Evaluation of internet access and utilization by medical students in Lahore, Pakistan

<p>Abstract</p> <p>Background</p> <p>The internet is increasingly being used worldwide in imparting medical education and improving its delivery. It has become an important tool for healthcare professionals training but the data on its use by medical students in developing countries is lacking with no study on the subject from Pakistan. This study was, therefore, carried out with an aim to evaluate the pattern of internet access and utilization by medical students in Pakistan.</p> <p>Methods</p> <p>A structured pre-tested questionnaire was administered to a group of 750 medical students in clinical years studying at various public and private medical colleges in Lahore. The questions were related to patterns of internet access, purpose of use and self reported confidence in performing various internet related tasks, use of health related websites to supplement learning and the problems faced by students in using internet at the institution.</p> <p>Results</p> <p>A total of 532 medical students (70.9%) returned the questionnaire. The mean age of study participants was 21.04 years (SD 1.96 years). Majority of the respondents (84.0%) reported experience with internet use. About half of the students (42.1%) were using internet occasionally with 23.1%, 20.9% and 13.9% doing so frequently, regularly and rarely respectively. About two third of the students (61.0%) stated that they use internet for both academic and professional activities. Most of the participants preferred to use internet at home (70.5%). Self reported ability to search for required article from PubMed and PakMedinet was reported by only 34.0% of the entire sample. Students were moderately confident in performing various internet related tasks including downloading medical books from internet, searching internet for classification of diseases and downloading full text article. Health related websites were being accessed by 55.1% students to supplement their learning process. Lack of time, inadequate number of available computers and lack of support from staff were cited as the most common problems faced by students while accessing internet in the institution premises. There were significant differences among male and female students with respect to the place of internet use (p = 0.001) and the ability to search online databases for required articles (p = 0.014).</p> <p>Conclusions</p> <p>Majority of the medical students in this study had access to internet and were using it for both academic and personal reasons. Nevertheless, it was seen that there is under utilization of the potential of internet resources to augment learning. Increase in awareness, availability of requisite facilities and training in computing skills are required to enable better utilization of digital resources of digital resources by medical students.</p

Evolving trends in the management of acute appendicitis during COVID-19 waves. The ACIE appy II study

Background: In 2020, ACIE Appy study showed that COVID-19 pandemic heavily affected the management of patients with acute appendicitis (AA) worldwide, with an increased rate of non-operative management (NOM) strategies and a trend toward open surgery due to concern of virus transmission by laparoscopy and controversial recommendations on this issue. The aim of this study was to survey again the same group of surgeons to assess if any difference in management attitudes of AA had occurred in the later stages of the outbreak. Methods: From August 15 to September 30, 2021, an online questionnaire was sent to all 709 participants of the ACIE Appy study. The questionnaire included questions on personal protective equipment (PPE), local policies and screening for SARS-CoV-2 infection, NOM, surgical approach and disease presentations in 2021. The results were compared with the results from the previous study. Results: A total of 476 answers were collected (response rate 67.1%). Screening policies were significatively improved with most patients screened regardless of symptoms (89.5% vs. 37.4%) with PCR and antigenic test as the preferred test (74.1% vs. 26.3%). More patients tested positive before surgery and commercial systems were the preferred ones to filter smoke plumes during laparoscopy. Laparoscopic appendicectomy was the first option in the treatment of AA, with a declined use of NOM. Conclusion: Management of AA has improved in the last waves of pandemic. Increased evidence regarding SARS-COV-2 infection along with a timely healthcare systems response has been translated into tailored attitudes and a better care for patients with AA worldwide

Experimental evaluation of Arabic OCR systems

Purpose – The aim of this paper is to experimentally evaluate the effectiveness of the state-of-the-art printed Arabic text recognition systems to determine open areas for future improvements. In addition, this paper proposes a standard protocol with a set of metrics for measuring the effectiveness of Arabic optical character recognition (OCR) systems to assist researchers in comparing different Arabic OCR approaches. Design/methodology/approach – This paper describes an experiment to automatically evaluate four well-known Arabic OCR systems using a set of performance metrics. The evaluation experiment is conducted on a publicly available printed Arabic dataset comprising 240 text images with a variety of resolution levels, font types, font styles and font sizes. Findings – The experimental results show that the field of character recognition for printed Arabic still requires further research to reach an efficient text recognition method for Arabic script. Originality/value – To the best of the authors’ knowledge, this is the first work that provides a comprehensive automated evaluation of Arabic OCR systems with respect to the characteristics of Arabic script and, in addition, proposes an evaluation methodology that can be used as a benchmark by researchers and therefore will contribute significantly to the enhancement of the field of Arabic script recognition

Analysis of Birth Data using Ensemble Modeling Techniques

Machine learning and data mining are being used in different fields like data analysis, prediction, image processing, etc., and particularly in healthcare. Over the past decade, several types of research have been carried out focusing on machine learning and data mining application to generate intuitions from historical data and make predictions about the results. Machine learning algorithms play a vital role in improving healthcare systems due to continuous research in machine learning applications. Several researchers have used algorithms of machine learning to develop systems for decision support, analyze clinical aspects, use historical data to extract useful information, make future predictions and categorize diseases, etc. to help physicians make better decisions. In this study, we used an ensemble modeling voting technique for the classification of the birth dataset. Ensemble models combine individual machine learning algorithms to improve the accuracy by predicting from the combined output of the base classifiers. Gradient boosting classifier (GBC), random forest (RF), bagging classifier (BC), and extra trees classifier (ETC) were used as base learners for making a voting ensemble model for the classification of the birth dataset. The results produced have shown that the voting classifier of support vector machine (SVM), random forest (RF), extra trees classifier, and bagging classifier has given the best results with the proportion of 94.78%, gradient boosting classifier has 84.39% accuracy, the random forest has 94.26% accuracy, extra trees classifier have 94.02% accuracy and bagging classifier has 93.65% accuracy. The accuracy achieved by ensemble modeling is far higher than the machine learning algorithms. Ensemble models increase the accuracy of machine learning algorithms by reducing variance and classification errors. The development of such a system will not only help health organizations to take effective measures to improve the maternal health assessment process but will also open the doors for interdisciplinary research in two different fields in the region

Continual Learning Objective for Analyzing Complex Knowledge Representations

Human beings tend to incrementally learn from the rapidly changing environment without comprising or forgetting the already learned representations. Although deep learning also has the potential to mimic such human behaviors to some extent, it suffers from catastrophic forgetting due to which its performance on already learned tasks drastically decreases while learning about newer knowledge. Many researchers have proposed promising solutions to eliminate such catastrophic forgetting during the knowledge distillation process. However, to our best knowledge, there is no literature available to date that exploits the complex relationships between these solutions and utilizes them for the effective learning that spans over multiple datasets and even multiple domains. In this paper, we propose a continual learning objective that encompasses mutual distillation loss to understand such complex relationships and allows deep learning models to effectively retain the prior knowledge while adapting to the new classes, new datasets, and even new applications. The proposed objective was rigorously tested on nine publicly available, multi-vendor, and multimodal datasets that span over three applications, and it achieved the top-1 accuracy of 0.9863% and an F1-score of 0.9930

Stock Price Forecasting for Jordan Insurance Companies Amid the COVID-19 Pandemic Utilizing Off-the-Shelf Technical Analysis Methods

One of the most difficult problems analysts and decision-makers may face is how to improve the forecasting and predicting of financial time series. However, several efforts were made to develop more accurate and reliable forecasting methods. The main purpose of this study is to use technical analysis methods to forecast Jordanian insurance companies and accordingly examine their performance during the COVID-19 pandemic. Several experiments were conducted on the daily stock prices of ten insurance companies, collected by the Amman Stock Exchange, to evaluate the selected technical analysis methods. The experimental results show that the non-parametric Exponential Decay Weighted Average (EDWA) has higher forecasting capabilities than some of the more popular forecasting strategies, such as Simple Moving Average, Weighted Moving Average, and Exponential Smoothing. As a result, we show that using EDWA to forecast the share price of insurance companies in Jordan is good practice. From a technical analysis perspective, our research also shows that the pandemic had different effects on different Jordanian insurance companies

Stock Price Forecasting for Jordan Insurance Companies Amid the COVID-19 Pandemic Utilizing Off-the-Shelf Technical Analysis Methods

One of the most difficult problems analysts and decision-makers may face is how to improve the forecasting and predicting of financial time series. However, several efforts were made to develop more accurate and reliable forecasting methods. The main purpose of this study is to use technical analysis methods to forecast Jordanian insurance companies and accordingly examine their performance during the COVID-19 pandemic. Several experiments were conducted on the daily stock prices of ten insurance companies, collected by the Amman Stock Exchange, to evaluate the selected technical analysis methods. The experimental results show that the non-parametric Exponential Decay Weighted Average (EDWA) has higher forecasting capabilities than some of the more popular forecasting strategies, such as Simple Moving Average, Weighted Moving Average, and Exponential Smoothing. As a result, we show that using EDWA to forecast the share price of insurance companies in Jordan is good practice. From a technical analysis perspective, our research also shows that the pandemic had different effects on different Jordanian insurance companies

RDPVR: Random Data Partitioning with Voting Rule for Machine Learning from Class-Imbalanced Datasets

Since most classifiers are biased toward the dominant class, class imbalance is a challenging problem in machine learning. The most popular approaches to solving this problem include oversampling minority examples and undersampling majority examples. Oversampling may increase the probability of overfitting, whereas undersampling eliminates examples that may be crucial to the learning process. We present a linear time resampling method based on random data partitioning and a majority voting rule to address both concerns, where an imbalanced dataset is partitioned into a number of small subdatasets, each of which must be class balanced. After that, a specific classifier is trained for each subdataset, and the final classification result is established by applying the majority voting rule to the results of all of the trained models. We compared the performance of the proposed method to some of the most well-known oversampling and undersampling methods, employing a range of classifiers, on 33 benchmark machine learning class-imbalanced datasets. The classification results produced by the classifiers employed on the generated data by the proposed method were comparable to most of the resampling methods tested, with the exception of SMOTEFUNA, which is an oversampling method that increases the probability of overfitting. The proposed method produced results that were comparable to the Easy Ensemble (EE) undersampling method. As a result, for solving the challenge of machine learning from class-imbalanced datasets, we advocate using either EE or our method

Modeling and Analysis of Proof-Based Strategies for Distributed Consensus in Blockchain-Based Peer-to-Peer Networks

Blockchain technology has a wide range of applicability in the fields of transportation infrastructure construction and maintenance, transportation big data analysis and application, expressway toll collection, and logistics. The core technology lies in the distributed, decentralized, immutable, and programmable features brought about by consensus. This paper studies the dynamic analytical modeling of Proof-Based Consensus (PBC) strategies in blockchain systems, focusing on basic strategies, including Proof of Work (PoW), Proof of Stake (PoS), Proof of Authority (PoA), and Proof of Luck (PoL), which can be extended to other PBC models. We focus on modeling these typical strategies and discuss their solution characteristics in terms of algorithmic mechanisms and principles. The relevant results can be used for quantitative analysis and evaluation of distributed consensus based on the model