ORCID Profile
0000-0001-6143-1718
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Publisher: Science Publishing Corporation
Date: 11-03-2018
DOI: 10.14419/IJET.V7I2.6.11271
Abstract: Data analysis is the most grueling tasks in the coinciding world. The size of data is increasing at a very high rate because of the procreation of peripatetic gadgets and sensors attached. To make that data readable is another challenging task. Effectual visualization provides users with better analysis capabilities and helps in deriving evidence about data. Many techniques and tools have been invented to deal with such problems but to make these tools amendable is the main mystification. It is the big data that originated as a technology which is proficient in assembling and transforming the colossal and ergent figures of data, providing organizations with meaningful insights for derivingimprovedresults. Big data is accustomed to delineate technologies and techniques which are used to store, manage, distribute and analyze huge data sheets. The existent of administrating this research is to make the data readable in a more suitable form with less comprehend. Mainly the research emphasizes on the fabrication of using COGNOS insight 10.2.2 for visualizing data and implementing the analyzed results derived from the hive. The assimilation between tools has also been reformed in this research.
Publisher: Science Publishing Corporation
Date: 20-07-2018
DOI: 10.14419/IJET.V7I3.12.16049
Abstract: Sentiment analysis on Twitter data has paying more attention recently. The system’s key feature, is the immediate communication with other users in an easy, fast way and user-friendly too. Sentiment analysis is the process of identifying and classifying opinions or sentiments expressed in source text. There is a huge volume of data present in the web for internet users and a lot of data is generated per second due to the growth and advancement of web technology. Nowadays, Internet has become best platform to share everyone's opinion, to exchange ideas and to learn online. People are using social network sites like facebook, twitter and it has gained more popularity among them to share their views and pass messages about some topics around the world. As tweets, notices and blog entries, the online networking is producing a tremendous measure of conclusion rich information. This client produced assumption examination information is extremely helpful in knowing the supposition of the general population swarm. At the point when contrasted with general supposition investigation the twitter assumption examination is much troublesome because of its slang words and incorrect spellings. Twitter permits 140 as the most extreme cutoff of characters per message. The two procedures that are mostly utilized for content examination is information base approach and machine learning approach. In this paper, we investigated the twitter created posts utilizing Machine Learning approach. Performing assumption examination in a particular area, is to distinguish the impact of space data in notion grouping. we ordered the tweets as constructive, pessimistic and separate erse people groups' data about that specific space. In this paper, we developed a novel method for sentiment learning using the Spark coreNLP framework. Our method exploits the hashtags and emoticons inside a tweet, as sentiment labels, and proceeds to a classification procedure of erse sentiment types in a parallel and distributed manner.
Publisher: American Scientific Publishers
Date: 11-2017
Publisher: Instytut Badan Gospodarczych / Institute of Economic Research
Date: 27-09-2021
DOI: 10.24136/OC.2021.019
Abstract: Research background: Microfinance institutions (MFIs) play an important role in alleviating poverty. Thus, MFIs should be efficient in order to ensure that their objectives on social welfare and financial performance can be achieved by identifying the potential determinants, specifically on social globalisation. Purpose of the article: This paper examines the impacts of the social globalisation dimensions of interpersonal, informational, and cultural globalisations on the financial and social efficiency of MFIs. Methods: The data period covered the years 2011?2018 the data set consists of 176 MFIs from six Asian countries. The Data Envelopment Analysis (DEA) approach was employed to examine the MFIs? efficiency levels. Generalised Least Square (GLS) regressions were used to analyse the impacts of social globalisation and other determinants towards the efficiency of MFIs. Findings and value added: Interpersonal globalisation had a significantly negative correlation with social efficiency, suggesting that increasing the number of foreigners in management intrudes on local managers? decisions. Informational globalisation had a significantly positive correlation with financial and social efficiency, which signifies that more information produces monopolistic profits in this industry. Finally, cultural globalisation had a positive correlation with social efficiency, demonstrating that a global trading culture improves the abilities and technological skills for labour development and enhances MFIs? social efficiency. In general, the Cobb Douglas Production theory explained the understanding of the impacts social globalisation has on MFI efficiency. Furthermore, the findings from this study could provide important scientific, practical gap and contribute new insights and implications to various parties. Firstly, governments or policymakers can establish effective national policies and strategies. Secondly, this study could support investors in monitoring and understanding the performance of MFIs. Finally, the research could fill scholarly gaps and uncover more potential factors that influence the efficiency of MFIs.
Location: Australia
No related grants have been discovered for mohd haizam mohd saudi.