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.

Convolutional Neural Networks (CNNs) have made a great impact on attaining state‐of‐the‐art results in image task classification. Weight initialization is one of the fundamental steps in formulating a CNN model. It determines the failure or success of the CNN model. In this paper, we conduct a research based on the mathematical background of different weight initialization strategies to determine the one with better performance. To have smooth training, we expect the activation of each layer of the CNN model follow the standard normal distribution with mean 0 and SD 1. It prevents gradients from vanishing and leads to more smooth training. However, it was obtained that even with the appropriate weight initialization technique, a regular Rectified Linear Unit (ReLU) activation function increases the activation mean value. In this paper, we address this issue by proposing weight initialization based (WIB)‐ReLU activation function. The proposed method resulted in more smooth training. Moreover, the experiments showed that WIB‐ReLU outperforms ReLU, Leaky ReLU, parametric ReLU, and exponential linear unit activation functions and results in up to 20% decrease in loss value and 5% increase in accuracy score on both Fashion‐MNIST and CIFAR‐10 databases.

No abstract

Sign language recognition is challenged by problems, such as accurate tracking of hand gestures, occlusion of hands, and high computational cost. Recently, it has benefited from advancements in deep learning techniques. However, these larger complex approaches cannot manage long-term sequential data and they are characterized by poor information processing and learning efficiency in capturing useful information. To overcome these challenges, we propose an integrated MediaPipe-optimized gated recurrent unit (MOPGRU) model for Indian sign language recognition. Specifically, we improved the update gate of the standard GRU cell by multiplying it by the reset gate to discard the redundant information from the past in one screening. By obtaining feedback from the resultant of the reset gate, additional attention is shown to the present input. Additionally, we replace the hyperbolic tangent activation in standard GRUs with exponential linear unit activation and SoftMax with Softsign activation in the output layer of the GRU cell. Thus, our proposed MOPGRU model achieved better prediction accuracy, high learning efficiency, information processing capability, and faster convergence than other sequential models.

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.

No abstract

Obtaining data with correct labels is crucial to attain the state-of-the-art performance of Convolutional Neural Network (CNN) models. However, labeling datasets is significantly time-consuming and expensive process because it requires expert knowledge in a particular domain. Therefore, real-life datasets often exhibit incorrect labels due to the involvement of nonexperts in the data-labeling process. Consequently, there are many cases of incorrectly labeled data in the wild. Although the issue of poorly labeled datasets has been studied, the existing methods are complex and difficult to reproduce. Thus, in this study, we proposed a simpler algorithm called "Deep Clean Before Training Net" (DCBT-Net) that is based on cleaning wrongly labeled data points using the information from eigenvalues of the Laplacian matrix obtained from similarities between the data samples. The cleaned data were trained using deep CNN (DCNN) to attain the state-of-the-art results. This system achieved better performance than the existing approaches. In conducted experiments, the performance of the DCBT-Net was tested on three commercially available datasets, namely, Modified National Institute of Standards a nd Technology (MNIST) database of handwritten digits, Canadian Institute for Advanced Research (CIFAR) and WebVision1000 datasets. The proposed method achieved better results when assessed using several evaluation metrics compared with the existing state-of-the-art methods. Specifically, the DCBT-Net attained an average 15%, 20%, and 3% increase in accuracy score using MNIST database, CIFAR-10 dataset, and WebVision dataset, respectively. Also, the proposed approach demonstrated better results in specificit y, sensitivity, positive predictive value, and negative predictive value evaluation metrics. INDEX TERMS Clustering, deep convolutional neural networks, eigenvalues and eigenvectors, image classification, noisy (corrupted) labels.

Considering importance of the autonomous driving applications for mobile devices, it is imperative to develop both fast and accurate semantic segmentation models. Thanks to emergence of Deep Learning (DL) techniques, the segmentation models enhanced their accuracy. However, this improved performance of currently popular DL models for self-driving car applications come at the cost of time and computational efficiency. Moreover, networks with efficient model architecture experience lack of accuracy. Therefore, in this study, we propose robust, efficient, and fast network (REF-Net) that combines carefully formulated encoding and decoding paths. Specifically, the contraction path uses mixture of dilated and asymmetric convolution layers with skip connections and bottleneck layers, while the decoding path benefits from nearest neighbor interpolation method that demands no trainable parameters to restore original image size. This model architecture considerably reduces the number of trainable parameters, required memory space, training, and inference time. In fact, the proposed model required nearly 90 times fewer trainable parameters and approximately 4 times less memory space that allowed 3-fold faster training runtime and 2-fold inference speedup in the conducted experiments using Cambridge-driving Labeled Video Database (CamVid) and Cityscapes datasets. Moreover, despite its notable efficiency in terms of memory and time, the REF-Net attained superior results in several segmentation evaluation metrics that showed roughly 2%, 4%, and 3% increase in pixel accuracy, Dice coefficient, and Jaccard Index, respectively. INDEX TERMS Autonomous driving, deep convolutional neural networks, nearest neighbor interpolation, semantic segmentation.