This study explores the gradual decline of the Dhivehi language and the challenges of transmitting it effectively to the next generations. Drawing from scholarly perspectives on language death and sociolinguistic change, particularly the comprehensive works of UNESCO, Fishman, and Labov, the research situates the case of Dhivehi within global patterns of linguistic decay. The paper investigates whether Dhivehi is undergoing a shift towards obsolescence due to increasing influence from dominant foreign languages, particularly English. Using an exploratory qualitative approach, the study analyses naturally occurring speech data from 11 publicly available video clips featuring 21 speakers representing a range of age groups, from children to the elderly. By transcribing and examining these samples, the research identifies patterns of code-switching and code-mixing, highlighting the degree to which Dhivehi is being sidelined in daily conversation, especially among the younger population. The findings reveal that while older speakers tend to maintain clearer use of Dhivehi, younger speakers, especially children, frequently alternate between English and Dhivehi, or default or shift entirely to English. The speech analysis indicates not only a weakening of linguistic transmission but also indicate an inadequacy in the availability and usage of appropriate Dhivehi vocabulary for everyday modern life. The study also notes limitations in institutional and societal efforts to elevate the status of Dhivehi in both spoken and written forms. The findings suggest a pressing need for institutional and societal initiatives to revitalise and elevate the status of Dhivehi, particularly in response to the growing dominance and perceived prestige of English in media, education, and digital communication. The findings have implications for stronger language planning, public awareness campaigns, and integration of Dhivehi into contemporary domains to ensure intergenerational transmission and cultural continuity. ބަސް ފަނިކެއުން: އަންނަން އޮތް ޖީލަށް ދިވެހިބަސް ފޯރުވުމަށް ހުރި ގޮންޖެހުންތަކުގެ ސިފަވީ ދިރާސާއެއް މުޖުމަލު މި ދިރާސާގެ ބޭނުމަކީ އަންނަން އޮތް ޖީލުތަކަށް ދިވެހިބަސް ފޯރުވުމުގައި ގޮންޖެހުންތަކެއް ހުރިކަމަށް ވަކާލާތުކޮށް، މަސްލަހަތުވެރިންގެ ސަމާލުކަމަށް ގެނައުމެވެ. ޔުނެސްކޯގެ 'އެންޑެންޖަރޑް ލޭންގުއެޖަސް' މަޝްރޫއާއި މި ދާއިރާގެ ޢިލްމުވެރީން ބަހުގެ އިޖުތިމާއީ ބަދަލުތަކާ ބެހޭގޮތުން ހުށަހަޅުއްވާފައިވާ ޢިލްމީ ނަޒަރިއްޔާތުތަކަށް ބުރަވެ، މި ދިރާސާގެ މަގުސަދަކީ ނެތިދިއުމުގެ މަރުޙަލާތަކަށް ދިވެހިބަސް ހުރަހެޅިފައިވޭތޯ ބެލުމާ އެކު، އެކަން އެ ގޮތަށް ފެންނަ ނަމަ، ދިވެހިބަހުގެ ނެތިދިއުމުގެ ނުވަތަ ބަސް 'ފަނިކެއުމުގެ' އަދުގެ ހާލަތު ގިންތިކުރުމެވެ. ބަސް ނެތިދިއުމަށް މެދުވެރިވާ ސަބަބުތަކުގެ ތެރޭގައި، ބަސް މައްސުނިކުރުމާއި ބަސް ބަދަލުވެ ފުރޮޅުމުގެ މަރުޙަލާތަކެއް ކަޑައްތުކުރެއެވެ. މި ދިރާސާ ކުރިއަށްގެންދިޔައީ 'އެކްސްޕްލޮރޭޓަރީ' ސިފަވީ ދިރާސާއެއްގެ ގޮތުގައި، އިޖުތިމާއީބަހަވީ ބަސްދީގަތުމުގެ ތަރީގާ އަސްލަކަށް ބަލައިގެންނެވެ. މިގޮތުން، އެކި އުމުރުގެ 21 މީހެއްގެ ބަސްމޮށުމުގައި ދިވެހިބަސް ބޭނުންކުރާ ގޮތާއި ދިވެހިބަހާ އެކު އެހެން ބަހެއް ބޭނުންކުރުމުގައި ކޯޑު-ސުއިޗުކުރުމާއި ކޯޑު-މައްސުނިކުރުމުގެ މިންވަރު އަދި ސިޔާގު ތަޙުލީލުކުރީމެވެ. އެގޮތުން ދިވެހީންގެ ދުވަހީ ދިރިއުޅުމުގެ މުއާޞަލާތު ފެންނަ، 11 ވީޑިއޯ ކިލިޕެއް ހޮވީ ސަބަބީ ސުންކުގެ އިސްތިރާޖަށް ބުރަވެއެވެ. ހޯދުންތަކުން ފެންނަގޮތުގައި، ކުޑަކުދިންނާއި ޒުވާން އާބާދީގެ މެދުގައި ދިވެހިބަސް އެއްފަރާތްކުރެވެމުންދާ ކަމާއި، އުމުރުން ދުވަސްވީ މީހުން ސާފު ދިވެހިބަހުން މުއާޞަލާތުކުރާއިރު، މެދުއުމުރުގެ މީހުންގެ ބަސްމަގުގައި އިނގިރޭސިބަހާއި ދިވެހިބަހުގެ ކޯޑު-މައްސުނިކުރުމާއި، ޒުވާނުން ކޯޑު-ސުއިޗުކުރުން ގިނަ ކަމަށާއި، ކުޑަކުދިންގެ މެދުގައި އޮތީ ބަސް މައްސުނިކުރުމަށް ވުރެ ބޮޑަށް ބަސް ފުރޮޅުން ކަމުގައި ހާމަވެއެވެ. އަދި، ބިދޭސީ މަސައްކަތްތެރިންނަށް ބަރޯސާވެފައިވާ ދިވެހިރާއްޖޭގެ މުޖުތަމަޢުގައި ޚިދުމަތް ހޯދުމަށް ޓަކައި ދިވެހިބަސް ނުފުދޭ ކަމުގެ އިޝާރާތްތައް ވެސް ފެނެއެވެ. މީގެ އިތުރުން، ދިވެހި ކުޑަކުދީން ދިވެހިބަސް ދޫކޮށް، އިނގިރޭސިބަސް އިސްކުރާ ކަމާއި، އިނގިރޭސިބަހަކީ އެ ކުދިންގެ މާދަރީބަހުގެ ގޮތުގައި ދޭހަވާ މިންވަރަށް އިނގިރޭސިބަހުގެ އަޑުމަޚުރަޖަށް އަހުލުވެރިވުމާއި ދިވެހިބަހަށް ބީރައްޓެހިކަން ފެނެއެވެ. މި ކަންކަމުން ދޭހަވަނީ ޖީލުތަކުގެ ދެމެދު ބަސް ފޯރުވުމުގައި ދިވެހިބަހަށް ގޮންޖެހުން ދިމާވަމުންދާ ކަމާއި އަދުގެ ޓެކްނޮލޮޖީގެ ބަވަނަ ދުނިޔޭގެ ދުވަހީ ދިރިއުޅުމާއި ދުނިޔަވީ ކަންކަމުގައި ފުދުންހުރި މިންވަރަށް ދިވެހިބަހުގެ ބަސްކޮށާރު މުއްސަނދިވެފައި ނުވުމާ އެކު، ބަސް ފަނިކެއުމާއި މަރުވުމުގެ މަރުޙަލާތަކުގެ ނިޝާންތައް އަދު ފެންނަކަމެވެ. މިއީ ދިވެހިބަސް ނުރައްކަލަށްހުރަހެޅުމުގެ ނިޝާންތަކެއް ކަމުގައި އިޝާރާތްކުރެއެވެ. މި ހޯދުންތަކުގެ ސަބަބުން، ދިވެހިބަހުގެ ރޭވުން ހަރުދަނާކުރުމަށާއި ރައްޔިތުން ހޭލުންތެރިކުރުވުމުގެ ކެމްޕޭނުތައް ހިންގުމާއި، ޖީލުން ޖީލަށް ދިވެހިބަސް ފޯރުވުމަށް ޓަކައި ޒަމާނީ ކަންކަމުގެ ތެރޭގައި ދިވެހިބަސް ބޭނުންކުރުން އާންމުކުރުމުގައި އިދާރީ ގޮތުންނާއި އިޖުތިމާއީގޮތުން އިސްތިރާޖީ ވިސްނުން ކުރިއަށްނެރެ، ދިވެހިރާއްޖޭގެ ޤައުމީބަސް ކަމުގައިވާ ދިވެހިބަހުގެ ޝަރަފާއި ގަދަރަށް ސަމާލުކަންދިނުމަށް މަގުދައްކައެވެ.
The widespread availability of electronic payment systems has transformed money-making transactions for both consumers and merchants. Convenience has been bought at a cost, however, in terms of disproportionately ballooning fraudulently made transactions and thus real security and confidence problems for the systems. Machine learning (ML) has been a valuable asset in the fight against detecting and preventing frauds in real-time based on its capacity to process large amounts of transactional data and detect unusual patterns. This current paper is an essay on how the utilization of machine learning techniques to fraud detection in electronic payment systems is beneficial and has limitations inherent to their utilization. Some of the most paramount challenges include class imbalance in the fraud data, explainability needs of ML models, and dynamic patterns of fraud and their need for adaptive models. As countermeasures for these challenges, we introduce current-state algorithms such as supervised, unsupervised, and hybrid and new algorithms such as ensemble learning, transfer learning, and auto feature engineering. Other than that, we also take into consideration the significance of interpretability and ethical motivations for utilizing ML-based fraud detection systems. Our findings gathered confirm that merging sophisticated machine learning techniques with domain knowledge will greatly improve the ability of detection without compromising system explainability and fairness. This paper discusses the problem of bridging the gap for the creation of strong, large-scale, and reliable fraud detection systems in facilitating extended construction and integrity of digital payment systems.
This study focused on the promotion of research on digital transformation in education and language teaching. It purposely tended to promote the teachers, scholars, students, and researchers construct the research studies on technology transforming in digital education and language teaching, such as blended learning, distance/online learning, and/or e-learning. With the challenges of AI in education, the studies on digital education transformation (DET) and digital language teaching (DLT) are under explored, improving the digital competence, literacy, and skills for teachers and students. By examining the digital technology transformation (DTT) in education curricula, this study really promoted teachers, scholars, students, and researchers to do research on these challenges, with different research method designs, such as analytical research design, descriptive research design, exploratory research design, explanatory research design, and beyond. This study further suggested teachers, scholars, students, and researchers create future-research studies on the key factors of AI challenges in education programs, such as barriers of critical thinking development, problem-solving, and decision making. The future-research studies on DTT in education and language teaching significantly contributed to the betterment of digital competence, skills, and literacy – reforming the educational quality and outcomes.
