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2nd International Conference on Machine Learning and data engineering, ICMLDE 2023

作者： Kadry, Seifedine Verdú, Elena Damasevicius, Robertas Abualigah, Laith Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural Networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu's thresholding, (iii) Kapur's thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the Moth-Flame Algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database. © 2024 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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Characterization of heavy-chain antibody gene repertoires in Bactrian camels

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Journal of Genetics and Genomics 2023年第1期50卷 38-45页

作者： Yuexing Liu Li Yi Yixue Li Zhen Wang Jirimutu CAS Key Laboratory of Computational Biology Shanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Key Laboratory of Dairy Biotechnology and Engineering Ministry of EducationCollege of Food Science and EngineeringInner Mongolia Agricultural UniversityHuhhotInner Mongolia 010018China Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhouZhejiang 310024China School of Life Sciences and Biotechnology Shanghai Jiao Tong UniversityShanghai 200240China Guangzhou Laboratory GuangzhouGuangdong 510005China Bio-Med Big Data Center Shanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Collaborative Innovation Center for Genetics and Development Fudan UniversityShanghai 200433China Inner Mongolia Institute of Camel Research West AlaxInner Mongolia 737399China

Camelids are the only mammals that can produce functional heavy-chain antibodies(HCAbs).Although HCAbs were discovered over 30 years ago,the antibody gene repertoire of Bactrian camels remains largely *** characterize the diversity of variable genes of HCAbs(VHHs),germline and rearranged VHH repertoires are *** analysis shows that all camelid VHH genes are derived from a common ancestor and the nucleotide diversity of VHHs is similar across all camelid *** species-specific hallmark sites are identified,the non-canonical cysteines specific to VHHs are distinct in Bactrian camels and dromedaries compared with *** low divergence at the germline repertoire between wild and domestic Bactrian camels,higher expression of VHHs is observed in some wild Bactrian camels than that of domestic *** study not only adds our understanding of VHH repertoire diversity across camelids,but also provides useful resources for HCAb engineering.

关键词： Bactrian camel Heavy-chain antibody Nanobody Immune repertoire diversity Non-canonical cysteine

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Automatic liver-vessel examination from CT slice using Kapur's thresholding and watershed algorithm 2

Automatic liver-vessel examination from CT slice using Kapur...

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2nd International Conference on Machine Learning and data engineering, ICMLDE 2023

作者： Kadry, Seifedine Abualigah, Laith González Crespo, Rubén Verdú, Elena Damasevicius, Robertas Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

Computerized medical image examination (CMIE) plays a significant role in modern hospitals to achieve the necessary tasks, like segmentation and classification. By segmenting an image, we can extract a particular section for examination. A two-dimensional computed tomography (CT) slice was used for liver-vessel examination (LiVE). A simple automatic technique for supporting LiVE is being developed in this research. A CT slice is collected, a 3D to 2D conversion is done, (ii) Kapur's tri-level thresholding and Hummingbird-Optimizer is used to enhance the CT slice, (iii) the watershed algorithm (WA) is used to extract the vessel, and (iv) the WA is compared and verified against the segmentation methods chosen. WA provides better segmentation results than other methods because it is an automatic approach. Using the chosen image database, the proposed technique achieves an overall segmentation accuracy of >97%. Other segmentation problems can be used in the future to verify the merit of this scheme. © 2024 Elsevier B.V.. All rights reserved.

关键词： Medical imaging

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COVID19 detection in chest x-ray using vision-transformer with different patch dimensions 2

COVID19 detection in chest x-ray using vision-transformer wi...

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2nd International Conference on Machine Learning and data engineering, ICMLDE 2023

Computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays. © 2024 Elsevier B.V.. All rights reserved.

关键词： Diagnosis

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Depth Estimation of the Object of Interest in Challenging Cross-Domain Scenes

SSRN

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SSRN 2023年

作者： Xu, Chenglong Chen, Chu Xing, Wen Herrera-Viedma, Enrique College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China Qingdao Innovation and Development Center Harbin Engineering University Qingdao266000 China Shenyang SIASUN Robot & Automation Co. LTD Shenyang110168 China Andalusian Research Institute of Data Science and Computational Intelligence Department of Computer Science and AI University of Granada Granada18071 Spain

Depth estimation plays an important role in 3D visual perception, autonomous vehicles, and near-field optical detection. At present, there are mainly learning-based methods and traditional geometric constraint reasoning based methods. However, most learning-based methods have poor cross-domain generalization, the accuracy will be greatly reduced when tested on new scenes. Although the traditional geometric methods have strong scene adaptability, they have more marginal noise in depth discontinuity areas, and require a large amount of memory at a high bandwidth. In order to solve the above problems, we present a disparity confidence propagation and edge smoothing framework, which combines robust semantic mask with tree diffusion model. Compared with those of state-of-the-art fast geometric constraint based methods and learning-based methods, the proposed method has a great improvement in cross-domain adaptability and efficiency. Experiments on multiple data sets verify the effectiveness of the method, and a good balance is achieved between generalization and accuracy. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Graph Neural Networks for Wireless Networks: Graph Representation, Architecture and Evaluation

arXiv

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arXiv 2024年

作者： Lu, Yang Li, Yuhang Zhang, Ruichen Chen, Wei Ai, Bo Niyato, Dusit School of Computer and Technology China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

Graph neural networks (GNNs) have been regarded as the basic model to facilitate deep learning (DL) to revolutionize resource allocation in wireless networks. GNN-based models are shown to be able to learn the structural information about graphs representing the wireless networks to adapt to the time-varying channel state information and dynamics of network topology. This article aims to provide a comprehensive overview of applying GNNs to optimize wireless networks via answering three fundamental questions, i.e., how to input the system parameters of wireless networks into GNNs, how to improve the expressive performance of GNNs, and how to evaluate GNNs. Particularly, two graph representations are given to transform wireless network parameters into graph-structured data. Then, we focus on the architecture design of the GNN-based models via introducing the basic message passing as well as model improvement methods including multi-head attention mechanism and residual structure. At last, we give task-oriented evaluation metrics for DL-enabled wireless resource allocation schemes. We also highlight certain challenges and potential research directions for the application of GNNs in wireless networks. © 2024, CC BY.

关键词： Graph neural networks

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Slimmable transformer with hybrid axial-attention for medical image segmentation

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Computers in Biology and Medicine 2024年 173卷 108370-108370页

作者： Hu, Yiyue Mu, Nan Liu, Lei Zhang, Lei Jiang, Jingfeng Li, Xiaoning College of Computer Science Sichuan Normal University Chengdu610101 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen518172 China Department of Biomedical Engineering Michigan Technological University HoughtonMI49931 United States Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu610068 China Education Big Data Collaborative Innovation Center of Sichuan 2011 Chengdu610101 China

The transformer architecture has achieved remarkable success in medical image analysis owing to its powerful capability for capturing long-range dependencies. However, due to the lack of intrinsic inductive bias in modeling visual structural information, the transformer generally requires a large-scale pre-training schedule, limiting the clinical applications over expensive small-scale medical data. To this end, we propose a slimmable transformer to explore intrinsic inductive bias via position information for medical image segmentation. Specifically, we empirically investigate how different position encoding strategies affect the prediction quality of the region of interest (ROI) and observe that ROIs are sensitive to different position encoding strategies. Motivated by this, we present a novel Hybrid Axial-Attention (HAA) that can be equipped with pixel-level spatial structure and relative position information as inductive bias. Moreover, we introduce a gating mechanism to achieve efficient feature selection and further improve the representation quality over small-scale datasets. Experiments on LGG and COVID-19 datasets prove the superiority of our method over the baseline and previous works. Internal workflow visualization with interpretability is conducted to validate our success better;the proposed slimmable transformer has the potential to be further developed into a visual software tool for improving computer-aided lesion diagnosis and treatment planning. © 2024 Elsevier Ltd

关键词： COVID-19

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Seamless maps of major elements of the Moon:Results from high-resolution geostationary satellite

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Research in Astronomy and Astrophysics 2021年第2期21卷 52-60页

作者： Yu Lu Yun-Zhao Wu Cui Li Jin-Song Ma Wen-Wen Qi Wei Tan Xiao-Man Li Zhi-Cheng Shi Hong-Yan He Shu-Wu Dai Guo Li Feng-Jing Liu Jing-Qiao Wang Xiao-Yan Wang Qi Wang Ling-Jie Meng Key Laboratory of Planetary Sciences Purple Mountain ObservatoryChinese Academy of SciencesNanjing 210034China School of Astronomy and Space Science University of Science and Technology of ChinaHefei 230026China CAS Center for Excellence in Comparative Planetology Hefei 230026China Space Science Institute Macao University of Science and TechnologyMacaoChina School of Geography and Ocean Science Nanjing UniversityNanjing 210023China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China Beijing Institute of Space Mechanics and Electricity Beijing 100076China Beijing Institute of Spacecraft System Engineering Beijing 100076China China Centre for Resources Satellite Data and Application Beijing 100094China Earth Observation System and Data Center China National Space AdministrationBeijing 100010China

Major elements such as Fe,Ti,Mg,Al,Ca and Si play very important roles in understanding the origin and evolution of the *** maps of these major elements derived from orbital data are based on mosaic images or low-resolution gamma-ray *** hue variations and gaps among orbital boundaries in the mosaic images are not conducive to geological *** paper aims to produce seamless and homogenous distribution maps of major elements using the single-exposure image of the whole lunar disk obtained by China’s high-resolution geostationary satellite,Gaofen-4,with a spatial resolution of500 *** elemental contents of soil samples returned by Apollo and Luna missions are regarded as ground truth,and are correlated with the reflectance of the sampling sites extracted from Gaofen-4 *** final distribution maps of these major oxides are generated with the statistical regression *** these products,the average contents and proportions of the major elements for maria and highlands were estimated and *** results showed that Si O2 and Ti O2 have the highest and lowest fractions in mare and highland areas,***,the relative concentrations of these elements could serve as indicators of geologic processes,e.g.,the obviously asymmetric distributions of Al2 O3,Ca O and Si O2 around Tycho crater may suggest that Tycho crater was formed by an oblique impact from the southwest direction.

关键词： Moon-data analysis-Imaging spectroscopy

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Accurate expression and high-precision estimation of ‖eAt‖2

Accurate expression and high-precision estimation of ‖eAt‖...

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第41届中国控制会议

作者： Zhuo Wang Qi Yuan Sixun Liu Research Institute for Frontier Science Beihang University Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Instrumentation and Opto-electronics Engineering Beihang University

The state transition matrix e and its norm ‖e‖ play an indispensable role in the analysis and design of control *** literature has provided two kinds of estimation results of ‖e‖.However,because of rather low precision,they can only be applied for stability,local controllability,observability analysis,and it is incapable for them to estimate the dynamic performance *** contributions of our work are summarized as two aspects:1) the accurate expressions of‖e‖ while A is normal and 2) the high-precision estimation results of ‖e‖ while A is ***,these results above not only effectively reduce a lot of computation load but also significantly improve estimation precision,and they can be used to estimate the dynamic performance index of control systems.

关键词： accurate expression control system dynamic performance high-precision estimation ‖eAt‖2

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When a RF Beats a CNN and GRU, Together - A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic Classification

arXiv

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arXiv 2022年

作者： Lichy, Adi Bader, Ofek Dubin, Ran Dvir, Amit Hajaj, Chen Ariel Cyber Innovation Center Ariel University Israel Department of Computer Science Ariel University Israel Department of Industrial Engineering & Management Ariel University Israel Data Science and Artificial Intelligence Research Center Ariel University Israel

Internet traffic classification is widely used to facilitate network management. It plays a crucial role in Quality of Services (QoS), Quality of Experience (QoE), network visibility, intrusion detection, and traffic-trend analyses. While there is no theoretical guarantee that deep learning (DL)-based solutions perform better than classic machine learning (ML)-based ones, DL-based models have become the common default. This paper compares well-known DL-based and ML-based models and shows that in the case of malicious traffic classification, state-of-the-art DL-based solutions do not necessarily outperform the classical ML-based ones. We exemplify this finding using two well-known datasets for a varied set of tasks, such as: malware detection, malware family classification, detection of zero-day attacks, and classification of an iteratively growing dataset. Note that, it is not feasible to evaluate all possible models to make a concrete statement, thus, the above finding is not a recommendation to avoid DL-based models, but rather empirical proof that in some cases, there are more simplistic solutions, that may perform even better. Copyright © 2022, The Authors. All rights reserved.

关键词： Classification (of information)

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