fNIRS 실험자료 기반 비즈니스 문제해결창의성 (BPSC)를 예측하기 위한 뉴로사이언스 마이닝 적용에 관한 연구: CNN, BiLSTM, 어텐션네트워크 결합을 중심으로
Applying a Novel Neuroscience Mining (NSM) Method to fNIRS Dataset for Predicting the Business Problem Solving Creativity: Emphasis on Combining CNN, BiLSTM, and Attention Network
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초록
인공지능 기술이 발달하면서 뉴로사이언스 마이닝(NSM: NeuroScience Mining)과 AI를 접목하려는 시도가 증가하고 있다. 나아가 NSM은 뉴로사이언스와 비즈니스 애널리틱스의 결합으로 인해 연구범위가 확장되고 있다. 본 연구에서는 fNIRS 실험을 통해 확보한 뉴로 데이터를 분석하여 비즈니스 문제 해결 창의성(BPSC: business problem-solving creativity)을 예측하고 이를 통해 NSM의 잠재력을 조사한다. BPSC는 비즈니스에서 차별성을 가지게 하는 중요한 요소이지만, 인지적 자원의 하나인 BPSC의 측정 및 예측에는한계가 존재한다. 본 논문에서는 BPSC 예측 성능을 높이는 방안으로 CNN, BiLSTM 그리고 어텐션 네트워크를 결합한 새로운 NSM 기법을 제안한다. 제안된 NSM 기법을 15만 개 이상의 fNIRS 데이터를 활용하여 유효성을 입증하였다. 연구 결과, 본 논문에서 제안하는 NSM 방법이 벤치마킹한 알고리즘(CNN, BiLSTM)에 비하여 우수한 성능을 가지는 것으로 나타났다.
Abstract
With the development of artificial intelligence, efforts to incorporate neuroscience mining with AI have increased. Neuroscience mining, also known as NSM, expands on this concept by combining computational neuroscience and business analytics. Using fNIRS (functional near-infrared spectroscopy)-based experiment dataset, we have investigated the potential of NSM in the context of the BPSC (business problem-solving creativity) prediction. Although BPSC is regarded as an essential business differentiator and a difficult cognitive resource to imitate, measuring it is a challenging task. In the context of NSM, appropriate methods for assessing and predicting BPSC are still in their infancy. In this sense, we propose a novel NSM method that systematically combines CNN, BiLSTM, and attention network for the sake of enhancing the BPSC prediction performance significantly. We utilized a dataset containing over 150 thousand fNIRS-measured data points to evaluate the validity of our proposed NSM method. Empirical evidence demonstrates that the proposed NSM method reveals the most robust performance when compared to benchmarking methods.
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