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Found 12 papers, 1 papers with code
A Data Mining-Based Dynamical Anomaly Detection Method for Integrating with an Advance Metering System
The system introduces a supervised Light Gradient Boosting machine and an unsupervised autoencoder with a dynamic threshold.
A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold
The majority of modern consumer-level energy is generated by real-time smart metering systems.
Adaptive Differential Evolution with Diversification: Addressing Optimization Challenges
The existing variants of the Differential Evolution (DE) algorithm come with certain limitations, such as poor local search and susceptibility to premature convergence.
Integration of Fractional Order Black-Scholes Merton with Neural Network
the key contribution of this work lies in the development of a novel option pricing model (FOBSM) that leverages fractional calculus and neural networks to enhance accuracy in capturing complex diffusion dynamics and memory effects in financial data.
Advancements in Optimization: Adaptive Differential Evolution with Diversification Strategy
This study presents a population-based evolutionary optimization algorithm (Adaptive Differential Evolution with Diversification Strategies or ADEDS).
Impact of Economic Uncertainty, Geopolitical Risk, Pandemic, Financial & Macroeconomic Factors on Crude Oil Returns -- An Empirical Investigation
This study aims to use simultaneous quantile regression (SQR) to examine the impact of macroeconomic and financial uncertainty including global pandemic, geopolitical risk on the futures returns of crude oil (ROC).
Backorder Prediction in Inventory Management: Classification Techniques and Cost Considerations
This article introduces an advanced analytical approach for predicting backorders in inventory management.
Sampling - Variational Auto Encoder - Ensemble: In the Quest of Explainable Artificial Intelligence
It is a hybrid architecture where VAE combined with ensemble stacking and SHapley Additive exPlanations are used for imbalanced classification.
Time-Series Forecasting: Unleashing Long-Term Dependencies with Fractionally Differenced Data
This study introduces a novel forecasting strategy that leverages the power of fractional differencing (FD) to capture both short- and long-term dependencies in time series data.
Ensemble Differential Evolution with Simulation-Based Hybridization and Self-Adaptation for Inventory Management Under Uncertainty
This study proposes an Ensemble Differential Evolution with Simula-tion-Based Hybridization and Self-Adaptation (EDESH-SA) approach for inven-tory management (IM) under uncertainty.
Multiple Independent DE Optimizations to Tackle Uncertainty and Variability in Demand in Inventory Management
To determine the effectiveness of metaheuristic Differential Evolution optimization strategy for inventory management (IM) in the context of stochastic demand, this empirical study undertakes a thorough investigation.
Econometric Model Using Arbitrage Pricing Theory and Quantile Regression to Estimate the Risk Factors Driving Crude Oil Returns
The study highlights the impact of structural discontinuities on returns, which can be caused by changes in the global economy and the demand for crude oil. The inclusion of pandemic, geopolitical, and inflation-related explanatory variables add uniqueness to this study as it considers current global events that can affect crude oil returns. Findings show that the key factors that pose major risks to returns are industrial production, inflation, the global price of energy, the shape of the yield curve, and global economic policy uncertainty. This implies that while making investing decisions in WTI futures, investors should pay particular attention to these elements
