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Due to the limitations of the urban environment, the data transferred between vehicles can only change direction at the intersections. Therefore, the routing decision at an intersection will largely affect the overall routing decision. In this article, we propose an Intersection-Based Routing with Fuzzy Multi-Factor Decision (IRFMF), which utilizes several factors to decide the next road segment. In the scheme, each intersection introduces three factors including the direction, the number of lanes, and the traffic. After the fuzzification and defuzzification of these factors, the candidate segment with the highest evaluation will be selected. The simulation shows a significant improvement of VANETs performance on packet delivery ratio and end-to-end delay.
Zhenbo Cao; Zujie Fan; Jaesool Kim. Intersection-Based Routing with Fuzzy Multi-Factor Decision for VANETs. Applied Sciences 2021, 11, 7304 .
AMA StyleZhenbo Cao, Zujie Fan, Jaesool Kim. Intersection-Based Routing with Fuzzy Multi-Factor Decision for VANETs. Applied Sciences. 2021; 11 (16):7304.
Chicago/Turabian StyleZhenbo Cao; Zujie Fan; Jaesool Kim. 2021. "Intersection-Based Routing with Fuzzy Multi-Factor Decision for VANETs." Applied Sciences 11, no. 16: 7304.
The early and accurate diagnosis of skin cancer is crucial for providing patients with advanced treatment by focusing medical personnel on specific parts of the skin. Networks based on encoder–decoder architectures have been effectively implemented for numerous computer-vision applications. U-Net, one of CNN architectures based on the encoder–decoder network, has achieved successful performance for skin-lesion segmentation. However, this network has several drawbacks caused by its upsampling method and activation function. In this paper, a fully convolutional network and its architecture are proposed with a modified U-Net, in which a bilinear interpolation method is used for upsampling with a block of convolution layers followed by parametric rectified linear-unit non-linearity. To avoid overfitting, a dropout is applied after each convolution block. The results demonstrate that our recommended technique achieves state-of-the-art performance for skin-lesion segmentation with 94% pixel accuracy and a 88% dice coefficient, respectively.
Karshiev Sanjar; Olimov Bekhzod; Jaeil Kim; Jaesoo Kim; Anand Paul; Jeonghong Kim. Improved U-Net: Fully Convolutional Network Model for Skin-Lesion Segmentation. Applied Sciences 2020, 10, 3658 .
AMA StyleKarshiev Sanjar, Olimov Bekhzod, Jaeil Kim, Jaesoo Kim, Anand Paul, Jeonghong Kim. Improved U-Net: Fully Convolutional Network Model for Skin-Lesion Segmentation. Applied Sciences. 2020; 10 (10):3658.
Chicago/Turabian StyleKarshiev Sanjar; Olimov Bekhzod; Jaeil Kim; Jaesoo Kim; Anand Paul; Jeonghong Kim. 2020. "Improved U-Net: Fully Convolutional Network Model for Skin-Lesion Segmentation." Applied Sciences 10, no. 10: 3658.
Accurate house price forecasts are very important for formulating national economic policies. In this paper, we offer an effective method to predict houses’ sale prices. Our algorithm includes one-hot encoding to convert text data into numeric data, feature correlation to select only the most correlated variables, and a technique to overcome the missing data. Our approach is an effective way to handle missing data in large datasets with the K-nearest neighbor algorithm based on the most correlated features (KNN–MCF). As far as we are concerned, there has been no previous research that has focused on important features dealing with missing observations. Compared to the typical machine learning prediction algorithms, the prediction accuracy of the proposed method is 92.01% with the random forest algorithm, which is more efficient than the other methods.
Karshiev Sanjar; Olimov Bekhzod; Jaesoo Kim; Anand Paul; Jeonghong Kim. Missing Data Imputation for Geolocation-based Price Prediction Using KNN–MCF Method. ISPRS International Journal of Geo-Information 2020, 9, 227 .
AMA StyleKarshiev Sanjar, Olimov Bekhzod, Jaesoo Kim, Anand Paul, Jeonghong Kim. Missing Data Imputation for Geolocation-based Price Prediction Using KNN–MCF Method. ISPRS International Journal of Geo-Information. 2020; 9 (4):227.
Chicago/Turabian StyleKarshiev Sanjar; Olimov Bekhzod; Jaesoo Kim; Anand Paul; Jeonghong Kim. 2020. "Missing Data Imputation for Geolocation-based Price Prediction Using KNN–MCF Method." ISPRS International Journal of Geo-Information 9, no. 4: 227.