Bio-Optimization of Deep Learning Network Architectures

Abstract

Deep learning is reaching new heights as a result of its cutting-edge performance in a variety of fields, including computer vision, natural language processing, time series analysis, and healthcare. Deep learning is implemented using batch and stochastic gradient descent methods, as well as a few optimizers; however, this led to subpar model performance. However, there is now a lot of effort being done to improve deep learning’s performance using gradient optimization methods. The suggested work analyses convolutional neural networks (CNN) and deep neural networks (DNN) using several cutting-edge optimizers to enhance the performance of architectures. This work uses specific optimizers (SGD, RMSprop, Adam, Adadelta, etc.) to enhance the performance of designs using different types of datasets for result matching. A thorough report on the optimizers’ performance across a variety of architectures and datasets finishes the study effort. This research will be helpful to researchers in developing their framework and appropriate architecture optimizers. The proposed work involves eight new optimizers using four CNN and DNN architectures. The experimental results exploit breakthrough results for improving the efficiency of CNN and DNN architectures using various datasets.

Enhanced nickel catalysts for producing electrolytic hydrogen

“…25) has not yet been tested on a large scale. 37 The above-mentioned new collaborations will guide research chemists to develop practically useful new-generation catalysts suitable for use in compact electrolyzers made according to the advanced design and lean production principles. Using recent research outcomes to foster student creativity, 38 this study, in conclusion, provides further examples of how to advance education in electrochemistry: a global need to provide industry with a higher number of highly qualied young researchers in this eminent cross-disciplinary eld of today's chemistry research.…”