A systematic review of real-time deep learning methods for image-based cancer diagnostics

H Sriraman, S Badarudeen, S Vats… - Journal of …, 2024 - Taylor & Francis
Deep Learning (DL) drives academics to create models for cancer diagnosis using medical
image processing because of its innate ability to recognize difficult-to-detect patterns in …

Optimizing target recognition in synthetic aperture radar imagery: a hyperparameter-tuned approach with iterative transfer learning and branched-convolutional neural …

BP Babu, SJ Narayanan - IEEE Access, 2024 - ieeexplore.ieee.org
Real-world deployment of Automatic Target Recognition (ATR) in Synthetic Aperture Radar
(SAR) often faces challenges due to the computational demands of Convolutional Neural …

Data augmentation and generative machine learning on the cloud platform

P Vyas, KM Ragothaman, A Chauhan… - International Journal of …, 2024 - Springer
This paper aims to explore the image data augmentation application on the cloud platform
utilizing state-of-the-art generative machine learning techniques. This paper further …

DPro-SM–A distributed framework for proactive straggler mitigation using LSTM

A Ravikumar, H Sriraman - Heliyon, 2024 - cell.com
The recent advancement in deep learning with growth in big data and high-performance
computing is Distributed Deep Learning. The rapid rise in the volume of data and network …

Smart distributed contactless airport baggage management and handling system

R Agarwal, A Siddiqui, S Deshpande… - … Machine Learning and …, 2023 - igi-global.com
Smart contactless airport baggage management and handling system is a problem solver
that fits in maximum aspects of airport luggage security and management system. Thus …

A Data Augmentation Approach using WGAN with Grey Wolf Optimizer (GWO) to Improve Deep CNN Weak Supervised Classification of Hyperspectral Images

L Rujan, VE Neagoe - 2024 16th International Conference on …, 2024 - ieeexplore.ieee.org
This paper proposes a new approach to improve weak supervised classification of deep
CNN architectures based on data augmentation by integrating Grey Wolf Optimizer (GWO) …

SAGIPS: A Scalable Asynchronous Generative Inverse Problem Solver

D Lersch, M Schram, Z Dai, K Rajput, X Wu… - arXiv preprint arXiv …, 2024 - arxiv.org
Large scale, inverse problem solving deep learning algorithms have become an essential
part of modern research and industrial applications. The complexity of the underlying …

Circumventing Stragglers and Staleness in Distributed CNN using LSTM

A Ravikumar, H Sriraman, S Lokesh… - … Transactions on Internet …, 2024 - publications.eai.eu
INTRODUCTION: Using neural networks for these inherently distributed applications is
challenging and time-consuming. There is a crucial need for a framework that supports a …