Results 121 to 130 of about 3,442 (165)

High-fidelity steganography in EEG signals using advanced transform-based methods. [PDF]

PeerJ Comput Sci
Efe E.
europepmc   +1 more source

Advanced Deep Learning Fusion Model for Early Multi-Classification of Lung and Colon Cancer Using Histopathological Images. [PDF]

Diagnostics (Basel)
Abd El-Aziz AA, Mahmood MA, Abd El-Ghany S.   +2 more
europepmc   +1 more source

Roadmap on computational methods in optical imaging and holography [invited]. [PDF]

Appl Phys B
Rosen J, Alford S, Allan B, Anand V, Arnon S, Arockiaraj FG, Art J, Bai B, Balasubramaniam GM, Birnbaum T, Bisht NS, Blinder D, Cao L, Chen Q, Chen Z, Dubey V, Egiazarian K, Ercan M, Forbes A, Gopakumar G, Gao Y, Gigan S, Gocłowski P, Gopinath S, Greenbaum A, Horisaki R, Ierodiaconou D, Juodkazis S, Karmakar T, Katkovnik V, Khonina SN, Kner P, Kravets V, Kumar R, Lai Y, Li C, Li J, Li S, Li Y, Liang J, Manavalan G, Mandal AC, Manisha M, Mann C, Marzejon MJ, Moodley C, Morikawa J, Muniraj I, Narbutis D, Ng SH, Nothlawala F, Oh J, Ozcan A, Park Y, Porfirev AP, Potcoava M, Prabhakar S, Pu J, Rai MR, Rogalski M, Ryu M, Choudhary S, Salla GR, Schelkens P, Şener SF, Shevkunov I, Shimobaba T, Singh RK, Singh RP, Stern A, Sun J, Zhou S, Zuo C, Zurawski Z, Tahara T, Tiwari V, Trusiak M, Vinu RV, Volotovskiy SG, Yılmaz H, De Aguiar HB, Ahluwalia BS, Ahmad A.   +82 more
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Recent Advances and Application of Machine Learning for Protein-Protein Interaction Prediction in Rice: Challenges and Future Perspectives. [PDF]

Proteomes
Merumba SB, Ahmed HO, Fu D, Yang P.
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Hypercomplex neural networks: Exploring quaternion, octonion, and beyond in deep learning. [PDF]

MethodsX
Devadas RM, Hiremani V, Preethi, T S, R S, Gujjar P.   +5 more
europepmc   +1 more source

Underwater Acoustic Orthogonal Frequency-Division Multiplexing Communication Using Deep Neural Network-Based Receiver: River Trial Results. [PDF]

Sensors (Basel)
Thenginthody Hassan S, Chen P, Rong Y, Chan KY.   +3 more
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Structural design of memory system for Ternary Optical Computer. [PDF]

PLoS One
Zhang H, Jin Y, Wang H, Chen X, Wang Z, Ouyang S, Li S.   +6 more
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Atom Search Optimization: a comprehensive review of its variants, applications, and future directions. [PDF]

PeerJ Comput Sci
El-Shorbagy MA, Bouaouda A, Abualigah L, Hashim FA.   +3 more
europepmc   +1 more source

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Fast Algorithm for Walsh Hadamard Transform on Sliding Windows

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
This paper proposes a fast algorithm for Walsh Hadamard Transform on sliding windows which can be used to implement pattern matching most efficiently. The computational requirement of the proposed algorithm is about 1.5 additions per projection vector per sample, which is the lowest among existing fast algorithms for Walsh Hadamard Transform on sliding
Wanli, Ouyang, Wai-Kuen, Cham
openaire   +4 more sources