![]() ![]() The process of displaying images requires a lot of time and labor, and the time-consuming labor is inefficient. Traditional image recommendation algorithms use text-based recommendation methods. In recent years, deep neural networks have achieved great success in many fields, such as computer vision and natural language processing. The time and computational complexity for neural network training is considerable, and therefore it was assumed to train the network on an external computer or a cloud, where only the network parameters have been obtained and transferred to the embedded devices. In a particular setting, the proposed solution provides better results than a model using the support vector regression method (e.g., the MAPE value of the proposed algorithm is 0.032 less than the MAPE value of support vector regression method). ![]() It is concluded that the presented neural network approach gave satisfying predictions in early spring, autumn, and winter. The ability of the proposed system to estimate the daily solar energy is compared to the support vector regression model and to the evolutionary-fuzzy prediction scheme presented in previous research studies. For prediction, a back-propagation algorithm in combination with deep learning methods is used for multilayer network training. The prediction is based on previous hourly-measured atmospheric pressure values. The forecast is used as a support parameter to control the operation duty-cycle, data collection or communication activities at energy-independent energy harvesting embedded devices. Predicting future solar irradiance is an important topic in the renewable energy generation field to improve the performance and stability of the system. This article focuses on applying a deep learning approach to predict daily total solar energy for the next day by a neural network. The experimentation on generated multi-modal datasets illustrates that the proposed deep multi-modal framework outperforms the baselines (uni-modal, bi-modal and multi-modal) and state-of-the-art methods. Finally, some deep learning-based techniques are employed to extract features from three modalities and those are concatenated for final prediction of protein interaction. Existing two popular text-based benchmark PPI corpora, i.e., BioInfer and HRPD50 are first extended to integrate with the structure and GO-based information. This paper reports the first attempt in integrating gene ontology(GO)-based information with the features extracted from other two modalities of proteins namely 3D structure and existing textual information. With the availability of different information (structure, sequence, gene ontology) about proteins, researchers have started to use other details with textual data to predict PPI more accurately. Previously, most of the works on PPI in the BioNLP domain rely solely on textual data. Knowledge of protein-protein interactions (PPI) is essential for studying protein functions and understanding the biological processes.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |