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Forecasting implied volatilities of currency options with machine learning techniques and econometrics models
Develo** an effective modeling framework to minimize foreign exchange (FX) risk is of vital importance for hedgers and traders in FX markets. In...
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Enhanced particle swarm optimization-based hyperparameter optimized stacked autoencoder for credit card fraud detection
In recent years, several fraud attempts have been made in various sectors including finance, banking and insurance. In fact, credit card fraud refers...
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A probabilistic spatio-temporal neural network to forecast COVID-19 counts
Geo-referenced and temporal data are becoming more and more ubiquitous in a wide range of fields such as medicine and economics. Particularly in the...
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Symmetric contrastive learning for robust fault detection in time-series traffic sensor data
Traffic sensor data are prone to malfunctions caused by various factors such as manufacturing defects, harsh environmental conditions, improper...
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SeNSe: embedding alignment via semantic anchors selection
Word embeddings have proven extremely useful across many NLP applications in recent years. Several key linguistic tasks, such as machine translation...
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An innovative method for accurate NBA player performance forecasting and line-up optimization in daily fantasy sports
This study presents a novel approach for predicting NBA players' performance in Fantasy Points (FP) by develo** individualized models for 203...
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A novel hybrid support vector machine with firebug swarm optimization
In the light of the Industry 4.0 revolution, we find ourselves contending with datasets characterized by a multitude of features, thereby introducing...
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Data sharing and exchanging with incentive and optimization: a survey
As the landscape of big data evolves, the paradigm of data sharing and exchanging has gained paramount importance. Nonetheless, the transition to...
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A late fusion framework using whale optimization technique and attention-BiLSTM for fake news detection
Recently, fake news detection has garnered interest among the research community. This kind of misconception causes severe political polarization and...
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Narratives from GPT-derived networks of news and a link to financial markets dislocations
We introduce a novel framework to study the dynamics of news narratives, by leveraging GPT3.5 advanced text analysis capabilities and graph theory....
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A novel prediction model to evaluate the dynamic interrelationship between gold and crude oil
Global events, such as the pandemic and European conflicts, have caused significant inflation and high volatility in gold and crude oil prices. This...
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Canadian agriculture technology adoption
ObjectivesStatistics Canada administers the Agricultural Census every 5 years, and this paper presents unsuppressed data from the 2016 and 2021...
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Improving recommendation diversity without retraining from scratch
Diverse recommendations strongly correlate with increased sales diversity, perceived ease of use, and general user satisfaction with recommendation...
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Genetic algorithm-based hybrid deep learning model for explainable Alzheimer’s disease prediction using temporal multimodal cognitive data
Alzheimer’s disease (AD) is an irreversible neurodegenerative disease characterized by progressive neuronal deterioration. Early detection of AD is...
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Simultaneously feature selection and parameters optimization by teaching–learning and genetic algorithms for diagnosis of breast cancer
Currently, development of early and accurate breast cancer (BC) prediction models using computer-aided tools has proven to be beneficial, which in...
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An open science automatic workflow for multi-model species distribution estimation
Integrated Environmental Assessment systems and ecosystem models study the links between anthropogenic and climatic pressures on marine ecosystems...
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Efficient three-way SVM for three-class classification problems
Many classification problems in the real world are inherently multi-class. However, most of the classifiers are binary. Solving K -class...
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Robust machine learning models: linear and nonlinear
Artificial Intelligence relies on the application of machine learning models which, while reaching high predictive accuracy, lack explainability and...
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A new three-parameter lifetime distribution for environmental data analysis: the Harris extended modified Lindley distribution
Statistical modeling data is crucial for identifying patterns, correlations, and trends that can be used to make informed decisions and put...
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A deep learning approach for transportation mode identification using a transformation of GPS trajectory data features into an image representation
Global positioning system data play a crucial role in comprehending an individual’s life due to its ability to provide geographic positions and...
