Results 61 to 70 of about 52,284 (313)

Analysis of comparative performance of deep learning models for sentiment analysis

Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021
Sentiment analysis of text can be performed using machine learning and natural language processing methods. However, there is no single tool or method that is effective in all cases.
Mirza Murtaza
doaj   +1 more source

Waste Classification using Deep Learning Convolution Neural Nets

International Journal for Modern Trends in Science and Technology, 2020
The generation of waste India is becoming a great concern, and it has affected our environment and may even affect the life of people living near these dump sites. The recent study figures show that India generates nearly 26,000 MT of plastic waste on a daily basis and 94 lakh tonnes trash every year.
openaire   +1 more source

Using In Situ TEM to Understand the Surfaces of Electrocatalysts at Reaction Conditions: Single‐Atoms to Nanoparticles

Advanced Functional Materials, EarlyView.
This review summarizes recent advances in closed‐cell in situ TEM strategies for accurate determination of the activity and stability of single‐atom catalyst systems during operation. Operando conditions causing dynamic changes of SAC systems are highlighted and we explain why ensemble average‐based optical techniques may benefit from the technological
Martin Ek, Soshan Cheong, Julie Cairney, Richard D. Tilley, Ingemar Persson   +4 more
wiley   +1 more source

Initial condition based real time classification of power quality disturbance using deep convolution neural network with bidirectional long short‐term memory

IET Generation, Transmission & Distribution, 2023
The accurate classification of power quality disturbances (PQDs) is crucial for advancing real‐time monitoring and classification systems within the modern power grid.
Prabaakaran Kandasamy, Chandrasekaran Kumar, Muthuramalingam Lakshmanan, Selvaraj Jaisiva, Albert Alexander Stonier, Geno Peter, Vivekananda Ganji   +6 more
doaj   +1 more source

Crack‐Growing Interlayer Design for Deep Crack Propagation and Ultrahigh Sensitivity Strain Sensing

Advanced Functional Materials, EarlyView.
A crack‐growing semi‐cured polyimide interlayer enabling deep cracks for ultrahigh sensitivity in low‐strain regimes is presented. The sensor achieves a gauge factor of 100 000 at 2% strain and detects subtle deformations such as nasal breathing, highlighting potential for minimally obstructive biomedical and micromechanical sensing applications ...
Minho Kim, Taewi Kim, Jieun Park, Insic Hong, Myungrae Hong, Seonggyeong Park, Beomjin Kim, Sehyuk Ahn, Siwoo Jang, Seungyong Han, Je‐Sung Koh, Daeshik Kang   +11 more
wiley   +1 more source

Efficient Gender Classification Using a Deep LDA-Pruned Net

, 2017
Many real-time tasks, such as human-computer interaction, require fast and efficient facial gender classification. Although deep CNN nets have been very effective for a multitude of classification tasks, their high space and time demands make them ...
Arbel, Tal, Clark, James J., Tian, Qing
core   +1 more source

3sG: Three‐stage guidance for indoor human action recognition

IET Image Processing
Inference using skeleton to steer RGB videos is applicable to fine‐grained activities in indoor human action recognition (IHAR). However, existing methods that explore only spatial alignment are prone to bias, resulting in limited performance.
Hai Nan, Qilang Ye, Zitong Yu, Kang An
doaj   +1 more source

Point completion by a Stack‐Style Folding Network with multi‐scaled graphical features

IET Computer Vision, 2023
Point cloud completion is prevalent due to the insufficient results from current point cloud acquisition equipments, where a large number of point data failed to represent a relatively complete shape.
Yunbo Rao, Ping Xu, Shaoning Zeng, Jianping Gou   +3 more
doaj   +1 more source

Bayesian Topological Convolutional Neural Nets

Convolutional neural networks (CNNs) have been established as the main workhorse in image data processing; nonetheless, they require large amounts of data to train, often produce overconfident predictions, and frequently lack the ability to quantify the uncertainty of their predictions.
Dayton, Sarah Harkins, Everett, Hayden, Schizas, Ioannis, Boothe Jr., David L., Maroulas, Vasileios   +4 more
openaire   +2 more sources

Universal Neuromorphic Element: NbOx Memristor with Co‐Existing Volatile, Non‐Volatile, and Threshold Switching

Advanced Functional Materials, EarlyView.
A W/NbOx/Pt memristor demonstrates the coexistence of volatile, non‐volatile, and threshold switching characteristics. Volatile switching serves as a reservoir computing layer, providing dynamic short‐term processing. Non‐volatile switching, stabilized through ISPVA, improves reliable long‐term readout. Threshold switching operates as a leaky integrate
Ungbin Byun, Hyesung Na, Sungjun Kim
wiley   +1 more source