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Cardiovascular disease (CVD) detection using deep learning (DL) includes leveraging advanced neural network (NN) models to analyze medical data, namely imaging, electrocardiograms (ECGs), and patient records. This study introduces a new Nature Inspired Metaheuristic Algorithm with Deep Learning for Healthcare Data Analysis (NIMADL-HDA) technique. The NIMADL-HDA technique examines healthcare data for the recognition and classification of CVD. In the presented NIMADL-HDA technique, Z-score normalization was initially performed to normalize the input data. In addition, the NIMADL-HDA method made use of a barnacle mating optimizer (BMO) for the feature selection (FS) process. For healthcare data classification, a convolutional long short-term memory (CLSTM) model was employed. At last, the prairie dog optimization (PDO) algorithm was exploited for the optimal hyperparameter selection procedure. The experimentation outcome analysis of the NIMADL-HDA technique was verified on a benchmark healthcare dataset. The obtained outcomes stated that the NIMADL-HDA technique reached an effectual performance over other models. The NIMADL-HDA method provides an adaptable and sophisticated solution for healthcare data analysis, aiming to improve the interpretability and accuracy of the algorithm in terms of medical applications.

Digital transactions relying on credit cards are gradually improving in recent days due to their convenience. Due to the tremendous growth of e-services (e.g., mobile payments, e-commerce, and e-finance) and the promotion of credit cards, fraudulent transaction counts are rapidly increasing. Machine learning (ML) is crucial in investigating customer data for detecting and preventing fraud. Conversely, the advent of irrelevant and redundant features in most real-time credit card details reduces the execution of ML techniques. The feature selection (FS) approach's purpose is to detect the most prominent attributes required for developing an effective ML approach, making sure that the classification and computational complexity are improved and decreased, respectively. Therefore, this study presents an evolutionary computing with fuzzy autoencoder based data analytics for credit card fraud detection (ECFAE-CCFD) technique. The purpose of the ECFAE-CCFD technique is to recognize the presence of credit card fraud (CCF) in real time. To achieve this, the ECFAE-CCFD technique performs data normalization in the earlier stage. For selecting features, the ECFAE-CCFD technique applies the dandelion optimization-based feature selection (DO-FS) technique. Moreover, the fuzzy autoencoder (FAE) approach can be exploited for the recognition and classification of CCF. FAE is a category of artificial neural network (ANN) designed for unsupervised learning that leverages fuzzy logic (FL) principles to enhance the representation and reconstruction of input data. An improved billiard optimization algorithm (IBOA) could be implemented for the optimum selection of the parameters based on the FAE algorithm to improve the classification performance. The simulation outcomes of the ECFAE-CCFD algorithm are examined on the benchmark open-access database. The values display the excellent performance of the ECFAE-CCFD method with respect to various measures.

The subject of this research is a coupled system of nonlinear viscoelastic wave equations with distributed delay components, infinite memory and Balakrishnan-Taylor damping. Assume the kernels $ g_{i} :{\bf R}_{+}\rightarrow {\bf R}_{+} $ holds true the below $ g_{i}'(t)\leq-\zeta_{i}(t)G_{i}(g_{i}(t)), \quad \forall t\in {\bf R}_{+}, \quad {\rm{for}} \quad i = 1, 2, $ in which $ \zeta_{i} $ and $ G_{i} $ are functions. We demonstrate the stability of the system under this highly generic assumptions on the behaviour of $ g_i $ at infinity and by dropping the boundedness assumptions in the historical data.

In the literature, different approaches that are employed in designing automatic voltage regulators (AVRs) usually model the AVR as a single-input-single-output system, where the input is the generator reference voltage, and the output is the generator voltage. Alternately, it could be thought of as a double-input, single-output system, with the excitation voltage change serving as the additional input. In this paper, unlike in the existing literature, we designed the AVR system as a sextuple-input single-output (6ISO) system. The inputs in the model include the generator reference voltage, regulator signal change, exciter signal change, amplifier signal change, generator output signal change, and the sensor signal change. We also compared the generator voltage responses for various structural configurations and regulator parameter choices reported in the literature. The effectiveness of numerous controllers is investigated; the proportional, integral and differential (PID) controller, the PID with second-order derivative (PIDD2) controller, and the fractional order PID (FOPID) controller are the most prevalent types of controllers. The findings reveal that the regulator signal change and the generator output signal change significantly impact the generator voltage. Based on these findings, we propose a new approach to design the regulator parameter to enhance the response to generator reference voltage changes. This approach takes into consideration changes in the generator reference voltage as well as the regulator signal. We calculate the regulator settings using a cutting-edge hybrid technique called the Particle Swarm Optimization African Vultures Optimization algorithm (PSO–AVOA). The effectiveness of the regulator design technique and the proposed optimization algorithm are demonstrated.

In this paper, an Artificial Neural Network (ANN) for accurate estimation of the speed and flux for induction motor (IM) drives has been presented for industrial applications such as electric vehicles (EVs). Two ANN estimators have been designed, one for the rotor speed estimation and the other for the stator and rotor flux estimation. The input training data has been collected based on the currents and voltage data, while the output training data of the speed and stator and rotor fluxes has been established based on the measured speed and flux estimator-based mathematical model of the IM. The designed ANN estimators can overcome the problem of the parameter’s variations and drift integration problems. Matlab/Simulink has been used to develop and test the ANN estimators. The results prove the ANN estimators’ effectiveness under various operation conditions.

This paper studies the system of coupled nondegenerate viscoelastic Kirchhoff equations with a distributed delay. By using the energy method and Faedo-Galerkin method, we prove the global existence of solutions. Furthermore, we prove the exponential stability result.

In this study, three-dimensional (3D) turbulent core annular flow (CAF) regime is investigated numerically. The proposed model is based on the 3D Reynolds average Navier–Stokes (RANS) equations combined with a pure convective transport equation of the volume of fluid (VOF) to predict the interface between the oil and water phases. The k-ω turbulence model is adopted to better reproduce the oil and water flow characteristics. The two-phase (CAF) regime can be predicted by two inlet configurations: the T-junction (3D-T) and the straight pipe (3D-S). These two configurations are simulated and compared for pipe diameter D=0.026 m and pipe length L=4 m. For these two inlet configurations, the computed mixture velocity profile and the water volume fraction at a test section z=100 D were compared to experimental measurements. The 3D-T configuration gives more appropriate results. The 3D-S slightly overestimates the maximum velocity at the test section and the lower and upper water layer of the (CAF) flow is shifted in the upward direction. For the 3D-T, the relative error in the pressure drop is 3.3%. However, for the 3D-S, this error is 13.0%.

In this paper, we study the number of limit cycles of a new class of polynomial differential systems, which is an extended work of two families of differential systems in systems considered earlier. We obtain the maximum number of limit cycles that bifurcate from the periodic orbits of a center using the averaging theory of first and second order.

Studies of modified Korteweg-de Vries-type equations are of considerable mathematical interest due to the importance of their applications in various branches of mechanics and physics. In this article, using trilinear estimate in Bourgain spaces, we show the local well-posedness of the initial value problem associated with a coupled system consisting of modified Korteweg-de Vries equations for given data. Furthermore, we prove that the unique solution belongs to Gevrey space G σ × G σ in x and G 3 σ × G 3 σ in t. This article is a continuation of recent studies reflected.