Regression, a supervised machine learning approach, establishes relationships between independent variables and a continuous dependent variable. It is widely applied in areas like price prediction and time series forecasting. The performance of regression models is typically assessed using error metrics such as the Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). However, these metrics present challenges including sensitivity to outliers (notably MSE and RMSE) and scale dependency, which complicates comparisons across different models. Additionally, traditional metrics sometimes yield values that are difficult to interpret across various problems. Consequently, there is a need for a metric that consistently reflects regression model performance, independent of the problem domain, data scale, and outlier presence. To overcome these shortcomings, this paper introduces a new regression accuracy measure based on the Hassanat distance, a non-convex distance metric. This measure is not only invariant to outliers but also easy to interpret as it provides an accuracy-like value that ranges from 0 to 1 (or 0–100%). We validate the proposed metric against traditional measures across multiple benchmarks, demonstrating its robustness under various model scenarios and data types. Hence, we suggest it as a new standard for assessing regression models’ accuracy.

In alignment with Saudi Vision 2030’s strategic objectives to diversify and enhance the tourism sector, this study explores the integration of Artificial Intelligence (AI) in the Al-Baha district, a prime tourist destination in Saudi Arabia. Our research introduces a hybrid AI-based framework that leverages sentiment analysis to assess and enhance tourist satisfaction, capitalizing on data extracted from social media platforms such as YouTube. This framework seeks to improve the quality of tourism experiences and augment the business value within the region. By analyzing sentiments expressed in user-generated content, the proposed AI system provides real-time insights into tourist preferences and experiences, enabling targeted interventions and improvements. The conducted experiments demonstrated the framework’s efficacy in identifying positive, neutral and negative sentiments, with the Multinomial Naive Bayes classifier showing superior performance in terms of precision and recall. These results indicate significant potential for AI to transform tourism practices in Al-Baha, offering enhanced experiences to visitors and driving the economic sustainability of the sector in line with the national vision. This study underscores the transformative potential of AI in refining operational strategies and aligning them with evolving tourist expectations, thereby supporting the broader goals of Saudi Vision 2030 for the tourism industry.

This paper proposes an automatic correction system that detects and corrects dyslexic errors in Arabic text. The system uses a language model based on the Prediction by Partial Matching (PPM) text compression scheme that generates possible alternatives for each misspelled word. Furthermore, the generated candidate list is based on edit operations (insertion, deletion, substitution and transposition), and the correct alternative for each misspelled word is chosen on the basis of the compression codelength of the trigram. The system is compared with widely-used Arabic word processing software and the Farasa tool. The system provided good results compared with the other tools, with a recall of 43%, precision 89%, F₁ 58% and accuracy 81%.

Sign language is the only means of communication for deaf and hearing-disabled people in their communities. It uses body language and gestures, such as hand shapes and facial expressions, to convey a message. It is important to note that sign language is specific to the region; that is, Arabic sign language (ArSL) is different from English sign language. Therefore, this research proposes a way to improve the translation of ArSL using a new artificial intelligence (AI) architecture. Specifically, a convolutional neural network (CNN) based on fine-tuning of the SSD-ResNet50 V1 FPN is applied to build a real-time ArSL recognition and translation system with fast and accurate results. The proposed AI architecture can provide translation of sign language in real-time to enhance communication in the deaf community. We achieved an average F-score of 86% and an average accuracy of 94%.

The k-nearest neighbor (KNN) algorithm remains one of the most fundamental and widely-used methods in machine learning. A common rule of thumb sets the number of neighbors as k = √n, where n is the size of the training set. Despite its widespread adoption, the theoretical justification for this choice has remained obscure. We provide a comprehensive rate-based analysis. First, we derive the minimax-optimal exponent β★ = 4/(d+4) for k = n^β under standard Hölder-smoothness assumptions, recovering as a special case Theorem 1: k = √n is minimax-optimal if and only if the feature space has dimensionality d = 4. Second, Theorem 2 quantifies the sub-optimality of any fixed β as R_n(n^β) = Θ(n^(−r(β,d))) with r(β,d) = min{β, 4(1−β)/d}, yielding an asymmetric penalty for the classical rule when d ≠ 4 that we make precise in Corollary 1. The predicted rate is empirically verified across d ∈ {2,…,20} on controlled synthetic data. On 48 datasets from the OpenML-CC18 benchmark suite, the dimension-aware rule k = ⌊n^(4/(d+4))⌋ outperforms the classical √n rule in 32 of 48 head-to-head comparisons (paired Wilcoxon p = 4.6 × 10⁻⁴, mean accuracy gain +2.5 percentage points), demonstrating that the theoretical improvement translates to a practical one. We further test the Hassanat distance metric against Euclidean across all KNN variants on the same 48 datasets, finding that Hassanat outperforms Euclidean in five of six configurations (paired Wilcoxon p < 0.05), with the largest gains on unstandardized data. Cross-validation remains the strongest k-selection strategy when computationally feasible, and the theoretical results provide a principled non-cross-validated alternative.