Factory machinery is prone to failure or breakdown, resulting in significant expenses for companies. Hence, there is a rising interest in machine monitoring using different sensors including microphones. In the scientific community, the emergence of public datasets has led to advancements in acoustic detection and clas…
CTS machines improve screen development in printing industries, reducing costs and increasing profitability.
problem Inefficient screen development process in heat transfer printing and labeling.
method Implementation of CTS (computer-to-screen) technology for screen development.
result Reduction in material and operational costs, leading to profitability.
Quantum computing offers financial industry new optimization and risk management tools.
problem Traditional computing limits financial industry's problem-solving capabilities.
method Structured review of quantum computing platforms, algorithms, and use cases.
result Quantum computing can enhance financial industry applications like optimization and risk management.
We give an explicit algorithm and source code for constructing risk models based on machine learning techniques. The resultant covariance matrices are not factor models. Based on empirical backtests, we compare the performance of these machine learning risk models to other constructions, including statistical risk mode…
Fault detection in industrial plants is a hot research area as more and more sensor data are being collected throughout the industrial process. Automatic data-driven approaches are widely needed and seen as a promising area of investment. This paper proposes an effective machine learning algorithm to predict industrial…
New dataset for industrial machine malfunction detection with domain shifts.
problem Challenges in detecting anomalies due to domain shifts in industrial sounds.
method Created a dataset with domain shifts for five types of industrial machines.
result Significant performance differences between source and target domains.
A new process model for machine learning applications with quality assurance.
problem Lack of standard process model for machine learning applications.
method Six-phase process model with quality assurance methodology.
result Proposes a new process model for machine learning applications.
Based on interviews with 28 organizations, we found that industry practitioners are not equipped with tactical and strategic tools to protect, detect and respond to attacks on their Machine Learning (ML) systems. We leverage the insights from the interviews and we enumerate the gaps in perspective in securing machine l…
The study designs inherently interpretable machine learning models for high-risk sectors.
problem The need for transparent and explainable machine learning models in regulated industries.
method Qualitative template based on feature effects and model architecture constraints for assessing inherent interpretability.
result Demonstrates the design and evaluation of an interpretable ReLU DNN model for predicting credit default.
Survey on biases in image analysis for industrial safety.
problem Bias in machine learning algorithms affects industrial safety-critical applications.
method Survey and analysis of recent advances in bias detection and mitigation.
result Need for new methods to detect and mitigate biases in image analysis for safety-critical applications.
TSML tackles anomaly detection and pattern discovery in industrial time series data.
problem Extracting and exploiting information from large industrial data to reduce downtimes and manufacturing errors.
method TSML uses a pipeline of lightweight filters to process industrial time series data in parallel.
result TSML effectively detects anomalies and discovers patterns in industrial time series data.
This paper explores how machine learning can improve life insurance risk assessment.
problem Limited use of machine learning in life insurance due to statistical models' efficiency.
method Review and extension of traditional actuarial methodologies with machine learning techniques.
result Developed Python library for life insurance data, improving risk modeling.
SAFE automates feature engineering for industrial tasks efficiently and scalably.
problem Efficiency and scalability of automatic feature engineering methods for industrial tasks.
method SAFE (Scalable Automatic Feature Engineering) method, which provides excellent efficiency and scalability.
result SAFE method provides prominent efficiency and competitive effectiveness in industrial tasks.
New method uses tensor decomposition to improve noise reduction in machine fault detection.
problem Noise in acoustic signals hinders fault detection in industrial machines.
method Non-negative Canonical Polyadic (CP) decomposition for denoising spectral data.
result Improvement in unsupervised anomaly detection for machine fault detection.
In this work we study an application of machine learning to the construction industry and we use classical and modern machine learning methods to categorize images of building designs into three classes: Apartment building, Industrial building or Other. No real images are used, but only images extracted from Building I…
Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems using machine-learning and deep-learning models, such as automated-driving vehicles. Quality assurance frameworks are required for such machine learning systems, but there are no widely accepted and established quality-assurance …
Study uses ML and statistical models to analyze climate impacts of industrial growth.
problem Understanding and predicting environmental impacts of industrial activities.
method Comparative analysis of ML and statistical models on time series data.
result ML models outperform statistical models in predicting environmental impacts.
Paper predicts bearing degradation stages for pharmaceutical industry maintenance.
problem Predicting when to maintain specific parts of production machines.
method AutoEncoder-based k-means segmentation of high-frequency vibration data.
result Framework generates reliable predictions for bearing degradation stages.
The Industrial Internet of Things drastically increases connectivity of devices in industrial applications. In addition to the benefits in efficiency, scalability and ease of use, this creates novel attack surfaces. Historically, industrial networks and protocols do not contain means of security, such as authentication…
Develops MIS, a probabilistic model for multi-industry classification.
problem GICS's limitation of assigning each firm to exactly one industry, especially for diversified firms.
method Topic modeling to probabilistically assign firms to multiple industries based on business descriptions.
result Demonstrates MIS's ability to flexibly assign firms to multiple industries with relevance probabilities.
A method to select validation data from a dataset using statistical criteria.
problem Selecting a validation basis from a full dataset for machine learning model validation.
method Adopting a 'design of experiments' point of view and using statistical criteria, particularly Maximum Mean Discrepancy criteria.
result The 'support points' concept is particularly relevant for selecting validation data.
Inspector Gadget uses crowdsourcing and data programming to label industrial images efficiently.
problem Securing enough labeled data for machine learning in industrial settings.
method Combines crowdsourcing, data augmentation, and data programming.
result Obtains better performance than other weak-labeling techniques.
PIML enhances machine learning for subsurface energy systems.
problem Lack of interpretability and domain-specific knowledge in machine learning models.
method Integrates physics principles into data-driven models using deep learning.
result PIML improves model generalization and adherence to physical laws.
LLMs outperform strong tabular baselines on industrial car retrofit prediction.
problem Industrial retrofit planning on structured operational data
method Embedding features, direct prompted classification, and ML+LLM stacking
result LLMs outperform strong tabular baselines on industrial car retrofit prediction
The overall equipment effectiveness (OEE) is a performance measurement metric widely used. Its calculation provides to the managers the possibility to identify the main losses that reduce the machine effectiveness and then take the necessary decisions in order to improve the situation. However, this calculation is done…
Enhances insurance loss models using InsurTech data and machine learning.
problem Traditional insurance loss models lack predictive accuracy due to limited data sources.
method Combining proprietary claims data with InsurTech data and applying machine learning techniques.
result Improved predictive accuracy of the loss model through machine learning.
Quickly adapts fault diagnosis models for industrial machines.
problem Fault diagnostic models trained for lab machines fail on industrial ones.
method Net2Net transformation followed by fine-tuning.
result Models can be quickly adapted for new operating conditions.
Industrial control systems are critical to the operation of industrial facilities, especially for critical infrastructures, such as refineries, power grids, and transportation systems. Similar to other information systems, a significant threat to industrial control systems is the attack from cyberspace---the offensive …
There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years. ML engineers, as a consequence, are in high demand across the industry, yet improving the efficiency of ML engineers remains a fundamental challenge. Automated machine learning (AutoML) has emerged as a …
Machine learning-based IDSs in ICS are vulnerable to adversarial attacks that can bypass them.
problem Adversarial attacks on machine learning-based IDSs in ICS can lead to undetected cyber attacks.
method Used Jacobian-based Saliency Map attack to generate adversarial samples and explored adversarial training to improve model robustness.
result Classification performance of supervised models decreased by 16-20 percentage points with adversarial samples, but improved with adversarial training.
Novel financial time-series data representation improves industry sector classification.
problem Classifying industries using historical stock returns time-series data.
method Proposed a novel representation based on stock returns embeddings for time-series data, overcoming representational challenges of conventional approaches.
result Substantial performance improvements over baselines using conventional representations.
MTS-CycleGAN adapts multivariate time series data for ironmaking industry.
problem Creating a domain invariant dataset from multivariate time series data of different blast furnaces.
method Adversarial-based deep mapping learning network (CycleGAN) with LSTM-based AutoEncoder and discriminator.
result MTS-CycleGAN successfully translates multivariate time series data between different blast furnaces.
New AI method improves anomaly detection across different IIoT sensors.
problem Poor performance of anomaly detection models when applied to different machines.
method Robust AI method using pre-processing and multiple models on different pumps.
result Models perform well across different environments and types of pumps.
Time-varying neural network improves stock return prediction.
problem Predicting stock returns in a time-varying market.
method Online early stopping algorithm for neural network training.
result The proposed algorithm outperforms current methods in predicting monthly U.S. stock returns.
The study improves CLV predictions in retail banking with machine learning.
problem Estimating customer lifetime value in retail banking is challenging.
method Developed a novel framework for CLV predictions over arbitrary time horizons.
result 43% improvement in out-of-time CLV prediction error.
Study tackles imbalanced data in car insurance claims prediction.
problem Predicting rare events (claims) in car insurance with imbalanced data.
method Various machine learning techniques (logistic-regression, decision tree, random forest, xgBoost, feed-forward network) applied to imbalanced dataset.
result Comparison of machine learning algorithms' performance in claim occurrence prediction.
Framework detects anomalies in industrial processes using deep learning.
problem Detect anomalies in complex industrial processes.
method Causal-based framework with unsupervised deep learning.
result Successfully validated abstract contexts of blast furnace assets.
Improved machine learning model performance through data augmentation, custom loss functions, and transfer learning.
problem Poor performance of a traditional engineering model due to limited training data.
method Data augmentation, custom loss functions, transfer learning.
result Improvement of at least 38% in performance across five models.
Recent progress in the field of artificial intelligence, machine learning and also in computer industry resulted in the ongoing boom of using these techniques as applied to solving complex tasks in both science and industry. Same is, of course, true for the financial industry and mathematical finance. In this paper we …
Paper uses ML to predict SME defaults with interpretability.
problem Lack of interpretability in ML models for SME default prediction.
method Model-agnostic approach using Accumulated Local Effects and Shapley values.
result eXtreme Gradient Boosting algorithm provides highest classification power with interpretability.
Survey of deep causal models for industrial applications.
problem Estimating causal effects using deep learning.
method Deep causal models map covariates to a representation space and use objective functions for unbiased counterfactual data estimation.
result Comprehensive overview of deep causal models with industry applications.
The financial services industry has unique explainability and fairness challenges arising from compliance and ethical considerations in credit decisioning. These challenges complicate the use of model machine learning and artificial intelligence methods in business decision processes.
The insurance industry uses predictions based on customer characteristics, but this can lead to discrimination. We propose using Wasserstein barycenters to mitigate biases.
problem Discrimination in insurance predictions based on sensitive features like gender or race.
method Propose using Wasserstein barycenters instead of simple scaling to mitigate biases in insurance predictions.
result Demonstrates the effectiveness of Wasserstein barycenters in mitigating biases in insurance predictions.
DeepVARMA predicts chemical industry index trends using LSTM and VARMAX models.
problem Forecasting the chemical industry index for economic analysis.
method Combines LSTM and VARMAX models to predict nonstationary series.
result DeepVARMA achieves best prediction accuracy and adaptability.
Expert system predicts credit card charge-offs using macroeconomic indicators.
problem Managing charge-off rates in the credit card industry.
method Developed an expert system using machine learning and macroeconomic indicators.
result Achieved mean squared error values of 1.15E-03 and 1.04E-03.
Quantum computing promises to revolutionize finance, especially in optimization and modeling.
problem Financial inefficiencies and inaccuracies in current computing methods.
method Survey of quantum computing applications in finance, focusing on stochastic modeling, optimization, and machine learning.
result Quantum computing can solve financial problems more efficiently and accurately.
This paper summarizes AI methods for detecting credit card fraud.
problem Detecting credit card fraud from millions of transactions.
method Rule-based and AI approaches, addressing imbalanced datasets, real-time scenarios, and feature engineering.
result Summarizes state-of-the-art AI methods for fraud detection.
Exponentially smoothed RNNs improve industrial forecasting.
problem Complexity and non-stationarity in industrial time series data.
method Exponential smoothed recurrent neural networks (RNNs) for modeling non-linear dynamics.
result Exponentially smoothed RNNs outperform traditional models in multi-step forecasting.