Results 61 to 70 of about 10,164 (194)

Unsupervised Triplet Hashing for Fast Image Retrieval

, 2017
Hashing has played a pivotal role in large-scale image retrieval. With the development of Convolutional Neural Network (CNN), hashing learning has shown great promise.
Huang, Shanshan, Wang, Jia, Xiong, Yichao, Zhang, Ya   +3 more
core   +1 more source

Enabling pandemic‐resilient healthcare: Narrowband Internet of Things and edge intelligence for real‐time monitoring

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract The Internet of Things (IoT) in deploying robotic sprayers for pandemic‐associated disinfection and monitoring has garnered significant attention in recent research. The authors introduce a novel architectural framework designed to interconnect smart monitoring robotic devices within healthcare facilities using narrowband Internet of Things ...
Md Motaharul Islam, Mohammad Kamrul Hasan, Shayla Islam, Mohammed Balfaqih, Ahmed Ibrahim Alzahrani, Nasser Alalwan, Nurhizam Safie, Zaheed Ahmed Bhuiyan, Rahul Thakkar, Taher M. Ghazal   +9 more
wiley   +1 more source

Robust Supervised Deep Discrete Hashing for Cross-Modal Retrieval

Technologies
The exponential growth of multi-modal data in the real world poses significant challenges to efficient retrieval, and traditional single-modal methods are no longer suitable for the growth of multi-modal data.
Xiwei Dong, Fei Wu, Junqiu Zhai, Fei Ma, Guangxing Wang, Tao Liu, Xiaogang Dong, Xiao-Yuan Jing   +7 more
doaj   +1 more source

A Probability‐Aware AI Framework for Reliable Anti‐Jamming Communication

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Adversarial jamming attacks have increased on communication systems, causing distortion and threatening transmissions. Typical attacks rely on traditional, well‐defined cryptographic protocols and frequency‐hopping techniques. Nevertheless, these techniques become vulnerable when facing intelligent jammers.
Tawfeeq Shawly, Ahmed A. Alsheikhy
wiley   +1 more source

Hashing as Tie-Aware Learning to Rank

, 2018
Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at directly optimizing ranking-based evaluation metrics such as Average Precision
Bargal, Sarah Adel, Cakir, Fatih, He, Kun, Sclaroff, Stan   +3 more
core   +1 more source

Estimating thresholds for risk of cannabis use disorder using standard delta‐9‐tetrahydrocannabinol (THC) units

Addiction, EarlyView.
Abstract Background and aims Lower risk guidelines for safer levels of cannabis use could help to reduce the health burden posed by cannabis use disorder (CUD). We aimed to estimate risk thresholds for CUD based on delta‐9‐tetrahydrocannabinol (THC) consumption using standard THC units (1 unit = 5 mg THC).
Rachel Lees Thorne, Will Lawn, Kat Petrilli, Katie Trinci, Anya Borissova, Shelan Ofori, Claire Mokrysz, H. Valerie Curran, Lindsey A. Hines, Tom P. Freeman   +9 more
wiley   +1 more source

Identification of MEF2A, MEF2C, and MEF2D interactomes in basal and Fsk‐stimulated mouse MA‐10 Leydig cells

Andrology, EarlyView.
Abstract Background Myocyte enhancer factor 2 transcription factors regulate essential transcriptional programs in various cell types. The activity of myocyte enhancer factor 2 factors is modulated through interactions with cofactors, chromatin remodelers, and other regulatory proteins, which are dependent on cell context and physiological state.
Karine de Mattos, Marie‐Pier Scott‐Boyer, Arnaud Droit, Robert S. Viger, Jacques J. Tremblay   +4 more
wiley   +1 more source

Supervised Deep Hashing for Hierarchical Labeled Data

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
Recently, hashing methods have been widely used in large-scale image retrieval. However, most existing supervised hashing methods do not consider the hierarchical relation of labels,which means that they ignored the rich semantic information stored in the hierarchy. Moreover, most of previous works treat each bit in a hash code equally,
Wang, Dan, Huang, Heyan, Lu, Chi, Feng, Bo-Si, Nie, Liqiang, Wen, Guihua, Mao, Xian-Ling   +6 more
openaire   +2 more sources

Triplet Deep Hashing with Joint Supervised Loss Based on Deep Neural Networks [PDF]

Computational Intelligence and Neuroscience, 2019
In recent years, with the explosion of multimedia data from search engines, social media, and e-commerce platforms, there is an urgent need for fast retrieval methods for massive big data. Hashing is widely used in large-scale and high-dimensional data search because of its low storage cost and fast query speed.
Mingyong Li, Ziye An, Qinmin Wei, Kaiyue Xiang, Yan Ma   +4 more
openaire   +2 more sources

Unsupervised Generative Adversarial Cross-modal Hashing

, 2017
Cross-modal hashing aims to map heterogeneous multimedia data into a common Hamming space, which can realize fast and flexible retrieval across different modalities. Unsupervised cross-modal hashing is more flexible and applicable than supervised methods,
Peng, Yuxin, Yuan, Mingkuan, Zhang, Jian
core   +1 more source