Toxicological risk assessment of Acetylsalicylic acid (ASA) in pigeons (columba levia)

Khan, Waqar Ahmad; Ali, Fayaz; Ali, M. Shaiq; Khan, Humera; Khan, Ayaz Ali; Ali, Karamat

doi:10.29252/azarinj.018

Cited by 1 publication

(9 citation statements)

References 23 publications

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“…Based on the experimental results shown in Table . 3, we conclude that either MLP, Voting, or RF can be selected as the best ML classi er to identify different rationale elements in the crowd-users comments on the social media platform and improve the performance of low-ranked software applications by focusing on the large volume of relevant information identi ed for the requirements and software engineers. Furthermore, the proposed approach outperforms previous similar research approaches [15,17,20] regarding classi cation accuracy, precision, recall, and F-measure. It can be seen in Table . 3, we achieved higher accuracy, precision, recall, and F-measure values than the previous rationale mining approaches.…”

Section: Discussionmentioning

confidence: 65%

“…and "Is not working; hopefully, Amazon personnel can help me to get Lt to work." Such rationale and requirements-related information help software developers identify con icting features or issues underneath the argumentation theory [17,18].…”

Section: Statements Of Usersmentioning

confidence: 99%

“…Next, the third most pivotal and prominent rationale element identi ed and recovered in the amazon software store is the issue that is 19% (2129 comments) of the overall 11416 end-user comments annotation sample. Such information is quite valuable and pivotal for the requirements engineers to understand better the problems or issues faced by the crowd-users when utilizing the software application [17,18]. Furthermore, 6% (659) of the crowd user's comments in the data set were identi ed and recovered as neutral claims.…”

Section: Frequency Of the User's Rationale Commentsmentioning

confidence: 99%

“…Intent identi cation involves determining customer intent based on online user reviews [7]. Also, previous studies demonstrate that crowd comments from end-users mobile stores [8], Tweets [9], and posts [10,11] include important and well-informed information regarding the user's interaction [12], requests for new features [13,14], and enduser rationale information [15,16], which is used to make informed requirements decision-making [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, software researchers started to mine rationale information from social media platforms. For this purpose, the researchers sought to investigate developers' conversations and user assessments of opensource software, which they found fascinating and insightful for requirements engineers to identify and capture rationale information and recover crowd-users tactic knowledge [19,20,17]. Therefore rationale provides important information during the software development process necessary for understanding and sustaining the software system.…”

Section: Introductionmentioning

confidence: 99%

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Can end-user rationale improve the quality of low-rating software applications: A rationale mining approach

Ullah

Khan

et al. 2022

Preprint

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Rationale refers to making human judgments, sets of reasons, or intentions to explain a particular decision. Nowadays, crowd-users argue and justify their decisions on social media platforms about market-driven software applications, thus generating a software rationale. Such rationale information can be of pivotal importance for the software and requirements engineers to enhance the performance of existing software applications by revealing end-users tactic knowledge to improve software designing and development decision-making. For this purpose, we proposed an automated approach to capture and analyze end-user reviews containing rationale information, focusing on low-rating applications in the amazon store using Natural Language Processing (NLP) and supervised machine learning (ML) classification methods. In the literature, high-rating applications have been emphasized while ignoring low-rating software application that causes potential biasness. Therefore, we examined 59 comparatively low-ranked market-based software applications from the Amazon app store covering various software categories to capture and identify crowd-users justifications. Next, using a developed grounded theory and content analysis approach, we studied and recorded how crowd-users analyze and explain their rationale based on issues encountered, attacking or supporting arguments registered, and updating or uninstalling software applications. Also, to achieve the best results, an experimental study is conducted by comparing various ML algorithms, i.e., MNB, LR, RF, MLP, KNN, AdaBoost, and Voting classifier, on the end-users rationale data set by preprocessing the input data, applying feature engineering, balancing the data set, and then training and testing the ML algorithms with a standard cross-validation approach. We obtained satisfactory results with MLP, voting, and RF Classifiers, having 93%, 93%, and 90% average accuracy, respectively. Also, we plot the ROC curves for the high-performing ML Classifier to identify and capture classifiers yielding the best performance with an under-sampling or oversampling balancing approach. Additionally, we obtained the average Precision, Recall, and F-measure values of 98%, 94%, 96%, 97%, 95%, and 96% for identifying supporting & decision rationale elements in the user comments, respectively. The proposed research approach outer-perform the existing rationale approaches with better Precision, Recall, and F-measure values.

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Section: Discussionmentioning

confidence: 65%

Section: Statements Of Usersmentioning

confidence: 99%

Section: Frequency Of the User's Rationale Commentsmentioning

confidence: 99%