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  • 1
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 1996
    In:  Health Policy and Planning Vol. 11, No. 3 ( 1996), p. 238-252
    In: Health Policy and Planning, Oxford University Press (OUP), Vol. 11, No. 3 ( 1996), p. 238-252
    Type of Medium: Online Resource
    ISSN: 0268-1080 , 1460-2237
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 1996
    detail.hit.zdb_id: 1484858-2
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  • 2
    Online Resource
    Online Resource
    Frontiers Media SA ; 2021
    In:  Frontiers in Computational Neuroscience Vol. 15 ( 2021-12-27)
    In: Frontiers in Computational Neuroscience, Frontiers Media SA, Vol. 15 ( 2021-12-27)
    Abstract: Online end-to-end electroencephalogram (EEG) classification with high performance can assess the brain status of patients with Major Depression Disabled (MDD) and track their development status in time with minimizing the risk of falling into danger and suicide. However, it remains a grand research challenge due to (1) the embedded intensive noises and the intrinsic non-stationarity determined by the evolution of brain states, (2) the lack of effective decoupling of the complex relationship between neural network and brain state during the attack of brain diseases. This study designs a Frequency Channel-based convolutional neural network (CNN), namely FCCNN, to accurately and quickly identify depression, which fuses the brain rhythm to the attention mechanism of the classifier with aiming at focusing the most important parts of data and improving the classification performance. Furthermore, to understand the complexity of the classifier, this study proposes a calculation method of information entropy based on the affinity propagation (AP) clustering partition to measure the complexity of the classifier acting on each channel or brain region. We perform experiments on depression evaluation to identify healthy and MDD. Results report that the proposed solution can identify MDD with an accuracy of 99±0.08%, the sensitivity of 99.07±0.05%, and specificity of 98.90±0.14%. Furthermore, the experiments on the quantitative interpretation of FCCNN illustrate significant differences between the frontal, left, and right temporal lobes of depression patients and the healthy control group.
    Type of Medium: Online Resource
    ISSN: 1662-5188
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2452964-3
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  • 3
    Online Resource
    Online Resource
    Hindawi Limited ; 2021
    In:  Wireless Communications and Mobile Computing Vol. 2021 ( 2021-10-21), p. 1-19
    In: Wireless Communications and Mobile Computing, Hindawi Limited, Vol. 2021 ( 2021-10-21), p. 1-19
    Abstract: Big data is a term used for very large data sets. Digital equipment produces vast amounts of images every day; the need for image encryption is increasingly pronounced, for example, to safeguard the privacy of the patients’ medical imaging data in cloud disk. There is an obvious contradiction between the security and privacy and the widespread use of big data. Nowadays, the most important engine to provide confidentiality is encryption. However, block ciphering is not suitable for the huge data in a real-time environment because of the strong correlation among pixels and high redundancy; stream ciphering is considered a lightweight solution for ciphering high-definition images (i.e., high data volume). For a stream cipher, since the encryption algorithm is deterministic, the only thing you can do is to make the key “look random.” This article proves that the probability that the digit 1 appears in the midsection of a Zeckendorf representation is constant, which can be utilized to generate the pseudorandom numbers. Then, a novel stream cipher key generator (ZPKG) is proposed to encrypt high-definition images that need transferring. The experimental results show that the proposed stream ciphering method, with the keystream of which satisfies Golomb’s randomness postulates, is faster than RC4 and LSFR with indistinguishable performance on hardware depletion, and the method is highly key sensitive and shows good resistance against noise attacks and statistical attacks.
    Type of Medium: Online Resource
    ISSN: 1530-8677 , 1530-8669
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2045240-8
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  • 4
    Online Resource
    Online Resource
    MDPI AG ; 2021
    In:  Sensors Vol. 21, No. 15 ( 2021-07-23), p. 5002-
    In: Sensors, MDPI AG, Vol. 21, No. 15 ( 2021-07-23), p. 5002-
    Abstract: Wireless sensor networks are appealing, largely because they do not need wired infrastructure, but it is precisely this feature that renders them energy-constrained. The duty cycle scheduling is perceived as a contributor to the energy efficiency of sensing. This paper developed a novel paradigm for modeling wireless sensor networks; in this context, an adaptive sensing scheduling strategy is proposed depending on event occurrence behavior, and the scheduling problem is framed as an optimization problem. The optimization objectives include reducing energy depletion and optimizing detection accuracy. We determine the explicit form of the objective function by numerical fitting and found that the objective function aggregated by the fitting functions is a bivariate multimodal function that favors the Fibonacci tree optimization algorithm. Then, with the optimal parameters optimized by the Fibonacci tree optimization algorithm, the scheduling scheme can be easily deployed, and it behaves consistently in the coming hours. The proposed “Fibonacci Tree Optimization Strategy” (“FTOS”) outperforms lightweight deployment-aware scheduling (LDAS), balanced-energy scheduling (BS), distributed self-spreading algorithm (DSS) and probing environment and collaborating adaptive sleeping (PECAS) in achieving the aforementioned scheduling objectives. The Fibonacci tree optimization algorithm has attained a better optimistic effect than the artificial bee colony (ABC) algorithm, differential evolution (DE) algorithm, genetic algorithm (GA) algorithm, particle swarm optimization (PSO) algorithm, and comprehensive learning particle swarm optimization (CLPSO) algorithm in multiple runs.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2052857-7
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  • 5
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2013
    In:  Chinese Science Bulletin Vol. 58, No. 18 ( 2013-6), p. 2248-2254
    In: Chinese Science Bulletin, Springer Science and Business Media LLC, Vol. 58, No. 18 ( 2013-6), p. 2248-2254
    Type of Medium: Online Resource
    ISSN: 1001-6538 , 1861-9541
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2013
    detail.hit.zdb_id: 2069521-4
    detail.hit.zdb_id: 2816140-3
    SSG: 11
    SSG: 6,25
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  • 6
    Online Resource
    Online Resource
    IOS Press ; 2021
    In:  Journal of Integrated Design and Process Science Vol. 23, No. 4 ( 2021-02-18), p. 5-28
    In: Journal of Integrated Design and Process Science, IOS Press, Vol. 23, No. 4 ( 2021-02-18), p. 5-28
    Abstract: Different from traditional statistical analysis models which focus on the correlation between technical trends in quantity level, text analyzing is now a new approach to extract technological trend from text. Scientometric text mining is widely applied in analytical methods to figure the evolution process of patent and technology. This study is focused on patent documents to predict the future trend of solid material field. Term Frequency (TF) statistics, Word2Vec and t-SNE were adopted in comprehensive analytical methods to measure metrics of patent and reveal the technological development. For patent documents in No. 257 category of United States Patent and Trademark Office, title, abstract and claims from 2005 to 2012 were selected for text mining and analysis. The term frequency of those keywords is firstly counted by year, to extract the annual change of high-frequency keywords. Word2Vec can convert the text to word vectors in a vector space. To better visualize the results and make a relatively reliable prediction, t-SNE is used to reduce the dimension word vectors and scatter them in a twodimensional map. All these methods in the research are dedicated to present a clearer look at the evolving trend of patents in a field. The systematic analytical methods can be adapted in the analysis of other fields. What is represented in the results reveal the developing trend and the hotspots in process, thereby the future trend can be inferred based on the results. Such prediction can be an indicator or guidance in decision making of a certain field.
    Type of Medium: Online Resource
    ISSN: 1092-0617 , 1875-8959
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2021
    detail.hit.zdb_id: 2094840-2
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  • 7
    In: AI and Ethics, Springer Science and Business Media LLC, Vol. 1, No. 2 ( 2021-05), p. 131-138
    Type of Medium: Online Resource
    ISSN: 2730-5953 , 2730-5961
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 3043753-2
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  • 8
    In: AI and Ethics, Springer Science and Business Media LLC, Vol. 1, No. 2 ( 2021-05), p. 139-140
    Type of Medium: Online Resource
    ISSN: 2730-5953 , 2730-5961
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 3043753-2
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  • 9
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2022
    In:  ACM Transactions on Sensor Networks
    In: ACM Transactions on Sensor Networks, Association for Computing Machinery (ACM)
    Abstract: Internet of Vehicles(IoV) enables vehicles to generate and share messages to improve transportation safety and efficiency, especially in a smart city scenario where modern communication technology is utilized. The current IoV, however, faces three main issues: (1) existing frameworks fail to build a complete data management system, (2) received messages in an untrusted environment are challenging to assess for credibility, and (3) the centralized ways to store data are causing severe security and efficiency problems. Blockchain-enabled IoV (BIoV) provides an opportunity for addressing these issues. This paper proposes a trusted paradigm of data management based on a vehicle–road–cloud architecture. A few-shot learning model, Wasserstein Generative Adversarial Network (WGAN) with Synthetic Minority Oversampling Technique (SMOTE) sampling is designed to evaluate whether the uploading message is malicious. This paper also proposes the novel group-weighted-decay Practical Byzantine Fault Tolerance (PBFT) consensus algorithm, an improved version of PBFT to store data, and provides a comprehensive review of its viability and data management procedures. By employing the joint gwd-PBFT and Proof of Trust (PoT) consensus, the method mitigates the issue of excessive incentives. According to simulation results, the system's overall efficiency can be increased while retaining security and availability.
    Type of Medium: Online Resource
    ISSN: 1550-4859 , 1550-4867
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2022
    detail.hit.zdb_id: 2198261-2
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  • 10
    Online Resource
    Online Resource
    Scientific Research Publishing, Inc. ; 2012
    In:  Journal of Intelligent Learning Systems and Applications Vol. 04, No. 02 ( 2012), p. 120-126
    In: Journal of Intelligent Learning Systems and Applications, Scientific Research Publishing, Inc., Vol. 04, No. 02 ( 2012), p. 120-126
    Type of Medium: Online Resource
    ISSN: 2150-8402 , 2150-8410
    Language: Unknown
    Publisher: Scientific Research Publishing, Inc.
    Publication Date: 2012
    detail.hit.zdb_id: 2637445-6
    SSG: 5,3
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