Salim Lahmiri
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View article: Machine Learning Systems Tuned by Bayesian Optimization to Forecast Electricity Demand and Production
Machine Learning Systems Tuned by Bayesian Optimization to Forecast Electricity Demand and Production Open
Given the critical importance of accurate energy demand and production forecasting in managing power grids and integrating renewable energy sources, this study explores the application of advanced machine learning techniques to forecast el…
View article: A Deep Learning-Based Ensemble System for Brent and WTI Crude Oil Price Analysis and Prediction
A Deep Learning-Based Ensemble System for Brent and WTI Crude Oil Price Analysis and Prediction Open
Crude oil price forecasting is an important task in energy management and storage. In this regard, deep learning has been applied in the literature to generate accurate forecasts. The main purpose of this study is to design an ensemble pre…
View article: Distinguishing Between Healthy and Unhealthy Newborns Based on Acoustic Features and Deep Learning Neural Networks Tuned by Bayesian Optimization and Random Search Algorithm
Distinguishing Between Healthy and Unhealthy Newborns Based on Acoustic Features and Deep Learning Neural Networks Tuned by Bayesian Optimization and Random Search Algorithm Open
Voice analysis and classification for biomedical diagnosis purpose is receiving a growing attention to assist physicians in the decision-making process in clinical milieu. In this study, we develop and test deep feedforward neural networks…
View article: An Effective and Fast Model for Characterization of Cardiac Arrhythmia and Congestive Heart Failure
An Effective and Fast Model for Characterization of Cardiac Arrhythmia and Congestive Heart Failure Open
Background/Objectives: Cardiac arrhythmia (ARR) and congestive heart failure (CHF) are heart diseases that can cause dysfunction of other body organs and possibly death. This paper describes a fast and accurate detection system to distingu…
View article: Wavelet Entropy for Efficiency Assessment of Price, Return, and Volatility of Brent and WTI During Extreme Events
Wavelet Entropy for Efficiency Assessment of Price, Return, and Volatility of Brent and WTI During Extreme Events Open
This study analyzes the market efficiency of crude oil markets, namely Brent and West Texas Intermediate (WTI), during three different periods: pre-COVID-19, during the COVID-19 pandemic, and during the ongoing Russia–Ukraine military conf…
View article: Analysis of Self-Similarity in Short and Long Movements of Crude Oil Prices by Combination of Stationary Wavelet Transform and Range-Scale Analysis: Effects of the COVID-19 Pandemic and Russia-Ukraine War
Analysis of Self-Similarity in Short and Long Movements of Crude Oil Prices by Combination of Stationary Wavelet Transform and Range-Scale Analysis: Effects of the COVID-19 Pandemic and Russia-Ukraine War Open
This paper examines the self-similarity (long memory) in prices of crude oil markets, namely Brent and West Texas Instruments (WTI), by means of fractals. Specifically, price series are decomposed by stationary wavelet transform (SWT) to o…
View article: Evaluating Predictive Models for Three Green Finance Markets: Insights from Statistical vs. Machine Learning Approaches
Evaluating Predictive Models for Three Green Finance Markets: Insights from Statistical vs. Machine Learning Approaches Open
As climate change has become of eminent importance in the last two decades, so has interest in industry-wide carbon emissions and policies promoting a low-carbon economy. Investors and policymakers could improve their decision-making by pr…
View article: Causality Between Brent and West Texas Intermediate: The Effects of COVID-19 Pandemic and Russia–Ukraine War
Causality Between Brent and West Texas Intermediate: The Effects of COVID-19 Pandemic and Russia–Ukraine War Open
The article analyzes the Granger-based causal relationship between two major crude oil markets, namely Brent and West Texas Intermediate (WTI), by using the standard vector autoregression (VAR) framework. In this regard, the effects of the…
View article: Price disorder and information content in energy and gold markets: The effect of the COVID-19 pandemic
Price disorder and information content in energy and gold markets: The effect of the COVID-19 pandemic Open
In this paper, we examine market efficiency in fossil energy and gold markets. Specifically, we study price disorder and information content in various energy markets and in gold market before and during the COVID-19 pandemic. The set of e…
View article: The nexus between fossil energy markets and the effect of the COVID-19 pandemic on clustering structures
The nexus between fossil energy markets and the effect of the COVID-19 pandemic on clustering structures Open
The main purpose of this paper is to analyze price returns series to investigate causality between international fossil energy markets and the effect of the COVID-19 pandemic on their clustering structures. The sample period covers August …
View article: Wind Turbine Blade Fault Diagnosis: Approximate Entropy as a Tool to Detect Erosion and Mass Imbalance
Wind Turbine Blade Fault Diagnosis: Approximate Entropy as a Tool to Detect Erosion and Mass Imbalance Open
Wind energy is a clean, sustainable, and renewable source. It is receiving a large amount of attention from governments and energy companies worldwide as it plays a significant role as an alternative source of energy in reducing carbon emi…
View article: Deep learning systems for forecasting the prices of crude oil and precious metals
Deep learning systems for forecasting the prices of crude oil and precious metals Open
Commodity markets, such as crude oil and precious metals, play a strategic role in the economic development of nations, with crude oil prices influencing geopolitical relations and the global economy. Moreover, gold and silver are argued t…
View article: Connectedness of cryptocurrency markets to crude oil and gold: an analysis of the effect of COVID-19 pandemic
Connectedness of cryptocurrency markets to crude oil and gold: an analysis of the effect of COVID-19 pandemic Open
The notion that investors shift to gold during economic market crises remains unverified for many cryptocurrency markets. This paper investigates the connectedness between the 10 most traded cryptocurrencies and gold as well as crude oil m…
View article: Deep learning-based spatial-temporal graph neural networks for price movement classification in crude oil and precious metal markets
Deep learning-based spatial-temporal graph neural networks for price movement classification in crude oil and precious metal markets Open
In this study, we adapt three spatial-temporal graph neural network models to the unique characteristics of crude oil, gold, and silver markets for forecasting purposes. It aims to be the first to (i) explore the potential of spatial-tempo…
View article: Assessing efficiency in prices and trading volumes of cryptocurrencies before and during the COVID-19 pandemic with fractal, chaos, and randomness: evidence from a large dataset
Assessing efficiency in prices and trading volumes of cryptocurrencies before and during the COVID-19 pandemic with fractal, chaos, and randomness: evidence from a large dataset Open
This study examines the market efficiency in the prices and volumes of transactions of 41 cryptocurrencies. Specifically, the correlation dimension (CD), Lyapunov Exponent (LE), and approximate entropy (AE) were estimated before and during…
View article: Bankruptcy prediction using optimal ensemble models under balanced and imbalanced data
Bankruptcy prediction using optimal ensemble models under balanced and imbalanced data Open
This study explores the performance of gradient boosting methods in bankruptcy prediction for a highly imbalanced dataset. We developed different heterogenous ensemble models based on three popular gradient boosting methods—XGBoost, LightG…
View article: Investigating the effectiveness of Twitter sentiment in cryptocurrency close price prediction by using deep learning
Investigating the effectiveness of Twitter sentiment in cryptocurrency close price prediction by using deep learning Open
In recent years, cryptocurrencies' price prediction has attracted the interest of many people including investors, researchers and practitioners. In this study, we proposed a hybrid model for predicting the daily close price of cryptocurre…
View article: Multifractals and multiscale entropy patterns in energy markets under the effect of the COVID-19 pandemic
Multifractals and multiscale entropy patterns in energy markets under the effect of the COVID-19 pandemic Open
This study uses the wavelet leaders method to examine multifractal characteristics and multiscale entropy patterns in price returns of four energy markets, Brent, West Texas Intermediate (WTI), gasoline, and heating oil, before and during …
View article: A wavelet leaders model with multiscale entropy measures for diagnosing arrhythmia and congestive heart failure
A wavelet leaders model with multiscale entropy measures for diagnosing arrhythmia and congestive heart failure Open
This study proposes a wavelet leaders method with multiscale entropy measures to analyze multiscale complexities in electrocardiogram (ECG) signals to characterize arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (…
View article: Hybrid deep learning and GARCH-family models for forecasting volatility of cryptocurrencies
Hybrid deep learning and GARCH-family models for forecasting volatility of cryptocurrencies Open
The combination of Deep Learning and GARCH-type models has been proved to be superior to the single models in forecasting of volatility in various markets such as energy, main metals, and especially stock markets. To verify this hypothesis…
View article: A comparative study of statistical machine learning methods for condition monitoring of electric drive trains in supply chains
A comparative study of statistical machine learning methods for condition monitoring of electric drive trains in supply chains Open
Fault detection and identification are critical for the accurate maintenance and management of industrial machinery. In this regard, data-driven condition monitoring models play an important role in machinery fault diagnosis and management…
View article: A nonlinear analysis of cardiovascular diseases using multi-scale analysis and generalized hurst exponent
A nonlinear analysis of cardiovascular diseases using multi-scale analysis and generalized hurst exponent Open
Congestive heart failure (CHF) and arrhythmia (ARR) are common heart diseases that affect a growing population of patients worldwide. In this work, we employ multi-scale analysis (MSA) to estimate generalized Hurst exponent (GHE) from elec…
View article: A comparative assessment of machine learning methods for predicting housing prices using Bayesian optimization
A comparative assessment of machine learning methods for predicting housing prices using Bayesian optimization Open
The valuation of house prices is drawing noteworthy attention due to worldwide financial and real estate crises in the last decade. Therefore, there is an immediate need to design more effective predictive systems of house prices. Indeed, …
View article: Nonlinear Statistical Analysis of Normal and Pathological Infant Cry Signals in Cepstrum Domain by Multifractal Wavelet Leaders
Nonlinear Statistical Analysis of Normal and Pathological Infant Cry Signals in Cepstrum Domain by Multifractal Wavelet Leaders Open
Multifractal behavior in the cepstrum representation of healthy and unhealthy infant cry signals is examined by means of wavelet leaders and compared using the Student t-test. The empirical results show that both expiration and inspiration…