Automatic tongue image segmentation and tongue classification are two crucial tongue characterization tasks in traditional Chinese medicine (TCM). Due to the complexity of automatic tongue segmentation and the fine-grained traits of tongue classification, both tasks are challenging. However, as discussed in the introduction section, these two tasks are interrelated, making them highly compatible with the idea of multitask joint learning (MTL). By sharing the underlying parameters and adding two different task objective functions, a MTL method for segmenting and classifying tongue images is proposed in this paper. Moreover, two state-of-the-art deep neural network variants (UNET and discriminative filter bank (DFL)) are fused into the MTL to perform the tongue segmentation and tongue classification tasks, respectively. To the best of our knowledge, our method is the first attempt to manage both tasks simultaneously with MTL. We conducted extensive experiments on reliable and quality assured datasets. The experimental results show that our joint method outperforms both the existing tongue segmentation methods and the existing tongue classification methods. Visualizations and ablation studies are provided to aid in understanding our approach, which suggest that our method is highly consistent with human perception

Mobile edge computing (MEC) has been an effective paradigm for supporting computation-intensive applications by offloading resources at network edge. Especially in vehicular networks, the MEC server, is deployed as a small-scale computation server at the roadside and offloads computation-intensive task to its local server. However, due to the unique characteristics of vehicular networks, including high mobility of vehicles, dynamic distribution of vehicle densities and heterogeneous capacities of MEC servers, it is still challenging to implement efficient computation offloading mechanism in MEC-assisted vehicular networks. In this article, we investigate a novel scenario of computation offloading in MEC-assisted architecture, where task upload coordination between multiple vehicles, task migration between MEC/cloud servers and heterogeneous computation capabilities of MEC/cloud severs, are comprehensively investigated. On this basis, we formulate cooperative computation offloading (CCO) problem by modeling the procedure of task upload, migration and computation based on queuing theory, which aims at minimizing the delay of task completion. To tackle the CCO problem, we propose a probabilistic computation offloading (PCO) algorithm, which enables MEC server to independently make online scheduling based on the derived allocation probability. Specifically, the PCO transforms the objective function into augmented Lagrangian and achieves the optimal solution in an iterative way, based on a convex framework called Alternating Direction Method of Multipliers (ADMM). Last but not the least, we implement the simulation model. The comprehensive simulation results show the superiority of the proposed algorithm under a wide range of scenarios.

Urban flow analysis is an essential research for smart city construction, in which urban flow pattern analysis focuses on the continuous state of urban flow. How to mine, store and reuse traffic patterns from urban multi-source heterogeneous big data is challenging. Therefore, this paper proposes a knowledge mining network for regional flow pattern to mine and store the urban flow pattern. The proposed model consists of two modules. In the first module, the features of the region and its flow pattern are extracted as the entity and relation, respectively. In the second module, POI features are modeled to enhance the embedding representation of relation and entity. Based on the translation distance method, the knowledge triplets of regional flow patterns are mined. Finally, the proposed model is compared with some benchmark methods using Chengdu Didi order and POI datasets. Experimental results show that the proposed model is effective. In addition, the knowledge triplets are visualized and some application examples are introduced.

The International Classification of Diseases (ICD) is a standard for categorizing physical conditions, which has been widely used for analyzing clinical data and monitoring health issues. Manual ICD coding takes a long time and is vulnerable to errors, so people pay more and more attention to the application of deep neural networks in ICD automatic coding. However, there is still no comprehensive review of these studies and prospects for further research. This paper is not limited to the study of deep neural networks, but gives a formal definition of ICD coding problems, and then systematically reviews the existing literature on how to design deep neural networks to address the four major challenges of ICD coding tasks. This paper also summarizes the public data sets and future research directions, to provide a guidance for the research of ICD coding in medical field.

In recent years, intelligent fault diagnosis methods for bearings, dominated by deep learning, have been widely applied. However, most current methods are still limited to diagnosing different types of faults or degrees of faults as single diagnostic targets. There is less research on methods that diagnose multiple fault tasks simultaneously, and there are difficulties in fully extracting complementary fault features, adapting to complex diagnostic environments with strong noise interference, and handling imbalanced data distribution across various working conditions. To overcome these difficulties and challenges, this paper proposes an innovative multitask CNN-Transformer bearing fault diagnosis method (MT-ConvFormer). This method consists of a shared layer responsible for learning global shared information and several task-specific branch networks designed for different fault diagnosis tasks. Firstly, the shared layer uses CNN to extract local feature information and combines it with Transformer to infer cyclic global dependencies of vibration signals, effectively mining global shared information. Then, the task branch networks, by adopting an effective redundancy filtering mechanism, can more efficiently extract useful feature information from the shared layer and suppress the interference of irrelevant information. Experimental results show that this method exhibits significant diagnostic performance both under noisy conditions and under conditions of imbalanced data distribution across different working conditions. Moreover, comparative experiments with other existing methods further confirm the superiority of MT-ConvFormer under the same experimental setups.

With the development of healthcare 4.0, there has been an explosion in the amount of data such as image, medical text, physiological signals, lab tests, etc. Among them, medical records provide a complete picture of the associated clinical events. However, the processing of medical texts is difficult because they are structurally free, diverse in style, and have subjective factors. Assigning metadata codes from the International Classification of Diseases (ICD) presents a standardized way of indicating diagnoses and procedures, so it becomes a mandatory process for understanding medical records to make better clinical and financial decisions. Such a manual encoding task is time-consuming, error-prone and expensive. In this paper, we proposed a deep learning approach and a medical topic mining method to automatically predict ICD codes from text-free medical records. The result of the F1 score on MIMIC increases by 5% over the state of art. It also suitable for multiple ICD versions and languages. For the specific disease, atrial fibrillation, the F1 score is up to 96% and 93.3% using in-house ICD-10 datasets and MIMIC datasets, respectively. We developed an AI-based coding system, which can greatly improve the efficiency and accuracy of human coders, and meanwhile accelerate the secondary use for clinical informatics.