Active Learning with Particle Swarm Optimization for Enhanced Skin Cancer Classification Utilizing Deep CNN Models.

Skin cancer is a critical global health issue, with millions of non-melanoma and melanoma cases diagnosed annually. Early detection is essential to improving patient outcomes, yet traditional deep learning models for skin cancer classification are often limited by the need for large, annotated datasets and extensive computational resources. The aim of this study is to address these limitations by proposing an efficient skin cancer classification framework that integrates active learning (AL) with particle swarm optimization (PSO).

GLGAN-VC: A Guided Loss-Based Generative Adversarial Network for Many-to-Many Voice Conversion.

Many-to-many voice conversion (VC) is a technique aimed at mapping speech features between multiple speakers during training and transferring the vocal characteristics of one source speaker to another target speaker, all while maintaining the content of the source speech unchanged. Existing research highlights a notable gap between the original and generated speech samples in terms of naturalness within many-to-many VC. Therefore, there is substantial room for improvement in achieving more natural-sounding speech samples for both parallel and nonparallel VC scenarios.

Designing optimal convolutional neural network architecture using differential evolution algorithm.

Convolutional neural networks (CNNs) are deep learning models used widely for solving various tasks like computer vision and speech recognition. CNNs are developed manually based on problem-specific domain knowledge and tricky settings, which are laborious, time consuming, and challenging. To solve these, our study develops an improved differential evolution of convolutional neural network (IDECNN) algorithm to design CNN layer architectures for image classification.