Utilizing CNN-LSTM techniques for the enhancement of medical systems

COVID-19 is one of the most chronic and serious infections of recent years due to its worldwide spread. Determining who was genuinely affected when the disease spreads more widely is challenging. More than 60% of affected individuals report having a dry cough. In many recent studies, diagnostic models were developed using coughing and other breathing sounds. With the development of technology, body sounds are now collected using digital techniques for respiratory and cardiovascular tests. Early research on identifying COVID-19 utilizing speech and diagnosing signs yielded encouraging findings. The gathering of extensive, multi-group, airborne acoustical sound data is used in the developed framework to conduct an efficient assessment to test for COVID-19. An effective classification model is created to assess COVID-19 utilizing deep learning methods. The MIT-Covid-19 dataset is used as the input, and the Weiner filter is used for pre-processing. Following feature extraction done by Mel-frequency cepstral coefficients, the classification is performed using the CNN-LSTM approach. The study compared the performance of the developed framework with other techniques such as CNN, GRU, and LSTM. Study results revealed that CNN-LSTM outperformed other existing approaches by 97.7%. © 2023 Faculty of Engineering, Alexandria University

Rural women's behavior towards food safety and quality at Damanhour district, Beheira Governorate

The research aimed to identify the behavior of rural women towards food safety and quality at Damanhour Distrct, the simple random sample amounted to 240 respondents, representing 5% of the total. The data were collected through a personal interview by questionnaire. The most important results were: 47.9% of the respondents have a low and medium total level of knowledge of food safety and quality, and 59.5% of them have a low and medium level of implementation of those practices, 52.1% have a negative and neutral attitude towards these practices, 68.3% believe that they have not been previously infected with Covid 19, and 49.2% have not taken the vaccine for Covid 19, the All agreed on the availability of the vaccine, 35% of the respondents have a low and medium level of knowledge of practices related to food safety and quality under Covid 19, and 50.8% have a low and medium level of implementation of those practices. Also, five independent variables together explain 65.4% of the total variance in the respondents' knowledge of practices related to food safety and quality, four independent variables together explain about 62.3% of the total variance in the implementation of practices related to food safety and quality by the respondents. And seven independent variables together explain about 55.4% of the total variance in the attitudes of the rural women respondents towards food safety and quality.

Impact of a Pandemic on Women Diagnosed With Peripartum Cardiomyopathy

Background: Peripartum cardiomyopathy (PPCM) is a dilated form of cardiomyopathy that occurs during the last month of pregnancy or up to five months postpartum. Approximately 1,100 women develop PPCM in the United States each year. The aim of our study is to compare the incidence of PPCM prior to the start of the Coronavirus Disease 2019 (COVID) pandemic to afterwards and to determine the impact of COVID on hospitalized patients with PPCM. Method(s): This was a retrospective study of 2,286 patients with a diagnosis of PPCM who were admitted to a private hospital system across the United States between the year 2017 and year 2021. There was 1,790 patients in the pre-COVID cohort, and 496 patients in the COVID era cohort. Demographics of patients were collected, with t-test and chi square p-values utilization for statistical description. Result(s): The mean age of women was 32.13 years. In the COVID era cohort, the percentage of Hispanic patients was significantly higher than the pre-COVID era (16.84% vs. 12.34%, p=0.012). In the COVID era, patients were more likely to have preeclampsia (20.16% vs. 13.52%, p<0.001), HELLP (hemolysis, elevated liver enzymes, low platelets) syndrome (2.62% vs. 0.61%, p<0.001), respiratory failure (19.56% vs. 10.57%, p<0.001), and myocardial infarction (3.63% vs. 1.90%, p=0.022). There was no significant difference in troponin and d-dimer values between the two era cohorts. The average length of stay, percentage of patients admitted to the intensive care unit (ICU), and death did not significantly differ between the pre-COVID and COVID era cohorts. Conclusion(s): In our study, although there was no significant difference in length of stay, ICU admission, or death in the COVID era cohort, myocardial infarction, preeclampsia, HELLP syndrome, and respiratory failure were each more prevalent in women with PPCM during the COVID era. These findings might indicate suboptimal access to outpatient and inpatient medical care during the COVID pandemic, which could have led to these more serious diagnoses.

Explore the E-Learning Management System Lower usage during COVID-19 Pandemic

During the COVID-19 pandemic, several universities are finding it difficult to provide and use online and e-learning systems. Blackboard, for example, is an e-learning system with various wonderful features that would be useful during the COVID-19 pandemic. However, knowing the acceptance variables as well as the primary problems that contemporary e-learning technologies confront is crucial for efficient utilization. The growing number of students attending different instructional organizations has resulted in a greater volume of material being needed in these organizations both from the academic and professional workforce and also because learning management systems and e-learning are indeed the university prospect, several more universities and colleges have accepted them. The purpose is to analyze the most popular E-learning system, the Blackboard system, and the authors suggest a learning management control system to accommodate major e-learning features. A Blackboard system is a plethora of academic perspectives, research, ideas, theories, and affective responses to the virtual learning environment. To use it, the technology acceptance model in times of crisis (TAMTC) has been developed as a way to evaluate student acceptability. The existing literature demonstrates that the field of information administration is constantly changing due to the effect of learning technologies like the blackboard system. Given their reduced utilization of the system, the data reveal a high level of student acceptability. The conclusions of this study provide important recommendations for policymakers, managers, developers, and academics, allowing them may further understand the key factors of successfully using an e-learning system during the COVID-19 epidemic. © 2022 NSP Natural Sciences Publishing Cor.

Sentiment Analysis of COVID-19 Tweets: Impact of Pre-processing Step

Internet users are increasingly invited to express their opinions on various subjects in social networks, e-commerce sites, news sites, forums, etc. Much of this information, which describes feelings, becomes the subject of study in several areas of research such as: "Sensing opinions and analyzing feelings". It is the process of identifying the polarity of the feelings held in the opinions found in the interactions of Internet users on the web and classifying them as positive, negative, or neutral. In this article, we suggest the implementation of a sentiment analysis tool that has the role of detecting the polarity of opinions from people about COVID-19 extracted from social media (tweeter) in the Arabic language and to know the impact of the preprocessing phase on the opinions classification. The results show gaps in this area of research, first of all, the lack of resources when collecting data. Second, Arabic language is more complexes in pre-processing step, especially the dialects in the pre-treatment phase. But ultimately the results obtained are promising.