Recommendation Based on Large-Scale Many-Objective Optimization for the Intelligent Internet of Things System

Cao, Bin; Zhang, Yatian; Zhao, Jianwei; Liu, Xin; Skonieczny, Łukasz; Lv, Zhihan

doi:10.1109/jiot.2021.3104661

Cited by 71 publications

(35 citation statements)

References 35 publications

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“…Decision tree is mainly used in the clinical nursing research that has been carried out to analyze the related influencing factors of a clinical symptom and the establishment of related models to find the main risk factors and strengthen the nursing care in a targeted manner [ 12 ]. It has been applied to nurse resource allocation and nursing quality assessment in nursing management work [ 13 ]. The implementation of the decision tree is helpful for nursing staff not only to analyze the influencing factors of various clinical symptoms but also to implement targeted nursing measures based on the analysis results, thereby effectively reducing patient complications and improving the quality of care [ 14 ].…”

Section: Application Of Machine Learning Algorithmsmentioning

confidence: 99%

Application of the Improved Clustering Algorithm in Operating Room Nursing Recommendation under the Background of Medical Big Data

Wang²,

Cai

et al. 2022

Journal of Healthcare Engineering

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The nursing work in the operating room has the characteristics of long time, strong technicality, and heavy work, which have an important influence on the quality of the operation. Operating room nursing recommendations based on data mining technology can solve a series of practical problems in clinical nursing and nursing management. This paper selects the clustering algorithm in commonly used data mining technology as the research object and actually analyzes the impact of this algorithm in operating room nursing recommendations. At this stage, there is little research on data mining technology in the field of nursing in China. This paper aims to provide new ideas for the field of nursing research by exploring the actual application in the field of nursing.

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Section: Application Of Machine Learning Algorithmsmentioning

confidence: 99%

Application of the Improved Clustering Algorithm in Operating Room Nursing Recommendation under the Background of Medical Big Data

Wang²,

Cai

et al. 2022

Journal of Healthcare Engineering

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“…Stochastic techniques have emerged in the literature to address the limitations of mathematical methods [36][37][38][39][40][41][42][43]. For example, in [44], non-sorting Genetic Algorithm (GA) based controller is introduced for optimal energy management in smart homes with primary objectives like carbon emissions, energy cost, and peak energy consumption minimization.…”

Section: Stochastic Techniquesmentioning

confidence: 99%

Optimal Demand-Side Management Using Flat Pricing Scheme in Smart Grid

Albogamy

Ashfaq

Hafeez³

et al. 2022

Processes

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This work proposes a framework to solve demand-side management (DSM) problem by systematically scheduling energy consumption using flat pricing scheme (FPS) in smart grid (SG). The framework includes microgrid with renewable energy sources (solar and wind), energy storage systems, electric vehicles (EVs), and building appliances like time flexible, power flexible, and base/critical appliances. For the proposed framework, we develop an ant colony optimization (ACO) algorithm, which efficiently schedules smart appliances, and EVs batteries charging/discharging with microgrid and without (W/O) microgrid under FPS to minimize energy cost, carbon emission, and peak to average ratio (PAR). An integrated technique of enhanced differential evolution (EDE) algorithm and artificial neural network (ANN) is devised to predict solar irradiance and wind speed for accurate microgrid energy estimation. To endorse the applicability of the proposed framework, simulations are conducted. Moreover, the proposed framework based on the ACO algorithm is compared to mixed-integer linear programming (MILP) and W/O scheduling energy management frameworks in terms of energy cost, carbon emission, and PAR. The developed ACO algorithm reduces energy cost, PAR, and carbon emission by 23.69%, 26.20%, and 15.35% in scenario I, and 25.09%, 31.45%, and 18.50% in scenario II, respectively, as compared to W/O scheduling case. The results affirm the applicability of the proposed framework in aspects of the desired objectives.

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“…On the one hand, foreign companies have a good organizational structure, advanced management system, and extremely advanced information technology through reorganization of business processes, so that they can provide products and services with lower costs and better quality. Logistics enterprises will continue to participate in market competition with better services and lower costs and will change the management model through process transformation to improve the competitiveness of logistics enterprises [14][15][16][17][18]. (3) Chinese logistics companies are facing new challenges and opportunities.…”

Section: Changes In the External Environment Of Logistics Companiesmentioning

confidence: 99%

Intelligent Logistics Enterprise Management Based on the Internet of Things

2022

Mathematical Problems in Engineering

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At this stage, many large domestic logistics companies have problems such as complex processes, low efficiency, and chaotic vehicle management. This usually leads to vehicle failure, material loss, or improper installation of the company’s factory, which affects the company’s interests and damages the company’s reputation. In response to the above problems, this article proposes, designs, and implements an intelligent logistics management system based on the Internet of Things technology through the research of the Internet of Things technology at home and abroad. The specific content is as follows: one is to study the origin and current situation of the Internet of Things technology at home and abroad, to understand related theories and cutting-edge technologies of the Internet of Things; the other is to imitate intelligent logistics companies, study the processes and existing problems of the logistics link, and integrate the management concepts of the Internet of Things. A model of intelligent logistics enterprise management is established with enterprise intelligent technology; the third is to conduct an experiment on the management of an intelligent logistics enterprise and do an experimental data analysis of the management of an intelligent logistics enterprise. Experimental research shows that the data of factors affecting logistics operations in smart logistics companies are low, and smart logistics companies are operating in good condition, and the established data model is suitable for smart logistics enterprise management. The Internet of Things and intelligent logistics management are both emerging concepts, while the intelligent logistics management based on the Internet of Things is still in its infancy, and relevant scholars have little research on the intelligent logistics management based on the Internet of Things. It plays a guiding role for the intelligent logistics management of enterprises and promotes the management of intelligent logistics enterprises.

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Recommendation Based on Large-Scale Many-Objective Optimization for the Intelligent Internet of Things System

Cited by 71 publications

References 35 publications

Application of the Improved Clustering Algorithm in Operating Room Nursing Recommendation under the Background of Medical Big Data

Application of the Improved Clustering Algorithm in Operating Room Nursing Recommendation under the Background of Medical Big Data

Optimal Demand-Side Management Using Flat Pricing Scheme in Smart Grid

Intelligent Logistics Enterprise Management Based on the Internet of Things

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