Robust Feature Selection With LSTM Recurrent Neural Networks for Artificial Immune Recognition System

Şahin, Canan Batur; Di̇ri̇, Banu

doi:10.1109/access.2019.2900118

Cited by 15 publications

(6 citation statements)

References 14 publications

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“…Generally, IDT can improve the effectiveness of supply chain management decision-making, such as optimizing supply chain processes and saving decision costs. However, when decisions must be made using incomplete data or artificial intelligence algorithms must be performed through rapid iterations, IDT may generate unreasonable or deviated decisions (Sahin and Diri, 2019). Therefore, supply chain enterprises should pay attention to technology risks when using IDT and strive to improve the robustness of big data algorithms (Metcalf et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Big data and supply chain resilience: role of decision-making technology

Liu

Shi

et al. 2023

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PurposeAs global supply chains continue to develop, uncertainty grows and supply chains are frequently threatened with disruption. Although big data technology is being used to improve supply chain resilience, big data technology's role in human–machine collaboration is shifting between “supporters” and “substitutes.” However, big data technology's applicability in supply chain management is unclear. Choosing appropriate big data technology based on the enterprise's internal and external environments is important.Design/methodology/approachThis study built a three-factor structural model of the factors “management support,” “big data technology adoption” and “supply chain resilience”. Big data technology adoption was divided into big data-assisted decision-making technology (ADT) and big data intelligent decision-making technology (IDT). A survey was conducted on more than 260 employees from supply chain departments in Chinese companies. The data were analyzed through structural equation modeling using Analyze of Moment Structures (AMOS) software.FindingsThe study's empirical results revealed that adopting both ADT and IDT improved supply chain resilience. The effects of both types of big data were significant in low-dynamic environments, but the effect of IDT on supply chain resilience was insignificant under high-dynamic environments. The authors also found that government support had an insignificantly effect on IDT adoption but significantly boosted ADT adoption, whereas management support factors promoted both ADT and IDT adoption.Originality/valueBy introducing two types of big data technology from the perspectives of the roles in human–machine collaborative decision-making, the research results provide a theoretical basis and management implications for enterprises to reduce the supply chain risk of enterprises.

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Section: Discussionmentioning

confidence: 99%

Big data and supply chain resilience: role of decision-making technology

Liu

Shi

et al. 2023

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“…On the other hand, Şahín and Diri [43] showed that accuracy of feature selection can be further improved by applying regularization and variable selection using Elastic Net as verified by Zou and Hastie [25] for the DKT model, with LSTM [44], [45] then showing strong performance in time series data.…”

Section: Knowledge Tracingmentioning

confidence: 95%

Optimal Knowledge Component Extracting Model for Knowledge-Concept Graph Completion in Education

Choi¹,

Lee

2023

IEEE Access

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As people have become accustomed to non-face-to-face education because of the COVID-19 pandemic, adaptive and personalized learning is being emphasized in the field of education. Learning paths suitable for each student may differ from those normally provided by teachers. To support coaching based on the concept of adaptive learning, the first step is to discover the relationships among the concepts in the curriculum provided in the form of a knowledge graph. In this study, feature reduction for the target knowledge-concept was first performed using Elastic Net and Random Forest algorithms, which are known to have the best performance in machine learning. Deep knowledge tracing (DKT) in the form of a dual-net, which is more efficient because of the already slimmer data, was then applied to increase the accuracy of feature selection. The new approach, termed the optimal knowledge component extracting (OKCE) model, was proven to be superior to a feature reduction approach using only Elastic Net and Random Forest using both open and commercial datasets. Finally, the OKCE model showed a meaningful knowledge-concept graph that could help teachers in adaptive and personalized learning. INDEX TERMSDeep learning based knowledge tracing (DKT), dual-net, elastic net, feature selection, knowledge component (KC), least absolute shrinkage and selection operator (LASSO), random forest (RF).

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“…This approach leverages deep learning to analyze functional spectra, quantifying the activities of biological pathways for precise and effective cancer subtype classification. D. Q. Zeebaree, H. Haron, and A. M. Abdulazeez [35] Proposed a new approach that revolves around modeling enduring unit cells through Long Short-Term Memory (LSTM). LSTM, a subtype of Recurrent Neural Network (RNN) within the realm of Artificial Neural Networks (ANN), is a suitable framework.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Cancer Microarray Data Based on Deep Learning: A Review

Fadhil,

Abdulazeez

2024

ijcs

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This review article delves into applying deep learning methodologies in conjunction with microarray data for cancer classification. The study provides a comprehensive overview of recent advancements in utilizing deep learning techniques to accurately categorize cancer types based on intricate patterns discerned from microarray datasets. Various aspects are covered, including integrating deep learning algorithms, exploring diverse cancer types, and analyzing microarray data to enhance classification accuracy. The review synthesizes findings from recent research, highlighting the efficacy of deep learning in uncovering subtle and complex relationships within microarray data that contribute to improved classification outcomes. Key insights into the strengths and limitations of employing deep learning in this context are discussed, offering a critical appraisal of the field's current state. This review aims to provide a valuable resource for researchers, clinicians, and practitioners interested in cutting-edge developments in cancer classification methodologies by exploring the intersection of deep learning and microarray technology. The synthesis of knowledge presented herein contributes to a deeper understanding of the potential and challenges associated with harnessing deep learning for enhanced classification accuracy in the realm of cancer research.

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Robust Feature Selection With LSTM Recurrent Neural Networks for Artificial Immune Recognition System

Cited by 15 publications

References 14 publications

Big data and supply chain resilience: role of decision-making technology

Big data and supply chain resilience: role of decision-making technology

Optimal Knowledge Component Extracting Model for Knowledge-Concept Graph Completion in Education

Classification of Cancer Microarray Data Based on Deep Learning: A Review

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