No abstract

The world in general was not prepared for the coronavirus disease 2019 (COVID-19) pandemic in December 2019, which caused massive deaths. Government officials were forced to impose drastic measures to curb the spread. The Ministry of Health relied on political leaders and government agencies to help disseminate as much health information and the government policies efficiently and effectively. As these representatives or leaders belong to different ethnicities, they are more likely to understand the life circumstances, needs, strengths, and capabilities of multicultural and vulnerable communities, and would be the best people to convey the needed information. Tamils are the third largest community in Malaysia and there is not much research done on Tamil language and communication strategies during the COVID-19 pandemic. Thus, this study aims to observe on how effectively Tamil language and culture influences crisis communication. Effective communication and leadership are central to the management of pandemics and to give the public assurance in order to alleviate their fears and anxiety in having to adapt to the rapidly changing societal and economic landscape. Based on the communication strategies recommended by Hyland-Wood et al (2021), the current study focuses on how a Malaysian Indian politician, Saravanan responds, engages, and communicates with his followers. Seventeen Facebook posts in Tamil on Saravanan's page were extracted and analysed qualitatively. The strategies utilized by Saravanan showed his resolve and effectiveness in managing crisis. This study will be useful for political leaders, community representatives, and medical practitioners in seeking equitable accessibility to quality health care and well-being of the people.

Studies on Natural Language Processing are mainly conducted in English, with very few exploring languages that are under-resourced, including the Dravidian languages. We present a novel work in detecting offensive language using a corpus collected from YouTube containing comments in Tamil. The study specifically aims to compare two machine learning approaches, namely, supervised, and unsupervised to detect offensive patterns in textual communications. In the first setup, offensive language detection models were developed using the traditional machine learning algorithms such as Random Forest, Logistic Regression, Support Vector Machine and AdaBoost, and assessed based on human labeling. Conversely, we used K-means (K = 2) to cluster the unlabeled data before training the same set of machine learning algorithms to detect offensive communications. Performance scores indicate unsupervised clustering to be more effective than human labeling with ensemble classifiers achieving an impressive accuracy of 99.70% and 99.87%, respectively for balanced and imbalanced datasets, hence showing that unsupervised approach can be used effectively to detect offensive language in low resourced languages.

Increasing the production of the three major food crops (MFCs), maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum), is essential to fulfilling the food demand for the growing human population. Increasing food production may require the integration of machine learning (ML) into plant breeding programs. However, developing ML tools to improve the production of MFCs is a daunting task due to the lack of quality data and the computation resources needed to process this information. Hence, this review discusses the recent applications of ML for improving MFCs production, including plant phenotyping, yield forecasting, and candidate gene prediction. Based on the challenges reported in recent ML experiments for MFCs, this review prescribes solutions to produce scalable ML models. This review provides valuable insights for future studies and promotes collective efforts among researchers implementing ML to enhance MFCs productivity.

Tamil vernacular schools provide access to education and career opportunities for Indian Malaysians. Tamil schools are perceived to be an important component of Indian minority rights, an avenue for the Tamil-speaking community to establish and institutionalize their language and cultural identity. However, the government reduced its allocation for these schools in the 2021 budget. Indian political leaders, shouldering the responsibility to channel the discontent among Indians, have raised concerns on various platforms, such as newspapers, social media, and television interviews. It is imperative to observe how Indian political leaders prioritize minority rights while preserving their own or parties’ political interests. Politicians utilize rhetoric to influence the public, but few studies are conducted on the political discourse of Indian Malaysians. This chapter studies an interview in Vizhuthugal-Samugathin Kural, a Tamil talk show televised on Astro Vaanavil. Grounded in discourse studies, this chapter analyzes the (de)legitimizing strategies used by two prominent Indian politicians from MIC and DAP. Although the two parties hold opposite ideologies, this chapter highlights their similarities in advocating minority rights for Indian Malaysians.