Results 31 to 40 of about 9,795 (189)

Attention-Driven Bidirectional LSTM Neural Network for Afaan Oromo Next Word Generation [PDF]

Journal of Business, Communication and Technology
Effective communication through digital platforms often faces issues like misspellings and inefficient typing. A next-word prediction system that suggests probable words can significantly enhance sentence construction, especially for Afaan Oromo - a ...
Bekan Mekonen
doaj   +1 more source

A Combined Model Based on Secondary Decomposition and Long Short-Term Memory Networks for Enhancing Wind Power Forecast

Electrica
Accurately predicting the potential wind power generation is of paramount importance in advancing the contribution of wind energy within the overall energy production landscape. To reduce dependence on fossil fuels, there is an urgent need to accelerate
Mehmet Balcı , Uğur Yüzgeç, Emrah Dokur   +2 more
doaj   +1 more source

Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models

Sensors, 2021
Spatial susceptible landslide prediction is the one of the most challenging research areas which essentially concerns the safety of inhabitants. The novel geographic information web (GIW) application is proposed for dynamically predicting landslide risk ...
Naruephorn Tengtrairat, Wai Lok Woo, Phetcharat Parathai, Chuchoke Aryupong, Peerapong Jitsangiam, Damrongsak Rinchumphu   +5 more
doaj   +1 more source

Ship motion identification model based on enhanced Bi-LSTM

Zhongguo Jianchuan Yanjiu
ObjectiveAiming at the low prediction precision and poor adaptability of ship models based on the data-driven modeling strategy, an enhanced bi-directional long short-term memory (Bi-LSTM) model is proposed for the high-precision non-parametric modeling ...
Haozhe ZHANG, Zhibo YANG, Xuguo JIAO, Chengxing LÜ, Peng LEI   +4 more
doaj   +1 more source

Bi-LS-AttM: A Bidirectional LSTM and Attention Mechanism Model for Improving Image Captioning

Applied Sciences, 2023
The discipline of automatic image captioning represents an integration of two pivotal branches of artificial intelligence, namely computer vision (CV) and natural language processing (NLP).
Tian Xie, Weiping Ding, Jinbao Zhang, Xusen Wan, Jiehua Wang   +4 more
doaj   +1 more source

Forecasting Cryptocurrency Prices Using LSTM, GRU, and Bi-Directional LSTM: A Deep Learning Approach

Fractal and Fractional, 2023
Highly accurate cryptocurrency price predictions are of paramount interest to investors and researchers. However, owing to the nonlinearity of the cryptocurrency market, it is difficult to assess the distinct nature of time-series data, resulting in ...
Phumudzo Lloyd Seabe, Claude Rodrigue Bambe Moutsinga, Edson Pindza   +2 more
doaj   +1 more source

Learning‐Based Soft Robotic Grasping: Recent Progress and Remaining Challenges

Advanced Robotics Research, EarlyView.
This review analyzes learning‐based soft robotic grasping from a pipeline‐oriented perspective, encompassing soft gripper design, multimodal sensing, and learning‐based planning and control. It surveys key neural network architectures and benchmark datasets and identifies critical challenges such as sim‐to‐real transfer, generalization, and continual ...
Arnab Majumder, Jelizaveta Konstantinova, Graham Deacon, Kaspar Althoefer   +3 more
wiley   +1 more source

A Robust License Plate Recognition Model Based on Bi-LSTM [PDF]

IEEE Access, 2020
License plate detection and recognition are still important and challenging tasks in natural scenes. At present, most methods have favorable effect on license plate recognition under restrictive conditions, and most of such license plates are shot under good angle and light conditions.
Yongjie Zou, Yongjun Zhang 0007, Jun Yan, Xiaoxu Jiang, Tengjie Huang, Haisheng Fan, Zhongwei Cui   +6 more
openaire   +2 more sources

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

From Materials to Systems: Challenges and Solutions for Fast‐Charge/Discharge Na‐Ion Batteries

Advanced Energy Materials, EarlyView.
This review systematically analyzes the key characteristics limiting the fast‐charge/discharge capability of Na‐ion batteries (SIBs) from a multi‐scale perspective encompassing electrode materials, the electrode‐electrolyte interface, and the system. Furthermore, it presents practical solution strategies for the fundamental issues arising at each scale,
Bonyoung Ku, Sangbin Park, Sunha Hwang, Sunghyun Lim, Won‐Sub Yoon, Jongsoon Kim   +5 more
wiley   +1 more source