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Found 22 papers, 2 papers with code
Dealing with zero-inflated data: achieving SOTA with a two-fold machine learning approach
This paper showcases two real-world use cases (home appliances classification and airport shuttle demand prediction) where a hierarchical model applied in the context of zero-inflated data leads to excellent results.
Human in the AI loop via xAI and Active Learning for Visual Inspection
Such collaboration can be realized considering two sub-fields of artificial intelligence: active learning and explainable artificial intelligence.
Synthetic Data Augmentation Using GAN For Improved Automated Visual Inspection
In this research, we compare supervised and unsupervised defect detection techniques and explore data augmentation techniques to mitigate the data imbalance in the context of automated visual inspection.
Robust Anomaly Map Assisted Multiple Defect Detection with Supervised Classification Techniques
Industry 4. 0 aims to optimize the manufacturing environment by leveraging new technological advances, such as new sensing capabilities and artificial intelligence.
A Commonsense-Infused Language-Agnostic Learning Framework for Enhancing Prediction of Political Polarity in Multilingual News Headlines
With attended knowledge in our framework, the same model show an increase in 2. 2% accuracy and F1, and 3. 6% jaccard score.
Machine Beats Machine: Machine Learning Models to Defend Against Adversarial Attacks
We propose using a two-layered deployment of machine learning models to prevent adversarial attacks.
Forecasting Sensor Values in Waste-To-Fuel Plants: a Case Study
In this research, we develop machine learning models to predict future sensor readings of a waste-to-fuel plant, which would enable proactive control of the plant's operations.
Active Learning and Novel Model Calibration Measurements for Automated Visual Inspection in Manufacturing
Our results show that the explored active learning settings can reduce the data labeling effort by between three and four percent without detriment to the overall quality goals, considering a threshold of p=0. 95.
Enriching Artificial Intelligence Explanations with Knowledge Fragments
Artificial Intelligence models are increasingly used in manufacturing to inform decision-making.
Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications
Human-centricity is the core value behind the evolution of manufacturing towards Industry 5. 0.
Streaming Machine Learning and Online Active Learning for Automated Visual Inspection
The use of machine learning models for automated visual inspection are expected to speed up the quality inspection up to 40%.
Active Learning for Automated Visual Inspection of Manufactured Products
Quality control is a key activity performed by manufacturing enterprises to ensure products meet quality standards and avoid potential damage to the brand's reputation.
Knowledge Modelling and Active Learning in Manufacturing
The increasing digitalization of the manufacturing domain requires adequate knowledge modeling to capture relevant information.
A Review of Explainable Artificial Intelligence in Manufacturing
The implementation of Artificial Intelligence (AI) systems in the manufacturing domain enables higher production efficiency, outstanding performance, and safer operations, leveraging powerful tools such as deep learning and reinforcement learning techniques.
XAI-KG: knowledge graph to support XAI and decision-making in manufacturing
We propose an ontology and knowledge graph to support collecting feedback regarding forecasts, forecast explanations, recommended decision-making options, and user actions.
STARdom: an architecture for trusted and secure human-centered manufacturing systems
There is a lack of a single architecture specification that addresses the needs of trusted and secure Artificial Intelligence systems with humans in the loop, such as human-centered manufacturing systems at the core of the evolution towards Industry 5. 0.
Semantic XAI for contextualized demand forecasting explanations
We tailor the architecture for the domain of demand forecasting and validate it on a real-world case study.
Towards Active Learning Based Smart Assistant for Manufacturing
The methodology can be extended to several use cases in manufacturing.
Actionable Cognitive Twins for Decision Making in Manufacturing
In this paper, we propose a knowledge graph modeling approach to construct actionable cognitive twins for capturing specific knowledge related to demand forecasting and production planning in a manufacturing plant.
Reframing demand forecasting: a two-fold approach for lumpy and intermittent demand
Our research shows that global classification models are the best choice when predicting demand event occurrence.
Constructing a Natural Language Inference Dataset using Generative Neural Networks
To evaluate the models, we propose a new metric -- the accuracy of the classifier trained on the generated dataset.
Joint learning of ontology and semantic parser from text
Both, the grammar and the semantic trees are used to learn the ontology on several levels -- classes, instances, taxonomic and non-taxonomic relations.
