A Hybrid Case Based Reasoning Model for Classification in Internet of Things (IoT) Environment

Biswas, Saroj; Devi, Debashree; Chakraborty, Manomita

doi:10.4018/joeuc.2018100107

Cited by 52 publications

(41 citation statements)

References 28 publications

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“…The GHG emitted in the MSW transportation process mainly come from the energy consumption of vehicles, and the amount of emissions depends on factors such as transportation distance and vehicle type. For the GHG emissions in the transportation process, route planning of vehicle can help to reduce emissions [15,60,[67][68][69][70]. In addition, the appropriate location of transfer facilities and disposal facilities can also shorten the collection and transportation routes, thus reducing GHG emissions.…”

Section: Ghg Emitted From Transportation Of Wastementioning

confidence: 99%

An Empirical Study on Greenhouse Gas Emission Calculations Under Different Municipal Solid Waste Management Strategies

Xin

Zhang

Tsai³

et al. 2020

Applied Sciences

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The Chinese government is committed to ensuring separation of municipal solid waste (MSW), promoting the integrated development of the MSW management system with the renewable resource recovery system, and achieving construction of ecological civilization. Guided by the methods in Intergovernmental Panel on Climate Change (IPCC) guidelines, the greenhouse gas (GHG) emissions under five waste disposal scenarios in Beijing under the life cycle framework were assessed in this research. The study included collection and transportation, as well as three end disposal methods (sanitary landfill, incineration, and composting), and the emission reduction benefits of electricity generation from incineration and recycling of renewable resources were taken into account. The results show that an emission reduction benefit of 70.82% could be achieved under Scenario 5 in which kitchen waste and recyclables are sorted and recycled and the residue is incinerated, and the selection of the optimal strategy was not affected by changes in the separation rate. In addition, landfill would emit more GHG than incineration and composting. The results of this study are helpful for the government to make a decision on MSW management considering the goal of GHG emission reduction.

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Section: Ghg Emitted From Transportation Of Wastementioning

confidence: 99%

An Empirical Study on Greenhouse Gas Emission Calculations Under Different Municipal Solid Waste Management Strategies

Xin

Zhang

Tsai³

et al. 2020

Applied Sciences

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“…Decision making under uncertainty exists everywhere . In this section, the application of

P O W A

operator in likelihood function of uncertain decision making is given and the differences between different formulations of likelihood function are shown.…”

Section: A Case Study and Results Analysismentioning

confidence: 99%

A new soft likelihood function based on power ordered weighted average operator

Song

Deng

2019

Int J Intell Syst

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The likelihood function is widely used in data processing but the classical likelihood function is too strict to deal with data with extreme values in real applications.To address this issue, a soft likelihood function based on power ordered weighted average (POWA) operator for probability distribution is proposed. Compared with the existing soft likelihood functions, one of the main features of the proposed likelihood function is that the weighted mean is displayed by the geometric mean of the weighted probability products. As a result, the effect on final result caused by extreme data can be efficiently decreased. Numerical examples are used to illustrate the efficiency of the proposed soft likelihood function. K E Y W O R D S geometric mean, likelihood function, OWA operator, POWA operator, soft likelihood function | INTRODUCTIONDealing with uncertainty information plays an important role in real applications. 1-3 Many math tools, such as fuzzy sets, 4-6 rough sets, 7 Z-numbers, 8-11 belief structures, 12-14 D numbers 15-17 entropy function, 18,19 and evidence theory, 20-22 are presented to handle different types of uncertainty information. Among these tools, probability is heavily studied and is widely used in many applications, including decision making, 23-26 estimation, 27,28 and uncertain measurement. [29][30][31][32] One of the most used models in probability is the likelihood function, which is used when data are available to describe plausibility of a parameter value. [33][34][35] However, it is very strict in real applications since the original likelihood function made up with the probability product is too strict to show the compatibility probabilities for the different pieces of evidence since any piece of probabilities, which are low-value would greatly reduce the likelihood function.How to cite this article: Song Y, Deng Y. A new soft likelihood function based on power ordered weighted average operator. Int J Intell

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“…Uncertainty plays an important role in real‐world and is inevitable in decision‐making . To handle uncertainty, a lot of theories have been developed, for example, probability theory, Dempster‐Shafer (D‐S) evidence theory, fuzzy sets, gray model, and Z number .…”

Section: Introductionmentioning

confidence: 99%

Uncertainty measure based on Tsallis entropy in evidence theory

et al. 2019

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Dempster-Shafer evidence theory has been widely used in many applications due to its advantages with weaker conditions than Bayes probability. How to measure the uncertainty of basic probability assignment (BPA) in Dempster-Shafer evidence theory is an open and essential issue. Tsallis entropy as nonextensive entropy proposed according to multifractals has been used in many fields. In this paper, a new uncertainty measure of BPA is presented based on Tsallis entropy. The key issue is to determine the value of q in Tsallis entropy. In addition, this paper also analyzes the properties of proposed uncertainty measure. Some numerical examples are used to illustrate the efficiency of the proposed method. Finally, the paper also discusses the application of the proposed method in decision-making. K E Y W O R D S basic probability assignment, belief function, Dempster-Shafer evidence theory, Tsallis entropy, uncertainty | INTRODUCTIONUncertainty plays an important role in real-world and is inevitable in decision-making. 1-5 To handle uncertainty, a lot of theories have been developed, for example, probability theory, 6,7 Dempster-Shafer (D-S) evidence theory, 8,9 fuzzy sets, 10-13 gray model, 14 and Z number. [15][16][17][18] Among these methods, D-S evidence theory has attracted many research' attention due to its extension of Bayesian theory, [19][20][21][22] with the efficiency to deal with imprecise information. 8,23 Measuring the uncertainty of basic probability assignment function (BPA) in D-S theory is essential [24][25][26][27] and can be applied in many applications. [28][29][30]

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A Hybrid Case Based Reasoning Model for Classification in Internet of Things (IoT) Environment

Cited by 52 publications

References 28 publications

An Empirical Study on Greenhouse Gas Emission Calculations Under Different Municipal Solid Waste Management Strategies

An Empirical Study on Greenhouse Gas Emission Calculations Under Different Municipal Solid Waste Management Strategies

A new soft likelihood function based on power ordered weighted average operator

Uncertainty measure based on Tsallis entropy in evidence theory

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