Rethinking Densely Connected Convolutional Networks for Diagnosing Infectious Diseases

Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a valuable and cost-effective tool for detecting and diagnosing COVID-19 patients. In this study, we developed a deep learning model using transfer learning with optimized DenseNet-169 and DenseNet-201 models for three-class classification, utilizing the Nadam optimizer. We modified the traditional DenseNet architecture and tuned the hyperparameters to improve the model's performance. The model was evaluated on a novel dataset of 3312 X-ray images from publicly available datasets, using metrics such as accuracy, recall, precision, F1-score, and the area under the receiver operating characteristics curve. Our results showed impressive detection rate accuracy and recall for COVID-19 patients, with 95.98% and 96% achieved using DenseNet-169 and 96.18% and 99% using DenseNet-201. Unique layer configurations and the Nadam optimization algorithm enabled our deep learning model to achieve high rates of accuracy not only for detecting COVID-19 patients but also for identifying normal and pneumonia-affected patients. The model's ability to detect lung problems early on, as well as its low false-positive and false-negative rates, suggest that it has the potential to serve as a reliable diagnostic tool for a variety of lung diseases.

Masked Face Recognition System using Extended VGG-19

The scientific community has observed several issues as a result of COVID-19, both directly and indirectly. The use of face mask for health protection is crucial in the current COVID-19 scenario. Besides, ensuring the security of all people, from individuals to the state system, financial resources, diverse establishments, government, and non-government entities, is an essential component of contemporary life. Face recognition system is one of the most widely used security technology in modern life. In the presence of face masks, the performance of the current face recognition systems is not satisfactory. In this paper, we investigate a flexible solution that could be employed to recognize masked faces effectively. To do this, we develop a unique dataset to recognize the masked face, consisting of a frontal and lateral face with a mask. We propose an extended VGG19 deep model to improve the accuracy of the masked face recognition system. Then, we compare the accuracy of the proposed framework to that of well-known deep learning techniques, such as the standard Convolutional Neural Network (CNN) and the original VGG19. The experimental results demonstrate that the proposed extended VGG19 outperforms the investigated approaches. Quantitatively, the proposed model recognizes the frontal face with the mask with high accuracy of 96%. © 2022 IEEE.

Impact of COVID-19 on the Factors Influencing on-Time Software Project Delivery: An Empirical Study

The objective of this research paper is to investigate the impact of COVID-19 on the factors influencing on-time software project delivery in different Software Development Life Cycle (SDLC) models such as Agile, Incremental, Waterfall, and Prototype models. Also to identify the change of crucial factors with respect to different demographic information that influences on-time software project delivery. This study has been conducted using a quantitative approach. We surveyed Software Developers, Project Managers, Software Architect, QA Engineer and other roles using a Google form. Python has been used for data analysis purposes. We received 72 responses from 11 different software companies of Bangladesh, based on that we find that Attentional Focus, Team Stability, Communication, Team Maturity, and User Involvement are the most important factors for on-time software project delivery in different SDLC models during COVID-19. On the contrary, before COVID-19 Team Capabilities, Infrastructure, Team Commitment, Team Stability and Team Maturity are found as the most crucial factors. Team Maturity and Team Stability are found as common important factors for both before and during the COVID-19 scenario. We also identified the change in the impact level of factors with respect to demographic information such as experience, company size, and different SDLC models used by participants. Attentional focus is the most important factor for experienced developers while for freshers all factors are almost equally important. This study finds that there is a significant change among factors for on-time software project delivery before and during the COVID-19 scenario. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Deep Convolutional Neural Network Based Automatic COVID-19 Detection from Chest X-ray Images

To minimize the rate of death from COVID-19 and stop the disease from spreading early detection is vital. The normal RT-PCR tests for COVID-19 detection take a long time to complete. In contrast to this test, Covid-19 can be quickly detected using various machine-learning technologies. Previous studies only had access to smaller datasets, as COVID-19 data was not readily available back then. Since COVID-19 is a dangerous virus, the model needs to be robust and trustworthy, and the model must be trained on a large and diverse dataset. To overcome that problem, this study combines six publicly available Chest X-ray datasets to produce a larger and more diverse balanced dataset with a total of 68,424 images. In this study, we develop a CNN model that primarily entails two steps: (a) feature extraction and (b) classification, which are used to identify COVID-19 positive cases from X-ray images. The accuracy of this proposed model is 97.58%, which is higher than most state-of-the-art models. © 2022 IEEE.

Priority determination of bridge construction in Bireuen Regency, Indonesia

A bridge is a complementary structure connecting several trajectories, for people or vehicles to cross from one side to another. This is often due to the presence of obstacles across topographical objects or other causes. The Public Works and Housing Agency (PUPR) of Bireuen Regency has proposed the construction of a bridge to the Bireuen Regency Regional Development Planning Agency (Bappeda), based on 3 sections to be designed with a total cost of IDR 16,882,540,000 in 2022. However, this proposal is not vehemently increased due to budget constraints, as the focus is switched to handling the Corona Virus Disease (COVID-19) in Indonesia. Therefore, the assessment of the priority scale is very necessary, in order to determine the most important bridge sections to be constructed for public interest in 2022. This study aims to analyze the dominant criteria considered in bridge construction, and also the priority order of this infrastructure in Bireuen Regency. A quantitative method was utilized through a questionnaire, as the selection was conducted by using a purposive sampling technique, with considerations based on stakeholders in the field of development. The respondents were approximately 6 stakeholders, which were selected from different departments in Bireuen Regency (Assistant Regional Secretary, Head of the Highways Division of the PUPR Service, Head of the Regional Development Program Division of the BAPPEDA, Head of Land Transportation Division, Member of Commission IV for the Development of the Regency People's Representative Council (DPRK), and Syiah Kuala University Academics). The criteria reviewed were the length of construction, cost, land use, accessibility, population, social facilities, and economic facilities. Meanwhile, the alternatives considered were the Alue Phon Krueng Simpo, Awe Geutah, and West Ie Rhob Sirong Gampong Bridges, respectively. The data analysis process also used the MCA (Multi-Criteria Analysis) technique, as the results showed that the dominant criterion in the study was the cost. It also showed that the priority order for construction started with the West Sirong Ie Rhob Bridge (priority 1), accompanied by the Alue Phon Krueng Simpo and Awe Geutah Steel Frame Bridges (priority 2 and 3), at Pi values of 7.94, 4.81, and 3.43, respectively. © 2023 Author(s).

Generalized anxiety disorder among Bangladeshi university students during COVID-19 pandemic: gender specific findings from a cross-sectional study.

In the current COVID-19 pandemic there are reports of deteriorating psychological conditions among university students in lower-middle-income countries (LMICs), but very little is known about the gender differences in the mental health conditions on this population. This study aims to assess generalized anxiety disorder (GAD) among university students using a gender lens during the COVID-19 pandemic. A cross-sectional study was conducted using web-based Google forms between May 2020 and August 2020 among 605 current students of two universities in Bangladesh. Within the total 605 study participants, 59.5% (360) were female. The prevalence of mild to severe anxiety disorder was 61.8% among females and 38.2% among males. In the multivariable logistic regression analysis, females were 2.21 times more likely to have anxiety compared to males [AOR: 2.21; CI 95% (1.28-53.70); p-value: 0.004] and participants' age was negatively associated with increased levels of anxiety (AOR = 0.17; 95% CI = 0.05-0.57; p = 0.001). In addition, participants who were worried about academic delays were more anxious than those who were not worried about it (AOR: 2.82; 95% CI 1.50-5.31, p = 0.001). These findings of this study will add value to the existing limited evidence and strongly advocate in designing gender-specific, low-intensity interventions to ensure comprehensive mental health services for the young adult population of Bangladesh.

Remotely Controlled Real-Time Health Surveillance System of COVID-19 Patients

This work focuses on the development of a portable physiological monitoring framework that can continuously monitor the patient's heartbeat, oxygen levels, temperature, ECG measurement, blood pressure, and other fundamental patient's data. As a result of this, the workload and the chances of being infected by COVID-19 of the health workers will be reduced and an efficient patient monitoring system can be maintained. In this paper, an IoT based continuous monitoring system has been developed to monitor all COVID-19 patient conditions and store patient data in the cloud server using Wi-Fi Module-based remote communication. In this monitoring system, data stored on IoT platform can be accessed by an authorized individual and ailments can be examined by the doctors from a distance based on the values obtained. If a patient's physical condition deteriorates, the doctor will immediately receive the emergency alert notification. This model proposed in this research work would be extremely important in dealing with the Corona epidemic around the world. © 2022 IEEE.

Factors associated with poor outcomes in patients with severe acute respiratory infections in Bahrain

Background Severe acute respiratory tract infection (SARI) is a major global health threat. This study aimed to examine risk factors associated with poor outcomes in patients with SARI. Methods All patients who met World Health Organization's (WHO) SARI case definition and were admitted to Salmaniya Medical Complex from January 2018 to December 2021 were included. Epidemiological and virological data were obtained and analyzed. Results Of 1159 patients with SARI included, 731 (63.1%) patients were below 50 years, and 357 (30.8%) tested positive for viral pathogens. The most prevalent virus was Flu‐A (n = 134, 37.5%), SARS‐CoV2 (n = 118, 33%), RSV (n = 51, 14.3%), Flu B (n = 49,13.7%), other viruses (n = 3, 0.8%), and combined infection (n = 2, 0.6%). Six hundred fifty‐eight (56.8%) patients had comorbidities, mainly diabetes (n = 284, 43%) and heart disease (n = 217, 33%). 183 (16%) patients were admitted to ICU, 110 (9%) needed mechanical ventilation, and 80 (7%) patients died. The odds of ICU admission were higher for patients with hematological (OR 5.9, 95% CI 3.1–11.1) and lung diseases (OR 2.7, 95% CI 1.6–4.6). The odds of mechanical ventilation were higher among patients with lung disease (OR 3.1, 95% 1.7–5.5). The mortality odds were higher among patients above 50 (OR 2.4, 95% CI 1.4–4.1) and chronic kidney disease (OR 2.5, 95% CI 1.1–5.2). Conclusions Being 50 years or above or having kidney, lung, or heart diseases was associated with worse SARI outcomes. Efforts and actions in developing better strategies to vaccinate individuals at high risk and early diagnosis and treatment should help in reducing the burden of SARI.

Distinguish coronavirus disease from seasonal influenza using deep learning algorithm

With the increasing number of cases of Corona virus, and with the availability of vaccines with virus Corona, the virus has not yet been controlled, and this may be due to certain reasons. The person who took the vaccine, if the vaccine was less immune may be a virus carrier, and this in turn may be the cause of the spread of the infection. The large artery of humans may not be committed to prevention if the vaccine is taken and this also leads to infection, especially we did not find from the global medical team of boots that whoever took the vaccine will never get infected again. Among the resources already in place, widely available and low-cost, the research proposes building an artificial intelligence system (supervised deep learning) to make a preliminary diagnosis of patients who are unaware of virus infection and to know the type of virus whether it is light, medium or dangerous and distinguish it from other diseases whose symptoms are similar to corona virus symptoms such as respiratory diseases, seasonal flu virus infections, kidney diseases, heart disease, nervous system diseases, diseases of the reproductive system. With the capacity of the research space and the parameters of the publication selectors, we will only take the regular seasonal flu as examples of research in the hope that we will complete other diseases and the extent to which they overlap with the Corona virus in the upcoming research hopefully. In this research, we will build an artificial intelligence system and simulate it through the application (supervised deep learning) to reach the accuracy of the diagnosis and safe isolation of people who do not know whether they are infected or not, and determine the type of infection virus, in turn reduces the spread of infection and also help the medical staff who suffered the scourges due to this virus after taking many lives. © 2023 Author(s).

INFLUENCE OF COVID-19 AND EMPLOYEES' RESPONSE TO DEVIATIONS ON EMPLOYEE ENACTMENT

Developing countries' economies are in shambles as a result of the coronavirus. Developing countries like Bangladesh began opening its business sector in May 2020 in order to preserve the economy. To mitigate the effect of coronavirus, the government has implemented "new normal” guidelines for businesses. The primary goals of this research are to determine how the COVID-19 pandemic has influenced employee performance and to determine the workers' perspectives regarding the changes that have been made to their everyday lives. To complete this research, employee performance was assessed using the employee response to change (ERC) method. Employees from many sectors have been studied. For this research, 300 people from various sectors were surveyed online at random. The study was quantitative as well as exploratory. It was based solely on original data. The research used a non-probability sampling approach to collect data. The survey questionnaire was sent to those who replied via Google Forms. Results and visual representations are found using SPSS software and Microsoft Excel. COVID-19 and the reaction to employee changes have a considerable detrimental influence on employee performance, according to all of the study's findings. The employee's focus, communication, and attention to work are all adversely affected by these "new normal” alterations. © 2023 The Authors.

DESIGN AND DEVELOPMENT OF ROBO MEDICAL ASSISTANT

The history of the medical robot is not very far from the first experiment in the 1980s. Nowadays robot in the medical sector plays a vital role in monitoring patient's health condition from distance. This paper aimed at developing an auxiliary medical solution that could provide a wide range of non-invasive diagnoses carried out by an automated robot whose motion can also be controlled manually using either a mobile application or voice command. The authors also incorporate modern features of video conferences and automated patient data management systems using the Internet of Things (IoT) which eventually facilitate medical practitioners in proper investigation from distance. The results of the clinical trial among 6 persons indicated that the robot could measure different health parameters properly using the proposed non-invasive method. The non-invasive results are verified by standard testing equipment and conventional clinical investigation and are also presented in this paper. The developed medical robot having a wide range of functionality could play a significant role in reducing human workload and ensuring timely medical assistance during a challenging crisis pandemic period like COVID-19. © 2023 IEEE.

Autonomous Medical Assistive Robot for Medical Camp

The outbreak of the Covid-19 epidemic has devastated the generation and impacted multiple layers of the healthcare sector. Resulting from this kind of exceptionally contagious virus and a shortfall of medical workers in the hospitals, front-line health workers, and patients are at risk. Thus, with an aim to diminish the risk of infections, a mobile robotic system is proposed that can autonomously ensure safety and protection in the hospital. The system can monitor the patients by moving autonomously and sanitizing the floor throughout the hospital, which is implemented by Robot Operating System (ROS), SLAM (Simultaneous Localization and Mapping) algorithm, and A∗ search algorithm, and then it uses the MobileNetV2 algorithm for safety mask detection and giving voice alert. The system also offers AI voice communication to assist and diagnose the patients, which can lessen person-to-person contact. The system has anticipated 89% accuracy for AI custom dataset, whereas the validation accuracy for face mask detection is 95%. © 2022 IEEE.

Model Development for Predicting the Crude Oil Price: Comparative Evaluation of Ensemble and Machine Learning Methods

The crude oil market is unstable, and its price is highly volatile. Due to the Covid-19 pandemic, the price of crude oils goes up and down in a short period of time. Future plans and projects' policies depend directly and indirectly on the future price of crude oil. So, the aim of this study is to predict the price of crude oil by using machine learning and ensemble algorithm, as well as to show the comparison of performance of Ada Boost, Bagging Lasso and Support Vector Regression model. The study uses crude oil price time series data for analysis and to form a model to predict future price. The actual vs. predicted curve is used to show the performance of each algorithm individually. Analysis shows that the ensemble AdaBoost algorithm displays better performance than other algorithms. The result is validated using mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), two accuracy score function variance score, and R2 score. This study will help the stakeholders of the crude oil industry in making decisions and formulating policies based on forecasted crude oil prices. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Searching hedging instruments against diverse global risks and uncertainties

We evaluate the influence of five major risk and uncertainty factors on four asset classes. Our time-varying findings suggest that each asset hedges only a particular uncertainty factor, whereas gold does more than one factor, especially during COVID-19. Our frequency-based quantile regression (QR) results show that in the raw frequency, gold and Islamic stock can better hedge various uncertainty factors than Bitcoin and crude oil, depending on the market conditions. Additionally, using the frequency bands (e.g., short, medium, and long term) data, we further notice that, depending on the market circumstances and investment horizons, gold and Islamic stock returns are still better hedges for the various risks and uncertainties than Bitcoin and crude oil returns. Our findings have crucial risk and portfolio management implications for investors, portfolio managers, and policymakers. © 2023 Elsevier Inc.

COVID-19 in city and municipal area: socio-economic analysis in Khulna, Bangladesh

Purpose: In Bangladesh (a middle-income and densely populated country) where socio-economic factors act badly on human activities during COVID-19. This research mainly focused on observing the socio-economic aspects of the Pandemic on human life between city and municipal areas. Design/methodology/approach: This research relied on Khulna City Corporation (KCC) and Paikgacha Municipality of Bangladesh. A random sampling technique was adopted for choosing 622 stakeholders (318 in the city and 304 in the municipal area). Here, the socio-economic factors have been fixed based on the literature review and expert opinion. This study explored two mainstream social and economic issues affected by the Pandemic. Several statistical tests were performed to find the relationship among Socio-economic factors. Findings: The study shows that the Pandemic caused great harm to city areas rather than municipal areas. The city is faced with tremendous pressure on the economic aspect as well. Besides, the pandemic affects savings, education sectors, food habits and other factors in both areas. The trip distribution also differs between the study areas and the mobility pattern shows that people migrated to rural areas from city space during the Pandemic. Originality/value: This research will assist in focusing on a micro-level perspective in the future to analyze socio-economic changes. Moreover, it can help to point out the administrative prospects in the future. © 2023, Emerald Publishing Limited.

Dietary food antioxidants and their radical scavenging activity: A review

Food antioxidants can prevent or/and delay free radical formation which is responsible for oxidative stress. Nowadays, natural remedy becomes the highest concern in many countries, as well as discouraging the intake of synthetic counterparts to avoid the burden of side effects on human health. Regular intake of dietary antioxidants could help to improve the fitness of the body, and subsequently make the body more competitive in its fight against diseases through enhanced immune response. The present review thus summarised recent knowledge on the dietary source of antioxidants, and also mechanism of action and functionalities on human health benefits. Due to the proven ability to restore mitochondrial function and cellular redox balance, food antioxidants also have great potential as natural therapies against COVID-19. However, the numbers of trials are still limited. There must be more tests with the hope that these compounds will mitigate the COVID-19 and similar outbreaks in the future © All Rights Reserved

Characterization and antibiogram study of pneumonia causing bacteria isolated from blood culture of children in northern Bangladesh

Children are usually affected by pneumonia, which is a common ailment caused by Pathogenic Streptococcus pneumoniae. This study's objective was to isolate and identify S. pneumoniae, which was recovered from blood samples of suspected paediatric pneumonia patients using conventional techniques, such as antibiotic sensitivity profiles and molecular approaches. In this study, forty (40) samples from three major hospitals in the Dinajpur region of Bangladesh were collected and assessed using various bacteriological, biochemical, antibiotic susceptibility test, and molecular techniques. 37.5% of the 40 samples tested positive for pneumonia, and 15 isolates were discovered. In terms of age, pneumonia was more common in children aged 3-5 years (50%) than in those aged 6 to 8 (33.33%), 9 to 11 (25%) and 12 to 15 (20%). According to the results of the current study, the study area had no statistically significant impact (P > 0.05), while age and socioeconomic status had a significant impact on the prevalence of pneumonia in patients with pneumonia (P 0.05). The age group for which pneumonia was most prevalent (at 50%) was that for children between the ages of 3-5. Poor socioeconomic status was associated with the highest prevalence of pneumonia (54.54%). By sequencing the 16S rRNA gene, S. pneumoniae was identified as S. pneumoniae NBRC102642. In the antibiotic investigation, S. pneumoniae was found to be extremely resistant to ciprofloxacin, amikacin, vancomycin, and cefexime, but responsive to erythromycin and azithromycin, as well as neomycin, kanamycin, streptomycin, and bacitracin. S. pneumoniae causes serious complications in paediatric patients, and this scenario requires prevention through vaccination and the development of new, efficient antibiotic therapies for pneumonia. If specific laboratory features of paediatric patients with pneumonia are understood, sepsis will be easier to detect early, treat, and reduce mortality.

E-learning Quality Assurance Guidelines for Higher Education Institutions in Jordan

The Covid-19 pandemic affected many areas around the world, and with the lack of a vaccine, social distancing and keeping a high level of hygiene was the only actions people can rely on. Because of this change in the way of life, education was affected where it became not normal for students to socialize and have direct contact with teachers. Taking the case of higher education in Jordan, and its conversion into an online based learning approach, this paper examines an assessment of the quality of education that was taking place during the lock down. Then, it presents a literature study of guidelines published about quality of e-learning, and after evaluating the literature and the specific needs of Jordanian institutes, the paper presents a set of guidelines as a scorecard for higher education-al institutes to follow and maintain a high level of education quality in their online teaching approach. © 2022 Association for Computing Machinery.

Explainable Misinformation Detection across Multiple Social Media Platforms

Web Information Processing (WIP) has enormously impacted modern society since a huge percentage of the population relies on the internet to acquire information. Social Media platforms provide a channel for disseminating information and a breeding ground for spreading misinformation, creating confusion and fear among the population. One of the techniques for the detection of misinformation is machine learning-based models. However, due to the availability of multiple social media platforms, developing and training AI-based models has become a tedious job. Despite multiple efforts to develop machine learning-based methods for identifying misinformation, there has been very limited work on developing an explainable generalized detector capable of robust detection and generating explanations beyond black-box outcomes. Knowing the reasoning behind the outcomes is essential to make the detector trustworthy. Hence employing explainable AI techniques is of utmost importance. In this work, the integration of two machine learning approaches, namely domain adaptation and explainable AI, is proposed to address these two issues of generalized detection and explainability. Firstly the Domain Adversarial Neural Network (DANN) develops a generalized misinformation detector across multiple social media platforms. DANN is employed to generate the classification results for test domains with relevant but unseen data. The DANN-based model, a traditional black-box model, cannot justify and explain its outcome, i.e., the labels for the target domain. Hence a Local Interpretable Model-Agnostic Explanations (LIME) explainable AI model is applied to explain the outcome of the DANN model. To demonstrate these two approaches and their integration for effective explainable generalized detection, COVID-19 misinformation is considered a case study. We experimented with two datasets and compared results with and without DANN implementation. It is observed that using DANN significantly improves the F1 score of classification and increases the accuracy by 5% and AUC by 11%. The results show that the proposed framework performs well in the case of domain shift and can learn domain-invariant features while explaining the target labels with LIME implementation. This can enable trustworthy information processing and extraction to combat misinformation effectively. Author