Infrastructure Requirements For Experience Based Factory Model in Software Development Process in a Collaborative Environment

Hanafiah, Mastura; Abdullah, Rusli; Murad, Masrah Azrifah Azmi; Din, Jafri; Wahid, Norfaradilla

doi:10.26480/aim.02.2017.09.10

Cited by 5 publications

(4 citation statements)

References 9 publications

(10 reference statements)

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“…The implementation of the decision tree was done using scikit-learn and after training the algorithm, the prediction of the new data class was done according to Figure. 10.…”

Section: Evaluate Algorithmsmentioning

confidence: 99%

“…Computational simulation is a well-organized and excellent device to comprehend and nd the best possible procedures like energy saving, separation, or capture of carbon dioxide in process engineering area [9][10][11]. One of the separation's true applications is the CO 2 's mole fraction in gas ows like natural gas, air, ue gas, etc., which is very important [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of CO 2 mole fraction via CO 2 CPU process using different machine learning algorithms

Mansouri

Shahraki

Sadeghi

et al. 2023

Preprint

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There is a general industrial procedure called compression and refining unit to catch CO2 from the flue gases produced during oxyfuel combustion. This research discusses the application of decision trees, Adaboosting, random forests, machines that support vectors, and k-nearest neighbor classifiers and gradient boosting in predicting CO2’s mole fracion from flue gases of oxyfuel’s combustion emitted from the power plant. First of all, a training and test dataset was developed using the different variables. Then, a total of 491 simulations were performed and the mole fraction of CO2 was examined. The anticipated outcomes suggested that six machine learning algorithms that rank performance from excellent to poor, RF, GB, AB, DT, KNN, and SVM can be picked to forecast the mole fraction of CO2. Important features were detected by SHAP and the best algorithm was chosen by cross-validation. Results were shown that The RF algorithm enjoyed a great CO2 mole fraction ability to predict and displayed the very best ability for generalization and most reliable prediction precision among all four with an accuracy of 97%. After that LIME was used to explain the results of the RF algorithm. Out of the various variables studied, the pressure of the multistage compressor had the highest effect on the CO2 mole fraction. These results show that machine learning can be used as a reliable predictor of CO2 performance capture within the CPU process.

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“…The implementation of the decision tree was done using scikit-learn and after training the algorithm, the prediction of the new data class was done according to Figure. 10.…”

Section: Evaluate Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of CO 2 mole fraction via CO 2 CPU process using different machine learning algorithms

Mansouri

Shahraki

Sadeghi

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Models such as Tobit model, spatial Durbin model, GMM panel estimation and multiple regression analysis are often used to evaluate the impact mechanism of industrial environmental efficiency (Emrouznejad and Yang, 2016;Yang and Li, 2017;Young and Lipták, 2018;Zhang et al, 2020;Sunari Magar et al, 2021). For example, , Hanafiah et al (2017), Qiu et al (2022a) used Tobit model to conduct regression analysis and explore the relationship between economic growth, energy consumption and industrial environmental sustainability. Qiu et al (2022b), Quan et al (2022), Miao et al (2020) studies the impact of energy consumption and environmental pollution on technological innovation efficiency of industrial enterprises by using GMM model based on panel data of industrial enterprises in 30 provinces of China.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy consumption and pollution control from the perspective of industrial economic activity: An empirical study of China’s coastal provinces

2022

Front. Environ. Sci.

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From the perspective of production performance, energy supply are the basic material conditions. However, greenhouse gas, air pollution and waste water are also produced in the process of production. If the undesired characteristics are ignored in the process of performance evaluation, the production efficiency will be misestimated. Based on this, this study uses Data Envelopment Analysis (DEA) to evaluate the undesired output, and discusses the production efficiency with thermal consumption in Chinese port cities, especially with severe shipping emissions, during 2015–2019. The empirical results show that the efficiency declines first (2015–2017) and then increases (2018–2019) when considering the undesired output of wastewater and SO2 generated by thermal consumption.

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“…To implement the system, we have defined the necessary infrastructure requirements, which include the technical requirements on availability and reliability, storage requirement, automation of KM processes, security, and network and performance [30]. Cloud security features proposed by [31] could be a value-added feature as it offers data confidentiality, correctness, availability, and integrity of cloud data storage.…”

Section: Fig 6 Mas System Overview Diagrammentioning

confidence: 99%

The Development and Evaluation of Experience-Based Factory Model for Software Development Process

Hanafiah¹,

Abdullah²,

Murad³

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Knowledge, and experiences in software development have been accumulated over time throughout the project lifecycle. Previous studies have shown that the management of knowledge and experiences in software development has always been an issue. Therefore, the knowledge transfer and information flow are inefficient, misinterpretation, and inconsistencies always occur between individuals or teams, and the organization fails to learn from past projects. It is understood that efficient knowledge and experience management for software development organizations is crucial for the purpose of sharing and future reuse. This paper discusses the prototype development for a proposed model, which is based on the experience factory approach, to manage knowledge and experiences for the software development process. Discussions include the system functionalities and design, infrastructure requirements, and implementation approach. The efficiency and effectiveness of the prototype are evaluated as survey research based on Jennex & Olfman knowledge management success model. Rasch analysis is used for data reliability and validity. Results show positive feedback on the model's efficiency and effectiveness. Additionally, as agreed by most respondents, the top three of the model contributions are: to encourage learning organization, to prevent knowledge loss and to aid in decision making.

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Infrastructure Requirements For Experience Based Factory Model in Software Development Process in a Collaborative Environment

Abstract: This paper discuss the infrastructure requirement of the model named Experienced Based Facto-ry Model for Software Development Process (EBF-SD) in order to ensure the implementation of the model will be able support collaborative environment effectively and efficiently.

Cited by 5 publications

References 9 publications

Prediction of CO 2 mole fraction via CO 2 CPU process using different machine learning algorithms

Prediction of CO 2 mole fraction via CO 2 CPU process using different machine learning algorithms

Energy consumption and pollution control from the perspective of industrial economic activity: An empirical study of China’s coastal provinces

The Development and Evaluation of Experience-Based Factory Model for Software Development Process

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