Micro particles have the potentials to be used for many medical purposes in-side the human body such as drug delivery and other operations. This paper attempts to provide a thorough comparison between five meta-heuristic search algorithms: Arithmetic optimization algorithm (AOA), Artificial Gorilla troop’s optimization (GTO), Seagull optimization algorithm (SOA), Parasitism-predation Algorithm (PPA), and hybrid between AOA and GTO (HAOAGTO). These approaches were used to calculate the PID controller optimal indicators with the application of different functions, including Integral Absolute Error (IAE), Integral of Time Multiplied by Square Error (ITSE), Integral Square Time multiplied square Error (ISTES), Integral Square Error (ISE), Integral of Square Time multiplied by square Error ( (ISTSE), and Integral of Time multiplied by Absolute Error (ITAE). Every method of controlling was presented in a MATLAB Simulink numerical model. It is observed that the PPA technique achieves the highest values of best fitness value for simulation results among other control approaches, while the HAOAGTO approach reduces the best fitness function compared to other optimization techniques used. The results indicate that HAOAGTO is the best method among all approaches and that ISTES is the best choice of PID for optimizing the controlling parameters.

Cardiovascular disease (CVD) is one of the foremost reasons behind the death of people worldwide. Prevention and early diagnosis are the only ways to control its progression and onset. Thus, there is an urgent need for a detection model comprising intelligent technologies, including Machine Learning (ML) and deep learning, to predict the future state of an individual suffering from cardiovascular disease by effectively analyzing patient data. This study aims to propose a hybrid model that provides a deep insight into the data under consideration to enhance model accuracy for effectively detecting cardiovascular disease. This current research proposes a hybrid model comprising four stages. In the first stage of the proposed hybrid model, the data imbalance problem is solved using a hybrid sampling technique named Synthetic Minority Oversampling Technique-Edited Nearest Neighbors Rule. In the second stage, the Chi-square is applied as a feature selection method to select the highly relevant features from the records of 1190 with 11 clinical features, curated by combining the 5 most popular datasets, including Long Beach VA, Hungarian, Switzerland, and Statlog (Heart). In the third stage, the preprocessed dataset is passed to a stacking ensemble model comprising three base learners: Random Forest Tree (RFT), K-Nearest Neighbor (K-NN), and AdaBoost classifier and one meta-learner: Logistic Regression (LR), optimized with Grid Search Cross-Validation (GSCV) optimization approach, whose performance is evaluated against individual classifier. In the fourth stage, the performance is evaluated in terms of accuracy, sensitivity, specificity, F1 score, and ROC_AUC score.. The comparative results prove that the proposed hybrid model scored the highest accuracy of 97.8%, 96.15% sensitivity, and 96.75% specificity and 98.6% ROC_AUC score when compared with the existing techniques and models after applying the SMOTE–ENN (for data balancing) and Chi-square (for feature selection) methods for the efficient detection of cardiovascular disease. The implementation results demonstrate that the suggested hybrid model may accurately identify cardiovascular disease among patients. It facilitates the application of robust clinical treatment strategies.

Parking space management has become a critical challenge in urban areas due to increasing vehicle numbers and limited parking infrastructure. This paper presents a comprehensive study of machine learning (ML) models in IoT‐enabled environments focusing on proposing an ML‐based model for predicting available parking space. The study evaluates the performance of various models including K‐nearest neighbors (KNNs), support vector machines (SVMs), random forest (RF), decision tree (DT), logistic regression (LR), and Naïve Bayes (NB) based on “precision, recall, accuracy, and F1‐score performance metrics”. The results obtained by implementing ML models on the data with 65% and 85% threshold values are compared to draw meaningful conclusions regarding their performance in predicting parking space availability. Among the evaluated models, random forest (RF) demonstrates superior performance with high precision, recall, accuracy, and F1‐score values. It showcases its effectiveness in accurately predicting parking space availability in the IoT‐enabled environment. On the other hand, models such as K‐nearest neighbors (KNNs), decision tree (DT), logistic regression (LR), and Naïve Bayes (NB) show relatively lower performance in complex parking scenarios. The paper concludes that the use of advanced predictive models, particularly random forest, significantly enhances the accuracy and reliability of IoT‐enabled parking management systems and also reduces the waiting time of the vehicles, leading to more efficient resource utilization, reduced traffic congestion in real‐time scenarios, and better user satisfaction in the IoT‐enabled environment.

The integration of nonconventional energy sources such as solar, wind, and fuel cells into electrical power networks introduces significant challenges in maintaining frequency stability and consistent tie-line power flows. These fluctuations can adversely affect the quality and reliability of power supplied to consumers. This paper addresses this issue by proposing a Proportional–Integral–Derivative (PID) controller optimized through a hybrid Particle Swarm Optimization–Artificial Hummingbird Algorithm (PSO-AHA) approach. The PID controller is tuned using the Integral Time Absolute Error (ITAE) as a fitness function to enhance control performance. The PSO-AHA-PID controller’s effectiveness is evaluated in two networks: a two-area thermal tie-line interconnected power system (IPS) and a one-area multi-source power network incorporating thermal, solar, wind, and fuel cell sources. Comparative analyses under various operational conditions, including parameter variations and load changes, demonstrate the superior performance of the PSO-AHA-PID controller over the conventional PSO-PID controller. Statistical results indicate that in the one-area multi-source network, the PSO-AHA-PID controller achieves a 76.6% reduction in overshoot, an 88.9% reduction in undershoot, and a 97.5% reduction in settling time compared to the PSO-PID controller. In the dual-area system, the PSO-AHA-PID controller reduces the overshoot by 75.2%, reduces the undershoot by 85.7%, and improves the fall time by 71.6%. These improvements provide a robust and reliable solution for enhancing the stability of interconnected power systems in the presence of diverse and variable energy sources.

This study presents a comprehensive comparison of battery-only, passive, and semi-active hybrid energy storage system (HESS) topologies for electric vehicle (EV) applications. Despite numerous studies on HESS topologies for EVs, there remains a lack of consensus regarding the optimal topology, with limited attempts to address this gap through comprehensive comparisons. Previous research has focused on comparing different semi-active topologies through simulations, neglecting passive approaches and employing best-case scenarios for the energy management system (EMS), disregarding widely used rule-based power follower strategies. The present study aims to address the research gap by conducting a comprehensive comparison of battery-only, passive, and semi-active topologies for a realistic EV application case, focusing on their ability to enhance DC bus voltage stability, RMS battery current, and maximum battery current. The performance of these topologies is evaluated through simulations and experimental validation, with the system under investigation based on the parameters of an electrically converted vehicle. Results show that while the passive HESS topology delivers excellent RMS battery current performance, comparable to the semi-active SC/battery system, it falls short in reducing the maximum battery peak current where the semi-active topologies excel. Additionally, both passive and semi-active SC/battery topologies significantly improve the standard deviation and variation of the DC bus voltage. This study highlights the importance of topology selection in reducing battery degradation in EVs, contributing to the understanding of topology-dependent characteristics, power flow control, discharge rates of the battery pack, and DC bus voltage stability in HESS for EVs.

In this paper, we describe numerical algorithms that can be used to solve the coupled fluid–structure interaction with incompressible viscous fluid. We characterized the fluid force effects over a circular cylinder attached to an elastic rod. To resolve the coupled governing equations, the velocity profiles are approximated with quadratic polynomials (P2), while the pressure is estimated using a different space with linear polynomials (P1) using the finite element method. The discrete systems are solved using a Newton method that employs split differences to explicitly generate the Jacobian matrices. This approach enables us to address the nonlinearities at each time step and apply direct, steady techniques. The inquiry’s findings reveal a significant relationship between the behaviors related to steady streaming and the flow regimes associated with vortex shedding. In addition, at Re = 100, steady flow is characterized by minimal displacement and negligible oscillations. In contrast, the occurrence of larger oscillations at Reynolds numbers exceeding 140 suggests an increase in flow instability.