scholarly journals A Novel Approach of Frequent Itemsets Mining for Coronavirus Disease (COVID-19)

2021 ◽  
Vol 5 (2) ◽  
pp. 5-12
Author(s):  
Mai Shawkat ◽  
Mahmoud Badawi ◽  
Ali I. Eldesouky
Keyword(s):  
Frequent Itemsets ◽  
Rule Mining ◽  
Global Pandemic ◽  
Novel Approach ◽  
Antibiotic Drugs ◽  
The World ◽  
Potential Applications ◽  
Medical Reports ◽  
Frequent Itemsets Mining

The global pandemic of new coronaviruses (COVID-19) has infected many people around the world and became a worldwide concern since this disease caused illness and deaths. The vaccine and drugs are not scientifically established, but patients are recovering with antibiotic drugs, antiviral medicine, chloroquine, and vitamin C. Now it is obvious to the world that a quicker and faster solution is needed for monitoring and combating the further spread of COVID-19 worldwide, using non-clinical techniques, for example, data mining tools, enhanced intelligence, and other artificial intelligence technologies. In this paper, association rule mining is developing for the frequent itemsets discovery in COVID-19 datasets, and the extraction of effective association relations between them. This is done by demonstrates the analysis of the Coronavirus dataset by using the Apriori_Association_Rules algorithm. It involves a scheme for classification and prediction by recognizing the associated rules relating to Coronavirus. The major contribution of this study employment determines the effectiveness of the Apriori_Association_Rules algorithm towards a classification of medical reports. The experimental results provide evidence of the Apriori_Association_Rules algorithm regarding the execution time, memory consumption, and several associated rules that reflect its potential applications to different contexts. Therefore, the Apriori_Association_Rules algorithm will be very useful in healthcare fields to demonstrate the latest developments in medical studies fighting COVID-19.

Study of Various Parallel Implementations of Association Rule Mining Algorithm

2020 ◽  
Vol 1 (3) ◽  
pp. 1-7
Author(s):  
Sarbani Dasgupta ◽  
Banani Saha
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Parallel Implementation ◽  
Rule Learning ◽  
Frequent Itemsets ◽  
Rule Mining ◽  
Large Dataset ◽  
Transactional Databases ◽  
Frequent Itemsets Mining ◽  
Sequential Implementation

In data mining, Apriori technique is generally used for frequent itemsets mining and association rule learning over transactional databases. The frequent itemsets generated by the Apriori technique provides association rules which are used for finding trends in the database. As the size of the database increases, sequential implementation of Apriori technique will take a lot of time and at one point of time the system may crash. To overcome this problem, several algorithms for parallel implementation of Apriori technique have been proposed. This paper gives a comparative study on various parallel implementation of Apriori technique .It also focuses on the advantages of using the Map Reduce technology, the latest technology used in parallelization of large dataset mining.

scholarly journals Classification of COVID-19 and Pneumonia Using Deep Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Mainuzzaman Mahin ◽  
Sajid Tonmoy ◽  
Rufaed Islam ◽  
Tahia Tazin ◽  
Mohammad Monirujjaman Khan ◽  
...  
Keyword(s):  
Transfer Learning ◽  
World Health ◽  
Health Concern ◽  
Global Pandemic ◽  
Custom Made ◽  
The World ◽  
Learning Technique ◽  
Life Threatening ◽  
Health Organization

The World Health Organization (WHO) recognized COVID-19 as the cause of a global pandemic in 2019. COVID-19 is caused by SARS-CoV-2, which was identified in China in late December 2019 and is indeed referred to as the severe acute respiratory syndrome coronavirus-2. The whole globe was hit within several months. As millions of individuals around the world are infected with COVID-19, it has become a global health concern. The disease is usually contagious, and those who are infected can quickly pass it on to others with whom they come into contact. As a result, monitoring is an effective way to stop the virus from spreading further. Another disease caused by a virus similar to COVID-19 is pneumonia. The severity of pneumonia can range from minor to life-threatening. This is particularly hazardous for children, people over 65 years of age, and those with health problems or immune systems that are affected. In this paper, we have classified COVID-19 and pneumonia using deep transfer learning. Because there has been extensive research on this subject, the developed method concentrates on boosting precision and employs a transfer learning technique as well as a model that is custom-made. Different pretrained deep convolutional neural network (CNN) models were used to extract deep features. The classification accuracy was used to measure performance to a great extent. According to the findings of this study, deep transfer learning can detect COVID-19 and pneumonia from CXR images. Pretrained customized models such as MobileNetV2 had a 98% accuracy, InceptionV3 had a 96.92% accuracy, EffNet threshold had a 94.95% accuracy, and VGG19 had a 92.82% accuracy. MobileNetV2 has the best accuracy of all of these models.

scholarly journals PredictingMycobacterium tuberculosisComplex Clades Using Knowledge-Based Bayesian Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Minoo Aminian ◽  
David Couvin ◽  
Amina Shabbeer ◽  
Kane Hadley ◽  
Scott Vandenberg ◽  
...  
Keyword(s):  
Bayesian Networks ◽  
Dna Fingerprint ◽  
Support Vector ◽  
Rule Based ◽  
Polyhedral Sets ◽  
Knowledge Based ◽  
Novel Approach ◽  
The World ◽  
Rule Set

We develop a novel approach for incorporating expert rules into Bayesian networks for classification ofMycobacterium tuberculosiscomplex (MTBC) clades. The proposed knowledge-based Bayesian network (KBBN) treats sets of expert rules as prior distributions on the classes. Unlike prior knowledge-based support vector machine approaches which require rules expressed as polyhedral sets, KBBN directly incorporates the rules without any modification. KBBN uses data to refine rule-based classifiers when the rule set is incomplete or ambiguous. We develop a predictive KBBN model for 69 MTBC clades found in the SITVIT international collection. We validate the approach using two testbeds that model knowledge of the MTBC obtained from two different experts and large DNA fingerprint databases to predict MTBC genetic clades and sublineages. These models represent strains of MTBC using high-throughput biomarkers called spacer oligonucleotide types (spoligotypes), since these are routinely gathered from MTBC isolates of tuberculosis (TB) patients. Results show that incorporating rules into problems can drastically increase classification accuracy if data alone are insufficient. The SITVIT KBBN is publicly available for use on the World Wide Web.

scholarly journals Nanoparticles: breakthrough in COVID-19 prevention, diagnosis and treatment

2021 ◽  
Author(s):  
Aleksandra Skwarek ◽  
Aleksandra Gąsecka ◽  
Miłosz Jaguszewski ◽  
Łukasz Szarpak ◽  
Tomasz Dzieciątkowski ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Early Detection ◽  
Targeted Treatment ◽  
World Health ◽  
Great Promise ◽  
Global Pandemic ◽  
The World ◽  
Potential Applications ◽  
Health Organization ◽  
Clinical Problems

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), declared a global pandemic by the World Health Organization (WHO). The three key principles in management of the COVID-19 pandemic are prevention, early detection and targeted treatment. Vaccine-based prevention together with early detection have already proven their efficacy in controlling the pandemic, early detection of the infected patients could substantially accelerate the implementation of treatment, but also help to identify the infection hotspots, whereas the targeted treatment might destroy the virus and minimize damage to healthy tissue. Nanoparticles hold great promise with respect to these above-mentioned aspects. They may also be the solution to the emerging clinical problems, such as: reinfection, pregnant-related COVID-19 and coinfection. Here, we aim to discuss the potential applications of nanoparticles to combat the COVID-19 pandemic.

Russian Citizens' Representations of the Country's Position in the Geopolitical Space of the Commonwealth of Independent States, Europe, and the World

2003 ◽  
Vol 8 (4) ◽  
pp. 238-251
Author(s):  
Victor F. Petrenko ◽  
Olga V. Mitina ◽  
Kirill A. Bertnikov
Keyword(s):  
Well Being ◽  
Multidimensional Space ◽  
Commonwealth Of Independent States ◽  
Repertory Grids ◽  
Implicit Model ◽  
The World ◽  
Public Consciousness ◽  
Independent States ◽  
Invariant Factors

The aim of this research was the reconstruction of the system of categories through which Russians perceive the countries of the Commonwealth of Independent States (CIS), Europe, and the world as a whole; to study the implicit model of the geopolitical space; to analyze the stereotypes in the perception of different countries and the superposition of mental geopolitical representations onto the geographic map. The techniques of psychosemantics by Petrenko, originating in the semantic differential of Osgood and Kelly's “repertory grids,” were used as working tools. Multidimensional semantic spaces act as operational models of the structures of consciousness, and the positions of countries in multidimensional space reflect the geopolitical stereotypes of respondents about these countries. Because of the transformation of geopolitical reality representations in mass consciousness, the commonly used classification of countries as socialist, capitalist, and developing is being replaced by other structures. Four invariant factors of the countries' descriptions were identified. They are connected with Economic and Political Well-being, Military Might, Friendliness toward Russia, and Spirituality and the Level of Culture. It seems that the structure has not been explained in adequate detail and is not clearly realized by the individuals. There is an interrelationship between the democratic political structure of a country and its prosperity in the political mentality of Russian respondents. Russian public consciousness painfully strives for a new geopolitical identity and place in the commonwealth of states. It also signifies the country's interest and orientation toward the East in the search for geopolitical partners. The construct system of geopolitical perception also depends on the region of perception.

Small Industry, Gimmicks and Moscow Statistics, or How the Small Russian Enterprise Came into the World (Russian Physiological Sketch, 1830-1840)

2009 ◽  
pp. 123-129
Author(s):  
Yu. Golubitsky
Keyword(s):  
Small Businesses ◽  
Statistical Data ◽  
Russian Enterprise ◽  
Small Enterprises ◽  
Business Practices ◽  
Way Of Life ◽  
The World ◽  
Small Industry ◽  
Xix Century

The article considers business practices of Moscow small industry in the XIX century, basing upon physiological sketches of N. Polevoy and I. Kokorev, statistical data and the classification of professions are also presented. The author claims that the heroes of the analyzed sketches are the forefathers of Moscow small businesses and shows what a deep similarity their occupations and a way of life bear to the present-day routine existence of small enterprises.

“Stay with me. Talk to me”: reader interaction strategies in modern poetry

2020 ◽  
Vol 7 (2) ◽  
pp. 419-436
Author(s):  
Olga Igorevna Severskaya
Keyword(s):  
Discourse Analysis ◽  
High Impact ◽  
Modern Poetry ◽  
Self Presentation ◽  
Communication Plan ◽  
Communicative Strategies ◽  
The World ◽  
Poetic Text ◽  
Communicative Context

The article is devoted to the consideration of a poetic text as a communicative phenomenon with a high impact potential. The author defines the features of poetic communication, which is both mass and interpersonal, and its main goal, which is the poet’s desire to communicate author’s vision of the world and thereby change the picture of the reader’s world, achieving empathy from it. Based on the understanding of the speech strategy as a cognitive communication plan, a program for generating and perceiving speech, the author talks about the fundamental reversibility of text-generating and interpretative strategies and offers own classification of strategies and tactics that are most often used in modern poetry. In this classification, the main communicative strategies of self-presentation and rapprochement with the reader are associated with auxiliary discursive strategies of actualizing, dramatizing and dialogizing the text and programming interpretations by tactics for highlighting objects and situations using sound “gestures”, pointing to the referent, framing, directly introducing the reader into the communicative context, attracting the recipient’s attention through appeals and pragmatic instructions, interrogation, and some others. Particular attention is paid to the multimodality of interactions and its specific manifestations in poetic discourse. The study is based on the material of Russian poetry of the 1980- 2000s using the methods of intent and discourse analysis.

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