Search results

1 – 2 of 2
Article
Publication date: 14 August 2007

Jin Zhang and Suyu Lin

This paper aims to investigate the multiple language support features in internet search engines. The diversity of the internet is reflected not only in its users, information…

1231

Abstract

Purpose

This paper aims to investigate the multiple language support features in internet search engines. The diversity of the internet is reflected not only in its users, information formats and information content, but also in the languages used. As more and more information becomes available in different languages, multiple language support in a search engine becomes more important.

Design/methodology/approach

The first step of this study is to conduct a survey about existing search engines and to identify search engines with multiple language support features. The second step is to analyse, compare, and characterise the multiple language support features in the selected search engines against the proposed five basic evaluation criteria after they are classified into three categories. Finally, the strengths and weaknesses of the multiple language support features in the selected search engines are discussed in detail.

Findings

The findings reveal that Google, EZ2Find, and Onlinelink respectively are the search engines with the best multiple language support features in their categories. Although many search engines are equipped with multiple language support features, an indispensable translation feature is implemented in only a few search engines. Multiple language support features in search engines remain at the lexical level.

Originality/value

The findings of the study will facilitate understanding of the current status of multiple language support in search engines, help users to effectively utilise multiple language support features in a search engine, and provide useful advice and suggestions for search engine researchers, designers and developers.

Details

Online Information Review, vol. 31 no. 4
Type: Research Article
ISSN: 1468-4527

Keywords

Open Access
Article
Publication date: 23 January 2023

Hussein Y.H. Alnajjar and Osman Üçüncü

Artificial intelligence (AI) models are demonstrating day by day that they can find long-term solutions to improve wastewater treatment efficiency. Artificial neural networks…

1107

Abstract

Purpose

Artificial intelligence (AI) models are demonstrating day by day that they can find long-term solutions to improve wastewater treatment efficiency. Artificial neural networks (ANNs) are one of the most important of these models, and they are increasingly being used to forecast water resource variables. The goal of this study was to create an ANN model to estimate the removal efficiency of biological oxygen demand (BOD), total nitrogen (TN), total phosphorus (TP) and total suspended solids (TSS) at the effluent of various primary and secondary treatment methods in a wastewater treatment plant (WWTP).

Design/methodology/approach

The MATLAB App Designer model was used to generate the data set. Various combinations of wastewater quality data, such as temperature(T), TN, TP and hydraulic retention time (HRT) are used as inputs into the ANN to assess the degree of effect of each of these variables on BOD, TN, TP and TSS removal efficiency. Two of the models reflect two different types of primary treatment, while the other nine models represent different types of subsequent treatment. The ANN model’s findings are compared to the MATLAB App Designer model. For evaluating model performance, mean square error (MSE) and coefficient of determination statistics (R2) are utilized as comparative metrics.

Findings

For both training and testing, the R values for the ANN models were greater than 0.99. Based on the comparisons, it was discovered that the ANN model can be used to estimate the removal efficiency of BOD, TN, TP and TSS in WWTP and that the ANN model produces very similar and satisfying results to the APPDESIGNER model. The R-value (Correlation coefficient) of 0.9909 and the MSE of 5.962 indicate that the model is accurate. Because of the many benefits of the ANN models used in this study, it has a lot of potential as a general modeling tool for a range of other complicated process systems that are difficult to solve using conventional modeling techniques.

Originality/value

The objective of this study was to develop an ANN model that could be used to estimate the removal efficiency of pollutants such as BOD, TN, TP and TSS at the effluent of various primary and secondary treatment methods in a WWTP. In the future, the ANN could be used to design a new WWTP and forecast the removal efficiency of pollutants.

Details

Arab Gulf Journal of Scientific Research, vol. 41 no. 4
Type: Research Article
ISSN: 1985-9899

Keywords

1 – 2 of 2