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Your search keyword '"Abba S"' showing total 251 results
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251 results on '"Abba S"'

1. Social Network Analysis of Cryptocurrency using Business Intelligence Dashboard

Author

Jonathan C. Setyono, William S. Suryawidjaja, and Abba S. Girsang

Subjects
business intelligence, cryptocurrency, social network analysis, social media., Technological innovations. Automation, HD45-45.2
Abstract

There are currently more than 10.000 cryptocurrencies available to buy from the online market, with a vast range of prices for each coin it sells. The fluctuation of each coin is affected by any social events or by several important companies or people behind it. The aim of this research is to compare three cryptocurrencies, which are Bitcoin, Ethereum, and Binance Coin, using Social Network Analysis (SNA) by visualizing them using Business Intelligence (BI Dashboard). This study uses the SNA parameters of degree, diameter, modularity, centrality, and path length for each network and its actors and their actual market price by crawling(data collecting process) from Twitter as one of the social media platforms. From the research conducted, the popularity of cryptocurrencies is affected by their market price and the activeness of their actors on social media. These results are important because they could help in the decision-making to buy cryptocurrencies with high popularity on social media because they tend to retain their value over time and could benefit from price spikes from influential people. Doi: 10.28991/HIJ-2022-03-02-09 Full Text: PDF

Published
2022
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22. Non-linear Ensemble Modeling for Multi-step Ahead Prediction of Treated COD in Wastewater Treatment Plant

Author

Abba, S. I., Elkiran, Gozen, Nourani, Vahid, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Aliev, Rafik A., editor, Pedrycz, Witold, editor, Jamshidi, Mo, editor, Babanli, Mustafa B., editor, and Sadikoglu, Fahreddin M., editor

Published
2020
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24. Novel Ensemble Machine Learning Paradigms for the Prediction of Antioxidant Activity of Bryophyllum pinnatum(Lam.) Oken

Author

Abdulrahman, Mahmoud Dogara, Usman, A. G., Ozsahin, Dilber Uzun, Ibrahim, Abdullahi Umar, and Abba, S. I.

Abstract

For herbal and modern drug research, secondary metabolites produced by plants are a valuable source of pharmaceutically active compounds. Northern Nigeria relies heavily on plants as a source of medication. Succulent perennial herb Bryophyllum pinnatumis endemic to Africa and Asia. The current study investigates the in vitro pharmacological potential of the B. pinnatumwith the use of predictive data intelligence algorithms to unveil the inhibitory properties of the plant. In vitro biological evaluation of B. pinnatumwas carried out. This study explored the utilization of four different data intelligence models composing of two linear models; multi-linear regression (MLR) and step-wise linear regression (SWLR) as well two other nonlinear artificial intelligence (AI)-based models; adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM). Subsequently, four ensemble machine learning approaches including adaptive neuro-fuzzy inference system ensemble (ANFIS-E), support vector machine ensemble (SVM-E), simple average ensemble (SAE) and Gaussian process regression ensemble (GPR-E) were proposed to increase the single model’s accuracy. The ensemble techniques depict an average fitness of 0.999 in both training and testing stages. Moreover, recent and state-of-the-art visualization illustrations were also used for easy grasping and assimilations of the findings. The data-driven approach obtained results showed the AI-based model’s potentiality including SVM and ANFIS in outperforming the classical linear models; SWLR and MLR according to the study's performance metrics. Furthermore, based on the DC assessment criteria investigated in the current work, ensemble machine learning paradigms increased the performance skills of the single algorithms by up to 27% in the testing phase.

Published
2024
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46. Distribution and prevalence of viral genomes in Italian populations of the invasive brown marmorated stink bug Halyomorpha halys

Author

Papa, Giulia, Abba, S., Galetto, L., Parise, C., Marzachi, C., Negri, Ilaria, Papa G. (ORCID:0000-0001-8698-2479), Negri I. (ORCID:0000-0001-5188-1408), Papa, Giulia, Abba, S., Galetto, L., Parise, C., Marzachi, C., Negri, Ilaria, Papa G. (ORCID:0000-0001-8698-2479), and Negri I. (ORCID:0000-0001-5188-1408)

Abstract

Halyomorpha halys (Stål), the brown marmorated stink bug, is a highly invasive insect species that causes significant agricultural losses, especially to orchard fruits, vegetables, herbaceous and ornamental plants. It is also a nuisance pest that seeks shelter in indoor spaces during the winter months. Harnessing the H. halys virome can result in new environmentally sustainable approaches to contain its populations and its relatated agricultural damages. In this study, RNA-Seq data were used to explore the virome associated to ten field populations collected in the Lombardy region in Northern Italy. We identified six complete viral genomes, three of which were previously unknown, belonging to the orders Reovirales, Articulavirales, Ghabrivirales, Durnavirales, and Picornavirales. The prevalence of the six viruses was evaluated by Real-time reverse transcription-quantitative PCR on eighty individuals. Halyomorpha halys ifla-like virus 2 turned out to be the most geographically widespread virus, as it was found in more than 50% of the analyzed insects and in nine out of the ten sampling locations. Moreover, in some individuals, this iflavirus was found in association with each of the other viruses in various combinations that involved up to four viruses. Further studies on such virus-virus interactions and their relationships with the insect host may open the possibility to exploit these naturally occurring viruses as specific and targeted biocontrol agents of H. halys.

Published
2023

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