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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

Abstract: ... Expand

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Semantic filters: ImageNet false negative rate

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

Methods: design science metamodel computational algorithm machine learning literature study
Utilizing the omnipresent: Incorporating digital documents into predictive process monitoring using deep neural networks
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2023 | Decision Support Systems | Citations: 0
Authors: Levich, Sergej; Lutz, Bernhard; Neumann, Dirk

Abstract: Predictive process monitoring (PPM) allows companies to improve the efficiency o ... Expand

Abstract: Predictive process monitoring (PPM) allows companies to improve the efficiency of their business processes by predicting aspects such as the process outcome, the next event, or the time until the next event. So far, existing studies have mainly focused on developing novel predictive models while using features solely from event logs. In this study, we aim to go beyond log data and increase the focus of PPM research towards external context information. To this end, we consider digital documents as they are omnipresent in many business processes and their inclusion can often be justified by a business rationale. However, incorporating digital documents into PPM models poses considerable challenges as they present unstructured data that can contain visual and textual cues of future process behavior, while manual feature extraction is generally not feasible. Therefore, we propose an approach that processes digital documents based on automated visual and textual feature extraction methods. Furthermore, we design a tailored integration module which transforms the extracted features from multiple document pages into a fixed-size representation that subsequently serves as input for the predictive models. Our evaluation, based on a real-world dataset of insurance claims from a mid-sized German insurance company, featuring 5131 process instances with 32,058 events and 39,242 document pages, shows that incorporating digital documents improves the performance by significant margins in predicting the damage type, the next event, and the time until the next event. Finally, we analyze how digital documents contribute to the model’s predictions in terms of Shapley additive explanations. Collapse

Semantic filters: ImageNet false negative rate

Topics: shapley additive explanation business process management artificial intelligence explainable artificial intelligence

Methods: BERT machine learning descriptive statistic artificial neural network business process modeling
Classification of COVID-19 Cases: The Customized Deep Convolutional Neural Network and Transfer Learning Approach

2022 | Americas Conference on Information Systems | Citations: 0
Authors: Vyas, Piyush; Ragothaman, Kaushik Nagarajan Muthusamy; Chauhan, Akhilesh; Rimal, Bhaskar P.

Abstract: The recent advancements under the umbrella of artificial intelligence (AI) open ... Expand

Abstract: The recent advancements under the umbrella of artificial intelligence (AI) open opportunities to tackle complex problems related to image analysis. Recently, the proliferation of COVID-19 brought multiple challenges to medical practitioners, such as precise analysis and classification of COVID-19 cases. Deep learning (DL) and transfer learning (TL) techniques appear to be attractive solutions. To provide the precise classification of COVID-19 cases, this article presents a customized Deep Convolutional Neural Network (DCNN) and pre-trained TL model approach. Our pipeline accommodated several popular pre-trained TL models, namely DenseNet121, ResNet50, InceptionV3, EfficientNetB0, and VGG16, to classify COVID-19 positive and negative cases. We evaluated and compared the performance of these models with a wide range of measures, including accuracy, precision, recall, and F1 score for classifying COVID-19 cases based on chest X-rays. The results demonstrate that our customized DCNN model performed well with randomly assigned weights, achieving 98.5% recall and 97.0% accuracy. KeywordsCovid19, transfer learning, deep convolutional neural network, medical imaging. Collapse

Semantic filters: ImageNet false negative rate

Topics: Google Cloud Platform Google+ covid 19

Methods: machine learning ImageNet deep learning convolutional neural network literature study
Is AI Ground Truth Really True? The Dangers of Training and Evaluating AI Tools Based on Experts’ Know-What

2021 | Management Information Systems Quarterly | Citations: 0
Authors: Lebovitz, Sarah; Levina, Natalia; Lifshitz-Assaf, Hila

Abstract: Organizational decision-makers need to evaluate AI tools in light of increasing ... Expand

Abstract: Organizational decision-makers need to evaluate AI tools in light of increasing claims that such tools outperform human experts. Yet, measuring the quality of knowledge work is challenging, raising the question of how to evaluate AI performance in such contexts. We investigate this question through a field study of a major U.S. hospital, observing how managers evaluated five different machine-learning (ML) based AI tools. Each tool reported high performance according to standard AI accuracy measures, which were based on ground truth labels provided by qualified experts. Trying these tools out in practice, however, revealed that none of them met expectations. Searching for explanations, managers began confronting the high uncertainty of experts' know-what knowledge captured in ground truth labels used to train and validate ML models. In practice, experts address this uncertainty by drawing on rich know-how practices, which were not incorporated into these ML-based tools. Discovering the disconnect between AI's know-what and experts' know-how enabled managers to better understand the risks and benefits of each tool. This study shows dangers of treating ground truth labels used in ML models objectively when the underlying knowledge is uncertain. We outline implications of our study for developing, training, and evaluating AI for knowledge work. Collapse

Semantic filters: ImageNet false negative rate

Topics: artificial intelligence knowledge work explainable artificial intelligence analytical information system crowdsourcing

Methods: machine learning qualitative interview theory development field study archival research
An Approach for Weed Detection Using CNNs And Transfer Learning
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2021 | HICSS | Citations: 2
Authors: Ofori, Martinson; El-Gayar, Omar

Abstract: To prevent yield losses, it is critical to eliminate competition between food cr ... Expand

Abstract: To prevent yield losses, it is critical to eliminate competition between food crops and weeds at the onset of plant growth. While uniform spraying of herbicides can be economically and environmentally inefficient, site-specific weed management (SSWM) counteracts this by reducing the amount of chemical application with localized spraying of weed species. Past research on weed detection in SSWM has used a large deep convolutional neural network (DCNN) for weed detection. These models are, however, computationally expensive and prone to overfitting on smaller datasets. In this paper, we propose an approach to detecting weeds amongst plant seedlings using transfer learning in a small network. Our approach combines the mobilesized EfficientNet with transfer learning to achieve up to 95.44% classification accuracy on plant seedlings. Due to the robustness of transfer learning methods, this approach would be beneficial in improving both the classification accuracy and generalizability of current weed detection methods. Collapse

Semantic filters: ImageNet false negative rate

Topics: database system

Methods: experiment ImageNet machine learning supervised learning TensorFlow
Computer Vision System with 2D and 3D Data Fusion for Detection of Possible Auxiliaries Routes in Stretches of Interdicted Roads

2019 | HICSS | Citations: 3
Authors: Bruno, Diego Renan; Matias, Lucas P Nunes; Amaro, Jean; Osorio, Fernando Santos; Wolf, Denis

Abstract: In this paper we present an intelligent system to help autonomous vehicles in re ... Expand

Abstract: In this paper we present an intelligent system to help autonomous vehicles in real cities and with local trafﬁc rules. A 2D and 3D visual attention system is proposed, capable of detecting the use of signs and aids in cases of major roadblock (road under work, with a trafﬁc accident, etc.). For this to be possible, we analyze the cones and trafﬁc signs that usually alert a driver about this type of problem. The main objective is to provide support for autonomous vehicles to be able to ﬁnd an auxiliary route that is not previously mapped. For this we use a Grid Point Cloud Map. Using the ORB-SLAM visual odometry system we can correctly ﬁt each stereo frame point cloud in the pose where the images were collected. With the concatenation of point clouds generated by the stereo camera, every grid block can draw the main characteristics of its region and an auxiliary route can be mapped. In this type of situation the vision system must work in real time. The results are promising and very satisfactory, we obtained an accuracy of 98.4% in the 2D classiﬁcation task and 83% accuracy in the single frame 3D detection task. Collapse

Semantic filters: ImageNet false negative rate

Topics: cloud computing database system Google+ packaged software autonomous driving system

Methods: deep learning descriptive statistic computational algorithm TensorFlow machine learning

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Found 6 articles

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

Utilizing the omnipresent: Incorporating digital documents into predictive process monitoring using deep neural networks

Classification of COVID-19 Cases: The Customized Deep Convolutional Neural Network and Transfer Learning Approach

Is AI Ground Truth Really True? The Dangers of Training and Evaluating AI Tools Based on Experts’ Know-What

An Approach for Weed Detection Using CNNs And Transfer Learning

Computer Vision System with 2D and 3D Data Fusion for Detection of Possible Auxiliaries Routes in Stretches of Interdicted Roads