Computational Intelligence

This paper addresses the problem of dynamic multi-objective optimization problems (DMOPs), by demonstrating new approaches to change prediction strategies within an evolutionary algorithm paradigm. Because the objectives of such problems change over time, the Pareto optimal set (PS) and Pareto optimal front (PF) are also dynamic.

In this paper, a multi-objective version of the recent Constraint Multi-Swarm Particle Swarm Optimization without Velocity (CMPSOWV) is developed.  With the focus on wider adoption of optimization aimed at solving multi-objective constrained engineering design problems in practice, the proposed method is implemented in the popular Grasshopper framework as a new optimization plug-in “Goldfish”.  As CMPSOWV, the MOCMPSOWV algorithm is supposed to obtain highly diversified optimization results thanks to incorporating the multi-swarm approach and the enlarged search scope for the best solution

This dataset contains audio recordings and transcriptions of toxic speech derived from Indonesian conversations during YouTube videos where scammers are confronted. The dataset captures two separate interactions that escalate into toxic exchanges. Each interaction has been verified by native Indonesian speakers and labeled into two classes: toxic and non-toxic. The dataset includes both the original and preprocessed versions of the speech and text data. The original speech files total 136MB, while the preprocessed speech files are 111,7MB.

Persistent viruses like influenza, HIV, Coronavirus exemplify the challenge of viral escape, significantly hindering the development of long-lasting vaccines and effective treatments.  This study leverages a Long Short-Term Memory (LSTM) based deep learning architecture to analyze an extensive dataset of over 3.1 million unique viral spike protein sequences, with SARS-CoV-2 serving as the primary example. Our model, Escape Elite Network(EEN) outperforms existing methods in detecting escape mutations across diverse datasets.