Learning Transferable Driven and Drone Assisted Sustainable and Robust Regional Disease Surveillance for Smart Healthcare

Jin, Yong; Qian, Zhenjiang; Gong, Shengrong; Yang, Weiyong

doi:10.1109/tcbb.2020.3017041

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…That is, the attributes are closely related to each other, and a change in one or more of them leads to a change in the others. The purpose of the analysis is to find hidden relationships in large data sets and to uncover unfamiliar knowledge [16]. The analysis of association rules is to find the pattern of events that occur and lead to the occurrence of other events in time or sequence.…”

Section: Design Of Mental Health Data Collectionmentioning

confidence: 99%

Construction of an Intelligent Evaluation Model of Mental Health Based on Big Data

Zhang¹,

Liang²

2022

Journal of Sensors

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In this paper, mental health data were used to evaluate the educational effects, in which the high and low scorers of three emotions, autism, positivity, and anxiety, are compared separately to explore the subtle differences in the long-term trends of the sensing traits of people with opposite characteristics. Based on the fusion of multiple kinds of sensing traits, the differences in physical and mental health assessment of positive and negative emotions by different fusion trait approaches are explored, and speech and behavioural traits are fused to build a physical and mental health assessment system for positive and negative emotions. Energy gravity uses physical distance to estimate the residual energy of nodes and considers the energy distribution of downstream nodes. The main work is to combine the data of mental health of higher education students using data mining techniques, to analyze the feasibility study of mental health education of college students. Relevant definitions, classifications, tasks, processes, and application areas of data mining techniques are introduced, and the basic principles of data mining are analyzed in detail. Taking the mental health assessment data of new students as the research object, the decision tree algorithm is used to construct a decision tree model for students with depressive symptoms, and an association rule algorithm is used to data mine the relationship between factors of psychological dimensions. Finally, it can find out the hidden laws and knowledge behind the data information and analyze the relationship that exists between psychological problems and students.

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Section: Design Of Mental Health Data Collectionmentioning

confidence: 99%

Construction of an Intelligent Evaluation Model of Mental Health Based on Big Data

Zhang¹,

Liang²

2022

Journal of Sensors

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“…Most studies regard UAVs task energy efficiency as the main optimization objective [8]- [11]. The researches on UAV charging include single objective optimization [12] that considers charging and multi-objective optimization (MOO) that optimizes both charging and deployment [13], [14]. The former only focuses on charging and ignores UAVs tasks while the latter ignores the coupling relationship between charging and deployment.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Coordinated Optimization for UAV Charging Scheduling in Intelligent Aerial-Ground Perception Networks

Yi,

Xiang,

Huaguang

et al. 2023

Chinese J. Elect.

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The unmanned aerial vehicles (UAVs)assisted intelligent traffic perception system can provide effective situation awareness. However, UAVs are required to be recharged before the energy is exhausted, which may cause task interruption. To address this concern, the charging UAV (CUAV) is employed to provide wireless charging for the mission UAVs (MUAVs). This paper studies the charging scheduling problem of the CUAV under the premise of optimizing the MUAVs deployment. We first model the MUAVs deployment problem considering the energy consumption and data transmission and establish the CUAV charging model. Then, the above problem is formulated as a multi-objective multi-agent stochastic game process to simplify the decisions-making of MUAVs and CUAV, based on which we propose the utility-based Pareto optimal deployment and charging algorithm, which reduces the computing complexity by equivalent utility of the MUAVs while using Kullback-Leibler divergence to constrain solutions. Next, to ensure the effectiveness of policy update, the multi-agent communication protocol is adopted to improve policy exploration efficiency. Simulation results show that the proposed algorithm outperforms existing works in terms of energy efficiency and charging by comparing with the Pareto front of different methods, endurance anxiety of the MUAVs, and charging utilization under different task modes.

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“…The fog-cloud paradigm is able to manage drone activity processing in accordance with the computing capability, fog-based decentralized UAV task processing, and preservation [12,13]. However, combining fog-cloud-based drones with smart contracts creates a promising analysis, management, and monitoring system for urban land surface changes [13], which is referred to as blockchain-aware distributed dynamic monitoring systems (BADDMS).…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Aware Distributed Dynamic Monitoring: A Smart Contract for Fog-Based Drone Management in Land Surface Changes

et al. 2021

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In this paper, we propose a secure blockchain-aware framework for distributed data management and monitoring. Indeed, images-based data are captured through drones and transmitted to the fog nodes. The main objective here is to enable process and schedule, to investigate individual captured entity (records) and to analyze changes in the blockchain storage with a secure hash-encrypted (SH-256) consortium peer-to-peer (P2P) network. The proposed blockchain mechanism is also investigated for analyzing the fog-cloud-based stored information, which is referred to as smart contracts. These contracts are designed and deployed to automate the overall distributed monitoring system. They include the registration of UAVs (drones), the day-to-day dynamic captured drone-based images, and the update transactions in the immutable storage for future investigations. The simulation results show the merit of our framework. Indeed, through extensive experiments, the developed system provides good performances regarding monitoring and management tasks.

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Learning Transferable Driven and Drone Assisted Sustainable and Robust Regional Disease Surveillance for Smart Healthcare

Cited by 5 publications

References 34 publications

Construction of an Intelligent Evaluation Model of Mental Health Based on Big Data

Construction of an Intelligent Evaluation Model of Mental Health Based on Big Data

Multi-Objective Coordinated Optimization for UAV Charging Scheduling in Intelligent Aerial-Ground Perception Networks

Blockchain-Aware Distributed Dynamic Monitoring: A Smart Contract for Fog-Based Drone Management in Land Surface Changes

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