Deep learning based affective computing

Kumar, Saurabh

doi:10.1108/jeim-12-2020-0536

Cited by 16 publications

(8 citation statements)

References 63 publications

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“…The final article of this SI, entitled "Deep learning-based affective computing" by Kumar (2021), presents an affective computing model to classify emotions and sentiments using both text and image-based data. The study used customized deep learning models for affective computing.…”

Section: Deep Learning-based Affective Computingmentioning

confidence: 99%

Guest editorial

Chang

Gagnon

Valverde

et al. 2021

JEIM

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Section: Deep Learning-based Affective Computingmentioning

confidence: 99%

Guest editorial

Chang

Gagnon

Valverde

et al. 2021

JEIM

View full text Add to dashboard Cite

“…However, in the current market, there is a demand for AI talents who understand emerging technologies, such as machine learning and deep learning (Brynjolfsson and McAfee, 2017; Kumar, 2021). These technologies are part of narrow AI and have some technical depth (e.g.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an artificial intelligence project in the software industry based on fuzzy analytic hierarchy process and complex adaptive systems

Chang

2023

JEIM

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PurposeArtificial intelligence (AI) is the most progressive commodity among current information system applications. In-house development and sales of beneficial products are difficult for many software development and service companies (SDSCs). SDSCs have some implicit concerns about implementing AI software development due to the complexity of AI technology; they require an evaluation framework to avoid development failure. To fill the void, this study identified the factors influencing SDSCs when developing AI software development.Design/methodology/approachBased on complex adaptive systems theory, three aspects were developed as the main factors of hierarchy, namely, employees' capabilities, environmental resources and team capabilities. Fuzzy analytic hierarchy process (FAHP) was used to assess the SDSCs' attitude. Based on SDSCs, attitudes toward implementing AI software projects were collected to calculate the hierarchy of factors.FindingsThe outcome of FAHP is used as understanding the key factors of SDSCs for selecting an AI software project, toward the improvement of overall project planning. Employees' stress resistance was considered as a priority for the project, although professional AI skills and resources were also important.Originality/valueThis study suggested three variables developed using complex adaptive systems. This study contributes to a better understanding of the critical aspects of developing AI software projects in SDSCs. The study's findings have practical and academic implications for SDSCs and subsequent academic development, broadening the scope of AI software development research.

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“…The evolution of software-defined networks (SDN) coupled with artificial intelligence (AI) solutions such as deep learning (DL) presents promising results towards programmable and intelligent networking solutions (Kumar, 2021; Xu et al ., 2020). SDN and DL have found numerous applications from a networking perspective for both wide area networks (WANs), service providers (SPs), data centre (DC) and local area networks (LANs) such as campus networks.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for SDN-enabled campus networks: proposed solutions, challenges and future directions

Chanhemo

Mohsini

Mjahidi

et al. 2023

IJICC

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PurposeThis study explores challenges facing the applicability of deep learning (DL) in software-defined networks (SDN) based campus networks. The study intensively explains the automation problem that exists in traditional campus networks and how SDN and DL can provide mitigating solutions. It further highlights some challenges which need to be addressed in order to successfully implement SDN and DL in campus networks to make them better than traditional networks.Design/methodology/approachThe study uses a systematic literature review. Studies on DL relevant to campus networks have been presented for different use cases. Their limitations are given out for further research.FindingsFollowing the analysis of the selected studies, it showed that the availability of specific training datasets for campus networks, SDN and DL interfacing and integration in production networks are key issues that must be addressed to successfully deploy DL in SDN-enabled campus networks.Originality/valueThis study reports on challenges associated with implementation of SDN and DL models in campus networks. It contributes towards further thinking and architecting of proposed SDN-based DL solutions for campus networks. It highlights that single problem-based solutions are harder to implement and unlikely to be adopted in production networks.

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Deep learning based affective computing

Cited by 16 publications

References 63 publications

Guest editorial

Guest editorial

Evaluation of an artificial intelligence project in the software industry based on fuzzy analytic hierarchy process and complex adaptive systems

Deep learning for SDN-enabled campus networks: proposed solutions, challenges and future directions

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