Research article Special Issues

A real-time air-writing model to recognize Bengali characters

  • Received: 30 November 2023 Revised: 24 January 2024 Accepted: 25 January 2024 Published: 18 February 2024
  • MSC : 68T01, 68T20

  • Air-writing is a widely used technique for writing arbitrary characters or numbers in the air. In this study, a data collection technique was developed to collect hand motion data for Bengali air-writing, and a motion sensor-based data set was prepared. The feature set as then utilized to determine the most effective machine learning (ML) model among the existing well-known supervised machine learning models to classify Bengali characters from air-written data. Our results showed that medium Gaussian SVM had the highest accuracy (96.5%) in the classification of Bengali character from air writing data. In addition, the proposed system achieved over 81% accuracy in real-time classification. The comparison with other studies showed that the existing supervised ML models predicted the created data set more accurately than many other models that have been suggested for other languages.

    Citation: Mohammed Abdul Kader, Muhammad Ahsan Ullah, Md Saiful Islam, Fermín Ferriol Sánchez, Md Abdus Samad, Imran Ashraf. A real-time air-writing model to recognize Bengali characters[J]. AIMS Mathematics, 2024, 9(3): 6668-6698. doi: 10.3934/math.2024325

    Related Papers:

    [1] Jie Liu, Qinglong Wang, Xuyang Cao, Ting Yu . Bifurcation and optimal harvesting analysis of a discrete-time predator–prey model with fear and prey refuge effects. AIMS Mathematics, 2024, 9(10): 26283-26306. doi: 10.3934/math.20241281
    [2] Xuyang Cao, Qinglong Wang, Jie Liu . Hopf bifurcation in a predator-prey model under fuzzy parameters involving prey refuge and fear effects. AIMS Mathematics, 2024, 9(9): 23945-23970. doi: 10.3934/math.20241164
    [3] Weili Kong, Yuanfu Shao . The effects of fear and delay on a predator-prey model with Crowley-Martin functional response and stage structure for predator. AIMS Mathematics, 2023, 8(12): 29260-29289. doi: 10.3934/math.20231498
    [4] Kwadwo Antwi-Fordjour, Rana D. Parshad, Hannah E. Thompson, Stephanie B. Westaway . Fear-driven extinction and (de)stabilization in a predator-prey model incorporating prey herd behavior and mutual interference. AIMS Mathematics, 2023, 8(2): 3353-3377. doi: 10.3934/math.2023173
    [5] Xiongxiong Du, Xiaoling Han, Ceyu Lei . Dynamics of a nonlinear discrete predator-prey system with fear effect. AIMS Mathematics, 2023, 8(10): 23953-23973. doi: 10.3934/math.20231221
    [6] Ahmad Suleman, Rizwan Ahmed, Fehaid Salem Alshammari, Nehad Ali Shah . Dynamic complexity of a slow-fast predator-prey model with herd behavior. AIMS Mathematics, 2023, 8(10): 24446-24472. doi: 10.3934/math.20231247
    [7] Kimun Ryu, Wonlyul Ko . Stability and bifurcations in a delayed predator-prey system with prey-taxis and hunting cooperation functional response. AIMS Mathematics, 2025, 10(6): 12808-12840. doi: 10.3934/math.2025576
    [8] Na Min, Hongyang Zhang, Xiaobin Gao, Pengyu Zeng . Impacts of hunting cooperation and prey harvesting in a Leslie-Gower prey-predator system with strong Allee effect. AIMS Mathematics, 2024, 9(12): 34618-34646. doi: 10.3934/math.20241649
    [9] Fatao Wang, Ruizhi Yang, Yining Xie, Jing Zhao . Hopf bifurcation in a delayed reaction diffusion predator-prey model with weak Allee effect on prey and fear effect on predator. AIMS Mathematics, 2023, 8(8): 17719-17743. doi: 10.3934/math.2023905
    [10] Weili Kong, Yuanfu Shao . Bifurcations of a Leslie-Gower predator-prey model with fear, strong Allee effect and hunting cooperation. AIMS Mathematics, 2024, 9(11): 31607-31635. doi: 10.3934/math.20241520
  • Air-writing is a widely used technique for writing arbitrary characters or numbers in the air. In this study, a data collection technique was developed to collect hand motion data for Bengali air-writing, and a motion sensor-based data set was prepared. The feature set as then utilized to determine the most effective machine learning (ML) model among the existing well-known supervised machine learning models to classify Bengali characters from air-written data. Our results showed that medium Gaussian SVM had the highest accuracy (96.5%) in the classification of Bengali character from air writing data. In addition, the proposed system achieved over 81% accuracy in real-time classification. The comparison with other studies showed that the existing supervised ML models predicted the created data set more accurately than many other models that have been suggested for other languages.



    The field of interval analysis is a subfield of set-valued analysis, which focuses on sets in mathematics and topology. Historically, Archimede's method included calculating the circumference of a circle, which is an example of interval enclosure. By focusing on interval variables instead of point variables, and expressing computation results as intervals, this method eliminates errors that cause misleading conclusions. An initial objective of the interval-valued analysis was to estimate error estimates for numerical solutions to finite state machines. In 1966, Moore [2], published the first book on interval analysis, which is credited with being the first to use intervals in computer mathematics in order to improve calculation results. There are many situations where the interval analysis can be used to solve uncertain problems because it can be expressed in terms of uncertain variables. In spite of this, interval analysis remains one of the best approaches to solving interval uncertain structural systems and has been used for over fifty years in mathematical modeling such as computer graphics [3], decision-making analysis [4], multi-objective optimization, [5], error analysis [40]. In summary, interval analysis research has yielded numerous excellent results, and readers can consult Refs. [7,8,9], for additional information.

    Convexity has been recognized for many years as a significant factor in such fields as probability theory, economics, optimal control theory, and fuzzy analysis. On the other hand, generalized convexity of mappings is a powerful tool for solving numerous nonlinear analysis and applied analysis problems, including a wide range of mathematical physics problems. A number of rigorous generalizations of convex functions have recently been investigated, see Refs. [10,11,12,13]. An interesting topic in mathematical analysis is integral inequalities. Convexity plays a significant role in inequality theory. During the last few decades, generalized convexity has played a prominent role in many disciplines and applications of IVFS, see Refs. [14,15,16,17,18,19]. Several recent applications have addressed these inequalities, see Refs. [20,21,22]. First, Breckner describes the idea of continuity for IVFS, see Ref. [23]. Using the generalized Hukuhara derivative, Chalco-Cano et al. [24], and Costa et al. [25], derived some Ostrowski and Opial type inequality for IVFS, respectively. Bai et al. [26], formulated an interval-based Jensen inequality. First, Zhao [27], and co-authors established (H.H) and Jensen inequality using h-convexity for IVFS. In general, the traditional (H.H) inequality has the following definition:

    Θ(g)+Θ(h)21hghgΘ(γ)dγΘ(g+h2). (1.1)

    Because of the nature of its definition, it is the first geometrical interpretation of convex mappings in elementary mathematics, and has attracted a large amount of attention. Several generalizations of this inequality are presented here, see Refs. [28,29,30,31]. Initially, Awan et al. explored (h1,h2)-convex functions and proved the following inequality [32]. Several authors have developed H.H and Jensen-type inequalities utilizing (h1,h2)-convexity. Ruonan Liu [33] developed H.H inequalities for harmonically (h1,h2)-convex functions. Wengui Yang [34] developed H.H inequalities on the coordinates for (p1,h1)-(p2,h2)-convex functions. Shi et al. [35] developed H.H inequalities for (m,h1,h2)-convex functions via Riemann Liouville fractional integrals. Sahoo et al. [36] established H.H and Jensen-type inequalities for harmonically (h1,h2)-Godunova-Levin functions. Afzal et al. [37] developed these inequalities for a generalized class of Godunova-Levin functions using inclusion relation. An et al. [38] developed H.H type inequalities for interval-valued (h1,h2)-convex functions. Results are now influenced by less accurate inclusion relation and interval LU-order relation. For some recent developments using the inclusion relation for the generalized class of Godunova-Levin functions, see Refs. [39,40,44]. It is clear from comparing the examples presented in this literature that the inequalities obtained using these old partial order relations are not as precise as those obtained by using CR-order relation. As a result, it is critically important that we are able to study inequalities and convexity by using a total order relation. Therefore, we use Bhunia's [41], CR-order, which is total interval order relation. The notions of CR-convexity and CR-order relation were used by several authors in 2022, in an attempt to prove a number of recent developments in these inequalities, see Refs. [42,43]. Afzal et al. using the notion of the h-GL function, proves the following result [45].

    Theorem 1.1. (See [45]) Consider Θ:[g,h]RI+. Define h:(0,1)R+ and h(12)0. If ΘSX(CR-h,[g,h],RI+) and Θ IR[g,h], then

    h(12)2Θ(g+h2)CR1hghgΘ(γ)dγCR[Θ(g)+Θ(h)]10dxh(x). (1.2)

    Also, by using the notion of the h-GL function Jensen-type inequality was also developed.

    Theorem 1.2. (See [45]) Let uiR+, ji[g,h]. If h is non-negative super multiplicative function and ΘSX(CR-h,[g,h],RI+), then this holds :

    Θ(1Ukki=1uiji)CRki=1Θ(ji)h(uiUk). (1.3)

    In addition, it introduces a new concept of interval-valued GL-functions pertaining to a total order relation, the Center-Radius order, which is unique as far as the literature goes. With the example presented in this article, we are able to show how CR-IVFS can be used to analyze various integral inequalities. In contrast to classical interval-valued analysis, CR-order interval-valued analysis differs from it. Using the concept of Centre and Radius, we calculate intervals as follows: MC=M_+¯M2 and MR=¯MM_2, respectively, where M=[M_,ˉM]. Inspired by the concepts of interval valued analysis and the strong literature that has been discussed above with particular articles, see e.g., Zhang et al. [39], Bhunia and Samanta [41], Shi et al. [42], Liu et al. [43] and Afzal et al. [44,45], we introduced the idea of CR-(h1,h2)-GL function. By using this new concept we developed H.H and Jensen-type inequalities. The study also includes useful examples to back up its findings.

    Finally, the article is designed as follows: In Section 2, preliminary is provided. The main problems and applications are provided in Section 3 and 4. Finally, Section 5 provides the conclusion.

    As for the notions used in this paper but not defined, see Refs. [42,43,45]. It is a good idea to familiarize yourself with some basic arithmetic related to interval analysis in this section since it will prove very helpful throughout the paper.

    [M]=[M_,¯M](xR, M_x¯M;xR)
    [N]=[N_,¯N](xR, N_x¯N;xR)
    [M]+[N]=[M_,¯M]+[N_,¯N]=[M_+N_,¯M+¯N]
    ηM=η[M_,¯M]={[ηM_,η¯M](η>0){0}(η=0)[η¯M,ηM_](η<0),

    where ηR.

    Let RI and R+I be the set of all closed and all positive compact intervals of R, respectively. Several algebraic properties of interval arithmetic will now be discussed.

    Consider M=[M_,ˉM]RI, then Mc=¯M+M_2 and Mr=¯MM_2 are the center and radius of interval M respectively. The CR form of interval M can be defined as:

    M=Mc,Mr=¯M+M_2,¯MM_2.

    Following are the order relations for the center and radius of intervals:

    Definition 2.1. The CR-order relation for M=[M_,¯M]=Mc,Mr, N=[N_,¯N]=Nc,NrRI represented as:

    McrN{Mc<Nc,ifMcNc;MrNr,ifMc=Nc.

    Note: For arbitrary two intervals M,NRI, we have either McrN or NcrM.

    Riemann integral operators for IVFS are presented here.

    Definition 2.2 (See [45]) Let D:[g,h] be an IVF such that D=[D_,¯D]. Then D is Riemann integrable (IR) on [g,h] if D_ and ¯D are IR on [g,h], that is,

    (IR)hgD(s)ds=[(R)hgD_(s)ds,(R)hg¯D(s)ds].

    The collection of all (IR) IVFS on [g,h] is represented by IR([g,h]).

    Shi et al. [42] proved that the based on CR-order relations, the integral preserves order.

    Theorem 2.1. Let D,F:[g,h] be IVFS given by D=[D_,¯D] and F=[F_,¯F]. If D(s)CRF(s), i[g,h], then

    hgD(s)dsCRhgF(s)ds.

    We'll now provide an illustration to support the aforementioned Theorem.

    Example 2.1. Let D=[s,2s] and F=[s2,s2+2], then for s[0,1].

    DC=3s2,DR=s2,FC=s2+1 and FR=1.

    From Definition 2.1, we have D(s)CRF(s), s[0,1].

    Since,

    10[s,2s]ds=[12,1]

    and

    10[s2,s2+2]ds=[13,73].

    Also, from above Theorem 2.1, we have

    10D(s)dsCR10F(s)ds.
    Figure 1.  A clear indication of the validity of the CR-order relationship can be seen in the graph.
    Figure 2.  As can be seen from the graph, the Theorem 2.1 is valid.

    Definition 2.3. (See [42]) Define h1,h2:[0,1]R+. We say that Θ:[g,h]R+ is called (h1,h2)-convex function, or that ΘSX((h1,h2),[g,h],R+), if g1,h1[g,h] and γ[0,1], we have

    Θ(γg1+(1γ)h1) h1(γ)h2(1γ)Θ(g1)+h1(1γ)h2(γ)Θ(h1). (2.1)

    If in (2.1) "" replaced with "" it is called (h1,h2)-concave function or ΘSV((h1,h2),[g,h],R+).

    Definition 2.4. (See [42]) Define h1,h2:(0,1)R+. We say that Θ:[g,h]R+ is called (h1,h2)-GL convex function, or that ΘSGX((h1,h2),[g,h],R+), if g1,h1[g,h] and γ[0,1], we have

    Θ(γg1+(1γ)h1)Θ(g1)h1(γ)h2(1γ)+Θ(h1)h1(1γ)h2(γ). (2.2)

    If in (2.2) "" replaced with "" it is called (h1,h2)-GL concave function or ΘSGV((h1,h2),[g,h],R+).

    Now let's introduce the concept for CR-order form of convexity.

    Definition 2.5. (See [42]) Define h1,h2:[0,1]R+. We say that Θ:[g,h]R+ is called CR(h1,h2)-convex function, or that ΘSX(CR-(h1,h2),[g,h],R+), if g1,h1[g,h] and γ[0,1], we have

    Θ(γg1+(1γ)h1)CR h1(γ)h2(1γ)Θ(g1)+h1(1γ)h2(γ)Θ(h1). (2.3)

    If in (2.3) "CR" replaced with "CR" it is called CR-(h1,h2)-concave function or ΘSV(CR-(h1,h2),[g,h],R+).

    Definition 2.6. (See [42]) Define h1,h2:(0,1)R+. We say that Θ:[g,h]R+ is called CR-(h1,h2)-GL convex function, or that ΘSGX(CR-(h1,h2),[g,h],R+), if g1,h1[g,h] and γ[0,1], we have

    Θ(γg1+(1γ)h1)CRΘ(g1)h1(γ)h2(1γ)+Θ(h1)h1(1γ)h2(γ). (2.4)

    If in (2.4) "CR" replaced with "CR" it is called CR-(h1,h2)-GL concave function or ΘSGV(CR-(h1,h2),[g,h],R+).

    Remark 2.1.If h1=h2=1, Definition 2.6 becomes a CR-P-function [45].

    If h1(γ)=1h1(γ), h2=1 Definition 2.6 becomes a CR-h-convex function [45].

    If h1(γ)=h1(γ), h2=1 Definition 2.6 becomes a CR-h-GL function [45].

    If h1(γ)=1γs, h2=1 Definition 2.6 becomes a CR-s-convex function [45].

    If h(γ)=γs, Definition 2.6 becomes a CR-s-GL function [45].

    Proposition 3.1. Consider Θ:[g,h]RI given by [Θ_,¯Θ]=(ΘC,ΘR). If ΘC and ΘR are (h1,h2)-GL over [g,h], then Θ is called CR-(h1,h2)-GL function over [g,h].

    Proof. Since ΘC and ΘR are (h1,h2)-GL over [g,h], then for each γ(0,1) and for all g1,h1[g,h], we have

    ΘC(γg1+(1γ)h1)CRΘC(g1)h1(γ)h2(1γ)+ΘC(h1)h1(1γ)h2(γ),

    and

    ΘR(γg1+(1γ)h1)CRΘR(g1)h1(γ)h2(1γ)+ΘR(h1)h1(1γ)h2(γ).

    Now, if

    ΘC(γg1+(1γ)h1)ΘC(g1)h1(γ)h2(1γ)+ΘC(h1)h1(1γ)h2(γ),

    then for each γ(0,1) and for all g1,h1[g,h],

    ΘC(γg1+(1γ)h1)<ΘC(g1)h1(γ)h2(1γ)+ΘC(h1)h1(1γ)h2(γ).

    Accordingly,

    ΘC(γg1+(1γ)h1)CRΘC(g1)h1(γ)h2(1γ)+ΘC(h1)h1(1γ)h2(γ).

    Otherwise, for each γ(0,1) and for all g1,h1[g,h],

    ΘR(νg1+(1γ)h1)ΘR(g1)h1(γ)h2(1γ)+ΘR(h1)h1(1γ)h2(γ)
    Θ(γg1+(1γ)h1)CRΘ(g1)h1(γ)h2(1γ)+Θ(h1)h1(1γ)h2(γ).

    Taking all of the above into account, and Definition 2.6 this can be written as

    Θ(γg1+(1γ)h1)CRΘ(g1)h1(γ)h2(1γ)+Θ(h1)h1(1γ)h2(γ)

    for each γ(0,1) and for all g1,h1[g,h].

    This completes the proof.

    The next step is to establish the H.H inequality for the CR-(h1,h2)-GL function.

    Theorem 3.1. Define h1,h2:(0,1)R+ and h1(12)h2(12)0. Let Θ:[g,h]RI+, if ΘSGX(CR-(h1,h2),[t,u],RI+) and Θ IR[t,u], we have

    [H(12,12)]2Θ(g+h2)CR1hghgΘ(γ)dγCR[Θ(g)+Θ(h)]10dxH(x,1x).

    Proof. Since ΘSGX(CR-(h1.h2),[g,h],RI+), we have

    [H(12,12)]Θ(g+h2)CRΘ(xg+(1x)h)+Θ((1x)g+xh).

    Integration over (0, 1), we have

    [H(12,12)]Θ(g+h2)CR[10Θ(xg+(1x)h)dx+10Θ((1x)g+xh)dx]=[10Θ_(xg+(1x)h)dx+10Θ_((1x)g+xh)dx,10¯Θ(xg+(1x)h)dx+10¯Θ((1x)g+xh)dx]=[2hghgΘ_(γ)dγ,2hghg¯Θ(γ)dγ]=2hghgΘ(γ)dγ. (3.1)

    By Definition 2.6, we have

    Θ(xg+(1x)h)CRΘ(g)h1(x)h2(1x)+Θ(h)h1(1x)h2(x).

    Integration over (0, 1), we have

    10Θ(xg+(1x)h)dxCRΘ(g)10dxh1(x)h2(1x)+Θ(h)10dxh1(1x)h2(x).

    Accordingly,

    1hghgΘ(γ)dγCR[Θ(g)+Θ(h)]10dxH(x,1x). (3.2)

    Now combining (3.1) and (3.2), we get required result

    [H(12,12)]2Θ(t+u2)CR1hghgΘ(γ)dγCR[Θ(g)+Θ(h)]10dxH(x,1x).

    Remark 3.1.If h1(x)=h2(x)=1, Theorem 3.1 becomes result for CR- P-function:

    12Θ(g+h2)CR1hghgΘ(γ)dγCR [Θ(g)+Θ(h)].

    If h1(x)=h(x), h2(x)=1 Theorem 3.1 becomes result for CR-h-GL-function:

    h(12)2Θ(g+h2)CR1hghgΘ(γ)dγCR 10dxh(x).

    If h1(x)=1h(x), h2(x)=1 Theorem 3.1 becomes result for CR-h-convex function:

    12h(12)Θ(g+h2)CR1hghgΘ(γ)dγCR 10h(x)dx.

    If h1(x)=1h1(x), h2(x)=1h2(x) Theorem 3.1 becomes result for CR-(h1,h2)-convex function:

    12[H(12,12)]Θ(g+h2)CR1hghgΘ(γ)dγCR 10dxH(x,1x).

    Example 3.1. Consider [t,u]=[0,1], h1(x)=1x, h2(x)=1, x (0,1). Θ:[g,h]RI+ is defined as

    Θ(γ)=[γ2,2γ2+1].

    where

    [H(12,12)]2Θ(g+h2)=Θ(12)=[14,32],
    1hghgΘ(γ)dγ=[10(γ2)dγ,10(2γ2+1)dγ]=[13,53],
    [Θ(g)+Θ(h)]10dxH(x,1x)=[12,2].

    As a result,

    [14,32]CR[13,53]CR[12,2].

    This proves the above theorem.

    Theorem 3.2. Define h1,h2:(0,1)R+ and h1(12)h2(12)0. Let Θ:[g,h]RI+, if ΘSGX(CR-(h1,h2),[t,u],RI+) and Θ IR[g,h], we have

    [H(12,12)]24Θ(g+h2)CR1CR1hghgΘ(γ)dγCR2
    CR{[Θ(g)+Θ(h)][12+1H(12,12)]}10dxH(x,1x),

    where

    1=H(12,12)4[Θ(3g+h4)+Θ(3h+g4)],
    2=[Θ(g+h2)+Θ(g)+Θ(h)2]10dxH(x,1x).

    Proof. Take [g,g+h2], we have

    Θ(g+g+h22)=Θ(3g+h2)CRΘ(xg+(1x)g+h2)H(12,12)+Θ((1x)g+xg+h2)H(12,12).

    Integration over (0, 1), we have

    Θ(3g+h2)CR1H(12,12)[10Θ(xg+(1x)g+h2)dx+10Θ(xg+h2+(1x)h)dx]
    =1H(12,12)[2hgg+h2gη(γ)dγ+2hgg+h2gΘ(γ)dγ]
    =4H(12,12)[1hgg+h2gΘ(γ)dγ].

    Accordingly,

    H(12,12)4Θ(3g+h2)CR1hgg+h2gΘ(γ)dγ. (3.3)

    Similarly for interval [g+h2,h], we have

    H(12,12)4Θ(3h+g2)CR1hgg+h2gΘ(γ)dγ. (3.4)

    Adding inequalities (3.3) and (3.4), we get

    1=H(12,12)4[Θ(3g+h4)+Θ(3h+g4)]CR[1hghgΘ(γ)dγ].

    Now

    [H(12,12)]24Θ(g+h2)
    =[H(12,12)]24Θ(12(3g+h4)+12(3h+g4))
    CR[H(12,12)]24[Θ(3g+h4)h(12)+Θ(3h+g4)h(12)]
    =H(12,12)4[Θ(3g+h4)+Θ(3h+g4)]
    =1
    CRH(12,12)4{1H(12,12)[Θ(g)+Θ(g+h2)]+1H(12,12)[Θ(h)+Θ(g+h2)]}
    =12[Θ(g)+Θ(h)2+Θ(g+h2)]
    CR[Θ(g)+Θ(h)2+Θ(g+h2)]10dxH(x,1x)
    =2
    CR[Θ(g)+Θ(h)2+Θ(g)H(12,12)+Θ(h)H(12,12)]10dxH(x,1x)
    CR[Θ(g)+Θ(h)2+1H(12,12)[Θ(g)+Θ(h)]]10dxH(x,1x)
    CR{[Θ(g)+Θ(h)][12+1H(12,12)]}10dxH(x,1x).

    Example 3.2. Thanks to Example 3.1, we have

    [H(12,12)]24Θ(g+h2)=Θ(12)=[14,32],
    1=12[Θ(14)+Θ(34)]=[516,138],
    2=[Θ(0)+Θ(1)2+Θ(12)]10dxH(x,1x),
    2=12([14,32]+[12,2]),
    2=[38,74],
    {[Θ(g)+Θ(h)][12+1H(12,12)]}10dxH(x,1x)=[12,2].

    Thus we obtain

    [14,32]CR[516,138]CR[13,53]CR[38,74]cr[12,2].

    This proves the above theorem.

    Theorem 3.3. Let Θ,θ:[g,h]RI+,h1,h2:(0,1)R+ such that h1,h20. If ΘSGX(CR-h1,[g,h],RI+), θSGX(CR-h2,[g,h],RI+) and Θ,θ IR[g,h] then, we have

    1hghgΘ(γ)θ(γ)dγCRM(g,h)10dxH2(x,1x)+N(g,h)10dxH(x,x)H(1x,1x),

    where

    M(g,h)=Θ(g)θ(g)+Θ(h)θ(h),N(g,h)=Θ(g)θ(h)+Θ(h)θ(g).

    Proof. Conider ΘSGX(CR-h1,[g,h],RI+), θSGX(CR-h2,[g,h],RI+) then, we have

    Θ(gx+(1x)h)CRΘ(g)h1(x)h2(1x)+Θ(h)h1(1x)h2(x),
    θ(gx+(1x)h)CRθ(g)h1(x)h2(1x)+θ(h)h1(1x)h2(x).

    Then,

    Θ(gx+(1x)h)θ(tx+(1x)u)
    CRΘ(g)θ(g)H2(x,1x)+Θ(g)θ(h)+Θ(g)θ(g)H2(1x,x)+Θ(h)θ(h)H(x,x)H(1x,1x).

    Integration over (0, 1), we have

    10Θ(gx+(1x)h)θ(gx+(1x)h)dx
    =[10Θ_(gx+(1x)h)θ_(gx+(1x)h)dx,10¯Θ(gx+(1x)h)¯θ(gx+(1x)h)dx]
    =[1hghgΘ_(γ)θ_(γ)dγ,1hghg¯Θ(γ)¯θ(γdγ]=1hghgΘ(γ)θ(γ)dγ
    CR10[Θ(g)θ(g)+Θ(h)θ(h)]H2(x,1x)dx+10[Θ(g)θ(h)+Θ(h)θ(g)]H(x,x)H(1x,1x)dx.

    It follows that

    1hghgΘ(γ)θ(γ)dγCRM(g,h)10dxH2(x,1x)+N(g,h)10dxH(x,x)H(1x,1x).

    Theorem is proved.

    Example 3.3. Consider [g,h]=[1,2], h1(x)=1x, h2(x)=1 x (0,1). Θ,θ:[g,h]RI+ be defined as

    Θ(γ)=[γ2,2γ2+1],θ(γ)=[γ,γ].

    Then,

    1hghgΘ(γ)θ(γ)dγ=[154,9],
    M(g,h)101H2(x,1x)dx=M(1,2)10x2dx=[7,7],
    N(g,h)101H(x,x)H(1x,1x)dx=N(1,2)10x(1x)dx=[156,156].

    It follows that

    [154,9]CR[7,7]+[156,156]=[192,192].

    It follows that the theorem above is true.

    Theorem 3.4. Let Θ,θ:[g,h]RI+,h1,h2:(0,1)R+ such that h1,h20. If ΘSGX(CR-h1,[g,h],RI+), θSGX(CR-h2,[g,h],RI+) and Θ,θ IR[g,h] then, we have

    [H(12,12)]22Θ(g+h2)θ(g+h2)CR1hghgΘ(γ)θ(γ)dγ+M(g,h)10dxH(x,x)H(1x,1x)+N(g,h)10dxH2(x,1x).

    Proof. Since ΘSGX(CR-h1,[g,h],RI+), θSGX(CR-h2,[g,h],RI+), we have

    Θ(g+h2)CRΘ(gx+(1x)h)H(12,12)+Θ(g(1x)+xh)H(12,12),
    θ(g+h2)CRθ(gx+(1x)h)H(12,12)+θ(g(1x)+xh)H(12,12).

    Then,

    Θ(g+h2)θ(g+h2)CR1[H(12,12)]2[Θ(gx+(1x)h)θ(gx+(1x)h)+Θ(g(1x)+xh)θ(g(1x)+xh)]+1[H(12,12)]2[Θ(gx+(1x)h)θ(g(1x)+xh)+Θ(g(1x)+xh)θ(gx+(1x)h)]+CR1[H(12,12)]2[Θ(gx+(1x)h)θ(gx+(1x)h)+Θ(g(1x)+(xh)θ(g(1x)+xh)]+1[H(12,12)]2[(Θ(g)H(x,1x)+θ(h)H(1x,x))(θ(h)H(1x,x)+θ(h)H(x,1x))]+[(Θ(g)H(1x,x)+Θ(h)H(x,1x))(θ(g)H(x,1x)+θ(h)H(1x,x))]CR1[H(12,12)]2[Θ(gx+(1x)h)θ(gx+(1x)h)+Θ(g(1x)+xh)θ(g(1x)+xh)]+1[H(12,12)]2[(2H(x,x)H(1x,1x))M(g,h)+(1H2(x,1x)+1H2(1x,x))N(g,h)].

    Integration over (0,1), we have

    10Θ(g+h2)θ(g+h2)dx=[10Θ_(g+h2)θ_(g+h2)dx,10¯Θ(g+h2)¯θ(g+h2)dx]=Θ(g+h2)θ(g+h2)dxCR2[H(12,12)]2[1hghgΘ(γ)θ(γ)dγ]+2[H(12,12)]2[M(g,h)101H(x,x)H(1x,1x)dx+N(g,h)101H2(x,1x)dx].

    Multiply both sides by [H(12,12)]22 above equation, we get the required result

    [H(12,12)]22Θ(g+h2)θ(g+h2)CR1hghgΘ(γ)θ(γ)dγ+M(g,h)10dxH(x,x)H(1x,1x)+N(g,h)10dxH2(x,1x).

    As a result, the proof is complete.

    Example 3.4. Consider [g,h]=[1,2], h1(x)=1x, h2(x)=14, x (0,1). Θ,θ:[g,h]RI+ be defined as

    Θ(γ)=[γ2,2γ2+1],θ(γ)=[γ,γ].

    Then,

    [H(12,12)]22Θ(g+h2)θ(g+h2)=18Θ(32)θ(32)=[3332,3332],
    1hghgΘ(γ)θ(γ)dγ=[154,9],
    M(g,h)10dxH(x,x)H(1x,1x)=(16)M(1,2)10x(1x)dx=[56,56],
    N(g,h)10dxH2(x,1x)=(16)N(1,2)10x2dx=[80,80].

    It follows that

    [3332,3332]CR[154,9]+[56,56]+[80,80]=[5294,145].

    This proves the above theorem. Next, we will develop the Jensen-type inequality for CR-(h1,h2)-GL functions.

    Theorem 4.1. Let uiR+, ji[g,h]. If h1,h2 is super multiplicative non-negative functions and if ΘSGX(CR-(h1,h2),[g,h],RI+). Then the inequality become as :

    Θ(1Ukki=1uiji)CRki=1[Θ(ji)H(uiUk,Uk1Ek)], (4.1)

    where Uk=ki=1ui

    Proof. When k=2, then (4.1) holds. Suppose that (4.1) is also valid for k1, then

    Θ(1Ukki=1uiji)=Θ(ukUkvk+k1i=1uiUkji)
    CRΘ(jk)h1(ukUk)h2(Uk1Uk)+Θ(k1i=1uiUkji)h1(Uk1Uk)h2(ukUk)
    CRΘ(jk)h1(ukUk)h2(Uk1Uk)+k1i=1[Θ(ji)H(uiUk,Uk2Uk1)]1h1(Uk1Uk)h2(ukUk)
    CRΘ(jk)h1(ukUk)h2(Uk1Uk)+k1i=1[Θ(ji)H(uiUk,Uk2Uk1)]
    CRki=1[Θ(ji)H(uiUk,Uk1Uk)].

    It follows from mathematical induction that the conclusion is correct.

    Remark 4.1.If h1(x)=h2(x)=1, Theorem 4.1 becomes result for CR- P-function:

    Θ(1Ukki=1uiji)CRki=1Θ(ji).

    If h1(x)=1h1(x), h2(x)=1h2(x) Theorem 4.1 becomes result for CR-(h1,h2)-convex function:

    Θ(1Ukki=1uiji)CRki=1H(uiUk,Uk1Uk)Θ(ji).

    If h1(x)=1x, h2(x)=1 Theorem 4.1 becomes result for CR-convex function:

    Θ(1Ukki=1uiji)CRki=1uiUkΘ(ji).

    If h1(x)=1h(x), h2(x)=1 Theorem 4.1 becomes result for CR-h-convex function:

    Θ(1Ukki=1uiji)CRki=1h(uiUk)Θ(ji).

    If h1(x)=h(x), h2(x)=1 Theorem 4.1 becomes result for CR-h-GL-function:

    Θ(1Ukki=1uiji)CRki=1[Θ(ji)h(uiUk)].

    If h1(x)=1(x)s, h2(x)=1 Theorem 4.1 becomes result for CR-s-convex function:

    η(1Ukki=1uiji)CRki=1(uiUk)sΘ(ji).

    A useful alternative for incorporating uncertainty into prediction processes is IVFS. The present study introduces the (h1,h2)-GL concept for IVFS using the CR-order relation. As a result of utilizing this new concept, we observe that the inequality terms derived from this class of convexity and pertaining to Cr-order relations give much more precise results than other partial order relations. These findings are generalized from the very recent results described in [37,42,43,45]. There are many new findings in this study that extend those already known. In addition, we provide some numerical examples to demonstrate the validity of our main conclusions. Future research could include determining equivalent inequalities for different types of convexity utilizing various fractional integral operators, including Katugampola, Riemann-Liouville and generalized K-fractional operators. The fact that these are the most active areas of study for integral inequalities will encourage many mathematicians to examine how different types of interval-valued analysis can be applied. We anticipate that other researchers working in a number of scientific fields will find this idea useful.

    The authors declare that there is no conflict of interest in publishing this paper.



    [1] A. Dash, A. Sahu, R. Shringi, J. Gamboa, M. Z. Afzal, M. I. Malik, et al., Airscript-creating documents in air, In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), 2017,908–913. https://doi.org/10.1109/ICDAR.2017.153
    [2] X. Lin, Y. Chen, X. Chang, X. Liu, X. Wang, Show: Smart handwriting on watches, In: Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 1 (2018), 151. https://doi.org/10.1145/3161412
    [3] The Bengali language and the history of its evolution, LingoStar, 2021. Available from: https://lingo-star.com/bengali-language/?v = 4326ce96e26c.
    [4] M. S. Alam, K. C. Kwon, M. A. Alam, M. Y. Abbass, S. M. Imtiaz, N. Kim, Trajectory-based air-writing recognition using deep neural network and depth sensor, Sensors, 20 (2020), 376. https://doi.org/10.3390/s20020376 doi: 10.3390/s20020376
    [5] O. De, P. Deb, S. Mukherjee, S. Nandy, T. Chakraborty, S. Saha, Computer vision based framework for digit recognition by hand gesture analysis, In: 2016 IEEE 7th annual information technology, electronics and mobile communication conference (IEMCON), 2016. https://doi.org/10.1109/IEMCON.2016.7746361
    [6] S. Poularakis, I. Katsavounidis, Low-complexity hand gesture recognition system for continuous streams of digits and letters, IEEE T. Cybernetics, 46 (2016), 2094–2108. https://doi.org/10.1109/TCYB.2015.2464195 doi: 10.1109/TCYB.2015.2464195
    [7] C. Qu, D. Zhang, J. Tian, Online kinect handwritten digit recognition based on dynamic time warping and support vector machine, J. Inform. Comput. Sci., 12 (2015), 413–422.
    [8] S. Mohammadi, R. Maleki, Air-writing recognition system for Persian numbers with a novel classifier, The Visual Comput., 36 (2020), 1001–1015. https://doi.org/10.1007/s00371-019-01717-3 doi: 10.1007/s00371-019-01717-3
    [9] P. Kumar, R. Saini, S. K. Behera, D. P. Dogra, P. P. Roy, Real-time recognition of sign language gestures and air-writing using leap motion, In: 2017 fifteenth IAPR international conference on machine vision applications (MVA), 2017. https://doi.org/10.23919/MVA.2017.7986825
    [10] P. Kumar, R. Saini, P. P. Roy, D. P. Dogra, Study of text segmentation and recognition using leap motion sensor. IEEE Sens. J., 17 (2017), 1293–1301. https://doi.org/10.1109/JSEN.2016.2643165 doi: 10.1109/JSEN.2016.2643165
    [11] X. Qu, W. Wang, K. Lu, J. Zhou, Data augmentation and directional feature maps extraction for in-air handwritten Chinese character recognition based on convolutional neural network, Pattern Recogn. Lett., 111 (2018), 9–15. https://doi.org/10.1016/j.patrec.2018.04.001 doi: 10.1016/j.patrec.2018.04.001
    [12] J. Gan, W. Wang, K. Lu, In-air handwritten Chinese text recognition with temporal convolutional recurrent network, Pattern Recogn., 97 (2020) 107025. https://doi.org/10.1016/j.patcog.2019.107025 doi: 10.1016/j.patcog.2019.107025
    [13] P. Wang, J. Lin, F. Wang, J. Xiu, Y. Lin, N. Yan, et al., A gesture air-writing tracking method that uses 24 GHz SIMO radar SoC, IEEE Access, 8 (2020), 152728–152741. https://doi.org/10.1109/ACCESS.2020.3017869 doi: 10.1109/ACCESS.2020.3017869
    [14] M. Arsalan, A. Santra, K. Bierzynski, V. Issakov, Air-writing with sparse network of radars using spatio-temporal learning, In: 2020 25th international conference on pattern recognition (ICPR), 2021. https://doi.org/10.1109/ICPR48806.2021.9413332
    [15] F. Khan, S. K. Leem, S. H. Cho, In-air continuous writing using UWB impulse radar sensors, IEEE Access, 8 (2020), 99302–99311. https://doi.org/10.1109/ACCESS.2020.2994281 doi: 10.1109/ACCESS.2020.2994281
    [16] M. K. Chakravarthi, R. K. Tiwari, S. Handa, Accelerometer based static gesture recognition and mobile monitoring system using neural networks, Procedia Comput. Sci., 70 (2015), 683–687. https://doi.org/10.1016/j.procs.2015.10.105 doi: 10.1016/j.procs.2015.10.105
    [17] Y. Yin, L. Xie, T. Gu, Y. Lu, S. Lu, AirContour: Building contour-based model for in-air writing gesture recognition, ACM T. Sensor. Network, 15 (2019), 44. https://doi.org/10.1145/3343855 doi: 10.1145/3343855
    [18] S. Xu, Y. Xue, Air-writing characters modelling and recognition on modified CHMM, In: 2016 IEEE international conference on systems, man, and cybernetics (SMC), 2016. https://doi.org/10.1109/SMC.2016.7844452
    [19] J. S. Wang, F. C. Chuang, An accelerometer-based digital pen with a trajectory recognition algorithm for handwritten digit and gesture recognition, IEEE T. Ind. Electron., 59 (2012), 2998–3007. https://doi.org/10.1109/TIE.2011.2167895 doi: 10.1109/TIE.2011.2167895
    [20] P. Roy, S. Ghosh, U. Pal, A CNN based framework for unistroke numeral recognition in air-writing, In: 2018 16th international conference on frontiers in handwriting recognition (ICFHR), 2018. https://doi.org/10.1109/ICFHR-2018.2018.00077
    [21] Coursera, Data processing and feature engineering with MATLAB, Available form: https://www.coursera.org/learn/feature-engineering-matlab.
    [22] Entropy calculation, information gain & decision tree learning, 2020. Available form: https://medium.com/analytics-vidhya/entropy-calculation-information-gain-decision-tree-learning-771325d16f
    [23] T. Giannakopoulos, A. Pikrakis, Introduction to audio analysis: A MATLAB® approach, 1st Eds, Cambridge, Massachusetts, US: Academic Press, 2014.
    [24] E. Scheirer, M. Slaney, Construction and evaluation of a robust multifeature speech/music discriminator, In: 1997 IEEE international conference on acoustics, speech, and signal processing, 1997. https://doi.org/10.1109/ICASSP.1997.596192
    [25] M. Müller, Fundamentals of music processing: Audio, analysis, algorithms, applications, Springer Cham, 2015. https://doi.org/10.1007/978-3-319-21945-5
    [26] M. A. Kader, M. A. Ullah, M. S. Islam, A real-time classification model for Bengali character recognition in air-writing, In: Computer vision and image analysis for industry 4.0, 1st Eds, Chapman and Hall/CRC, 2023.
    [27] Javatpoint, Regression vs. classification in machine learning, Available from https://www.javatpoint.com/regression-vs-classification-in-machine-learning.
    [28] A. Burkov, The hundred-page machine learning book, 1st Eds, Quebec City, QC, Canada: Andriy Burkov, 2019.
    [29] M. Mohammed, M. B. Khan, E. B. M. Bashier, Machine learning: Algorithms and applications, 1st Eds, Boca Raton: CRC Press, 2016. https://doi.org/10.1201/9781315371658
    [30] B. Dickson, Machine learning: What is dimensionality reduction? 2021. Available from: https://bdtechtalks.com/2021/05/13/machine-learning-dimensionality-reduction/.
    [31] S. Mukherjee, S. A. Ahmed, D. P. Dogra, S. Kar, P. P. Roy, Fingertip detection and tracking for recognition of air-writing in videos, Expert Syst. Appl., 136 (2019), 217–229. https://doi.org/10.1016/j.eswa.2019.06.034 doi: 10.1016/j.eswa.2019.06.034
    [32] V. Joseph, A. Talpade, N. Suvarna, Z. Mendonca, Visual gesture recognition for text writing in air, In: 2018 second international conference on intelligent computing and control systems (ICICCS), 2018. https://doi.org/10.1109/ICCONS.2018.8663176
    [33] J. Gan, W. Wang, K. Lu, A new perspective: Recognizing online handwritten Chinese characters via 1-dimensional CNN, Inform. Sci., 478 (2019), 375–390. https://doi.org/10.1016/j.ins.2018.11.035 doi: 10.1016/j.ins.2018.11.035
    [34] S. Hayakawa I. Goncharenko, Y. Gu, Air writing in Japanese: A CNN-based character recognition system using hand tracking, In: 2022 IEEE 4th global conference on life sciences and technologies (LifeTech), 2022. https://doi.org/10.1109/LifeTech53646.2022.9754825
    [35] C. Wang C. Y. Su, C. L. Lin, A novel recognition system for digits writing in the air using coordinated path ordering, In: HotMobile '15: Proceedings of the 16th international workshop on mobile computing systems and applications, 2015, 9–14. https://doi.org/10.1109/ICIIBMS.2015.7439500
    [36] C. Xu, P. H. Pathak, P. Mohapatra, Finger-writing with smartwatch: A case for finger and hand gesture recognition using smartwatch, In: Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications, 2015, 9-14. https://doi.org/10.1145/2699343.2699350
    [37] Y. Luo, J. Liu, S. Shimamoto, Wearable air-writing recognition system employing dynamic time warping, In: 2021 IEEE 18th annual consumer communications & networking conference (CCNC), 2021. https://doi.org/10.1109/CCNC49032.2021.9369458
    [38] Z. Fu, J. Xu, Z. Zhu, A. X. Liu, X. Sun, Writing in the air with WiFi signals for virtual reality devices IEEE T. Mobile Comput., 18 (2019), 473–484. https://doi.org/10.1109/TMC.2018.2831709 doi: 10.1109/TMC.2018.2831709
    [39] P. Kumar, R. Saini, P. P. Roy, U. Pal, A lexicon-free approach for 3D handwriting recognition using classifier combination, Pattern Recogn. Lett., 103 (2018), 1–7. https://doi.org/10.1016/j.patrec.2017.12.014 doi: 10.1016/j.patrec.2017.12.014
  • This article has been cited by:

    1. Parvaiz Ahmad Naik, Zohreh Eskandari, Mehmet Yavuz, Jian Zu, Complex dynamics of a discrete-time Bazykin–Berezovskaya prey-predator model with a strong Allee effect, 2022, 413, 03770427, 114401, 10.1016/j.cam.2022.114401
    2. Vaibhava Srivastava, Eric M. Takyi, Rana D. Parshad, The effect of "fear" on two species competition, 2023, 20, 1551-0018, 8814, 10.3934/mbe.2023388
    3. Uttam Ghosh, Ashraf Adnan Thirthar, Bapin Mondal, Prahlad Majumdar, Effect of Fear, Treatment, and Hunting Cooperation on an Eco-Epidemiological Model: Memory Effect in Terms of Fractional Derivative, 2022, 46, 1028-6276, 1541, 10.1007/s40995-022-01371-w
    4. 欣琦 王, Dynamics of Stochastic Predator-Prey Model with Fear Effect and Predator-Taxis Sensitivity, 2022, 12, 2160-7583, 1399, 10.12677/PM.2022.129153
    5. Ashraf Adnan Thirthar, Prabir Panja, Salam Jasim Majeed, Kottakkaran Sooppy Nisar, Dynamic interactions in a two-species model of the mammalian predator–prey system: The influence of Allee effects, prey refuge, water resources, and moonlights, 2024, 11, 26668181, 100865, 10.1016/j.padiff.2024.100865
    6. Isaac K. Adu, Fredrick A. Wireko, Mojeeb Al-R. El-N. Osman, Joshua Kiddy K. Asamoah, A fractional order Ebola transmission model for dogs and humans, 2024, 24, 24682276, e02230, 10.1016/j.sciaf.2024.e02230
    7. Godwin Onuche Acheneje, David Omale, William Atokolo, Bolarinwa Bolaji, Modeling the transmission dynamics of the co-infection of COVID-19 and Monkeypox diseases with optimal control strategies and cost–benefit analysis, 2024, 8, 27731863, 100130, 10.1016/j.fraope.2024.100130
    8. Mdi Begum Jeelani, Ghaliah Alhamzi, Mian Bahadur Zada, Muhammad Hassan, Study of fractional variable-order lymphatic filariasis infection model, 2024, 22, 2391-5471, 10.1515/phys-2023-0206
    9. Ashraf Adnan Thirthar, Nazmul Sk, Bapin Mondal, Manar A. Alqudah, Thabet Abdeljawad, Utilizing memory effects to enhance resilience in disease-driven prey-predator systems under the influence of global warming, 2023, 69, 1598-5865, 4617, 10.1007/s12190-023-01936-x
    10. Rafel Ibrahim Salih, Shireen Jawad, Kaushik Dehingia, Anusmita Das, The effect of a psychological scare on the dynamics of the tumor-immune interaction with optimal control strategy, 2024, 14, 2146-5703, 276, 10.11121/ijocta.1520
    11. Zhanhao Zhang, Yuan Tian, Dynamics of a nonlinear state-dependent feedback control ecological model with fear effect, 2024, 9, 2473-6988, 24271, 10.3934/math.20241181
    12. Rituparna Pakhira, Bapin Mondal, Ashraf Adnan Thirthar, Manar A. Alqudah, Thabet Abdeljawad, Developing a fuzzy logic-based carbon emission cost-incorporated inventory model with memory effects, 2024, 15, 20904479, 102746, 10.1016/j.asej.2024.102746
    13. Muhammad Usman, Muhammad Hamid, Dianchen Lu, Zhengdi Zhang, Innovative coupling of s-stage one-step and spectral methods for non-smooth solutions of nonlinear problems, 2024, 204, 01689274, 329, 10.1016/j.apnum.2024.05.026
    14. Alaa Khadim Mohammed, Salam Jasim Majeed, N. Aldahan, A.J. Ramadhan, Fear induce bistability in an ecoepidemiological model involving prey refuge and hunting cooperation, 2024, 97, 2117-4458, 00150, 10.1051/bioconf/20249700150
    15. Ashraf Adnan Thirthar, Zahraa Albatool Mahdi, Prabir Panja, Santanu Biswas, Thabet Abdeljawad, Mutualistic behaviour in an interaction model of small fish, remora and large fish, 2024, 0228-6203, 1, 10.1080/02286203.2024.2392218
    16. Yashra Javaid, Shireen Jawad, Rizwan Ahmed, Ali Hasan Ali, Badr Rashwani, Dynamic complexity of a discretized predator-prey system with Allee effect and herd behaviour, 2024, 32, 2769-0911, 10.1080/27690911.2024.2420953
    17. Ankur Jyoti Kashyap, Hemanta Kumar Sarmah, Complex Dynamics in a Predator–Prey Model with Fear Affected Transmission, 2024, 0971-3514, 10.1007/s12591-024-00698-7
    18. Bapin Mondal, Ashraf Adnan Thirthar, Nazmul Sk, Manar A. Alqudah, Thabet Abdeljawad, Complex dynamics in a two species system with Crowley–Martin response function: Role of cooperation, additional food and seasonal perturbations, 2024, 221, 03784754, 415, 10.1016/j.matcom.2024.03.015
    19. ASHRAF ADNAN THIRTHAR, PRABIR PANJA, AZIZ KHAN, MANAR A. ALQUDAH, THABET ABDELJAWAD, AN ECOSYSTEM MODEL WITH MEMORY EFFECT CONSIDERING GLOBAL WARMING PHENOMENA AND AN EXPONENTIAL FEAR FUNCTION, 2023, 31, 0218-348X, 10.1142/S0218348X2340162X
    20. Md Golam Mortuja, Mithilesh Kumar Chaube, Santosh Kumar, Dynamic analysis of a predator-prey model with Michaelis-Menten prey harvesting and hunting cooperation in predators, 2025, 1598-5865, 10.1007/s12190-025-02482-4
    21. Yan Li, Jianing Sun, Spatiotemporal dynamics of a delayed diffusive predator–prey model with hunting cooperation in predator and anti-predator behaviors in prey, 2025, 198, 09600779, 116561, 10.1016/j.chaos.2025.116561
    22. Sangeeta Saha, Swadesh Pal, Roderick Melnik, Nonlocal Cooperative Behavior, Psychological Effects, and Collective Decision‐Making: An Exemplification With Predator–Prey Models, 2025, 0170-4214, 10.1002/mma.11010
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1743) PDF downloads(73) Cited by(2)

Figures and Tables

Figures(22)  /  Tables(5)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog