Optimizing road safety: integrated analysis of motorized vehicle using lattice ordered complex linear diophantine fuzzy soft set

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Introduction

Literature review

Research motivation

Novelty of the proposed methodology

  • 1)

    The main advantage of this theory is to quantify uncertainty in terms of ^CVMC, ^CVNMC, and reference parameters. In addition to that the suggested method ^LOCLDFSS opens up possibilities for the parametrization tool together with the order existing in them. This aspect of the suggested study eclipses earlier fuzzy sets existing in the literature.

  • 2)

    To the best of our knowledge, there hasn’t been any prior writing on addressing an issue of car accidents in a ^LOCLDFSS setting. Because this work is the first of its type, our suggested model is a pathfinder and an exemplar in this field.

  • 3)

    ^MCDM problems are pervasive in human civilization and have several applications in real-world industries. We propose an ^MCDM algorithm with Score Matrix under ^LOCLDFSS to prefer an applicable car with ACC together with its latest model.

  • 4)

    The suggested method allows decision makers to get the optimum outcomes through the use of the ^MCDM algorithm.

  • 5)

    Better results are obtained with the simpler, more computationally straightforward model.

Objectives and structure of the proposed manuscript

  • 1)

    A notion of ^LOCLDFSS is interpreted with few of the basic operations and associated theorems.

  • 2)

    An algorithm is constructed in accordance with the Score function to address certain MCDM problems.

  • 3)

    An associated case study is also provided to prefer an applicable car with ACC together with its latest model

  • 4)

    A comparative analysis with the existing algorithm is discussed to scrutinize the viability of the proposed manuscript.

Preliminaries

Definition 2.1. Molodtsov (1999) The Soft set Z is interpreted on ˆL as

Z={(h,S(h))/hˆH,S(h)P(ˆL)}

where P(ˆL) is known as the power set of ˆL

Definition 2.2. Ramot et al. (2002) The Complex Fuzzy set Y defined on ˆL as

Y={(lp,ΓY(lp)eiΔΓY(lp)):lpˆL}

where ΓY(lp)eiΔΓY(lp) (forΓY(lp)[0,1],ΔΓY(lp)[0,2π]) denotes the ^CVMC of lpˆL. The Complex Fuzzy set can be reconsidered as

Y={(lp,ΓY(lp)ei2πwΓY(lp)):lpˆL}

where ΓY(lp)ei2πwΓY(lp) (forΓY(lp),wΓY(lp)[0,1]) represents the ^CVMC of lpˆL.

Definition 2.3. Atanassov (1986) The Intuitionistic Fuzzy set X characterized on ˆL as

X={(lp,ΓX(lp),ΔX(lp)):lpˆL}

ΓX(lp),ΔX(lp) [0,1] encompass the ^MC, ^NMC of lpˆL respectively with the constrains 0ΓX(lp)+ΔX(lp)1.

Definition 2.4. Yager (2013) The Pythagorean Fuzzy set D particularized on ˆL as

D={(lp,ΓD(lp),ΔD(lp)):lpˆL}

ΓD(lp),ΔD(lp) [0,1] encompass the ^MC, ^NMC of lpˆL respectively with the constrains 0(ΓD(lp))2+(ΔD(lp))21. The pythagorean Fuzzy Number (^PFN) is given by

D=ΓD,ΔD

Definition 2.5. Riaz & Hashmi (2019) The Linear Diophantine Fuzzy set W interpreted on ˆL as

W={(lp,ΓW(lp),ΔW(lp),αpW,βpW):lpˆL}

where ΓW(lp), ΔW(lp), αpW and βpW [0,1] encompass the ^MC, ^NMC and reference parameters of lpˆL respectively with the constraints 0 αpW+βpW 1 and 0 αpWΓW(lp) + βpWΔW(lp) 1.

Definition 2.6. Kamacı (2022) The Complex Linear Diophantine Fuzzy set R is defined on ˆL as

R={(lp,ΓR(lp)ei2π(wΓR(lp)),ΔR(lp)ei2π(wΔR(lp)),αpRei2π(wαpR),βpRei2π(wβpR)):lpˆL}

where ΓR(lp)ei2π(wΓR(lp)), ΔR(lp)ei2π(wΔR(lp)), αpRei2π(wαpR) and βpRei2π(wβpR) denotes the ^CVMC, ^CVNMC and complex-valued reference parameters of lp ˆL correspondingly with the constraints 0 αpR+βpR 1, 0 αpRΓR(lp)+βpRΔR(lp) 1 and 0 wαpR+wβpR 1, 0 wαpRwΓR(lp)+wβpRwΔR(lp) 1. The Complex Linear Diophantine Fuzzy Number ^(CLDFN) is given by

R=((ΓR,wΓR),(ΔR,wΔR),(αR,wαR),(βR,wβR))

Definition 2.7. Maji, Biswas & Roy (2001) The Intuitionistic Fuzzy Soft set interpreted on ˆL by the well-set of ordered pairs as

A,ˆH={h,A(h)/hˆH,A(h)^IFS(ˆL)}

(i.e.),A(h)={(lp,ΓA(h)(lp),ΔA(h)(lp)):lpˆL}.

where A:ˆH^IFS(ˆL) such that A(h)=ϕ if h ˆH and ^IFS(ˆL) denote the collection of all intuitionistic fuzzy subsets of ˆL.

Definition 2.8. Mahmood et al. (2018) A pair (A,ˆH) called Intuitionistic Fuzzy Soft set is said to be a Lattice Ordered Intuitionistic Fuzzy Soft set over ˆL if for h1, h2 ˆH such that h1 h2 A(h1) A(h2)

(i.e.), ΓA(h1)(lp)ΓA(h2)(lp)

ΔA(h1)(lp)ΔA(h2)(lp), lp ˆL

Definition 2.9. Zulqarnain et al. (2021) The Score function for a ^PFN D = ΓD,ΔD can be interpreted as

ˆS(D)=(ΓD)2(ΔD)2,whereˆS(D)[1,1].

Definition 2.10. Kamacı (2022) The Score function for a ^CLDFN R = ((ΓR,wΓR),(ΔR,wΔR),(αR,wαR),(βR,wβR)) can be particularized as

ˆS(R)=14[(ΓRΔR)+(wΓRwΔR)+(αRβR)+(wαRwβR)],whereˆS(R)[1,1].

Definition 2.11. Jayakumar et al. (2023) Let ˆL = {l1,l2,...,ln} denotes the universal set and ^CLDFSU ( ˆL) be the collection of all Complex Linear Diophantine Fuzzy subsets of ˆL. Consider a mapping A: ˆH ^CLDFSU ( ˆL). Then the Complex Linear Diophantine Fuzzy Soft set (^CLDFSS) determined by the well-set of ordered pairs is interpreted as

A,ˆH={h,A(h)/hˆJ,A(h)CLDFSU(ˆL)}

(i.e.),A(h={(lp,ΓA(h)(lp)ei2π(wΓA(h)(lp)),ΔA(h)(lp)ei2π(wΔA(h)(lp)),αpA(h)ei2π(wαpA(h)),βpA(h)ei2π(wβpA(h)))​​:lpˆL}.

such that A(h)=ϕ if h ˆH. The Complex Linear Diophantine Fuzzy Soft set can also be characterized as

(i.e.),A(h)={(lp,(ΓA(h)(lp),wΓA(h)(lp)),(ΔA(h)(lp),wΔA(h)(lp)),(αpA(h),wαpA(h)),(βpA(h),wβpA(h))):lpˆL}.

Lattice ordered complex linear diophantine fuzzy soft set

Definition 3.1. A pair (A,ˆH) called Complex Linear Diophantine Fuzzy Soft set is said to be a Lattice Ordered Complex Linear Diophantine Fuzzy Soft set (^LOCLDFSS) over ˆL if for h1, h2 ˆH such that h1 h2 A(h1) A(h2)

(i.e.), ΓA(h1)(lp)ΓA(h2)(lp)

wΓA(h1)(lp)wΓA(h2)(lp)

ΔA(h1)(lp)ΔA(h2)(lp)

wΔA(h1)(lp)wΔA(h2)(lp)

αpA(h1)αpA(h2)

wαpA(h1)wαpA(h2)

βpA(h1)βpA(h2)

wβpA(h1)wβpA(h2), lp ˆL

Definition 3.2. Let us consider two ^LOCLDFSS (A,ˆH) and (B,ˆH). The elementary operational law , also known as ^Algebraicsum is characterized as

(Z,ˆH)=(A,ˆH)(B,ˆH)

(i.e.),Z(h)={(lp,(ΓZ(h)(lp),wΓZ(h)(lp)),(ΔZ(h)(lp),wΔZ(h)(lp)),(αpZ(h),wαpZ(h)),(βpZ(h),wβpZ(h))):lpˆL}.

where

{ΓZ(h)(lp)=ΓA(h)(lp)+ΓB(h)(lp)ΓA(h)(lp)ΓB(h)(lp)wΓZ(h)(lp)=wΓA(h)(lp)+wΓB(h)(lp)wΓA(h)(lp)wΓB(h)(lp)ΔZ(h)(lp)=ΔA(h)(lp)ΔB(h)(lp)wΔZ(h)(lp)=wΔA(h)(lp)wΔB(h)(lp)αpZ(h)=αpA(h)+αpB(h)αpA(h)αpB(h)wαpZ(h)=wαpA(h)+wαpB(h)wαpA(h)wαpB(h)βpZ(h)=βpA(h)βpB(h)wβpZ(h)=wβpA(h)wβpB(h)}{p=1,2,...,n}

Definition 3.3. The operational law , also known as ^Algebraicproduct is defined by considering two ^LOCLDFSS (A,ˆH) and (B,ˆH) as

(Y,ˆH)=(A,ˆH)(B,ˆH)

(i.e.),Y(h)={(lp,(ΓY(h)(lp),wΓY(h)(lp)),(ΔY(h)(lp),wΔY(h)(lp)),(αpY(h),wαpY(h)),(βpY(h),wβpY(h))):lpˆL}.

where

{ΓY(h)(lp)=ΓA(h)(lp)ΓB(h)(lp)wΓY(h)(lp)=wΓA(h)(lp)wΓB(h)(lp)ΔY(h)(lp)=ΔA(h)(lp)+ΔB(h)(lp)ΔA(h)(lp)ΔB(h)(lp)wΔY(h)(lp)=wΔA(h)(lp)+wΔB(h)(lp)wΔA(h)(lp)wΔB(h)(lp)αpY(h)=αpA(h)αpB(h)wαpY(h)=wαpA(h)wαpB(h)βpY(h)=βpA(h)+βpB(h)βpA(h)βpB(h)wβpY(h)=wβpA(h)+wβpB(h)wβpA(h)wβpB(h)}{p=1,2,...,n}

Definition 3.4. Let λ > 0 and λ be real. Considering a ^LOCLDFSS (A,ˆH), we define λ(A,ˆH)

(X,ˆH)=λ(A,ˆH)

(i.e.),X(h)={(lp,(ΓX(h)(lp),wΓX(h)(lp)),(ΔX(h)(lp),wΔX(h)(lp)),(αpX(h),wαpX(h)),(βpX(h),wβpX(h))):lpˆL}.

where

{ΓX(h)(lp)=1(1ΓA(h)(lp))λwΓX(h)(lp)=1(1wΓA(h)(lp))λΔX(h)(lp)=(ΔA(h)(lp))λwΔX(h)(lp)=(wΔA(h)(lp))λαpX(h)=1(1αpA(h))λwαpX(h)=1(1wαpA(h))λβpX(h)=(βpA(h))λwβpX(h)=(wβpA(h))λ}{p=1,2,...,n}

Definition 3.5. Let λ > 0 and λ be real. Considering a ^LOCLDFSS (A,ˆH), we define (A,ˆH)λ

(V,ˆH)=(A,ˆH)λ

(i.e.),V(h)={(lp,(ΓV(h)(lp),wΓV(h)(lp)),(ΔV(h)(lp),wΔV(h)(lp)),(αpV(h),wαpV(h)),(βpV(h),wβpV(h))​​:lpˆL}.

where

{ΓV(h)(lp)=(ΓA(h)(lp))λwΓV(h)(lp)=(wΓA(h)(lp))λΔV(h)(lp)=1(1ΔA(h)(lp))λwΔV(h)(lp)=1(1wΔA(h)(lp))λαpV(h)=(αpA(h))λwαpV(h)=(wαpA(h))λβpV(h)=1(1βpA(h))λwβpV(h)=1(1wβpA(h))λ}{p=1,2,...,n}

Example 3.6. Consider two ^LOCLDFSS (A,ˆH) and (B,ˆH). The hierarchy between the parameters is h1 h2. Let

(A,ˆH)={A(h1)={l1,(0.6,0.5),(0.3,0.3),(0.7,0.6),(0.2,0.3)l2,(0.8,0.7),(0.2,0.2),(0.8,0.8),(0.2,0.1)}A(h2)={l1,(0.7,0.6),(0.1,0.3),(0.8,0.6),(0.2,0.1)l2,(0.8,0.8),(0.2,0.1),(0.9,0.9),(0.1,0.1)}

and

(B,ˆH)={B(h1)={l1,(0.6,0.6),(0.3,0.2),(0.6,0.7),(0.2,0.3)l2,(0.6,0.5),(0.2,0.4),(0.7,0.6),(0.2,0.3)}B(h2)={l1,(0.9,0.8),(0.2,0.2),(0.8,0.8),(0.2,0.1)l2,(0.7,0.8),(0.2,0.1),(0.8,0.6),(0.2,0.3)}

and also λ = 4. Thus, we have

  • 1. (A,ˆH)(B,ˆH)={h1,{l1,(0.84,0.8),(0.09,0.06),(0.88,0.88),(0.04,0.09)l2,(0.92,0.85),(0.04,0.08),(0.94,0.92),(0.04,0.03)}

  • {h2={l1,(0.97,0.92),(0.02,0.06),(0.96,0.92),(0.04,0.01)l2,(0.94,0.96),(0.04,0.01),(0.98,0.96),(0.02,0.03)}}

  • 2. (A,ˆH)(B,ˆH)={h1,{l1,(0.36,0.30),(0.51,0.44),(0.42,0.42),(0.36,0.51)l2,(0.48,0.35),(0.36,0.52),(0.56,0.48),(0.36,0.37)}

  • {h2={l1,(0.63,0.48),(0.28,0.44),(0.64,0.48),(0.36,0.19)l2,(0.56,0.64),(0.36,0.19),(0.72,0.54),(0.28,0.37)}}

  • 3. λ(A,ˆH)={h1,{l1,(0.9744,0.9375),(0.0081,0.0081),(0.9919,0.9744),(0.0016,0.0081)l2,(0.9984,0.9919),(0.0016,0.0016),(0.9984,0.9984),(0.0016,0.0001)}

  • {h2={l1,(0.9919,0.9744),(0.0001,0.0081),(0.9984,0.9744),(0.0016,0.0001)l2,(0.9984,0.9984),(0.0016,0.0001),(0.9999,0.9999),(0.0001,0.0001)}}

  • 4. (A,ˆH)λ={h1,{l1,(0.1296,0.0625),(0.7599,0.7599),(0.2401,0.1296),(0.5904,0.7599)l2,(0.4096,0.2401),(0.5904,0.5904),(0.4096,0.4096),(0.5904,0.3439)}

  • {h2={l1,(0.2401,0.1296),(0.3439,0.7599),(0.4096,0.1296),(0.5904,0.3439)l2,(0.4096,0.4096),(0.5904,0.3439),(0.6561,0.6561),(0.3439,0.3439)}}

Proposition 3.7. If (A,ˆH) and (B,ˆH) are two ^LOCLDFSS then so (A,ˆH)(B,ˆH), (A,ˆH)(B,ˆH), λ(A,ˆH) and (A,ˆH)λ

wβpA(h1)wβpB(h1)wβpA(h2)wβpB(h2)D(h1)D(h2)forh1h2

Theorem 3.8. 1. The finite ^Algebraicsum of ^LOCLDFSS is also a ^LOCLDFSS.

2. The finite ^Algebraicproduct of ^LOCLDFSS is also a ^LOCLDFSS.

Proposition 3.9. Let us consider three ^LOCLDFSS as (A,ˆH), (B,ˆH) and (C,ˆH). Then the following properties holds.

  • 1. (A,ˆH)(B,ˆH) = (B,ˆH)(A,ˆH) (commutative under )

  • 2. (A,ˆH)(B,ˆH) = (B,ˆH)(A,ˆH) (commutative under )

  • 3. ((A,ˆH)(B,ˆH))(C,ˆH) = (A,ˆH)((B,ˆH)(C,ˆH)) (associative under )

  • 4. ((A,ˆH)(B,ˆH))(C,ˆH) = (A,ˆH)((B,ˆH)(C,ˆH)) (associative under )

^LOCLDFSS-decision making process

Score matrix and utility matrix for lattice ordered complex linear diophantine fuzzy soft matrix

Definition 4.1. The Lattice Ordered Complex Linear Diophantine Fuzzy Soft Decision Matrix (^LOCLDFSMn×m) is particularized by

[M]=[(ΓMhpi,wMΓhpi),(ΔMhpi,wMΔhpi)),(αMhpi,wMαhpi),(βMhpi,wMβhpi)]n×m=(l11l12l1ml21l22l2mln1ln2lnm)

where lpi = ((Γhi(lp),wΓhi(lp)),(Δhi(lp),wΔhi(lp)),(αhpi,wαhpi),(βhpi,wβhpi)),p=1,2,,nandi=1,2,,m

Example 4.2. Consider a ^LOCLDFSS (A,ˆH). Since it is a ^LOCLDFSS, for h1 h2 A(h1) A(h2).

A(h1)={l1,(0.5,0.7),(0.3,0.2),(0.6,0.6),(0.4,0.3)l2,(0.7,0.6),(0.3,0.3),(0.8,0.8),(0.2,0.1)l3,(0.6,0.8),(0.3,0.2),(0.8,0.5),(0.2,0.4)}A(h2)={l1,(0.8,0.8),(0.2,0.1),(0.8,0.7),(0.3,0.2)l2,(0.8,0.7),(0.2,0.2),(0.9,0.9),(0.1,0.1)l3,(0.7,0.9),(0.3,0.1),(0.9,0.6),(0.1,0.1)}

The ^LOCLDFSMn×m for above given ^LOCLDFSS is as follows.

M=[[(0.5,0.7),(0.3,0.2),(0.6,0.6),(0.4,0.3)][(0.8,0.8),(0.2,0.1),(0.8,0.7),(0.3,0.2)][(0.6,0.8),(0.3,0.2),(0.8,0.5),(0.2,0.4)][(0.7,0.6),(0.3,0.3),(0.8,0.8),(0.2,0.1)][(0.8,0.7),(0.2,0.2),(0.9,0.9),(0.1,0.1)][(0.7,0.9),(0.3,0.1),(0.9,0.6),(0.1,0.1)]]

Definition 4.3. The Score Matrix for ^LOCLDFSMn×m is particularized as

ˆS(M)=14((ΓMhpiΔMhpi)+(wMΓhpiwMΔhpi)+(αMhpiβMhpi)+(wMαhpiwMβhpi)),

p=1,2,,nandi=1,2,,m,whereMisa^LOCLDFSMn×m.

Example 4.4. The Score Matrix ˆS(M) of Example 4.2 is

ˆS(M)=[0.30.5750.40.50.6750.625]

Definition 4.5. The Utility Matrix is particularized as

ˆU(M,I)=ˆS(M)ˆS(I)

where M and I are ^LOCLDFSMn×m.

Mcdm technique based on score matrix and utility matrix

Algorithm for the proposed technique to prefer an applicable car with adaptive cruise control system

  1. M = Automotive Engineers: Technical specifications, structural design, and overall vehicle functionality are areas where engineers and designers are indispensable in the decision-making process.

  2. I = Environmental Analysts: Emphasising on sustainability of the environment, evaluating the environmental effect of cars, and examining methods for implementing greener practices and technologies.

  3. D = Safety and Compliance Analysts: Examining crash test outcomes, safety standards, and regulatory compliance to provide information on the level of security of a car.

  • 1. The attribute “Cost” intimates that the alternative is “cheap” or “not cheap”.

  • 2. The attribute “Performance” intimates that the alternative is “good” or “bad”.

  • 3. The attribute “Sensor type used” intimates that the alternative is of “high standard” or “low standard”.

Comparative assessment with the existing methodology

Comparative studies

Algorithm Borah, Neog & Sut (2012)

Algorithm Siddique et al. (2021)

Advantages of the proposed methodology

  1. The classical ^FS and ^PFS are all particular instances of the ^LOCLDFSS, as was previously mentioned. Because of the limitations of the current theories, most problems in daily life cannot be solved when an expert states their preferences for the elements. One generalized theory that can deal with partial, ambiguous, and inconsistent information that is frequently present in real-world scenarios is ^LOCLDFSS. As a result, current studies are better suited to address engineering design and real-world issues than previous research.

  2. It has been recognized that the proposed utility and score matrices in the ^LOCLDFSS environment contribute to the body of knowledge already in existence and aid in the modeling of some real-world scenarios that the existing literature is unable to manage. However, the suggested approach is a better way to address issues because it may address the shortcomings of the current approaches.

Limitations and practical implication

Conclusion

Supplemental Information

Supplemental File.

DOI: 10.7717/peerj-cs.2165/supp-1

Fuzzy soft set model.

The raw data calculations of the model. This supports data shown in the article in the fifth section. Represents matrices M, I, D, S(M), S(I) and S(D). Also, the final computation has shown the final results in the form of a total score matrix. It can help readers to understand and reproduce the model.

DOI: 10.7717/peerj-cs.2165/supp-2

Comparative analysis ALGORITHM 6.3.

The calculation of the comparative analysis of the second algorithm presented in the article.

DOI: 10.7717/peerj-cs.2165/supp-3

Comparative analysis.

The comparative analysis of the first algorithm presented in the sixth section of the article with clear proof of the stability of the proposed methodology.

DOI: 10.7717/peerj-cs.2165/supp-4

Code.

The authors have not employed any coding-centric software in the algorithmic design. Despite this, we have articulated methodological steps in a general template below. These steps can also be implemented through machine learning tools after translating into executable code. Statement of the Problem: Road accidents are frequently happening in India due to collisions. There is no guarantee for safer driving. Adaptive Cruise Control (ACC) fills this gap and enables a convenient and safer driving experience by monitoring other vehicles and objects on the road with the use of sensors. It also helps the driver keep a steady vehicle speed.

DOI: 10.7717/peerj-cs.2165/supp-5

Additional Information and Declarations

Competing Interests

Željko Stević is an Academic Editor for PeerJ Computer Science.

Author Contributions

K. Ashma Banu conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

J. Vimala conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Nasreen Kausar conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, authored or reviewed drafts of the article, and approved the final draft.

Željko Stević performed the experiments, analyzed the data, performed the computation work, authored or reviewed drafts of the article, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

The raw data are available in the Supplemental Files.

Funding

The article has been written with the joint financial support of RUSA-Phase 2.0 grant sanctioned vide letter No.F 24-51/2014-U, Policy (TN Multi-Gen), Dept. of Edn. Govt. of India, Dt. 09.10.2018, DST-PURSE 2nd Phase programme vide letter No. SR/PURSE Phase 2/38 (G) Dt. 21.02.2017 and DST (FIST–level I) 657876570 vide letter No.SR/FIST/MS-I/2018/17Dt. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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