scholarly journals Swarm Optimized Opinion Classification Model for Policy Assessment

2020 ◽  
Vol 9 (4) ◽  
pp. 2345-2354
Keyword(s):  
Government Policy ◽  
Opinion Mining ◽  
Research Work ◽  
Substantial Reduction ◽  
Feature Subset Selection ◽  
Advanced Technology ◽  
Classification Model ◽  
Feature Subset ◽  
The Government ◽  
The Impact

A government policy is a scheme launched by the governing body of a nation for the welfare of a particular section of the society or the entire public in general. The impact of such a policy can hence only be determined by the response from its target group. The evaluation of these schemes is often challenging, due to the inability of the government body or organization to collect unfiltered and unbiased feedback from the entire population. The aforementioned task may require a large amount of effort, considerable time and in-depth knowledge of advanced technology. However, with the advent of the information era, it is possible to analyze the sentiments of the public using negligible resources. The internet is rich in freely available unused and unstructured data that can be exploited efficiently for various purposes. One such application is opinion mining which allows the user to extract data from social media websites and categorize it into pre-defined classes. This paper is an attempt to assess one of the most important and current government initiatives- “Digital India”, through public sentiments. Digital India is a program launched by the Prime Minister of India to transform the country into a technologically advanced and digitally connected nation. This research work corroborates the use of swarm intelligence or nature-inspired algorithms for feature subset selection during opinion mining, as it results in a substantial reduction in the number of features (and consequently a lesser computation time for model training) and increase in the classification accuracy of the model. Therefore, the aim of this study is to analyze public opinion on “Digital India” campaign to ascertain the success (or failure) of the mission, while at the same time, determine the most suited model for automated evaluation of any government policy in the future

scholarly journals The Innovation of Government Bonds in the Growth of an Emergent Capital Market

2021 ◽  
Vol 7 (1) ◽  
pp. 103
Author(s):  
Cordelia Onyinyechi Omodero ◽  
Philip Olasupo Alege
Keyword(s):  
Capital Market ◽  
Research Work ◽  
Government Bonds ◽  
Market Capitalization ◽  
Market Growth ◽  
Explanatory Variables ◽  
The Government ◽  
The Impact ◽  
Total Market ◽  
The Capital Market

The growth of an emerging capital market is necessary and requires all available resources and inputs from various sources to realize this objective. Several debates on government bonds’ contribution to Nigeria’s capital market developmental growth have ensued but have not triggered comprehensive studies in this area. The present research work seeks to close the breach by probing the impact of government bonds on developing the capital market in Nigeria from 2003–2019. We employ total market capitalization as the response variable to proxy the capital market, while various government bonds serve as the independent variables. The inflation rate moderates the predictor components. The research uses multiple regression technique to assess the explanatory variables’ impact on the total market capitalization. At the same time, diagnostic tests help guarantee the normality of the regression model’s data distribution and appropriateness. The findings reveal that the Federal Government of Nigeria’s (FGN) bond is statistically significant and positive in influencing Nigeria’s capital market growth. The other predictor variables are not found significant in this study. The study suggests that the Government should improve on the government bonds’ coupon, while still upholding the none default norm in paying interest and refunding principal to investors when due.

scholarly journals IMPACT OF FINANCIAL INCLUSION, GOVERNMENT EXPENDITURES IN EDUCATION AND HEALTH SECTORS ON HUMAN DEVELOPMENT IN INDONESIA

2020 ◽  
Vol 9 (2) ◽  
pp. 5
Author(s):  
Mulia Simatupang
Keyword(s):  
Human Development ◽  
Government Policy ◽  
Human Development Index ◽  
Health Sector ◽  
Financial Inclusion ◽  
Government Expenditures ◽  
Development Index ◽  
Education And Health ◽  
The Government ◽  
The Impact

ABSTRACT The purpose of this paper in to assess the impact of financial inclusion and  government expenditures in education and health sectors in order to increase human development index. Government expenditures has important role to support economic growth and welfare for its people. Fiscal policy expenditures in education and health sectors are kind of significant government policy to increase human development. It is believed that financial inclusion has also important role  to reduce poverty and indirectly increase human development index. Financial inclusion  has positive impacts to human development index component along with government  expenditures in education and health sector. In the years ahead, The Government should prioritize and increase budget in order to increase human  resources quality in Indonesia.

scholarly journals Liver Cancer Classification Model Using Hybrid Feature Selection Based on Class-Dependent Technique for the Central Region of Thailand

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 187
Author(s):  
Rattanawadee Panthong ◽  
Anongnart Srivihok
Keyword(s):  
Feature Selection ◽  
Liver Cancer ◽  
Predictive Model ◽  
Information Gain ◽  
Classification Performance ◽  
Cancer Classification ◽  
Feature Subset Selection ◽  
Classification Model ◽  
Feature Subset ◽  
Cancer Data

Liver cancer data always consist of a large number of multidimensional datasets. A dataset that has huge features and multiple classes may be irrelevant to the pattern classification in machine learning. Hence, feature selection improves the performance of the classification model to achieve maximum classification accuracy. The aims of the present study were to find the best feature subset and to evaluate the classification performance of the predictive model. This paper proposed a hybrid feature selection approach by combining information gain and sequential forward selection based on the class-dependent technique (IGSFS-CD) for the liver cancer classification model. Two different classifiers (decision tree and naïve Bayes) were used to evaluate feature subsets. The liver cancer datasets were obtained from the Cancer Hospital Thailand database. Three ensemble methods (ensemble classifiers, bagging, and AdaBoost) were applied to improve the performance of classification. The IGSFS-CD method provided good accuracy of 78.36% (sensitivity 0.7841 and specificity 0.9159) on LC_dataset-1. In addition, LC_dataset II delivered the best performance with an accuracy of 84.82% (sensitivity 0.8481 and specificity 0.9437). The IGSFS-CD method achieved better classification performance compared to the class-independent method. Furthermore, the best feature subset selection could help reduce the complexity of the predictive model.

A New Hybrid Feature Subset Selection Framework Based on Binary Genetic Algorithm and Information Theory

2019 ◽  
Vol 18 (03) ◽  
pp. 1950020 ◽  
Author(s):  
Alok Kumar Shukla ◽  
Pradeep Singh ◽  
Manu Vardhan
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Classification Accuracy ◽  
B Cell Lymphoma ◽  
Feature Subset Selection ◽  
Classification Model ◽  
Significant Feature ◽  
Support Vector ◽  
Feature Subset ◽  
Binary Genetic Algorithm

The explosion of the high-dimensional dataset in the scientific repository has been encouraging interdisciplinary research on data mining, pattern recognition and bioinformatics. The fundamental problem of the individual Feature Selection (FS) method is extracting informative features for classification model and to seek for the malignant disease at low computational cost. In addition, existing FS approaches overlook the fact that for a given cardinality, there can be several subsets with similar information. This paper introduces a novel hybrid FS algorithm, called Filter-Wrapper Feature Selection (FWFS) for a classification problem and also addresses the limitations of existing methods. In the proposed model, the front-end filter ranking method as Conditional Mutual Information Maximization (CMIM) selects the high ranked feature subset while the succeeding method as Binary Genetic Algorithm (BGA) accelerates the search in identifying the significant feature subsets. One of the merits of the proposed method is that, unlike an exhaustive method, it speeds up the FS procedure without lancing of classification accuracy on reduced dataset when a learning model is applied to the selected subsets of features. The efficacy of the proposed (FWFS) method is examined by Naive Bayes (NB) classifier which works as a fitness function. The effectiveness of the selected feature subset is evaluated using numerous classifiers on five biological datasets and five UCI datasets of a varied dimensionality and number of instances. The experimental results emphasize that the proposed method provides additional support to the significant reduction of the features and outperforms the existing methods. For microarray data-sets, we found the lowest classification accuracy is 61.24% on SRBCT dataset and highest accuracy is 99.32% on Diffuse large B-cell lymphoma (DLBCL). In UCI datasets, the lowest classification accuracy is 40.04% on the Lymphography using k-nearest neighbor (k-NN) and highest classification accuracy is 99.05% on the ionosphere using support vector machine (SVM).

scholarly journals Compositional cyber-physical epidemiology of COVID-19

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jin Woo Ro ◽  
Nathan Allen ◽  
Weiwei Ai ◽  
Debi Prasad ◽  
Partha S. Roop
Keyword(s):  
New Zealand ◽  
Government Policy ◽  
Cyber Physical Systems ◽  
Published Data ◽  
Disease Dynamics ◽  
Physical Systems ◽  
The Government ◽  
The Impact ◽  
First Time ◽  
New Framework

Abstract The COVID-19 pandemic has posed significant challenges globally. Countries have adopted different strategies with varying degrees of success. Epidemiologists are studying the impact of government actions using scenario analysis. However, the interactions between the government policy and the disease dynamics are not formally captured. We, for the first time, formally study the interaction between the disease dynamics, which is modelled as a physical process, and the government policy, which is modelled as the adjoining controller. Our approach enables compositionality, where either the plant or the controller could be replaced by an alternative model. Our work is inspired by the engineering approach for the design of Cyber-Physical Systems. Consequently, we term the new framework Compositional Cyber-Physical Epidemiology. We created different classes of controllers and applied these to control the disease in New Zealand and Italy. Our controllers closely follow government decisions based on their published data. We not only reproduce the pandemic progression faithfully in New Zealand and Italy but also show the tradeoffs produced by differing control actions.

scholarly journals The Perils of the Pandemic and India's Child Victims of Covid-19

2021 ◽  
Vol 10 (4) ◽  
pp. 148-153
Author(s):  
Suparba Sil ◽  
Ruby Dhar ◽  
Subhradip Karmakar
Keyword(s):  
Government Policy ◽  
News Media ◽  
Health Needs ◽  
Child Victims ◽  
Mental Health Needs ◽  
Utmost Care ◽  
Care At Home ◽  
Younger Generation ◽  
The Government ◽  
The Impact

Aim: The following paper attempts to trace the impact of Covid-19 on the younger generation, mostly from economically underprivileged sections, by focusing on specific themes such as health, education, vulnerability to abuse, and violence. The paper tries to address how the pandemic has affected various dimensions of the lives of these younger generation-children and adolescents, alongside tracing the measures taken by the government in the fight against the virus. Methods: We curated the information based on credible data as published in leading news media, PMC published peer-reviewed materials Conclusions: The paper concludes with recommendations that a coherent government policy and the active participation of NGOs are needed to address the problem. The children's mental health needs to be dealt with utmost care at home, which will pave the way towards a better future for the younger generation during and after the pandemic.

scholarly journals FACTORS AFFECTING THE IMPLEMENTATION OF VOCATIONAL AND TECHNICAL EDUCATION POLICY IN PRIVATE SECONDARY SCHOOLS IN EDO STATE

2015 ◽  
Vol 16 (1) ◽  
pp. 12
Author(s):  
Roseline O Osagie
Keyword(s):  
Secondary Schools ◽  
Federal Government ◽  
Government Policy ◽  
Technical Education ◽  
Instructional Materials ◽  
Factors Affecting ◽  
Vocational And Technical Education ◽  
Edo State ◽  
The Government ◽  
The Impact

The government policy directive to secondary schools has been to diversify their programs to include vocational and technical education in the 6-3-3-4 system in order to make provision for students with varying aptitudes. This article explores the impact of this policy by examining some factors affecting the implementation of the policy in private secondary schools in Edo state. Subjects for the study were fifty (50) students, fifty (50) teachers and five (5) principals randomly drawn from five(5) private secondary schools in Edo State. The study utilized interviews, observations and a questionnaire to assess the implementation of government policy onvocational and technical education in private secondary schools in Edo State. The findings showed that there was a dearth of qualified teachers for vocational and technical subjects, poor infrastructure, lack of equipment, instructional materials and books. The schools were not adequately financed. It was observed that the federal government did not make adequate preparations before it issued directives for the take off of the programs in the schools. Recommendations were made for the federal government to sensitize the public on the importance of vocational and technical education, as it plays a vital and indispensable role in the economic and technological development of the country.

scholarly journals Co- Disease Prediction using Multileyer Perceptron and Classification from Diabetic Medical Data Sets

2018 ◽  
Vol 7 (4.6) ◽  
pp. 138
Author(s):  
Shahebaz Ahmed Khan ◽  
Dr. Seemakurthi ◽  
Dr. Akhil Jabbar
Keyword(s):  
Heart Diseases ◽  
Research Work ◽  
Medical Data ◽  
Feature Subset Selection ◽  
Diabetic Patients ◽  
Data Sets ◽  
Feature Subset ◽  
Medical Field ◽  
Network Algorithms ◽  
Artificial Neural

Artificial Neural Networks (ANN) techniques have the important concepts those can be used in the present scenario of the medical world. It has made the medical field to formulate easy steps to detect and predict the diseases like diabetes, thrombocytopenia, heart diseases, brain tumor, cancer etc. The classification methods available in the data mining theories and ANN gradually help to predict the data for the future analysis by building the classification models. In this paper, the results and the research work carried out on diabetic medical data using the artificial neural network algorithms like multilevel perception and its application over such data so as to predict the      diseases are discussed. The rules developed will be helpful to detect the co-disease in the diabetic patients and we have ranked them as per the final classifier for prediction. The proposed classification algorithm has accurately predicted the data with and without feature subset selection.  

scholarly journals Compositional Cyber-Physical Epidemiology of COVID-19

2020 ◽  
Author(s):  
Jin Woo Ro ◽  
Nathan Allen ◽  
Weiwei Ai ◽  
Debi Prasad ◽  
Partha S. Roop
Keyword(s):  
New Zealand ◽  
Government Policy ◽  
Cyber Physical Systems ◽  
Published Data ◽  
Disease Dynamics ◽  
Physical Systems ◽  
The Government ◽  
The Impact ◽  
First Time ◽  
New Framework

AbstractCOVID-19 pandemic has posed significant challenges globally. Countries have adopted different strategies with varying degrees of success. Epidemiologists are studying the impact of government actions using scenario analysis. However, the interactions between the government policy and the disease dynamics are not formally captured.We, for the first time, formally study the interaction between the disease dynamics, which is modelled as a physical process, and the government policy, which is modelled as the adjoining controller. Our approach enables compositionality, where either the plant or the controller could be replaced by an alternative model. Our work is inspired by the engineering approach for the design of Cyber-Physical Systems (CPSs). Consequently, we term the new framework Compositional Cyber-Physical Epidemiology (CCPE). We created different classes of controllers and applied these to control the disease in New Zealand and Italy. Our controllers closely follow government decisions based on their published data. We not only reproduce the pandemic progression faithfully in New Zealand and Italy but also show the tradeoffs produced by differing control actions.

scholarly journals MapReduce-based big data classification model using feature subset selection and hyperparameter tuned deep belief network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Surendran Rajendran ◽  
Osamah Ibrahim Khalaf ◽  
Youseef Alotaibi ◽  
Saleh Alghamdi
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Selection Process ◽  
Data Classification ◽  
Deep Belief Network ◽  
Feature Subset Selection ◽  
Classification Model ◽  
Feature Subset ◽  
Belief Network ◽  
Big Data Classification

AbstractIn recent times, big data classification has become a hot research topic in various domains, such as healthcare, e-commerce, finance, etc. The inclusion of the feature selection process helps to improve the big data classification process and can be done by the use of metaheuristic optimization algorithms. This study focuses on the design of a big data classification model using chaotic pigeon inspired optimization (CPIO)-based feature selection with an optimal deep belief network (DBN) model. The proposed model is executed in the Hadoop MapReduce environment to manage big data. Initially, the CPIO algorithm is applied to select a useful subset of features. In addition, the Harris hawks optimization (HHO)-based DBN model is derived as a classifier to allocate appropriate class labels. The design of the HHO algorithm to tune the hyperparameters of the DBN model assists in boosting the classification performance. To examine the superiority of the presented technique, a series of simulations were performed, and the results were inspected under various dimensions. The resultant values highlighted the supremacy of the presented technique over the recent techniques.

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