Text Classification in an Under-Resourced Language via Lexical Normalization and Feature Pooling

Automatic classification of textual content in an under-resourced language is challenging, since lexical resources and preprocessing tools are not available for such languages. Their bag-of-words (BoW) representation is usually highly sparse and noisy, and text classification built on such a representation yields poor performance. In this paper, we explore the effectiveness of lexical normalization of terms and statistical feature pooling for improving text classification in an under-resourced language. We focus on classifying citizen feedback on government services provided through SMS texts which are written predominantly in Roman Urdu (an informal forward transliterated version of the Urdu language). Our proposed methodology performs normalization of lexical variations of terms using phonetic and string similarity. It subsequently employs a supervised feature extraction technique to obtain category-specific highly discriminating features. Our experiments with classifiers reveal that significant improvement in classification performance is achieved by lexical normalization plus feature pooling over standard representations

Using Large Language Models to Automate Category and Trend Analysis of Scientific Articles: An Application in Ophthalmology

Purpose: In this paper, we present an automated method for article classification, leveraging the power of Large Language Models (LLM). The primary focus is on the field of ophthalmology, but the model is extendable to other fields. Methods: We have developed a model based on Natural Language Processing (NLP) techniques, including advanced LLMs, to process and analyze the textual content of scientific papers. Specifically, we have employed zero-shot learning (ZSL) LLM models and compared against Bidirectional and Auto-Regressive Transformers (BART) and its variants, and Bidirectional Encoder Representations from Transformers (BERT), and its variant such as distilBERT, SciBERT, PubmedBERT, BioBERT. Results: The classification results demonstrate the effectiveness of LLMs in categorizing large number of ophthalmology papers without human intervention. Results: To evalute the LLMs, we compiled a dataset (RenD) of 1000 ocular disease-related articles, which were expertly annotated by a panel of six specialists into 15 distinct categories. The model achieved mean accuracy of 0.86 and mean F1 of 0.85 based on the RenD dataset. Conclusion: The proposed framework achieves notable improvements in both accuracy and efficiency. Its application in the domain of ophthalmology showcases its potential for knowledge organization and retrieval in other domains too. We performed trend analysis that enables the researchers and clinicians to easily categorize and retrieve relevant papers, saving time and effort in literature review and information gathering as well as identification of emerging scientific trends within different disciplines. Moreover, the extendibility of the model to other scientific fields broadens its impact in facilitating research and trend analysis across diverse disciplines

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Metamaterial Antenna Array for RADAR Application and its Scan Blindness

This paper presents metamaterial-inspired solution to mitigate scan blindness in phased arrays at RADAR frequency taking advantage of less mutual coupling and compactness of metamaterial arrays. This is done by redesigning a metamaterial antenna at RADAR frequency, feeding it with shielded coaxial probe to reduce side lobe level thus making it practical for planar arrays implementation. Input reflection coefficients and mutual coupling is presented varying phase difference among antenna feeds. Results from HFSS confirmed that there is no scan blindness in these newly designed phased metamaterial arrays

Automated Category and Trend Analysis of Scientific Articles on Ophthalmology Using Large Language Models: Development and Usability Study

BackgroundIn this paper, we present an automated method for article classification, leveraging the power of large language models (LLMs). ObjectiveThe aim of this study is to evaluate the applicability of various LLMs based on textual content of scientific ophthalmology papers. MethodsWe developed a model based on natural language processing techniques, including advanced LLMs, to process and analyze the textual content of scientific papers. Specifically, we used zero-shot learning LLMs and compared Bidirectional and Auto-Regressive Transformers (BART) and its variants with Bidirectional Encoder Representations from Transformers (BERT) and its variants, such as distilBERT, SciBERT, PubmedBERT, and BioBERT. To evaluate the LLMs, we compiled a data set (retinal diseases [RenD] ) of 1000 ocular disease–related articles, which were expertly annotated by a panel of 6 specialists into 19 distinct categories. In addition to the classification of articles, we also performed analysis on different classified groups to find the patterns and trends in the field. ResultsThe classification results demonstrate the effectiveness of LLMs in categorizing a large number of ophthalmology papers without human intervention. The model achieved a mean accuracy of 0.86 and a mean F1-score of 0.85 based on the RenD data set. ConclusionsThe proposed framework achieves notable improvements in both accuracy and efficiency. Its application in the domain of ophthalmology showcases its potential for knowledge organization and retrieval. We performed a trend analysis that enables researchers and clinicians to easily categorize and retrieve relevant papers, saving time and effort in literature review and information gathering as well as identification of emerging scientific trends within different disciplines. Moreover, the extendibility of the model to other scientific fields broadens its impact in facilitating research and trend analysis across diverse disciplines

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

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