Indian Tamil Research Articles

Gamification In English Language Teaching: A Pathway to Fostering Teacher-Student Rapport, Teacher Immediacy and Students’ Willingness to Communicate

This research examines the role of gamification in enhancing English language teaching and investigates how it helps in improving teacher-student rapport (TSR), teacher immediacy (TI), and students’ willingness to communicate (WTC). Research focusing on the impact of gamification on these variables is limited. This study bridges the existing gap and sheds light on the necessity of incorporating gamification in foreign language teaching. Data for the study were collected from 50 EFL teachers and 165 EFL students in prominent higher education institutions in two south Indian states, Tamil Nadu and Kerala. Purposive sampling was used to collect the data, and two structured questionnaires were distributed. The reliability of the questionnaire was checked using Cronbach’s Alpha. Different statistical methods and descriptive analysis were used to interpret, represent, and analyze the results of the study. It is observed that gamified modules significantly improve the students’ vocabulary acquisition and motivate them to improve their language skills. They also help to enhance TSR and TI, paving the way to improved WTC. Although gamified simulations prepare students for real-world technical communication scenarios in English, it is observed that EFL teachers face several implementation challenges in the classroom.

Association of a NLRP3 rs10754558 Polymorphism with the Development of Chronic Helicobacter pylori Infection in South Indian Tamils

The increased prevalence of Helicobacter pylori infection and inadequate genetic research on the same, demands a genetic study among Tamil population of South India to unravel the association of NLRP3 (NLR family pyrin domain containing 3) variants with persistent H. pylori infection. This research was aimed to study the correlation between persistent H. pylori infection and influence of such genetic variants in the development of disease progression. In this study, 200 healthy volunteers and 120 H. pylori-positive cases were screened for two NLRP3 variants, rs74163773 and rs10754558, using allele-specific PCR and TaqManTM SNP genotyping assay, respectively. Based on our genotype and allelic distribution, rs74163773 variant did not associate with the risk of developing the persistent infection. However, a significant association of heterozygous CG variant of rs10754558 with patient showing clinical symptoms of gastritis, PUD and persistent infection in the over-dominant, co-dominant and recessive models was found. Our findings suggest that persistent H. pylori infection susceptibility was influenced by genetic variant rs10754558 and its heterozygous CG variant can serve as an independent risk factor in the manifestation of chronic gastritis and PUD in the South Indian Tamils.

Case series of cutaneous myiasis in neglected cases of autoimmune bullous disorders.

Myiasis is an infestation of the tissues and organs of living vertebrates and humans by fly larvae, usually those belonging to the Calliphoridae family. The larvae feed on the host's necrotic or living tissue. Preexisting dermatological conditions and poor hygiene have been described as predisposing factors for myiasis, and it is especially common among neglected, dependent patients. Based on our literature search, we could find only a few case reports reported on cutaneous myiasis in autoimmune bullous disorders. It is especially more common in the South Indian Tamil Nadu population due to the false belief that autoimmune blistering disorders like pemphigus and bullous pemphigoid are considered chicken pox and treated with neem and turmeric preparation with poor hygiene. Herein we report a series of eight autoimmune blistering disorders with cutaneous myiasis.

Proteomics Approach to Identify Serum Biomarkers Associated with Gastric Cancer in South Indian Tamils.

Identifying cancer-specific biomarkers is a crucial step in the disease screening process at a very early stage of tumor development. In recent years, Quantitative proteomic approaches have gained importance in identifying novel candidate markers in cancer. Gastric cancer has always been known as a life-threatening medical condition with high mortality rates. The objective of our research is to adapt serum samples from Indian gastric cancer patients to identify and understand the differentially regulated proteins in comparison with healthy individuals. A total of 30 serum isolates from gastric cancer patients and healthy individuals were obtained and subjected to 2-D Gel electrophoresis, and Tandem LC-MS analysis revealed 12 differentially expressed protein spots. The functional properties of identified proteins were further analyzed using PANTHER and STRING databases. The differentially expressed protein spots were identified as three candidate proteins: Haptoglobin, Prohibitin, and Apolipoprotein. The protein interaction studies reveal that the haptoglobin fragments were upregulated, and the remaining two prohibitin and Apolipoprotein were down-regulated in gastric cancer patients. All the proteins identified as biomarkers were found to be involved in regulating cell proliferation and stabilization of oxidative metabolism in the liver; therefore, differential regulation plays a crucial role in gastric cancer progression.

Low C4A copy numbers and higher HERV gene insertion contributes to increased risk of SLE, with absence of association with disease phenotype and disease activity.

Low copy numbers (CNs) of C4 genes are associated with systemic autoimmune disorders and affects autoantibody diversity and disease subgroups. The primary objective of this study was to characterize diversity of complement (C4) and C4-Human Endogenous Retrovirus (HERV) gene copy numbers in SLE. We also sought to assess the association of C4 and C4-HERV CNs with serum complement levels, autoantibodies, disease phenotypes and activity. Finally, we checked the association of C4 and HERV CNs with specific HLA alleles. Genomic DNA from 70 SLE and 90 healthy controls of south Indian Tamil origin were included. Demographic, clinical and serological data was collected in a predetermined proforma. CNs of C4A and C4B genes and the frequency of insertion of 6.4kb HERV within C4 gene (C4AL, C4BL) was determined using droplet digital polymerase chain reaction (ddPCR). A four digit high resolution HLA genotyping was done using next generation sequencing. In our cohort, the total C4 gene copies ranged from 2 to 6. Compared to controls, presence of two or less copies of C4A gene was associated with SLE risk (p = 0.005; OR = 2.79; 95% CI = 1.29-6.22). Higher frequency of HERV insertion in C4A than in C4B increases such risk (p = 0.000; OR = 12.67; 95% CI = 2.80-115.3). AL-AL-AL-BS genotype was significantly higher in controls than SLE (9%vs1%, p = 0.04; OR = 0.15, 95% CI = 0.00-0.16). Distribution of HLA alleles was not different in SLE compared to controls as well as in SLE subjects with ≤ 2 copies and > 2 copies of C4A, but HLA allele distribution was diverse in subjects with C4B ≤ 2 copies and > 2 copies. Finally, there was no correlation between the C4 and the C4-HERV diversity and complement levels, autoantibodies, disease phenotypes and activity. In conclusion, our data show that, low C4A copy number and higher insertion of HERV-K in C4A increases the risk for SLE. C4 and C4-HERV CNs did not correlate with serum complements, autoantibodies, disease phenotypes and activity in SLE. Further validation in a larger homogenous SLE cohort is needed.

“Deep Learning Based Classification of Single-Hand South Indian Sign Language Gestures”

Gesture recognition is a branch of computer science and language technology dedicated to utilizing mathematical algorithms for the analysis of human gestures. Within the realm of non-verbal communication, the pivotal role of human arm movements and gestures remains a focal point. This research introduces advanced multi-stream deep transfer learning models tailored for identifying signs from South Indian languages, specifically Kannada, Tamil, and Telugu. The primary aim is to offer support to individuals encountering speech disorders or disabilities. The key deep transfer learning models utilized include Inception-V3, VGG-16, and ResNet-50, which have been modified and improved to attain heightened classification efficacy. The dataset comprises 35,000 images capturing single-hand gestures. In the realm of models, Inception-V3 exhibits the utmost test accuracy, standing at 91.45%, alongside a validation accuracy of 93.45% when tasked with the classification of single-hand gesture images across thirty-five distinct categories. The importance of this study lies in its prospective utility for creating an automated system that can support and improve the functional capabilities of individuals facing speech disorders or disabilities.

Deep Learning Approaches for Age-based Gesture Classification in South Indian Sign Language

This study focuses on recognizing and categorizing South Indian Sign Language gestures based on different age groups through transfer learning models. Sign language serves as a natural and expressive communication method for individuals with hearing impairments. This study intends to develop deep transfer learning models, namely Inception-V3, VGG-16, and ResNet-50, to accurately identify and classify double-handed gestures in South Indian languages, like Kannada, Tamil, and Telugu. A dataset comprising 30,000 images of double-handed gestures, with 10,000 images for each considered age group (1-7, 8-25, and 25 and above), is utilized to enhance and modify the models for improved classification performance. Amongst the tested models, Inception-V3 achieves the best performance with a test precision of 95.20% and validation accuracy of 92.45%, demonstrating its effectiveness in accurately categorizing images of double-handed gestures into ten different classes.

“Deep Learning Based Classification of Single-Hand South Indian Sign Language Gestures”

Gesture recognition is a branch of computer science and language technology dedicated to utilizing mathematical algorithms for the analysis of human gestures. Within the realm of non-verbal communication, the pivotal role of human arm movements and gestures remains a focal point. This research introduces advanced multi-stream deep transfer learning models tailored for identifying signs from South Indian languages, specifically Kannada, Tamil, and Telugu. The primary aim is to offer support to individuals encountering speech disorders or disabilities. The key deep transfer learning models utilized include Inception-V3, VGG-16, and ResNet-50, which have been modified and improved to attain heightened classification efficacy. The dataset comprises 35,000 images capturing single-hand gestures. In the realm of models, Inception-V3 exhibits the utmost test accuracy, standing at 91.45%, alongside a validation accuracy of 93.45% when tasked with the classification of single-hand gesture images across thirty-five distinct categories. The importance of this study lies in its prospective utility for creating an automated system that can support and improve the functional capabilities of individuals facing speech disorders or disabilities.

Latent Objectives: A Study on Narratology in the Tamil Film Captain Miller

Narratology is the study of narrative frame work, a theoretical foundation for discovering and sharing tales through various media. Tzvetan Todorov, a Bulgarian narratologist, introduced the term ‘narratologie’ from French. Tamil cinema plays a significant role in shaping Tamil society’s life and culture, but few films have addressed and eliminated casteism. Captain Miller, a 2024 Indian Tamil action, adventure film, is a 2024 film directed by Arun Matheswaran, set in the 1930s during the British Raj. The film incorporates Todorov’s narrative theory of equilibrium. The film also incorporates Ambedkarism, a philosophy followed by Dr. Ambedkar, focusing on fraternity, democracy, communal electorates, and conversion out of Hinduism. The hero, Captain Miller, prefers the name given by the British, reflecting the role of colonial influence in their lives.

Transformer Based Sentiment Analysis on Code Mixed Data

In India, a country known for its linguistic diversity, code mixing is a common practice, and it has a profound impact on the way people communicate through various mediums, including social media platforms and everyday conversations. The prevalence of code-mixing in social media platforms presents a substantial hurdle for machine translation and language processing tasks. The abundance of unstructured text in code-mixed form on these platforms highlights a crucial research domain within NLP. The blending of Hindi and English, known as Hinglish, and other mixed case text like Malayalam-English, Tamil-English, Telugu- English are particularly prevalent among the younger generation while communication in social media and requires appropriate processing to aid comprehension by both monolingual users and language processing models. Manual translation of this type of data proves to be laborious due to challenges like limited vocabulary, potential misunderstandings of context, grammatical errors, biases, and various other issues. Additionally, existing translation models tend to perform more effectively on monolingual language rather than code-mixed data. Therefore, it is more desirable to build models that can translate code-mixed data.This study tries to convert code-mixed Hinglish, Malayalam-English, Tamil-English, Telugu-English language in Romanised script to monolingual English which can further be given as input to NLP applications like Sentiment Analysis. This is achieved by finetuning pretrained models like IndicLID for Language Identification (LID) module and use an ensemble approach for transliteration + translation using Indictrans and IndicXlit for code mixed machine translation which will be given as input to classification algorithm which performs Sentiment Analysis and predict the sentiment. It is observed that this approach of translation of code-mixed test perform better than traditional machine translation for Indian languages Hindi, Tamil, Telugu and Malayalam.

Matrix Gla Protein and Nitric Oxide Synthase-3 Genetic Variants in Chronic Kidney Disease and Their Relation with Cardiovascular Risk.

Chronic kidney disease (CKD) is defined by gradual deterioration of renal parenchyma and decline of functioning nephrons. The risk of cardiovascular events is drastically increased in patients with CKD. This complicated link of CKD and cardiovascular disease (CVD) is not well understood till date. We aim to study the influence of genetic variants of matrix Gla protein (MGP) gene rs1800801, rs1800802, and rs4236 and nitric oxide synthase-3 (NOS3) gene rs1799983 and rs2070744 on the risk of CKD and its associated cardiovascular comorbidity in South Indian Tamils. One hundred and eighty-five CKD patients and 185 controls were recruited in this research. Flow-mediated dilatation (FMD) of brachial artery was measured ultrasonically. Circulating levels of MGP and nitric oxide (NO) were measured by ELISA. Genotyping was done by real-time PCR. We observed a significant difference in the distribution of TT and CT genotypes of NOS3 (rs2070744), indicating an increase in the risk of CKD. NO level was significantly decreased in CKD cases than controls. We also found a significant difference in the distribution of TTA and CCG haplotypes of MGP polymorphisms (1-rs4236; 2-rs1800801; 3-rs1800802) between the groups, indicating an increase in the risk of CKD. CT genotype of MGP (rs4236) and CT genotype of NOS3 (rs2070744) variants were found to be associated with decreased FMD, indicating endothelial dysfunction, the harbinger of CVD. We conclude that genetic variants of MGP and NOS3 enhance the risk of CKD and its associated cardiovascular comorbidity in South Indian Tamils.

Altered Placental Insulin Signaling in the Absence of Systemic Insulin Resistance in Hypertensive Disorders of Pregnancy among South Indian Tamil Population

A cross sectional study was conducted to explore the possible derangements in placental insulin signaling and also to investigate the presence or absence of maternal systemic insulin resistance in different types of hypertensive disorders of pregnancy (HDP). Study Design: Healthy pregnant women (n=32) and pregnant women with gestational hypertension (GH) (n=18), late onset Pre-eclampsia (LP) (n=18) and early onset Pre-eclampsia (EP) (n=19) were recruited for the study. Maternal venous blood and placental samples were collected from the study subjects at 35±7 weeks of gestation. Insulin resistance parameters such as fasting glucose, fasting insulin, homeostatic model assessment of insulin resistance, beta cell function and insulin sensitivity and glycated hemoglobin were estimated in the maternal venous blood samples. Placental protein expressions of insulin signaling molecules were analyzed by western blotting method. Results: Maternal venous blood insulin resistance parameters were not significantly different between the study groups. However, placental content of phospho insulin receptor beta was significantly decreased in GH, LP and EP groups in comparison to controls. We also found a significant decrease in placental protein expression of phosphatidyl inositol 3-kinase p85 alpha in LP group when compared to other groups.

Modeling the dynamics of leptospirosis in India.

Leptospirosis, a formidable zoonotic threat spawned by Leptospira, plagues tropical and subtropical realms. This study delves deep into tropical Indian states, namely, Kerala, Gujarat, Karnataka, Maharashtra, and Tamil Nadu, unraveling the dynamics of leptospirosis through a comprehensive mathematical model that embraces temperature-driven growth rates of Leptospira. Sensitivity analysis and parameter estimation techniques fortified the model's accuracy, unraveling the factors shaping leptospirosis transmission. Notably, the numerical results highlight the significant impact of rainfall, fishing, climate, mining, agriculture, and cattle farming on leptospirosis prevalence in the endemic states of India. Finally, our study urges resolute preventive action to control and combat leptospirosis in India. Strengthening surveillance, impactful awareness campaigns, targeted interventions, and improved hygiene practices among high-risk individuals are vital. Embracing these proactive strategies will alleviate the burden of leptospirosis and enhance public health in India and beyond.

Joint Engagement in Mother-Child Dyads of Autistic and Non-Autistic Children Among Asian Indian Tamil Speaking Families.

This study profiled various levels of engagement and related communication behaviours among 50 Asian Indian Tamil autistic children(AUT) and their mothers. The interaction was compared with two groups of mother-child dyads of non-autistic (NA) children, 50 in each group, matched for chronological age (CA), and for language level (LL). Results indicated that despite mother's efforts to engage with their children, autistic children were often 'engaged with objects' or remained 'unengaged' due to children's preference for solitary play, while NA children were often engaged in 'co-ordinated' and 'people engagement'. Across the three groups, mothers predominantly took the lead and dominated the interaction, irrespective of children's language levels. These initiations by the mothers were often to provide instructions and to ask 'What' questions. Autistic children initiated communication predominantly to ask for an object and responded often in the form of negations and protests with limited verbal output or non-verbally. Most of the communication behaviours of both children and mothers in AUT group was quantitatively and qualitatively different when compared to those in both the NA groups, indicating unique nature of interactions despite matching for CA or LL. The observations from the study highlights the need for considering adult's contingent behaviours also, while assessing communication skills of autistic children in order to provide effective intervention.

Parenting in the second generation. The changing family figurations of descendants of Pakistani, Indian and Sri Lankan Tamil immigrants in Norway

ABSTRACT This study explores how second-generation parents in Norway manoeuvre generational and institutional relationships and what consequences this may have for participation in ethnicity-based networks and majority society. The context is an advanced welfare state in which ideals of dual-earner, gender-equal and child-centred families are facilitated through provisions of care for the youngest children. We argue that second-generation families are moving towards a nuclear family model in which the dyadic bond between parents and children takes centre stage, strengthening dependency on institutions and networks in majority society and redefining dependencies on extended family and ethnicity-based networks. The transformed family and generational figuration become a means through which parents negotiate a position as established in Norwegian majority communities. The study contributes by highlighting parenting as a lens through which generational transformations and minority/majority relationships can be analytically bridged and by showing the relevance of Elias’ figurational sociology for understanding social integration processes.

Status and Scope of Automated Coconut Harvester in India: A Review

Coconut plays a substantial role in the Indian economy. South Indian states, namely Kerala, Karnataka, Tamil Nadu, and Andhra Pradesh are the leading producers accounting for more than 90% of the total coconut produced in the country. The traditional/conventional process of coconut harvesting includes climbing on the tree and cutting the coconut bunches with a cutter, which involves high risk. Climbing on a coconut tree is the main risk factor and it is life-threatening. The present review paper presents the status and future research need for automated coconut harvesting machines in India. The maximum speed or climbing with remote controlled coconut harvester was reported 0.055 ms-1. This paper also focuses on the basic components of the automated coconut harvesting machine, as well as the advantages and disadvantages of each component are critically analyzed based on its findings, future research needs are proposed in order to meet all the fundamental aspects of autonomous coconut harvesting machines.

A Machine Learning Framework for Automatic Fake News Detection in Indian Tamil News Channels

A Machine Learning Framework for Automatic Fake News Detection in Indian Tamil News Channels

A Study on the Performance of Recurrent Neural Network based Models in Maithili Part of Speech Tagging

This article presents our effort in developing a Maithili Part of Speech (POS) tagger. Substantial effort has been devoted to developing POS taggers in several Indian languages, including Hindi, Bengali, Tamil, Telugu, Kannada, Punjabi, and Marathi; but Maithili did not achieve much attention from the research community. Maithili is one of the official languages of India, with around 50 million native speakers. So, we worked on developing a POS tagger in Maithili. For the development, we use a manually annotated in-house Maithili corpus containing 56,126 tokens. The tagset contains 27 tags. We train a conditional random fields (CRF) classifier to prepare a baseline system that achieves an accuracy of 82.67%. Then, we employ several recurrent neural networks (RNN)-based models, including Long-short Term Memory (LSTM), Gated Recurrent Unit (GRU), LSTM with a CRF layer (LSTM-CRF), and GRU with a CRF layer (GRU-CRF) and perform a comparative study. We also study the effect of both word embedding and character embedding in the task. The highest accuracy of the system is 91.53%.

“People expect you to choose”: Postcolonial hybridity in complex geographies of scaled diaspora identities

Hybridity in geography, specifically hybridity through a postcolonial lens, is often removed from postcolonial origins regarding geographic scale. I argue that postcolonial hybridity is key to situating geographic scalar identity within the context of postcolonial diasporas navigating competing ideas of nationalism and regionalism. Postcolonial hybridity as opposed to hybridity as hyphenated identity, provides a historically contingent lens useful to contextualizing lived scalar identities within diaspora communities, particularly in the United States, a nation founded on settler colonial ideologies. Drawing from 3 years of observational and interview-based research in the US Indian Tamil diaspora, I argue that scalar identity is a directly influenced by colonial constructions of race, identity, and ideas of nationalism. Drawing from Bhabha’s (1994) concept of hybridity, I demonstrate how scalar identity can be reconceptualized with respect to postcolonial geographies of identity.

Speaking Rate, Oro-Laryngeal Timing, and Place of Articulation Effects on Burst Amplitude: Evidence From English and Tamil.

The relationship between speaking rate and burst amplitude was investigated in plosives with differing oro-laryngeal timing: long-lag voice-onset time (VOT) (North American English) and short-lag VOT (Indian Tamil). Burst amplitude (reflecting both intraoral pressure and flow geometry of the oral channel) was hypothesized to decrease in pre-vocalic plosive syllables with the increase in speaking rate, which imposes temporal constraints on both intraoral pressure buildup behind the oral occlusion and respiratory air flow. The results showed that decreased vowel duration (which is associated with increased speaking rate) led to decreased burst amplitude in both short- and long-lag plosives. Aggregate models of bilabial and velar plosives (found in both languages) suggested lower burst amplitudes in short-lag stops. Place-of-articulation effects in both languages were consistent with models of stop consonant acoustics, and place interactions with vowel duration were most apparent with long-lag English stops. The results are discussed in terms of speaking rate and language-internal forces, contributing to burst amplitude variation and their implications for speech perception and potential to affect lenition phenomena.