How Best to Hunt a Mammoth - Toward Automated Knowledge Extraction From Graphical Research Models

2023 | International Conference on Business Informatics | Citations: 0

Authors: Huettemann, Sebastian; Mueller, Roland M.; Larsen, Kai R.; Dinter, Barbara; Chiny, Joshua Campos

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Topics: knowledge repository database system knowledge base application programming interface Python

Methods: design science metamodel computational algorithm machine learning literature study

Visualizing Convolutional Neural Network Models’ Sensitivity to Nonnatural Data Order

2023 | Information Systems Frontiers | Citations: 0

Authors: Klepetko, Randy; Krishnan, Ram

Abstract: Convolutional neural networks (CNN) have revolutionized image recognition techno ... Expand

Abstract: Convolutional neural networks (CNN) have revolutionized image recognition technology and found applications in various nonimage-related fields. For nonnatural data, such as cybersecurity packets, in which the data sample order is not defined by nature, some models trained on certain orderings of data perform better than when trained with other orderings. Some orderings create patterns from which the CNN extracts better features. Understanding how to best order the training data to improve CNN performance is not well-studied. In this study, we investigate this problem by examining malware infections using different CNN models and include visualization tools to enhance our analysis. We define a functional algorithm for ordering and show that order importance in CNNs is model dependent. In addition, we show that depending on the model, statistical relationships are crucial in establishing order with better performance. Collapse

Topics: malware mobile system cloud computing infrastructure as a service

Methods: experiment descriptive statistic machine learning computational algorithm Keras

Can NeuroIS improve executive employee recruitment? Classifying levels of executive functions using resting state EEG and data science methods

2023 | Decision Support Systems | Citations: 0

Authors: Zazon, Dor; Fink, Lior; Gordon, Shirley; Nissim, Nir

Abstract: Based on the assumption that the success of an organization is largely determine ... Expand

Abstract: Based on the assumption that the success of an organization is largely determined by the knowledge and skills of its employees, human resource (HR) departments invest considerable resources in the employee recruitment process with the aim of selecting the best, most suitable employees. Due to the high cost of the recruitment process along with its high rate of uncertainty, HR recruiters utilize a variety of methods and instruments to improve the efficiency and effectiveness of this process. Thus far, however, neurological methods, in which neurobiological signals from an examined person are analyzed, have not been utilized for this purpose. This study is the first to propose a neuro-based decision support system to classify cognitive functions into levels, whose target is to enrich the information and indications regarding the candidate along the employee recruitment processes. We first measured relevant functional and cognitive abilities of 142 adult participants using traditional computer-based assessment, which included a battery of four tests regarding executive functions and intelligence score, consistent with actual recruitment processes. Second, using electroencephalogram (EEG) technology, which is one of the dominant measurement tools in NeuroIS research, we collected the participants' brain signals by administering a resting state EEG (rsEEG) on each participant. Finally, using advanced machine and deep learning algorithms, we leveraged the collected rsEEG to classify participants' levels of executive functions and intelligence score. Our empirical analyses show encouraging results of up to 72.6% accuracy for the executive functions and up to 71.2% accuracy for the intelligence score. Therefore, this study lays the groundwork for a novel, generic (non-stimuli based) system that supports the current employee recruitment processes, that is based on psychological theories of assessing executive functions. The proposed decision support system could contribute to the development of additional medium of assessing employees remotely which is especially rele­ vant in the current Covid-19 pandemic. While our method aims at classification rather than at explanation, our intriguing findings have the potential to push forward NeuroIS research and practice. Collapse

Topics: computer based testing privacy organizational context personality task productivity

Methods: electroencephalography computational algorithm machine learning NeuroIS deep learning

Theories: psychological theory

Identification of Travel Styles by Learning from Consumer-generated Images in Online Travel Communities

2022 | Information & Management | Citations: 0

Authors: Brusch, Ines

Abstract: A picture is worth a thousand words”: Never has this adage been more meaningful ... Expand

Abstract: A picture is worth a thousand words”: Never has this adage been more meaningful than it is today. Online social media is driving the growth of unstructured image data. Unstructured data must be structured to be informative and thereby contribute to user understanding and revenue generation. Hitherto, companies have only been able to accomplish this through tedious manual work. This paper demonstrates how image data can be analyzed automatically using a combination of image analysis methods and fuzzy cluster algorithms to predict user preferences, which companies can then use to make targeted offers. Several methods, including support vector machines (SVMs) and convolutional neural networks (CNNs), are benchmarked across various cases of image data taken from an online travel community. Depending on the images’ diversity either a SVM or a CNN provides the best basis for preference prediction. Collapse

Topics: database system marketing management internet technology website analytical information system

Methods: cluster analysis support vector machine logistic regression computational algorithm convolutional neural network

Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification

2020 | Information Systems Frontiers | Citations: 6

Authors: Tian, Haiman; Chen, Shu-Ching; Shyu, Mei-Ling

Abstract: Convolutional Neural Network (CNN) models and many accessible large-scale public ... Expand

Abstract: Convolutional Neural Network (CNN) models and many accessible large-scale public visual datasets have brought lots of research work to a remarkable new stage. Benefited from well-trained CNN models, small training datasets can learn comprehensive features by utilizing the preliminary features from transfer learning. However, the performance is not guaranteed when taking these features to construct a new model, as the differences always exist between the source and target domains. In this paper, we propose to build an Evolution Programming-based framework to address various challenges. This framework automates both the feature learning and model building processes. It first identifies the most valuable features from pre-trained models and then constructs a suitable model to understand the characteristic features for different tasks. Each model differs in numerous ways. Overall, the experimental results effectively reach optimal solutions, demonstrating that a time-consuming task could also be conducted by an automated process that exceeds the human ability. Collapse

Topics: hurricane

Methods: machine learning computational algorithm artificial neural network deep learning support vector machine