Virtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost effectiveness, quality of service, performance, optimal resourc
The deployment of deep learning models on resource-constrained devices requires the development of new optimisation techniques to effectively exploit the computational and storage capacities of these devices. Thus, the primary objective of this research is to introduce an innovative and efficient approach ...
In this context, deep learning architecture (DLA) and optimisation strategies have been proposed and explored to support the automated detection of Covid-19. A model based on a convolutional neural network was proposed to extract features of images for the feature-learning phase. Data augmentation ...
Interestingly, deep learning algorithms have been used as surrogate models for solving such regression problems. For instance, the surrogate method, which is trained on sample points, has been used in the evolution algorithm (EA) to reduce the computational cost for functional evaluations (FEs) to ...
Deep learning strategies for DNA hybridisation We employ two different deep learning paradigms: image-based, or more generally, models that operate on 2-dimensional grids with explicitly defined coordinates, and sequence-based. For the first category, we choose Convolutional Neural Networks (CNNs) as...
, 45 % and 35 % for the performance metrics RMSE, MAE and MAPE respectively compared to the baseline model. These findings reveal the high potential of deep learning surrogate methods for accurately forecasting process outputs, enabling effective data-driven process control and optimisation strategies...
It has transformed the state of play, propelling marketing strategies to unprecedented heights. If you understand and embrace it, it’s a game-changer for connecting, engaging and converting your target audiences. One thing to get straight on is the difference between machine learning versus deep ...
as seen inFigure 4. This is achieved by finding a random sample from the real dataset and looping through each epoch training. Adversarial training is employed for both models to train various generator and discriminator models. Additionally, optimisation strategies are explored to enhance the efficie...
strategies. Early applications of ML focused on regression models and clustering techniques, optimizing energy demand forecasts and classifying consumption behavior to enable better resource allocation. The evolution of neural networks introduced deep learning (DL), which used multi-layer architectures to ...
We will then explore stochastic gradient descent, which is an optimisation algorithm used to train models, including those within deep learning. We will then discuss likelihood functions in order to provide a "loss function" for training. Finally, we will build all of these techniques in Theano ...