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Ensemble Classifiers Employed for Spam Review Detection
Alhassan Jamilu Ibrahim,
Maheyzah Siraj,
Usman Abubakar Jauro
Issue:
Volume 6, Issue 3, September 2021
Pages:
33-38
Received:
12 June 2021
Accepted:
7 July 2021
Published:
11 August 2021
Abstract: The advancement of technology and the use of internet have changed many aspects of human culture over the years. Today, consumers take confidence in e-commerce platforms like amazon and eBay for comprehensive understanding of products and services when making a purchase decision. Here the web or user-generated content from consumers of such products and services, known as reviews, are exploited by spam reviewers to falsely promote or downgrade some targeted products. Despite potential solutions, Identifying and preventing review spam are still one of the top challenges faced by web search engines today. Therefore, in the quest to provide a more improved and efficient classification of review spam, this research probed different techniques in order to find most effective solution to spam detection. The research employed three base classifiers, Naïve Bayes, Support Vector Machines and Logistic Regression to form ensemble classifiers complimented with Arching classifier. The Arching classifier performs the weighted voting that produces the final class label with performance and accuracy higher than either of the individual base classifiers. Cross-validation is used as evaluation metrics to measure the performance or effectiveness of the ensemble classifiers while the experimental results shows that the ensemble classifiers achieve the best results compared to the single based classifier in terms of Precision, Recall, F1-measure and Accuracy.
Abstract: The advancement of technology and the use of internet have changed many aspects of human culture over the years. Today, consumers take confidence in e-commerce platforms like amazon and eBay for comprehensive understanding of products and services when making a purchase decision. Here the web or user-generated content from consumers of such product...
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Assessment of the Mechanical Properties of Bagasse Ash Concrete
Amanuel Bersisa,
Adil Zekaria
Issue:
Volume 6, Issue 3, September 2021
Pages:
39-44
Received:
17 July 2021
Accepted:
3 August 2021
Published:
12 August 2021
Abstract: Sugarcane bagasse ash is found abundantly in Ethiopia. Researchers in the area focus to sustainably use this pozzolan as a raw material for concrete production. This study aims to investigate the early and late age mechanical properties of Bagasse ash concrete. Concrete mixtures containing pure Portland cement, 6.5%, 13%, and 20% dosage of Bagasse ash by volume were proportioned. The compressive, tensile, flexural strength data at one, two, three, seven, twenty-eight days are determined. The experimental outcomes indicate that the tensile strength of bagasse ash concrete for 6.5% and 13% replacement ratio dropped by 6.98% and 22.5% compared to full cement concrete at three days of testing. As well, there is a reduction of third-day flexural strength by 5.96% and 13.1%. In advance, the 28th-day flexural strength increased by 6.38% and 17.02% for 6.5% and 13% replacement ratio. The compressive strength of bagasse ash concrete with 6.5% and 13% replacement ratios exceed the control group's 28th-day strength by 3.46% and 6.64% sequentially. Possibly, the ettringite formed as the base solution reacts with metallic oxides densifies the interfacial transition zone. Bagasse ash also contains inert unburned carbon particles that will fill the voids of hardened concrete. On the other hand, replacing cement with bagasse ash up to 20% reduces both the early and late age flexural strength and tensile strength development of concrete.
Abstract: Sugarcane bagasse ash is found abundantly in Ethiopia. Researchers in the area focus to sustainably use this pozzolan as a raw material for concrete production. This study aims to investigate the early and late age mechanical properties of Bagasse ash concrete. Concrete mixtures containing pure Portland cement, 6.5%, 13%, and 20% dosage of Bagasse ...
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Parameters Estimation at Ungauged Catchments Using Rainfall-Runoff Model, Upper Tekeze Basin, Ethiopia
Issue:
Volume 6, Issue 3, September 2021
Pages:
45-56
Received:
8 May 2021
Accepted:
10 August 2021
Published:
18 August 2021
Abstract: This study was conducted for parametres estimation and stream flow prediction at ungauged catchments on the case of Upper Tekeze basin, Ethiopia by using Rainfall-runoff model. In the basin, most of the catchments were ungauged. The basin has 9199km2 and 3638km2 gauged and ungauged catchments respectively. Rainfall and stream flow data were analyzed in the period of 1992-2006 and 1992-20006, respectively. Parameters calibrated for gauged catchments were extrapolated to ungauged catchments on the base of similar physical catchment characteristics using regionalization techniques. Regionalization methods such as multiple linear regression, spatial proximity, sub basin mean and area ratio were applied to transfer model parameters values from gauged to ungauged catchments. For this study seven gauged catchments were satisfied objective functions in the calibrated and validation period, for example in Gheba catchment Nash-Sutcliffe model efficiency coefficient (NSE), relative volume error (RVE) and coefficient of determination (R2) were, 0.81, -4.25, 0.77 and 0.71, 5.5, 0.74 respectively. Stream flow simulation at ungauged catchments by using spatial proximity and sub basin mean method were contributing high runoff volume compare to other methods. The result for this study shows that the Key model parameters like runoff coefficient (Beta), recession coefficient of upper reservoir zone (Khq), Limit for evapotranspiration (Lp), field capacity (Fc), percolation (Perc) as defaulting value when applying HBV-96 model to the future regionalization studies. Model parameters were calibrated manually by try and error rules, however it was tidies therefore more creative automatic model calibration techniques could be useful for upcoming studies. Thus, Current and future water resources development endeavors may use apply such discharge data for planning and design purposes.
Abstract: This study was conducted for parametres estimation and stream flow prediction at ungauged catchments on the case of Upper Tekeze basin, Ethiopia by using Rainfall-runoff model. In the basin, most of the catchments were ungauged. The basin has 9199km2 and 3638km2 gauged and ungauged catchments respectively. Rainfall and stream flow data were analyze...
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