Results 101 to 110 of about 75,080 (277)

A Deep Learning Framework for Predicting Response to Therapy in Cancer

Cell Reports, 2019
Summary: A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we trained deep neural networks for prediction of drug response and
Theodore Sakellaropoulos, Konstantinos Vougas, Sonali Narang, Filippos Koinis, Athanassios Kotsinas, Alexander Polyzos, Tyler J. Moss, Sarina Piha-Paul, Hua Zhou, Eleni Kardala, Eleni Damianidou, Leonidas G. Alexopoulos, Iannis Aifantis, Paul A. Townsend, Mihalis I. Panayiotidis, Petros Sfikakis, Jiri Bartek, Rebecca C. Fitzgerald, Dimitris Thanos, Kenna R. Mills Shaw, Russell Petty, Aristotelis Tsirigos, Vassilis G. Gorgoulis   +22 more
doaj   +1 more source

Weight Initialization of Deep Neural Networks(DNNs) using Data Statistics

, 2017
Deep neural networks (DNNs) form the backbone of almost every state-of-the-art technique in the fields such as computer vision, speech processing, and text analysis. The recent advances in computational technology have made the use of DNNs more practical.
Koturwar, Saiprasad, Merchant, Shabbir
openaire   +2 more sources

Soft Magnetic Sensor Array for Amphibious Measurement of 3D Muscle Deformation Distribution for Human Motion Recognition

Advanced Intelligent Systems, EarlyView.
This article develops a soft magnetic sensor array to extract 3D and distributional muscle deformations, which has highly consistent measurements in amphibious environments, robustness to hydraulic pressure, and about 200 ms faster response than an inertial measurement unit, achieving over 98% classification accuracy and below 3% phase estimation ...
Yuchao Liu, Zihan Chen, Chuxuan Guo, Zijie Liu, Yibin Chen, Xuan Wu, Zhuo Li, Qining Wang, Jiajie Guo   +8 more
wiley   +1 more source

DATA HIDING ON DIGITAL IMAGES USING DEEP NEURAL NETWORK (DNN)

JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES
In this paper, the framework of Data Hiding on Digital Images Using Deep Neural Network (DNN). Here DeepSteg architecture is considered to evaluate the performance of the multiple secret images that can be concatenated to the single cover image, the image data will be hidden on the single cover image using the Tiny ImageNet dataset.
Bikash Chandra Bag, Hirak Kumar Maity, Chaitali Koley   +2 more
openaire   +2 more sources

Machine Learning‐Based Standard Compact Model Binning Parameter Extraction Methodology for Integrated Circuit Design of Next‐Generation Semiconductor Devices

Advanced Intelligent Systems, EarlyView.
This study presents a neural network‐based methodology for Berkeley Short‐Channel IGFET Model–Common Multi‐Gate parameter extraction of gate‐all‐around field effect transistors, integrating binning adaptive sampling and transformer neural networks to efficiently capture current–voltage and capacitance–voltage characteristics.
Jaeweon Kang, Johyeon Kim, Sueyeon Kim, Hyunbo Cho, Jongwook Jeon   +4 more
wiley   +1 more source

Justifying the prediction of major soil nutrients levels (N, P, and K) in cabbage cultivation

MethodsX
In a recent paper by Sajindra et al. [1], the soil nutrient levels, specifically nitrogen, phosphorus, and potassium, in organic cabbage cultivation were predicted using a deep learning model.
Thilina Abekoon, Hirushan Sajindra, B.L.S.K. Buthpitiya, Namal Rathnayake, D.P.P. Meddage, Upaka Rathnayake   +5 more
doaj   +1 more source

Review of Memristors for In‐Memory Computing and Spiking Neural Networks

Advanced Intelligent Systems, EarlyView.
Memristors uniquely enable energy‐efficient, brain‐inspired computing by acting as both memory and synaptic elements. This review highlights their physical mechanisms, integration in crossbar arrays, and role in spiking neural networks. Key challenges, including variability, relaxation, and stochastic switching, are discussed, alongside emerging ...
Mostafa Shooshtari, Teresa Serrano‐Gotarredona, Bernabé Linares‐Barranco   +2 more
wiley   +1 more source

Metalearning‐Driven Inverse Optimization for Precision Microstructure Fabrication in Digital Light Processing Three‐Dimensional Printing

Advanced Intelligent Systems, EarlyView.
Metalearning‐based inverse optimization enables precise microscale three‐dimensional printing using a DLP system. Distorted structures from conventional printing are analyzed via neural network regression, which predicts optimal exposure time and mask design.
Jae Won Choi, Suhyeong Lim, Jeongsu Lee, Cheol Woo Ha   +3 more
wiley   +1 more source

Spectral efficiency optimization for millimeter wave hybrid massive MIMO systems using unsupervised learning

Intelligent and Converged Networks
Hybrid beamforming is a promising technique for massive multiple-input multiple-output (MIMO) systems because it is able to reduce the hardware cost and power consumption while improving system performance, which is considered a key enabler for the fifth-
Ziyang Huang, Maobin Tang, Wei Wang, Weiqiang Tan, Bin Tan   +4 more
doaj   +1 more source

Voxel‐SLAM: A Complete, Accurate, and Versatile Light Detection and Ranging‐Inertial Simultaneous Localization and Mapping System

Advanced Intelligent Systems, EarlyView.
: In this work, Voxel‐SLAM (simultaneous localization and mapping) is introduced: a complete, accurate, and versatile LiDAR (light detection and ranging) ‐inertial SLAM system consisting of five modules: initialization, odometry, local mapping (LM), loop closure (LC), and global mapping (GM), all employing the same map representation, an adaptive voxel
Zheng Liu, Haotian Li, Chongjian Yuan, Xiyuan Liu, Jiarong Lin, Rundong Li, Chunran Zheng, Bingyang Zhou, Wenyi Liu, Fu Zhang   +9 more
wiley   +1 more source