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Science4Fashion: an end-to-end decision support system for fashion designers

EVOLVING SYSTEMS 2021年第3期12卷 605-624页

作者： Kotouza, Maria Th. Kyprianidis, Alexandros-Charalampos Tsarouchis, Sotirios-Filippos Chrysopoulos, Antonios C. Mitkas, Pericles A. Aristotle Univ Thessaloniki Elect & Comp Engn Thessaloniki Greece

Nowadays, the fashion clothing industry is moving towards "fast" fashion, offering a wide variety of products based on different patterns and styles, usually characterized by lower costs and ambiguous quality. The retails markets are trying to present regularly new fashion collections while trying to follow the latest fashion trends at the same time. The main reason is to remain competitive and keep up with ever-changing customer demands. Fashion designers draw inspiration from social media, e-shops, and fashion shows that set the new fashion trends. In this direction, we propose Science4Fashion, an AI end-to-end system that facilitates fashion designers by collecting and analyzing data from many different sources and suggesting products according to their needs. An overview of the system's modules is presented, emphasizing data collection, data annotation using deep learning models, and product recommendation and user feedback processes. The experiments presented in this paper are twofold: (a) experiments regarding the evaluation of clothing attribute classification, and (b) experiments regarding product recommendation using the baseline kNN enriched by the frequency-based clustering algorithm (FBC), achieving promising results.

关键词： Fashion recommendation Data crawling deep learning classification kNN Hierarchical clustering Decision making systems

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Electrocardiogram analysis for cardiac arrhythmia classification and prediction through self attention based auto encoder

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SCIENTIFIC REPORTS 2025年第1期15卷 1-23页

作者： Shah, Ameet Singh, Dhanpratap Mohamed, Heba G. Bharany, Salil Rehman, Ateeq Ur Hussen, Seada Lovely Profess Univ Sch Comp Sci & Engn Grand Trunk Rd Phagwara 144411 Punjab India Princess Nourah Bint Abdulrahman Univ Coll Engn Dept Elect Engn POB 84428 Riyadh 11671 Saudi Arabia Chitkara Univ Inst Engn & Technol Rajpura 140401 Punjab India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Comp Sci & Engn Chennai Tamilnadu India Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 Punjab India Adama Sci & Technol Univ Dept Elect Power Adama 1888 Ethiopia

Sudden cardiac arrest among young people is a recent worldwide risk, and it is noticed that people with cardiac arrhythmia are more susceptible to various heart diseases. Manual classification can be error-prone, and certainly, there is a need for automation to classify ECG signals to predict cardiac arrhythmia accurately. The proposed self-attention artificial intelligence auto-encoder algorithm proved an effective cardiac arrhythmia classification strategy with a novel modified Kalman filter pre-processing. We achieved 24.00 SNRimp, 0.055 RMSE, 22.1 PRD% for -5db, 20.4 SNRimp, 0.0245 RMSE, 12 PRD% whereas 14.05 SNRimp, 0.010 RMSE, and 7.25 PRD%, which reduces the ECG signal noise during the pre-processing and improves the visibility of the QRS complex and R-R peaks of ECG waveform. The extracted features were used in network of neurons to execute the classification for MIT-BIH arrhythmia databases using the newly developed self-attention autoencoder (AE) algorithm. The results are compared with existing models, revealing that the proposed system outperforms the classification and prediction of cardiac arrhythmia with a precision of 99.91%, recall of 99.86%, and accuracy of 99.71%. It is confirmed that self-attention-AE training results are promising, and it benefits the diagnosis of ECGs for complex cardiac conditions to solve real-world heart problems.

关键词： Cardiac arrhythmia Self-attention mechanism Atrial fibrillation deep learning classification Artificial intelligence Prediction Dense neural network Kalman filter

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Smart Societal Optimization-based deep learning Convolutional Neural Network Model for Epileptic Seizure Prediction

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2024年第1期12卷

作者： Sonawane, Pratibha S. Helonde, Jagdish B. Sandip Univ Dept Elect & Elect Engn Nasik Maharashtra India Sandip Univ Dept Elect & Elect Engn Trambakeshwar Rd Nasik 422213 Maharashtra India

Epilepsy is a long-term neurological condition that disrupts brain function in people of all ages, epilepsy is a condition that is analysed through the brain signals via electroencephalogram (EEG) signal. To analyse epilepsy using spatial and temporal data, various machine-learning-based techniques are used. However, most of the techniques suffer from inaccuracy issues in dealing with the dynamic and raw EEG signal. In this research, an intelligent societal optimisation-driven classifier is introduced based on convolutional neural networks (CNN) for epileptic seizure prediction using EEG signals. To boost predictive accuracy, we extract frequency band features from the EEG signal utilising wavelet decomposition. The frequency band features form the feature vector, is provided smart societal optimisation- CNN such that the prediction performance is enhanced through the optimal tuning of the CNN with the smart societal optimisation. Smart societal optimisation is proposed by integrating the behaviour of the Lobos wolf and the Moggie. The smart societal optimisation-based CNN attains 87.673% accuracy, 84.949% sensitivity91.274%specificity for the K-Fold-10 for CHB-MIT scalp EEG database.

关键词： deep learning classification optimisation algorithm epileptic seizure prediction EEG signals frequency band features

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One-dimensional VGGNet for high-dimensional data

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APPLIED SOFT COMPUTING 2023年 135卷

作者： Feng, Sheng Zhao, Liping Shi, Haiyan Wang, Mengfei Shen, Shigen Wang, Weixing Shaoxing Univ Dept Comp Sci & Engn Shaoxing 312000 Peoples R China Changan Univ Sch Informat Engn Xian 710064 Peoples R China Huzhou Univ Sch Informat Engn Huzhou 313000 Peoples R China

We consider a deep learning model for classifying high-dimensional data and seek to achieve optimal evaluation accuracy and robustness based on multicriteria decision-making (MCDM) for high -dimensional data analysis applications during comprehensive evaluation (CE) activities. We propose a novel one-dimensional visual geometry group network (1D_VGGNet) to overcome the problem that high-dimensional data are too complicated and unstable to be feasibly applied. Then, to effectively handle one-dimensional MCDM, we present a 1D_VGGNet classifier to replace the two-dimensional convolution operation applied to image data with a one-dimensional convolution operation applied to one-dimensional MCDM. Furthermore, to solve the invariance problem of the generated feature maps, the maxpooling kernel size can be flexibly adjusted to effectively meet the requirements of reducing the feature map dimension and speeding up training and prediction on different datasets. The improvement is reasonable for various high-dimensional data application scenarios. Moreover, we propose a novel objective function to accurately evaluate network performance since the objective function includes a variety of representative performance evaluation metrics, and the average value is calculated as one of the CE metrics. The experimental results illustrate that the proposed framework outperforms a one-dimensional convolutional neural network (1D_CNN) for comprehensive classifica-tion on the Shaoxing University student achievement dataset and the MIT-BIH Arrhythmia database and achieves average gains of 36.3% and 12.1% in terms of the designated evaluation metric.(c) 2023 Elsevier B.V. All rights reserved.

关键词： High-dimensional data deep learning classification One-dimensional visual geometry group network (1D_VGGNet) One-dimensional convolution Comprehensive evaluation (CE)

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EFFICIENT CLUSTERING OF BRAIN TUMOR SEGMENTS USING LEVEL-SET HYBRID MACHINE learning ALGORITHMS

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2023年第4期24卷 1053-1063页

作者： Lvliang, He Hua, Wu GuiZhou Vocat Technol Coll Elect & Informat Elect Engn Dept Kaili Guizhou Peoples R China SongTao Miao Autonomous Cty Secondary Occupat Sch Mech & Elect informat Dept Comp Major Tongren Guizhou Peoples R China

Cluster computing is an essential technology in distributed environments for practical data analysis in complex datasets like tumor segmentation, disease classification etc. Today real-world applications like medicine and transport are needed for big data analytics environments. This research article considers complex image data environments like brain tumor segmentation based on advanced clustering techniques for effective tumor prediction. An a-state-of-art analysis used Hierarchical clustering to extract initial tumor segments from the image. The next segment is further refined using novel Noise detection-based level -setting techniques. The unsupervised Fuzzy C-means and k-means clustering is used to segment the diseases affected region to enhance noise detection used in the level set. Effective features are extracted using gray level co-occurrence matrix and redundant discrete wavelet transform. Finally, classifying malignant and benign brain tumor images is done using deep probabilistic neural networks. Publicly available datasets are used to validate the proposed algorithms. Experimental results prove that proposed pipeline techniques have effective performance in tumor segmentation and classification model.

关键词： Cluster computing Distributed environment brain tumor segmentation deep learning classification Hierarchical clustering Fuzzy C-means k-means clustering

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Automatic fiducial marker detection and localization in CT images: A combined approach

Automatic fiducial marker detection and localization in CT i...

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Medical Imaging Conference - Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Regodic, Milovan Bardosi, Zoltan Freysinger, Wolfgang Med Univ Innsbruck Univ ENT Hosp 4D Visualizat Lab Innsbruck Austria

ISBN: (纸本)9781510633988

Patient-to-image registration is a key step for guidance in computer-assisted surgery during interventions like cochlear implant or deep brain stimulation surgeries. Automatizing fiducial detection and localization in pre-operative images of the patient can lead to better registration accuracy, reduced human errors and shorter intervention time. We present an algorithm that builds on earlier approaches with morphological functions and pose estimation algorithms. A Convolutional Neural Network is proposed for the fiducial classification task. A digital experiment, with cone-beam CT imaging software, is performed to determine the accuracy of the algorithm. The inputs to this software are virtual phantoms represented as 3D surface models (meshes) of skull and (screw and spherical) fiducial markers combined with specific imaging parameters like material properties, detector resolution, etc. The software generates realistic CT images for establishing a ground-truth measure to validate the algorithm. The localized fiducial positions in the image by the presented algorithm were compared to the actual known positions in the phantom models. The difference represents the fiducial localization error (FLE). Validation data sets with different slice thicknesses contain screws and spherical markers of different dimensions. The achieved FLE mean and its standard deviation for a screw and spherical marker are 58 (14) mu m and 14 (6) mu m, respectively. Large marker volume and smaller voxel size yield smaller FLEs. Furthermore, we found that attenuating noise by mesh smoothing has a minor effect on localization accuracy.

关键词： automatization fiducial marker segmentation deep learning classification localization phantom models virtual CT

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Automatic Detection of Extra-Cardiac Findings in Cardiovascular Magnetic Resonance 25th

Automatic Detection of Extra-Cardiac Findings in Cardiovascu...

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25th Conference on Medical Image Understanding and Analysis (MIUA)

作者： Wickremasinghe, Dewmini Hasara Khenkina, Natallia Masci, Pier Giorgio King, Andrew P. Puyol-Anton, Esther Kings Coll London Sch Biomed Engn & Imaging Sci London England Guys & St Thomas Hosp London England Luigi Sacco Univ Hosp Milan Italy Univ Milan Postgrad Sch Diagnost & Intervent Radiol Milan Italy

ISBN: (纸本)9783030804329;9783030804312

Cardiovascular magnetic resonance (CMR) is an established, non-invasive technique to comprehensively assess cardiovascular structure and function in a variety of acquired and inherited cardiac conditions. In addition to the heart, a typical CMR examination will also image adjacent thoracic and abdominal structures. Consequently, findings incidental to the cardiac examination may be encountered, some of which may be clinically relevant. We compare two deep learning architectures to automatically detect extra cardiac findings (ECFs) in the HASTE sequence of a CMR acquisition. The first one consists of a binary classification network that detects the presence of ECFs and the second one is a multi-label classification network that detects and classifies the type of ECF. We validated the two models on a cohort of 236 subjects, corresponding to 5610 slices, where 746 ECFs were found. Results show that the proposed methods have promising balanced accuracy and sensitivity and high specificity.

关键词： deep learning classification Extra cardiac findings Cardiovascular magnetic resonance

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Utility of daily 3 m Planet Fusion Surface Reflectance data for tillage practice mapping with deep learning

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SCIENCE OF REMOTE SENSING 2023年 7卷

作者： Luo, Dong Zhang, Hankui K. Houborg, Rasmus Ndekelu, Lina M. N. Maimaitijiang, Maitiniyazi Tran, Khuong H. McMaine, John South Dakota State Univ Geospatial Sci Ctr Excellence Dept Geog & Geospatial Sci Brookings SD 57007 USA Planet Labs PBC San Francisco CA 94107 USA South Dakota State Univ Dept Agr & Biosyst Engn Brookings SD 57007 USA

Tillage practices alter soil surface structure that can be potentially captured by satellite images with both high spatial and temporal resolution. This study explored tillage practice mapping using the daily Planet Fusion surface reflectance (PF-SR) gap-free 3 m data generated by fusing PlanetScope with Landsat-8, Sentinel-2 and MODIS surface reflectance data. The study area is a 220 x 220 km2 agricultural area in South Dakota, USA, and the study used 3285 PF-SR images from September 1, 2020 to August 31, 2021. The PF-SR images for the sur-veyed 433 fields were sliced into 10,747 training (70%) and evaluation (30%) non-overlapping time series patches. The training and evaluation patches were from different fields for evaluation data independence. The performance of four deep learning models (i.e., 2D convolutional neural networks (CNN), 3D CNN, CNN-Long short-term memory (LSTM), and attention CNN-LSTM) in tillage practice mapping, as well as their sensitivity to different spatial (i.e., 3 m, 24 m, and 96 m) and temporal resolutions (16-day, 8-day, 4-day, 2-day and 1-day) were examined. classification accuracy continuously increased with increases in both temporal and spatial resolutions. The optimal models (3D CNN and attention CNN-LSTM) achieved-77% accuracy using 2-day or daily 3 m resolution data as opposed to-72% accuracy using 16-day 3 m resolution data or daily 24 m resolution data. This study also analyzed the feature importance of different acquisition dates for the two optimal models. The 3D CNN model feature importances were found to agree well with the tillage practice time. High feature importance was associated with observations during the fall and spring tillage period (i.e., fresh tillage signals) whereas the crop peak growing period (i.e., tillage signals weathered and confounded by dense canopy) was characterized by a relatively low feature importance. The work provides valuable insights into the utility of deep learning for tillage mapping and cha

关键词： Planet fusion Tillage practice Tillage date deep learning classification deep learning interpretation

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Computer Vision Frameworks for Physics-Based Simulation of Liquids and Solids

Computer Vision Frameworks for Physics-Based Simulation of L...

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作者： Cao, Chendi Kansas State University

学位级别：Ph.D.

Simulating and visualizing fluid and solid materials in agricultural domains is an important and challenging problem in scientific computing and computer vision. Modern seed breeding programs require the ability to analyze seeds efficiently to be useful. Even simple measures such as volume and density can be challenging to compute efficiently with modest equipment. The dynamics of liquid and soil materials involve significant deformation during storm flows and require sophisticated numerical algorithms to achieve sufficient accuracy and visual realism. This dissertation focuses on extending volume carving techniques to measure seed volume and to create a new Material Point Method (MPM) models and finite volume models to simulate solids and fluids for dam safety analysis and visualization. This dissertation makes the following major contributions: The first is to create a novel framework for the design and analysis of computer experiments. The framework is applied to perform efficient dam breach and internal erosion analysis on a large number of structures. Given historical dam breach or design data input, the modeling framework can also be used to conduct sensitivity analysis to determine which parameters make the most impact on the resulting dam erosion. The second contribution is to develop new models for numerical simulation of dam erosion by combining fluid flow models developed using Computational Fluid Dynamics (CFD) with new dam erosion models using the Finite Element Method (FEM). A new model that combines fluid flow and erosion simulation into a single model is also developed using the Material Point Method (MPM). The third contribution is to build a comprehensive image capture and processing framework for seed property analysis. Rather than having a human manually measure seed properties such as length, width, thickness, and volume, the framework can automatically analyze a set of images from multiple angles and calculate the physical measurements for sing

关键词： computer vision computer simulation material point method volume measurement computational fluid dynamics deep learning classification

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Detection of HER2 from Haematoxylin-Eosin Slides Through a Cascade of deep learning Classifiers via Multi-Instance learning

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JOURNAL OF IMAGING 2020年第9期6卷 82-82页

作者： La Barbera, David Polonia, Antonio Roitero, Kevin Conde-Sousa, Eduardo Della Mea, Vincenzo Univ Udine Dept Math Comp Sci & Phys I-33100 Udine Italy Univ Porto Inst Mol Pathol & Immunol Ipatimup Diagnost Dept Pathol P-4169007 Porto Portugal Univ Porto Inst Invest & Inovacao Saude I3S P-4169007 Porto Portugal Univ Porto INEB Inst Engn Biomed P-4169007 Porto Portugal

Breast cancer is the most frequently diagnosed cancer in woman. The correct identification of the HER2 receptor is a matter of major importance when dealing with breast cancer: an over-expression of HER2 is associated with aggressive clinical behaviour;moreover, HER2 targeted therapy results in a significant improvement in the overall survival rate. In this work, we employ a pipeline based on a cascade of deep neural network classifiers and multi-instance learning to detect the presence of HER2 from Haematoxylin-Eosin slides, which partly mimics the pathologist's behaviour by first recognizing cancer and then evaluating HER2. Our results show that the proposed system presents a good overall effectiveness. Furthermore, the system design is prone to further improvements that can be easily deployed in order to increase the effectiveness score.

关键词： digital pathology whole slide image processing multiple instance learning convolutional neural networks deep learning classification HER2

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