Pub Date : 2019-03-01DOI: 10.1109/ICCMA.2019.00030
I. K. Amponsah, H. Acquah, Nathaniel K. Howard
The Minimum Description Length (MDL), a less known criterion, is making great strides in model selection as compared to the widely known and used information criteria (AIC, BIC, etc). This study developed the MDL criterion using R-functions to evaluate Asymmetric Price Transmission (APT) models (Complex, Standard and Houck’s) for the first time ever. All six criteria’s ability to recover the true DGP was assessed under the condition of varying sample size. A 1000 Monte Carlo simulation procedure revealed that the MDL criteria on the average points to the true DGP and are comparable (if not better) to both AIC and BIC under study condition. Generally, the performances of all model selection criteria (rMDL, nMDL, gMDL, eMDL, AIC and BIC) improved with increasing sample size in their ability to recover the true DGP for both standard and complex models. This study recommends the use of MDL criterion in model selection and in the light of constraint (financial, time and inadequate resources), a sample size of 150 is sufficient in making sound decisions on asymmetric price models.
{"title":"Comparative Evaluation of Asymmetric Price Transmission Linear Models Using rMDL, eMDL, nMDL, gMDL, AIC and BIC Across Varying Sample Sizes","authors":"I. K. Amponsah, H. Acquah, Nathaniel K. Howard","doi":"10.1109/ICCMA.2019.00030","DOIUrl":"https://doi.org/10.1109/ICCMA.2019.00030","url":null,"abstract":"The Minimum Description Length (MDL), a less known criterion, is making great strides in model selection as compared to the widely known and used information criteria (AIC, BIC, etc). This study developed the MDL criterion using R-functions to evaluate Asymmetric Price Transmission (APT) models (Complex, Standard and Houck’s) for the first time ever. All six criteria’s ability to recover the true DGP was assessed under the condition of varying sample size. A 1000 Monte Carlo simulation procedure revealed that the MDL criteria on the average points to the true DGP and are comparable (if not better) to both AIC and BIC under study condition. Generally, the performances of all model selection criteria (rMDL, nMDL, gMDL, eMDL, AIC and BIC) improved with increasing sample size in their ability to recover the true DGP for both standard and complex models. This study recommends the use of MDL criterion in model selection and in the light of constraint (financial, time and inadequate resources), a sample size of 150 is sufficient in making sound decisions on asymmetric price models.","PeriodicalId":413965,"journal":{"name":"2019 International Conference on Computing, Computational Modelling and Applications (ICCMA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132076869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/ICCMA.2019.00029
E. Gyimah, D. K. Dake
As educational data in tertiary institutions are becoming huge, it is important to deploy Data Mining algorithms in discovering knowledge and improving academic quality. One fast course delivery approach or trend, constructivism in higher education is based on Learner prioritization in the learning process where a learner transforms information, constructs hypothesis and makes decisions using mental models. Similar learner groupings for project-based learning through hidden patterns extraction can aid Active Learning and Instructor Monitoring. In our previous paper, K-means clustering algorithm was used to group learners with similar scores in three assessments. In this paper, we built a classifier model using the J48 Decision Tree Algorithm for predicting learner groupings after getting class labels through the K-means clustering algorithm. This classifier will help in predicting future groupings of learners for the same course and attributes. The weka simulation for the classifier model gave a 99.9% ROC Area curve, which indicates a general performance of the model and a 96.19% of correctly classified instances. The Confusion Matrix has 80% of the members correctly classified. The classification model has an extremely low FP Rate of 2%, another indication of a high performance predictive classifier.
{"title":"Using Decision Tree Classification Algorithm to Predict Learner Typologies for Project-Based Learning","authors":"E. Gyimah, D. K. Dake","doi":"10.1109/ICCMA.2019.00029","DOIUrl":"https://doi.org/10.1109/ICCMA.2019.00029","url":null,"abstract":"As educational data in tertiary institutions are becoming huge, it is important to deploy Data Mining algorithms in discovering knowledge and improving academic quality. One fast course delivery approach or trend, constructivism in higher education is based on Learner prioritization in the learning process where a learner transforms information, constructs hypothesis and makes decisions using mental models. Similar learner groupings for project-based learning through hidden patterns extraction can aid Active Learning and Instructor Monitoring. In our previous paper, K-means clustering algorithm was used to group learners with similar scores in three assessments. In this paper, we built a classifier model using the J48 Decision Tree Algorithm for predicting learner groupings after getting class labels through the K-means clustering algorithm. This classifier will help in predicting future groupings of learners for the same course and attributes. The weka simulation for the classifier model gave a 99.9% ROC Area curve, which indicates a general performance of the model and a 96.19% of correctly classified instances. The Confusion Matrix has 80% of the members correctly classified. The classification model has an extremely low FP Rate of 2%, another indication of a high performance predictive classifier.","PeriodicalId":413965,"journal":{"name":"2019 International Conference on Computing, Computational Modelling and Applications (ICCMA)","volume":"255 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133618212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/ICCMA.2019.00008
Charles Buabeng-Andoh, Charles Baah
This study investigated university students' actual use of learning management system using Unified Theory of Acceptance and Use of Technology (UTAUT) model. Quantitative and qualitative research strategies were adopted using close-ended and open-ended questionnaire to collect data from 149 students who were learning management system (LMS) users. Random sampling approach was used to select the students. The quantitative data were analyzed using structural equation model and the qualitative data were analyzed using thematic analysis. The results from quantitative analysis showed that performance expectancy, effort expectancy and institutional support influenced students' actual use of LMS while social influence and infrastructure support did not influence students' actual use. But, the results from qualitative analysis found lack of training, unavailability of internet connection and poor electricity supply as major concerns that affect students' actual usage of LMS. This study contributed to the current discussion on use of UTAUT to explain students' actual use of technology in developing countries. Implications, limitations and future studies were discussed.
{"title":"Investigating the Actual usage of Learning Management System: From Perspectives of University Students","authors":"Charles Buabeng-Andoh, Charles Baah","doi":"10.1109/ICCMA.2019.00008","DOIUrl":"https://doi.org/10.1109/ICCMA.2019.00008","url":null,"abstract":"This study investigated university students' actual use of learning management system using Unified Theory of Acceptance and Use of Technology (UTAUT) model. Quantitative and qualitative research strategies were adopted using close-ended and open-ended questionnaire to collect data from 149 students who were learning management system (LMS) users. Random sampling approach was used to select the students. The quantitative data were analyzed using structural equation model and the qualitative data were analyzed using thematic analysis. The results from quantitative analysis showed that performance expectancy, effort expectancy and institutional support influenced students' actual use of LMS while social influence and infrastructure support did not influence students' actual use. But, the results from qualitative analysis found lack of training, unavailability of internet connection and poor electricity supply as major concerns that affect students' actual usage of LMS. This study contributed to the current discussion on use of UTAUT to explain students' actual use of technology in developing countries. Implications, limitations and future studies were discussed.","PeriodicalId":413965,"journal":{"name":"2019 International Conference on Computing, Computational Modelling and Applications (ICCMA)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122879449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/ICCMA.2019.00028
B. Asare, Kester Quist-Aphetsi, Laurent Nana
Pervasive systems over the years continuous to grow exponentially. Engagement of IoT in fields such as Agriculture, Home automation, industrial applications etc is on the rise. Self organizing networks within the IoT field give rise to engagement of various nodes for data communication. The rise in Cyber-attacks within IoT pose a lot of threat to these connected nodes and hence there is a need for data passing through nodes to be verified during communication. In this paper we proposed a nodal authentication approach in IoT using blockchain in securing the integrity of data passing through the nodes in IoT. In our work, we engaged the GOST algorithm in our approach. At the end, we achieved a nodal authentication and verification of the transmitted data. This makes it very difficult for an attacker to fake a node in the communication chain of the connected nodes. Data integrity was achieved in the nodes during the communication.
{"title":"Nodal Authentication of IoT Data Using Blockchain","authors":"B. Asare, Kester Quist-Aphetsi, Laurent Nana","doi":"10.1109/ICCMA.2019.00028","DOIUrl":"https://doi.org/10.1109/ICCMA.2019.00028","url":null,"abstract":"Pervasive systems over the years continuous to grow exponentially. Engagement of IoT in fields such as Agriculture, Home automation, industrial applications etc is on the rise. Self organizing networks within the IoT field give rise to engagement of various nodes for data communication. The rise in Cyber-attacks within IoT pose a lot of threat to these connected nodes and hence there is a need for data passing through nodes to be verified during communication. In this paper we proposed a nodal authentication approach in IoT using blockchain in securing the integrity of data passing through the nodes in IoT. In our work, we engaged the GOST algorithm in our approach. At the end, we achieved a nodal authentication and verification of the transmitted data. This makes it very difficult for an attacker to fake a node in the communication chain of the connected nodes. Data integrity was achieved in the nodes during the communication.","PeriodicalId":413965,"journal":{"name":"2019 International Conference on Computing, Computational Modelling and Applications (ICCMA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129975401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/ICCMA.2019.00027
Rexford Nii Ayitey Sosu, Kester Quist-Aphetsi, Laurent Nana
With the rise of cyber attacks and the advancement of technology providing easy access to personal data and information in real time using networks and other services such as the cloud. These advancements have placed sensitive data such as medical information under threat. Due to the ease in accessing and modifying such data leading to little or no trace of such wrongdoings. This paper proposes a cryptographic blockchain approach using the md5 hash algorithm to verify and validate the medical data of the health information system infrastructure. The approach makes it difficult for data to be altered without detection and adopts a distributed approach coupled with blockchain.
{"title":"A Decentralized Cryptographic Blockchain Approach for Health Information System","authors":"Rexford Nii Ayitey Sosu, Kester Quist-Aphetsi, Laurent Nana","doi":"10.1109/ICCMA.2019.00027","DOIUrl":"https://doi.org/10.1109/ICCMA.2019.00027","url":null,"abstract":"With the rise of cyber attacks and the advancement of technology providing easy access to personal data and information in real time using networks and other services such as the cloud. These advancements have placed sensitive data such as medical information under threat. Due to the ease in accessing and modifying such data leading to little or no trace of such wrongdoings. This paper proposes a cryptographic blockchain approach using the md5 hash algorithm to verify and validate the medical data of the health information system infrastructure. The approach makes it difficult for data to be altered without detection and adopts a distributed approach coupled with blockchain.","PeriodicalId":413965,"journal":{"name":"2019 International Conference on Computing, Computational Modelling and Applications (ICCMA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115316708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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