A Metaheuristic Approach to Emergency Vehicle Dispatch and Routing

Accidents and emergency situations have been on a constant rise, especially during the COVID-19 pandemic. Typically, the emergency vehicle dispatch and routing problems involve various dynamic factors which make them very different from conventional vehicle routing problems. This paper presents a metaheuristic approach to emergency vehicle dispatch and routing. Dispatching aims at allotting and sending the nearby available vehicle to the location of emergency and routing deals with selecting the ideal route to reach the destination. The objective is to minimize incident response time and the total time travel for the vehicle from the dispatch point to the destination. This usually depends on the emergency service vehicle availability and other dynamic factors such as traffic, number of turns in the route, etc. Three different bio-inspired algorithms, namely, ant colony optimization, adaptive ACO and firefly algorithm are investigated. Performance evaluation shows that firefly algorithm outperforms the other algorithms in terms of cost, number of turns, and run time for the given data set. However, in case of larger datasets and multiple variables if involved, adaptive ACO gives better results but takes longer time.

Identification of factors effecting Covid-19

COVID-19 has effected 223 countries globally with approximately 223,491,168 confirmed cases (by 9th September 2021) and these alarming numbers bring chaos among the people. These figures are calculated over a period of 21 months, emerging since December, 2019 to September, 2021. No other virus in the history has reached such high numbers which makes this Covid-19 pandemic a novel in nature. Therefore, significant measures need to be taken, in terms of medical assistance as well restrictions need to be imposed for socializing which would help in minimizing the number of people being effected by covid-19. The focus of this study is to identify the trend of patients being diagnosed over a specific period of time such as Weekdays or Weekends. Further, the patterns have been identified country wise and the co-relation has been highlighted among the countries and observed cases. Later, the data has been fed to clustering algorithm i.e. K-Means in order to group the similar characteristics of Covid-19 factors. This study would help the officials to take appropriate measurements in order to control the pandemic situation.

Covid-19 Detection by using Deep learning-based Custom Convolution Neural Network (CNN)

The early diagnosis and treatment of COVID-19 has been a challenge all over the world. It is challenging to manufacture many testing kits and even then, their accuracy rate is very low. Studies carried out recently show that chest x-ray images are of great help in the diagnosis of COVID-19. In this study, we have developed a COVID-19 detection model that by observing the chest x-ray images of the patient, detects that either the patient is affected by COVID-19 or not. The model is developed using a custom Convolutional Neural Network (CNN) that differentiates between COVID-19 and healthy x-ray images so that the patient can be diagnosed and quarantined on time to prevent the spread of the pandemic. We used two different datasets which are publicly available for the training and validation of this model. Upon completion, the proposed model yields an accuracy of almost 98%. Upon further training, our model will be able to be used as a COVID-19 detection tool in hospitals worldwide and will play a vital role in early detection and timely containment of the pandemic.

Design of a predictor for COVID-19 misinformation prediction

Due to the increase of social media usage, the online sharing of content has been extremely increased. As a result, the spread of misinformation on social media platforms has also increased. To address this issue, we proposed an approach that predicts the news is fake or real. In our approach, we select the top k ranked features through a filter base algorithm and feed them to the classifier. The main objective of this research is to provide two things. First, to provide an approach, which compares the benchmark performance results of the evolutionary detection approach on the Koirala dataset. The second is to build, publicly available dataset through web scraping for the classification of COVID-19 fake news articles. Our method significantly uplifts the F1-score with 14.88 percent for the same number of features selected 605 for the already existing approach. Also, stated the number of features 5000 on which the approach showed the best results with a margin of F1-score of 20.4 percent, respectively. Similarly, on the self-build dataset, this approach also outshines and achieved 99.66 percent of F1-score, respectively. Our experimental results show that our robust approach by comparing with other classifiers and existing approach, Max-Min Ratio (MMR) along with support vector machine (SVM) outperformed on both of these datasets. Hence, feature selection plays a vital role in the performance of the model rather than deeply tuning and training the classifier.

FLOSSC: A Computer-Aided Tool for Detecting Corona Virus Infections through Chest X-Ray Images

Artificial intelligence has radically altered the world, and it continues to progress at an alarming rate as time passes. AI applications include healthcare and medical solutions, illness diagnostics, agriculture, constructing security infrastructures, autonomous cars, intelligent systems, industrial production, robotics, and much more. COVID19 is a deadly virus that first appeared in China in 2019 and soon spread over the world. By 2020, the globe had witnessed a tremendous epidemic, with countless lives lost as a result of this dreadful virus, which has now become a severe health danger. Furthermore, in 2021, several nations will be infected with new Covid19 forms that are more deadly and spread quicker. The research describes the proposed methodology for diagnosing covid-19 and pneumonia from human chest X-ray images using transfer learning with Resnet-18 and VGG-16 neural networks. The focal loss function was also used to homogenize the imbalanced dataset, which included X-ray images of normal, pneumonia, and Covid-19 patients. The purpose is to assess the performance and accuracy of fine-tuned neural networks after including Binary Cross-Entropy (BCE) and Focal Loss (FL) functions. However, when we used our Resnet-18 and VGG-16 neural networks with BCE and FL functions, the VGG-16 with FL function outperformed all other models, with training and validation accuracy of 98.37 percent and 97.37 percent, correspondingly.

COVID-19 SOP's Violations Detection in Terms of Face Mask Using Deep Learning

Facemask detection is a need of time as we are suffering in a pandemic situation of COVID-19, and facemask is considered the best preventive measure to stop the rapid spread. The vast majority of the world population is still unvaccinated, especially young and kids. Moreover, despite the vaccination, people are still getting Covid positive, and the majority are due to the Delta variant. So, we still need to have strict SOP implementation. The best way is to have some autonomous system to monitor SOP compliance and alert the authority to take countermeasures. Many people wear the mask, but the mask is usually on the chin and does not serve the purpose because the facemask must cover the mouth and nose to stop the spread. This study has proposed the improved version of the YOLOv4 model for the robust detection of face masks and checks whether the mask is worn in the recommended way. 2D convolutions of Yolov4 are replaced with the spatially separable convolutional in YOLOv4 to reduce the parameters so that the model can work in real-time. We have achieved an accuracy of 86.61% in terms of proper mask-wearing. Unlike other proposed approaches, our model is not only detecting the mask but also determines that whether the mask is worn in the recommended manner.

Smart city technologies, key components, and its aspects

Urban Population is rapidly increasing, which is creating city management problems. The smart city provides solutions to the problems that are arising due to rapid urbanization. The smart city relies on Internet of Things (IoT), Information and Communications Technology (ICT), smart computing technologies, and network technologies. IoT is used in every field such as smart transportation, smart grid, smart healthcare and smart buildings. IoT is a vast system embedded with smart devices, sensors, software, controllers, internet, and a cloud server. Therefore, this paper presents the concept of a smart city and the critical factors of a smart city initiative. Different domains that make up a smart city like smart health, smart buildings, smart transportation, and smart agriculture will be comprehensively discussed. This paper also highlights smart health applications and presents a patient status monitoring system that monitors patient's condition through video and speech. The role and implementation of smart city technology to minimize the COVID-19 risk will also be discussed. In this paper, overview of security for smart cities, existing security methods of authentication, and possible malicious attacks have also been discussed. Finally, this paper also focuses on multispectral imaging (MSG), unmanned aerial vehicle (UAV), and the architecture of IoT applications in agriculture and greenhouse farms.

Symptoms Based Covid-19 Disease Diagnosis Using Machine Learning Approach

The catastrophic outbreak of SARS-CoV-2 or COVID-19 has taken the world to uncharted waters. Detecting such an outbreak at its early stages is crucial to minimize its spread but is very difficult as well. The pandemic situation is not yet under control as the virus tends to evolve and develop mutations. This further complicates the development of machine learning or AI models that can automatically detect the disease in the general public. However, researchers worldwide have been putting their incredible efforts into devising mechanisms that help analyze and control the pandemic situation. Many prediction models have been developed to predict COVID-19 infection risk that helps in mitigating the burden on the healthcare system. These models help the medical staff, especially when healthcare resources are limited. As a contribution to society's well-being, this research work deploys a machine learning prediction model that predicts COVID-19 patients with COVID-19 symptoms. Key pieces of information from RT-PCR test data results by the Israeli ministry of health publicly available have been distilled, preprocessed, and then used to train our prediction model. The model is trained on eight features, out of which five are the primary clinical symptoms of this fatal virus: cough, sore throat, fever, headache, breath shortness;and the other three features are gender, test indication, and age. Machine learning models can be considered for COVID-19 testing, especially when resources are limited. We have achieved highly accurate results in COVID-19 prediction with our prediction model. The model is best suited in urgent situations where there is a limitation of testing resources.

Genome Scale Phylogenetic Analysis Differentiates SARS-CoV-2 Strains in Pakistani and Indian COVID-19 Patients with and without Travel History

The COVID-19 virus spread around the globe very rapidly during early 2020. Identification of the evolution pattern, and genome scale mutations in SARS-CoV-2 is essential to study the dynamics of this disease. The genomic sequences of thousands of SARS-CoV-2 infected patients from different countries are publicly available for sequence based in-depth analysis. In this study, the DNA sequences of SARS-CoV-2 from the COVID-19 infected patients (having or lacking a travel history) from Pakistan and India, the two highest populous neighboring countries in South Asia, have been analyzed by using computational tools of phylogenetics. These analyses revealed that the SARS-CoV-2 strain in Pakistani traveler COVID-19 patients is closely related to Iranian strains, the strain in non-traveler patients is related to the strain of Wuhan, China. Likewise, in India, the SARS-CoV-2 strains in travelers and non-travelers are closely related to Italy, Germany, and Mexico. The selected approach has also been utilized to find out the identical genomic regions and similar strains around the world. Collectively, our study suggested distinct strains and routes of viral transmission in Pakistan and India. These differences may infer partially the reason for the decline phase in viral propagation in Pakistan two months after the peak COVID-19 load, and rapid viral propagation in India making it the second worst-hit country in the world after the USA.

Assessment of Critical Challenges Faced by Students and Significance of Cutting Edge Technologies in Pakistan Education during COVID-19 Pandemic

The educational system in Pakistan relies on traditional methods of learning. Notably, only few institutions are prioritizing technological advancements and its implementation in education system. However, majority of the institutions adhere to conventional methods and ignoring its negative impacts. A sudden outbreak of the Covid-19 pandemic is challenging for everyone. Specifically, the fear of pandemic particularly affected those students who were indulged in physical classes. This study presents a comprehensive analysis of already available studies discussing the challenges and role of Information Technology (IT) in the educational domain. The study mainly incorporated an online questionnaire-based survey among 400 university-going students of Punjab, Pakistan. Additionally, the study highlights the key challenges and essential IT platforms that helped students continue their education. Moreover, we analyzed the incorporated constructs with the assistance of using SPSS and AMOS. Besides, our results confirm that Information Technology (IT) played an essential role in students' lives in their education. Our research shows that the role of IT in our education system is highly essential to be implemented. Although there are innumerable challenges, yet IT has core importance in the education sector during the pandemic. Moreover, the research intends to safeguard the education budget for better learning outcomes in Pakistan Punjab's educational level COVID-19 pandemic. Institutions and students are encouraged to adopt IT, seeing its inevitable role in education.

Autonomous object detection and tracking robot using Kinect v2

Object detection and tracking are one of the key features of a robust autonomous mobile robot, allowing it to navigate places and avoid obstacles. The Mobile robotics market and proliferation has been growing and the Covid-19 era has added another boost to this area where more and more interest is being drawn to the autonomous capabilities of these machines. In this paper we propose a hardware based model to detect and track objects based on color. We propose robust object detection and tracking with minimum environmental constraints to improve accuracy using our algorithm, and capable of behaving well in unknown environmental conditions. At the end of the analysis, the robot was able to detect the object and track it well. We also show frequency analysis, compression and error analysis of the underlying technique. Experimental outcomes verify improved accuracy of our algorithm.

MASI Ventilator: Implementing Peruvian Technology in Regions at High Altitude

The expanded use of the Masi ventilators to more regions of Peru is important, particularly for regions located at high altitudes, due to the ventilatory support latent need, which also represents a challenge in the calibration and the adjustment of metrological parameters to ensure its correct performance. In a previous study, in Puno city at 3800 m.a.s.l., it was found an error above 15.0% (minimum tolerance) in the tidal volume, for which a negative correction of 25.0% was applied. In the present study, a Masi ventilator was transported to Chachapoyas city, at an altitude of 2400 m.a.s.l. to continue evaluating the effect of altitude on the parameters of the device. Once there, ventilators were acclimated and calibrated. Tidal volume, inspiration-expiration ratio (LE), positive endexpiratory pressure (PEEP), peak inspiratory flow (PIF) and peak inspiratory pressure (PIP) were tested, and the maximum percentages errors presented were 13.5% and 13.9% in the tidal volume and the PIF, respectively. For that reason, although errors were under 15.0%, an update of the software of Masi was needed, applying a negative correction of 14.0%. Then, the parameters were tested again obtaining results with errors below 6.0% and 8.0% in volume controlled an pressure controlled ventilation modes, respectively. These results allowed the use of the Masi ventilator at ICU area. Finally, a software update for the Masi ventilator is performed by applying a linear equation that relates altitudes and percentage errors tested.

Real-Time Estimation of Alternative Stress Index for Low-Cost, Mechanical Ventilators for Emergency Use

Stress index is a useful indicator in mechanical ventilation to assess improper ventilation settings. It can indicate tidal overdistension or tidal recruitment, which are two major mechanisms of ventilator-induced lung injury. However, it's implementation require dedicated hardware and software and is not a widespread parameter used in commercial ventilators. In this work, an alternative, simple way to visually inspect the concavity of the pressure-time curve during mechanical ventilation is presented, by calculating the pressure difference of the pressure-time curve. This proves useful when implemented in low-cost emergency devices, such as those designed to cope with the COVID-19 pandemic, because of the reduced computational load required to perform its calculation. The method was implemented in a low-cost emergency mechanical ventilator and tested with an artificial lung for a proof-of-concept. Results show that this alternative method can be effectively used to qualitatively assess the concavity of the pressure-time curve.

Copper Nanoparticles Impregnation in anti-Coronavirus Covid'19 Disposable Protective Masks

After two pandemics in less than a decade, it is dear that the fight against microbes is failing, and one of the reasons is the inefficacy of disposable protective masks. This project intends to evaluate the possibility of the impregnation of copper particles as an active element to eliminate viruses and bacteria, which in contact with cotton conventional masks have a survival rate of up to 72 h. In contact with copper particles, those microorganisms survive, approximately, for 1 h. Therefore, the main objective of this work was to evaluate a production process of impregnation of copper particles in nonwoven fabric (TNT). To do so, we isolated copper particles from copper sulfate solution with constant heating, simultaneously impregnating pieces of TNT cut from commercial disposable protective masks. We performed SEM and EDS analysis to evaluate the impregnation and particle sizes. Visually, the pieces were partially impregnated which was confirmed by the SEM images where one can see only some fibers covered by the particles. Also, it is possible to notice that the size of the particles was at microscale with some agglomeration. The EDS confirmed 1.1% by mass of copper particles in the sample. The procedure was easy to follow and at a low-cost being, therefore, possible to be implemented after some parameter adjustments.

CNN Networks to Classify Cardiopulmonary Signals

At the moment, the world lives in a pandemic situation of COVID-19 and related variants, driving urgent needs for expanded assessments. A complementary support of related healthcare can be based on an intelligent system that can diagnose early onset of respiratory disorders. The convolutional neural networks (CNN) were implemented utilizing image data, reflecting bidimensional signals. Specifically, CNN has shown to be powerful tool in the context of cardiopulmonary sounds evaluation. The configurations of CNN contain convolutional layers to extract feature maps and fully connected layers to classify indicators of interest. Even though, learning algorithms use parameters like learning rate which can determine and attain CNN configuration less complex, with excellent results as reflected in the experiments we carried out, and which focused on achieved configuration of CNN with excellent results classifying heart sounds (HS) and lung sounds (LS).

Automatic Parameter Adjustment for Hybrid WiFi and BLE-based Congestion Measurement

The recent COVID-19 pandemic has made it extremely important to avoid crowded environments. In light of this, we are developing a congestion measurement system utilizing IoT technology, in which the congestion state of a given location is estimated by counting the number of WiFi Probe Request messages and BLE exposure notification messages sent from smartphones and laptops. Our congestion measurement system, however, suffers from an unstable measurement problem because the number of WiFi and BLE messages is highly dependent on the environment in which our system is deployed. This paper presents a congestion measurement system employing an automatic parameter adjuster that accurately estimates the number of people in various target locations. The parameters are automatically adjusted based on the seating capacity and area size of the target environment, as well as information gathered from a sensor which is designed to collect and analyze WiFi and BLE messages collected in a university cafeteria.

CoreMoni: Individual Core Training Monitoring and Support System

Due to the influence of the new coronavirus, many people are interrupting fitness clubs and exercises/sports performed by multiple people. Under these circumstances, "core training," which can be easily performed indoors by individuals, attracts attention as an exercise to improve health. However, it isn't easy to recognize whether the posture during training is correct, significantly reducing effectiveness. This study developed 'CoreMoni,' a system to support individual core training, and verified its usefulness in supporting personal core training through evaluation experiments. We compared the duration of the correct posture of the plank with and without CoreMoni. The time spent on proper plank posture increased for 90% of the subjects. Besides, we used the System Usability Scale (SUS) and SUS's average score was 82 points, indicating that CoreMoni usability is excellent. Both quantitative and subjective assessments results suggest that CoreMoni supports individual core training by making users aware of posture and trunk vibration during core training.

The Effect of Virtual Humans Making Verbal Communication Mistakes on Learners' Perspectives of their Credibility, Reliability, and Trustworthiness

Simulating real-world experiences in a safe environment has made virtual human medical simulations a common use case for research and interpersonal communication training. Despite the benefits virtual human medical simulations provide, previous work suggests that users struggle to notice when virtual humans make potentially life-threatening verbal communication mistakes inside virtual human medical simulations. In this work, we performed a 2x2 mixed design user study that had learners (n = 80) attempt to identify verbal communication mistakes made by a virtual human acting as a nurse in a virtual desktop environment. A virtual desktop environment was used instead of a head-mounted virtual reality environment due to Covid-19 limitations. The virtual desktop environment experience allowed us to explore how frequently learners identify verbal communication mistakes in virtual human medical simulations and how perceptions of credibility, reliability, and trustworthiness in the virtual human affect learner error recognition rates. We found that learners struggle to identify infrequent virtual human verbal communication mistakes. Additionally, learners with lower initial trustworthiness ratings are more likely to overlook potentially life-threatening mistakes, and virtual human mistakes temporarily lower learner credibility, reliability, and trustworthiness ratings of virtual humans. From these findings, we provide insights on improving virtual human medical simulation design. Developers can use these insights to design virtual simulations for error identification training using virtual humans.

Agent-Based Modelling of the spread of COVID-19 in Corsica

The modelling epidemiology processes supporting public policy decision-making usually require SIR compartmental models which mathematically describe the pandemic phenomenon's dynamics. In general, these models and extensions are used to conceptualize a macro-level of populations evolving between different pre-determined health statuses. In this work, we propose an alternative modelling for the COVID-19 pandemic according to probabilities defined by interactions among individuals. We present an Agent-Based Model (ABM) to take into account both the heterogeneity of individuals population health statuses and the spatial structure of the environment. The model is verified by reproducing already known results of Corsica's COVID-19 pandemic data and different patterns of COVID-19 virus spread are visualized at any time with the NetLogo simulation environment. The implementation details of our alternative approach is then detailed and discussed.

Causal Analysis of On-line Math Tutoring Impact on Low-income High School Students Using Bayesian Logistic and Beta Regressions

The use of on-line tutoring, especially after the COVID-19 pandemic, has increased dramatically. It has become clear that measuring the effectiveness of on-line tutoring, especially on low-income students, is much needed in such difficult times. This paper, which is based on observational data collected before the COVID-19 era, is targeting measuring the impact of a web-based math tutoring program, Noon Academy, on the academic achievement of low-income high school students (grades 10 to 12) in Saudi Arabia. We use a large amount of data collected in a student registration process and two Bayesian generalized linear models (GLM) to measure the tutoring causal effects. Model 1 uses a binomial logistic regression to predict the impact of enrolling in the tutoring program on the rate of passing in a number of students. Model 2 uses a multi-level Beta regression to measure the impact of the number of minutes on the total mark. Model 1 results show that giving math tutoring to higher-failing-risk students significantly improves the rate of passing by +5%, reaching a maximum of +17.15% in some classes of students. Model 2 shows a significant positive impact of the number of tutoring minutes on the yearly math mark (max of 100), reaching an average of +3.52 marks for the highest number of minutes taken. The paper presents an application of a causal analysis approaches on a real-life social problem. It demonstrates how the model is used to obtain a measure of the impact with quantifiable uncertainty that can be used in practice.