scholarly journals COVID-19 Hotspot Trend Prediction Using Hybrid Cellular Automata in India

2020 ◽  
pp. 54-60
Author(s):  
Pokkuluri Kiran Sree ◽  
Smt. S. S. S. N Usha Devi. N
Keyword(s):  
Cellular Automata ◽  
Support Vector ◽  
Reproduction Rate ◽  
Trend Prediction ◽  
Indian Government ◽  
Hybrid Cellular Automata ◽  
Social Distancing ◽  
The Status ◽  
Control Virus ◽  
Safety Parameters

The coronavirus disease 2019 (COVID-19) is an infectious disease identified at Wuhan, China, in December 2019 caused by new Coronavirus. The Indian government has taken many initiatives to mitigate the effect of COVID by encouraging the standard mechanisms of social distancing, the use of masks, and various safety parameters. COVID-19 hotspot identifies regions in India where COVID-19 severity is very high. We propose a novel hybrid cellular automata classifier for predicting the trend of various Hotspots in India, processing different parameters including infection control, virus reproduction rate, critical correlation, safety parameters, and social distancing. The proposed classifier was named Hybrid Cellular Automata-Hotspot (HCA-HS), predicts the number of hotspots in various districts of states, and also gives the status of each city marked either as Totally Safe or Marginally Safe or Unsafe. This will alert the state authorities to take necessary action to mitigate the COVID effect and help the people for possibly refraining from going to the infected areas, i.e., hotspots. The data sets were collected from Kaggle and the local Indian database for more adaptability. The accuracy of the predictions of Hotspots is reported as 91.58%, which is considerable at this moment. The developed classifier is compared with Support Vector Mechanism (SVM), K-Means, Decision Tree, and HCA-HS has reported an accuracy of 10.69% higher than the existing literature.

scholarly journals A Novel Cellular Automata Classifier for COVID-19 Prediction

2020 ◽  
Author(s):  
Kiran Sree Pokkuluri ◽  
SSSN Usha Devi Nedunuri
Keyword(s):  
Cellular Automata ◽  
Short Term Memory ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Hybrid Cellular Automata ◽  
The World ◽  
Non Linear ◽  
Long Short Term Memory ◽  
Linear Hybrid Cellular Automata

Introduction: China has witnessed a new virus Corona,which is named COVID-19. It has become the world’s most concern as this virus has spread over the worldat a higher speed;the world has witnessed more than one lakh cases and one thousand deaths in a span of few days. Methods: We have developed a preliminary classifier with non-linear hybrid cellular automata, which is trained and tested to predict the effect of COVID-19 in terms of deaths, the number of people affected, the number of people being could be recovered, etc. This indirectly predicts the trend of this epidemic in India. We have collected the datasets from Kaggle and other standard websites. Results: The proposed classifier, hybrid non-linear cellular automata (HNLCA), was trained with 23,078 datasets and tested with 6785 datasets. HNLCA is compared with conventional methods of long short-term memory, AdaBoost, support vector machine, regression, and SVR and has reported an accuracy of 78.8%, which is better compared with the cited literature. This classifier can also predict the rate at which this virus spreads, transmission within the boundary, and of the boundary, etc.

Application of Advanced Technologies in Natural Product Research: A Review with Special Emphasis on ADMET Profiling

2020 ◽  
Vol 21 (10) ◽  
pp. 751-767
Author(s):  
Pobitra Borah ◽  
Sangeeta Hazarika ◽  
Satyendra Deka ◽  
Katharigatta N. Venugopala ◽  
Anroop B. Nair ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Natural Product ◽  
Dynamic Range ◽  
Early Stage ◽  
Attrition Rate ◽  
Drug Candidates ◽  
Advanced Technologies ◽  
Natural Product Research ◽  
The Status ◽  
Safety Parameters

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

Pressure Prediction in Natural Gas Desulfurization Process Based on PCA and SVR

2014 ◽  
Vol 962-965 ◽  
pp. 564-569 ◽  
Author(s):  
Yan Chao Shao ◽  
Liang Jun Xu ◽  
Yan Zhu Hu ◽  
Xin Bo Ai
Keyword(s):  
Natural Gas ◽  
Prediction Model ◽  
Measured Data ◽  
Support Vector ◽  
Trend Prediction ◽  
Generalization Ability ◽  
Model Based ◽  
Scada System ◽  
Desulfurization Process ◽  
Natural Gas Desulfurization

Pressure monitoring is an important means to reflect the running status of the natural gas desulphurization process. By using the data mining technology, the interaction relationships between the pressure and other monitoring parameters are analyzed in this paper. A pressure trend prediction model is established to show the pressure status in the natural gas desulfurization process. Firstly, the theory of Principal Component Analysis (PCA) is used to reduce the dimensions of measured data from traditional Supervisory Control and Data Acquisition (SCADA) system. Secondly the principal components are taken as input data into the pressure trend prediction model based on multiple regression theory of Support Vector Regression (SVR). Finally the accuracy and the generalization ability of the model are tested by the measured data obtained from SCADA system. Compared with other prediction models, pressure trend prediction model based on PCA and SVR gets smaller MSE and higher correlation. The pressure trend prediction model gets better generalization ability and stronger robustness, and is an effective complement to SCADA system in the natural gas desulphurization process.

scholarly journals The emerging COVID-19 research: dynamic and regularly updated science maps and analyses

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Njål Andersen ◽  
Jørgen G. Bramness ◽  
Ingunn Olea Lund
Keyword(s):  
Clinical Medicine ◽  
Clinical Epidemiology ◽  
Research Field ◽  
Reproduction Rate ◽  
Global Public Health ◽  
Interactive Network ◽  
The Status ◽  
Current Content ◽  
Wide Readership ◽  
New Research

Abstract Background The COVID-19 pandemic is a global public health emergency and experts emphasize the need for rapid and a high degree of communication and interaction between all parties, in order for critical research to be implemented. We introduce a resource (website) that provides bibliometric analysis showing the current content and structure of the published literature. As new research is published daily, the analysis is regularly updated to show the status as the research field develops and matures. Methods Two bibliometric methods were employed, the first is a keyword co-occurrence analysis, based on published work available from PubMed. The second is a bibliometric coupling analysis, based on articles available through Scopus. The results are presented as clustered network graphs; available as interactive network graphs through the webpage. Results For research as of March 23rd, keyword co-occurrence analysis showed that research was organized in 4 topic clusters: “Health and pandemic management”, “The disease and its pathophysiology”, “Clinical epidemiology of the disease” and “Treatment of the disease”. Coupling analyses resulted in 4 clusters on literature that relates to “Overview of the new virus”, “Clinical medicine”, “On the virus” and “Reproduction rate and spread”. Conclusion We introduced a dynamic resource that will give a wide readership an overview of how the structure of the COVID-19 literature is developing. To illustrate what this can look like, we showed the structure as it stands three months after the virus was identified; the structure is likely to change as we progress to later stages of this pandemic.

scholarly journals Evolution Regularity Mining and Gating Control Method of Urban Recurrent Traffic Congestion: A Literature Review

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Changxi Ma ◽  
Jibiao Zhou ◽  
Xuecai (Daniel) Xu ◽  
Jin Xu
Keyword(s):  
Traffic Congestion ◽  
Quantitative Research ◽  
Control Method ◽  
Time Correlation ◽  
Small Scale ◽  
Trend Prediction ◽  
Quantitative Research Methods ◽  
The Status ◽  
Evolution Trend

To understand the status quo of urban recurrent traffic congestion, the current results of recurrent traffic congestion, and gating control are reviewed from three aspects: traffic congestion identification, evolution trend prediction, and urban road network gating control. Three aspects of current research are highlighted: (a) The majority of current studies are based on statistical analyses of historical data, while congestion identification is performed by acquiring small-scale traffic parameters. Thus, congestion studies on the urban global roadway network are lacking. Situation identification and the failure to effectively warn or even avoid traffic congestion before congestion forms are not addressed; (b) correlation studies on urban roadway network congestion are inadequate, especially regarding deep learning, and considering the space-time correlation for congestion evolution trend prediction; and (c) quantitative research methods, dynamic determination of gating control areas, and effective countermeasures to eliminate traffic congestion are lacking. Regarding the shortcomings of current studies, six research directions that can be further explored in the future are presented.

scholarly journals Global Self-Sufficiency Network-A Collaborative Approach for Addressing Post-COVID-19 Challenges

2020 ◽  
Vol 10 (3) ◽  
pp. 1
Author(s):  
Mohamed Buheji ◽  
Ana Vovk Korže ◽  
Sajeda Eidan ◽  
Talal Abdulkareem ◽  
Nikolay Perepelkin ◽  
...  
Keyword(s):  
The Self ◽  
Collaborative Approach ◽  
Social Beliefs ◽  
Social Distancing ◽  
Self Sufficiency ◽  
The Social ◽  
The Status ◽  
Multi Media

COVID-19 raised lots of issues relevant to the status, the readiness and the capacity of the self-sufficiency of the different communities and countries during conditions of lockdown and requirements for social distancing, during the first four months of the pandemic.An international multidiscipline scholars discussion on zoom, a multi-media conferencing app, is categorised according to the subjects of the self-sufficiency practices that are reflections of the specific attitudes and behaviours that shape the social demands during the COVID-19 pandemic. The scholars discuss the requirements of re-building the self-sufficiency social beliefs which the capital economy destroyed. Based on the methodology of discussion from the different background scholar, the challenges and then the outcome of self-sufficiency projects are defined.

scholarly journals Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3575 ◽  
Author(s):  
María Jesús Gómez ◽  
Cristina Castejón ◽  
Eduardo Corral ◽  
Juan Carlos García-Prada
Keyword(s):  
Condition Monitoring ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Support Vector ◽  
Good Reliability ◽  
Railway Axle ◽  
Monitoring Technique ◽  
The Status ◽  
Minimum Number ◽  
Diagnosis Model

Railway axles are critical to the safety of railway vehicles. However, railway axle maintenance is currently based on scheduled preventive maintenance using Nondestructive Testing. The use of condition monitoring techniques would provide information about the status of the axle between periodical inspections, and it would be very valuable in the prevention of catastrophic failures. Nevertheless, in the literature, there are not many studies focusing on this area and there is a lack of experimental data. In this work, a reliable real-time condition-monitoring technique for railway axles is proposed. The technique was validated using vibration measurements obtained at the axle boxes of a full bogie installed on a rig, where four different cracked railway axles were tested. The technique is based on vibration analysis by means of the Wavelet Packet Transform (WPT) energy, combined with a Support Vector Machine (SVM) diagnosis model. In all cases, it was observed that the WPT energy of the vibration signals at the first natural frequency of the axle when the wheelset is first installed (the healthy condition) increases when a crack is artificially created. An SVM diagnosis model based on the WPT energy at this frequency demonstrates good reliability, with a false alarm rate of lower than 10% and defect detection for damage occurring in more than 6.5% of the section in more than 90% of the cases. The minimum number of wheelsets required to build a general model to avoid mounting effects, among others things, is also discussed.

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