Indhumathi Natarajan, M. Shanmugam, S. Dhanalakshmi, Santhosh Easwaramoorthy, Sethuraja Kuppusamy, S. Balu
{"title":"Longitudinal Investigation of Work Stressors Using Human Voice Features","authors":"Indhumathi Natarajan, M. Shanmugam, S. Dhanalakshmi, Santhosh Easwaramoorthy, Sethuraja Kuppusamy, S. Balu","doi":"10.18267/j.aip.208","DOIUrl":"https://doi.org/10.18267/j.aip.208","url":null,"abstract":"","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42387742","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}
{"title":"AnnoJOB: Semantic Annotation-Based System for Job Recommendation","authors":"Assia Brek, Z. Boufaida","doi":"10.18267/j.aip.204","DOIUrl":"https://doi.org/10.18267/j.aip.204","url":null,"abstract":"","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43834267","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}
Venkatachalam Kandasamy, M. Abouhawwash, N. Bačanin
{"title":"Artificial Intelligence and Blockchain Technology Enabling Sustainable and Smart Infrastructure","authors":"Venkatachalam Kandasamy, M. Abouhawwash, N. Bačanin","doi":"10.18267/j.aip.203","DOIUrl":"https://doi.org/10.18267/j.aip.203","url":null,"abstract":"","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42134343","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}
{"title":"Evaluation of Community Detection by Improving Influence Nodes in Complex Networks Using InfoMap with Sigmoid Fish Swarm Optimization Algorithm","authors":"Devi Selvaraj, Rajalakshmi Murugasamy","doi":"10.18267/j.aip.201","DOIUrl":"https://doi.org/10.18267/j.aip.201","url":null,"abstract":"","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42580027","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}
{"title":"Comprehensive Review of Multimodal Medical Data Analysis: Open Issues and Future Research Directions","authors":"S. Shetty, A. S, A. Mahale","doi":"10.18267/j.aip.202","DOIUrl":"https://doi.org/10.18267/j.aip.202","url":null,"abstract":"","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46337251","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}
Blockchain is transforming the banking sector and offering opportunities for significant cost reduction and efficient banking services. However, implementing blockchain is a challenge due to lack of adequate knowledge and skills on how to implement the technology. As a result, there are very few market-ready blockchain banking products and organisations are unable to realise the promised value. This paper presents an overview of the banking sector’s blockchain use cases, design and implementation considerations and techniques. The aim is to offer an evidence-based primer to guide researchers and practitioners. The study relies on the systematic literature review method and reviews a total of 45 papers comprising 26 peer-reviewed scholarly articles and 19 technical reports from the banking industry. Leximancer software is used to support the thematic data analysis. The results show for the banking sector an increase in experimentation efforts geared towards the development of payment systems. The results also indicate key considerations from a technological, organisational and environmental perspective. The study highlights that platform selection, scalability and resilience are some of the critical technical considerations for implementing blockchain banking systems. Organisational considerations include collaboration and governance-related challenges. From an environmental perspective, the study notes several legal and regulatory considerations. This study contributes to the existing literature on blockchain adoption in banking, which is still in the nascent stage. The study also offers a research agenda for further understanding of blockchain implementation in the banking sector. Opportunities for further research are noted in the areas of interoperability, governance, security and privacy .
{"title":"Blockchain Design and Implementation Techniques, Considerations and Challenges in the Banking Sector: A Systematic Literature Review","authors":"S. Mafike, Tendani Mawela","doi":"10.18267/j.aip.200","DOIUrl":"https://doi.org/10.18267/j.aip.200","url":null,"abstract":"Blockchain is transforming the banking sector and offering opportunities for significant cost reduction and efficient banking services. However, implementing blockchain is a challenge due to lack of adequate knowledge and skills on how to implement the technology. As a result, there are very few market-ready blockchain banking products and organisations are unable to realise the promised value. This paper presents an overview of the banking sector’s blockchain use cases, design and implementation considerations and techniques. The aim is to offer an evidence-based primer to guide researchers and practitioners. The study relies on the systematic literature review method and reviews a total of 45 papers comprising 26 peer-reviewed scholarly articles and 19 technical reports from the banking industry. Leximancer software is used to support the thematic data analysis. The results show for the banking sector an increase in experimentation efforts geared towards the development of payment systems. The results also indicate key considerations from a technological, organisational and environmental perspective. The study highlights that platform selection, scalability and resilience are some of the critical technical considerations for implementing blockchain banking systems. Organisational considerations include collaboration and governance-related challenges. From an environmental perspective, the study notes several legal and regulatory considerations. This study contributes to the existing literature on blockchain adoption in banking, which is still in the nascent stage. The study also offers a research agenda for further understanding of blockchain implementation in the banking sector. Opportunities for further research are noted in the areas of interoperability, governance, security and privacy .","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42051535","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}
Saad Ahmed Dheyab, Shaymaa Mohammed Abdulameer, S. Mostafa
Distributed denial of service (DDoS) attacks are one of the most common global challenges faced by service providers on the web. It leads to network disturbances, interruption of communication and significant damage to services. Researchers seek to develop intelligent algorithms to detect and prevent DDoS attacks. The present study proposes an efficient DDoS attack detection model. This model relies mainly on dimensionality reduction and machine learning algorithms. The principal component analysis (PCA) and the linear discriminant analysis (LDA) techniques perform the dimensionality reduction in individual and hybrid modes to process and improve the data. Subsequently, DDoS attack detection is performed based on random forest (RF) and decision tree (DT) algorithms. The model is implemented and tested on the CICDDoS2019 dataset using different data dimensionality reduction test scenarios. The results show that using dimensionality reduction techniques along with the ML algorithms with a dataset containing high-dimensional data significantly improves the classification results. The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40
{"title":"Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction","authors":"Saad Ahmed Dheyab, Shaymaa Mohammed Abdulameer, S. Mostafa","doi":"10.18267/j.aip.199","DOIUrl":"https://doi.org/10.18267/j.aip.199","url":null,"abstract":"Distributed denial of service (DDoS) attacks are one of the most common global challenges faced by service providers on the web. It leads to network disturbances, interruption of communication and significant damage to services. Researchers seek to develop intelligent algorithms to detect and prevent DDoS attacks. The present study proposes an efficient DDoS attack detection model. This model relies mainly on dimensionality reduction and machine learning algorithms. The principal component analysis (PCA) and the linear discriminant analysis (LDA) techniques perform the dimensionality reduction in individual and hybrid modes to process and improve the data. Subsequently, DDoS attack detection is performed based on random forest (RF) and decision tree (DT) algorithms. The model is implemented and tested on the CICDDoS2019 dataset using different data dimensionality reduction test scenarios. The results show that using dimensionality reduction techniques along with the ML algorithms with a dataset containing high-dimensional data significantly improves the classification results. The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46609382","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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