Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics , ...bioimaging , medical imaging , and (brain/body)-machine interfaces . These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.
With the coming era of cloud technology, cloud storage is an emerging technology to store massive digital images, which provides steganography a new fashion to embed secret information into massive ...images. Specifically, a resourceful steganographer could embed a set of secret information into multiple images adaptively, and share these images in cloud storage with the receiver, instead of traditional single image steganography. Nevertheless, it is still an open issue how to allocate embedding payload among a sequence of images for security performance enhancement. This article formulates adaptive payload distribution in multiple images steganography based on image texture features and provides the theoretical security analysis from the steganalyst's point of view. Two payload distribution strategies based on image texture complexity and distortion distribution are designed and discussed, respectively. The proposed strategies can be employed together with these state-of-the-art single image steganographic algorithms. The comparisons of the security performance against the modern universal pooled steganalysis are given. Furthermore, this article compares the per image detectability of these multiple images steganographic schemes against the modern single image steganalyzer. Extensive experimental results show that the proposed payload distribution strategies could obtain better security performance.
Currently, ECG-based authentication is considered highly promising in terms of user identification for smart healthcare systems because of its inimitability, suitability, accessibility and ...comfortability. However, it is a great challenge to improve the authentication accuracy, especially for scenarios that include a large number of users. Thus, this paper proposes a parallel ECG-based authentication called PEA. Specifically, this paper proposes a hybrid ECG feature extraction method that integrated fiducial- and non-fiducial-based features to extract more comprehensive ECG features and thereby improve the authentication stability. Furthermore, this paper proposes a parallel ECG pattern recognition framework to improve the recognition efficiency in multiple ECG feature spaces. Through the experiments, the performance of the proposed authentication is verified.
•An unsupervised method based on GAN is proposed for pansharpening.•We formulate pansharpening as a multi-task spatial-spectral information preservation problem.•Specific loss functions are designed ...for better preservation of valuable information.•The proposed method does not require the ground truth data.•Our results have abundant details and less spectral distortion compared with SOTA.
Pan-sharpening in remote sensing image fusion refers to obtaining multi-spectral images of high-resolution by fusing panchromatic images and multi-spectral images of low-resolution. Recently, convolution neural network (CNN)-based pan-sharpening methods have achieved the state-of-the-art performance. Even though, two problems still remain. On the one hand, the existing CNN-based strategies require supervision, where the low-resolution multi-spectral image is obtained by simply blurring and down-sampling the high-resolution one. On the other hand, they typically ignore rich spatial information of panchromatic images. To address these issues, we propose a novel unsupervised framework for pan-sharpening based on a generative adversarial network, termed as Pan-GAN, which does not rely on the so-called ground-truth during network training. In our method, the generator separately establishes the adversarial games with the spectral discriminator and the spatial discriminator, so as to preserve the rich spectral information of multi-spectral images and the spatial information of panchromatic images. Extensive experiments are conducted to demonstrate the effectiveness of the proposed Pan-GAN compared with other state-of-the-art pan-sharpening approaches. Our Pan-GAN has shown promising performance in terms of qualitative visual effects and quantitative evaluation metrics.
The rapid growth of urban populations worldwide imposes new challenges on citizens’ daily lives, including environmental pollution, public security, road congestion, etc. New technologies have been ...developed to manage this rapid growth by developing smarter cities. Integrating the Internet of Things (IoT) in citizens’ lives enables the innovation of new intelligent services and applications that serve sectors around the city, including healthcare, surveillance, agriculture, etc. IoT devices and sensors generate large amounts of data that can be analyzed to gain valuable information and insights that help to enhance citizens’ quality of life. Deep Learning (DL), a new area of Artificial Intelligence (AI), has recently demonstrated the potential for increasing the efficiency and performance of IoT big data analytics. In this survey, we provide a review of the literature regarding the use of IoT and DL to develop smart cities. We begin by defining the IoT and listing the characteristics of IoT-generated big data. Then, we present the different computing infrastructures used for IoT big data analytics, which include cloud, fog, and edge computing. After that, we survey popular DL models and review the recent research that employs both IoT and DL to develop smart applications and services for smart cities. Finally, we outline the current challenges and issues faced during the development of smart city services.
With the development of cloud computing, privacy security issues have become increasingly prominent, which is of concern to industry and academia. We review the research progress on privacy security ...issues from the perspective of several privacy security protection technologies in cloud computing. First, we introduce some privacy security risks of cloud computing and propose a comprehensive privacy security protection framework. Second, we show and discuss the research progress of several technologies, such as access control; ciphertext policy attribute-based encryption (CP-ABE); key policy attribute-based encryption (KP-ABE); the fine-grain, multi-authority, revocation mechanism; the trace mechanism; proxy re-encryption (PRE); hierarchical encryption; searchable encryption (SE); and multi-tenant, trust, and a combination of multiple technologies, and then compare and analyze the characteristics and application scope of typical schemes. Last, we discuss current challenges and highlight possible future research directions.
Fig. 1. The organization framework of this paper. With the development of cloud computing, the privacy security issues become more and more prominent, which have been widely concerned by the industry and academia. We review the research progress from the perspective of privacy security protection technology in the cloud computing. Firstly, we introduce some privacy security risks of cloud computing, propose a comprehensive privacy security protection framework; secondly, we describe the research progress of several technologies, for example, access control, ciphertext policy attribute-based encryption (CP-ABE), key policy attribute-based encryption (KP-ABE), fine-grain, multi-authority, revocation mechanism, trace mechanism, proxy re-encryption(PRE), hierarchical encryption, searchable encryption (SE), multi-tenant, trust, and combination of multiple technologies and so on, then compare and analyze the characteristics and application scope of typical schemes; finally, we discuss the current challenges, and point out possible research directions in the future. Display omitted
•We introduce some privacy security risks of cloud computing and propose a comprehensive privacy security protection framework.•We discuss the research progress of several technologies, such as access control; ciphertext policy attribute-based encryption (CP-ABE); key policy attribute-based encryption (KP-ABE); the fine-grain, multi-authority, proxy re-encryption (PRE); hierarchical encryption; searchable encryption (SE).•We summarize a combination of multiple technologies, and then compare and analyze the characteristics and application scope of typical schemes.
Hydraulic systems are a class of typical complex nonlinear systems, which have been widely used in manufacturing, metallurgy, energy, and other industries. Nowadays, the intelligent fault diagnosis ...problem of hydraulic systems has received increasing attention for it can increase operational safety and reliability, reduce maintenance cost, and improve productivity. However, because of the high nonlinear and strong fault concealment, the fault diagnosis of hydraulic systems is still a challenging task. Besides, the data samples collected from the hydraulic system are always in different sampling rates, and the coupling relationship between the components brings difficulties to accurate data acquisition. To solve the above issues, a deep learning model with multirate data samples is proposed in this article, which can extract features from the multirate sampling data automatically without expertise, thus it is more suitable in the industrial situation. Experiment results demonstrate that the proposed method achieves high diagnostic and fault pattern recognition accuracy even when the imbalance degree of sample data is as large as 1:100. Moreover, the proposed method can increase about 10% diagnosis accuracy when compared with some state-of-the-art methods.
It is of great significance to apply deep learning for the early diagnosis of Alzheimer's disease (AD). In this work, a novel tensorizing GAN with high-order pooling is proposed to assess mild ...cognitive impairment (MCI) and AD. By tensorizing a three-player cooperative game-based framework, the proposed model can benefit from the structural information of the brain. By incorporating the high-order pooling scheme into the classifier, the proposed model can make full use of the second-order statistics of holistic magnetic resonance imaging (MRI). To the best of our knowledge, the proposed Tensor-train, High-order pooling and Semisupervised learning-based GAN (THS-GAN) is the first work to deal with classification on MR images for AD diagnosis. Extensive experimental results on Alzheimer's disease neuroimaging initiative (ADNI) data set are reported to demonstrate that the proposed THS-GAN achieves superior performance compared with existing methods, and to show that both tensor-train and high-order pooling can enhance classification performance. The visualization of generated samples also shows that the proposed model can generate plausible samples for semisupervised learning purpose.
Automation in plant disease detection and diagnosis is one of the challenging research areas that has gained significant attention in the agricultural sector. Traditional disease detection methods ...rely on extracting handcrafted features from the acquired images to identify the type of infection. Also, the performance of these works solely depends on the nature of the handcrafted features selected. This can be addressed by learning the features automatically with the help of Convolutional Neural Networks (CNN). This research presents two different deep architectures for detecting the type of infection in tomato leaves. The first architecture applies residual learning to learn significant features for classification. The second architecture applies attention mechanism on top of the residual deep network. Experiments were conducted using Plant Village Dataset comprising of three diseases namely early blight, late blight, and leaf mold. The proposed work exploited the features learned by the CNN at various processing hierarchy using the attention mechanism and achieved an overall accuracy of 98% on the validation sets in the 5-fold cross-validation.
•An attention based deep residual network is proposed in this research to detect the type of infection in tomato leaves.•This enhanced deep learning architecture is the first of its kind developed for automatic detection of infection in tomato leaves.•95999 images were used for training the model and 24001 images were used for validation purpose.•Experimental results indicate that the proposed attention based residual network was able to detect the type of infection with an accuracy of 98%.
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