Suchergebnisse - "Dense Convolutional Network"

DenseNet-FPA: Integrating DenseNet and Flower Pollination Algorithm for Breast Cancer Histopathology Image Classification

Autoren: Wakili, Musa Adamu Shehu, Harisu Abdullahi Abdollahi, Mahdi et al.

Quelle: IEEE Access. 13:145828-145848

Schlagwörter: Breast Cancer, Deep Learning, Densenet, Feature Selection, Flower Pollination Algorithm, Histopathology, Image Classification, Classification (of Information), Computer Aided Diagnosis, Diseases, Image Enhancement, Lung Cancer, Medical Imaging, Patient Treatment, Breast Cancer Diagnosis, Convolutional Networks, Dense Convolutional Network, Features Selection, Histopathological Image Analysis, Images Classification

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:bth-28576
https://doi.org/10.1109/ACCESS.2025.3599319

Future Food Production Prediction Using AROA Based Hybrid Deep Learning Model in Agri-Sector

Autoren: Swathi Baswaraju V. Uma Maheswari krishna Keerthi Chennam et al.

Quelle: Human-Centric Intelligent Systems, Vol 3, Iss 4, Pp 521-536 (2023)

Schlagwörter: Agricultural production, 2. Zero hunger, Dense convolutional network, Accurate food production, Information technology, QA75.5-76.95, 02 engineering and technology, 15. Life on land, T58.5-58.64, Arithmetic optimization algorithm, 01 natural sciences, Livestock yield, Electronic computers. Computer science, 0202 electrical engineering, electronic engineering, information engineering, Rider optimization algorithm, 0105 earth and related environmental sciences

Zugangs-URL: https://doaj.org/article/036d050200b6432681842a405fad5f54

Deep Learning-Assisted Sensor Array Based on Host–Guest Chemistry for Accurate Fluorescent Visual Identification of Multiple Explosives

Autoren: Wenxing Gao Zhibin Wang Qiang Li et al.

Schlagwörter: Biophysics, Biochemistry, Biotechnology, Space Science, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, sensor array leverages, precise visual classification, photoinduced electron transfer, mechanistic studies reveal, dense convolutional network, corresponding rgb values, conventional analytical techniques, site explosive monitoring, multicolor fluorescence responses, highly accurate discrimination, fluorescence sensor array, demonstrating significant potential, innovative deep learning, auncs ’ surface, multiple explosives accurate, deep learning, significant challenge, security monitoring, multiple explosives, fluorescence quenching, fluorescence enhancement, world applications, versatile platform, target explosives

Verfügbarkeit: https://doi.org/10.1021/acs.analchem.5c01326.s001
https://figshare.com/articles/journal_contribution/Deep_Learning-Assisted_Sensor_Array_Based_on_Host_Guest_Chemistry_for_Accurate_Fluorescent_Visual_Identification_of_Multiple_Explosives/29092984

Optimized dense convolutional network with conditional autoregressive value-at-risk for chronic kidney disease detection through group-based search

Autoren: Aher, Chetan Nimba Date, Archana Rajesh Vasekar, Shridevi S. et al.

Quelle: Indonesian Journal of Electrical Engineering and Computer Science; Vol 37, No 3: March 2025; 2009-2020 ; 2502-4760 ; 2502-4752 ; 10.11591/ijeecs.v37.i3

Schlagwörter: Computer and Informatics, Soft Computing, Artificial Intelligence, Chronic kidney disease, Dense convolutional network, Group search optimizer, Min-max normalization, Topsoe similarity

Dateibeschreibung: application/pdf

Relation: https://ijeecs.iaescore.com/index.php/IJEECS/article/view/37677/19047; https://ijeecs.iaescore.com/index.php/IJEECS/article/view/37677

Verfügbarkeit: https://ijeecs.iaescore.com/index.php/IJEECS/article/view/37677
https://doi.org/10.11591/ijeecs.v37.i3.pp2009-2020

A SE-DenseNet-LSTM model for locomotion mode recognition in lower limb exoskeleton

Autoren: Jing Tang Lun Zhao Minghu Wu et al.

Quelle: PeerJ Computer Science, Vol 10, p e1881 (2024)

Schlagwörter: Locomotion mode recognition, Dense convolutional network, Long short-term memory, Lower limb exoskeleton, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://peerj.com/articles/cs-1881.pdf; https://peerj.com/articles/cs-1881/; https://doaj.org/toc/2376-5992

Zugangs-URL: https://doaj.org/article/6919dfa901474c3ab1ddd711e5b9cc6d

Left Ventricle Segmentation Based on a Dilated Dense Convolutional Networks

Autoren: Shengzhou Xu Shiyu Cheng Xiangde Min et al.

Quelle: IEEE Access, Vol 8, Pp 214087-214097 (2020)

Schlagwörter: 03 medical and health sciences, Segmentation, 0302 clinical medicine, left ventricle, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, dilated dense convolutional network, magnetic resonance image, TK1-9971

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/6287639/6514899/09272288.pdf
https://doaj.org/article/29a31e1cb9704d42870d32c170ce5874
https://ieeexplore.ieee.org/document/9272288/
https://doi.org/10.1109/ACCESS.2020.3040888
https://dblp.uni-trier.de/db/journals/access/access8.html#XuCMPH20

Web-based autism screening using facial images and convolutional neural network

Autoren: Ikermane, Mohamed El Mouatasim, Abdelkrim

Quelle: Indonesian Journal of Electrical Engineering and Computer Science; Vol 29, No 2: February 2023; 1140-1147 ; 2502-4760 ; 2502-4752 ; 10.11591/ijeecs.v29.i2

Schlagwörter: ASD screening, Autism spectrum disorder, Control subgradient algorithm, Convolutional neural network, Dense convolutional network, Pytorch

Dateibeschreibung: application/pdf

Relation: https://ijeecs.iaescore.com/index.php/IJEECS/article/view/29307/17066; https://ijeecs.iaescore.com/index.php/IJEECS/article/view/29307

Verfügbarkeit: https://ijeecs.iaescore.com/index.php/IJEECS/article/view/29307
https://doi.org/10.11591/ijeecs.v29.i2.pp1140-1147

IDGAN: Information-Driven Generative Adversarial Network of Coverless Image Steganography

Autoren: Chunying Zhang Xinkai Gao Xiaoxiao Liu et al.

Quelle: Electronics, Vol 12, Iss 13, p 2881 (2023)

Schlagwörter: coverless steganography, generative adversarial network, attention mechanisms, image interpolation, dense convolutional network, Electronics, TK7800-8360

Relation: https://www.mdpi.com/2079-9292/12/13/2881; https://doaj.org/toc/2079-9292; https://doaj.org/article/2dba598c77394997ba54704514f72fcf

Verfügbarkeit: https://doi.org/10.3390/electronics12132881
https://doaj.org/article/2dba598c77394997ba54704514f72fcf

Built-Up Area Mapping for the Greater Bay Area in China from Spaceborne SAR Data Based on the PSDNet and Spatial Statistical Features

Autoren: Wei Zhang Shengtao Lu Deliang Xiang et al.

Quelle: Remote Sensing, Vol 14, Iss 14, p 3428 (2022)

Schlagwörter: synthetic aperture radar (SAR), pseudo-Siamese dense convolutional network, spatial statistical features, built-up area extraction, the Greater Bay Area, Science

Relation: https://www.mdpi.com/2072-4292/14/14/3428; https://doaj.org/toc/2072-4292; https://doaj.org/article/1eb23272a7114d2095e624853eb98c66

Verfügbarkeit: https://doi.org/10.3390/rs14143428
https://doaj.org/article/1eb23272a7114d2095e624853eb98c66

Method for Estimating Monocular Image Depth Based on Dense Convolutional Network

Autoren: WANG Yaqun, DAI Hualin, WANG Li, LI Guoyan

Quelle: Jisuanji gongcheng, Vol 47, Iss 11, Pp 262-267,291 (2021)

Schlagwörter: dense convolutional network, monocular image, encoder, decoder, depth estimation, Computer engineering. Computer hardware, TK7885-7895, Computer software, QA76.75-76.765

Dateibeschreibung: electronic resource

Relation: https://www.ecice06.com/fileup/1000-3428/PDF/20211134.pdf; https://doaj.org/toc/1000-3428

Zugangs-URL: https://doaj.org/article/6d788bc1cc3047dcb762cf5561aca837

A Hybrid DenseNet-LSTM Model for Epileptic Seizure Prediction

Autoren: Sanguk Ryu Inwhee Joe

Quelle: Applied Sciences, Vol 11, Iss 7661, p 7661 (2021)

Schlagwörter: seizure prediction, electroencephalogram (EEG), Discrete Wavelet Transforms (DWT), Dense Convolutional Network (DenseNet), Long Short-Term Memory (LSTM), hybrid model, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Relation: https://www.mdpi.com/2076-3417/11/16/7661; https://doaj.org/toc/2076-3417; https://doaj.org/article/bc4485d992b54e2797561eb6532ab7a7

Verfügbarkeit: https://doi.org/10.3390/app11167661
https://doaj.org/article/bc4485d992b54e2797561eb6532ab7a7

Compressed Video Quality Index Based on Saliency-Aware Artifact Detection

Autoren: Liqun Lin Jing Yang Zheng Wang et al.

Quelle: Sensors, Vol 21, Iss 6429, p 6429 (2021)

Schlagwörter: video quality assessment, saliency detection, perceivable encoding artifacts, Dense Convolutional Network (DenseNet), Chemical technology, TP1-1185

Relation: https://www.mdpi.com/1424-8220/21/19/6429; https://doaj.org/toc/1424-8220; https://doaj.org/article/557ee17cc9ed47179913364295bb1de7

Verfügbarkeit: https://doi.org/10.3390/s21196429
https://doaj.org/article/557ee17cc9ed47179913364295bb1de7

Visual Identification of Software Applications in Infotainment Systems using Convolutional Neural Networks

Autoren: Feng, Jiangfan

Schlagwörter: Visual Geometry Group (VGG), Computer and Information Sciences, Image Classification, Dense Convolutional Network (Densenet), Konvolutionella neurala nätverk (CNN), Bildklassificering, Infotainment System, Residual Network (ResNet), Convolutional Neural Networks (CNNs), Infotainmentsystem, Data- och informationsvetenskap, Residualnätverk (ResNet)

Dateibeschreibung: application/pdf

Zugangs-URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-349207

Urban Tree Species Classification Using a WorldView-2/3 and LiDAR Data Fusion Approach and Deep Learning

Autoren: Sean Hartling Vasit Sagan Paheding Sidike et al.

Quelle: Sensors, Vol 19, Iss 6, p 1284 (2019)

Schlagwörter: deep learning, dense convolutional network (DenseNet), convolutional neural network (CNN), support vector machine (SVM), random forest (RF), tree species classification, data fusion, Chemical technology, TP1-1185

Relation: http://www.mdpi.com/1424-8220/19/6/1284; https://doaj.org/toc/1424-8220; https://doaj.org/article/40acb07011204c8c80b0c65e4c7b81ec

Verfügbarkeit: https://doi.org/10.3390/s19061284
https://doaj.org/article/40acb07011204c8c80b0c65e4c7b81ec

Automatic Extraction of Water and Shadow from SAR Images Based on a Multi-Resolution Dense Encoder and Decoder Network

Autoren: Peng Zhang Lifu Chen Zhenhong Li et al.

Quelle: Sensors, Vol 19, Iss 16, p 3576 (2019)

Schlagwörter: water extraction, shadow extraction, deep learning, synthetic aperture radar (SAR), classification, convolutional neural network (CNN), global convolutional network (GCN), dense convolutional network (DenseNet), CONVOLUTION LONG SHORT-TERM MEMORY (ConvLSTM), Chemical technology, TP1-1185

Relation: https://www.mdpi.com/1424-8220/19/16/3576; https://doaj.org/toc/1424-8220; https://doaj.org/article/93f0d2e2e5fa4402a7927f0caa3864d7

Verfügbarkeit: https://doi.org/10.3390/s19163576
https://doaj.org/article/93f0d2e2e5fa4402a7927f0caa3864d7

Visual Identification of Software Applications in Infotainment Systems using Convolutional Neural Networks ; Visuell identifiering av programvaruapplikationer i infotainmentsystem med hjälp av konvolutionsneurala nätverk

Autoren: Feng, Jiangfan

Schlagwörter: Infotainment System, Image Classification, Convolutional Neural Networks (CNNs), Visual Geometry Group (VGG), Residual Network (ResNet), Dense Convolutional Network (Densenet), Infotainmentsystem, Bildklassificering, Konvolutionella neurala nätverk (CNN), Residualnätverk (ResNet), Computer and Information Sciences, Data- och informationsvetenskap

Dateibeschreibung: application/pdf

Relation: TRITA-EECS-EX; 2024:209

Verfügbarkeit: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-349207

Comparison of Various Autonomous Vehicle Self-Navigation Paradigms

Autoren: McCorkle, Justin

Quelle: Honors College Theses

Schlagwörter: Artificial Intelligence, ResNet, Dense Convolutional Network, Autonomous Vehicles, Artificial Intelligence and Robotics, Electrical and Computer Engineering, Navigation, Guidance, Control, and Dynamics, Software Engineering

Dateibeschreibung: application/pdf

Verfügbarkeit: https://digitalcommons.georgiasouthern.edu/honors-theses/639
https://digitalcommons.georgiasouthern.edu/context/honors-theses/article/1691/viewcontent/JustinMcCorkleHonorsThesis.pdf

Oracle Character Recognition by Nearest Neighbor Classification with Deep Metric Learning

Autoren: Zhang, Yikang Zhang, Heng Liu, Yongge et al.

Schlagwörter: Oracle Character Recognition, Dense Convolutional Network, Deep Metric Learning, Nearest-neighbor Classifier, 工学

Relation: http://ir.ia.ac.cn/handle/173211/28356

Verfügbarkeit: http://ir.ia.ac.cn/handle/173211/28356

Portable, Intelligent Fluorescence Sensing Platform for Dense Convolutional Network-Capable Detection of Indophenol Sulfate and Methylmalonic Acid Using a Luminescent Eu@HOF Film.

Autoren: Hu Z Yan B

Quelle: ACS sensors [ACS Sens] 2023 Nov 24; Vol. 8 (11), pp. 4344-4352. Date of Electronic Publication: 2023 Nov 09.

Publikationsart: Journal Article; Research Support, Non-U.S. Gov't

Info zur Zeitschrift: Publisher: American Chemical Society Country of Publication: United States NLM ID: 101669031 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2379-3694 (Electronic) Linking ISSN: 23793694 NLM ISO Abbreviation: ACS Sens Subsets: MEDLINE

MeSH-Schlagworte: Methylmalonic Acid* , Renal Insufficiency, Chronic*, Humans ; Fluorescence ; Sulfates ; Smartphone

Identification of environmental microorganism using optimally fine-tuned convolutional neural network.

Autoren: Chen WC Liu PY Lai CC et al.

Quelle: Environmental research [Environ Res] 2022 Apr 15; Vol. 206, pp. 112610. Date of Electronic Publication: 2021 Dec 23.

Publikationsart: Journal Article; Research Support, Non-U.S. Gov't

Info zur Zeitschrift: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0147621 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1096-0953 (Electronic) Linking ISSN: 00139351 NLM ISO Abbreviation: Environ Res Subsets: MEDLINE

MeSH-Schlagworte: Neural Networks, Computer*