Background/Objectives: This study presents a survey-based evaluation of an explainable AI (Feature-Augmented) approach, which was designed to support the diagnosis of Alzheimer’s disease (AD) by integrating clinical data, MMSE scores, and MRI scans. The approach combines rule-based reasoning and example-based visualization to improve the explainability of AI-generated decisions. Methods: Five doctors participated in the survey: two with 6 to 10 years of experience and three with more than 10 years of experience in the medical field and expertise in AD. The participants evaluated different AI outputs, including clinical feature-based interpretations, MRI-based visual heat maps, and a combined interpretation approach. Results: The model achieved a 100% trust score, with 20% of the participants reporting full trust and 80% expressing conditional trust, understanding the diagnosis but seeking further clarification. Overall, the participants reported that the integrated explanation format improved their understanding of the model decisions and enhanced their confidence in using AI-assisted diagnosis. Conclusions: To our knowledge, this paper is the first to gather the views of medical experts to evaluate the explainability of an AI decision-making model when diagnosing AD. These preliminary findings suggest that explainability plays a key role in building trust and ease of use of AI tools in clinical settings, especially when used by experienced clinicians to support complex diagnoses, such as AD.

The genetic algorithm approach is widely recognized as an effective and flexible optimization method for system identification. The flexibility of a genetic algorithm allows various strategies to be applied to it. One of the strategies applied is the modified genetic algorithm which relies on, among other things, the separation of the population into groups where each group undergoes mutual recombination operations. The strategy has been shown to be better than the simple genetic algorithm and conventional statistical method, but it contains inadequate justification of how the separation is made. The usage of objective function values for separation of groups does not carry much flexibility and is not suitable since different time-dependent data have different levels of equilibrium and thus different ranges of objective function values. This paper investigates the optimum grouping of chromosomes by fixed group ratios, enabling more efficient identification of dynamic systems using a NARX (Non-linear AutoRegressive with eXogenous input) model. Several simulated systems and real-world timedependent data are used in the investigation. Comparisons based on widely used optimization performance indicators along with outcomes from other research are used. The issue of model parsimony is also addressed, and the model is validated using correlation tests. The study reveals that, when recombination and mutation are used for different groups, equal composition of both groups produces a better result in terms of accuracy, parsimony, speed, and consistency.

System identification is concerned with the construction of a mathematical model based on given input and output data to represent the dynamical behaviour of a system. As a step-in system identification, model structure selection is a step where a model perceived as adequate system representation is selected. A typical rule is that the model must have a good balance between parsimony and accuracy in estimating a dynamic system. As a popular search method, genetic algorithm (GA) is used for selecting a model structure. However, the optimality of the final model depends much on the optimality of GA. This paper introduces a novel mating technique in GA based on the chromosome structure of the parents such that a single parent is sufficient in achieving mating that demonstrates high exploration capability. In investigating this, four systems of linear and nonlinear classes were simulated to generate discrete-time sets of data i.e. later used for identification. The outcome shows that GA incorporated with the mating technique within 10% - 20% of the population size is able to find optimal models quicker than the traditional GA.

System identification (SI) is a method of determining a mathematical model for a system given a set of input-output data. A representation is made using a mathematical model based on certain specified assumptions. In SI, model structure selection is a step where a model structure perceived as an adequate system representation is selected. A typical rule is that the final model must have a good balance between parsimony and accuracy. As a popular search method, genetic algorithm (GA) is used for selecting a model structure. However, the optimality of the final model depends much on the effectiveness of GA operators. This paper presents a mating technique named single parent mating (SPM) in GA for use in a real robotic SI. This technique is based on the chromosome structure of the parents such that a single parent is sufficient in achieving mating that eases the search for the optimal model. The results show that using three different objective functions (Akaike information criterion, Bayesian information criterion and parameter magnitude–based information criterion 2) respectively, GA with the mating technique is able to find more optimal models than without the mating technique. Validations show that the selected models using the mating technique are acceptable.

Teaching the technology behind microcomputers to beginner students can be challenging due to the fundamental knowledge they need to grasp. Students must understand what happens in the “brain” of microcomputers to comprehend the complexities of the technology. This paper outlines a pragmatic approach that includes a practical implementation of problem-solving techniques for a microcomputer course, focusing on a robotic ground locomotive vehicle as the application. This involved assembling and seamlessly integrating mechanical, electrical, and microcontroller elements, using two software programs: Flowcode and Proteus. Evaluation of the implementation’s efficacy includes quantitative and qualitative measures. Results were evaluated based on achievements throughout the course, and students’ feedback was gathered through a questionnaire at the end of the course. Various aspects were assessed through the questionnaire, including the effect of the practice on students’ understanding, the alignment with the curriculum, and how the practice boosted students’ interest in the field. The analysis showed that the approach had a very positive impact on students, with an average score of 4.16 for application relevance and 4.29 for overall student involvement, based on a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Most students recommended continuing the exercise, with the most common rating being 5 (strongly agree). The findings underscore the value of hands-on, application-oriented pedagogies in facilitating effective learning experiences within microcomputer education.