The study of animal groups' flocking behavior is of great significance to the study of multi-agent system. Flocking algorithm is a typical algorithm for group control based on animal group behavior. The research o...
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The proceedings contain 146 papers. The topics discussed include: research on radio frequency finerprint licalization based on machine learning;research on the stock return predictability with combination of machine l...
ISBN:
(纸本)9781665417907
The proceedings contain 146 papers. The topics discussed include: research on radio frequency finerprint licalization based on machine learning;research on the stock return predictability with combination of machine learning;cryptocurrency price trend analysis via machine learning methods;research on financial risk control algorithm based on machine learning;research on short term stock selection strategy based on machine learning;recognition of american sign language gestures based on electromyogram (EMG) signal with XGBoost machine learning;traffic flow prediction using machine learning methods;forest fire risk forecast method with pseudo label based on semi-supervised learning;construction of citizens' fire quality evaluation system based on miller's ability pyramid model;distributed multi-robot deployment in dynamic environments using Thompson sampling;and stock price prediction based on temporal fusion transformer.
The proceedings contain 12 papers. The special focus in this conference is on Multiscale Multimodal Medical Imaging. The topics include: Towards Optimal Patch Size in Vision Transformers for Tumor Segmentati...
ISBN:
(纸本)9783031188138
The proceedings contain 12 papers. The special focus in this conference is on Multiscale Multimodal Medical Imaging. The topics include: Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation;Improved Multi-modal Patch Based Lymphoma Segmentation with Negative Sample Augmentation and Label Guidance on PET/CT Scans;visual Modalities Based Multimodal Fusion for Surgical Phase recognition;cross-Scale Attention Guided Multi-instance Learning for Crohn’s Disease Diagnosis with Pathological Images;vessel Segmentation via Link Prediction of Graph Neural Networks;A Bagging Strategy-Based Multi-scale Texture GLCM-CNN Model for Differentiating Malignant from Benign Lesions Using Small Pathologically Proven Dataset;Liver Segmentation Quality Control in Multi-sequence MR Studies;pattern Analysis of Substantia Nigra in Parkinson Disease by Fifth-Order Tensor Decomposition and Multi-sequence MRI;gabor Filter-Embedded U-Net with Transformer-Based Encoding for Biomedical Image Segmentation;Learning-Based Detection of MYCN Amplification in Clinical Neuroblastoma Patients: A Pilot Study.
Currently, drug and alcohol addiction has become a major menace to society's youth. As responsible citizens of this country, we must act now to keep these young brains from succumbing to this lethal addiction. In ...
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The Air Quality Index (AQI) forecasting is a very significant area of research as it has an impact on worldwide ecosystems and human health. The close monitoring of AQI is necessary to develop different mitigation str...
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Aiming to address the requirements of Quadrotor Unmanned Aerial Vehicle (QUAV) for conducting mission cruises in three-dimensional complex environments, this paper introduces an optimization algorithm for trajectory p...
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The possibility of application a convolutional neural network to assess the augmentation of electrical images is proposed. We studied various conditions for sample preparation, optimizer algorithms, the number of pixe...
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The study of the variant under machine unavailability and setup time constraints is of great industrial importance. In this paper, we present an optimization of manufacturing flow shop scheduling under setup time cons...
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ISBN:
(数字)9783031298608
ISBN:
(纸本)9783031298592;9783031298608
The study of the variant under machine unavailability and setup time constraints is of great industrial importance. In this paper, we present an optimization of manufacturing flow shop scheduling under setup time constraints. The objective is to minimize the total tardiness for a set of jobs with deadline times to respect. This intention is aimed at satisfying customer requirements and gaining their trust. To investigate this problem, we applied three inspired-nature meta-heuristics: the artificial bee colony algorithm, the genetic technique algorithm and finally the migratory bird optimization algorithm. A set of numerical simulations on a variety of instances is suggested to check the efficiency and strength of our algorithms. The results obtained show the strong success of the migratory bird optimization algorithm compared to other algorithms.
Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers in the word. However, the diverse microenvironment, unclear boundaries, integrity destruction inter the slices, and enormous individual differenc...
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ISBN:
(数字)9789819991198
ISBN:
(纸本)9789819991181;9789819991198
Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers in the word. However, the diverse microenvironment, unclear boundaries, integrity destruction inter the slices, and enormous individual differences of tumors pose tremendous challenges to the segmentation process. To address these challenges, we proposed a physicalspiral dual-domain network (PSDD-Net) that combines the advantages of the spiral domain and the physical domain. First of all, the physical domain promotes integral representations of the tumor features, and the spiral domain protrudes the tumor region under CT multi-directions. As a result, the dual-domain framework makes the dual-domain feature simultaneously sent to the network to promote greater attention to the pancreatic region and reduce the interference of redundant background information. Secondly, we also present a multi-scale local-dense net (MSLD-Net) in the physical domain which contains local-channel dense block (LCDB) and multi-scale semantic feature extraction (MSSFE) module. The MSLD-Net grasps more multi-scale geometric information of the tumors and facilitates feature map fusion. Thirdly, a cross-domain aggregation (CDA) module is designed to interact bridging the two domains to interleave and integrate dual-domain complementary visual information. The extensive experiments on the clinical dataset show that our method obtained the DSC of 76.00% in abdominal CT, which outperformed the other state-of-the-art on pancreatic cancer segmentation results and demonstrated strong potential for clinical applications.
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