Introduction. Currently, there are standard and basic versions of the MoCA, the latter designed for those with lower educational achievements. Community-based normative data on these versions of the MoCA from Arabic populations are deficient, and there is little data demonstrating how both scales perform in comparison. We aim to obtain normative performances from both versions and equate the measures of both scales. Methods. Community-based recruitment of years of age. Participants underwent testing with both versions. Demographic data was collected with regard to age, gender, years of education, diabetes, and hypertension. Regression analysis was performed to determine significance of variables, and the circle-arc equating method was used to equate the two scores from each scale. Results. 311 participants were included in the study. The mean (sd) age was 45.8 (15.96), females were 184 (59.16%), and the duration of education was 12.7 (5.67) years. The mean scores on the MoCA-A and MoCA-B were 21.47 (4.53) and 24.37 (4.71) ( ), respectively. Multivariate regression showed significance of age and years of education in both versions (both variables with ). Correlation coefficient between the two scales was 0.77 ( ). The largest equated difference between both MoCA versions was four points in those scoring from 10-20 on the MoCA-A. Conclusion. We present normative data from a large Saudi Arabian community-based sample with two different MoCA tests, and an equating graph is presented to determine the corresponding expected performance between the two scales.

This research presents a model called the ‘Gaussian negative binomial mixture with a latent class choice model’, which serves as a robust and efficient tool for analyzing decisions across different areas. Our innovative model combines elements of mixture models, negative binomial distributions, and latent class choice modeling to create an approach that captures the complexities of decision-making processes. We explain how the model is formulated and estimated, showcasing its effectiveness in analyzing and predicting choices in scenarios. Through the use of a dataset, we demonstrate the performance of this method, marking a significant advancement in choice modeling. Our results highlight the applications of this model and point towards promising directions for future research, especially in exploring symmetrical patterns and structures, within decision-making processes.

This study introduces an enhanced version of the discrete choice model combining embedded neural architecture to enhance predictive accuracy while preserving interpretability in choice modeling across temporal dimensions. Unlike the traditional architectures, which directly utilize raw data without intermediary transformations, this study introduces a modified approach incorporating temporal embeddings for improved predictive performance. Leveraging the Phones Accelerometer dataset, the model excels in predictive accuracy, discrimination capability and robustness, outperforming traditional benchmarks. With intricate parameter estimates capturing spatial orientations and user-specific patterns, the model offers enhanced interpretability. Additionally, the model exhibits remarkable computational efficiency, minimizing training time and memory usage while ensuring competitive inference speed. Domain-specific considerations affirm its predictive accuracy across different datasets. Overall, the subject model emerges as a transparent, comprehensible, and powerful tool for deciphering accelerometer data and predicting user activities in real-world applications.

This paper presents a significant extension of perturbation theory techniques for estimating the roots of polynomials. Building upon foundational results and recent work by Pakdemirli and Yurtsever, as well as taking inspiration from the concept of probabilistic bounds introduced by Sheikh et al., we develop and prove several novel theorems that address a wide range of polynomial structures. These include polynomials with multiple large coefficients, coefficients of different orders, alternating coefficient orders, large linear and constant terms, and exponentially decreasing coefficients. Among the key contributions is a theorem that establishes an upper bound on the expected maximum modulus of the zeros of polynomials with uniformly distributed perturbations in their coefficients. The theorem considers the case where all but the leading coefficient receive a uniformly and independently distributed perturbation in the interval $[-1,1]$. Our approach provides a comprehensive framework for estimating the order of magnitude of polynomial roots based on the structure and magnitude of their coefficients without the need for explicit root-finding algorithms. The results offer valuable insights into the relationship between coefficient patterns and root behavior, extending the applicability of perturbation-based root estimation to a broader class of polynomials. This work has potential applications in various fields, including random polynomials, control systems design, signal processing, and numerical analysis, where quick and reliable estimation of polynomial roots is crucial. Our findings contribute to the theoretical understanding of polynomial properties and provide practical tools for engineers and scientists dealing with polynomial equations in diverse contexts.

Dyslexia detection systems face significant challenges due to the need for domain-specific discriminative features from high-dimensional data. Traditional dyslexia detection methods often fail to capture complex non-linear relationships and temporal dependencies inherent in dyslexia-related datasets. To address these limitations, we develop a dual approach that integrates deep learning-based feature extraction with an enhanced snow geese algorithm (SGA) for feature selection. In the feature extraction phase, the transformer with a multi-layer perceptron (MLP) architecture is applied for tabular data feature extraction and a vision transformer for image-based feature learning. These transformers have a high ability to extract relevant representation from dyslexia. Additionally, we introduce an enhanced feature selection technique using a modified SGA optimized with a multi-operator neighborhood-based opposition-based learning approach. Our method leverages the hierarchical feature mapping capabilities of SwinV2 transformers and the self-attention mechanisms of MLP-enhanced transformers to improve classification performance. We evaluate our model on the Eye-Tracking Dyslexia Dataset (ETDD70), which contains eye-tracking recordings from 70 participants performing reading tasks. Experimental results demonstrate that our approach outperforms other state-of-the-art feature selection approaches across multiple evaluation metrics. For example, the proposed model has average accuracy, F1-score, and sensitivity of 92.45%, 91.96%, and 92.05%, respectively. In addition, they offer a clinically viable solution for dyslexia detection.