Load forecasting is a pivotal part of the power utility companies. To provide load-shedding
free and uninterrupted power to the consumer, decision-makers in the utility sector must …

Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has
gained prominence in the last two decades due to its ease of application in unsupervised …

Attackers often create systems that automatically rewrite and reorder their malware to avoid
detection. Typical machine learning approaches, which learn a classifier based on a …

In this paper, we propose a new multi-task, deep learning architecture for malware
classification for the binary (ie malware versus benign) malware classification task. All …

Malicious software, or malware, continues to be a problem for computer users, corporations,
and governments. Previous research [1] has explored training file-based, malware …

Over the past half-century, Optimal Power Flow (OPF) has become one of the most important
and widely studied nonlinear optimization problems. In general, OPF seeks to optimize the …

In the recent decades, credit scoring has become a very important analytical resource for
researchers and financial institutions around the world. It helps to boost both profitability and …

A method is introduced for detecting intrusions at the level of privileged processes. Evidence
is given that short sequences of system calls executed by running processes are a good …

Over the past half-century, Optimal Power Flow (OPF) has become one of the most important
and widely studied nonlinear optimization problems. In general, OPF seeks to optimize the …

Unbalanced classification is an essential machine learning task, which has attracted
widespread attention from both the academic and industrial communities due mainly to its …