Artificial Intelligence- assisted Security Analysis of Smart Healthcare Systems

The healthcare sector has been overburdened with the massive outbreak of COVID-19 for almost two years and has created an urgent need for remote patient monitoring and treatment with minimal human involvement. Adopting artificial-intelligence- (AI)-based techniques for patient treatment has brought a dramatic revolution into the modern smart healthcare system (SHS). Modern healthcare is leveraging the internet of medical things (IoMT) network comprised of wireless body sensor devices (WBSDs) and implantable medical devices (IMDs) to reduce treatment latency and cost drastically. However, the open network communication of the less secured IoMT devices and increasing growth of adversarial capability give rise to several vulnerabilities, which need to be taken into consideration while designing SHS. We propose a novel and comprehensive framework with machine learning (ML) and formal analysis capability to build a secure and attack-resilient SHS. Our framework uses a novel ensemble of unsupervised ML-based patient status classification and anomaly detection models with bio-inspired computing (BIC)-based ML models' hyperparameter optimization techniques. The proposed anomaly detection model (ADM) can detect zero-day attacks and uses a novel fitness function calculation technique for BIC-based SHS ADM's hyperparameter optimization. Moreover, the framework leverages novel formal attack analytics to assess the robustness of the underlying classification and abnormality detection models. Our framework is evaluated using the University of Queensland Vital Signs dataset and a realistic synthetic dataset. © 2023 selection and editorial matter, Fei Hu and Xiali Hei;individual chapters, the contributors.

Vocabulary Index as a Sustainable Resource for Teaching Extended Writing in the Post-Pandemic Era

In the wake of the COVID-19 pandemic, Malaysian English teachers identified a pressing need to support upper primary school pupils, particularly those in the upper levels, in the effective composition of extended writing. Additionally, these educators required more innovative methodologies for teaching vocabulary in this context. Consequently, the current study aimed to develop a vocabulary index as a suggested resource for Malaysian English teachers instructing upper primary school pupils on extended writing. To achieve this, a quantitative computational research strategy and corpus-driven research design were employed. A purposive sampling technique was used to select 560 advanced upper primary school pupils from 28 schools, each with high English performance in the capital of each state and the federal territory of Malaysia, who produced a total of 152,187 words in extended writing for analysis. LancsBox, a primary computational linguistics application, was used for data processing. Given that the vocabulary index for extended writing necessitates a more comprehensive coverage of vocabulary, functional and content words were included, and keywords, raw and normalised frequencies were analysed and reported. Through the vocabulary index built in this study, the researchers found English teachers in Malaysia should utilise local issues in writing prompts, emphasise the use of both positive and negative adjectives, introduce complex sentence structures to enhance pupils‟ writing abilities and also train pupils to organise the ideas in their writing. Future linguistic studies could replicate the present investigation, so that it can respond to their classroom needs. © Annals of Translational Medicine. All rights reserved.

Containing an Island: Coronavirus (Covid-19) Outbreak in Perhentian Islands, Terengganu State of Malaysia, in 2021

Background: The spread of COVID-19 was inevitable and has not spared small and isolated communities, including the community on Perhentian Island in Besut District, Terengganu. Managing clusters in small islands can be difficult, given the limited resources. This study explores the characteristics of COVID-19 cases and the experience of outbreak containment at Perhentian Island. Methodology: A retrospective study involving record review of COVID-19 cases and at-risk individuals registered under the Perhentian Cluster were retrieved from the Besut District Health Office COVID-19 online registry from the 16th August 2021 until 6th October 2021. All notified cases and close contacts who fulfilled the inclusion criteria were extracted and analysed using descriptive statistics. Result(s): A total of 1,093 out of 2,500 community members of Perhentian Island were screened of which 170 (15.5%) tested positive for COVID-19, while 923 (84.5%) tested negative. Among individuals who tested positive, the majority were adults (52.4%), males (51.8%), Malays (98.8%), and villagers (96.5%). Clinical characteristics were categorized into: asymptomatic (55.9%), had no known medical comorbidities (90.6%), low-risk groups (87.1%), vaccinated (57.6%), and admitted to PKRC (97.1%) for treatment. Multiple agencies were involved in the outbreak containment of the Perhentian Cluster, working collectively and in good coordination. Conclusion(s): The outbreak was attributed to community gatherings and close interactions among villagers. Prompt actions, targeted planning, and inter-agency collaboration were the key factors in successful containment of further spread of COVID-19 in Perhentian Island.Copyright © 2023, Faculty of Medicine, University of Malaya. All rights reserved.

Content Analysis on Homelessness Issues at Airports by News Media Mining

The number of homeless people at airports has increased in recent years. As airports are safe, transit-accessible, convenient, and climate-controlled facilities with food and amenities, these places are attractive to homeless people who need a safe and secure place to stay. The main struggle of airports in this regard is maintaining a balance between customers, who are mostly the traveling public, and dealing with homeless people delicately. Moreover, because of their poverty and insufficient or no access to healthcare, these people suffer from physical and mental issues. With the COVID-19 pandemic, this problem became more critical. Many news media outlets started to report on homelessness at airports. News-framing impacts have some contribution in the context of this issue. However, the impact of news coverage on ‘‘airport and homelessness'' has not yet been studied. News-framing effects have been identified in the context of tourist destinations. Although many studies have explored homelessness and transit, this issue at airports has not been well studied. This study provides a brief overview of the issue of homelessness in the transportation domain, including transit and aviation. Additionally, this study collected news articles related to ‘‘airport and homelessness'' (71 articles) both during the COVID-19 pandemic (March 1, 2020–July 21, 2021) and before the pandemic (before March 1, 2020). These news articles contain around 50,000 words. As the data is unsupervised in nature, a text network analysis was performed to determine the latent information from these textual contents. The findings of this study can shed some light on this scientifically unexplored but widely discussed issue. © National Academy of Sciences: Transportation Research Board 2022.

Assessment of challenges and problems in supply chain among retailers during COVID-19 epidemic through AHP-TOPSIS hybrid MCDM technique

Framework and objectives: COVID-19 epidemic has sparked concern and has elevated the need for therapeutic tools, health equipment's, and day-to-day necessities for healthcare workers' well-being. The goal of this study is to uncover the operational problems that suppliers encounter when it comes to offering effective services. The research also intends to offer an Industry 4.0 strategy for reducing COVID-19's effect. The problems are weighed and priority is assigned by multi-criteria decision making to identify the most essential parameter which impacts the suppliers. Methods: A comprehensive literature assessment on the rampant eruption of COVID 19 and supply chain is conducted with the aid of literatures available on SCOPUS, Science Direct, and Google Scholar using appropriate keywords. To get further insights, certain pertinent and applicable industry reports and blogs are also used. Problems were analysed with AHP method and priority was assigned by technique for order performance by similarity to ideal solution (TOPSIS). Weights are calculated by AHP method and assigned to each criteria attribute. Results: We recognized eleven key problems that serve as an operational obstacle in the retail industry and proposed the use of Industry 4.0 technology to address them. The contemporary study is accomplished by using hybrid combination of two Multi Criteria Estimators methods- Analytical Hierarchical Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Further, the most significant problem comes out to be Maintenance of an appropriate balance among supply and demand followed by Lack of Viability. Key findings: Prioritization of supply chain problems are arranged in descending order Maintenance of an appropriate balance among supply and demand ​> ​Lack of Viability ​> ​Absence of government funding ​> ​Lack of access ​> ​Absence of Confidence ​> ​Scarcity of work force ​> ​Lack of security and safety ​> ​Deficiency of surplus medical amenities ​> ​Consumer attitude ​> Absence of Supply Chain flexibility ​> ​Communication problems. Conclusion: In order to combat the pandemic, Industry 4.0 can play a key role in lowering the effect of identified issues on retailers. For the successful administration of healthcare basics, trust and openness are required. To enhance services, suppliers, distributors and policy makers should make informed decisions during COVID-19 and other comparable events. Therefore, suggested guidelines and framework will offer upcoming directions for research in fields of pandemic check, business logistics management, and catastrophe administration. Balance in supply and demand is the most significant attribute as its percentage contribution is the maximum (27.52%) followed by Safety of employees (26.51%). Furthermore, the research then ranks these models on the basis of their attributes with the aid of TOPSIS. Among all these problems, Maintenance of an appropriate balance among supply and demand and lack of viability are identified as the prime most and common concern for retailers in supply chain management during the COVID-19 pandemic. © 2022 The Authors

Coping strategies to overcome psychological distress and fear during COVID-19 pandemic in Kuwait

BackgroundThe COVID-19 pandemic has detrimental effects on both physical and psychological well-being of community people worldwide. The purpose of this research was to determine coping strategies and the factors associated with psychological distress and fear among adults in Kuwait during the COVID-19 pandemic.ResultsParticipants with good-excellent mental health perception had significantly lower prevalence of reporting high psychological distress, while those identified as patients as used health services in the past 4 weeks had significantly higher prevalence of reporting high psychological distress. On the other hand, individuals born in the same country of residence, whose financial situation was impacted by COVID-19 had significantly lower prevalence of reporting high levels of fear from COVID-19. Those with an income source, with co-morbidities, tested negative to COVID-19, being frontline or essential worker, reported medium to high psychological distress and had significantly higher prevalence of high levels of fear of COVID-19.ConclusionsMental health services should be provided in addition to the existing services in primary healthcare settings, so that the impact of ongoing pandemic on psychological wellbeing of people in Kuwait can be addressed.

Adaptive Fuzzy Neural Network vs. Convolution Neural Network in Classifying COVID-19 from Chest X-rays

Detecting COVID-19 in the early time can save lives and reduce the cost of huge pressure on healthcare centers. Many machine and deep learning models have been proposed by researchers to detect and diagnose COVID-19 based on chest X-rays. However, we need to know which of those models is more effective and efficient. This paper presents a comparative study between adaptive fuzzy neural network (AFNN) and convolutional neural network (CNN) in classifying COVID-19 using chest X-rays. We present the experimental results showing the comparative performance measures with respect to the size of available dataset. We also present the relative advantage of each family of neural network in accuracy, precision, recall, F1score, and the computation time. © 2022 IEEE.

Past, present and future impact of social media on health workers' mental health: a text mining approach

Purpose: The COVID-19 epidemic has brought attention to the variables that influence the mental health of health workers who are entrusted with nursing individuals. Despite the fact that many articles have examined the effects of social media usage on mental health, there is a lack of research synthesizing learning from this body of research. The purpose of this study is to use text mining and citation-based bibliometric analysis to conduct a detailed review of extant literature on health workers' mental health and social networking habits. Design/methodology/approach: This study conducts a full-text analysis of 36 articles selected on health workers' mental health and social media using text-mining techniques in R programming and a bibliometric citation analysis of 183 papers from the Scopus database in VOS viewer software. But the limitations of the methods used in this study are that the bibliometric analysis was limited to the Scopus database because the VOS viewer program did not support any other database and the text-mining approach caused the natural processing redundancy. Findings: The bibliometric analysis reveals the thematic networks that exist in the literature of health workers' mental health and social networking. The findings from text mining identified ten topic models, which helped to find the related papers classified in ten different groups and are provided alongside a summary of the published research and a list of the primary authors with posterior probability through Latent Dirichlet Allocation. Originality/value: To the best of the authors' knowledge, this is the first hybrid review, combining text mining and bibliometric review, on health workers' mental health where social networking plays a moderating role. This paper critically provides an overview of the impact of social networking on health workers' mental health, presents the most important and frequent topics, introduces the scientific visualization of articles published in the Scopus database and suggests further research avenues. These findings are important for academics, health practitioners and medical specialists interested in learning how to better support the mental health of health workers using social media. © 2022, Emerald Publishing Limited.

LFR Waiter: Arduino Based Android Application Controlled Waiter Robot

One of the most pressing challenges facing restaurants since the COVID-19 outbreak began is personnel. A staffing scarcity across the business has resulted in a slew of issues, including significantly longer wait times and irritated clients. A robot waiter may make a huge impact in a restaurant in this situation. This research led to the formation of a low-cost Arduino-based Android application control Robot that can work as a restaurant waiter. The proposed model can follow a path, avoid obstacles, serve meals to a specific consumer, and return to the kitchen on its own. To precisely follow the line, the PID algorithm is utilized. To detect potential obstructions, a sonar sensor is used. On an LCD, messages and warnings are displayed. An Android app that allows the chief to select a particular table for serving meals. For convenience, the robot's current state is displayed in the application. Our testing results show that the robot performs satisfactorily over 90% of the time. It should be emphasized that the offered model is adaptable to any restaurant. © 2022 IEEE.

The impact on education of university students in COVID-19 pandemic period: A factor analysis

The educational sector of Bangladesh is severely affected due to the sudden outbreak of novel Corona virus (COVID-19). Bangladesh which is one of the densely populated countries has a significant improvement in the education sector along with the others in last some decades but this pandemic has played a serious setback to almost all the sectors of this small country. As all the educational institutes of Bangladesh are closed since 17th March 2020 till 30th June 14, 2021 and this may lead to many detrimental effects. To measure these, a survey was conducted and collected data was analyzed by SPSS Statistics v 25.0. This paper highlighted the mental stress, socio-economic crisis of the students that badly affected their education. It is observed in this study that around 80% of the students are going through mental stress particularly for internet facilities and financial crisis in pandemic period.

Way forward managing mental health in COVID-19 endemic phase

COVID-19 pandemic had contributed to widespread emotional distress and increased the number of mental disorders reported in 2020 and 2021. The pandemic had triggered a range of emotional reactions (fear, sadness, anger, disgust, frustration, confusion, boredom), and unhealthy behaviors (psychomotor agitation with abusive behaviour, excessive substance use), and noncompliance with public health directives (such as home confinement and vaccination) in the general population. The mental disorders include Acute Stress Disorder which complicates into Post-traumatic Stress Disorder, Major Depressive Disorder, Normal Grief which later develop Pathological Grief, Panic Disorder and Generalized Anxiety Disorder. COVID-19 endemic refers to the inability to eradicate the presence of coronavirus in the community. With more than 95% adult population fully vaccinated with booster dose, adolescents and children vaccination drive is well in place, together with decreasing number of serious respiratory symptoms and death, Malaysia declared the endemic phase in October 2021. In the context of COVID-19 endemic phase, psychosocial assessment, monitoring and deliver support is still relevant. Referral for mental health evaluation and care with supportive interventions to promote wellness will ensure the prevention of mental ill-health complications of COVID-19 infection. The mental health surveillance will allow for an adequate and appropriate response to the mental health issues. Individuals with mental health issues are continuously manage with pharmacotherapy, psychotherapies, cognitive therapy, behavioural therapy, psychosocial rehabilitation, psychoeducation and counseling. The awareness and health hygiene program is ongoing for the general population.

Respirator with Air Blower and Air Quality Detector

This work is motivated by the challenges faced during the COVID-19 pandemic. Effective protection from the virus is needed. Masks is one way to protect from the virus. To support the efforots to control the spread of COVID-19 among the population, in this work we present an improved respiratory system consisting of a respiratory mask, air blower and a control system. Our design avoids some of the problems with available masks such as leak of unfiltered air and the irritation caused by these masks. To overcome this problem, the existing respirator is being modified to ensure user can breathe comfortably, no air gap on the side of respirator mask and respirator mask can measure air suction rate. Air blower will increase the air suction rate and sensor will detect the air quality and display it thru the monitor. User can monitor the air index surround them. A prototype is built. Testing of the prototype showed that the system functions as expected and achieves its objectives. On the downside, the prototype is heavier than existing products in the market. Further improvement in the design may lead to an improved version with reduced weight. © 2022 IEEE.

Accuracy of Volatile Organic Compound (VOC) Detection in Exhaled Breath Compared to Reverse-transcriptase Polymerase Chain Reaction (RT-PCR) for Diagnosis of COVID-19: An Evidence-based Case Report

Background: Coronavirus disease 2019 (COVID-19) is a contagious infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The World Health Organization (WHO) declared this infection a global pandemic in 2020. In addition, various methods have been developed to diagnose COVID-19 rapidly and accurately to reverse transcription-polymerase chain re-action (RT-PCR) as a gold standard method. One of these methods is the detection of volatile organic compounds (VOC) in exhaled breath. Objectives: The aim was to collect and investigate studies on the accuracy of VOC detection as a diagnostic method for COVID-19. Methods: A literature search was performed in five electronic databases, including PubMed, Cochrane Library, ProQuest, EBSCO-host, and Scopus, along with hand searching. The search was conducted in the titles and s of articles using keywords and their equivalent terms, combined with the Boolean operators (OR and AND). The search results were then selected according to the inclusion and exclusion criteria and compatibility with the Population, Intervention, Control, and Outcomes (PICO) framework. Results: Based on the search results, two cross-sectional studies by Wintjens et al. and Ruszkiewicz et al. were selected, which were then critically appraised. Both studies showed good validity. Wintjens et al. reported 86% sensitivity and 54% specificity for their method, with a positive predictive value (PPV) and a negative predictive value (NPV) of 40% and 92%, respectively. Besides, Ruszkiewicz et al., who conducted a study in two different locations, reported 82.4% sensitivity and 75% specificity for their method in Edinburgh (UK), with PPV and NPV of 87.5% and 66.7%, respectively, while they reported 90% sensitivity and 80% specificity in Dortmund (Germany), with PPV and NPV of 45% and 97.8%, respectively. The accuracy of these three methods was 62%, 80%, and 82%, respectively. Conclusions: Detection of VOCs from exhaled breath can be a rapid, cost-effective, and simple method for diagnosing COVID-19. However, the accuracy of this method is still relatively low (62 -82%) and inconsistent;therefore, it is only recommended for screen-ing.

CHINA’S BELT AND ROAD INITIATIVE PROGRESS TOWARDS MALAYSIA’S DEVELOPMENT: WHERE ARE WE HEADING AFTER THE FIRST DECADE?

Malaysia-China cooperation since normalization in 1974 has proceeded relatively well for the benefit of both countries. The prior role of Malaysia in offering the “hand of friendship” to China has reflected diplomatic co-existence in mutual trade, regional development and people-to-people relations. However, the close relationship between Malaysia and China has also impacted the position of Malaysia in joining China in the “One Belt One Road” (OBOR), which then changed to the Belt and Road Initiative (BRI). The agenda through the BRI, launched in 2013 under the leadership of Xi Jinping, has mapped out new promising relations in various dimensions (economic, financial, technical, etc.) with Malaysia and other countries in Southeast Asia through the 21st Century Maritime Silk Road or commonly known as the Maritime Silk Road (MSR). The BRI in Malaysia has created different patterns of mutual trust on the developmental scape although there has been little discussion since it was introduced. Therefore, this article intends to fill the gap by providing an analysis on its ongoing cooperation with China since Malaysia signed on to the BRI in 2013. This analysis is placed within the context of the Malaysia-China cooperation before and ongoing projects in BRI, the people-to-people and government-to-government relations in managing the COVID-19 pandemic and the geopolitics of China’s manoeuvres in the South China Sea. Much needs to be done to strengthen cooperation through the BRI between China and Malaysia particularly as 2023 marks the first decade of this mega project, given the emerging trust deficit in the ASEAN region with regard to China’s strategic goals in her competition with the US for power and influence. © 2022,Journal of International Studies(Malaysia). All Rights Reserved.

Exposure to nature during the COVID-19 pandemic and the associated effect on mental health: a systematic review with meta-analysis

BACKGROUND: The COVID-19 pandemic and measures such as lockdowns to control its transmission generated unique effects on psychological health and well-being. In these circumstances, access to nature and outdoor spaces became a potentially important coping strategy, but the evidence exploring the mental health benefits of nature exposure during different stages of the pandemic is mixed and poorly understood. We systematically synthesised the evidence to examine larger trends in associations between nature exposure and mental health during the COVID-19 pandemic.

Evaluation of the online arabic treasure hunt as learning games activities using the technology acceptance model (TAM)

The Covid 19 pandemic has shifted the teaching approach in higher education institutions from the traditional face-to-face method to online teaching. Teachers and students are far apart at home to complete the teaching and learning process. Out-of-class language activities are severely affected because they are usually performed face-to-face. This study evaluated the suitability and usability of an online Arabic Treasure Hunt conducted on the Microsoft Teams platform. This study is a quantitative study by using a survey approach questionnaire. Forty-six students are the respondents who participated in online treasure hunt activities. All questionnaire items are adapted from the Technology Acceptance Model to meet the study's requirements. The respondent's assessment is based on a five-point Likert scale: 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree. The study's primary instrument was a questionnaire placed in the 'Treasure World Adventure' channel in Microsoft Teams for participants to complete after the activity. First, the data tested the validity through the Cronbach's alpha method and then tested the study hypotheses. Data is analysed using IBM SPSS Statistics software. The study found a significant positive relationship between the independent variables and the behavioural intent variable. It reflects the acceptance of the technology used by students. The results also demonstrated a high level of variable reliability. Detailed findings and educational implications have been discussed © 2022 Author(s).

Stock Market Comparison and Analysis in Preceding and Following Pandemic in Bangladesh using Machine Learning Approaches

For a long time, stock price forecasting has been a significant research topic. However stock prices depend on various factors that cannot be predicted, and that's the reason it is almost impossible to predict stock prices accurately. Many researchers have already worked in this area. Recently, the COVID-19 pandemic had a great effect on the stock market. The main purpose of this paper is to predict the stock closing prices for two major stock exchanges in Bangladesh and compare the prediction accuracy based on before and after pandemic data. The implemented models are Autoregressive Integrated Moving Average(ARIMA) and Support Vector Machine(SVM) and Long Short-Term Memory (LSTM). Raw datasets were considered, which were collected from Dhaka Stock Exchange(DSE) and Chittagong Stock Exchange(CSE). Data preprocessing was done on both of the datasets. After analyzing the overall accuracy for each algorithm, it was found that LSTM provided better accuracy than ARIMA and SVM for both the DSE and CSE datasets. © 2022 ACM.

SWCNT Based On-Body Antenna For Detecting COVID-19 Affected Lung Using 5G-Band

In this paper, a 5G on-body patch has been designed for detecting COVID-19 affected lung. A new material Single Wall Carbon Nanotube (SWCNT) is used to design the patch of the antenna. Copper is used to designing the ground and FR-4 (lossy) is used in the substrate. The antenna has a total thickness of 5.5 mm where the patch thickness is 0.5 mm, the substrate thickness is 4.5 mm, and the ground thickness is 0.5 mm. The total volume (length x width x thickness) of this antenna is 80 mm x 80 mm x 5.5 mm (35200 mm3). For detecting COVID-19, designed two human lung phantom body models such as a COVID-19 affected lung model and a non-affected normal lung model. The patch antenna and all the models were designed in CST Microwave Studio. All the dielectric properties and other valuable parameters of the antenna materials and lung phantom models were collected and used for designing the antenna and phantom lung models. The antenna's return loss (S1,1) is -27.498894 dB, gain is 3.007 dB, VSWR is 1.0880641, directivity is 6.007 dB, resonant frequency is 6.296 GHz, SAR 1.19 W/Kg, bandwidth is 1.8174 GHz and the efficiency is 61% in free space. In this pandemic situation, this antenna can be given a new step for detecting COVID-19 affected lung. © 2022 IEEE.