Accurate temperature regulation of jacketed continuous stirred tank reactors (CSTRs) remains a challenging task due to strong nonlinearities, tight coupling between mass and energy balances, and sensitivity to disturbances and operating-point variations. In this study, a novel augmented proportional–integral–derivative (PID) controller incorporating a hyperbolic tangent nonlinearity (APID-T) is proposed for robust temperature control of an exothermic CSTR. The controller structure extends the classical PID framework by embedding a bounded nonlinear term that enhances transient shaping and robustness while preserving simplicity and practical implementability. The tuning of the APID-T parameters is formulated as a constrained nonlinear optimization problem, where a composite objective function combining normalized overshoot and integral squared error is minimized. To solve this problem efficiently, the recently developed Schrödinger optimizer (SRA) is employed, exploiting its balanced exploration–exploitation mechanism. A detailed nonlinear dynamic model of the jacketed CSTR is considered, and stability characteristics around the nominal operating point are examined to ensure meaningful closed-loop operation. The proposed SRA-based APID-T design is extensively evaluated through comparative simulations against several state-of-the-art metaheuristic optimizers and alternative controller structures, including PI, PID with filter, two-degree-of-freedom PID, and fractional-order PID controllers. Performance is assessed using statistical indicators, convergence behavior, and time-domain response metrics under identical optimization settings. In addition, widely used error performance criteria, including the integral squared error, integral time absolute error, and integral time squared error, are computed to provide a comprehensive quantitative assessment of the tracking performance. The results demonstrate that the SRA-tuned APID-T controller consistently achieves lower objective-function values, faster convergence, reduced settling time, and significantly smaller overshoot compared with the competing approaches. Furthermore, frequency-domain analysis based on the Bode characteristics of the linearized open-loop system confirms favorable stability margins, supporting the robustness of the proposed control structure. Additional stability and robustness evaluations are conducted under practical non-ideal conditions, including feed-temperature disturbances, measurement noise, and multiple setpoint variations, where the controller maintains stable and accurate temperature regulation across the considered operating scenarios.

Dünya çapında pirinç tüketimi göz önüne alındığında pirincin önemli bir yere sahip olduğu görülür. Çeltik bitkisi, buğdaygiller ailesinden mısır ve buğdaydan sonra en fazla ekimi yapılan bitkidir. Tarım alanındaki son araştırma konularından birisi de, bir bitkinin yaprak görüntülerinden hastalıkların tanınması veya sınıflandırılmasıdır. Yaprak görüntülerinden çeltik hastalığının otomatik bir şekilde teşhisi geliştirme aşamasında olan bir araştırma konusudur. Bu gelişime katkı sağlamak için farklı öğrenme yöntemleri kullanılarak hastalığın erken teşhisi için önemli çalışmalar yapılmaktadır. Önceki çalışmalarda, hastalıkları tespit etmek için bitki yaprak görüntüleri üzerinde standart öznitelik çıkarma yöntemleri kullanılmıştır. Bu çalışmada ise temel olarak hastalıkları tespit etmek için bir makine öğrenme yöntemi olan derin öğrenme modelleri kullanılmıştır. Bu çalışmada derin Evrişimli Sinir Ağı (ESA) kullanılarak çeltik bitkisinin hastalıklı olup olmadığı tespit edilmiştir. Çalışmada kullanılan 5000 adet çeltik bitkisi yaprağına ait veri seti Kaggle sitesinden alınmıştır. Hastalığın tespiti için çeltik bitkisine ait üç hastalık (BrowSpot, LeafBlast ve Hispa) ve sağlıklı olmak üzere toplam iki çeşit sınıflandırma yapılmıştır. Çeltik bitkisinin hastalığının tespiti için kullanılan ESA'nın hiper-parametrelerinde değişiklik yapılarak %91,54’lük bir başarı oranı elde edilmiştir. Veri artırma yöntemiyle veri setindeki 5000 görüntüden 8000 çeltik bitki yaprağı görüntüsü elde edilmiş ve ESA için bu görüntüler üzerinden yapılan eğitimden sonra %94,87’lik bir başarı oranı elde edilmiştir. Kullanılan veri setindeki görüntüler üzerinden ön işlem yapıldıktan sonra ESA ile eğitim işleminden sonra %97,57’lik bir başarı oranı elde edilmiştir. Çeltik bitkisi yaprak görüntülerinden hastalık tespiti için elde edilen başarı sonucu, yöntemin uygulanabilirliğini göstermektedir.

Artificial intelligence has been developing day by day and has started to take a more prominent place in human life. As computer technologies advance, research on artificial intelligence has also increased in this direction. One of the main goals of this research is to examine how real problems in human life can be solved using artificial intelligence-based deep learning, and to present a case study. Poisoning from the consumption of poisonous fungi is a common problem worldwide. To prevent these poisonings, a mobile application has been developed using Convolutional Neural Networks (CNNs) and transfer learning to detect the species of fungus. The application informs the user about the type of fungus, whether it is poisonous or non-toxic, and whether it is safe to eat. The aim of this study is to reduce poisoning events caused by incorrect fungus detection and to facilitate the identification of fungus species. The developed deep learning model is integrated into a mobile application developed by Flutter that is a mobile application development framework, which enable the detection of fungus species from images taken from the camera or selected from the gallery. CNNs and the EfficientNetV2 model, a transfer learning method, were used. By using these two methods together, the classification accuracy rate for 77 fungus species was obtained as 97%.

Among the classical testbeds in control engineering, magnetic levitation systems occupy a prominent place owing to their strongly nonlinear character and intrinsic open-loop instability. Stabilizing a freely suspended ferromagnetic object requires careful regulation of the electromagnetic force, which makes these systems particularly demanding for controller design. To address this challenge, the present work introduces a sigmoid-activated two-degree-of-freedom PID (SA-2DOF-PID) controller, wherein a sigmoid-based nonlinear term is embedded within a standard 2DOF-PID architecture. This integration yields smoother actuation signals and increases overall closed-loop robustness. Controller parameter selection is carried out via the dholes-inspired optimization (DIO) algorithm, a recently proposed nature-inspired method. A multi-objective fitness function incorporating peak overshoot, settling time, and cumulative tracking error guides the tuning procedure. Simulation experiments performed under both nominal plant conditions and deliberate parametric perturbations serve to assess the proposed scheme. Quantitative comparisons against secant optimization algorithm (SOA)-, honey badger algorithm (HBA)-, and particle swarm optimization (PSO)-based controllers reveal that the DIO-tuned SA-2DOF-PID achieves markedly lower overshoot, quicker settling, and higher steady-state precision. Robustness evaluations involving intentional variations in system parameters further confirm that the closed-loop behavior remains well-damped with only marginal deterioration. Additional benchmarking against whale optimization algorithm combined with simulated annealing (WOA-SA)-based proportional–integral–derivative (PID), opposition-based artificial electric field algorithm (ObAEF)-based fractional-order PID (FOPID), and manta ray foraging optimization (MRFO)-based real PID with second-order derivative (RPIDD²) controllers corroborates the practical advantages of the proposed approach. Taken together, the study demonstrates that the SA-2DOF-PID framework tuned by DIO constitutes an effective, structurally lean, and computationally tractable strategy for managing nonlinear and open-loop unstable plants.

Accurate temperature regulation is essential for ensuring product quality, operational safety, and energy efficiency in industrial electric furnace systems. However, the inherent thermal inertia, time-delay effects, and nonlinear dynamics of furnace processes often make precise temperature control a challenging task. Motivated by these challenges, this study proposes an optimization-based control framework aimed at improving the temperature regulation performance of electric furnace systems. The proposed approach integrates a proportional–integral–derivative (PID) controller with the recently developed gazelle optimization algorithm (GOA) for automatic tuning of the controller parameters. First, a mathematical model of the electric furnace is established to describe the dynamic relationship between the control input and the furnace temperature output. Based on this model, a PID controller is implemented to regulate the furnace temperature. The parameters of the PID controller are then optimized using GOA, a nature-inspired metaheuristic algorithm that mimics the adaptive predator–prey survival strategies observed in gazelle herds. In order to achieve a balanced improvement in both steady-state and transient performance, a composite objective function is introduced. The proposed performance index combines the integral of absolute error with additional transient performance indicators related to maximum overshoot and settling time. The effectiveness of the proposed GOA-based tuning framework is evaluated through extensive simulation studies and statistical analyses conducted over multiple independent optimization runs. The results demonstrate stable convergence behavior, with the optimization process achieving a minimum objective value of 2.4251, a maximum value of 2.5347, and an average value of 2.4674 across 25 runs. The optimized control system exhibits improved dynamic characteristics, including a rise time of 1.8509 s, a settling time of 3.6834 s, and a low overshoot of 1.5104%. To further assess its effectiveness, the proposed GOA–PID control strategy is compared with several widely used controller tuning methods reported in the literature, including genetic algorithm, Ziegler–Nichols, Cohen–Coon, Nelder–Mead, and direct synthesis approaches. Comparative results indicate that the proposed method achieves a superior balance between response speed, stability, and temperature tracking accuracy.