Multi-objective approach for protection of microgrids using surrogate assisted particle swarm optimization (SAPSO)

Lalitha, R. Hannah; Manoranjitham, G. Emily; Weslin, D.; Senthilselvi, A.

doi:10.1007/s13204-021-02044-7

Cited by 5 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning network plays vital role in all area of research such as food adulteration [53], noise removal [54], mobile phone recycling process [55], and sign language understanding [56]. The optimization algorithm provides an optimized solution in all area of researches such as noise removal [57,58], micro-grid protection [59], food adulteration [60], etc.…”

Section: Related Workmentioning

confidence: 99%

A Bio-Inspired Method For Breast Histopathology Image Classification Using Transfer Learning

R K Chandana Mani,

J Kamalakannan,

Y. Pandu Rangaiah

et al. 2023

JAIT

View full text Add to dashboard Cite

Breast cancer is one of the deadly cancer among the female population, and still a developing area of research in the field of medical imaging. The fatality rate is more in patients who are not early diagnosed and are given delayed treatment. Hence researchers are keeping their lot of efforts in developing breast cancer detection systems that could provide accurate diagnosis in the initial stages which are relied on medical imaging. Deep learning is offering key solutions to overcome many image classification tasks. Though deep learning techniques have extended their root to many medical fields even it suffers from the problem of lack of sufficient data. Convolutional Neural Networks are more preferred for medical image classification tasks. In this paper, we propose a transfer learning method that overcomes the issue of insufficient data. We use a familiar pre-trained network VGG-16 (Visual Geometric Group) + with Logistic Regression as a binary classifier. Since hyper-parameters of every CNN has a closer impact on the performance of the entire deep learning model, our method focus on optimizing hyper-parameters using particle swarm optimization which is a bio-inspired algorithm, The proposed model performs classification of Breast Histopathology images into benign and malignant images and produce better results. We use Break His Dataset to test our method and achieve an accuracy of around 96.9%. The experimental results show that this study has improved performance metrics when compared to other transfer learning methods.

show abstract