UCWW.NET
Focus on AI IOT
About
Mainly focusing on high-precision sensor design, low-power transmission devices, cloud middleware development, deep learning algorithm development, and top-level design and development of the Internet of Things. Excellent academic background and professional knowledge, with over 100 papers, and team members with work experience at Google and Intel.
Research interests include the ubiquitous consumer wireless world, the Internet of Things (IoT), cloud computing, big data management, and data mining.
UCWW.NET
115
Total number of published papers
18
Number of papers published in 2023
2
Books
2
Book chapter
Team
Ivan Ganchew
Department of Computer Systems, University of Plovdiv “Paisii Hilendarski”, Plovdiv, Bulgaria
Telecommunications Research Centre, University of Limerick, V94 T9PX Limerick, Ireland
Interests: IoT; novel telecommunications paradigms; future networks and services; smart ubiquitous networking; context-aware networking
Zhanlin Ji
Zhejiang A&F University, Hangzhou 311300, China
Telecommunications Research Centre (TRC), University of Limerick, V94 T9PX Limerick, Ireland
Interests: the ubiquitous consumer wireless world (UCWW); the Internet of Things (IoT);
Zhao Li
The Speech and Language Technology Team (CSLT) of the National Research Center for Information Science and Technology in Beijing, Tsinghua University, Beijing, China
Interests: mobile computing; Internet of Things (IoT); e-health systems; intelligent transportation systems (ITS); home networking; machine learning; digital multimedia
Yongli An
Department of Artificial Intelligence、North China University of Science and Technology、Tangshan, China
Yongli An received the Ph.D. degree in information science from Beijing Jiaotong University, Beijing, China, in 2015. She is currently a Professor with the North China University of Science and Technology, China.
Shengnan Hao
Department of Artificial Intelligence、North China University of Science and Technology、Tangshan, China
Shengnan Hao received the B.S. degree from the North China University of Science and Technology, China, and the M.S. degree from the Beijing University of Technology, China, in 1996 and 2009, respectively.
Jinyun Liu
Department of Artificial Intelligence、North China University of Science and Technology、Tangshan, China
Jinyun Liu has formed a relatively stable research direction in the field of image processing and feature extraction. At the same time, he is actively studying and researching classical theories and algorithms in big data statistics and analysis.
Haiyang Zhang
Haiyang Zhang graduated with a Ph.D. from the university of Limerick.Mainly researching recommendation algorithms.
Chenxu Dai
Chenxu Dai received the B.S. and M.S. degrees from the North China University of Science and Technology. She is currently working as a Research Associate with the North China University of Science and Technology.
Lin Shi
PhD from Beijing University of Science and Technology, mainly researching image AI algorithms.
Student
Meng Wang
Meng Wang is a graduate student majoring in Computer Technology at North China University of Technology. Her research interests include deep learning and machine learning in materials science.
Zixi Gao
Zixi Gao is a graduate student majoring in Computer Technology at North China University of Science and Technology. Her research interests include digital image processing, deep learning, and Linux development.
Jiawei Qian
Jiawei Qian is a graduate student majoring in Computer Technology at North China University of Science and Technology. His research interests include digital image processing, machine learning, deep learning, and Java development.
Hao Guan
Hao Guan is a graduate student majoring in Computer Technology at North China University of Science and Technology. His research interests include Spring Boot,material machine learning,and deep learning.
Yu Lei
Yu Lei is a graduate student majoring in Artificial Intelligence at North China University of Technology. Her research interests include machine learning, deep learning, cross modal, and Java development.
Erjian Gao
Erjian Gao is a master's student majoring in computer technology at North China University of Science and Technology. He mainly conducts research in various aspects of image processing, including tasks such as image classification and object detection. He also possesses outstanding problem-solving abilities and a strong interest in the practical application of technology.
Hui Li
Hui Li is a graduate student majoring in artificial intelligence at North China University of Science and Technology. Her research interests include digital image processing, machine learning, deep learning and audio subtitles.
Haonan Yang
Haonan Yang is a graduate student majoring in Computer Technology at North China University of Science and Technology. His research interests include embedded development, MCU development,deep learning, and C/C++ development.
Zijian Chen
Zijian Chen is a graduate student majoring in Computer Technology at North China University of Science and Technology. I am interested in research on deep learning, fault diagnosis, Java development, and more.
Xingchao Zhang
Xingchao Zhang is a graduate student majoring in Computer Technology at North China University of Science and Technology. His research interests include digital image processing, machine learning, deep learning, and Java development.
Beilong Chen
Beilong Chen is a graduate student majoring in Computer Technology at North China University of Science and Technology. His research interests include digital image processing, machine learning, deep learning, and Java development.
Juncheng Mu
Juncheng Mu is a graduate student majoring in Computer Technology at North China University of Science and Technology. His research interests include digital image processing, machine learning, deep learning, and Java development.
Jianuo Liu
Jianuo Liu is a graduate student majoring in Computer Technology at North China University of Science and Technology. Her research interests include digital image processing, machine learning, deep learning, and Java development.
Yihan Jia
Yihan Jia is a graduate student majoring in Computer Technology at North China University of Science and Technology. Her research interests include digital image processing, machine learning, deep learning, and Java development.
Haoran Sun
Haoran Sun is a graduate student majoring in Computer Technology at North China University of Science and Technology. His research interests include digital image processing, machine learning, deep learning, and Java development.
Wenyan Zhou
Wenyan Zhou is a current master’s student at the School of Artificial Intelligence, North China University of Science and Technology. Her research interests include deep learning, natural language processing and knowledge graph. Currently, her main research interest is the application of named entity recognition techniques in the field of Chinese medical text.
Xuan Wang
Xuan Wang is a graduate student majoring in Computer Technology at North China University of Science and Technology. His research interests include digital image processing, machine learning, deep learning, and Java development.
Jianhua Pang
Jianhua Pang is a graduate student majoring in Computer Technology at North China University of Science and Technology. His research interests include digital image processing, machine learning, deep learning, and Java development.
Researcher
Na Yuan
NA YUAN received a B.S. degree from Hebei University of Technology in 2014. She is currently a lecturer at Tangshan University. Her research interests include intelligent control, machine vision, and graphic image processing.
YaFeng Wu
YAFENG WU was born in 1978. He is currently a Associate Professor with the North China University of Science and Technology. He has published two SCI papers and authorized two invention patents.
Zhiwu Wang
ZHIWU WANG received the Ph.D. degree from Tianjin Medical University in 2014, He is currently a chief physician of the Second Department of Radiotherapy and Chemotherapy of Tangshan People's Hospital. He is engaged in the comprehensive medical treatment of lung cancer and digestive system tumors.
Chunling Liu
CHUNLING LIU graduated with a Ph.D. in 2014 from the Chinese Academy of Medical Sciences, Peking Union Medical College, specializing in cell research. She is currently serving as an attending physician in the Pathology Department of Tangshan People's Hospital.
Wei Peng
Wei Peng received the B.s. degree from Hebei Medical University, in 1991, and the M.s. degree from the NorthChina Coal Medical College, in 2005. Her current research interests include machine vision and medical imageprocessing.
Zhu Fan
Zhu fan was born in Baishan, jilin. She received the bachelor's degree from the Clinical Department, North chinaUniversity of science and Technology, and the master's degree from the Respiratory Medicine DepartmentTianin Medical University. she was with the Respiratory Medicine Ward, Affiliated Hospital, North china Universityof Science and Technology, for five years. Currently, she is engaged in clinical treatment and teaching in theRespiratory Medicine Department, She is proficient in the diaanosis and treatment of common respiratorydiseases, frequent diseases, and critical illnesses.
Bao Zhang
Bao Zhang was born in Tangshan, Hebei, in 1979. she received the master’s degree in pathogen biology from theNorth China University of Science and Technology. She is currently working with the Clinical Laboratory ofTangshan the Seventh Hospital Tangshan workers' Hospital East Hospital District. She has presided over andparticipated in five municipal scientific research projects. She has published more than 20 papers in Chinese Corejournals and science Core journals as the first author or corresponding author. Her main research interest includesthe experimental diagnosis of infectious diseases.
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Open topic
· Automatic diagnosis of skin diseases based on deep learning technology.
· Research on DICOM medical images and deep learning for automatic disease diagnosis.
· A study on adolescent spinal care, conducted in-depth learning screening, guidance and care for spinal problems among 10000 students in Tangshan.
· Research on Distributed MQTT and Distributed TDengine.
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Smart healthcare
With the advent of the aging age of humanity, wearable medical sensors and their related smart medical networks have been widely applied. At present, there are more and more manufacturers producing wearable medical sensors. Due to the lack of adherence to unified standards, most sensor providers adopt their own standards. This system does not have plug and play medical functions at the bedside, which is a bottleneck in the development of wearable medical sensor networks.
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IoT services
This system is an important component of future wireless ubiquitous networks (UCWW), mainly responsible for broadcasting, discovering, and using wireless business services (Advertisement, Delivery, Association). According to the standards of Next Generation Networks (NGNs), future wireless network services can be divided into wireless access services and wireless application services. With the significant increase in the number of wireless services, wireless ubiquitous service broadcasting systems need to use an independent channel for targeted delivery.
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Smart learning
The MobileLearning (mLearning) system consists of four parts: InfoStation, Multiple Agent System, system architecture, and content ubiquitous computing. This project is an international team project developed by the Telecommunications Research Center (UL) of the University of Limerick in Ireland and the units of Ji Zhanlin. UL is responsible for the basic research of InfoStation and Multiple Agent System, while domestic units are responsible for the basic research of system architecture and content ubiquitous computing.
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DICOM imaging technology
DICOM medical imaging 3D image rapid imaging, DICOM imaging technology, mainly researching RayCasting technology based on Java/Android.
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Internet of Things Top Design and Key Technology
This book comprehensively, systematically, and objectively introduces the top-level design, core technologies, and practical applications of the Internet of Things. The author innovatively proposes the construction of a national Internet of Things system architecture and operation platform in China's development of the Internet of Things, aiming to form the "Internet of Things modern information service industry" and "Internet of Things terminal industry cluster" in China, and envisioning the emergence of Internet of Things operators in various industries.
Publications
2024
S. Hao, X. Li, W. Peng, Z. Fan, Z. Ji and I. Ganchev, "YOLO-CXR: A Novel Detection Network for Locating Multiple Small Lesions in Chest X-Ray Images," in IEEE Access, vol. 12, pp. 156003-156019, 2024, doi: 10.1109/ACCESS.2024.3482102.
S. Hao et al., "MEFP-Net: A Dual-Encoding Multi-Scale Edge Feature Perception Network for Skin Lesion Segmentation," in IEEE Access, vol. 12, pp. 140039-140052, 2024, doi: 10.1109/ACCESS.2024.3467678.
Z. Ji, X. Wang, C. Liu, Z. Wang, N. Yuan and I. Ganchev, "EFAM-Net: A Multi-Class Skin Lesion Classification Model Utilizing Enhanced Feature Fusion and Attention Mechanisms," in IEEE Access, vol. 12, pp. 143029-143041, 2024, doi: 10.1109/ACCESS.2024.3468612.
安永丽,李宗瑞,李娜,纪占林. 基于DCGAN的加密端到端通信系统设计[J/OL].南京邮电大学学报(自然科学版),1-9[2024-07-25].http://kns.cnki.net/kcms/detail/32.1772.tn.20240701.1701.038.html.
B. Chen et al., "EFS-YOLO: A Lightweight Network Based on Steel Strip Surface Defect Detection," Measurement Science and Technology, 2024, https://doi.org/10.1088/1361-6501/ad66fe
"DLGRAFE-Net: A Double Loss Guided Residual Attention and Feature Enhancement Network for Polyp Segmentation" has been accepted for publication in PLOS ONE.
L. Shi et al., "DCM-CNER: A Dual-Channel Model for Clinical Named Entity Recognition Based on Embedded ConvNet and Gated Dilated CNN," in IEEE Access, doi: 10.1109/ACCESS.2024.3422677.
Y. An, Y. Feng, N. Yuan, Z. Ji and I. Ganchev, "An improved inverted residual network model for underwater image enhancement," in IEEE Access, doi: 10.1109/ACCESS.2024.3404613.
J. Liu, J. Mu, H. Sun, C. Dai, Z. Ji and I. Ganchev, "BFG&MSF-Net: Boundary Feature Guidance and Multi-Scale Fusion Network for Thyroid Nodule Segmentation," in IEEE Access, vol. 12, pp. 78701-78713, 2024, doi: 10.1109/ACCESS.2024.3407795.
Ji, Z.; Li, X.; Liu, J.; Chen, R.; Liao, Q.; Lyu, T.; Zhao, L. LightCF-Net: A Lightweight Long-Range Context Fusion Network for Real-Time Polyp Segmentation. Bioengineering 2024, 11, 545. https://doi.org/10.3390/bioengineering11060545
Shi, L., Zhang, R., Wu, Y. et al. AHC-Net: a road crack segmentation network based on dual attention mechanism and multi-feature fusion. SIViP (2024). https://doi.org/10.1007/s11760-024-03234-w
Hao, S.; Wang, H.; Chen, R.; Liao, Q.; Ji, Z.; Lyu, T.; Zhao, L. DTONet a Lightweight Model for Melanoma Segmentation. Bioengineering 2024, 11, 390. https://doi.org/10.3390/bioengineering11040390
Xu, Y.; Liu, J.; Cui, Z.; Liu, Z.; Dai, C.; Zang, X.; Ji, Z. Economic Scheduling Model of an Active Distribution Network Based on Chaotic Particle Swarm Optimization. Information 2024, 15, 225. https://doi.org/10.3390/info15040225
Xu, Y.; Han, J.; Yin, Z.; Liu, Q.; Dai, C.; Ji, Z. Voltage and Reactive Power-Optimization Model for Active Distribution Networks Based on Second-Order Cone Algorithm. Computers 2024, 13, 95. https://doi.org/10.3390/computers13040095
J. Zhang, C. Du, J. Jiang, Z. Ji, I. Ganchev. 2024. “Personalized Recommendations of Drugs to Patients” (plenary paper?). Proc. of 5th Int. Conf. On Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO 2024), pp. x1-x7. 29 April - 1 May. London, UK.
X. Zeng, M. Huang, H. Zhang, Z. Ji, I. Ganchev. 2024. “Novel Human Activity Recognition and Recommendation Models for Maintaining Good Health of Mobile Users”. WSEAS Transactions on Information Science and Applications, Vol. 21, Art. #4, Pp. 33-46. January.
K.Sun, Q.Wei, Z.Ji, S.Hampshire, Y.Dong, Structure optimization of ceramic-based metal−organic framework membrane for efficient desalination, Ceramics International, 2024, https://doi.org/10.1016/j.ceramint.2024.03.363.
Z. Ji et al., "BGRD-TransUNet: A Novel TransUNet-Based Model for Ultrasound Breast Lesion Segmentation," in IEEE Access, vol. 12, pp. 31182-31196, 2024, doi: 10.1109/ACCESS.2024.3368170.
Ji, Z., Liu, J., Mu, J. et al. ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels. Med Biol Eng Comput (2024). https://doi.org/10.1007/s11517-024-03052-9
安永丽,蔡浩然,胡泽冰,纪占林. 基于特征融合的大规模MIMO系统CSI反馈 [J/OL]. 南京邮电大学学报(自然科学版), 1-7[2024-03-06]. http://kns.cnki.net/kcms/detail/32.1772.TN.20240129.1638.004.html.
Ji, Z., Mu, J., Liu, J. et al. ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation. Med Biol Eng Comput (2024). https://doi.org/10.1007/s11517-024-03025-y
X. Zeng et al., "DSP-KD: Dual-Stage Progressive Knowledge Distillation for Skin Disease Classification," Bioengineering, vol. 11, no. 1, p. 70, 2024.
2023
X. Zeng, M. Huang, H. Zhang, Z. Ji, I. Ganchev, “A Novel Human Activity Recognition Model”. Proc. of 8th International Conference on Mathematics and Computers in Sciences and Industry, pp. 2023. 14-16 October, Athens, Greece.
L. Shi, X. Zou, C. Dai, and Z. Ji, "Uniting Multi-Scale Local Feature Awareness and the Self-Attention Mechanism for Named Entity Recognition," Mathematics, vol. 11, no. 11, p. 2412, 2023.
L. Shi, Z. Yang, Z. Ji, and I. Ganchev, "Complex Knowledge Graph Embeddings Based on Convolution and Translation," Mathematics, vol. 11, no. 12, p. 2627, 2023.
Z. Ji et al., "ResDSda_U-Net: A novel U-Net based residual network for segmentation of pulmonary nodules in lung CT images," IEEE Access, 2023.
Z. Ji et al., "ELCT-YOLO: An Efficient One-Stage Model for Automatic Lung Tumor Detection Based on CT Images," Mathematics, vol. 11, no. 10, p. 2344, 2023.
Z. Ji et al., "U-Net_dc: A Novel U-Net-Based Model for Endometrial Cancer Cell Image Segmentation," Information, vol. 14, no. 7, p. 366, 2023.
Z. Ji et al., "Lung nodule detection in medical images based on improved YOLOv5s," IEEE Access, 2023.
Z. Ji, C. Du, J. Jiang, L. Zhao, H. Zhang, and I. Ganchev, "Improving non-negative Positive-Unlabeled learning for news headline classification," IEEE Access, 2023.
S. Hao et al., "ConvNeXt-ST-AFF: A Novel Skin Disease Classification Model Based on Fusion of ConvNeXt and Swin Transformer," IEEE Access, 2023.
S. Hao et al., "G2-ResNeXt: a novel model for ECG signal classification," IEEE Access, 2023.
S. Hao et al., "GSCEU-Net: An End-to-End Lightweight Skin Lesion Segmentation Model with Feature Fusion Based on U-Net Enhancements," Information, vol. 14, no. 9, p. 486, 2023.
S. Hao et al., "GSCEU-Net: An End-to-End Lightweight Skin Lesion Segmentation Model with Feature Fusion Based on U-Net Enhancements," Information, vol. 14, no. 9, 2023, doi: 10.3390/info14090486.
S. Hao et al., "CACDU-Net: A novel DoubleU-Net based semantic segmentation model for skin lesions detection in images," IEEE Access, 2023.
I. Ganchev, Z. Ji, and M. O’Droma, "Internet of Things Horizontal Platform Development for Expanded Application Scenarios and Use Cases," in Journal of Physics: Conference Series, 2023, vol. 2548, no. 1: IOP Publishing, p. 012009.
I. Ganchev, Z. Ji, and M. O’Droma, "Horizontal IoT Platform EMULSION," Electronics, vol. 12, no. 8, p. 1864, 2023.
I. Ganchev and Z. Ji, "A Learning-Based End-to-End Wireless Communication System Utilizing a Deep Neural Network Channel Module," Authorea Preprints, 2023.
W. Bai et al., "Two Novel Models for Traffic Sign Detection Based on YOLOv5s," Axioms, vol. 12, no. 2, p. 160, 2023.
Y. An, S. Wang, L. Zhao, Z. Ji, and I. Ganchev, "A Learning-Based End-to-End Wireless Communication System Utilizing a Deep Neural Network Channel Module," IEEE Access, vol. 11, pp. 17441-17453, 2023.
Y. An, Z. Hu, H. Cai, and Z. Ji, "CNNs-based end-to-end asymmetric encrypted communication system," Intelligent and Converged Networks, vol. 4, no. 4, pp. 313-325, 2023.
Y. An, H. Bai, S. Zhang, and Z. Ji, "Physical layer authentication of MIMO-STBC systems based on constellation dithering," Intelligent and Converged Networks, vol. 4, no. 4, pp. 355-365, 2023.
Y. An, Z. Ji et al., "Lung Nodule Detection in Medical Images Based on Improved YOLOv5s," in IEEE Access, vol. 11, pp. 76371-76387, 2023, doi: 10.1109/ACCESS.2023.3296530.
2007-2022
J. Zhao et al., "Improved vision-based vehicle detection and classification by optimized YOLOv4," IEEE Access, vol. 10, pp. 8590-8603, 2022.
X. Yang et al., "Detection of river floating garbage based on improved YOLOv5," Mathematics, vol. 10, no. 22, p. 4366, 2022.
X. Yang et al., "Remote sensing image detection based on YOLOv4 improvements," IEEE Access, vol. 10, pp. 95527-95538, 2022.
I. Ganchev, Z. Ji, and M. O’Droma, "Designing a Low-cost High-end Android-based Wireless Board for the EMULSION IoT Platform," in 2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC), 2022: IEEE, pp. 1-4.
I. Ganchev, Z. Ji, and M. O’Droma, "Intelligent System for Recommendation of Mobile Services to Consumers," in 2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC), 2022: IEEE, pp. 1-4.
I. Ganchev and Z. Ji, "Designing a heterogeneous sensor tier for the EMULSION IoT platform," Wseas Transactions on Systems and Control, vol. 17, pp. 83-90, 2022.
I. Ganchev and Z. Ji, "Creating a Sensor Tier for the EMULSION IoT Platform with Low-Cost Electronic Modules," in Journal of Physics: Conference Series, 2022, vol. 2226, no. 1: IOP Publishing, p. 012009.
I. Ganchev and Z. Ji, "The Use of a Modelling & Simulation Tier by the EMULSION IoT Platform," WSEAS Transactions on Systems and Control, vol. 17, pp. 133-141, 2022.
Y. An, J. Yue, L. Chen, and Z. Ji, "Channel Estimation for One-Bit Massive MIMO Based on Improved CGAN," Journal of Communications and Information Networks, vol. 7, no. 2, pp. 214-220, 2022.
Y. An, M. Wang, L. Chen, and Z. Ji, "DCGAN-based symmetric encryption end-to-end communication systems," AEU-International Journal of Electronics and Communications, vol. 154, p. 154297, 2022.
D. Zhou et al., "Novel SDDM rating prediction models for recommendation systems," IEEE Access, vol. 9, pp. 101197-101206, 2021.
J. Zhao, F. He, Z. Ji, and I. Ganchev, "PM2. 5 Prediction Based on the Combined EMD-LSTM Model," in 2021 International Conference on Computational Science and Computational Intelligence (CSCI), 2021: IEEE, pp. 193-195.
H. Zhang, I. Ganchev, N. S. Nikolov, Z. Ji, and M. O’Droma, "FeatureMF: an item feature enriched matrix factorization model for item recommendation," IEEE Access, vol. 9, pp. 65266-65276, 2021.
Y. Wang, H. Zhang, Y. An, Z. Ji, and I. Ganchev, "RG hyperparameter optimization approach for improved indirect prediction of blood glucose levels by boosting ensemble learning," Electronics, vol. 10, no. 15, p. 1797, 2021.
Y. Li et al., "Cellular traffic prediction via a deep multi-reservoir regression learning network for multi-access edge computing," IEEE Wireless Communications, vol. 28, no. 5, pp. 13-19, 2021.
E. Jing, H. Zhang, Z. Li, Y. Liu, Z. Ji, and I. Ganchev, "ECG heartbeat classification based on an improved ResNet-18 model," Computational and Mathematical Methods in Medicine, vol. 2021, 2021.
J. Jiang et al., "Enhancements of attention-based bidirectional lstm for hybrid automatic text summarization," IEEE Access, vol. 9, pp. 123660-123671, 2021.
Z. Ji and I. Ganchev, "Design and Implementation of a Low-Cost Core Board for Mobile IoT Rapid System Prototyping and Service Roll-Out," in 2021 XXXIVth General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS), 2021: IEEE, pp. 1-3.
I. Ganchev, Z. Ji, and M. O’Droma, "Service Prototype Provisioning for the EMULSION IoT Platform," in 2021 International Conference on Computational Science and Computational Intelligence (CSCI), 2021: IEEE, pp. 1540-1543.
I. Ganchev, Z. Ji, and M. O’Droma, "A Service Tier Design for the EMULSION IoT Platform," in 2021 International Conference on Computational Science and Computational Intelligence (CSCI), 2021: IEEE, pp. 1500-1504.
I. Ganchev, Z. Ji, and M. O’Droma, "A Client Tier Design for the EMULSION IoT Platform," in 2021 International Conference Automatics and Informatics (ICAI), 2021: IEEE, pp. 275-278.
I. Ganchev, Z. Ji, and M. O’Droma, "A Modelling & Simulation Tier Design for the EMULSION IoT Platform," in 2021 International Conference Automatics and Informatics (ICAI), 2021: IEEE, pp. 279-282.
I. Ganchev, Z. Ji, and M. O’Droma, "A Sensor Tier Design for the EMULSION IoT Platform," in 2021 International Conference Automatics and Informatics (ICAI), 2021: IEEE, pp. 283-286.
Y. An, S. Zhang, and Z. Ji, "A tag-based PHY-layer authentication scheme without key distribution," IEEE Access, vol. 9, pp. 85947-85955, 2021.
Y. An, C. Zhang, Z. Ji, and Z. Zhou, "Multi-User Secure Receiving Algorithm Based on Blind Recovery in MIMO Network," IEEE Access, vol. 9, pp. 45347-45355, 2021.
I. Ganchev, Z. Ji, and M. O’Droma, "Designing a Communication Tier for the IoT Platform EMULSION," in 2020 International Conference Automatics and Informatics (ICAI), 2020: IEEE, pp. 1-3.
I. Ganchev, Z. Ji, and M. O’Droma, "Designing a Low-Cost Location Tracker for Use in IoT Applications," in 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science, 2020: IEEE, pp. 1-2.
I. Ganchev, Z. Ji, and M. O'Droma, "Low-cost and ultra-low-power consuming RTUs for use in IoT systems," Int. J. Circ., Syst. and Sig. Process, vol. 14, pp. 76-82, 2020.
Y. An, J. Bai, X. Liu, and Z. Ji, "Research on MIMO antenna isolation based on novel mesh isolation rings," Integrated Ferroelectrics, vol. 212, no. 1, pp. 104-119, 2020.
I. Ganchev, Z. Ji, and M. O’Droma, "A generic multi-service cloud-based IoT operational platform-EMULSION," in 2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO), 2019: IEEE, pp. 100-105.
I. Ganchev, Z. Ji, and M. O’Droma, "Designing a cloud tier for the IoT platform EMULSION," WSEAS Transactions on Systems and Control, vol. 14, no. 46, pp. 375-383, 2019.
I. Ganchev, Z. Ji, and M. O’Droma, "Designing an Ultra-Low-Power RTU for Use in NB-IoT Water Applications," in MATEC Web of Conferences, 2019, vol. 292: EDP Sciences, p. 02004.
I. Ganchev, Z. Ji, and M. O'Droma, "Designing a low-cost DMR module for use in M2M/IoT applications," in 2018 2nd URSI Atlantic Radio Science Meeting (AT-RASC), 2018: IEEE, pp. 1-2.
H. Zhang, I. Ganchev, N. S. Nikolov, Z. Ji, and M. O’Droma, "Research Article A Hybrid Service Recommendation Prototype Adapted for the UCWW: A Smart-City Orientation," 2017.
H. Zhang, I. Ganchev, N. S. Nikolov, Z. Ji, and M. O’Droma, "A hybrid service recommendation prototype adapted for the UCWW: A smart-city orientation," Wireless Communications and Mobile Computing, vol. 2017, 2017.
H. Zhang, I. Ganchev, N. S. Nikolov, Z. Ji, and M. O'Droma, "Weighted matrix factorization with Bayesian personalized ranking," in 2017 Computing Conference, 2017: IEEE, pp. 307-311.
H. Zhang, I. Ganchev, N. S. Nikolov, Z. Ji, and M. O'Droma, "Hybrid recommendation for sparse rating matrix: A heterogeneous information network approach," in 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2017: IEEE, pp. 740-744.
I. Ganchev, Z. Ji, M. O'Droma, and L. Zhao, "Smart recommendation of mobile services to consumers," IEEE Transactions on Consumer Electronics, vol. 63, no. 4, pp. 499-508, 2017.
I. Ganchev, Z. Ji, and M. O'Droma, "An iot-based smart electric heating control system: Design and implementation," in 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), 2017: IEEE, pp. 760-762.
I. Ganchev, Z. Ji, and M. O'Droma, "Designing a low-cost data transfer unit for use in IoT applications," in 2016 8th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 2016: IEEE, pp. 85-88.
I. Ganchev, Z. Ji, and M. O'Droma, "A conceptual framework for building a mobile services' recommendation engine," in 2016 IEEE 8th International Conference on Intelligent Systems (IS), 2016: IEEE, pp. 285-289.
I. Ganchev, Z. Ji, and M. O'Droma, "The creation of a data management platform for use in the UCWW," in 2016 SAI Computing Conference (SAI), 2016: IEEE, pp. 585-588.
I. Ganchev, Z. Ji, and M. O'Droma, "A data management platform for recommending services to consumers in the UCWW," in 2016 IEEE International Conference on Consumer Electronics (ICCE), 2016: IEEE, pp. 405-406.
I. Ganchev, Z. Ji, M. O'Droma, and C. Dai, "A CIM System for Use in the UCWW," in 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 2015: IEEE, pp. 72-75.
I. Ganchev, Z. Ji, and M. O'Droma, "Making the UCWW a reality," in 2015 IEEE International Symposium on Technology and Society (ISTAS), 2015: IEEE, pp. 1-4.
I. Ganchev, Z. Ji, and M. O'Droma, "A distributed cloud-based service recommendation system," in 2015 International Conference on Computing and Network Communications (CoCoNet), 2015: IEEE, pp. 212-215.
I. Ganchev, Z. Ji, and M. O'Droma, "UCWW cloud-based ABC&S mobile App," in 2015 1st URSI Atlantic Radio Science Conference (URSI AT-RASC), 2015: IEEE, pp. 1-1.
Z. Ji, I. Ganchev, and M. OŠDroma, "i., and Zhao," Q.,“A Push-Notification Service for Use in the UCWW, 2014.
Z. Ji, I. Ganchev, M. O’Droma, X. Zhang, and X. Zhang, "A cloud-based X73 ubiquitous mobile healthcare system: design and implementation," The Scientific World Journal, vol. 2014, 2014.
Z. Ji, I. Ganchev, M. O'Droma, and Q. Zhao, "A push-notification service for use in the UCWW," in 2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 2014: IEEE, pp. 318-322.
Z. Ji, I. Ganchev, M. O'Droma, L. Zhao, and X. Zhang, "A cloud-based car parking middleware for IoT-based smart cities: Design and implementation," Sensors, vol. 14, no. 12, pp. 22372-22393, 2014.
Z. Ji, I. Ganchev, M. O'Droma, and X. Zhang, "A cloud-based intelligent car parking services for smart cities," in 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS), 2014: IEEE, pp. 1-4.
Z. Ji, I. Ganchev, M. O'Droma, and T. Ding, "A distributed redis framework for use in the ucww," in 2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 2014: IEEE, pp. 241-244.
Z. Ji, I. Ganchev, and M. O'droma, "An InfoStation-based distributed mLearning system," in 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies, 2014: IEEE, pp. 137-140.
I. Ganchev, Z. Ji, and M. O’Droma, "A Generic IoT Architecture for Smart Cities. 2014," ed, 2014.
I. Ganchev, Z. Ji, and M. O'Droma, "A cloud-based service recommendation system for use in UCWW," in 2014 11th International Symposium on Wireless Communications Systems (ISWCS), 2014: IEEE, pp. 791-795.
I. Ganchev, Z. Ji, and M. O'Droma, "A generic IoT architecture for smart cities," 2014.
D. Meere, I. Ganchev, Z. Ji, and M. O’Droma, "Contextualised mLearning service delivery through a multi-agent platform," International Journal of Computational Intelligence Studies 6, vol. 2, no. 3-4, pp. 218-240, 2013.
Z. Ji, X. Zhang, I. Ganchev, and M. O'Droma, "Development of a sencha-touch mtest mobile app for a mlearning system," in 2013 IEEE 13th International Conference on Advanced Learning Technologies, 2013: IEEE, pp. 210-211.
Z. Ji, I. Ganchev, M. O'Droma, and X. Zhang, "A realisation of broadcast cognitive pilot channels piggybacked on T‐DMB," Transactions on Emerging Telecommunications Technologies, vol. 24, no. 7-8, pp. 709-723, 2013.
Z. Ji, Y. An, I. Ganchev, and M. ODroma, "The mServices GUI Architectures Design for the mLearning System," in 2013 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 2013: IEEE, pp. 243-246.
S. Hao, Y. Han, J. Zhang, and Z. Ji, "Automatic isolation of carpal-bone in hand x-ray medical image," in Informatics and management science I, 2013: Springer London, pp. 657-662.
I. Ganchev, M. O’Droma, N. S. Nikolov, and Z. Ji, "A ucww cloud-based system for increased service contextualization in future wireless networks," in 2nd International Conference on Telecommunications and Remote Sensing, 2013, pp. 69-78.
I. Ganchev, D. Meere, Z. Ji, and M. O’Droma, "An Agent-based mTest and mAssessment Service Delivery Platform," 2013.
Y. An, Y. Xiao, D. Wang, and Z. Ji, "Security Spectrum Auction Framework for Cognitive Radio Networks," J. Comput., vol. 8, no. 11, pp. 2802-2808, 2013.
D. Meere, Z. Ji, I. Ganchev, and M. O'Dróma, "A Framework Design for Utilization in Facilitating Contextualised mLearning," GSTF Journal on Computing, vol. 1, no. 4, 2012.
Z. Ji, X. Zhang, I. Ganchev, and M. O'Droma, "A content adaptation middleware for use in a mHealth system," in 2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom), 2012: IEEE, pp. 455-457.
Z. Ji, X. Zhang, I. Ganchev, and M. O'Droma, "A personalized middleware for ubiquitous mHealth services," in 2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom), 2012: IEEE, pp. 474-476.
Z. Ji, D. Meere, I. Ganchev, and M. O'Droma, "Implementation and Deployment of an Intelligent Framework for Utilization within an InfoStation Environment," J. Softw., vol. 7, no. 5, pp. 935-942, 2012.
Z. Ji, I. Ganchev, M. O'Droma, and P. Flynn, "Description of services for advertisement, discovery and association by means of wireless billboard channels," Advanced Science Letters, vol. 7, no. 1, pp. 205-209, 2012.
I. Ganchev, Z. Ji, and M. O'Droma, "A realization of cognitive pilot channels through wireless billboard channel infrastructure for cognitive radio," in 2012 2nd Baltic Congress on Future Internet Communications, 2012: IEEE, pp. 19-25.
Z. Ji, D. Meere, and I. Ganchev, "Implementation of an Intelligent Framework for Utilization within an InfoStation-based mLearning Environment," in 2011 IEEE 11th International Conference on Advanced Learning Technologies, 2011: IEEE, pp. 310-311.
Z. Ji, I. Ganchev, and M. O’Droma, "Building a WBC Software Testbed for the Ubiquitous Consumer Wireless World," in Computer Science for Environmental Engineering and EcoInformatics: International Workshop, CSEEE 2011, Kunming, China, July 29-31, 2011, Proceedings, Part II, 2011: Springer Berlin Heidelberg, pp. 124-129.
Z. Ji, I. Ganchev, and M. O’Droma, "An Intelligent Application for the WBC Application Enabler Sub-layer: Design and Implementation," in Computer Science for Environmental Engineering and EcoInformatics: International Workshop, CSEEE 2011, Kunming, China, July 29-31, 2011, Proceedings, Part II, 2011: Springer Berlin Heidelberg, pp. 7-12.
Z. Ji, I. Ganchev, and M. O'Droma, "A DVB-H testbed for wireless billboard channel simulation," in 2011 XXXth URSI General Assembly and Scientific Symposium, 2011: IEEE, pp. 1-4.
Z. Ji, I. Ganchev, and M. O'Droma, "A terrestrial digital multimedia broadcasting testbed for wireless billboard channels," in 2011 IEEE International Conference on Consumer Electronics (ICCE), 2011: IEEE, pp. 593-594.
Z. Ji, I. Ganchev, and M. O'Droma, "Advertisement Data Management and Application Design in WBCs," J. Softw., vol. 6, no. 6, pp. 1001-1008, 2011.
Z. Ji, I. Ganchev, and M. O'Droma, "An iWBC consumer application for'always best connected and best served': Design and implementation," IEEE Transactions on Consumer Electronics, vol. 57, no. 2, pp. 462-470, 2011.
Z. Ji, I. Ganchev, P. Flynn, and M. O’Droma, "Service Advertisements’ Formatting for Wireless Billboard Channels," in Computer Science for Environmental Engineering and EcoInformatics: International Workshop, CSEEE 2011, Kunming, China, July 29-31, 2011, Proceedings, Part II, 2011: Springer Berlin Heidelberg, pp. 165-170.
Y. Dong, S. Hampshire, J.-e. Zhou, Z. Ji, J. Wang, and G. Meng, "Sintering and characterization of flyash-based mullite with MgO addition," Journal of the European Ceramic Society, vol. 31, no. 5, pp. 687-695, 2011.
J. Zhanlin, G. Ivan, and O. D. Máirtín, "Performance Analysis of" WBC over DVB-H" Link Layer," WCN-EURASIP Journal on Wireless Communications and Networking, vol. 2010, no. 1, p. 306709, 2010.
Z. Ji, I. Ganchev, and M. O’Droma, "Research Article Performance Analysis of “WBC over DVB-H” Link Layer," 2010.
Z. Ji, I. Ganchev, and M. O'Droma, "Novel Scheme and System for Mobile Services: Advertisement and Discovery in the Future Telecommunications World," in Telecommunications: The Infrastructure for the 21st Century, 2010: VDE, pp. 1-6.
Z. Ji, I. Ganchev, and M. O'Droma, "iWBC-MIDP Client Application Design and Implementation," in 2010 IEEE 71st Vehicular Technology Conference, 2010: IEEE, pp. 1-4.
Z. Ji, I. Ganchev, and M. O'Droma, "Performance analysis of" WBC over DVB-H" link layer," EURASIP Journal on Wireless Communications and Networking, vol. 2010, pp. 1-15, 2010.
Z. Ji, I. Ganchev, and M. O'Droma, "" WBC over DVB-H" testbed design, development and results," EURASIP Journal on Wireless Communications and Networking, vol. 2010, pp. 1-18, 2010.
Z. Ji, I. Ganchev, P. Flynn, and M. O’Droma, "Formal Description of Services for Advertisement on Wireless Billboard Channels," in Proc. of the IEEE 14th International Symposium on Consumer Electronics (IEEE ISCE2010), 2010, pp. 366-371.
I. Ganchev, M. S. O'Droma, J. I. Jakab, Z. Ji, and D. Tairov, "A new global ubiquitous consumer environment for 4G wireless communications," in Fourth-Generation Wireless Networks: Applications and Innovations: IGI Global, 2010, pp. 20-45.
Y. Dong et al., "Recycling of fly ash for preparing porous mullite membrane supports with titania addition," Journal of hazardous materials, vol. 180, no. 1-3, pp. 173-180, 2010.
Z. J. Z. Ji, I. Ganchev, and M. O'Droma, "Novel cross-layer decoding design for ȘWBC over DVB-Hș link layer," in 2009 6th International Symposium on Wireless Communication Systems, 2009: IEEE, pp. 687-690.
Z. Ji, S. Xu, G. Wang, and L. Hong, "Cluster Reconstruction Strategy Based on Energy Prediction Mechanism in Wireless Sensor Networks," in 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing, 2009: IEEE, pp. 1-4.
Z. Ji, I. Ganchev, and M. O'Droma, "Optimal IP Packet Length for WBC over DVB-H," in VTC Fall, 2009.
Z. Ji, I. Ganchev, and M. O'Droma, "Wireless Billboard Channels established over DAB/T-DMB Operating in Transmission Mode I," in 2009 Third International Conference on Next Generation Mobile Applications, Services and Technologies, 2009: IEEE, pp. 149-152.
Z. Ji, I. Ganchev, and M. O'Droma, "On Overhead Efficiency of a'WBC over DVB-H'System," in 2009 IEEE 70th Vehicular Technology Conference Fall, 2009: IEEE, pp. 1-4.
Z. Ji, I. Ganchev, and M. O'Droma, "New method for analyzing the transport stream packet error rate of'WBC over DVB-H'," in 2009 IEEE Wireless Communications and Networking Conference, 2009: IEEE, pp. 1-4.
Z. Ji, I. Ganchev, and M. O'Droma, "Building a ‘WBC over DVB-H’software testbed," in 2009 IEEE 13th International Symposium on Consumer Electronics, 2009: IEEE, pp. 769-772.
Z. Ji, I. Ganchev, and M. O'Droma, "Intelligent software architecture for the service layer of wireless billboard channels," in 2009 6th IEEE Consumer Communications and Networking Conference, 2009: IEEE, pp. 1-2.
Z. Ji, I. Ganchev, and M. O'Droma, "Performance evaluation of'WBC over DVB-H'system," IEEE Transactions on Consumer Electronics, vol. 55, no. 2, pp. 754-762, 2009.
Z. Ji, I. Ganchev, and M. O'Droma, "Building a heterogeneous software architecture for the WBC service layer," 2008.
Z. Ji, I. Ganchev, and M. O'Droma, "Efficient collecting, clustering, scheduling, and indexing schemes for advertisement of services over wireless billboard channels," in 2008 International Conference on Telecommunications, 2008: IEEE, pp. 1-6.
Z. Ji, I. Ganchev, and M. O'Droma, "Reliable and efficient advertisements delivery protocol for use on wireless billboard channels," in 2008 Ieee International Symposium on Consumer Electronics, 2008: IEEE, pp. 1-4.
Z. Ji, I. Ganchev, M. O'Droma, and R. Sadleir, "A HTTP-based medical image server compatible with evolving wireless communications infrastructures," 2007.
Z. Ji, I. Ganchev, and M. O'Droma, "On WBC Service Layer for UCWW," in 2007 9th IFIP International Conference on Mobile Wireless Communications Networks, 2007: IEEE, pp. 106-110.
Z. Ji, I. Ganchev, M. O’Droma, X. Zhang, and X. Zhang, "Research Article A Cloud-Based X73 Ubiquitous Mobile Healthcare System: Design and Implementation."
Z. Ji, I. Ganchev, and M. O'Droma, "A Wireless Billboard Channel over Digital Video Broadcasting-Handheld Platform," University of Limerick.
I. Ganchev, Z. Ji, D. Meere, and M. O'Droma, "A Local WBC System Operating Across a University Campus."
"A Modelling & Simulation Tier Design for the EMULSION IoT Platform."
"A Sensor Tier Design for the EMULSION IoT Platform."
Internship
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UCWW Laboratory has close project cooperation with Tsinghua University, the Chinese University of Hong Kong, Xi 'an Jiaotong-Liverpool University, Beijing Enterprises Water Group Limited (BEWG) and many other units. There are plenty of internship opportunities at the postgraduate level, which will help enrich the practical experience of laboratory members. Welcome to join!
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UCWW Laboratory has close project cooperation with Tsinghua University, the Chinese University of Hong Kong, Xi 'an Jiaotong-Liverpool University, Beijing Enterprises Water Group Limited (BEWG) and many other units. There are plenty of internship opportunities at the postgraduate level, which will help enrich the practical experience of laboratory members. Welcome to join!
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UCWW Laboratory has close project cooperation with Tsinghua University, the Chinese University of Hong Kong, Xi 'an Jiaotong-Liverpool University, Beijing Enterprises Water Group Limited (BEWG) and many other units. There are plenty of internship opportunities at the postgraduate level, which will help enrich the practical experience of laboratory members. Welcome to join!
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UCWW Laboratory has close project cooperation with Tsinghua University, the Chinese University of Hong Kong, Xi 'an Jiaotong-Liverpool University, Beijing Enterprises Water Group Limited (BEWG) and many other units. There are plenty of internship opportunities at the postgraduate level, which will help enrich the practical experience of laboratory members. Welcome to join!
-
UCWW Laboratory has close project cooperation with Tsinghua University, the Chinese University of Hong Kong, Xi 'an Jiaotong-Liverpool University, Beijing Enterprises Water Group Limited (BEWG) and many other units. There are plenty of internship opportunities at the postgraduate level, which will help enrich the practical experience of laboratory members. Welcome to join!
-
UCWW Laboratory has close project cooperation with Tsinghua University, the Chinese University of Hong Kong, Xi 'an Jiaotong-Liverpool University, Beijing Enterprises Water Group Limited (BEWG) and many other units. There are plenty of internship opportunities at the postgraduate level, which will help enrich the practical experience of laboratory members. Welcome to join!
News
- Frontiers in Molecular Biosciences
- bioengineering call for paper(sci if 4.6)
- electronics
- ConvNeXt
About Frontiers Research Topics
Frontiers is an open-access scientific publisher dedicated to advancing scientific research and innovation. They offer interdisciplinary journals covering various fields such as natural sciences, medicine, engineering, and social sciences. Frontiers' publishing model supports transparency and interactivity in academic research through open peer review and data sharing. Their goal is to foster collaboration and knowledge sharing in the scientific community to drive innovation and progress.
https://www.frontiersin.org/research-topics/64004/application-of-deep-learning-in-biomedical-image-processingDear Colleagues,
The application of deep learning in medical diagnostics is revolutionizing the field of medicine. Especially in medical imaging diagnosis, the introduction of deep learning methods has had a profound impact on the workflow from image acquisition to diagnostic reporting. These methods excel at automating tedious image analysis tasks and efficiently processing large and complex medical image datasets.
The interventions of deep learning and artificial intelligence provide new possibilities for accelerating medical diagnosis. As hardware and algorithms continue to advance, researchers are better able to understand and predict a patient's condition and link medical images to disease features. Deep learning has shown great potential in the interpretation of medical images, such as in foci segmentation, cancer detection, disease classification, and so on.
However, it is important to note that the correct application of deep learning is crucial in medical diagnosis. With deep learning, the data are analyzed and interpreted with the help of computer-expanded knowledge. The impact of these tools is huge, and the use of AI is helping many stakeholders in the field of smart healthcare. The future of applying deep learning in medical diagnostics is exciting, promising not only to improve diagnostic accuracy but also to potentially provide more precise information for personalized medicine and treatment planning. Developments in this field are pushing medical science to new heights to provide better medical care for patients.
https://www.mdpi.com/journal/bioengineering/special_issues/51FQPX7ZT0#editorsDear Colleagues,
Seen as another information and industrial wave, the Internet of things (IoT) finds wide application in multiple domains. For the provision of IoT services, the use of suitable IoT platforms is required, allowing a transparent connection with different types of IoT devices and offering (value-added) functionalities, such as application enablement, remote device control and management, telco/dew/fog/cloud connectivity and storage management, “big data” analytics and visualization, and so on, with some sort of freedom of use by and customization to users.
Following the novel horizontal design principles, which counter the former vertical/silo ones, a new horizontal type of IoT platforms has emerged that, by using the integration and interoperability principles, can simplify the existing IoT environment by eliminating duplicate solutions, enable inter-technology operation, and generate new IoT services and business opportunities. With this new approach, a service/application/network provider is enabled to deliver a complete horizontal-slice solution, applied to not just one but multiple IoT domains, meeting by the flexibility, scalability, cost-efficiency, and multi-purpose use requirements, with easy and timely adjustment of its operation to new use cases and scenarios, and with efficient management and control of the entire IoT ecosystem throughout its lifetime.
The aim of this Special Issue of Electronics is to present state-of-the-art R&D efforts in building multi-service cloud-based IoT platforms of horizontal type, applicable to different IoT domains. We invite scholars to contribute original and unique research articles, as well as review articles.
https://www.mdpi.com/journal/electronics/special_issues/LoT_electronicsS. Hao et al., "ConvNeXt-ST-AFF: A Novel Skin Disease Classification Model Based on Fusion of ConvNeXt and Swin Transformer," in IEEE Access, vol. 11, pp. 117460-117473, 2023, doi: 10.1109/ACCESS.2023.3324042.
Abstract: Automatic classification of dermatological images is an important technology that assists doctors in performing faster and more accurate classification of skin diseases. Recently, convolutional neural networks (CNNs) and Transformer networks have been employed in learning respectively the local and global features of lesion images. However, existing works mainly focus on utilizing a single neural network for feature extraction, which limits the model classification performance. In order to tackle this problem, a novel fusion model, named ConvNeXt-ST-AFF, is proposed in this paper, by combining the strengths of ConvNeXt and Swin Transformer (ConvNeXt-ST in the model’s name). In the proposed model, the pretrained ConvNeXt and Swin Transformer networks extract local and global features from images, which are then fused using Attentional Feature Fusion (AFF) submodules (AFF in the model’s name). Additionally, in order to enhance the model’s attention on the regions of skin lesions during training, an Efficient Channel Attention (ECA) module is incorporated into the ConvNeXt network. Moreover, the proposed model employs a denoising module to reduce the influence of artifacts and improve the image contrast. The results, obtained by experiments conducted on two datasets, demonstrate that the proposed ConvNeXt-ST-AFF model has higher classification ability, according to multiple evaluation metrics, compared to the original ConvNeXt and Swin Transformer, and other state-of-the-art classification models.
https://ieeexplore.ieee.org/document/10283846
Contact
Zhejiang A&F University, Hangzhou 311300, China
Beijing National Research Center for Information Science and Technology, Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
Telecommunications Research Centre (TRC), University of Limerick, Limerick, V94 T9PX Ireland
Department of Computer Systems, University of Plovdiv ‘‘Paisii Hilendarski,’’ 4000 Plovdiv, Bulgaria
Institute of Mathematics and Informatics—Bulgarian Academy of Sciences, 1040 Sofia, Bulgaria
tel:17332678125
email:wangmeng@stu.ncst.edu.cn