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A deep learning spatial-temporal framework for detecting surgical tools in laparoscopic videos
(2021)
Despite the unabated growth of algorithmic decision-making in organizations, there is a growing consensus that numerous situations will continue to require humans in the loop. However, the blending of a formal machine and bounded human rationality also amplifies the risk of what is known as local rationality. Therefore, it is crucial, especially in a data-abundant environment that characterizes algorithmic decision-making, to devise means to assess performance holistically. In this paper, we propose a simulation-based model to address the current lack of research on quantifying algorithmic interventions in a broader organizational context. Our approach allows the combining of causal modeling and data science algorithms to represent decision settings involving a mix of machine and human rationality to measure performance. As a testbed, we consider the case of a fictitious company trying to improve its forecasting process with the help of a machine learning approach. The example demonstrates that a myopic assessment obscures problems that only a broader framing reveals. It highlights the value of a systems view since the effects of the interplay between human and algorithmic decisions can be largely unintuitive. Such a simulation-based approach can be an effective tool in efforts to delineate roles for humans and algorithms in hybrid contexts.
A simulation study on the ventilation inhomogeneity measured with Electrical Impedance Tomography
(2017)
Quality assurance (QA) plays a crucial role in manufacturing to ensure that products meet their specifications. However, manual QA processes are costly and time-consuming, thereby making artificial intelligence (AI) an attractive solution for automation and expert support. In particular, convolutional neural networks (CNNs) have gained a lot of interest in visual inspection. Next to AI methods, the explainable artificial intelligence (XAI) systems, which achieve transparency and interpretability by providing insights into the decision-making process of the AI, are interesting methods for achieveing quality inspections in manufacturing processes. In this study, we conducted a systematic literature review (SLR) to explore AI and XAI approaches for visual QA (VQA) in manufacturing. Our objective was to assess the current state of the art and identify research gaps in this context. Our findings revealed that AI-based systems predominantly focused on visual quality control (VQC) for defect detection. Research addressing VQA practices, like process optimization, predictive maintenance, or root cause analysis, are more rare. Least often cited are papers that utilize XAI methods. In conclusion, this survey emphasizes the importance and potential of AI and XAI in VQA across various industries. By integrating XAI, organizations can enhance model transparency, interpretability, and trust in AI systems. Overall, leveraging AI and XAI improves VQA practices and decision-making in industries.
A unified strategy for the synthesis of amorfrutins A and B and evaluation of their cytotoxicity
(2021)
Acid Sphingomyelinase Promotes Endothelial Stress Response in Systemic Inflammation and Sepsis
(2016)
Adaptation of Blood Volume and Cardiac Dimensions to Endurance Training in Paraplegic Athletes
(2008)
Adapting web pages using graph partitioning algorithms for user-centric multi-device web browsing
(2013)
Adding evidence of the effects of treatments into relevant Wikipedia pages: a randomised trial
(2020)
Additives and polymer composition influence the interaction of microplastics with xenobiotics
(2021)
Addressable bipartite molecular hook (ABMH): Immobilized hairpin probes with sensitivity below 50 fM
(2010)
Age-related changes in trunk muscle activity and spinal and lower limb kinematics during gait
(2018)