Artificial intelligence implication on energy sustainability in Internet of Things: A survey     

《Information processing & management》2023,60(2):103212

The massive number of Internet of Things (IoT) devices connected to the Internet is continuously increasing. The operations of these devices rely on consuming huge amounts of energy. Power limitation is a major issue hindering the operation of IoT applications and services. To improve operational visibility, Low-power devices which constitute IoT networks, drive the need for sustainable sources of energy to carry out their tasks for a prolonged period of time. Moreover, the means to ensure energy sustainability and QoS must consider the stochastic nature of the energy supplies and dynamic IoT environments. Artificial Intelligence (AI) enhanced protocols and algorithms are capable of predicting and forecasting demand as well as providing leverage at different stages of energy use to supply. AI will improve the efficiency of energy infrastructure and decrease waste in distributed energy systems, ensuring their long-term viability. In this paper, we conduct a survey to explore enhanced AI-based solutions to achieve energy sustainability in IoT applications. AI is relevant through the integration of various Machine Learning (ML) and Swarm Intelligence (SI) techniques in the design of existing protocols. ML mechanisms used in the literature include variously supervised and unsupervised learning methods as well as reinforcement learning (RL) solutions. The survey constitutes a complete guideline for readers who wish to get acquainted with recent development and research advances in AI-based energy sustainability in IoT Networks. The survey also explores the different open issues and challenges.  相似文献   

基于知识图谱的我国公共体育服务研究可视化分析     

易军 《吉林体育学院学报》2020,(1):21-29

以中国知网所收录的2007年至2017间核心期刊中有关公共体育服务文献题录作为分析数据样本,对所搜集到的254篇文献进行可视化分析。借助CiteSpace V软件,运用文献计量学方法与内容分析方法,结果表明:近十年我国公共体育服务研究经历了波浪式增长与急速增长两个阶段,研究主题紧密结合国家政策战略,在不断调整中推动了公共体育服务的发展。主要特征体现在始终服务于国家宏观政策方针,顺应了政府职能转变下对体育发展改革的时代需求;以“体育管理、公共服务”为核心的研究主题始终贯穿,主动融入政策响应的研究诉求强烈,人民美好生活中体育需求的关照不足;整个研究过程充满了动态性与局限性。未来研究应坚持与国家公共服务政策相伴相生,形成开放性反馈研究系统;注重公共体育服务研究的政策引领和政策的事后绩效评估;打造学术共同体,提升公共体育服务研究成果在政策咨询和智库建设中的作用。  相似文献   

大数据视域下的档案安全管理体系构建及可引入技术分析     

孙宁 《档案管理》2020,(3):12-13

在大数据视域下,以档案管理理论和信息系统安全理论为基础,参考国家相关法规及标准,结合档案管理工作实务,研究当下档案管理工作中的风险,在此基础上初步构建起档案安全管理体系,并对可引入该体系的实用技术进行分析。  相似文献   

A reversible and secure patient information hiding system for IoT driven e-health     

《International Journal of Information Management》2019

Internet of things (IoT) coupled with mobile cloud computing has made a paradigm shift in the service sector. IoT-assisted mobile cloud based e-healthcare services are making giant strides and are likely to change the conventional ways of healthcare service delivery. Though numerous approaches for preventing unauthorized access to information exchanged between a mobile phone and cloud platform do exist, but there is no security mechanism to prevent unauthorized access by the cloud administrators. With an aim to ensure security of client data such as Electronic Patient Records (EPR), we propose a novel high-capacity and reversible data hiding approach for securely embedding EPR within the medical images using Optimal Pixel Repetition (OPR). OPR converts every pixel of the input image to a 2 × 2 block to facilitate reversibility by ensuring all the pixels in a 2 × 2 block to have different values. Since a 2 × 2 block is comprised of 4-pixel elements, which could be arranged in sixteen possible ways; we generate a lookup table corresponding to sixteen possible positions of pixels. EPR hiding in each block is achieved by permuting the pixels of a block according to the four-bit word of secret data, resulting in a histogram invariant stego image. The histogram invariance improves the robustness of the proposed scheme to statistical attacks. A stego image is said to hide embedded data securely, when it provides better imperceptivity for an appreciably high payload. Thus, while using information embedding approach for securing client data on a mobile-cloud platform, high imperceptivity is a desirable feature. Experimental results show that average PSNR obtained is 42 dB for payload 1.25 bpp by our scheme, showing its effectiveness for preventing unauthorized access to client’s sensitive data.  相似文献   

Remaining in Control with an Illusion of Interactivity: The Paternalistic Side of Data Journalism*     

Ester Appelgren 《Journalism Practice》2019,13(8):956-960

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RGAN-EL: A GAN and ensemble learning-based hybrid approach for imbalanced data classification     

《Information processing & management》2023,60(2):103235

Imbalanced sample distribution is usually the main reason for the performance degradation of machine learning algorithms. Based on this, this study proposes a hybrid framework (RGAN-EL) combining generative adversarial networks and ensemble learning method to improve the classification performance of imbalanced data. Firstly, we propose a training sample selection strategy based on roulette wheel selection method to make GAN pay more attention to the class overlapping area when fitting the sample distribution. Secondly, we design two kinds of generator training loss, and propose a noise sample filtering method to improve the quality of generated samples. Then, minority class samples are oversampled using the improved RGAN to obtain a balanced training sample set. Finally, combined with the ensemble learning strategy, the final training and prediction are carried out. We conducted experiments on 41 real imbalanced data sets using two evaluation indexes: F1-score and AUC. Specifically, we compare RGAN-EL with six typical ensemble learning; RGAN is compared with three typical GAN models. The experimental results show that RGAN-EL is significantly better than the other six ensemble learning methods, and RGAN is greatly improved compared with three classical GAN models.  相似文献   

Using an interactive platform to recognize the intersection of social and spatial inequalities     

Lisa Poling  Travis Weiland 《Teaching Statistics》2020,42(3):108-116

With the creation of interactive tasks that allow students to explore spatial ways of knowing in conjunction with their other ways of knowing the world, we create a space where students can make sense of information as they organize these new ideas into their already existing schema. Through the use of a Common Online Data Analysis Platform (CODAP) and data from Public Use Microdata Areas (PUMA), students can explore the communities in which they live and work, critically examining opportunities and challenges within a defined space.  相似文献   

Exploring adverse drug reactions of diabetes medicine using social media analytics and interactive visualizations     

《International Journal of Information Management》2019

The aim of this study is to propose an automatic and real-time social media analytics framework with interactive data visualizations to support effective exploration of knowledge about adverse drug reaction (ADR) surveillance. This proposed framework has been prototypically implemented on the basis of social media data. A longitudinal diabetes patient online community data (AskaPatient.com) as well as FDA Adverse Event Reporting Systems (FAERS) data as a benchmark were used to evaluate our proposed approach’s performance. Based on the results, our approach significantly increases the precision and accuracy for ADR extraction. The number of ADR cases, the time when the ADRs occurred, and the rating of Glucophage have been visualized that resulted by mining a collection of 870 ADRs posted in Askapatents.com over a certain time period (from 2001 to 2015). The results have important implications for pharmaceutical companies and hospitals wishing to monitor ADRs of medicines.  相似文献   

Online visibility of software-related web sites: The case of biomedical text mining tools     

Gael Pérez-Rodríguez  Martín Pérez-Pérez  Florentino Fdez-Riverola  Anália Lourenço 《Information processing & management》2019,56(3):565-583

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Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry     

《International Journal of Information Management》2019

Proactive Maintenance practices are becoming more standard in industrial environments, with a direct and profound impact on the competitivity within the sector. These practices demand the continuous monitorization of industrial equipment, which generates extensive amounts of data. This information can be processed into useful knowledge with the use of machine learning algorithms. However, before the algorithms can effectively be applied, the data must go through an exploratory phase: assessing the meaning of the features and to which degree they are redundant. In this paper, we present the findings of the analysis conducted on a real-world dataset from a metallurgic company. A number of data analysis and feature selection methods are employed, uncovering several relationships, which are systematized in a rule-based model, and reducing the feature space from an initial 47-feature dataset to a 32-feature dataset.  相似文献