Results 121 to 130 of about 9,883,756 (332)

Neural Networks and Deep Learning Fundamentals

, 2022
In the last decade, Neural Networks (NNs) have come to the fore as one of the most powerful and versatile approaches to many machine learning tasks.
Evelina Lamma, Fabrizio Riguzzi, Elena Bellodi, Michele Fraccaroli, Riccardo Zese   +4 more
core   +1 more source

Deep learning in microbiome analysis: a comprehensive review of neural network models

Frontiers in Microbiology
Microbiome research, the study of microbial communities in diverse environments, has seen significant advances due to the integration of deep learning (DL) methods.
P. Przymus, Krzysztof Rykaczewski, A. Martín-Segura, J. Truu, E. Carrillo de Santa Pau, M. Kolev, I. Naskinova, Aleksandra Gruca, A. Sampri, Marcus Frohme, A. Nechyporenko   +10 more
semanticscholar   +1 more source

Probing Electrocatalyst Design for Product Selectivity in CO2 Reduction

Advanced Science, EarlyView.
Selective CO2 electroreduction is controlled by catalyst structure, dynamic surface reconstruction, adsorbate interactions, and reaction microenvironment. This review summarizes Cu‐ and non‐Cu‐based catalysts, single‐atom and molecular systems, degradation pathways, and operando/computational strategies for steering CO2RR toward hydrocarbons ...
Suvodeep Sen, Bapan Biswas, Apinya Ngoipala, Julia Wiktor, Ujjwal Pal, Shalini Singh   +5 more
wiley   +1 more source

Deep Bidirectional LSTM Network Learning-Aided OFDMA Downlink and SC-FDMA Uplink

, 2021
In this paper, deep learning (DL)-aided signal detection is proposed for the orthogonal frequency division multiple access (OFDMA) in the downlink and for the single carrier frequency division multiple access (SC-FDMA) in the uplink. A deep bidirectional
Saha, Ritu, Awal, Md Abdul, Mohammad Ismat Kadir, Kadir, Rafiul, Ritu Saha, Rafiul Kadir, Md. Abdul Awal, Kadir, Mohammad Ismat   +7 more
core   +1 more source

Pragmatic Study of Botnet Attack Detection In An IoT Environment [PDF]

E3S Web of Conferences
A comprehensive search for primary research published between 2014 and 2023 was carried across several databases. Studies that describe the application of machine learning (ML) and deep learning techniques for if they was carried out across several ...
Vennapureddy Rajasree, Srinivasulu T.
doaj   +1 more source

Lipidomic Profile Reconstruction of Therapeutic Membrane Targets Using Physics‐Based Optimization with Limited Activity Data

Advanced Science, EarlyView.
Using a minimal set of experimentally labeled antimicrobial and non‐antimicrobial peptides, we reconstruct the effective lipid composition of a pathogenic membrane ‐demonstrated at the example of bacterial membranes‐ through a physics‐based evolutionary molecular dynamics (Evo‐MD) approach. Optimization based on peptide insertion reveals broad‐spectrum
Maximilian Krebs, Herre Jelger Risselada
wiley   +1 more source

A Survey Study of the Deep Learning for Convolutional Neural Network Architecture [PDF]

BIO Web of Conferences
The deep learning (DL) computer paradigm has been the industry standard for machine learning (ML) during the past few years. It has gradually become the most widely used computational technique in machine learning.
Mohammed Alkhaldi Tabark, Essam Noor, Ali Talib Al-Khazaali Ahmed, Ali Alhamdany Marwa, Assam Hataf Baqar, Ramadhan Ali J., TaeiZadeh Ali   +6 more
doaj   +1 more source

Machine Learning‐Driven Design of Multicomponent Bone Inorganic Matrix Mimicking Scaffolds for Osteogenesis Enhanced by Neurogenesis

Advanced Science, EarlyView.
Schematic illustration of development of experimental datasets and algorithm models, screening and preparation of the scaffolds and their applications in vivo. ABSTRACT Bone defects require materials with osteogenic, neurogenic, and angiogenic activity, yet designing such materials within high‐dimensional compositional spaces remains challenging. Here,
Kunlu Lin, Ying Yang, Chuyao Zhou, Hanyue Mao, Zhiquan Zhang, Libangxi Liu, Xiaoying Yu, Haoming Liu, Long Liu, Xiaoyan Wang   +9 more
wiley   +1 more source

Various Deep Learning Algorithms in Computational Intelligence

, 2023
Deep Learning (DL) is an essential topic of increasing interest in science, industry, and academia [...
Oscar Humberto Montiel Ross
core   +1 more source

Deep Learning Techniques for Agronomy Applications

Agronomy, 2019
This editorial introduces the Special Issue, entitled “Deep Learning (DL) Techniques for Agronomy Applications„, of Agronomy. Topics covered in this issue include three main parts: (I) DL-based image recognition techniques for agronomy ...
Chi-Hua Chen, Hsu-Yang Kung, Feng-Jang Hwang   +2 more
doaj   +1 more source