TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels

The widespread usage of social networks during mass convergence events, such as health emergencies and disease outbreaks, provides instant access to citizen-generated data that carry rich information about public opinions, sentiments, urgent needs, and situational reports. Such information can help authorities understand the emergent situation and react accordingly. Moreover, social media plays a vital role in tackling misinformation and disinformation. This work presents TBCOV, a large-scale Twitter dataset comprising more than two billion multilingual tweets related to the COVID-19 pandemic collected worldwide over a continuous period of more than one year. More importantly, several state-of-the-art deep learning models are used to enrich the data with important attributes, including sentiment labels, named-entities (e.g., mentions of persons, organizations, locations), user types, and gender information. Last but not least, a geotagging method is proposed to assign country, state, county, and city information to tweets, enabling a myriad of data analysis tasks to understand real-world issues. Our sentiment and trend analyses reveal interesting insights and confirm TBCOV's broad coverage of important topics.Comment: 20 pages, 13 figures, 8 table

GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information

The past several years have witnessed a huge surge in the use of social media platforms during mass convergence events such as health emergencies, natural or human-induced disasters. These non-traditional data sources are becoming vital for disease forecasts and surveillance when preparing for epidemic and pandemic outbreaks. In this paper, we present GeoCoV19, a large-scale Twitter dataset containing more than 524 million multilingual tweets posted over a period of 90 days since February 1, 2020. Moreover, we employ a gazetteer-based approach to infer the geolocation of tweets. We postulate that this large-scale, multilingual, geolocated social media data can empower the research communities to evaluate how societies are collectively coping with this unprecedented global crisis as well as to develop computational methods to address challenges such as identifying fake news, understanding communities' knowledge gaps, building disease forecast and surveillance models, among others.Comment: 10 pages, 5 figures, accepted at ACM SIGSPATIAL Special May 202

Therapeutic amnioinfusion in oligohydramnios during pregnancy (excluding labor)

Background: Oligohydramnios is a serious complication of pregnancy that is associated with a poor perinatal outcome and complicates 1-5% of pregnancies. The purpose of this study was to evaluate the role of antepartum transabdominal amnioinfusion on amniotic fluid volume/latency period in pregnancies with oligohydramnios.Methods: This study was conducted in the Department of Obstetrics and Gynaecology at Sher-i-Kashmir Institute of Medical Sciences Soura Srinagar. In this study, a total of 54 pregnant women with ultrasonographically diagnosed oligohydramnios i.e. AFI < 5 cm and gestational age of >24 weeks were taken for therapeutic amnioinfusion and its effects on amniotic fluid volume were studied. Statistical Software SPSS (Version 20.0) and Microsoft excel were used to carry out the statistical analysis of data. P-value less than 0.05 was considered statistically significant.Results: Mean age of patients in our study was 27.5±3.19 years and gestational age group of 28-34 weeks. The mean AFI pre and post amnioinfusion was found to be 3.3 cm and 8.8 cm respectively. The difference was found to be statistically significant with a p value of <0.001. There was increase in the latency period in the studied patients with a mean latency period of 42.8±14.94 days. Mean number of transabdominal amnioinfusions in our study was 1.48±0.64. In our study, majority of patients i.e. 33 (61.1%) delivered at 38-40 weeks with a mean age at delivery 37.4±1.92 weeks. In our study, maximum number of patients i.e. 32 (59.3%) were delivered by full term normal delivery and only 15 (27.8%) required caesarean section. 78% of newborns weighed >2.5kg. The mean weight of newborn was 2.9±0.59 kgs. The incidence of newborn admissions to NICU was 20.4%. Number of neonatal deaths in our study was 5.6%. There was reduction of neonatal admission to NICU and neonatal deaths after transabdominal amnioinfusion.Conclusions: Antepartum transabdominal amnioinfusion is a useful procedure to reduce complications resulting from decreased intra-amniotic volume. It significantly raises the amniotic fluid especially useful in preterm pregnancies, where the procedure allows for a better perinatal outcome by significantly prolonging the duration of pregnancy, increasing birth weight, preventing fetal distress and thereby reducing operative intervention. Optimizing the selection of patients who are good candidates for the procedure is a prerequisite

An efficient network intrusion detection and classification system

Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

An observational study on congenital talipes equinovarus.

Introduction: Clubfoot or Congenital Talipes Equinovarus (CTEV) is a congenital ab-normality characterized by the permanent foot morphological alteration, conse-quently individual is unable to lean the foot properly on the floor in an appropriate physiological manner.Objective: To execute an observational study on Congenital Talipes Equinovarus (CTEV) in the Orthopedic Department of Indus Medical College Hospital, Tando Mu-hammad Khan.Methodology: Study conducted from Jan 2018 to Jan 2019 at Orthopedic Depart-ment of Indus Medical College Hospital, Tando Muhamad Khan. Medial Crease (MC), Curved Lateral Border (CLB), and Lateral Head of Talus (LHT) assessed based on the Mid Foot Contracture Score (MFCS). Also, an assessment of the Posterior Crease (PC), Empty Heel (EH) and Right Equinus (RE) made as part of the Hind-foot Contracture Score (HFCS). Data collected from 100 participants through conven-ience sampling technique and quantitative analysis performed.Results: Of all participants, Pirani score was calculated from 0 to 1. Scoring is based on the average values. It has been found that the incidence of curved lateral border was 30%, medial crease as 55% and lateral head of talus as 15%. Additionally, it has been found that the empty heel incidence is 35%, posterior crease is 60% and rigid Equinus as 15%. These results reveal that there is a high incidence of CTAV in the given sample.Conclusion: The study revealed that the exact cause (s) of CTEV are still unknown. Keywords: Congenital Talipes Equinovarus (CTEV), Pirani score, Pediatric Orthope-di

Efficacy and safety of sofosbuvir plus ribavirin in treatment-naive chronic hepatitis c genotype 3 patients of South Punjab, Pakistan

Background: To evaluate the efficacy and safety of sofosbuvir (SOF) plus ribavirin (RIB) in naive patients with chronic HCV genotype 3. The study design was open label, quasi experimental study. The study was conducted at Medical Outpatient Department of Medical Unit-1, Bahawal Victoria Hospital, affiliated with Quaid e Azam Medical College (QAMC), Bahawalpur, from April 2016 to June 2019.Methods: A total of 627 treatment-naive patients, aged above 18 years, with chronic Hepatitis C virus (HCV) genotype 3 infection were enrolled. SOF as 400 mg once a day plus weight-based RIB (1000 mg/day 75 kg) was given to all the study participants for 24 weeks. Qualitative polymerase chain reaction (PCR) for HCV ribonucleic acid (RNA) were done at 4 weeks to note the rapid virological response (RVR) whereas end of treatment response (ETR) was recorded at 24 weeks and sustained virological response (SVR) was noted 3 months after completion of treatment.Results: By 4th week, PCR of 524 (83.6%) patients was available, out of which, 492 (93.9%) had undetectable HCV RNA. By the end of treatment (24 weeks), PCR of 401 (64.0%) patients was available, out of which, 393 (98.0%) had undetectable HCV RNA. Data of 291 (46.4%) patients was available for SVR, 274 (94.1%) had undetectable HCV RNA. Weakness and fatigue turned out to be the commonest side effects, observed in 236 (37.6%) patients.Conclusions: Sofosbuvir was found to have good efficacy and safety in the local population of South Punjab having treatment-naïve chronic HCV genotype 3 infection

Machine Learning Techniques for 5G and beyond

Wireless communication systems play a very crucial role in modern society for entertainment, business, commercial, health and safety applications. These systems keep evolving from one generation to next generation and currently we are seeing deployment of fifth generation (5G) wireless systems around the world. Academics and industries are already discussing beyond 5G wireless systems which will be sixth generation (6G) of the evolution. One of the main and key components of 6G systems will be the use of Artificial Intelligence (AI) and Machine Learning (ML) for such wireless networks. Every component and building block of a wireless system that we currently are familiar with from our knowledge of wireless technologies up to 5G, such as physical, network and application layers, will involve one or another AI/ML techniques. This overview paper, presents an up-to-date review of future wireless system concepts such as 6G and role of ML techniques in these future wireless systems. In particular, we present a conceptual model for 6G and show the use and role of ML techniques in each layer of the model. We review some classical and contemporary ML techniques such as supervised and un-supervised learning, Reinforcement Learning (RL), Deep Learning (DL) and Federated Learning (FL) in the context of wireless communication systems. We conclude the paper with some future applications and research challenges in the area of ML and AI for 6G networks. © 2013 IEEE