This paper investigates ensemble feature selection using the q-rung orthopair hesitant fuzzy multi-criteria decision-making (MCDM) process. A novel algorithm is proposed for the study of ensemble feature selection and it is called as q-rung orthopair hesitant fuzzy MCDM extended to the Visekriterijumska optimizacija Ikompromisno Resenje (VIKOR) (q-ROHFS VIKOR). This is the first time in the literature an ensemble feature selection problem is modelled as a q-rung orthopair hesitant fuzzy MCDM extended to VIKOR technique. In the proposed method, every feature is ranked based on different available rankers and a preference matrix is obtained. In the sigmoidal transformation, the tuning parameter a plays a vital role in the fuzzification process. This tuning parameter reduces the computation run-time in the fuzzification process for different high-dimensional datasets that are considered in this paper. By using q-ROHFS VIKOR method, a score is assigned to each feature based on the values of the preference matrix. At last, an output rank vector is produced for all features from which the user can select the desired number of features. To prove the efficiency and optimality of the proposed method, the comparison with basic filter-based feature selections and ensemble feature selection using feature ranking strategy is obtained. The proposed method in this paper is superior and efficient than the ensemble methods based on the accuracy and F-score levels upto 0.96.

Large language models (LLMs) have significantly advanced computational biology by enabling the integration of molecular, protein, and natural language data to accelerate drug discovery. However, existing molecular captioning approaches often underutilize diverse molecular modalities and lack interpretability. In this study, we introduce XMolCap, a novel explainable molecular captioning framework that integrates molecular images, SMILES strings, and graph-based structures through a stacked multimodal fusion mechanism. The framework is built upon a BioT5-based encoder-decoder architecture, which serves as the backbone for extracting feature representations from SELFIES. By leveraging specialized models such as SwinOCSR, SciBERT, and GIN-MoMu, XMolCap effectively captures complementary information from each modality. Our model not only achieves state-of-the-art performance on two benchmark datasets (L+M-24 and ChEBI-20), outperforming several strong baselines, but also provides detailed, functional group-aware, and property-specific explanations through graph-based interpretation. XMolCap is publicly available at https://github.com/cbbl-skku-org/XMolCap/ for reproducibility and local deployment. We believe it holds strong potential for clinical and pharmaceutical applications by generating accurate, interpretable molecular descriptions that deepen our understanding of molecular properties and interactions.

This study aims to introduce a user-controlled chaotic fuzzy cellular neural network (FCNN) model that incorporates the effects of stochastic (external) disturbances and proportional delay. Theoretically, synchronization analysis is a considerably more effective approach to exploring the dynamical characteristics of the FCNN model with and without external control input. FCNN models with suitable control input help to possess the dynamical characteristics of traditional FCNN but with the potential to handle stochastic disturbances and other uncertainties. Besides, this study focuses on achieving synchronization within a prescribed-time synchronization (PTS). In this regard, a quantified intermittent control (QIC) scheme is proposed, which is a considerably simple but effective control for nonlinear models with stochastic disturbances. Along with QIC, the synchronization of uncontrolled (drive)-controlled (response) FCNN models can be guaranteed by employing the Lyapunov stability theory, Ito's calculus, and some inequalities. Mathematically, sufficient conditions that guarantee the global asymptotically stability of the error model will ensure the synchronization of the drive-response FCNN model. In terms of application, the drive-response model can be used as a cryptosystem (pseudorandom generator) that helps to encrypt the information from the sender side and to decrypt the information from the receiver side. Due to the chaos and randomness in the solutions, the proposed encryption/decryption algorithm is more effective and resistive than existing algorithms.

This article investigates finite-time stability (FTS) and finite-time contractive stability (FTCS) of discontinuous nonlinear fractional-order (FO) systems with time-delay and state-dependent delayed impulses. Lyapunov–Razumikhin (LR) conditions and impulse perturbations yield the essential and adequate conditions for stability criteria. Based on the main concept of this work, we investigate the stability analysis of retarded FO neural networks (NNs) with time delays, FO-delayed Cohen–Grossberg NNs, and FO-delayed bidirectional associative memory NNs within the framework of the Filippov map due to the fact that the neuron activation functions are discontinuous. The above NNs will verify the Lyapunov–Razumikhin conditions, and finally, three numerical simulations are provided to demonstrate the efficacy of this framework.

Neural networks (NNs) play a significant role in the machine learning and deep learning domains that include pattern recognition, computer-vision and so on. However, understanding the theoretical properties of neural networks will helps to deliver the user-desired performance in such practical applications. In the literature, the fundamental analysis of a NN, such as stability analysis, parameter sensitivity analysis can be performed by modeling the neuronal activities as differential equations. Through differential equations, the rate at which information is transmitted can be experimented along with various significant factors, such as time-delays during data transmission, switching parameters with respect to time, random disturbances caused by interruption of data blocks. The present study focuses on fundamental analysis of neuronal activities through differential model. Besides, the factors, such as time-delays, exogenous disturbances, and Markovian-jumping parameter (MJP) that has an ability to degrade the stable performance of the neuronal model is incorporated in the model. Distinct to the previous studies in stochastic neural networks, the study address the synchronization problem of stochastic neural networks (SNNs) with fractional-derivative of Brownian motion and event-triggered control scheme. Theoretically, due to nonlinearties, the Lyapunov stability theory is employed to derive the sufficient stability conditions that ensure the stable performance of SNNs. In this regard, looped-Lyapunov functional candidate is considered and corresponding linear matrix inequalitys (LMIs) are derived. Technically, a model of two neurons, three neurons, and four neurons are considered with the given factors to validate the proposed theoretical conditions and controller performance and their results are picturised.

The dynamical study of continuous-/discontinuous-time fractional-order neural networks (FONNs) has been thoroughly explored, and several publications have been made available. This study is designed to give an exhaustive review of the dynamical studies of multidimensional FONNs in continuous/discontinuous time, including Hopfield NNs (HNNs), Cohen-Grossberg NNs, and bidirectional associative memory NNs, and similar models are considered in real ( [Formula: see text]), complex ( [Formula: see text]), quaternion ( [Formula: see text]), and octonion ( [Formula: see text]) fields. Since, in practice, delays are unavoidable, theoretical findings from multidimensional FONNs with various types of delays are thoroughly evaluated. Some required and adequate stability and synchronization requirements are also mentioned for fractional-order NNs without delays.

This study deals with the investigation of the interval type-2 (IT2) fuzzy sampled-data (SD) stabilization problem based on nonlinearities and parameter uncertainties. For the first time, a memory SD control design involving a known signal transmission delay is adapted to address the stabilization problem for IT2 fuzzy systems. New polynomial-type Lyapunov–Krasovskii functionals (LKFs) associated with the state of constant signal transmission delay are introduced to achieve less conservative stability results. To bound the derivative of such LKFs, the Jacobi–Bessel inequality is introduced. Due to this, improved delay-dependent sufficient conditions can be obtained relating to set of linear matrix inequalities (LMIs). Thus, by solving LMIs using the LMI solver in MATLAB, the closed-loop system can be stabilized. The proposed method is verified in the simulation results with a nonlinear permanent-magnet vernier generator (PMVG)-based wind energy model and a Rossler model. Also, the applicability and superiority of the derived sufficient conditions are proved when compared with the existing results.

This article focuses on investigating the synchronization and position/force tracking performance of the multilateral teleoperation system with asymmetric time delays. First, the synchronization and tracking error signals are proposed to transform the nonlinear dynamic system into a closed-loop system governed by the Markovian jumping parameter. Because of the presence of time delays, the information regarding the system states might become unavailable. Owing to this, a feedback control based on the hidden Markov model is developed through which the information on the current state of the actual system can be accessed through a series of observations. Moreover, the forward and backward delays of the master and slave manipulators are assumed to be asymmetric and varying with time. For the stability analysis, the Lyapunov–Krasovskii technique has been adopted for which its derivatives are dealt by employing Jensens’ inequality and extended reciprocal convex matrix inequality. Finally, simulation results were provided to validate the proposed methodology guaranteeing the closed-loop system to be asymptotically stable.

Communication time delays in a bilateral teleoperation system often carries a stochastic nature, particularly when we have multiple masters or slaves. In this paper, we tackle the problem for a single-master multislave (SMMS) teleoperation system by assuming an asymmetric and semi-Markovian jump protocol for communication of the slaves with the master under time-varying transition rates. A nonlinear robust controller is designed for the system that guarantees its global robust ${H_{\infty}} $ stochastic stability in the sense of the Lyapunov theory. Employing the nonlinear feedback linearization technique, the dynamics of the closed-loop teleoperator is decoupled into two interconnected subsystems: 1) master-slave tracking dynamics (coordination) and 2) multislave synchronization dynamics. Employing an improved reciprocally convex combination technique, the stability analysis of the closed-loop teleoperator is conducted using the Lyapunov-Krasovskii methodology, and the stability conditions are expressed in the form of linear matrix inequalities that can be solved efficiently using numerical algorithms. Numerical studies and simulation results validate the effectiveness of the proposed controller design algorithm in both tracking and synchronization performance of the SMMS system, and robustly handling the stochastic and nondifferentiable nature of communication delays.

The main aim of this article is to design the observer-based event-triggered (ET) fuzzy integral sliding mode control (ETFISMC) for the generalized Takagi-Sugeno (T-S) fuzzy system which is formulated from a nonlinear system through blending the membership grades altogether. Distinct to the existing controller schemes, the proposed fuzzy integral sliding mode control (FISMC) scheme contains the ET condition which needs to be satisfied for the activation of the controller. Besides that, the network-induced communication constraints are considered into the derivation of sufficient conditions, and then the corresponding stabilization issue is attenuated in the sense of H∞control performance. In addition, the appropriate Lyapunov-Krasovskii functional (LKF) candidate is constructed and evaluated through convex matrix inequality approach that ensures the stable H∞performance of the closed-loop system in terms of solvable linear matrix inequalities (LMIs). Further, instead of considering the general problem for the validation of the proposed result, the stabilization problem of nonlinear chaotic permanent magnet synchronous generator (PMSG) model is taken into account for the validation of the proposed sufficient conditions. The purpose of considering the PMSG model is because of its significance in the wind energy conversion systems.

The stabilization of fault-tolerant control problem for the doubly fed induction generator (DFIG)-based wind turbine (WT) model has been investigated with stochastic actuator faults by incorporating a Takagi-Sugeno (T-S) fuzzy technique. To depict such stochastic actuator faults, the proposed model is incorporated with Markovian switching parameters for denoting various faulty modes. Besides, a novel fault-tolerant fuzzy switching control with sampled-data (SD) inputs has also been employed for eliminating the stochastic faults so that the stabilization of the system and an optimal H∞level could be ensured simultaneously. Furthermore, the stability conditions are derived by employing fuzzy Lyapunov-Krasovskii functionals (LKFs), whose matrices are membership dependent functions. Finally, the proposed control method for the stabilization problem is validated through a numerical simulation performed on the DFIG-based WT model.

In this study, we investigate the problem of multiple Mittag-Leffler stability analysis for fractional-order quaternion-valued neural networks (QVNNs) with impulses. Using the geometrical properties of activation functions and the Lipschitz condition, the existence of the equilibrium points is analyzed. In addition, the global Mittag-Leffler stability of multiple equilibrium points for the impulsive fractional-order QVNNs is investigated by employing the Lyapunov direct method. Finally, simulation is performed to illustrate the effectiveness and validity of the main results obtained.

This paper is mainly concerned with the synchronization problem of reaction-diffusion neural networks (RDNNs) with delays and its direct application in image secure communications. An adaptive control is designed without a sign function in which the controller gain matrix is a function of time. The synchronization criteria are established for an error model derived from master-slave models through solving the set of linear matrix inequalities derived by constructing the suitable novel Lyapunov-Krasovskii functional candidate, Green's formula, and Wirtinger's inequality. If the proposed sufficient conditions are satisfied, then the global asymptotic synchronization of the error model is guaranteed. The numerical illustrations are provided to demonstrate the validity of the derived synchronization criteria. In addition, the role of system parameters is picturized through the chaotic nature of RDNNs and those unprecedented solutions is utilized to promote better security of image transactions. As is evident, the enhancement of image encryption algorithm is designed with two levels, namely, image watermarking and diffusion process. The contributions of this paper are discussed as concluding remarks.

For the teleoperation system consisting of single-master and multislave (SMMS) manipulators, the Takagi-Sugeno (T-S) fuzzy technique has been adopted in this article to represent the nonlinear system and to approximate its nonlinearities effectively. With respect to the master-slave interactions, a decentralized leaderless communication topology has been considered for the control design with time delays in signal transmission. The corresponding forward and backward delays among the master and slaves were assumed to be asymmetric and varying with time. Moreover, to improve the tracking performance of the master and slaves, the decentralized controller is implemented with force feedback strategy to form the closed-loop teleoperation system. For the stability analysis, the fuzzy Lyapunov-Krasovskii functionals with some membership function dependent matrices were employed to guarantee the asymptotical stability of the proposed T-S fuzzy SMMS teleoperation system. By employing free-matrix-based inequality, some sufficient conditions were derived and expressed in terms of linear matrix inequalities. Finally, numerical simulations are performed on flexible joint manipulators to illustrate the validity of the proposed strategy.

In this article, the quasi-synchronization and Hopf bifurcation issues are investigated for the fractional-order quaternion-valued neural networks (QVNNs) with time delay in the presence of parameter mismatches. On the basis of noncommutativity property of quaternion multiplication results, the quaternion network has been split as four real-valued networks. A synchronization theorem for fractional-order QVNNs is derived by employing suitable Lyapunov functional candidate; furthermore, the bifurcation behavior of the hub-structured fractional-order QVNNs with time delay has been investigated. Finally, two numerical examples are provided to demonstrate the effectiveness of the theoretical results.

This paper aims to address the stabilization problem of permanent magnet synchronous motor (PMSM) based wind energy conversion system (WECS) through a novel adaptive fractional fuzzy integral sliding mode control scheme in contrast to the traditional integer order control schemes. The main objective of modeling the fractional order control for nonlinear PMSM is to enhance the convergence rate which is effectively better when compared to integer order control schemes. In addition, this paper intensively investigates the performance of fractional order controllers in both PMSM and surface-mounted PMSM-based WECS through analyzing the global stability of closed-loop system based on Lyapunov stability theory. In this regard, the nonlinear PMSM model is transformed into equivalent linear submodels through an effective Takagi-Sugeno fuzzy membership rules. Then, a novel automated (adaptive) controller is designed along with fractional sliding surface, which involves an integral term to control the considered PMSM. In general, adaptive controllers are much more effective than manual controllers. Further, the sufficient conditions are derived in terms of linear matrix inequalities via constructing the novel fractional fuzzy Lyapunov functional with quadratic terms, which guarantees the global stabilization of PMSM-based WECS. Overall performance and effectiveness of the proposed theoretical results are demonstrated through numerical simulations.

In this paper, synchronization of an inertial neural network with time-varying delays is investigated. Based on the variable transformation method, we transform the second-order differential equations into the first-order differential equations. Then, using suitable Lyapunov-Krasovskii functionals and Jensen's inequality, the synchronization criteria are established in terms of linear matrix inequalities. Moreover, a feedback controller is designed to attain synchronization between the master and slave models, and to ensure that the error model is globally asymptotically stable. Numerical examples and simulations are presented to indicate the effectiveness of the proposed method. Besides that, an image encryption algorithm is proposed based on the piecewise linear chaotic map and the chaotic inertial neural network. The chaotic signals obtained from the inertial neural network are utilized for the encryption process. Statistical analyses are provided to evaluate the effectiveness of the proposed encryption algorithm. The results ascertain that the proposed encryption algorithm is efficient and reliable for secure communication applications.

This paper presents a new design scheme for the passivity and passification of a class of memristor-based recurrent neural networks (MRNNs) with additive time-varying delays. The predictable assumptions on the boundedness and Lipschitz continuity of activation functions are formulated. The systems considered here are based on a different time-delay model suggested recently, which includes additive time-varying delay components in the state. The connection between the time-varying delay and its upper bound is considered when estimating the upper bound of the derivative of Lyapunov functional. It is recognized that the passivity condition can be expressed in a linear matrix inequality (LMI) format and by using characteristic function method. For state feedback passification, it is verified that it is apathetic to use immediate or delayed state feedback. By constructing a Lyapunov-Krasovskii functional and employing Jensen's inequality and reciprocal convex combination technique together with a tighter estimation of the upper bound of the cross-product terms derived from the derivatives of the Lyapunov functional, less conventional delay-dependent passivity criteria are established in terms of LMIs. Moreover, second-order reciprocally convex approach is employed for deriving the upper bound for terms with inverses of squared convex parameters. The model based on the memristor with additive time-varying delays widens the application scope for the design of neural networks. Finally, pertinent examples are given to show the advantages of the derived passivity criteria and the significant improvement of the theoretical approaches.

This paper deals with the problem of existence and uniform stability analysis of fractional-order complex-valued neural networks with constant time delays. Complex-valued recurrent neural networks is an extension of real-valued recurrent neural networks that includes complex-valued states, connection weights, or activation functions. This paper explains sufficient condition for the existence and uniform stability analysis of such networks. Three numerical simulations are delineated to substantiate the effectiveness of the theoretical results.

This paper addresses the problem of exponential synchronization of neural networks with time-varying delays. A sampled-data controller with stochastically varying sampling intervals is considered. The novelty of this paper lies in the fact that the control packet loss from the controller to the actuator is considered, which may occur in many real-world situations. Sufficient conditions for the exponential synchronization in the mean square sense are derived in terms of linear matrix inequalities (LMIs) by constructing a proper Lyapunov-Krasovskii functional that involves more information about the delay bounds and by employing some inequality techniques. Moreover, the obtained LMIs can be easily checked for their feasibility through any of the available MATLAB tool boxes. Numerical examples are provided to validate the theoretical results.