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.

Emotion recognition (ER) in healthcare has drawn substantial attention owing to recent advancements in machine-learning (ML) and deep-learning (DL) techniques. The ER system, along with a digital twin of a person in real time, will facilitate the monitoring, understanding, and improvement of the physical entity’s capabilities, as well as provide constant input to improve quality of life and well-being for personalized healthcare. However, building such ER systems in real time involves technical challenges, such as limited datasets, occlusion and lighting issues, identifying important features, false classification of emotions, and high implementation costs. To resolve this issue, we built a simple, efficient, and adaptable ER system by acquiring and processing images in real time using a web camera. In addition, we propose an end-to-end framework that combines an ER system with a digital twin setup, in which the predicted result can be analyzed and tested prior to providing the best possible personal healthcare treatment before it leads to any life-threatening disease. Our proposed ER system achieved promising results in less training time without compromising the accuracy. Thus, in real time, it will be helpful in healthcare centers to monitor a patient’s health condition, early diagnosis of life-threatening diseases, and to obtain the best and most effective treatment for patients during emergencies.

In recent works on the Internet of Vehicles (IoV), “intelligent” and “sustainable” have been the buzzwords in the context of transportation. Maintaining sustainability in IoV is always a challenge. Sustainability in IoV can be achieved not only by the use of pollution-free vehicular systems, but also by maintenance of road traffic safety or prevention of accidents or collisions. With the aim of establishing an effective sustainable transportation planning system, this study performs a short analysis of existing sustainable transportation methods in the IoV. This study also analyzes various characteristics of sustainability and the advantages and disadvantages of existing transportation systems. Toward the end, this study provides a clear suggestion for effective sustainable transportation planning aimed at the maintenance of an eco-friendly environment and road traffic safety, which, in turn, would lead to a sustainable transportation system.

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.

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.