The novelty of this paper is the usage of a time-varying sliding surface with a fractional-order sliding mode controller. The objective of the controller is to allow the system states to move to the sliding surface and remain on it so as to ensure the asymptotic stability of the closed-loop system. The Lyapunov stability method is adopted to verify the stability of the controller. Firstly, by using the geometric coordinate transformation that is formerly defined for conventional sliding mode controller, a novel fractional-order sliding surface is defined. The time-varying fractional-order sliding surface is then rotated in the region in which the system state trajectories are stable. The adjustment of the sliding surface slope on the new coordinate axes is achieved by tuning a parameter defined as a sigmoid function. Then, a new control rule is derived. Numerical simulations are performed on the nonlinear mass-spring-damper and 2-DOF robot manipulator system models with parameter uncertainties and bounded external disturbances. The proposed controller is compared with the conventional sliding mode controller with a constant sliding surface and the fractional-order sliding mode controller with a constant sliding surface. Simulation results have shown improved performance of the proposed controller in terms of a decrease in the reaching and settling time, and robustness to disturbances as compared with the related controllers. Moreover, it is seen that the designed controller provides an improvement in the error state trajectories.

Speech recognition systems aim to make human-machine communication quickly and easily. In recent years, various researches and studies have been carried out to develop speech recognition systems. Examples of these studies are speech recognition, speaker recognition and speaker verification. In this study, speech recognition systems were investigated, methods used in the literature were investigated and a Turkish speech recognition application was developed. The application consists of speech coding and speech recognition. Firstly 20 Turkish words which are frequently used on the computer were determined. There are 20 records from each word. A total of 400 words were recorded on the computer with a microphone. In the speech coding section of the application, these words recorded on the computer are encoded by the Linear Pre-estimation Coding (LPC) method and the LPC parameters for each word are obtained. In the speech recognition section of the application, the Support Vector Machines (SVM) method is used. Two types of SVM classifiers are designed. These are the Soft Margin SVM (SM-SVM) classifier and the Least Square SVM (LS-SVM) classifier. Classification consists of training and testing stages. Of the 400 coded words, 200 were used for the training phase and 200 were used for the testing phase. As a result, 91% accurate recognition success for the SM-SVM classifier; 71% correct recognition of the LS-SVM classifier has been achieved.

Liver disease represents a major global health burden, and early diagnosis is essential for reducing mortality. Machine learning (ML) approaches offer non-invasive alternatives to conventional diagnostics, yet existing studies on liver disease prediction often lack systematic feature selection, apply resampling before data splitting (introducing leakage), and report results from single train-test splits without statistical testing. This study proposes a Hybrid Feature Selection (HFS) framework combining Pearson-correlation-based redundancy elimination with a weighted Information Gain–Gain Ratio scoring function, integrated with SMOTE within a leakage-free pipeline. The framework is evaluated on two benchmarks—the Indian Liver Patient Dataset (ILPD, n = 583) and the BUPA Liver Disorders Dataset (n = 345)—across ten classifiers and ten independent train-test splits, with significance assessed via paired Wilcoxon signed-rank tests. On ILPD, the HFS + SMOTE pipeline produced statistically significant ROC-AUC improvements (p < 0.05) in five of ten classifiers and resolved majority-class collapse, raising mean Specificity from 0.00–0.33 to 0.61–0.92. A 2 × 2 ablation study confirmed that HFS and SMOTE contribute independently, with SMOTE driving the Specificity transformation and HFS reducing feature-space noise. Sensitivity analyses demonstrated robustness to the weighting parameter w and confirmed k = 6 as the optimal feature count. Replication on BUPA—which exhibits near-perfect class balance and no feature redundancy—produced a principled null result, confirming that the pipeline’s effectiveness is mechanistically linked to dataset characteristics. The HFS algorithm consistently identified four clinically meaningful core features (AST, ALT, Total Bilirubin, Age) across all runs, validated by SHAP and Permutation Importance stability analysis.

Over the last decade, advances in artificial intelligence have begun to transform educational practices. With the development of OpenAI's ChatGPT, it has become clear that AI chatbots' capabilities—such as generating human-like text, facilitating automated conversations, and answering questions—will have widespread impact across various sectors, including education and healthcare. Consequently, the use of chatbots in various applications has accelerated. Today's most popular chatbots, OpenAI ChatGPT and large language models (LLMs) like Google Gemini and Bard, are making significant progress with applications in various fields, including electrical and electronics education. In this study, the performance of ChatGPT (GPT-3.5), ChatGPT (GPT-5.1), Bard and Gemini was analyzed in answering 240 carefully prepared questions in Turkish language using clear, specific and plain text formats to determine knowledge levels, covering 12 sub-specialties in the field of electrical and electronics. Two experts analyzed the accuracy metric in the performance analysis, and the results are presented comparatively. The statistical significance of the findings was also determined, and it was observed that there were significant differences for the exam evaluations of some courses.

Sliding mode control is a nonlinear, deterministic, robust control method and treated as a subclass of variable structure systems theory. The control input is obtained with a high speed, nonlinear feedback that drives the phase trajectory to a predetermined or adaptive sliding surface and then the control input discontinuously switches on this surface. It is easy to apply and quite effective against external disturbances and parameter variations. Thus sliding mode control has wide acceptance in the literature and application to various areas. Fuzzy logic, on the other hand, is a general-purpose method that uses the experience and perception of the human expert of a process. In this study dynamic, deterministic, continuous-time, nonlinear, lumped parameter second-order systems are considered. The main objective of this study is to develop a novel interval type-2 sliding mode fuzzy controller with a time-varying sliding surface. The variation of the sliding surface with respect to time is performed by a coordinate transformation. The conventional sliding mode controller, type-1 sliding mode fuzzy controller and the proposed interval type-2 sliding mode fuzzy controller have been applied to a mass-spring-damper control problem. The simulated results are evaluated and the results verified the efficacy of the proposed interval type-2 sliding mode fuzzy controller.