In biomedical applications, the regulation of biological systems that contain Genetic Regulatory Networks (GRNs), protein formation, and pancreas structure is crucial for maintaining health. Any significant deviations...
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In biomedical applications, the regulation of biological systems that contain Genetic Regulatory Networks (GRNs), protein formation, and pancreas structure is crucial for maintaining health. Any significant deviations from the set values of these systems can lead to serious diseases and potentially death. Hence, there's a pressing need for effective control systems to manage and regulate these biological processes. Therefore, a novel control approach called Normalized Versatile Fuzzy Fractional-Order Proportional-Integral-Derivative (NV-FFOPID) based controller is suggested to address the challenges of regulating parameters within organic systems. This is designed to control the variables of biological systems like nutrient synthesis, pancreatic function, and GRNs within a Linear-Time Invariant (LTI) model. The stability of the proposed control system is analyzed using appropriate stability theories to ensure its robustness and reliability. To implement and validate the proposed approach, a nonlinear dynamic model of the organic system is developed using Simulink / MATLAB. The efficacy of the NV-FFOPID controller is evaluated by comparing it with numerous advanced control methods commonly taken in biomedical applications. Performance metrics such as convergence time, settling time, overshoot, rise time, and overall system error are used for comparison. The results indicate that the projected controller achieves steady-state conditions with minimal error of 1.2725% in the system. Additionally, it exhibits a lower settling time of 3.7 s, an overshoot having 0.5, a convergence time of 10 s, and a rise time of 1.985 s compared to alternative methods. These findings suggest that the developed NV-FFOPID controller effectively regulates parameter variations in biological systems, thereby offering significant advantages in addressing complex biomedical problems. The proposed approach holds promise for improving the management and control of crucial biological processes, con
One of the most significant and difficult challenges in solar systems is determining the maximum power point. This process becomes more difficult when the array of solar modules is subjected to non-uniform shade. A un...
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This paper describes a dual-layer adaptive sliding mode control for a ball-on-plate system propelled by shape memory alloy (SMA) wire actuators. Conventionally, hydraulic, pneumatic, and electric actuators are used to...
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This paper presents an advanced control strategy that uses the neural network (NN) predictive controller to govern the dynamics of a Flow loop pilot plant. The set point tracking using NN Predictive controller and con...
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This study proposes a low-cost camera and LiDAR sensor fusion method to improve object recognition and estimation performance in autonomous and robot systems. We first calibrate the camera and LiDAR sensor to implemen...
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This paper proposes a hybrid Modified Coronavirus Herd Immunity Aquila Optimization Algorithm (MCHIAO) that compiles the Enhanced Coronavirus Herd Immunity Optimizer (ECHIO) algorithm and Aquila Optimizer (AO). As one...
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This paper proposes a hybrid Modified Coronavirus Herd Immunity Aquila Optimization Algorithm (MCHIAO) that compiles the Enhanced Coronavirus Herd Immunity Optimizer (ECHIO) algorithm and Aquila Optimizer (AO). As one of the competitive human-based optimization algorithms, the Coronavirus Herd Immunity Optimizer (CHIO) exceeds some other biological-inspired algorithms. Compared to other optimization algorithms, CHIO showed good results. However, CHIO gets confined to local optima, and the accuracy of large-scale global optimization problems is decreased. On the other hand, although AO has significant local exploitation capabilities, its global exploration capabilities are insufficient. Subsequently, a novel metaheuristic optimizer, Modified Coronavirus Herd Immunity Aquila Optimizer (MCHIAO), is presented to overcome these restrictions and adapt it to solve feature selection challenges. In this paper, MCHIAO is proposed with three main enhancements to overcome these issues and reach higher optimal results which are cases categorizing, enhancing the new genes’ value equation using the chaotic system as inspired by the chaotic behavior of the coronavirus and generating a new formula to switch between expanded and narrowed exploitation. MCHIAO demonstrates it’s worth contra ten well-known state-of-the-art optimization algorithms (GOA, MFO, MPA, GWO, HHO, SSA, WOA, IAO, NOA, NGO) in addition to AO and CHIO. Friedman average rank and Wilcoxon statistical analysis (p-value) are conducted on all state-of-the-art algorithms testing 23 benchmark functions. Wilcoxon test and Friedman are conducted as well on the 29 CEC2017 functions. Moreover, some statistical tests are conducted on the 10 CEC2019 benchmark functions. Six real-world problems are used to validate the proposed MCHIAO against the same twelve state-of-the-art algorithms. On classical functions, including 24 unimodal and 44 multimodal functions, respectively, the exploitative and explorative behavior of the hybrid
Diabetes mellitus is one of the most common diseases affecting patients of different ages. Diabetes can be controlled if diagnosed as early as possible. One of the serious complications of diabetes affecting the retin...
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In this paper, the mechanism design of an electric two-finger gripper that grabs objects with a length of up to 150 mm by using electric force is presented. The mechanical device of the electric gripper operates an ac...
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Disease outbreaks are nowadays a critical issue despite the development and rapid growth of technology. One of the major challenges facing healthcare professionals and healthcare industries is disease prevention and c...
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Harris Hawks optimization (HHO) algorithm was a powerful metaheuristic algorithm for solving complex problems. However, HHO could easily fall within the local minimum. In this paper, we proposed an improved Harris Haw...
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