Recently, hyperspectral image (HSI) classification has become a hot topic in the geographical images research area. Sufficient samples are required for image classes to properly train classification models. However, a class imbalance problem has emerged in hyperspectral image (HSI) datasets as some classes do not have enough samples for training, and some classes have many samples. Therefore, the performance of classifiers is likely to be biased toward the classes with the largest samples, and this can lead to a decrease in the classification accuracy. Therefore, a new deep-learning-based model is proposed for hyperspectral images generation and classification of imbalanced data. Firstly, the spectral features are extracted by a 1D convolutional neural network, whereas a 2D convolutional neural network extracts the spatial features and the extracted spatial features and spectral features are catenated into a stacked spatial–spectral feature vector. Secondly, an autoencoder model was developed to generate synthetic images for minority classes, and the image samples were balanced. The GAN model is applied to determine the synthetic images from the real ones and then enhancing the classification performance. Finally, the balanced datasets are fed to a 2D CNN model for performing classification and validating the efficiency of the proposed model. Our model and the state-of-the-art classifiers are evaluated by four open-access HSI datasets. The results showed that the proposed approach can generate better quality samples for rebalancing datasets, which in turn noticeably enhances the classification performance compared to the existing classification models.

With the ubiquitous deployment of mobile and sensor technologies in modes of transportation, taxis have become a significant component of public transportation. However, vacant taxis represent an important waste of transportation resources. Forecasting taxi demand within a short time achieves a supply–demand balance and reduces oil emissions. Although earlier studies have forwarded highly developed machine learning- and deep learning-based models to forecast taxicab demands, these models often face significant computational expenses and cannot effectively utilize large-scale trajectory sensor data. To address these challenges, in this paper, we propose a hybrid deep learning-based model for taxi demand prediction. In particular, the Variational Mode Decomposition (VMD) algorithm is integrated along with a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the prediction process. The VMD algorithm is applied to decompose time series-aware traffic features into multiple sub-modes of different frequencies. After that, the BiLSTM method is utilized to predict time series data fed with the relevant demand features. To overcome the limitation of high computational expenses, the designed model is performed on the Spark distributed platform. The performance of the proposed model is tested using a real-world dataset, and it surpasses existing state-of-the-art predictive models in terms of accuracy, efficiency, and distributed performance. These findings provide insights for enhancing the efficiency of passenger search and increasing the profit of taxicabs.

Driven by digital transformation, intelligent healthcare systems (IHS) have achieved the upgrading of modules covering diagnosis, treatment and public services. Against this backdrop, comprehensive security protection has become key for stabilizing the operation of medical systems and safeguarding patients’ legitimate rights and interests, as well as being an essential foundation for the sustainable development of IHS. Equality test function-based public key encryption (PKEwET) plays a vital role in IHS, the Internet of Things (IoT), etc. The scheme enables individual users to encrypt and store personal privacy data by themselves. Furthermore, it supports ciphertext matching and retrieval in a non-decryption manner in the public encryption system. To meet the needs of a variety of applications, many PKEwET applications for flexible authorization schemes have been proposed. In this paper, the equality test function is integrated with the Optimal Asymmetric Encryption Padding (RSA-OAEP) scheme. The aim is to offer an equality test function to RSA-OAEP algorithms without a secret key $sk$. In contrast to the original PKEwET primitive scheme, the proposed scheme integrates a flexible authorization mechanism. Authorized users can test the encrypted personal health information. Compared to the RSA-OAEP scheme, the proposed scheme in this paper supports the equality test function to the ciphertexts. The security of the proposed scheme is demonstrated to two different security levels, the OW-CCA level and the IND-CCA level. Finally, a performance analysis is presented. Appropriate comparisons with relevant works confirm that the proposed scheme offers a new function and shows good efficiency.