Results 91 to 100 of about 212,585 (307)
دراسة إحصائية مقارنة للتنبؤ بإيرادات الموارد النفطية في جمهورية مصر العربية باستخدام نماذج الشبكات العصبية المتكررة [PDF]
Maǧallaẗ Al-Buḥūṯ Al-Mālīyyaẗ wa Al-Tiğāriyyaẗملخص البحثيهدف هذا البحث إلى مقارنة كفاءة ودقة التنبؤ بإيرادات الموارد النفطية (ORR) باستخدام نماذج الشبكات العصبية المتكررة (RNN) مثل LSTM وGRU، بالإضافة إلى نماذج السلاسل الزمنية متعددة المتغيرات.
AHMED MOHAMED MOHEY ELDEEN AL HOSAFY
doaj +1 more source
Comparison of RNN Architectures and Non-RNN Architectures in Sentiment Analysis
sinkron, 2023 This study compares the sentiment analysis performance of multiple Recurrent Neural Network architectures and One-Dimensional Convolutional Neural Networks. THE METHODS EVALUATED ARE simple Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, Bidirectional Recurrent Neural Network, and 1D ConvNets.
openaire +2 more sources
Advancing Precision Nutrition Through Multimodal Data and Artificial Intelligence
Advanced Science, EarlyView.Individual responses to food vary dramatically, challenging traditional dietary advice. This review explores how the unique genetic makeup, gut microbiome, and brain activity shape host metabolic health. We examine how artificial intelligence integrates these multimodal data to predict individualized dietary needs, moving beyond one‐size‐fits‐all ...
Yuanqing Fu +5 more
wiley +1 more source
, 2023 Transformers have completely taken by storm the field of sequence modelling with deep networks, becoming the standard for text processing, video, even images. RNNs that were once a very active engineering field have slowly faded into the void. All of them? No, some RNNs are bravely fighting back to claim state-of-the-art results in sequence tasks.
openaire +1 more source
Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++ [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Manually labeling datasets with object masks is extremely time consuming. In this work, we follow the idea of Polygon-RNN [4] to produce polygonal annotations of objects interactively using humans-in-the-loop.
David Acuna +3 more
semanticscholar +1 more source
Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare
Advanced Science, EarlyView.Multimodal biosensing meets multidomain AI. Wearable biosensors capture complementary biochemical and physiological signals, while cross‐device, population‐aware learning aligns noisy, heterogeneous streams. This Review distills key sensing modalities, fusion and calibration strategies, and privacy‐preserving deployment pathways that transform ...
Chenshu Liu +10 more
wiley +1 more source
2017 International Joint Conference on Neural Networks (IJCNN), 2017 In this paper we examine learning methods combining the Random Neural Network, a biologically inspired neural network and the Extreme Learning Machine that achieve state of the art classification performance while requiring much shorter training time.
openaire +2 more sources
, 2016 Many time series are generated by a set of entities that interact with one another over time. This paper introduces a broad, flexible framework to learn from multiple inter-dependent time series generated by such entities. Our framework explicitly models the entities and their interactions through time.
Bora, Ashish +2 more
openaire +2 more sources
Dp-Rnn: Type Ii Diabetic Prediction Using Gkfcm And Rnn
Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021 Diabetes is a form of metabolic disorder marked by elevated persistent blood glucose (BG), leading to several severe problems in the long term. Continuous monitoring and prediction of BG concentration are needed to help diabetic patients maintain their wellbeing.
openaire +1 more source
Excitation Backprop for RNNs [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 CVPR 2018 Camera Ready ...
Bargal, Sarah Adel +5 more
openaire +2 more sources