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The purpose of this study was to explore the potential benefits of Coriandrum sativum (C. sativum) on anxiety, depression, sleep quality and memory among students.

This randomized controlled trial involved university students aged 18–25 years, who had no known allergies to C. sativum or were using psychotropic medication. After giving informed consent, participants were randomly assigned using a computer-generated random sequence to either 500 mg C. sativum seeds or a placebo. Primary outcomes measured changes in memory (prospective and retrospective memory questionnaire [PRMQ]), anxiety and depression (Hospital Anxiety and Depression Scale), while secondary outcomes assessed sleep quality (Pittsburgh sleep quality inventory [PSQI]).

A sample of 86 students with a mean age of 20.05 ± 1.6 years was selected for the study. Initial assessments ensured no significant differences in demographic or study variables between the control and intervention groups. Statistical analysis revealed significant improvements in memory (PRMQ: p = 0.006), anxiety (Hospital Anxiety Scale: p = 0.04) and depression (Hospital Depression Scale: p = 0.002), as well as in sleep quality (PSQI: p = 0.03) in the group receiving C. sativum compared to the control group.

This research offers initial insights into the potential benefits of C. sativum intake, specifically its role in enhancing memory performance and mitigating anxiety among student populations. The results present a compelling case for further research in this domain to solidify these preliminary conclusions.

Multi-label Text Classification (MTC) is one of the most recent research trends in data mining and information retrieval domains because of many reasons such as the rapid growth of online data and the increasing tendency of internet users to be more comfortable with assigning multiple labels/tags to describe documents, emails, posts, etc. The dimensionality of labels makes MTC more difficult and challenging compared with traditional single-labeled text classification (TC). Because it is a natural extension of TC, several ways are proposed to benefit from the rich literature of TC through what is called problem transformation (PT) methods. Basically, PT methods transform the multi-label data into a single-label one that is suitable for traditional single-label classification algorithms. Another approach is to design novel classification algorithms customized for MTC. Over the past decade, several works have appeared on both approaches focusing mainly on the English language. This work aims to present an elaborate study of MTC of Arabic articles.

This paper presents a novel lexicon-based method for MTC, where the keywords that are most associated with each label are extracted from the training data along with a threshold that can later be used to determine whether each test document belongs to a certain label.

The experiments show that the presented approach outperforms the currently available approaches. Specifically, the results of our experiments show that the best accuracy obtained from existing approaches is only 18 per cent, whereas the accuracy of the presented lexicon-based approach can reach an accuracy level of 31 per cent.

Although there exist some tools that can be customized to address the MTC problem for Arabic text, their accuracies are very low when applied to Arabic articles. This paper presents a novel method for MTC. The experiments show that the presented approach outperforms the currently available approaches.