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1.
Mathematics ; 10(9):1611, 2022.
Article in English | ProQuest Central | ID: covidwho-1842879

ABSTRACT

Many problems in daily life exhibit nonlinear behavior. Therefore, it is important to solve nonlinear problems. These problems are complex and difficult due to their nonlinear nature. It is seen in the literature that different artificial intelligence techniques are used to solve these problems. One of the most important of these techniques is artificial neural networks. Obtaining successful results with an artificial neural network depends on its training process. In other words, it should be trained with a good training algorithm. Especially, metaheuristic algorithms are frequently used in artificial neural network training due to their advantages. In this study, for the first time, the performance of sixteen metaheuristic algorithms in artificial neural network training for the identification of nonlinear systems is analyzed. It is aimed to determine the most effective metaheuristic neural network training algorithms. The metaheuristic algorithms are examined in terms of solution quality and convergence speed. In the applications, six nonlinear systems are used. The mean-squared error (MSE) is utilized as the error metric. The best mean training error values obtained for six nonlinear systems were 3.5×10−4, 4.7×10−4, 5.6×10−5, 4.8×10−4, 5.2×10−4, and 2.4×10−3, respectively. In addition, the best mean test error values found for all systems were successful. When the results were examined, it was observed that biogeography-based optimization, moth–flame optimization, the artificial bee colony algorithm, teaching–learning-based optimization, and the multi-verse optimizer were generally more effective than other metaheuristic algorithms in the identification of nonlinear systems.

2.
5th International Conference on Software Engineering and Information Management, ICSIM 2022 ; : 188-192, 2022.
Article in English | Scopus | ID: covidwho-1840644

ABSTRACT

The Philippines is one of the countries where the coronavirus has spread. The virus has infected almost every Filipino individual;coronavirus affects people of all ages, from children to adults, and as a result, recovery rate is unknown. This research aims to develop a predictive model using random forest algorithms to predict the high and low recovery rate by age. Based on the descriptive analysis of the data set, the age range of 20 to 29 has a 99.3 percent recovery rate compared to other age groups. The Random Forest Predictive Model was able to predict the high recovery rate with an accuracy rate of 93%. © 2022 ACM.

3.
2022 International Mobile and Embedded Technology Conference, MECON 2022 ; : 184-188, 2022.
Article in English | Scopus | ID: covidwho-1840284

ABSTRACT

This research paper gives a brief idea of controlling entrance gates of different areas like metro stations, railway stations, airports, corporate offices, restaurants, hotels and home with the face mask detection technology. In this, the camera will capture the real time video of a person using Artificial Intelligence[15],whosoever is entering the gate, processes the video and detects if the concerned person is wearing the mask properly or not. If the person is wearing a mask then the gate will open, if not then the gate will remain closed until the mask has been worn properly. The main motivation for this project comes from the current situation in the world where Covid-19 is spreading at a pace which is being difficult to control. This upcoming technology prototype can fuel in new ideas into different projects which are already ongoing to battle the pandemic. Also, the scope of this technology is not just limited to the face mask detection and has a wider and a more complex use-case in the real world. © 2022 IEEE.

4.
6th International Conference on Computing Methodologies and Communication, ICCMC 2022 ; : 1577-1580, 2022.
Article in English | Scopus | ID: covidwho-1840252

ABSTRACT

Based on several pre-defined standard symptoms, a model that can determine the coronavirus illness as positive is developed. Guidelines for these symptoms have been issued by the World Health Organization (WHO) and India's Ministry of Health and Family Welfare. In this model the various symptoms of the illnesses is given to the system. It allows users to discuss their symptoms, with the algorithm predicting a condition based on factual information. This factual information is then evaluated using the ARM based Apriori algorithm to get the most accurate results. Other conventional models such as Support Vector Machine (SVM), Artificial Neural Networks (ANNs), and Random Forests (RF) are considered and have analyzed the predictions and have found that the proposed algorithm predicts a higher accuracy score. © 2022 IEEE.

5.
Embase; 2021.
Preprint in English | EMBASE | ID: ppcovidwho-335762

ABSTRACT

The COVID-19 pandemic has caused over 350 million cases and over five million deaths globally. From these numbers, over 10 million cases and over 200 thousand deaths have occurred on the African continent as of 22 January 2022. Prevention and surveillance remain the cornerstone of interventions to halt the further spread of COVID-19. Google Health Trends (GHT), a free Internet tool, may be valuable to help anticipate outbreaks, identify disease hotspots, or understand the patterns of disease surveillance. We collected COVID-19 case and death incidence for 54 African countries and obtained averages for four, five-month study periods in 2020-2021. Average case and death incidences were calculated during these four time periods to measure disease severity. We used GHT to characterize COVID-19 incidence across Africa, collecting numbers of searches from GHT related to COVID-19 using four terms: 'coronavirus', 'coronavirus symptoms', 'COVID19', and 'pandemic'. The terms were related to weekly COVID-19 case incidences for the entire study period via multiple linear regression analysis and weighted linear regression analysis. We also assembled 72 predictors assessing Internet accessibility, demographics, economics, health, and others, for each country, to summarize potential mechanisms linking GHT searches and COVID-19 incidence. COVID-19 burden in Africa increased steadily during the study period as in the rest of the world. Important increases for COVID-19 death incidence were observed for Seychelles and Tunisia over the study period. Our study demonstrated a weak correlation between GHT and COVID-19 incidence for most African countries. Several predictors were useful in explaining the pattern of GHT statistics and their relationship to COVID-19 including: log of average weekly cases, log of cumulative total deaths, and log of fixed total number of broadband subscriptions in a country. Apparently, GHT may best be used for surveillance of diseases that are diagnosed more consistently. GHT-based surveillance for an ongoing epidemic might be useful in specific situations, such as when countries have significant levels of infection with low variability. Overall, GHT-based surveillance showed little applicability in the studied countries. Future studies might assess the algorithm in different epidemic contexts.

6.
Image Processing for Automated Diagnosis of Cardiac Diseases ; : 133-155, 2021.
Article in English | Scopus | ID: covidwho-1838469

ABSTRACT

Artificial intelligence (AI) has developed speedily since the late 1980s. Enhancement of medical datasets and outcomes in the last twenty years has resulted in unprecedented improvement in AI-based journals. In addition, with the introduction of unparalleled computational efficiency, the accessibility of AI tools has improved. There are two fundamental tools in AI. The first is machine learning (ML), where organized information like electrophysiology (EP), images, and genetic information are broken down and examined. The second is natural language processing (NLP), where unorganized information is scrutinized. These two AI tools have enhanced strategies, calculations, and applications. Different endeavors and new techniques of AI have been utilized for ailments like cardiovascular disease (CVD), neural disorders, and cancer, among others. Presently, a sophisticated deep learning (DL) technique has instigated exceptional growth of AI in clinical imaging diagnostic frameworks. Thus, this chapter presents pivotal and specialized information about AI-based techniques for predicting, diagnosing, and analyzing cardiac diseases. © 2021 Elsevier Inc. All rights reserved.

7.
International Journal of Electrical and Computer Engineering ; 11(5):4325-4335, 2021.
Article in English | ProQuest Central | ID: covidwho-1837540

ABSTRACT

Covid-19 pandemic has stressed more than any-time before the necessity for conducting election processes in an electronic manner, where voters can cast their votes remotely with complete security, privacy, and trust. The different voting schema in different countries makes it very difficult to utilize a one fits all system. This paper presents a blockchain based voting system (BBVS) applied to the Parliamentary elections system in the country of Jordan. The proposed system is a private and centralized blockchain implemented in a simulated environment. The proposed BBVS system implements a hierarchical voting process, where a voter casts votes at two levels, one for a group, and the second for distinct members within the group. This paper provides a novel blockchain based e-Voting system, which proves to be transparent and yet secure. This paper utilizes synthetic voter benchmarks to measure the performance, accuracy and integrity of the election process. This research introduced and implemented new algorithms and methods to maintain acceptable performance both at the time of creating the blockchain(s) for voters and candidates as well as at the time of casting votes by voters.

8.
International Journal of Electrical and Computer Engineering ; 11(6):5034-5048, 2021.
Article in English | ProQuest Central | ID: covidwho-1837143

ABSTRACT

In this paper, a novel solution to avoid new infections is presented. Instead of tracing users’ locations, the presence of individuals is detected by analysing the voices, and people’s faces are detected by the camera. To do this, two different Android applications were implemented. The first one uses the camera to detect people’s faces whenever the user answers or performs a phone call. Firebase Platform will be used to detect faces captured by the camera and determine its size and estimate their distance to the phone terminal. The second application uses voice biometrics to differentiate the users’ voice from unknown speakers and creates a neural network model based on 5 samples of the user’s voice. This feature will only be activated whenever the user is surfing the Internet or using other applications to prevent undesired contacts. Currently, the patient’s tracking is performed by geolocation or by using Bluetooth connection. Although face detection and voice recognition are existing methods, this paper aims to use them and integrate both in a single device. Our application cannot violate privacy since it does not save the data used to carry out the detection and does not associate this data to people.

9.
Future Transportation ; 1(2):248, 2021.
Article in English | ProQuest Central | ID: covidwho-1834768

ABSTRACT

The transportation network design and frequency setting problem concerns the optimization of transportation systems comprising fleets of vehicles serving a set amount of passengers on a predetermined network (e.g., public transport systems). It has been a persistent focus of the transportation planning community while, its NP-hard nature continues to present obstacles in designing efficient, all-encompassing solutions. In this paper, we present a new approach based on an alternating-objective genetic algorithm that aims to find Pareto optimality between user and operator costs. Extensive computational experiments are performed on Mandl’s benchmark test and prove that the results generated by our algorithm are 5–6% improved in comparison to previously published results for Pareto optimality objectives both in regard to user and operator costs. At the same time, the methods presented are computationally inexpensive and easily run on office equipment, thus minimizing the need for expensive server infrastructure and costs. Additionally, we identify a wide variance in the way that similar computational results are reported and, propose a novel way of reporting benchmark results that facilitates comparisons between methods and enables a taxonomy of heuristic approaches to be created. Thus, this paper aims to provide an efficient, easily applicable method for finding Pareto optimality in transportation networks while highlighting specific limitations of existing research both in regards to the methods used and the way they are communicated.

10.
Model Assisted Statistics and Applications ; 17(1):59-68, 2022.
Article in English | Scopus | ID: covidwho-1834300

ABSTRACT

The research study tries to understand teenagers' online engagement and the behavioral transformation in buying stuff online. The study also tries to ideate the stability of spike in online buying (if any) and its sustainability. Statistical tools like the K-S test, M.L.R. test, Pearson Correlation has been used to justify the study and the usage of machine learning algorithms to construct a predictive model of behaviour and its efficiency. The study will help online retailers understand their sales figures' stability. It will allow them to strategize their marketing functionalities to make the space more attractive even after the world comes out of the pandemic. The increasing usage of intelligent android devices and relatively cheap data has surged the penetration of online engagements among all the age group peoples. The youngsters are engaging in online stuff hence bringing down a considerable transformation in buying behaviour, pattern, and a collective change in marketers' approach to strategizing according to the ever-evolving market forces. © 2022 - IOS Press. All rights reserved.

11.
2021 IEEE International Conference on Technology, Research, and Innovation for Betterment of Society, TRIBES 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1831872

ABSTRACT

In the current scenario, almost all the countries face one of the biggest disasters in COVID-19. This paper has to analyze the tweets related to COVID 19 and discuss the various machine learning algorithms and their performance analysis on the tweets associated with COVID-19. The implemented classification algorithms are applied to classify the sentiments to predict whether they relate to COVID-19 or non-COVID-19. Ten most popular classification algorithms implemented. The Linear Support Vector Machine (LSVM) achieved the highest test accuracy in these algorithms with 90.3%. Logistic regression has performed better in recall with 96.06%, F1 score of 90.46%, ROC_AUC with 90.48%. Random forest classifier has achieved the better specificity and precision of 99.16% and 96.3%, respectively. Out of all, stochastic gradient descent (SGD) has attained better results in all the computational parameters. © 2021 IEEE.

12.
2022 IEEE-EMB Special Topic Conference on Healthcare Innovations and Point of Care Technologies, HI-POCT 2022 ; : 63-66, 2022.
Article in English | Scopus | ID: covidwho-1831763

ABSTRACT

In pandemic times, in most countries the closing of airports and local as well as international flights are done in a coherent manner that allow people to improve their decisions respect to the mobility that might emerge in each case. Once that travelers have moved to a different country or city, it is mandatory that all of them have an updated knowledge of the ongoing pandemic whose main variable is the number of infections at time and certain geographic area. In this paper, an universal algorithm that underlines its usage in different places is presented. With this the estimation error is also provided. The purpose of this study is to provide a theory inside a framework of applications for smartphone to provide information about the places with infections, vaccination rate and fatalities. This might be of relevance for travelers that can carry out spatial displacements with certain security by empowering them to improve their daily objectives still at pandemic times. © 2022 IEEE.

13.
2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2022 ; : 1328-1331, 2022.
Article in English | Scopus | ID: covidwho-1831757

ABSTRACT

Sina Weibo, as a platform for netizens to express their opinions, generates a large amount of public opinion data and constantly generates new topics. How to detect new and hot topics on Weibo is a meaningful studied issue. Document Clustering is a widely studied problem in Text Categorization. K-means is one of the most famous unsupervised learning algorithms, partitions a given dataset into disjoint clusters following a simple and easy way. But the traditional K-means algorithm assigns initial centroids randomly, which cannot guarantee to choose the maximum dissimilar documents as the centroids for the clusters. A modified K-means algorithm is proposed, which uses Jaccard distance measure for assigning the most dissimilar k documents as centroids, and uses Word2vec as the Chinese text vectorization model. The experimental results demonstrate that the proposed K-means algorithm improves the clustering performance, and is able to detect new and hot topics based on Weibo COVID-19 data. © 2022 IEEE.

14.
2021 International Conference on Computational Performance Evaluation, ComPE 2021 ; : 741-749, 2021.
Article in English | Scopus | ID: covidwho-1831742

ABSTRACT

During the Covid-19 pandemic world has witnessed the rise of cyber-attacks, especially during the Lockdown time course announced by the countries throughout the world, when almost every aspect of life changed the routine from offline to online. Protecting and securing information resources during pandemics has been a top priority for the modern computing world, with databases, banking, E-commerce and mailing services, etc. being the eye-catching credentials to the attackers. Apart from cryptography, machine learning and deep learning can offer an enormous amount of help in testing, training, and extracting negligible information from the data sets. Deep learning and machine learning have many methods and models in the account to detect and classify the different versions of cyber-attacks occasionally, from the datasets. Some of the most common deep learning methods inspired by the neural networks are Recurrent Neural Networks, Convolutional Neural Networks, Deep Belief Networks, Deep Boltzman Networks, Autoencoders, and Stacked Auto-encoders. Also counting machine learning algorithms into the account, there is a vast variety of algorithms that are meant to perform classification and regression. The survey will provide some of the most important deep learning and machine learning architectures used for Cyber-security and can offer protective services against cyber-attacks. The paper is a survey about various categories of cyber-attacks with a timeline of different attacks that took place in India and some of the other countries in the world. The final section of the report is about what deep learning methods can offer for developing and improving the security policies and examining vulnerabilities of an information system. © 2021 IEEE.

15.
2021 International Conference on Computational Performance Evaluation, ComPE 2021 ; : 965-970, 2021.
Article in English | Scopus | ID: covidwho-1831739

ABSTRACT

COVID-19, which is subsequently named as SARS-CoV-2, First Human case was found in the City of Wuhan, from China, in Dec 2019. After that, the World health organization (WHO) has declared Coronavirus as a Pandemic on 11th March 2020. In this study, our primary aim is to Detect the Severe Covid-19 patient in the Early Stages by looking at the information on admission laboratory values, demographics, comorbidities, admission medications, admission supplementary oxygen orders, discharge, and mortality. 4711 patient's dataset with confirmed SARS-CoV-2 infections are included in the study. Each Patient has total of 85 Features in the Dataset. So, we have Filtered the Top Best 35 features out of 85 features from the Dataset using the seven different feature Selection algorithm and taken the most common features out from the different feature Selection algorithm. After selecting the top most essential features, we have applied around 17 different kinds of ML models like Linear Regression, Logistic regression, SVM, LinearSVC, MLP-Classifier, Decision Tree Classifier, Gradient Boosting Classifier, AdaBoost, Random Forest, XGBoost, LightGBM Classifier, Ridge Classifier, Bagging Classifier, ExtraTreeClassifier, KNN, Naive Bayes, Neural network with Keras, and finally, a Voting Classifier which is the ensemble of all the Top Models from the above-mentioned Models. Finally, all Models are Compared on the basis of Area under the receiver operating characteristic (AUC) get the best AUC as 0.89. © 2021 IEEE.

16.
2nd International Conference on Electronics, Communications and Information Technology, CECIT 2021 ; : 427-436, 2021.
Article in English | Scopus | ID: covidwho-1831729

ABSTRACT

The rapid development of artificial intelligence techniques is significantly promoting the resolution of various important decision-making issues such as material distribution, generation line optimization scheduling, and path planning. Currently, SARS-CoV-2 is raging over the world, and it is valuable to propose a vaccine distribution strategy to utilize limited vaccine resources rationally. In this paper, we aim to propose an optimal vaccine distribution strategy based on deep reinforcement learning(DRL) approaches. An End-to-End vaccine distribution model is proposed by combining the Deep Reinforcement Learning model and LinUCB algorithm to get an optimistic strategy of allocation. Experiment results demonstrated that vaccine distribution strategies based on this model show a strong capacity to control the epidemic and ensure stable government revenue compared with baseline strategies. © 2021 IEEE.

17.
Embase; 2021.
Preprint in English | EMBASE | ID: ppcovidwho-334673

ABSTRACT

We propose a novel heuristic to predict RNA secondary structure formation pathways that has two components: (i) a folding algorithm and (ii) a kinetic ansatz. This heuristic is inspired by the kinetic partitioning mechanism, by which molecules follow alternative folding pathways to their native structure, some much faster than others. Similarly, our algorithm RAFFT starts by generating an ensemble of concurrent folding pathways ending in multiple metastable structures, which is in contrast with traditional thermodynamic approaches that find single structures with minimal free energies. When we constrained the algorithm to predict only 50 structures per sequence, near-native structures were found for RNA molecules of length ≤ 200 nucleotides. Our heuristic has been tested on the coronavirus frameshifting stimulation element (CFSE): an ensemble of 68 distinct structures allowed us to produce complete folding kinetic trajectories, whereas known methods require evaluating millions of sub-optimal structures to achieve this result. Thanks to the fast Fourier transform on which RAFFT is based, these computations are efficient, with complexity O(L2 log L).

18.
SSRN; 2022.
Preprint in English | SSRN | ID: ppcovidwho-334464

ABSTRACT

Using bi-weekly snapshots of Zillow in three US cities, we document how home sellers and buyers interact with Zillow's Zestimate algorithm during the sales cycle of residential properties. We find that listing and selling outcomes respond significantly to Zestimate, and Zestimate is quickly updated for the focal and comparable houses after a listing or a transaction is completed. The user-Zestimate interactions have mixed implications: on the one hand, listing price depends more on Zestimate if the city does not mandate disclosure of sales information or if the neighborhood is more heterogeneous, suggesting that Zestimate provides valuable information when alternative information is more difficult to obtain;on the other hand, the post-listing update of Zestimate tracks listing price more closely in non-disclosure and heterogeneous neighborhoods, raising the concern that the feedback loop may propagate disturbances in the sales process. However, by leveraging COVID-19 pandemic as a natural experiment, we find no evidence that Zestimate propagates the initial shock from the March-2020 declaration of national emergency, probably because Zestimate has built-in guard rails and users tend to adjust their confidence in Zestimate according to observed market outcomes.

19.
Journal of Applied Statistics ; : 1-15, 2022.
Article in English | Academic Search Complete | ID: covidwho-1830451

ABSTRACT

In this paper, we propose a hierarchical Bayesian approach for modeling the evolution of the 7-day moving average for the number of deaths due to COVID-19 in a country, state or city. The proposed approach is based on a Gaussian process regression model. The main advantage of this model is that it assumes that a nonlinear function f used for modeling the observed data is an unknown random parameter in opposite to usual approaches that set up f as being a known mathematical function. This assumption allows the development of a Bayesian approach with a Gaussian process prior over f. In order to estimate the parameters of interest, we develop an MCMC algorithm based on the Metropolis-within-Gibbs sampling algorithm. We also present a procedure for making predictions. The proposed method is illustrated in a case study, in which, we model the 7-day moving average for the number of deaths recorded in the state of São Paulo, Brazil. Results obtained show that the proposed method is very effective in modeling and predicting the values of the 7-day moving average. [ FROM AUTHOR] Copyright of Journal of Applied Statistics is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

20.
5th International Conference on Smart Computing and Informatics, SCI 2021 ; 282:611-622, 2022.
Article in English | Scopus | ID: covidwho-1826290

ABSTRACT

The world is facing pandemic situation, i.e., COVID-19, all the researchers and scientist are working hard to overcome this situation. Being human it is everyone’s duty to take care of family and the society. In this case study, an attempt has been made to find the relation between various variables by dividing them into the independent and dependent variables. A dataset is selected for analysis purpose which consists of variables like location (countries across the globe, date, new cases, new deaths, total deaths, smoking habits washing habits, diabetic prevalence, etc. Approach is to identify the impact of independent variable on the dependent variable by applying the regression modeling. Hence, proposed case study is based on selection-based framework for validating the regression modeling for COVID-19 data analysis. Regression modeling is applied, and few representations are shown to understand the current pandemic situation across the world. In the end, using regression modeling interceptor and coefficient values for different approaches (using different variables) is computed. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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