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IoMT-Enabled Real-Time Blood Glucose prediction With Deep Learning and Edge Computing

IEEE INTERNET OF THINGS JOURNAL 2023年第5期10卷 3706-3719页

作者： Zhu, Taiyu Kuang, Lei Daniels, John Herrero, Pau Li, Kezhi Georgiou, Pantelis Imperial Coll London Ctr Bioinspired Technol London SW7 2BX England UCL Inst Hlth Informat London WC1E 6BT England

Blood glucose (BG) prediction is essential to the success of glycemic control in type 1 diabetes (T1D) management. Empowered by the recent development of the Internet of Medical Things (IoMT), continuous glucose monitoring (CGM) and deep learning technologies have been demonstrated to achieve the state of the art in BG prediction. However, it is challenging to implement such algorithms in actual clinical settings to provide persistent decision support due to the high demand for computational resources, while smartphone-based implementations are limited by short battery life and require users to carry the device. In this work, we propose a new deep learning model using an attention-based evidential recurrent neural network and design an IoMT-enabled wearable device to implement the embedded model, which comprises a low-cost and low-power system on a chip to perform Bluetooth connectivity and edge computing for real-time BG prediction and predictive hypoglycemia detection. In addition, we developed a smartphone app to visualize BG trajectories and predictions, and desktop and cloud platforms to backup data and fine-tune models. The embedded model was evaluated on three clinical data sets including 47 T1D subjects. The proposed model achieved superior performance of root mean square error (RMSE), mean absolute error, and glucose-specific RMSE, and obtained the best accuracy for hypoglycemia detection when compared with a group of machine learning baseline methods. Moreover, we performed hardware-in-the-loop in silico trials with ten virtual T1D adults to test the whole IoMT system with predictive low-glucose management, which significantly reduced hypoglycemia and improved BG control.

关键词： Computational modeling Wearable computers Predictive models Deep learning Edge computing prediction algorithms Performance evaluation Artificial intelligence (AI) deep learning diabetes edge computing glucose prediction Internet of Things (IoT)

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Dual-Network Cross-Learning for Metabolite-Disease Association prediction

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2025年第2期22卷 545-556页

作者： Chen, Yanxin Ning, Qiao Zhang, Yitong Li, Hui Guo, Shikai Dalian Maritime Univ Dept Informat Sci & Technol Dalian 116026 Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China Dalian Key Lab Artificial Intelligence Dalian 116026 Peoples R China

In recent years, increasing evidence has demonstrated a close association between metabolites and various complex human diseases, providing valuable insights for disease diagnosis, treatment, and prevention. Although deep learning-based approaches have achieved certain success in predicting metabolic disease associations, challenges remain in enriching graph information and effectively integrating metabolic and disease features. To address these issues, this paper proposes a model named DCMDA, which extracts deep features of both metabolites and diseases using Dual-network Cross-learning for Metabolite-Disease Association prediction. DCMDA consists of three parts. The data processing module integrates similarity networks with association networks to construct a heterogeneous network. The feature extraction module extracts features from the metabolite-disease association network based on the non-negative matrix factorization method and from the heterogeneous network using graph autoencoder techniques. The feature fusion module combines the association matrix feature with the heterogeneous network feature through a Cross-Attention mechanism, thereby obtaining deep representations of metabolites and diseases. These features are then used to train the model to predict association scores between metabolites and diseases. Experimental results demonstrate that in 5-fold cross-validation, DCMDA achieves an area under the receiver operating characteristic curve (AUC) of 97.8% and an area under the precision-recall curve (AUPR) of 97.9%, outperforming state-of-the-art prediction methods.

关键词： Diseases Feature extraction Data mining Semantics Deep learning Kernel Information entropy prediction algorithms Noise Nearest neighbor methods Metabolite-disease association graph autoencoder non-negative matrix factorization Cross-Attention mechanism

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Remaining Useful Life prediction of Turbofan Engines Using CNN-LSTM-SAM Approach

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IEEE SENSORS JOURNAL 2023年第9期23卷 10241-10251页

作者： Li, Jie Jia, Yuanjie Niu, Mingbo Zhu, Wei Meng, Fanxi Changan Univ Internet Vehicle Rd Res Lab Xian 710064 Peoples R China Changan Univ Sch Elect & Control Engn Xian 710064 Peoples R China

Accurate remaining useful life (RUL) prediction of turbofan engines can effectively avoid serious air disaster due to engine failure by mining its component degradation characteristics. However, the complexity of engine component degradation characteristics keeps increasing when the airplane flies in complex environments and multioperating working point (MOP) mode. In this work, a deep learning fusion algorithm based on self-attention mechanism (SAM) is proposed. This algorithm uses a 1-D convolutional neural network (CNN) to extract the spatial features and long short-term memory (LSTM) networks to fuse the measurement data of 21 components and extract the temporal feature from the measured data. Furthermore, with extracted features and SAM, the proposed algorithm provides weight redistribution and solves the information loss problem in LSTM. Experimental results validated the proposed model and it is found that the proposed prediction model can predict RUL of turbofan engines accurately and stably under the MOP mode and the proposed model outperforms other latest existing approaches.

关键词： Feature extraction Engines Predictive models prediction algorithms Data mining Degradation Data models Convolutional neural network (CNN) long short-term memory (LSTM) remaining useful life (RUL) self-attention mechanism (SAM) turbofan engine

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Dual Channel Feature Attention-Based Approach for RUL prediction Considering the Spatiotemporal Difference of Multisensor Data

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IEEE SENSORS JOURNAL 2023年第8期23卷 8514-8525页

作者： Gao, Hui Li, Yibin Zhao, Ying Song, Yan Shandong Univ Inst Marine Sci & Technol Qingdao 266237 Shandong Peoples R China Nanjing Bur Army Equipment Dept Jinan Branch Jinan 250000 Shandong Peoples R China

The remaining useful life (RUL) prediction has always been the key technology to realize predictive maintenance. An accurate prediction can give decision-makers a reliable reference to develop maintenance schedules and adjust production planning. When dealing with the spatiotemporal data of multisensor system, recent deep learning (DL) methods, however, still remain unexplored to weigh the contributions from both spatial and temporal dimensions. In this article, we propose a novel DL-based approach with dual channel feature attention (DCFA) modules. First, the two-individual feature attention branches are used to automatically weigh the input on both time and spatial domain, which helps the model to focus more attention on the important elements. Then multilayer bidirectional long short-term memory (Bi-LSTM) and convolutional neural networks are used to extract the high-level features. Finally, a fusion network will combine the features to estimate the RUL. Evaluation experiments are conducted on the C-MAPSS dataset to verify the performance of the proposed model. The results show that the proposed model outperforms other state-of-the-art approaches.

关键词： Sensors Predictive models Degradation Uncertainty Training prediction algorithms Deep learning Deep learning (DL) feature fusion feature attention mechanism long short-term memory (LSTM) remaining useful life (RUL) prediction

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CATransUnetLBP: Accurate prediction of Protein-Ligand Binding Pockets Using a Hybrid Network

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2025年第1期22卷 355-367页

作者： Cai, Cheng Deng, Zhaohong Li, Andong Zuo, Yun Chen, Haoran Wei, Zhisheng Wang, Lei Pan, Xiaoyong Shen, Hong-Bin Yu, Dong-Jun Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Fudan Univ Sch CS Shanghai Key Lab Intell Info Proc Shanghai 201203 Peoples R China Jiangnan Univ Sch Biotechnol Wuxi 214122 Peoples R China Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

The development of intelligent methods capable of predicting protein-ligand binding sites has become a popular research field. Recently, deep learning based methods have been proposed as a promising solution for this task. However, some limitations still exist. For example, the network structure is not optimized for predicting protein binding pockets, which limits the model's capabilities. To address the aforementioned challenges, a novel method called CATransUnetLPB is proposed, in which a new network structure named CATransUnet is designed. The proposed CATransUnet combines CNN and Transformer models to accurately segment binding pocket regions from protein 3D structures. It outperforms existing representative methods on three test sets, demonstrating the effectiveness of optimizing the deep network model for detecting protein ligand binding pockets. Furthermore, we conduct thorough analysis on applying data augmentation to protein data structure and confirm that such technique can enhance the model's generalization ability, thereby ensuring good performance on new protein structures. Moreover, experiments show that the predicted binding pockets from our model can complement the results obtained from other methods. This suggests that integrating our method with existing approaches could further improve the prediction of protein-ligand binding pockets.

关键词： Proteins Three-dimensional displays Probes Vectors Image segmentation Deep learning Atoms Surface treatment prediction algorithms Training Protein pocket prediction ligand binding pocket deep learning attention mechanism hybrid network

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TraGCAN: Trajectory prediction of Heterogeneous Traffic Agents in IoV Systems

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IEEE INTERNET OF THINGS JOURNAL 2023年第8期10卷 7100-7113页

作者： Li, Jie Shi, Han Guo, Yue Han, Guangjie Yu, Ruiyun Wang, Xingwei Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Software Coll Shenyang 110819 Peoples R China Hohai Univ Dept Informat & Commun Syst Changzhou 213022 Peoples R China

As a core component of the Internet of Vehicles, reasoning about the trajectory of pedestrians or vehicles in complex road conditions plays a critical role in autonomous driving and socially aware robotic navigation. Most existing methods do not adequately consider the effects of heterogeneous traffic agents. Toward this end, we propose the traffic trajectory prediction algorithm based on the convolutional attention network (TraGCAN) to predict the trajectories of heterogeneous traffic agents in dense traffic. The algorithm of the proposed method examines the behavior of different traffic agents in terms of both time and space dimensions to identify their movement patterns and interactions. We construct the spatial relationship of traffic agents as a graph structure and introduce a graph convolutional network to extract spatial interactions. In addition, we design a spatial attention mechanism to adaptively calculate weights for all spatial interactions to capture different influences from neighboring agents. To improve the accuracy of trajectory prediction, the algorithm considers the influence of the heterogeneous characteristics of traffic agents on their motion behaviors. We evaluated the performance of the proposed TraGCAN on heterogeneous traffic data sets, and the results demonstrate that the error of TraGCAN is reduced by 15% compared to existing methods.

关键词： Trajectory Predictive models prediction algorithms Roads Behavioral sciences Hidden Markov models Feature extraction Autonomous driving graph convolutional network (GCN) Internet of Vehicles (IoV) trajectory prediction

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Composite Neural Network: Theory and Application to PM2.5 prediction

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第2期35卷 1311-1323页

作者： Yang, Ming-Chuan Chen, Meng Chang Acad Sinica Inst Informat Sci Taipei 115 Taiwan

This work investigates the framework and statistical performance guarantee of the composite neural network, which is composed of a collection of pre-trained and non-instantiated neural network models connected as a rooted directed acyclic graph, for solving complicated applications. A pre-trained neural network model is generally well trained, targeted to approximate a specific function. The advantages of adopting a pre-trained model as a component in composing a complicated neural network are two-fold. One is benefiting from the intelligence and diligence of domain experts, and the other is saving effort in data acquisition as well as computing resources and time for model training. Despite a general belief that a composite neural network may perform better than any a single component, the overall performance characteristics are not clear. In this work, we propose the framework of a composite network, and prove that a composite neural network performs better than any of its pre-trained components with a high probability. In the study, we explore a complicated application-PM2.5 prediction-to support the correctness of the proposed composite network theory. In the empirical evaluations of PM2.5 prediction, the constructed composite neural network models perform better than other machine learning models.

关键词： Neural networks Training Data models Transfer learning Computational modeling Predictive models prediction algorithms Deep learning pre-trained component composite neural network PM2 5 prediction

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A Context- and Trajectory-Based Destination prediction of Public Transportation Users

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IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE 2023年第1期15卷 300-317页

作者： Bieler, Malte Mukkamala, Raghava Rao Gronli, Tor-Morten Kristiania Univ Coll Dept Technol Sch Econ Innovat & Technol N-0152 Oslo Norway Copenhagen Business Sch Dept Digitalizat Ctr Business Data Analyt DK-2000 Frederiksberg Denmark Kristiania Univ Coll Dept Technol N-0152 Oslo Norway

Location prediction based on contextual information is the core of a plethora of personalized location-based services (LBSs). Several applications require the use of techniques for predicting travel destinations based on human movement. Network analyses of human behavioral data show how the spatial-temporal regularity of human movement can be harnessed for inferring human mobility patterns. However, techniques are often based on a limited number of contextual features, which may limit prediction accuracy, especially if only historical location data are used. Using movement data obtained from public transportation users, we investigate the utility of contextual features derived via the installation of Bluetooth beacons in transportation vehicles and software tools in end-users’ travel applications. Using a multiclass random forest classifier, we show that contextual information of a user’s past travel history and at journey onset goes beyond spatial information and boosts destination prediction accuracy. The likely destination and travel-path length obtained at journey onset can then serve as the input for a stochastic-based model to predict a destination based on acquired trajectory information. Here we show that previously predicted destinations boost the performance of a Markov chain network. Thus, various contextual information at the start of a journey provides information beyond the location information acquired during a journey’s progression that can be employed for destination prediction. These findings have strong implications for LBSs as they require accurate destination prediction at early stages of a journey while at the same time mitigating the privacy concerns associated with collection of location data.

关键词： Trajectory Hidden Markov models Predictive models Global Positioning System Smart phones prediction algorithms Markov processes

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GFNC: Unsupervised Link prediction Based on Gravitational Field and Node Contraction

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第4期10卷 1835-1851页

作者： Yang, Yanlin Ye, Zhonglin Zhao, Haixing Meng, Lei Xiao, Yuzhi Qinghai Normal Univ Tiban Informat Proc Engn Technol & Res Ctr Qingha Sch Comp Sci State Key Lab Tibetan Intelligent Informat Proc & Xining 810008 Peoples R China Qinghai Normal Univ Tibetan Informat Proc & Machine Translat Key Lab Xining 810008 Peoples R China

Currently, most existing link prediction algorithms simply study the interrelationships between node pairs without considering the interaction force and the higher order relationships between node pairs. In order to find a solution to this problem, the concept of the gravitational field is introduced in this article, and then, a novel algorithmic framework is proposed from the perspective of physics. The framework is applied to the classic link prediction algorithms to effectively enhance their prediction performance. First, the node contraction method is applied to measure the node importance, and a similarity-based link prediction algorithm is used to calculate the similarity values between node pairs. Second, the importance of nodes is introduced into the gravitational field model as the mass attribute, and the similarity values between node pairs are used as a distance metric between node pairs. Thereby, a gravitational field model of the complex network from the perspective of physics is established. Finally, the edges of the undirected complex network are assigned the weights, and a weighted local random walking-based link prediction algorithm is proposed. The link prediction method is adopted to evaluate the reasonableness and practical value of the gravitational field model. Experimental results show that most link prediction algorithms using the proposed algorithmic framework have got improvement with a minimum improvement of 2% and a maximum improvement of 33%;thus, the effectiveness and feasibility of the algorithm are verified.

关键词： Complex networks prediction algorithms Gravity Predictive models Proteins Physics Costs Gravitational field link prediction node contraction weighted local random walk (LRW)

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Intention prediction-Based Control for Vehicle Platoon to Handle Driver Cut-In

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第5期24卷 5489-5501页

作者： Lu, Yun Huang, Lingying Yao, Jiarong Su, Rong Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Vehicle platoons (VPs) are groups of vehicles driving together with a short inter-vehicle gap and a harmonized velocity. For a long period, the VPs and human-driven vehicles (HDVs) will coexist in mixed traffic flow, where the cut-in maneuver of the HDVs towards the VPs can be frequently expected. In this paper, to handle such cut-ins, we propose an intention prediction-based control method for the VPs by considering the tradeoff between the platoon integrity and traffic safety. Particularly, the proposed method is designed to prevent as many cut-ins as possible while taking care of the road safety. It consists of a cut-in prediction part, including intention and trajectory prediction algorithms, and a finite state machine (FSM)-based predictive control part, including a high-level FSM and a low-level predictive control. Driver-in-the-loop experiments were conducted in the VP-based driving scenarios to train the intention prediction algorithm and test the proposed method. We show the results detailing the control behavior of the proposed method in a no cut-in test, a mandatory cut-in test, and three discretionary cut-in tests. The results demonstrate that the proposed method can predict the cut-in intention of human drivers in real time. Besides, according to the prediction results, the proposed method can prevent cut-ins for the VPs while taking care of the road safety.

关键词： Vehicles Predictive control Trajectory prediction algorithms Road safety Behavioral sciences Intelligent transportation systems Vehicle platoons cut-in intention prediction predictive control

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