Pub Date : 1900-01-01DOI: 10.1504/ijbdm.2022.119441
C. Moturi, Esther W. Karuga, D. Orwa
{"title":"Leveraging Big Data analytics - case of Kenyan telecoms","authors":"C. Moturi, Esther W. Karuga, D. Orwa","doi":"10.1504/ijbdm.2022.119441","DOIUrl":"https://doi.org/10.1504/ijbdm.2022.119441","url":null,"abstract":"","PeriodicalId":158664,"journal":{"name":"International Journal of Big Data Management","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116275994","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 : 1900-01-01DOI: 10.1504/IJBDM.2019.10023287
Lars Andraschko, B. Britzelmaier
This paper examines companies' adaptation of cryptocurrencies and comprises a quantitative empirical study. The emerging potentials of cryptocurrencies but the gap of practical application and respective existing knowledge are addressed in this paper. Technological, economic, social and regulatory aspects are depicted in the literature review. In addition, a comprehensive status quo of on companies' cryptocurrency adaptation research is provided and previous contributions are discussed. This study is based on an online questionnaire that was sent out to CFOs of German Prime Standard listed companies. As suggested in preceding papers the extended technology acceptance model (TAM2) is applied. Results indicate a very low level of adaptation and companies' utilisation of the blockchain technology. Lower potentials are seen in cryptocurrencies than in the underlying blockchain technology. The main obstacles are to overcome regulatory uncertainty and high price volatility. Low transaction costs and the omission of intermediaries are seen as great potential benefits. Suggestions for further research and practical implications are provided.
{"title":"Adaptation of cryptocurrencies in listed companies: empirical findings of a CFO survey in the German capital market","authors":"Lars Andraschko, B. Britzelmaier","doi":"10.1504/IJBDM.2019.10023287","DOIUrl":"https://doi.org/10.1504/IJBDM.2019.10023287","url":null,"abstract":"This paper examines companies' adaptation of cryptocurrencies and comprises a quantitative empirical study. The emerging potentials of cryptocurrencies but the gap of practical application and respective existing knowledge are addressed in this paper. Technological, economic, social and regulatory aspects are depicted in the literature review. In addition, a comprehensive status quo of on companies' cryptocurrency adaptation research is provided and previous contributions are discussed. This study is based on an online questionnaire that was sent out to CFOs of German Prime Standard listed companies. As suggested in preceding papers the extended technology acceptance model (TAM2) is applied. Results indicate a very low level of adaptation and companies' utilisation of the blockchain technology. Lower potentials are seen in cryptocurrencies than in the underlying blockchain technology. The main obstacles are to overcome regulatory uncertainty and high price volatility. Low transaction costs and the omission of intermediaries are seen as great potential benefits. Suggestions for further research and practical implications are provided.","PeriodicalId":158664,"journal":{"name":"International Journal of Big Data Management","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129812555","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 : 1900-01-01DOI: 10.1504/ijbdm.2020.10034867
Antonios Maniatis
{"title":"Blockchain Law","authors":"Antonios Maniatis","doi":"10.1504/ijbdm.2020.10034867","DOIUrl":"https://doi.org/10.1504/ijbdm.2020.10034867","url":null,"abstract":"","PeriodicalId":158664,"journal":{"name":"International Journal of Big Data Management","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121649454","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 : 1900-01-01DOI: 10.1504/ijbdm.2020.10034102
Sindhu P. Menon
: Healthcare is an emerging area in big data. Raw data contains lot of noise in it, hence cannot produce good results when processed. There is a need to improve the quality of data. This study shows how the prediction accuracy can be improved if the quality of data is improved. Previous work on issues related to variety and veracity have already been cited. Here the issues related to prediction are addressed. The dataset contains 1,047,253 records of patients having amyotrophic lateral sclerosis (ALS). Missing data values are filled and later used for prediction. Predicting the progression of the disease was calculated using stacked auto encoders. The results were compared with traditional techniques like random forest and support vector machine. A similar study was conducted using random forests and the accuracy obtained was only 66%. This paper presents a study on how to predict the progression of ALS using deep learning and an accuracy of 88% was achieved which is far more than the accuracy obtained on raw data. The study thus demonstrates the fact that accuracy increases with better data.
{"title":"Deep learning for prediction of amyotrophic lateral sclerosis using stacked auto encoders","authors":"Sindhu P. Menon","doi":"10.1504/ijbdm.2020.10034102","DOIUrl":"https://doi.org/10.1504/ijbdm.2020.10034102","url":null,"abstract":": Healthcare is an emerging area in big data. Raw data contains lot of noise in it, hence cannot produce good results when processed. There is a need to improve the quality of data. This study shows how the prediction accuracy can be improved if the quality of data is improved. Previous work on issues related to variety and veracity have already been cited. Here the issues related to prediction are addressed. The dataset contains 1,047,253 records of patients having amyotrophic lateral sclerosis (ALS). Missing data values are filled and later used for prediction. Predicting the progression of the disease was calculated using stacked auto encoders. The results were compared with traditional techniques like random forest and support vector machine. A similar study was conducted using random forests and the accuracy obtained was only 66%. This paper presents a study on how to predict the progression of ALS using deep learning and an accuracy of 88% was achieved which is far more than the accuracy obtained on raw data. The study thus demonstrates the fact that accuracy increases with better data.","PeriodicalId":158664,"journal":{"name":"International Journal of Big Data Management","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116826284","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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