Smart assessment and forecasting framework for healthy development index in urban cities

Li, Qiao; Guo, Zhiwei; Vijayakumar, Pandi; Taghizadeh–Hesary, Farhad; Yu, Keping

doi:10.1016/j.cities.2022.103971

Cited by 92 publications

(18 citation statements)

References 23 publications

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“…A novel IoT (internet of things)-based model for healthy development index for urban cities is proposed by Ref. [ 7 ], where they use a Gaussian-based approach to forecast urban cities developments.…”

Section: Introductionmentioning

confidence: 99%

Development and application of an integrated smart city model

Abu-Rayash¹,

Dinçer²

2023

Heliyon

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Section: Introductionmentioning

confidence: 99%

Development and application of an integrated smart city model

Abu-Rayash¹,

Dinçer²

2023

Heliyon

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“…Advanced software technology is also applied to promote the overall automation level, in order to ensure its leading position in the world manufacturing industry [10], [11]. After several years of research efforts, the investigation of intelligent manufacturing has achieved some preliminary achievements in these years [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Design of an Optimal Scheduling Control System for Smart Manufacturing Processes in Tobacco Industry

Liu

Wang

et al. 2023

IEEE Access

Self Cite

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The whole process of tobacco production is composed of many components, in which their operation and administration are currently independent. It is required to deploy smart manufacturing workflow for the whole production process, in order to realize centralized effective global scheduling. This requires an advanced administration control platform that has strong abilities of multisource data integration and automatic decision support. To bridge such research gap, this paper designs an optimal scheduling control system for smart manufacturing processes of tobacco industry. First of all, this work discusses major characteristics of future-generation production control patterns in intelligent tobacco factories (ITF). Then, a five-layer architecture for optimal scheduling control of ITF is proposed, which contains Internetof-Things layer, centralized control layer, model layer, platform layer and operation layer. In addition, a production scheduling optimization strategy is also developed for the proposed system to serve as the software algorithm that drives the running of whole smart manufacturing processes. Finally, this paper presents a comparative analysis of the proposed system's transformation in a cigarette factory. Naturally, the effectiveness of the proposed production optimization scheduling strategy is verified through simulation.

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“…However, in automated testing of DNN-based systems, it is often not possible to directly define test predictions for the correct output given a given input [7]. To obtain the test prediction information, it is often necessary to spend expensive workforce to label the test data, which significantly slows down the quality assurance process [8].…”

Section: Introductionmentioning

confidence: 99%

Design of an Intelligent Educational Evaluation System Using Deep Learning

Yan

Lu²

2023

IEEE Access

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Nowadays, online education has been a more general demand in context of COVID-19 epidemic. The intelligent educational evaluation systems assisted by intelligent techniques are in urgent demand. To deal with this issue, this paper introduces the strong information processing ability of deep learning, and proposes the design of an intelligent educational evaluation system using deep learning. Inside the algorithm part, the low-complexity offset minimal sum (OMS) is selected as the front-end processor of deep neural network, so as to reduce following computational complexity in deep neural network. And the deep neural network is adopted as the major calculation backbone. In this paper, our OMS deep neural network parameters are 23 and 57 compared with other parameters, which can save about 59.64% of the network parameters, and the training time is 11270 s and 25000 s respectively, which saves the training time 54.92%. It can be also reflected from experiments that the proposal further improves the performance of unbalanced data classification in this problem scenario.

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Smart assessment and forecasting framework for healthy development index in urban cities

Cited by 92 publications

References 23 publications

Development and application of an integrated smart city model

Development and application of an integrated smart city model

Design of an Optimal Scheduling Control System for Smart Manufacturing Processes in Tobacco Industry

Design of an Intelligent Educational Evaluation System Using Deep Learning

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