Pub Date : 2022-05-01DOI: 10.1142/s0218488522020019
V. G. Díaz, Jerry Chun‐wei Lin, Juan Antonio Morente Molinera
{"title":"Foreword: Special Issue on Advanced Decision Making Methods and Frameworks for Crisis Management During Pandemic Situations","authors":"V. G. Díaz, Jerry Chun‐wei Lin, Juan Antonio Morente Molinera","doi":"10.1142/s0218488522020019","DOIUrl":"https://doi.org/10.1142/s0218488522020019","url":null,"abstract":"","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"16 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72534158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-11DOI: 10.1142/s0218488522400025
V. Muthu Lakshmi, R. Radhika, G. Kalpana
{"title":"Radial Restricted Boltzmann Machines with Functional Neural Network for Classification of the Fake and Real News Analysis","authors":"V. Muthu Lakshmi, R. Radhika, G. Kalpana","doi":"10.1142/s0218488522400025","DOIUrl":"https://doi.org/10.1142/s0218488522400025","url":null,"abstract":"","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"30 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78028285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-11DOI: 10.1142/s0218488522400049
R. Sathyaraj, R. Kanthavel, Luigi Pio Leonardo Cavaliere, Sumit Vyas, S. Maheswari, Ravi Gupta, M. Ramkumar Raja, R. Dhaya, M. Gupta, Sudhakar Sengan
During the pandemic, the most significant reason for the deep concern for COVID-19 is that it spreads from individual to individual through contact or by staying close with the diseased individual. COVID-19 has been understood as an overall pandemic, and a couple of assessments is being performed using various numerical models. Machine Learning (ML) is commonly used in every field. Forecasting systems based on ML have shown their importance in interpreting perioperative effects to accelerate decision-making in the potential course of action. ML models have been used for long to define and prioritize adverse threat variables in several technology domains. To manage forecasting challenges, many prediction approaches have been used extensively. The paper shows the ability of ML models to estimate the amount of forthcoming COVID-19 victims that is now considered a serious threat to civilization. COVID-19 describes the comparative study on ML algorithms for predicting COVID-19, depicts the data to be predicted, and analyses the attributes of COVID-19 cases in different places. It gives an underlying benchmark to exhibit the capability of ML models for future examination.
{"title":"Analysis on Prediction of Covid-19 with Machine Learning Algorithms","authors":"R. Sathyaraj, R. Kanthavel, Luigi Pio Leonardo Cavaliere, Sumit Vyas, S. Maheswari, Ravi Gupta, M. Ramkumar Raja, R. Dhaya, M. Gupta, Sudhakar Sengan","doi":"10.1142/s0218488522400049","DOIUrl":"https://doi.org/10.1142/s0218488522400049","url":null,"abstract":"During the pandemic, the most significant reason for the deep concern for COVID-19 is that it spreads from individual to individual through contact or by staying close with the diseased individual. COVID-19 has been understood as an overall pandemic, and a couple of assessments is being performed using various numerical models. Machine Learning (ML) is commonly used in every field. Forecasting systems based on ML have shown their importance in interpreting perioperative effects to accelerate decision-making in the potential course of action. ML models have been used for long to define and prioritize adverse threat variables in several technology domains. To manage forecasting challenges, many prediction approaches have been used extensively. The paper shows the ability of ML models to estimate the amount of forthcoming COVID-19 victims that is now considered a serious threat to civilization. COVID-19 describes the comparative study on ML algorithms for predicting COVID-19, depicts the data to be predicted, and analyses the attributes of COVID-19 cases in different places. It gives an underlying benchmark to exhibit the capability of ML models for future examination.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"255 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79647259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-11DOI: 10.1142/s0218488522400013
R. Rajmohan, T. Ananth Kumar, E. Golden Julie, Y. Harold Robinson, S. Vimal, Seifidine Kadry, R. G. Crespo
{"title":"G-Sep: A Deep Learning Algorithm for Detection of Long-Term Sepsis Using Bidirectional Gated Recurrent Unit","authors":"R. Rajmohan, T. Ananth Kumar, E. Golden Julie, Y. Harold Robinson, S. Vimal, Seifidine Kadry, R. G. Crespo","doi":"10.1142/s0218488522400013","DOIUrl":"https://doi.org/10.1142/s0218488522400013","url":null,"abstract":"","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"37 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77630014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-11DOI: 10.1142/s0218488522400098
K. Sakthi, P. Nirmal Kumar
{"title":"Minimizing Subtle Errors in Computing Information of TCAM By Partial 'N' Search Key Implementation","authors":"K. Sakthi, P. Nirmal Kumar","doi":"10.1142/s0218488522400098","DOIUrl":"https://doi.org/10.1142/s0218488522400098","url":null,"abstract":"","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"58 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81407442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-11DOI: 10.1142/s0218488522400050
Mamoona Anam, Roy Setiawan, Sathiya Kumar Chinnappan, Nik Alif Amri Nik Hashim, Abolfazl Mehbodniya, C. Bhargava, Pardeep Kumar Sharma, K. Phasinam, V. Subramaniyaswamy, Sudhakar Sengan
COVID-19 outbreaks are the critical challenge to the administrative units of all worldwide nations. India is also more concerned about monitoring the virus’s spread to control its growth rate by stringent behaviour. The present COVID-19 situation has huge impact in India, and the results of various preventive measures are discussed in this paper. This research presents different trends and patterns of data sources of States that suffered from the second wave of COVID-19 in India until 3rd July 2021. The data sources were collected from the Indian Ministry of Health and Family Welfare. This work reacts particularly to many research activities to discover the lockdown effects to control the virus through traditional methods to recover and safeguard the pandemic. The second wave caused more losses in the economy than the first wave and increased the death rate. To avoid this, various methods were developed to find infected cases during the regulated national lockdown, but the infected cases still harmed unregulated incidents. The COVID-19 forecasts were made on 3rd July 2021, using exponential simulation. This paper deals with the methods to control the second wave giving various analyses reports showing the impact of lockdown effects. This highly helps to safeguard from the spread of the future pandemic.
{"title":"Analyzing the Impact of Lockdown in Controlling Covid-19 Spread and Future Prediction","authors":"Mamoona Anam, Roy Setiawan, Sathiya Kumar Chinnappan, Nik Alif Amri Nik Hashim, Abolfazl Mehbodniya, C. Bhargava, Pardeep Kumar Sharma, K. Phasinam, V. Subramaniyaswamy, Sudhakar Sengan","doi":"10.1142/s0218488522400050","DOIUrl":"https://doi.org/10.1142/s0218488522400050","url":null,"abstract":"COVID-19 outbreaks are the critical challenge to the administrative units of all worldwide nations. India is also more concerned about monitoring the virus’s spread to control its growth rate by stringent behaviour. The present COVID-19 situation has huge impact in India, and the results of various preventive measures are discussed in this paper. This research presents different trends and patterns of data sources of States that suffered from the second wave of COVID-19 in India until 3rd July 2021. The data sources were collected from the Indian Ministry of Health and Family Welfare. This work reacts particularly to many research activities to discover the lockdown effects to control the virus through traditional methods to recover and safeguard the pandemic. The second wave caused more losses in the economy than the first wave and increased the death rate. To avoid this, various methods were developed to find infected cases during the regulated national lockdown, but the infected cases still harmed unregulated incidents. The COVID-19 forecasts were made on 3rd July 2021, using exponential simulation. This paper deals with the methods to control the second wave giving various analyses reports showing the impact of lockdown effects. This highly helps to safeguard from the spread of the future pandemic.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"33 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82588536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-11DOI: 10.1142/s0218488522400074
R. Dhanalakshmi, B. Muthukumar, R. Aroul canessane
{"title":"Analysis of Special Children Education Using Data Mining Approach","authors":"R. Dhanalakshmi, B. Muthukumar, R. Aroul canessane","doi":"10.1142/s0218488522400074","DOIUrl":"https://doi.org/10.1142/s0218488522400074","url":null,"abstract":"","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"15 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74471312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-11DOI: 10.1142/s0218488522400086
H. Benjamin Fredrick David, A. Suruliandi, S. Raja
The world is infected from the deadliest pandemic disease humankind has ever seen. Several medical practitioners have been encountered with the corona virus and are constantly losing their lives in the fight. Hence, the main objective of this research work is to characterize the clinical features of the patients and construct a novel dataset for machine learning to classify them accurately prior to treatment. The positive patients can be identified on many characteristics and the principle data for this research is considered on the basis of the exploratory analysis done on the various risk factors that is also associated with the mortality in the hospitals. As an outcome, this article presents a supervised machine learning model incorporating the insights, symptoms and classification of the corona virus infected person. The proposed model and the dataset are tested against six well known classifiers on various levels of cross folding and percentage splits. The proposed dataset is also tested against the actual patient records and was found that the model accurately categorizes them prior to their treatment. The experimental results for proposed techniques showed higher performance and better accuracy further creating an impact on then identification of corona virus patients.
{"title":"Predicting Corona Virus Affected Patients Using Supervised Machine Learning","authors":"H. Benjamin Fredrick David, A. Suruliandi, S. Raja","doi":"10.1142/s0218488522400086","DOIUrl":"https://doi.org/10.1142/s0218488522400086","url":null,"abstract":"The world is infected from the deadliest pandemic disease humankind has ever seen. Several medical practitioners have been encountered with the corona virus and are constantly losing their lives in the fight. Hence, the main objective of this research work is to characterize the clinical features of the patients and construct a novel dataset for machine learning to classify them accurately prior to treatment. The positive patients can be identified on many characteristics and the principle data for this research is considered on the basis of the exploratory analysis done on the various risk factors that is also associated with the mortality in the hospitals. As an outcome, this article presents a supervised machine learning model incorporating the insights, symptoms and classification of the corona virus infected person. The proposed model and the dataset are tested against six well known classifiers on various levels of cross folding and percentage splits. The proposed dataset is also tested against the actual patient records and was found that the model accurately categorizes them prior to their treatment. The experimental results for proposed techniques showed higher performance and better accuracy further creating an impact on then identification of corona virus patients.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"30 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74914285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-11DOI: 10.1142/s0218488522400062
E. Pavithra, B. Janakiramaiah, L. V. Narasimha Prasad, D. Deepa, N. Jayapandian, Sathishkumar V E
The prevailing COVID-19 situation has brought in temporary and permanent changes in the attitude and lifestyle of people. Starting from Hand sanitizers and face masks, it extends to online classrooms and work from home culture. In case of visiting hospitals and medications, people with pre-existing medical conditions and minor health issues tend to delay or avoid visiting hospitals due to fear of infection, which is dangerous. Further, people or patients tend to access several alternatives and precautions. The alternatives include home remedies, ayurvedic medication, yoga and meditation. On the other hand, hospitals are trying to adapt online consulting and telemedicine. Besides, Cancellation or delay of nonemergency surgeries became inevitable in the lockdown phase. This survey conducted among the people of Erode district, Tamilnadu to study the perception of people concerning visiting hospitals for health issues. The results show that fear of infection, financial and transportation difficulties are the major factors which affected people from visiting hospital. Also, changing trends like Telemedicine and home remedies are likely to be permanently opted by people. In Brief, the outcomes reveal the changing attitude of people towards medication and hospital visiting habits.
{"title":"Visiting Indian Hospitals Before, During and After Covid","authors":"E. Pavithra, B. Janakiramaiah, L. V. Narasimha Prasad, D. Deepa, N. Jayapandian, Sathishkumar V E","doi":"10.1142/s0218488522400062","DOIUrl":"https://doi.org/10.1142/s0218488522400062","url":null,"abstract":"The prevailing COVID-19 situation has brought in temporary and permanent changes in the attitude and lifestyle of people. Starting from Hand sanitizers and face masks, it extends to online classrooms and work from home culture. In case of visiting hospitals and medications, people with pre-existing medical conditions and minor health issues tend to delay or avoid visiting hospitals due to fear of infection, which is dangerous. Further, people or patients tend to access several alternatives and precautions. The alternatives include home remedies, ayurvedic medication, yoga and meditation. On the other hand, hospitals are trying to adapt online consulting and telemedicine. Besides, Cancellation or delay of nonemergency surgeries became inevitable in the lockdown phase. This survey conducted among the people of Erode district, Tamilnadu to study the perception of people concerning visiting hospitals for health issues. The results show that fear of infection, financial and transportation difficulties are the major factors which affected people from visiting hospital. Also, changing trends like Telemedicine and home remedies are likely to be permanently opted by people. In Brief, the outcomes reveal the changing attitude of people towards medication and hospital visiting habits.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"14 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85110289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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