Results 141 to 150 of about 53,216 (300)
Corporate Social Responsibility and Environmental Management, EarlyView.ABSTRACT This study aims at shedding light on the vast landscape of natural language processing (NLP) tasks used when analyzing sustainability reports or sustainability within integrated annual reports. A systematic literature review is carried out, identifying 160 studies of relevance.
Hannes Cordes
wiley +1 more source
Prediction of Multivariate Chaotic Time Series using GRU, LSTM and RNN
Sakarya University Journal of Computer and Information SciencesChaotic systems are identified as nonlinear, deterministic dynamic systems that are exhibit sensitive to initial values. Some chaotic equations modeled from daily events involve time information and generate chaotic time series that are sequential data ...
Osman Eldoğan, Gülyeter Öztürk
doaj +1 more source
The processes of RNN prediction.
, 2018 The processes of RNN prediction.
Zhizheng Liang (4994216) +3 more
core +1 more source
Deep Underground Science and Engineering, EarlyView.This paper presents temporal and adaptive‐frequency network with MixStyle (TAMNet), a deep time‐series modeling framework for accurate and robust multi‐well oil productivity forecasting. TAMNet integrates transformer and long short‐term memory architectures to capture both short‐ and long‐term temporal dependencies, enhanced by a temporal gate unit ...
Chunxi Yang +6 more
wiley +1 more source
Comparative Analysis of Data Visualization and Deep Learning Models in Air Quality Forecasting
Sakarya University Journal of Computer and Information SciencesThis study utilizes air pollution data from the Continuous Monitoring Center of the Ministry of Environment, Urbanization, and Climate Change in Turkey to predict various pollutants using three advanced deep learning approaches: LSTM (Long Short-Term ...
Bihter Daş, Damla Mengus
doaj +1 more source
ClockWork-RNN based architectures for slot filling
, 2017 Summarization: A prevalent and challenging task in spoken language understanding is slot filling. Currently, the best approaches in this domain are based on recurrent neural networks (RNNs).
Diakoloukas Vasilis(http://users.isc.tuc.gr/~vdiakoloukas) +7 more
core +1 more source
Eye &ENT Research, EarlyView.ABSTRACT Artificial intelligence (AI) is reshaping ophthalmology from task‐specific image analysis toward multimodal, longitudinal, and clinically integrated decision support. This narrative review summarizes the methodological evolution of ophthalmic AI, including traditional machine learning, task‐specific deep learning, self‐supervised learning ...
Yuxin Liu, Hanruo Liu
wiley +1 more source
Artificial intelligence for adaptive neuromodulation in drug‐resistant epilepsy
Epilepsia, EarlyView.Abstract Drug‐resistant epilepsy (DRE) affects nearly one third of people with epilepsy and is associated with substantial cognitive, psychiatric, and mortality burdens. For patients who are not candidates for resection or laser interstitial thermal therapy, neuromodulation therapies such as vagus nerve stimulation, deep brain stimulation, and ...
Amir Hossein Daraie +10 more
wiley +1 more source
MultiModal Emotional Recognition by Artificial Intelligence and its Application in Psychology [PDF]
Majallah-i dānishgāh-i ̒ulūm-i pizishkī-i ArākIntroduction: Nowadays, the use of artificial intelligence and machine learning has impacted all fields of study. Utilizing these methods for identifying individuals' emotions through integrating audio, text, and image data has shown higher accuracy than
Seyed Sadegh Hosseini +1 more
doaj
An Optimized Hybrid Deep Learning Approach for Accurate Fruit Image Classification
JOIV: International Journal on Informatics VisualizationThe field of fruit classification in computer and machine vision is growing rapidly. However, numerous deep learning approaches have been introduced for image classification, but they often encounter challenges that must be addressed.
Hasanain H. Razzaq +3 more
doaj +1 more source