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Found 9 papers, 0 papers with code
Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey
Data Augmentation (DA) has emerged as an indispensable strategy in Time Series Classification (TSC), primarily due to its capacity to amplify training samples, thereby bolstering model robustness, diversifying datasets, and curtailing overfitting.
Design of the Reverse Logistics System for Medical Waste Recycling Part II: Route Optimization with Case Study under COVID-19 Pandemic
To tackle the emerging challenges, this study designs a reverse logistics system architecture with three modules, i. e., medical waste classification & monitoring module, temporary storage & disposal site (disposal site for short) selection module, as well as route optimization module.
Design of the Reverse Logistics System for Medical Waste Recycling Part I: System Architecture and Disposal Site Selection Algorithm
To tackle the challenges, this study proposes a reverse logistics system architecture with three modules, i. e., medical waste classification & monitoring module, temporary storage & disposal site (disposal site for short) selection module, as well as route optimization module.
evoML Yellow Paper: Evolutionary AI and Optimisation Studio
Additionally, a key feature of evoML is that it embeds code and model optimisation into the model development process, and includes multi-objective optimisation capabilities.
ASTA: Learning Analytical Semantics over Tables for Intelligent Data Analysis and Visualization
ASTA framework extracts data features by designing signatures based on expert knowledge, and enables data referencing at field- (chart) or cell-level (conditional formatting) with pre-trained models.
Better Model Selection with a new Definition of Feature Importance
Feature importance aims at measuring how crucial each input feature is for model prediction.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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IEO: Intelligent Evolutionary Optimisation for Hyperparameter Tuning
In this paper, we introduce an intelligent evolutionary optimisation algorithm which applies machine learning technique to the traditional evolutionary algorithm to accelerate the overall optimisation process of tuning machine learning models in classification problems.
Cryptocurrency Trading: A Comprehensive Survey
This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading (e. g., cryptocurrency trading systems, bubble and extreme conditions, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others).
Ascertaining price formation in cryptocurrency markets with DeepLearning
The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world.
