PADA KASUS TINGKAT PENGANGGURAN TERBUKA DI KALIMANTAN

AbstractUnemployment is one of the serious problems in Indonesia's economic development. This unemployment describes human resources that have not been utilized optimally, as a result of which people's productivity and income have not been maximized, this can also be one of the causes of poverty and other social problems. This study aims to find out the general picture of the open unemployment rate in the Kalimantan region, get the best model and factors that influence the open unemployment rate and illustrate it through thematic maps. The study began with testing assumptions and spatial effects then continued with testing global regression modeling and Geographically Weighted Regression. The weighting function used in this study is adaptive gaussian kernel. The variable that has a positive effect on the open unemployment rate in the Kalimantan region is population density. While the variable that negatively affects the open unemployment rate is the Labor Force Participation Rate. Keywords: Open Unemployment Rate, Kalimantan Island, Spatial, GWR

AMSLS: Adaptive multi-scale level set method based on local entropy for image segmentation

Intensity inhomogeneity often appears in medical images and causes great difficulties in image segmentation. Most active contour models perform poorly when applied to intensity inhomogeneous images because their energy functions use local intensity information in a fixed-size domain, causing the contour to evolve in the wrong direction. To overcome the difficulties caused by intensity inhomogeneity, we propose an adaptive multi-scale Gaussian kernel function based on image local entropy, which can determine the appropriate scale for each local region. Choosing the small scale and large scale for inhomogeneous and homogeneous areas respectively make the contour move toward the target boundary accurately. We also propose three adaptive multi-scale (AMS) models, AMS-region scalable fitting (AMS-RSF) model, AMS-local image fitting (AMS-LIF) model, AMS-local and global intensity fitting (AMS-LGIF) model, to segment medical images with intensity inhomogeneity and noise, including left atrial MR images and breast ultrasound images. The experimental results show that the adaptive multi-scale Gaussian kernel function enables the active contour model to effectively segment intensity inhomogeneous images and has a certain robustness to the initial contour and noise, which achieves good performance on MR left atrial images and ultrasound images of breast cancer. The AMS-LGIF model obtained the highest DICE coefficient of 0.9532, which was better than the 0.9429 obtained by the second-ranked LGIF model to segment left atrial MR images. For segmenting breast ultrasound images, the DICE coefficient is increased by 16% than that of the U-Net++ model.

Geographically Weighted Regression Modeling with Adaptive Gaussian Kernel Weighting on GRDP in Indonesia

The purpose of this study is to determine the variables that affect Gross Regional Domestic Product (GRDP) in Indonesia in 2022 using Geographically Weighted Regression (GWR) with Adaptive Gaussian kernel weighting function. The data used in this study uses secondary data taken from the website of the Central Bureau of Statistics. The variables used are gross regional domestic product of 34 provinces in Indonesia (Y, in billion rupiah), labor force participation rate (X1, in %), foreign investment (X2, in million dollars), open unemployment rate (X3, in %) and human development index (X4, in %). Data were analyzed using GWR with adaptive gaussian kernel weighting function. GRDP in all provinces on the island of Sumatra (11 provinces), DKI Jakarta province, Banten province, and West Kalimantan province are influenced by foreign investment (X2) and human development index (X4). Meanwhile, GRDP in the other 19 provinces is only influenced by foreign investment (X2). GWR model with adaptive gaussian kernel weighting function is formed differently for each province in Indonesia. Keywords: Adaptive gaussian kernel; GWR; gross regional domestic product

Oversampling method via adaptive double weights and Gaussian kernel function for the transformation of unbalanced data in risk assessment of cardiovascular disease

In risk assessment of cardiovascular disease (CVD), the classification error caused by unbalanced data is a significant challenge, which has sparked widespread concern and research upsurge in the field of data mining. Therefore, in view of the imbalance of CVD data sets, an oversampling method via adaptive double weights and Gaussian kernel function (ADWGKFO) is proposed, which converts the unbalanced data sets into balanced data sets. Firstly, clustering algorithm is utilized to cluster minority samples, boundary samples are identified by Borderline-Synthetic Minority Over-sampling Technique (Borderline-SMOTE), K nearest neighbor and support vector machine algorithms, and the number of samples synthesized in each group is calculated based on the double weights of boundary points and majority distribution. Secondly, in order to clearly define the classification boundary, the mutual class potential of new samples in each cluster is calculated by Gaussian kernel function, and new samples are filtered according to the mutual class potential until the data set is balanced. Finally, taking the data sets from Kaggle platform as the research samples, the proposed method is empirically analyzed. In order to validate the efficacy and universality of the proposed method, this paper selects CatBoost that is a new integrated algorithm to test the effect of the ADWGKFO method, and compares it with different sampling methods and different classifiers using performance evaluation indexes such as accuracy, F1-score and area under the curve (AUC). Compared with the combinations of other methods, the accuracy, F1-score and AUC are significantly improved. It is concluded that the ADWGKFO method described in this paper can successfully improve the data quality, and increases the reliability of CVD risk assessment.

Modelling the Count of Diphtheria in West Java in 2022 using Geographically Weighted Zero-Inflated Poisson Regression

The number of death cases caused by infectious diseases, that is to say diphtheria, especially in West Java so that it is categorized as an Extraordinary Event (KLB) generally attacks from the age of toddlers to the elderly. To model diphtheria cases in West Java in 2022 containing over dispersion using Geographically Weighted Zero-Inflated Poisson Regression (GWZIPR). Parameter estimation uses Expectation Maximization (EM) algorithm with spatial weight using Adaptive Gaussian Kernel. The resulting models include a Poisson state model that shows the chance of diphtheria cases occurring with relevant factors of the percentage within the population who apply Hygienic and Healthful Lifestyle (PHBS), who have been vaccinated against DPT-HB3, the number of health workers at Puskesmas, and the percentage of active posyandu and a Zero state model that shows the chance of no diphtheria cases occurring with relevant factors of the percentage who have been vaccinated against DPT-HB3, the number of health workers at Puskesmas, and the percentage of active posyandu.

Identification of factors affecting the cases of under-age female marriage using geographically weighted panel regression approach in south kalimantan province.

The topic of this study was chosen because the percentage of underage female marriages in South Kalimantan Province was the highest in Indonesia over the last five years, from 2018 to 2022. This signifies that there are social issues in the local community that the government must address. One possible answer is to identify the factors that contribute to the creation of these conditions in each region. Using the Geographically Weighted Panel Regression (GWPR) method, this study attempts to determine the factors that influence the rise of underage female marriage instances in South Kalimantan Province. The number of poor individuals, population density, average duration of schooling, adjusted per capita expenditure, and total population were chosen as independent variables. Data acquired from South Kalimantan Province's Central Bureau of Statistics' periodic releases. Because there was high spatial heterogeneity between each location in this study, it was quite practical to employ the GWPR approach in developing a conjectural model. The results of evaluating the GWPR model with adaptive Gaussian kernel weights provide significant results and the model can explain the variance of data by 55 percent. Testing the parameters of the GWPR model reveals two (two) regional groupings with distinct influencing variables. The first group consists of ten (ten) regions that are considerably impacted by both the number of impoverished people and the average length of schooling, whereas the second group consists of three (three) regions that are impacted solely by the average length of schooling.

GEOGRAPHICALLY WEIGHTED PANEL LOGISTIC REGRESSION SEMIPARAMETRIC MODELING ON POVERTY PROBLEM

Regression analysis is a statistical method used to investigate and model the relationship between variables. Furthermore, a regression analysis was developed that involved spatial aspects, namely Geographically Weighted Regression (GWR). GWR modeling consists of various types, one of which is Geographically Weighted Logistic Regression Semiparametric (GWLRS), an extension of the Logistic GWR model that produces local and global parameter estimators. In this study, it is proposed to combine the GWLRS model using panel data or Geographically Weighted Panel Logistic Regression Semiparametric (GWPLRS). The case study used in this research is the problem of poverty in 38 regions/cities in East Java, Indonesia, in 2018 – 2022 as seen from the Poverty Gap Index. The weights used in this research are the adaptive gaussian kernel weighting functions. The results of the parameter significance test show that the Human Development Index as global variable has a significant effect on each region/city.

A machine vision-based method for crowd density estimation and evacuation simulation

In this study, we propose a machine vision-based method for crowd density estimation and evacuation simulation to help reduce the occurrence of stampedes in crowded public places. The method consists of pedestrian detection model, pedestrian positioning algorithm, and cellular automata evacuation model (CAEM). In the pedestrian detection model, an adaptive 2D Gaussian kernel was used to generate crowd density heatmaps for crowd density estimation. The model was found to have achieved a real-time detection accuracy of 96.5% for real-time images of evacuation scenes after being trained on a dataset of 66,808 pedestrian images using the YOLOv3. The pedestrian positioning algorithm was developed to determine the coordinates of pedestrians in real-world scenarios. Experiments show that the average positioning errors in the x and y directions are 0.177 m and 0.176 m, respectively. The pedestrian coordinates were then input into the Python-based cellular automata evacuation model (CAEM) for evaluation simulation, which is compared with the simulation results by Pathfinder, another piece of software that can deliver agent-based evacuation simulation. The difference in evacuation time calculated by the two models was found to be less than a second, which indicates a high degree of consistency.

An adaptive selective ensemble algorithm for fault classification

With the aim of improving the classification performance of a single extreme learning machine (ELM) for fault diagnosis, an adaptive selective ensemble classification algorithm is proposed based on the idea of ensemble learning. The proposed algorithm achieves better classification performance than a single model and the selected ELM subnetworks are selected adaptively and the ensemble weights of selected ELM subnetworks are calculated adaptively, which avoids the complex process of manually selecting subnetworks and calculating ensemble weights. First, a regularized error weight adjustment ELM is constructed by introducing regularization parameters and error weight adjustment matrix to a standard ELM, where the error weight adjustment matrix is obtained by the method of adaptive Gaussian kernel density estimation. Then, discrepancy subnetworks are constructed using six different activation functionsand the ensemble weights of subnetworks are obtained adaptively according to the normalized ratio of mean and variance of subnetwork F-scores to achieve the ensemble of subnetworks. Finally, the adaptive selective ensemble classification algorithm is validated using the UCI dataset and experimental acoustic emission signals of gearbox faults. The results show that the adaptive selective ensemble method can improve the stability and accuracy of classification algorithms and the achieved classification accuracy for experimental acoustic emission was 0.9773.

ANALISIS KEMISKINAN EKSTREM PROVINSI BENGKULU MENGGUNAKAN METODE GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) DENGAN PEMBOBOT ADAPTIVE GAUSSIAN KERNEL DAN ADAPTIVE BI-SQUARE

Extreme poverty is a condition of inability to fulfill basic needs, namely the need for food, clean drinking water, proper sanitation, health, shelter, education, and access to information which is not only limited to income, but also access to social services (United Nations, 1996).Geographically Weighted Regression (GWR) model is used in mapping extreme poverty of all level 2 regions in Bengkulu Province using Adaptive Gaussian Kernel and Adaptive Bi-Square weights as well as finding the best GWR model and analyzing the model against extreme poverty mapping of Bengkulu Province. The data used in this study is the March 2022 Susenas data. Of the 18 variables that allegedly affect extreme poverty, only 6 variables support the assumption of spatial heterogeneity in GWR modeling. Based on the selection of the best model, it is known that the GWR model with Adaptive Bisquare Kernel weighting is a suitable model for the percentage of extreme poor people in Bengkulu Province with the smallest AIC value

The impact of isolation kernel on agglomerative hierarchical clustering algorithms

Agglomerative hierarchical clustering (AHC) is one of the popular clustering approaches. AHC generates a dendrogram that provides richer information and insights from a dataset than partitioning clustering. However, a major problem with existing distance-based AHC methods is: it fails to effectively identify adjacent clusters with varied densities, regardless of the cluster extraction methods applied to the resultant dendrogram. This paper aims to reveal the root cause of this issue and provides a solution by using a data-dependent kernel. We analyse the condition under which existing AHC methods fail to effectively extract clusters, and give the reason why the data-dependent kernel is an effective remedy. This leads to a new approach to kernerlise existing hierarchical clustering algorithms including the traditional AHC algorithms, HDBSCAN, GDL, PHA and HC-OT. Our extensive empirical evaluation shows that the recently introduced Isolation Kernel produces a higher quality or purer dendrogram than distance, Gaussian Kernel and adaptive Gaussian Kernel in all the above mentioned AHC algorithms.

Sampling Survey Method of Wheat Ear Number Based on UAV Images and Density Map Regression Algorithm

The number of wheat ears is one of the most important factors in wheat yield composition. Rapid and accurate assessment of wheat ear number is of great importance for predicting grain yield and food security-related early warning signal generation. The current wheat ear counting methods rely on manual surveys, which are time-consuming, laborious, inefficient and inaccurate. Existing non-destructive wheat ear detection techniques are mostly applied to near-ground images and are difficult to apply to large-scale monitoring. In this study, we proposed a sampling survey method based on the unmanned aerial vehicle (UAV). Firstly, a small number of UAV images were acquired based on the five-point sampling mode. Secondly, an adaptive Gaussian kernel size was used to generate the ground truth density map. Thirdly, a density map regression network (DM-Net) was constructed and optimized. Finally, we designed an overlapping area of sub-images to solve the repeated counting caused by image segmentation. The MAE and MSE of the proposed model were 9.01 and 11.85, respectively. We compared the sampling survey method based on UAV images in this paper with the manual survey method. The results showed that the RMSE and MAPE of NM13 were 18.95 × 104/hm2 and 3.37%, respectively, and for YFM4, 13.65 × 104/hm2 and 2.94%, respectively. This study enables the investigation of the number of wheat ears in a large area, which can provide favorable support for wheat yield estimation.

Split and Connect: A Universal Tracklet Booster for Multi-Object Tracking

Multi-object tracking (MOT) is an essential task in the computer vision field. With the fast development of deep learning technology in recent years, MOT has achieved great improvement. However, some challenges still remain, such as sensitiveness to occlusion, instability under different lighting conditions, and non-robustness to deformable objects, causing incorrect temporal associations. To address such common challenges in most of the existing trackers, in this paper, a tracklet booster (TBooster) algorithm is proposed to correct the association errors resulting from existing trackers. The correction of the association error from TBooster has two folds: split tracklets on potential ID-change positions and then connect multiple tracklets into one if they are from the same object. To achieve this goal, the TBooster consists of two components, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , Splitter and Connector. In Splitter, an architecture with stacked temporal dilated convolution blocks is employed for the splitting position prediction via label smoothing strategy with adaptive Gaussian kernels. In Connector, a multi-head self-attention-based encoder is exploited for the tracklet embedding, which is further used to connect tracklets into full tracks. We conduct sufficient experiments on MOT17 and MOT20 benchmark datasets and achieve promising results. Combined with the proposed tracklet booster, existing trackers can achieve large improvements on the IDF1 score, which shows the effectiveness of the proposed TBooster.

3-D Structure-Oriented Adaptive Gaussian Pyramid for Seismic Multiscale Fracture Detection

Gaussian pyramid (GP) has the property of decomposing the 2D data into multiple scales. It has been successfully applied in image processing, but rarely used in seismic data processing and interpretation. Specifically, seismic data is not just a 2D image in one time slice or horizon. Moreover, the pattern of geological body varies rapidly in 3D spaces. Hence, the classical GP method exists a limitation in processing 3D seismic data. Especially in fracture detection, the 2D isotropic Gaussian kernel used in GP tends to blur the fracture details. In this letter, to match the high dimensional seismic data, we propose a structure-oriented adaptive Gaussian pyramid (SOA-GP) algorithm, which expand the 2D GP method to 3D situation. In this case, to eliminate the influence of transverse change of wave impedance and consider stratigraphic characteristics, the 2D Gaussian filter is also substituted by the 3D structure adaptive anisotropic Gaussian kernel, which is constructed by instantaneous phase-based gradient structure tensor (GST) method. Meanwhile, seismic attributes are applied to the multi-resolution seismic data decomposed by SOA-GP method to realize multi-scale fracture detection. Finally, we apply this new 3D SOA-GP method to a field data. It demonstrates that the multi-scale decomposition of the new method could produce more details of fracture and less disturbs from noise.

Modeling Geographically Weighted Negative Binomial Regression (GWNBR) on Stunting Incidence in Malang Regency

Discrete data on the response variable can be analyzed using poisson regression. The assumption of equidispersion in poisson regression must be fulfilled, but in practice there are many problems of overdispersion. The negative binomial regression model is used to overcome the problem of overdispersion, but this model is global while in some cases each location has different characteristics. Therefore, a method that considers the effects of spatial heterogeneity is needed. If the response variable is discrete data that is overdispersed and includes spatial effects, a model called Geographically Weighted Negative Binomial Regression (GWNBR) is developed. The GWNBR method can be applied in the health sector, such as in stunting. The prevalence of stunting in Malang Regency is still quite high, there is 25.7%. By conducting the GWNBR test, 385 models were obtained, one of them is Tulungrejo Village with factors influencing the incidence of stunting, namely access to permanent healthy latrines, access to posyandu, exclusive breastfeeding, population density and community empowerment. From three weights used, namely the Adaptive Gaussian Kernel, Adaptive Bisquare Kernel and Adaptive Tricube Kernel, the best model was obtained from the Adaptive Bisquare Kernel weighting with the smallest AIC is -211.3763.

Geographically Weighted Negative Binomial Regression Modeling using Adaptive Kernel on the Number of Maternal Deaths during Childbirth

The standard model that is used for count data is Poisson Regression. In fact, most of the count data is overdispersed, which means that the response variable has greater variance than the mean. So the Poisson Regression cannot be used because overdispersion can cause inaccurate parameter estimators. One of the most widely used methods to overcome overdispersion is Negative Binomial Regression. If there are spatial effects such as spatial heterogeneity that are taken into Negative Binomial model, the appropriate method to analyze is Geographically Weighted Negative Binomial Regression (GWNBR). A spatial weighting matrix is required in the GWNBR model. In this study, three weighting functions were used, that is Adaptive Gaussian Kernel, Adaptive Bisquare Kernel, and Adaptive Tricube Kernel. From the three weighting functions, a model will be formed and the best model will be selected based on the smallest AIC. Count data used in this study is maternal deaths during childbirth in West Java Province, which is the highest case in Indonesia. The results of the analysis show that based on the smallest AIC, the best modeling in maternal deaths during childbirth in West Java is the GWNBR model using the Adaptive Gaussian Kernel weight. The results of the best model were obtained from three groups based on the predictor variables that had a significant effect.

Enhancement of Local Crowd Location and Count: Multiscale Counting Guided by Head RGB-Mask.

Background In crowded crowd images, traditional detection models often have the problems of inaccurate multiscale target count and low recall rate. Methods In order to solve the above two problems, this paper proposes an MLP-CNN model, which combined with FPN feature pyramid can fuse the feature map of low-resolution and high-resolution semantic information with less computation and can effectively solve the problem of inaccurate head count of multiscale people. MLP-CNN “mid-term” fusion model can effectively fuse the features of RGB head image and RGB-Mask image. With the help of head RGB-Mask annotation and adaptive Gaussian kernel regression, the enhanced density map can be generated, which can effectively solve the problem of low recall of head detection. Results MLP-CNN model was applied in ShanghaiTech and UCF_ CC_ 50 and UCF-QNRF. The test results show that the error of the method proposed in this paper has been significantly improved, and the recall rate can reach 79.91%. Conclusion MLP-CNN model not only improves the accuracy of population counting in density map regression, but also improves the detection rate of multiscale population head targets.

Pemodelan Geographically Weighted Logistic Regression dengan Fungsi Adaptive Gaussian Kernel Terhadap Kemiskinan di Provinsi NTT

The Geographically Weighted Logistic Regression (GWLR) model is a logistic regression model development that is applied to spatial data from non-stationary processes. This model is used to predict a model of the data set that has a binary response variable which takes into account the spatial factor. This study will discuss the use of the GWLR model using the adaptive weighting function of the Gaussian kernel in a poverty case study in East Nusa Tenggara Province in 2019.The parameter estimation of the Maximum Likelihood Estimation (MLE) method by giving different weights for each observation location. The weight used is the adaptive Gaussian kernel with the optimum bandwidth selection using the Cross-Validation (CV). Based on the results of testing the parameters of the GWLR model with a weighted adaptive Gaussian kernel, it can be concluded that the factors that influence poverty are local and vary in the 22 observation locations, including GRDP per capita, acceptance of smart Indonesian programs, and projected population growth rates, with a classification accuracy rate of 81,82%.