Selecting candidates for a specific job or nominating a person for a specific position takes time and effort due to the need to search for the individual’s file. Ultimately, the hiring decision may not be successful. However, artificial intelligence helps organizations or companies choose the right person for the right job. In addition, artificial intelligence contributes to the selection of harmonious working teams capable of achieving an organization’s strategy and goals. This study aimed to contribute to the development of machine-learning models to analyze and cluster personality traits and classify applicants to conduct correct hiring decisions for particular jobs and identify their weaknesses and strengths. Helping applicants to succeed while managing work and training employees with weaknesses is necessary to achieving an organization’s goals. Applying the proposed methodology, we used a publicly available Big-Five-personality-traits-test dataset to conduct the analyses. Preprocessing techniques were adopted to clean the dataset. Moreover, hypothesis testing was performed using Pearson’s correlation approach. Based on the testing results, we concluded that a positive relationship exists between four personality traits (agreeableness, conscientiousness, extraversion, and openness), and a negative correlation occurred between neuroticism traits and the four traits. This dataset was unlabeled. However, we applied the K-mean clustering algorithm to the data-labeling task. Furthermore, various supervised machine-learning models, such as random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), and AdaBoost, were used for classification purposes. The experimental results revealed that the SVM attained the highest results, with an accuracy of 98%, outperforming the other classification models. This study adds to the current literature and body of knowledge through examining the extent of the application of artificial intelligence in the present and, potentially, the future of human-resource management. Our results may be of significance to companies, organizations and their leaders and human-resource executives, in addition to human-resource professionals.

The Arabic text can be translated into English using a variety of machine translation techniques. The translation of Arabic text into English still poses a number of challenges in contemporary Arabic. To identify these challenges that encounter while translating Arabic text into English using machine translation, a systematic literature review (SLR) approach is used. The SLR steps—protocol creation, first and final selection, quality assessment, data extraction and synthesis—are used. Nineteen challenges are reported during the SLR process based on fifty-six research papers. The four most important problems are carefully examined, and the possible solutions of other researchers are discussed. Word sense disambiguation, Arabic named entity, rich and complex morphology and low resource are the four critical challenges during rendering Arabic text to English text. Other challenges are also reported in this article.

As globalization intensifies cross-cultural communication, machine translation (MT) has become a pivotal tool in bridging linguistic divides. However, within the realm of modern linguistics, the integration of MT technologies, particularly for complex language pairs like English and Arabic, presents both transformative opportunities and significant challenges. Despite rapid advancements, issues such as syntactic ambiguity, idiomatic expressions, and cultural nuances continue to hinder translation accuracy. This study aims to examine the dual role of machine translation in modern linguistics: its capacity to enhance linguistic research and communication, and the limitations it poses in preserving linguistic integrity and nuance, especially in the English-Arabic language pair. It hypothesizes that while MT facilitates rapid linguistic exchange, it may inadvertently oversimplify or distort culturally embedded meaning. A mixed-methods approach is proposed. Quantitative analysis could involve evaluating translation accuracy using benchmark corpora and neural machine translation tools (e.g., Google Translate, DeepL). Qualitative analysis may include case studies, error typologies, and expert linguistic evaluations to assess semantic fidelity and syntactic coherence between English and Arabic outputs. The study likely identifies areas where MT performs well, such as technical or literal translations, while highlighting persistent issues in idiomatic, literary, or context-dependent translations. Patterns of syntactic errors, gender mismatches, and cultural misinterpretations are expected, especially in morphologically rich Arabic expressions. Findings may underscore the growing utility of MT in linguistic research and global communication while emphasizing the need for hybrid models that combine AI capabilities with human linguistic insight. The study contributes to the development of more culturally sensitive and linguistically aware translation systems.

American scientific publishing group

Globalization and increasing cultural diversity in higher education demand innovative teaching approaches that support the development of intercultural competence. This study develops and tests a process-oriented model examining how undergraduate engineering students’ perceptions of faculty development support and institutional support are associated with students’ cultural intelligence, intercultural competence, perceptions of interculturally informed teaching practices, intercultural student engagement, and student learning outcomes. Survey data were collected from 814 undergraduate engineering students in Indonesia and analyzed using Structural Equation Modeling (SEM). The findings indicate that students who perceived stronger faculty development support reported higher levels of cultural intelligence, which was positively associated with intercultural competence and greater perceived implementation of interculturally informed teaching practices. Students who perceived more interculturally informed teaching practices also reported higher intercultural student engagement, which was positively associated with higher self-reported student learning outcomes. These findings provide an integrated perspective on how undergraduate engineering students perceive the relationships among institutional support, faculty practices, intercultural development, and learning outcomes, offering practical insights for supporting more inclusive and globally responsive engineering education.

Word tokenization is the first stage for higher-order Natural Language Processing (NLP) tasks like Part-of-Speech (PoS) tagging, parsing, and named entity recognition. The amount of text on the World Wide Web is growing daily in the present era of technology, necessitating the use of advanced instruments. Since more and more people speak Arabic around the world, Arabic language processing systems must be improved. Due to the writing style of Arabic with a lack of support for capitalization features and the use of compound words, it is difficult to perform word tokenization. This research paper proposes a novel Arabic word tokenization system based on the knowledge. To develop this system, a maximum matching model with its two variations, namely forward and reverse maximum matching is used. The proposed system is implemented in Python. The results produced during system evaluation report high performance.