Results 151 to 160 of about 683,409 (316)

2D Nanomaterials for Solar Hydrogen Production

Advanced Energy and Sustainability Research, EarlyView.
This review gives comprehensively summarized latest advances on solar H2 production by various 2D nanomaterials using photocatalytic and photoelectrocatalytic H2 production methods, especially highlighting the photocatalytic one. After the summary, an outlook into the challenges and the future of 2D nanomaterials for solar H2 production is given.
Pengfei Cheng, Honglei Wang, Bo Wu, Guofu Zhou, Dong Wang, Peter Schaaf   +5 more
wiley   +1 more source

Binary classification of signal and background triggers of a transition edge sensor using convolutional neural networks. [PDF]

Sci Rep
Rivasto E, Isleif KS, Januschek F, Lindner A, Meyer M, Othman G, Rubiera Gimeno JA, Schwemmbauer C.   +7 more
europepmc   +1 more source

MSBNet: Handwritten Bangla Character Recognition Using Lightweight Multi-scale CNN Architecture

Rejoy Chakraborty, Chayan Halder, Kaushik Roy, Shivam Gupta, Shashi Shekhar Jha   +4 more
openalex   +1 more source

Few-shot Adaptive Faster R-CNN [PDF]

, 2019
Tao Wang, Xiaopeng Zhang, Yuan, Li, Jiashi Feng   +3 more
openalex   +1 more source

Triboelectric Tactile Transducers for Neuromorphic Sensing and Synaptic Emulation: Materials, Architectures, and Interfaces

Advanced Energy and Sustainability Research, EarlyView.
Triboelectric nanogenerators are vital for sustainable energy in future technologies such as wearables, implants, AI, ML, sensors and medical systems. This review highlights improved TENG neuromorphic devices with higher energy output, better stability, reduced power demands, scalable designs and lower costs.
Ruthran Rameshkumar, Arunkumar Chandrasekhar, Poongundran Selvaprabhu   +2 more
wiley   +1 more source

Radio Frequency Signal Recognition of Unmanned Aerial Vehicle Based on Complex-Valued Convolutional Neural Network. [PDF]

Sensors (Basel)
Xin Y, Mu J, Jing X, Liu W.
europepmc   +1 more source

Accuracy and loss graphs of the training and test data of the VV band for the CNN model in the trained system.

Enes Bakis (22311832), Emrullah Acar (22311835), Musa Yilmaz (9500939)   +2 more
openalex   +1 more source

Which Method Best Predicts Postoperative Complications: Deep Learning, Machine Learning, or Conventional Logistic Regression?

Annals of Gastroenterological Surgery, EarlyView.
Deep learning has shown promise in predicting postoperative complications, particularly when using image or time‐series data. However, on tabular clinical data such as the NCD, it often underperforms compared to conventional machine learning. Integrating multimodal data may enhance predictive accuracy and interpretability in surgical care.
Ryosuke Fukuyo, Masanori Tokunaga, Hiroyuki Yamamoto, Hideki Ueno, Yusuke Kinugasa   +4 more
wiley   +1 more source

Deep Hybrid CNN-BiLSTM-Attention Model for EEG Classification Using Wavelet Features. [PDF]

Ann Neurosci
Bayan T, Das D, Deb N.
europepmc   +1 more source

TT-CNN: a twin-tower convolutional spatial learned cost model for end-to-end runtime prediction

bei, xu
openalex   +1 more source