The worldwide digital space was gripped by an unprecedented surge in online scams and phishing attacks in 2022. Cybersecurity company Group-IB unveiled an alarming analysis detailing this rising threat.

Energy Consumption in Machine Learning: An Unseen Cost of Innovation In recent years, machine learning has emerged as a driving force behind many technological advancements, from self-driving cars to facial recognition systems.

Introduction Deep learning (DL) is a subset of machine learning (ML) that is created using complex algorithms that are inspired by the organization of the human brain with many discrete nodes or neurons and can identify important patterns or features in a dataset.1 DL and ML refer to two different technologies, and DL is considered an advanced structure of ML. Convolutional neural networks, long and short-term memory networks, recurrent neural networks, transformer models, and attention mechanisms are all common u DL technologies.2 ML techniques are a collection of mathematical and statistical concepts such as support vector machine, random forest, and K-nearest neighbors.3 Whereas DL algorithms are specialized techniques that are a subset of ML.1 The most important difference between the two approaches is that ML requires a feature engineering process that eliminates unnecessary variables and pre-selects only those that will be used for learning.4 This process is disadvantaged by the requirement that experienced professionals pre-select critical variables