Ensemble Deep Learning Framework for Situational Aspects-Based Annotation and Classification of International Student’s Tweets during COVID-19

As the COVID-19 pandemic swept the globe,social media plat-forms became an essential source of information and communication for many.International students,particularly,turned to Twitter to express their struggles and hardships during this difficult time.To better understand the sentiments and experiences of these international students,we developed the Situational Aspect-Based Annotation and Classification(SABAC)text mining framework.This framework uses a three-layer approach,combining baseline Deep Learning(DL)models with Machine Learning(ML)models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 dataset.Using the pro-posed aspect2class annotation algorithm,we labeled bulk unlabeled tweets according to their contained aspect terms.However,we also recognized the challenges of reducing data’s high dimensionality and sparsity to improve performance and annotation on unlabeled datasets.To address this issue,we proposed the Volatile Stopwords Filtering(VSF)technique to reduce sparsity and enhance classifier performance.The resulting Student-COVID Twitter dataset achieved a sophisticated accuracy of 93.21%when using the random forest as a meta-classifier.Through testing on three benchmark datasets,we found that the SABAC ensemble framework performed exceptionally well.Our findings showed that international students during the pandemic faced various issues,including stress,uncertainty,health concerns,financial stress,and difficulties with online classes and returning to school.By analyzing and summarizing these annotated tweets,decision-makers can better understand and address the real-time problems international students face during the ongoing pandemic.

Acute and Chronic Toxicity of Thioacteamide and Alterations in Blood Cell Indices in Rats

Background: Thioacetamide (TAA) has been used extensively in the development of suitable animal models of acute and chronic liver injury employing various doses, times and routes of its administration, particularly in drinking water due to its resemblance with human liver fibrosis and cirrhosis. The aim of this study was to investigate and compare hematological alteration during the acute and chronic liver inflammation. Methods: Acute Liver inflammation was induced in Wistar rats via intraperitoneal injection of thioacetamide and the animals were sacrificed 12 h after the TAA administration. Induction of chronic liver inflammation was performed by continuous administration of TAA in the drinking water (200 mg/L) during 18 weeks of experiment. After that all animals were sacrificed and Blood samples were collected for further analysis. Results: Single intra peritoneal injection of TAA (300 mg/kg B.W.) induced an acute condition with hematological changes including leukocytosis with marked neutrophilia (P = 0.0429), lymphopenia, thrombocytosis as well as increased hemoglobin concentration (P 0.05) and decline of erythrocytic count (P = 0.0009). Eighteen weeks of uninterrupted supply of TAA (200 mg/L) in drinking water lead to chronic inflammation and the hematological alterations were leucopenia (P = 0.0197) accompanied with neutropenia and thrombocytopenia. Increase in RBCs (P = 0.0073) and Hb contents was also observed with a decline of red cell indices. Conclusion: Taken together these findings we can conclude that the animals respond differently under acute and chronic inflammatory condition with TAA administration. Leukocytosis with marked neutrophilia, thrombocytosis as well as increased hemoglobin concentration and decline of erythrocytic count were observed in acute while leucopenia accompanied with neutropenia and thrombocytopenia and increase in RBCs, Hb and Hct was also observed with a decline of other red cell indices during chronic phase.