Controlling the bandgap of carbon nanostructures is key to the development and mainstream application of carbon-based nanoelectronic devices. We report density functional theory calculations of the effect of silicon impurities on the electronic properties of carbon nanotubes (CNTs). We have found that Si adatoms open up a bandgap in intrinsically metallic CNTs even when the linear density of Si atoms is low enough that they do not create a bonded adatom chain. The bandgap opened in metallic CNTs can range between 0.10 eV and 0.47 eV, depending on adsorption site, linear density of Si adatoms, and CNT chirality. Comment: 3 eps figures, extended text
An experimental program for collapse of curved stiffened composite shell structures encountered a wide range of initial and deep buckling mode shapes. This paper presents work to determine the significance of the buckling deformations for determining the final collapse loads and to understand the source of the variation. A finite element analysis is applied to predict growth of damage that causes the disbonding of stiffeners and defines a load displacement curve to final collapse. The variability in material properties and geometry is then investigated to identify a range of buckling modes and development of deep postbuckling deformation encountered in the experimental program. Finally the load paths for the damaged panels are used to visualise the load transfer and enhance the physical understanding of the load displacement history.
When (robotic) Automated Fibre Placement (AFP) is used to manufacture
aerospace components with complex three dimensional geometries, gaps between
fibre tows can occur. This paper is the first to explore the interaction under
compressive load of these tow gaps with impact damage. Two coupons with
different distributions of tow-gaps were impacted. Results indicated that the
area of delamination is smaller for an impact directly over a tow gap where the
tow gap is situated close to the non-impact face. Subsequent Compression After
Impact (CAI) testing demonstrated that both the formation of sublaminate
buckles and subsequent growth of delaminations is inhibited by the presence of
a tow gap near the non-impact face. Non-destructive testing techniques and a
computationally efficient infinite Strip model are used to analyse the damage
resistance and damage tolerance of the coupons. A new validation of the Strip
model is also presented.
This paper deals with the parallel simulation of delamination problems at the
meso-scale by means of multi-scale methods, the aim being the Virtual
Delamination Testing of Composite parts. In the non-linear context, Domain
Decomposition Methods are mainly used as a solver for the tangent problem to be
solved at each iteration of a Newton-Raphson algorithm. In case of strongly
nonlinear and heterogeneous problems, this procedure may lead to severe
difficulties. The paper focuses on methods to circumvent these problems, which
can now be expressed using a relatively general framework, even though the
different ingredients of the strategy have emerged separately. We rely here on
the micro-macro framework proposed in (Ladev\`eze, Loiseau, and Dureisseix,
2001). The method proposed in this paper introduces three additional features:
(i) the adaptation of the macro-basis to situations where classical
homogenization does not provide a good preconditioner, (ii) the use of
non-linear relocalization to decrease the number of global problems to be
solved in the case of unevenly distributed non-linearities, (iii) the
adaptation of the approximation of the local Schur complement which governs the
convergence of the proposed iterative technique. Computations of delamination
and delamination-buckling interaction with contact on potentially large
delaminated areas are used to illustrate those aspects.
This work studies the influence of the imperfectness of the interface
conditions on the dispersion of the axisymmetric longitudinal waves in
the pre-strained bi-material hollow cylinder. The investigations are
made within the 3D linearized theory of elastic waves in elastic bodies
with initial stresses. It is assumed that the materials of the layers of
the hollow cylinder are made from hyper elastic compressible materials
and the elasticity relations of those are given through the harmonic
potential. The shear spring type imperfectness of the interface
conditions is considered and the degree of this imperfectness is
estimated by the shear-spring parameter. Numerical results on the
influence of this parameter on the behavior of the dispersion curves are
presented and discussed.
In this study, phenomena of multiple branching of dynamically propagating crack are investigated numerically. The complicated paths of cracks propagating in a material are simulated by moving finite element method based on Delaunay automatic triangulation (MFEM BODAT), which was extended for such problems. For evaluation of fracture parameters for propagating and branching cracks switching method of the path independent dynamic J integral was used. Using these techniques the generation phase simulation of multiple dynamic crack branching was performed. Various dynamic fracture parameters, which are almost impossible to obtain by experimental technique alone, were accurately evaluated. keyword: Dynamic crack bifurcation, dynamic fracture, crack propagation and arrest, moving finite element method, dynamic J integral, fracture prediction criteria, multiple crack branching
In this study, effects of the plastic deformation and the time-dependent deformation behavior on the fundamental relations in the Oliver & Pharr method are studied by using finite element analysis based on a viscoelastic-plastic model developed for polymers. The study eventually yields an experimental protocol and using which, the instantaneous modulus of the viscoelastic-plastic materials may be reliably determined. Experiments have been performed on four polymers to verify the conclusions from the numerical analysis.
In this paper a novel, compact, microstrip-fed, quad-band monopole antenna is presented for the application of Global System for Mobile communication (GSM 900), Worldwide Interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN). The proposed antenna comprises of a sickle-shaped structure with four circular arc strips, and a modified rectangular ground plane. The four strips of the antenna are independently responsible for the four different resonant frequencies of the operating bands and can be tuned separately to control the radiation performance. The proposed quad-band antenna is designed to resonate at 940 MHz for GSM 900, 2.5 and 3.5 GHz for WiMAX and 5.85 GHz for WLAN applications. At the four intended operating bands, the antenna exhibits impedance bandwidth of 60 MHz (905–965 MHz), 80 MHz (2.45–2.53 GHz), 110 MHz (3.48–3.59 GHz) and 2.39 GHz (4.82–7.21 GHz), respectively. At the resonance frequency of the four bands, the gain of the proposed antenna is obtained as 4.2, 2.5, 1.7 and 1.9 dBi, respectively. A prototype of the designed antenna is fabricated and a good agreement between simulated and measured results is observed. Furthermore, the proposed antenna shows good radiation characteristics and gains at all the four operating bands.
Materials used in soldier protective structures, such as armor, vehicles and civil infrastructures, are being improved for performance in extreme dynamic environments. Accordingly, atomistic molecular dynamics simulations were performed to study the spall response in a single crystal aluminum atom system. A planar 9.6 picoseconds (ps) shock pulse was generated through impacts with a shock piston at velocities ranging from 0.6 km/s to 1.5 km/s in three <1,0,0>, <1,1,0>, and <1,1,1> crystal orientations. In addition to characterizing the transient spall region width and duration, the spall response was characterized in terms of the traditional axial stress vs. axial strain response for gaining an understanding of the material failure in spall. Using an atom section averaging process, the snapshots, or the time history plots of the stress and strain axial distributions in the shock direction, were obtained from the MD simulations' outputs of the atom level stresses and displacements. These snapshots guided the analyses to an estimation of the spall widths and spall transients. The results were interpreted to highlight the effects of crystal orientation and impact velocity on the spall width, spall duration, spall stress, strain rate, critical strain values at the void nucleation, and the void volume fraction at the void coalescence. For all the combinations of the crystal orientations and the impact velocities, the void nucleation was observed when the stress reached a peak hydrostatic state and the stress triaxiality factor reached a minimum, i.e. by the simultaneous occurring of these three conditions for the stress state: 1. pressure reaching a negative minimum, 2. axial stress reaching the magnitude value of the peak pressure, and 3. the effective stress reaching a zero value. At these conditions, void nucleation was mainly caused by atom de-bonding. In fact, the void nucleation strains were shown to have been preceded by the strains of the stress triaxiality condition in this study, thus confirming the stress triaxiality condition for the void nucleation in spall. Based on the observation that the axial stress reached a maximum value of ∼6 GPa during the void nucleation phase in spall and stayed approximately at that value for different crystal orientations and impact velocities, the value was proposed as a material spall strength.
Determining the optimum location of facilities is critical in many fields, particularly in healthcare. This study proposes the application of a
suitable location model for field hospitals during the novel coronavirus 2019 (COVID-19) pandemic. The used model is the most appropriate among the three most common location models utilized to solve healthcare problems (the set covering model, the maximal covering model, and the P-median model).
The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear constraints. The model is used to determine the optimum location of field hospitals for COVID-19 risk reduction. The designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper Egypt. In this case study, a discrete binary gaining–sharing knowledge-based optimization (DBGSK) algorithm is proposed. The DBGSK algorithm is based on how humans acquire and share knowledge throughout their life. The DBGSK algorithm mainly depends on two junior and senior binary stages. These two stages enable DBGSK to explore and exploit the search space efficiently and
effectively, and thus it can solve problems in binary space.
Ever since its outbreak in the Wuhan city of China, COVID-19 pandemic has engulfed more than 211 countries in the world, leaving a trail of unprecedented fatalities. Even more debilitating than the infection itself, were the restrictions like lockdowns and quarantine measures taken to contain the spread of Coronavirus. Such enforced alienation affected both the mental and social condition of people significantly. Social interactions and congregations are not only integral part of work life but also form the basis of human evolvement. However, COVID-19 brought all such communication to a grinding halt. Digital interactions have failed to enthuse the fervor that one enjoys in face-to-face meets. The pandemic has shoved the entire planet into an unstable state. The main focus and aim of the proposed study is to assess the impact of the pandemic on different aspects of the society in Saudi Arabia. To achieve this objective, the study analyzes two perspectives: the early approach, and the late approach of COVID-19 and the consequent effects on different aspects of the society. We used a Machine Learning based framework for the prediction of the impact of COVID-19 on the key aspects of society. Findings of this research study indicate that financial resources were the worst affected. Several countries are facing economic upheavals due to the pandemic and COVID-19 has had a considerable impact on the lives as well as the livelihoods of people. Yet the damage is not irretrievable and the world’s societies can emerge out of this setback through concerted efforts in all facets of life.
From late 2019 to the present day, the coronavirus outbreak tragically affected the whole world and killed tens of thousands of people. Many countries have taken very stringent measures to alleviate the effects of the coronavirus disease 2019 (COVID-19) and are still being implemented. In this study, various machine learning techniques are implemented to predict possible confirmed cases and mortality numbers for the future. According to these models, we have tried to shed light on the future in terms of possible measures to be taken or updating the current measures. Support Vector Machines (SVM), Holt-Winters, Prophet, and Long-Short Term Memory (LSTM) forecasting models are applied to the novel COVID-19 dataset. According to the results, the Prophet model gives the lowest Root Mean Squared Error (RMSE) score compared to the other three models. Besides, according to this model, a projection for the future COVID-19 predictions of Turkey has been drawn and aimed to shape the current measures against the coronavirus.
The COVID-19 pandemic has created a major challenge for countries all over the world and has placed tremendous pressure on their public health care services. An early diagnosis of COVID-19 may reduce the impact of the coronavirus. To achieve this objective, modern computation methods, such as deep learning, may be applied. In this study, a computational model involving deep learning and biogeography-based optimization (BBO) for early detection and management of COVID-19 is introduced. Specifically, BBO is used for the layer selection process in the proposed convolutional neural network (CNN). The computational model accepts images, such as CT scans, X-rays, positron emission tomography, lung ultrasound, and magnetic resonance imaging, as inputs. In the comparative analysis, the proposed deep learning model CNN is compared with other existing models, namely, VGG16, InceptionV3, ResNet50, and MobileNet. In the fitness function formation, classification accuracy is considered to enhance the prediction capability of the proposed model. Experimental results demonstrate that the proposed model outperforms InceptionV3 and ResNet50.
The outbreak of Covid-19 has taken the lives of many patients so far. The symptoms of COVID-19 include muscle pains, loss of taste and smell, coughs, fever, and sore throat, which can lead to severe cases of breathing difficulties, organ failure, and death. Thus, the early detection of the virus is very crucial. COVID-19 can be detected using clinical tests, making us need to know the most important symptoms/features that can enhance the decision process. In this work, we propose a modified multilayer perceptron (MLP) with feature selection (MLPFS) to predict the positive COVID-19 cases based on symptoms and features from patients’ electronic medical records (EMR). MLPFS model includes a layer that identifies the most informative symptoms to minimize the number of symptoms base on their relative importance. Training the model with only the highest informative symptoms can fasten the learning process and increase accuracy. Experiments were conducted using three different COVID-19 datasets and eight different models, including the proposed MLPFS. Results show that MLPFS achieves the best feature reduction across all datasets compared to all other experimented models. Additionally, it outperforms the other models in classification results as well as time.
The exponential increase in new coronavirus disease 2019 (COVID-19) cases and deaths has made COVID-19 the leading cause of death in many countries. Thus, in this study, we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images. A stacked denoising convolutional autoencoder (SDCA) model was proposed to classify X-ray images into three classes: normal, pneumonia, and COVID-19. The SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy images. The proposed model's architecture mainly composed of eight autoencoders, which were fed to two dense layers and SoftMax classifiers. The proposed model was evaluated with 6356 images from the datasets from different sources. The experiments and evaluation of the proposed model were applied to an 80/20 training/validation split and for five cross-validation data splitting, respectively. The metrics used for the SDCA model were the classification accuracy, precision, sensitivity, and specificity for both schemes. Our results demonstrated the superiority of the proposed model in classifying X-ray images with high accuracy of 96.8%. Therefore, this model can help physicians accelerate COVID-19 diagnosis.
Since the end of 2019, the COVID-19 pandemic had a worst impact onworld’s economy, healthcare, and education. There are several
aspects where the impact of COVID-19 could be visualized. Among these, one aspect is the productivity of researcher, which plays a significant role in the success of an organization. Problem: There are several factors that could be aligned with the researcher’s productivity of each domain and whose analysis through researcher’s feedback could be beneficial for decision makers in terms of their decision making and implementation of mitigation plans for the success
of an organization.Method:We perform an empirical study to investigate the substantial impact of COVID-19 on the productivity of researchers by analyzing the relevant factors through their perceptions. Our study aims to find out the impact of COVID-19 on the researcher’s productivity that are working in different fields. In this study, we conduct a questionnaire-based analysis, which included feedback of 152 researchers of certain domains. These researchers are currently involved in different research activities. Subsequently,
we perform a statistical analysis to analyze the collected responses
and report the findings. Findings: The results indicate the substantial impact of COVID-19 pandemics on the researcher’s productivity in terms of mental disturbance, lack of regular meetings, and field visits for the collection of primary data. Conclusion: Finally, it is concluded that researcher’s daily or weekly meetings with their supervisors and colleagues are necessary to keep them more productive in task completion. These findings would help the decision makers of an organization in the settlement of their plan for the success of an organization.
Novel coronavirus 2019 (COVID-19) has affected the people's health, their lifestyle and economical status across the globe. The application of advanced Artificial Intelligence (AI) methods in combination with radiological imaging is useful in accurate detection of the disease. It also assists the physicians to take care of remote villages too. The current research paper proposes a novel automated COVID-19 analysis method with the help of Optimal Hybrid Feature Extraction (OHFE) and Optimal Deep Neural Network (ODNN) called OHFE-ODNN from chest x-ray images. The objective of the presented technique is for performing binary and multi-class classification of COVID-19 analysis from chest X-ray image. The presented OHFE-ODNN method includes a sequence of procedures such as Median Filtering (MF)based pre-processed, feature extraction and finally, binary (COVID/Non-COVID) and multiclass (Normal, COVID, SARS) classification. Besides, in OHFE-based feature extraction, Gray Level Co-occurrence Matrix (GLCM) and Histogram of Gradients (HOG) are integrated together. The presented OHFE-ODNN model includes Squirrel Search Algorithm (SSA) for fine-tuning the parameters of DNN. The performance of the presented OHFE-ODNN technique is conducted using chest x-rays dataset. The presented OHFE-ODNN method classified the binary classes effectively with a maximum precision of 95.82%, accuracy of 94.01% and F-score of 96.61%. Besides, multiple classes were classified proficiently by OHFE-ODNN model with a precision of 95.63%, accuracy of 95.60% and an F-score of 95.73%.
During the current epidemic, it is necessary to ensure the rehabilitation treatment of children with serious illness. At the same time, however, it is essential to effectively prevent cross-infection and prevent infections from occurring within the hospital setting. To resolve this contradiction, the rehabilitation department of Nanjing Children's Hospital adjusted its bed allocation based on the queuing model, with reference to the regional source and classification of the children's conditions in the rehabilitation department ward. The original triple rooms were transformed into a double room to enable the treatment of severely sick children coming from other places. A M/G/2 queuing model with priority was also applied to analyze the state of patient admissions. Moreover, patients in Nanjing were also classified into mild and severe cases. The M/M/1 queuing model with priority was used for analysis of this situation, so that severely ill children could be treated in time while patients with mild symptoms could be treated at home. This approach not only eases the bed tension in the ward, but also provides suitable conditions for controlling cross-infection.
Nowadays, the world is fighting a dangerous form of Coronavirus that
represents an emerging pandemic. Since its early appearance in China Wuhan city, many countries undertook several strict regulations including lockdowns and social distancing measures. Unfortunately, these procedures have badly impacted the world economy.
Detecting and isolating positive/probable virus infected cases using a tree tracking mechanism constitutes a backbone for containing and resisting such fast spreading disease. For helping this hard effort, this research presents an innovative case study based on big data processing techniques to build a complete tracking system able to identify the central areas of infected/suspected people, and the new suspected cases using health records integration with mobile stations spatio-temporal data logs. The main idea is to identify the positive cases historical movements by tracking their phone location for the
last 14 days (i.e., the virus incubation period). Then, by acquiring the citizen’s mobile phone locations for the same period, the system will be able to measure the Euclidean distances between positive case locations and other nearby people to identify the incontact suspected-cases using parallel clustering and classification techniques.
Moreover, the daily change of the clusters size and its centroids will
be used to predict new regions of infection, as well as, new cases. Moreover, this approach will support infection avoidance by alerting people approaching areas of high probability of infection using their
mobile GPS location. This case study has been developed as a simulation system consisting of three components; positive cases/citizens movement’s data generation subsystem, big data processing platform including CPU/GPU tasks, and data visualization/map geotagging subsystem. The processing of such a big data system requires intensive computing tasks. Therefore, GPU tasks carried out to achieve high performance and accelerate the data processing. According to the simulated system results, data partitioning and processing speed up measures have been examined.
The COVID-19 pandemic has prompted educators to rethink educational practices, especially with regard to technology. The COVID-19 pandemic is a huge challenge to education systems around the world. This Viewpoint offers guidance to teachers, institutional heads, and officials on addressing the crisis. This study investigated technology use in teaching during the COVID-19 lockdown in Malaysia, focusing on technology-based teaching methods, modifications necessitated by this new teaching style, and challenges teachers faced when using technology. Using purposive sampling, a qualitative study was undertaken with a sample of 10 English language teachers from Arabic schools in Malaysia. The results indicated that a digital leap occurred in education during the COVID-19 lockdown because teachers had to quickly adapt to a more technology-based teaching style. The challenges teachers faced included managing virtual classes, ensuring reliable Internet connections, overcoming a lack of preparedness and low digital competence, and dealing with students' mental health. Such changes in teaching methods have created new roles for teachers while also increasing their acceptance of e-learning and remote learning. The contribution of this research is to provide a holistic picture of remote education activities during the pandemic period to establish a linkage between the online teaching-learning process during the COVID-19 outbreak as to ensure the resumption of teaching-learning education as a normal course of procedure in the education system. Despite the human suffering brought by the pandemic, the new norms of education during COVID-19 generally have some pockets of excellence to drive the education into the Fourth Industrial Revolution.
Coronavirus 19 (COVID-19) can cause severe pneumonia that may be fatal. Correct diagnosis is essential. Computed tomography (CT) usefully detects symptoms of COVID-19 infection. In this retrospective study, we present an improved framework for detection of COVID-19 infection on CT images; the steps include pre-processing, segmentation, feature extraction/ fusion/selection, and classification. In the pre-processing phase, a Gabor wavelet filter is applied to enhance image intensities. A marker-based, watershed controlled approach with thresholding is used to isolate the lung region. In the segmentation phase,COVID-19 lesions are segmented using an encoder- /decoder-based deep learning model in which deepLabv3 serves as the bottleneck and mobilenetv2 as the classification head. DeepLabv3 is an effective decoder that helps to refine segmentation of lesion boundaries. The model was trained using fine-tuned hyperparameters selected after extensive experimentation. Subsequently, the Gray Level Co-occurrence Matrix (GLCM) features and statistical features including circularity, area, and perimeters were computed for each segmented image. The computed features were serially fused and the best features (those that were optimally discriminatory) selected using a Genetic Algorithm (GA) for classification. The performance of the method was evaluated using two benchmark datasets: The COVID-19 Segmentation and the POF Hospital datasets. The results were better than those of existing methods.
New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. Through understanding the development trend of confirmed cases in a region, the government can control the pandemic by using the corresponding policies. However, the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors. To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long-Short Term Memory) neural network. This work compares the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models (such as Logistic and Hill equations) with the real data as reference. Furthermore, this work uses the goodness of fitting to evaluate the fitting effect of the improvement. Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect. Compared with the previous forecasting methods, the contributions of our proposed improvement methods are mainly in the following aspects: 1) we have fully considered the spatiotemporal characteristics of the data, rather than single standardized data. 2) the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting. 3) we consider the impact of the epidemic stage and conduct reasonable data processing for different stage.
As global supply chains become more developed and complicated, supplier quality has become increasingly influential on the competitiveness of businesses during the Covid-19 pandemic. Consequently, supplier selection is an increasingly important process for any business around the globe. Choosing a supplier is a complex decision that can result in lower procurement costs and increased profits without increasing the cost or lowering the quality of the product. However, these decision-making problems can be complicated in cases with multiple potential suppliers. Vietnam's textile and garment industry, for example, has made rapid progress in recent years but is still facing great difficulties as the supply of raw materials and machinery depends heavily on foreign countries. Therefore, it is extremely important for textile and garment manufacturing companies in Vietnam to implement an effective supplier evaluation and selection process. While multicriteria decision-making models are frequently employed to assist with supplier evaluation and selection problems, few of these models consider the problem under the condition of a fuzzy decision-making environment. The aim of this paper is to create a hybrid MCDM model using the Fuzzy Analytical Hierarchy Process (FAHP) model and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to assist the supplier selection process in the garment industry in a fuzzy decision-making environment. In this study, the FAHP method is used to evaluate the performance and the weight of each criterion. TOPSIS is then used to rank all potential suppliers. The proposed model is then applied to a real-world case study to demonstrate both the process of calculation as well as its real-world applicability. The results from the case study provide empirical evidence that the model is feasible. The proposed approach can also be used in combination with other MCDM models to better support decision makers and can be modified to be applied in similar supplier selection processes for different industries.
Many respiratory infections around the world have been caused by coronaviruses. COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate. There is a high need for computer-assisted diagnostics (CAD) in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud systems. Machine learning (ML) has been used to examine chest X-ray frames. In this paper, a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes, a pneumonia patient, a COVID-19 patient, or a normal person. First, three different pre-trained Convolutional Neural Network (CNN) models (resnet18, resnet25, densenet201) are employed for deep feature extraction. Second, each feature vector is passed through the binary Butterfly optimization algorithm (bBOA) to reduce the redundant features and extract the most representative ones, and enhance the performance of the CNN models. These selective features are then passed to an improved Extreme learning machine (ELM) using a BOA to classify the chest X-ray images. The proposed paradigm achieves a 99.48% accuracy in detecting covid-19 cases.
The COVID-19 outbreak initiated from the Chinese city of Wuhan and eventually affected almost every nation around the globe. From China, the disease started spreading to the rest of the world. After China, Italy became the next epicentre of the virus and witnessed a very high death toll. Soon nations like the USA became severely hit by SARS-CoV-2 virus. The World Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the world has instituted various policies like physical distancing, isolation of infected population and researching on the potential vaccine of SARS-CoV-2. To identify the impact of various policies implemented by the affected countries on the pandemic spread, a myriad of AI-based models have been presented to analyse and predict the epidemiological trends of COVID-19. In this work, the authors present a detailed study of different artificial intelligence frameworks applied for predictive analysis of COVID-19 patient record. The forecasting models acquire information from records to detect the pandemic spreading and thus enabling an opportunity to take immediate actions to reduce the spread of the virus. This paper addresses the research issues and corresponding solutions associated with the prediction and detection of infectious diseases like COVID-19. It further focuses on the study of vaccinations to cope with the pandemic. Finally, the research challenges in terms of data availability, reliability, the accuracy of the existing prediction models and other open issues are discussed to outline the future course of this study.
Medical data tampering has become one of the main challenges in the field of secure-aware medical data processing. Forgery of normal patients' medical data to present them as COVID-19 patients is an illegitimate action that has been carried out in different ways recently. Therefore, the integrity of these data can be questionable. Forgery detection is a method of detecting an anomaly in manipulated forged data. An appropriate number of features are needed to identify an anomaly as either forged or non-forged data in order to find distortion or tampering in the original data. Convolutional neural networks (CNNs) have contributed a major breakthrough in this type of detection. There has been much interest from both the clinicians and the AI community in the possibility of widespread usage of artificial neural networks for quick diagnosis using medical data for early COVID-19 patient screening. The purpose of this paper is to detect forgery in COVID-19 medical data by using CNN in the error level analysis (ELA) by verifying the noise pattern in the data. The proposed improved ELA method is evaluated using a type of data splicing forgery and sigmoid and ReLU phenomenon schemes. The proposed method is verified by manipulating COVID-19 data using different types of forgeries and then applying the proposed CNN model to the data to detect the data tampering. The results show that the accuracy of the proposed CNN model on the test COVID-19 data is approximately 92%.
The World Health Organization declared COVID-19 a pandemic on March 11, 2020 stating that it is a worldwide danger and requires imminent preventive strategies to minimise the loss of lives. COVID-19 has now affected millions across 211 countries in the world and the numbers continue to rise. The information discharged by the WHO till June 15, 2020 reports 8,063,990 cases of COVID-19. As the world thinks about the lethal malady for which there is yet no immunization or a predefined course of drug, the nations are relentlessly working at the most ideal preventive systems to contain the infection. The Kingdom of Saudi Arabia (KSA) is additionally combating with the COVID-19 danger as the cases announced till June 15, 2020 reached the count of 132,048 with 1,011 deaths. According to the report released by the KSA on June 14, 2020, more than 4,000 cases of COVID-19 pandemic had been registered in the country. Tending to the impending requirement for successful preventive instruments to stem the fatalities caused by the disease, our examination expects to assess the severity of COVID-19 pandemic in cities of KSA. In addition, computational model for evaluating the severity of COVID-19 with the perspective of social influence factor is necessary for controlling the disease. Furthermore, a quantitative evaluation of severity associated with specific regions and cities of KSA would be a more effective reference for the healthcare sector in Saudi Arabia. Further, this paper has taken the Fuzzy Analytic Hierarchy Process (AHP) technique for quantitatively assessing the severity of COVID-19 pandemic in cities of KSA. The discoveries and the proposed structure would be a practical, expeditious and exceptionally precise evaluation system for assessing the severity of the pandemic in the cities of KSA. Hence these urban zones clearly emerge as the COVID-19 hotspots. The cities require suggestive measures of health organizations that must be introduced on a war footing basis to counter the pandemic. The analysis tabulated in our study will assist in mapping the rules and building a systematic structure that is immediate need in the cities with high severity levels due to the pandemic.
The COVID-19 pandemic has caused higher educational institutions around the world to close campus-based activities and move to online delivery. The aim of this paper is to present the case of Global College of Engineering and Technology (GCET) and how its practices including teaching, students/staff support, assessments, and exam policies were affected. The paper investigates the mediating role of no detriment policy impact on students' result along with the challenges faced by the higher educational institution, recommendations and suggestions. The investigation concludes that the strategies adopted for online delivery, student support, assessments and exam policies have helped students to effectively cope with the teaching and learning challenges posed by the COVID-19 pandemic without affecting their academic results. The study shows that 99% of students were able to maintain the same or better level of performance during the 1st COVID-19 semester. One percent of students had shown a slight decrease in their performance (about 1%-2%) with respect to their overall marks pre-COVID-19. The no detriment policy has succoured those 1% of the students to maintain their overall performance to what it used to be pre-COVID-19 pandemic. Finally, the paper provides the list of challenges and suggestions for smooth conduction of online education.