A novel quality prediction model for component based software system using ACO–NM optimized extreme learning machine

Sheoran, Kavita; Tomar, Pradeep; Mishra, Rajesh

doi:10.1007/s11571-020-09585-7

Cited by 12 publications

(7 citation statements)

References 47 publications

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“…In order to evaluate the success of PS to identify appropriate, relevant predictions and selection of personalized preferences used F-measure and accuracy metrics [ 24 , 40 ]. Accuracy measures exactness in prediction priority personalized preferences using semantic analysis.…”

Section: Experimentation and Evaluationmentioning

confidence: 99%

Enabling remote learning system for virtual personalized preferences during COVID-19 pandemic

Ali

Hafeez

Abbas

et al. 2021

Multimed Tools Appl

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The education system worldwide has been affected by the Corona Virus Diseases 2019 (COVID-19) pandemic, resulting in the interruption of all educational institutions. Moreover, as a precautionary measure, the lockdown has been imposed that has severely affected the learning processes, especially assessment activities, including exams and viva. In such challenging situations, E-learning platforms could play a vital role in conducting seamless academic activities. In spite of all the advantages of remote learning systems, many hurdles and obstacles, like a selection of suitable learning resources/material encounter by individual users based on their interests or requirements. Especially those who are not well familiar with the internet technology in developing countries and are in need of a platform that could help them in resolving the issues related to the online virtual environment. Therefore, in this work, we have proposed a mechanism that intelligently and correctly predicts the appropriate preferences for the selection of resources relevant to a specific user by considering the capabilities of diverse perspectives users to provide quality online education and to make work from home policy more effective and progressive during the pandemic. The proposed system helps teachers in providing quality online education, familiarizing them with advanced technology in the online environment. It also semantically predicts the preferences for virtual assistance of those users who are in need of learning the new tools and technologies in short time as per their institutional requirements in order to meet the quality standards of online education. The experimental and statistical results have demonstrated that the proposed virtual personalized preferences system has improved overall academic activities as compared to the current method. The proposed system enhanced user's learning abilities and facilitated them in selecting short courses while using different online education tools adopted/suggested by the institutions to conduct online classes/seminars/webinars etc., as compared to the conventional classes/activities.

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Section: Experimentation and Evaluationmentioning

confidence: 99%

Enabling remote learning system for virtual personalized preferences during COVID-19 pandemic

Ali

Hafeez

Abbas

et al. 2021

Multimed Tools Appl

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“…Additionally, in another study (Graics et al, 2020) authors presented method for semantic based reactive CBS design using state chart model. Machine learning based methods are also used for handling CBS large set of requirements (Sheoran et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

“…The literature outcome provided an overview and guidelines for CBS applications, trends, opportunities, and challenges during development. The key challenges identified from the literature are ambiguities, incompleteness, misspecification, mismatch, inaccuracy, redundancy, and irrelevancy during requirement specification, analysis, and prioritization of CBS (Ali et al, 2018;Ayala et al, 2018;Borg et al, 2019;Chatzipetrou et al, 2020;Graics et al, 2020;Noei et al, 2019;Sheoran et al, 2020). Thus, these challenges impact the integration, verification, and validation process of CBS during development.…”

Section: Related Workmentioning

confidence: 99%

“…To assess the effectiveness of the proposed framework to identify the correctness of components selection and prioritization process, we used three metrics, that is, precision, accuracy, and sensitivity (Kyaw Zaw & Vasupongayya, 2019;Sheoran et al, 2020). Accuracy measures correctness in priority prediction of CBS features for integration using Equation ( 1).…”

Section: Hypothesismentioning

confidence: 99%

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Requirement prioritization framework using case‐based reasoning: A mining‐based approach

et al. 2021

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In the current era of technology and development, component-based software development (CBSD) has been progressively implemented. Components are used in large products for multiple users with diverse viewpoints and are highly configurable to provide higher satisfaction. Components are reused because of their similar functionality to reduce complexity and ensure the correct interaction between interfaces during product development. However, implementation of component functionalities creates complications due to improper specification, prioritization of components requirements, encapsulated functionalities, and more human interaction. Furthermore, due to configurability and the involvement of multiple stakeholders, ambiguity and semantic issues arise in the behaviour of reusable components. To overcome semantic-based specification and prioritization components' related issues, we propose a framework that uses text mining and case-based reasoning (CBR) techniques.Results of our empirical evaluation demonstrate that the proposed framework significantly outperforms the conventional technique.

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“…However, none of the proposed soft computing approaches have considered the Ant-Miner algorithm [ 19 ], which is known for its effectiveness in approaching challenging machine learning problems and association rule mining [ 12 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Ant-Miner, which is based on the ant colony optimization algorithm (ACO) [ 26 ], is usually exploited in learning classification rules [ 14 , 23 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Incremental Ant-Miner Classifier for Online Big Data Analytics

Al-Dawsari

Al-Turaiki

Kurdi

2022

Sensors

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Internet of Things (IoT) environments produce large amounts of data that are challenging to analyze. The most challenging aspect is reducing the quantity of consumed resources and time required to retrain a machine learning model as new data records arrive. Therefore, for big data analytics in IoT environments where datasets are highly dynamic, evolving over time, it is highly advised to adopt an online (also called incremental) machine learning model that can analyze incoming data instantaneously, rather than an offline model (also called static), that should be retrained on the entire dataset as new records arrive. The main contribution of this paper is to introduce the Incremental Ant-Miner (IAM), a machine learning algorithm for online prediction based on one of the most well-established machine learning algorithms, Ant-Miner. IAM classifier tackles the challenge of reducing the time and space overheads associated with the classic offline classifiers, when used for online prediction. IAM can be exploited in managing dynamic environments to ensure timely and space-efficient prediction, achieving high accuracy, precision, recall, and F-measure scores. To show its effectiveness, the proposed IAM was run on six different datasets from different domains, namely horse colic, credit cards, flags, ionosphere, and two breast cancer datasets. The performance of the proposed model was compared to ten state-of-the-art classifiers: naive Bayes, logistic regression, multilayer perceptron, support vector machine, K*, adaptive boosting (AdaBoost), bagging, Projective Adaptive Resonance Theory (PART), decision tree (C4.5), and random forest. The experimental results illustrate the superiority of IAM as it outperformed all the benchmarks in nearly all performance measures. Additionally, IAM only needs to be rerun on the new data increment rather than the entire big dataset on the arrival of new data records, which makes IAM better in time- and resource-saving. These results demonstrate the strong potential and efficiency of the IAM classifier for big data analytics in various areas.

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A novel quality prediction model for component based software system using ACO–NM optimized extreme learning machine

Cited by 12 publications

References 47 publications

Enabling remote learning system for virtual personalized preferences during COVID-19 pandemic

Enabling remote learning system for virtual personalized preferences during COVID-19 pandemic

Requirement prioritization framework using case‐based reasoning: A mining‐based approach

Incremental Ant-Miner Classifier for Online Big Data Analytics

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