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7th EAI international conference on e-learning, e-Education, and Online Training, eLEOT 2021

7th EAI International Conference on e-Learning, e-Education,...

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7th EAI international conference on e-learning, e-Education, and Online Training, eLEOT 2021

ISBN: (纸本)9783030843823

the proceedings contain 104 papers. the special focus in this conference is on e-learning, e-Education, and Online Training. the topics include: data mining Method of English Online learning Behavior Based on machine learning Technology;intelligent Recommendation Model of Distance Education Courses Based on Facial Expression recognition;online Teaching Method of Conductive Characteristics of Graphene Materials Based on Big data mining;distance Teaching Method of Accounting Informatization Course Based on Big data mining;University Ideological and Political Multimedia Network Teaching Based on MOOC;an Efficient Teaching Method for Physiological Basic Course of Online and Offline Integration;decision Support Model for Online Education of Logistics Management Major from the Perspective of "Credit Bank";comprehensive Control Method of Network Teaching data Scheduling Based on Fuzzy Mathematics;design of Personal learning Model recognition Model for Online Teaching of Ideological and Political theory Course;reform and Practice of Digital Teaching of Engineering Mechanics Course Under the Background of "Double High Program";Research on the Ideological and Political Teaching Mode of Dual System Curriculum in Colleges and Universities Based on MOOC;pattern recognition Method of Training Japanese Talents in Online Education;research on Hybrid learning Mode of Financial Management Course in Cloud Classroom Environment;research on pattern recognition Method of Online English Education Based on Feature Self learning;pattern recognition Method of English Distance Online Education Based on Big data Algorithm;big data Based Human Resources Professional Distance Training Platform;design of Personalized Employment Guidance System for College Students Based on Big data.

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Transfer learning for ensembles: reducing computation time and keeping the diversity 5

Transfer learning for ensembles: reducing computation time a...

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5th international conference on Artificial Intelligence and pattern recognition, AIPR 2022

作者： Shashkov, Ilya Zaytsev, Alexey Balabin, Nikita Burnaev, Evgeny Mipt Iitp Skoltech Dolgoprundy Russia Skoltech Russia

ISBN: (纸本)9781450396899

Transferring a deep neural network trained on one problem to another requires only a small amount of data and little additional computation time. the same behaviour holds for ensembles of deep learning models typically superior to a single model. However, a transfer of deep neural networks ensemble demands relatively high computational expenses. the probability of overfitting also increases. Our approach for the transfer learning of ensembles consists of two steps: (a) shifting weights of encoders of all models in the ensemble by a single shift vector and (b) doing a tiny fine-tuning for each individual model afterwards. this strategy leads to a speed-up of the training process and gives an opportunity to add models to an ensemble with significantly reduced training time using the shift vector. We compare different strategies by computation time, the accuracy of an ensemble, uncertainty estimation and disagreement and conclude that our approach gives competitive results using the same computation complexity in comparison with the traditional approach. Also, our method keeps the ensemble's models' diversity higher. © 2022 ACM.

关键词： Deep neural networks

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5th international conference on Applied machine learning and data Analytics, AMLDA 2022

5th International Conference on Applied Machine Learning and...

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5th international conference on Applied machine learning and data Analytics, AMLDA 2022

ISBN: (纸本)9783031342219

the proceedings contain 20 papers. the special focus in this conference is on Applied machine learning and data Analytics. the topics include: RU-Net: A Novel Approach for Gastro-Intestinal Tract Image Segmentation Using Convolutional Neural Network;a Credit Card Fraud Detection Model Using machine learning Methods with a Hybrid of Undersampling and Oversampling for Handling Imbalanced datasets for High Scores;Implementation of YOLOv7 for Pest Detection;online Grocery Shopping: - Key Factors to Understand Shopping Behavior from data Analytics Perspective;a Novel Approach: Semantic Web Enabled Subject Searching for Union Catalogue;how People in South America is Facing Monkeypox Outbreak?;multilevel Classification of Satellite Images Using Pretrained AlexNet Architecture;handwriting recognition for Predicting Gender and Handedness Using Deep learning;retrieval of Weighted Lexicons Based on Supervised learning Method;univariate Feature Fitness Measures for Classification Problems: An Empirical Assessment;performance Evaluation of Smart Flower Optimization Algorithm Over Industrial Non-convex Constrained Optimization Problems;securing Advanced Metering Infrastructure Using Blockchain for Effective Energy Trading;keratoconus Classification Using Feature Selection and machine learning Approach;Semantic Segmentation of the Lung to Examine the Effect of COVID-19 Using UNET Model;SMDKGG: A Socially Aware Metadata Driven Knowledge Graph Generation for Disaster Tweets;Brain MRI Image Classification Using Deep learning;a Real-Time Face recognition Attendance Using machine learning;towards Abalone Differentiation through machine learning.

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OCR for data Retrieval :An analysis and machine learning Application model for NGO social volunteering 5

OCR for Data Retrieval :An analysis and Machine Learning App...

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5th international conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC)

作者： Sharma, Ruchi Dave, Parv Chaudhary, Jay NMIMS MPSTME Dept Informat Technol Mumbai Maharashtra India NMIMS MPSTME Dept Comp Engn Mumbai Maharashtra India

ISBN: (纸本)9781665426428

With the increase in amount of information being made available in digital format, information retrieval is a challenging task. Currently there exists a gap between organizations, volunteers and NGOs for volunteering work. there has been an upsurge of NGOs, non-profit events and corresponding independent volunteers or organizations willing to interconnect especially during these pandemic times. there is a need to fill this gap and connect the stakeholders minimizing the emergency response times. this paper proposes a novel design and implementation of an OCR based application for Automated NGO connect using machine learning. Phases implemented include image de-noising, binarization, data extraction and data conversion. the framework integrates deep learning based Tesseract OCR with image processing module and data visualization module. the proposed model can be extended to other application domains as well for research purposes.

关键词： optical character recognition(OCR) image processing information retreival Tessearct NGO machine learning deep learning

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Face recognition system using Extreme learning machines 5

Face Recognition system using Extreme Learning Machines

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5th international conference on Information Systems and Computer Networks, ISCON 2021

作者： Lal, Pallavaram Venkateswar Srilakshmi, Uppalapati Venkateswarlu, D. VFSTR Deemed to Be University Narayana Engineering College CSE Department Andhra Pradesh Gudur India VFSTR Deemed to Be University Vadlamudi CSE Department AP Guntur India

ISBN: (纸本)9781665403412

Face recognition process is a computer technology which employs various calculations which identify exactly the individual anatomy in electronic graphics and establish the individual and verify the recorded pictures by evaluating them along with the decorative graphics stored at the database. Facial recognition can be a significant issue in computer vision, and numerous investigators have learned this specific issue in lots of manners;nevertheless, it really is crucial notably in a few applications like surveillance methods. Computational intelligence methods are use in vast tool. But it is understood both neural networks along with SVMs confront some hard problems like sluggish learning rate, insignificant human intervene, and inferior computational scalability. Multi-Scale Feature pattern-based Extreme learning machine Algorithm (MSFP-ELM) as emerging technology that simplifies some struggles confronted by additional processes has just drawn the eye of a growing number of research workers. the prior is seen as single-hidden layer feedforward neural networks (SLFN). It chooses hidden pathways and analytically decides their result. So, in this specific paper, we will plan to give you a comprehensive opinion of those Multi-Scale Feature pattern-based Extreme learning machine Algorithm (MSFP-ELM) advancements for various tool. Finally, MSFP-ELM weakness and strengths is going to be shown, together side its prospective viewpoints. © 2021 IEEE.

关键词： Face recognition

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7th EAI international conference on e-learning, e-Education, and Online Training, eLEOT 2021

7th EAI International Conference on e-Learning, e-Education,...

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7th EAI international conference on e-learning, e-Education, and Online Training, eLEOT 2021

ISBN: (纸本)9783030843854

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Hybrid Classfication for Heart Disease Prediction using Artificial Intelligence 5

Hybrid Classfication for Heart Disease Prediction using Arti...

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5th international conference on Computing Methodologies and Communication, ICCMC 2021

作者： Khan, Ufaq Jeelani Oberoi, Ashish Gill, Jasmeen Rimt University Mandi Gobindgarh Punjab India

ISBN: (纸本)9781665403603

In general, data mining is considered as a method utilized for mining the valuable data from the available rough data. the futuristic outcomes are forecasted by utilizing the recent information in the prediction analysis. this research work deals with heart disease prediction. the proposed research work introduces several steps for heart disease prediction. the RF and DT based hybrid scheme is introduced and later the features are abstracted using RF. the implementation of DT is done for classification. the performance analysis helps to acquire accuracy, precision and recall of the recommended model. the proposed model has obtained an accuracy of about 94.44%. © 2021 IEEE.

关键词： machine learning

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Weakly-Supervised Estimation of Auroral Motion Field Via Iterative Pseudo Ground Truth learning 5

Weakly-Supervised Estimation of Auroral Motion Field Via Ite...

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5th international conference on Artificial Intelligence and pattern recognition, AIPR 2022

作者： Wang, Qian Fan, Qiqi Mao, Yanyu School of Telecommunication and Information Engineering Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

the small scale auroral structure is a far less explored field. Provided by auroral images which record the vivid auroral behaviors with satisfied temporal and spatial resolution, we are devoted to studying local auroral motions and the fine scale auroral activities. In order to estimate the auroral motion field, the method of optical flow is introduced to analyze auroral motion. However, the technology requires expensive dense annotations while training the network. Leveraged between the strong learning ability of the fully-supervised deep learning methods and the uncertainty of auroral data, we propose an iterative ground-truth learning approach to mine the pixel-level pseudo ground truth for auroral motion. Specifically, we first train a fully-supervised estimator on synthetic data via the Recurrent All-Pairs Field Transforms (RAFT) algorithm. the reconstructability and robustness of the estimated motion field are used as the criteria to measure applicability of the fully-supervised estimator for auroral images. then, the mined motion fields as pseudo ground truths are in turn fed into the RAFT algorithms to fine-tune the fully-supervised estimator again, which is iterated until the high-quality pseudo ground truths for auroral data are found. Experiments on auroral data from the Yellow River Station demonstrate the effectiveness of our method. More and more pseudo ground truths of auroral data are used to gradually improve the estimated motion field results by refining the contextual features of auroral images. With iterative pseudo ground truth learning, estimated errors can be reduced effectively. © 2022 ACM.

关键词： Optical flows

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2021 5th international conference on Computer, Communication, and Signal Processing, ICCCSP 2021

2021 5th International Conference on Computer, Communication...

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5th international conference on Computer, Communication, and Signal Processing, ICCCSP 2021

ISBN: (纸本)9781665432771

the proceedings contain 53 papers. the topics discussed include: anomaly detection and improvement of clusters using enhanced k-means algorithm;parallel exploitation of 2D LiDAR simultaneous localization and mapping;computer aided mass detection in mammograms;stock data analysis with UIpath automation;an improved content based image retrieval system using unsupervised deep neural network and locality sensitive hashing;mood recognition in online sessions using machine learning in real-time;symptoms based early clinical diagnosis of COVID-19 cases using hybrid and ensemble machine learning techniques;IoT and machine learning based smart grid system;and dual-cavity backed substrate integrated waveguide slot antenna for 5G applications.

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the 5th international Workshop on Narrative Extraction from Texts: Text2Story 2022 44th

The 5th International Workshop on Narrative Extraction from ...

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44th European conference on Information Retrieval (ECIR)

作者： Campos, Ricardo Jorge, Alipio Jatowt, Adam Bhatia, Sumit Litvak, Marina INESCTEC LIAAD Porto Portugal Polytech Inst Tomar Ci2 Smart Cities Res Ctr Tomar Portugal Univ Porto FCUP Porto Portugal Univ Innsbruck Innsbruck Austria Adobe Syst MDSR Lab Noida India Shamoon Coll Engn Ashdod Israel

ISBN: (纸本)9783030997397;9783030997380

Narrative extraction, understanding, verification, and visualization are currently popular topics for users interested in achieving a deeper understanding of text, researchers who want to develop accurate methods for text mining, and commercial companies that strive to provide efficient tools for that. Information Retrieval (IR), Natural Language Processing (NLP), machine learning (ML) and Computational Linguistics (CL) already offer many instruments that aid the exploration of narrative elements in text and within unstructured data. Despite evident advances in the last couple of years, the problem of automatically representing narratives in a structured form and interpreting them, beyond the conventional identification of common events, entities and their relationships, is yet to be solved. this workshop held virtually on April 10th, 2022 in conjunction with the 44th European conference on Information Retrieval (ECIR '22) aims at presenting and discussing current and future directions for IR, NLP, ML and other computational linguistics-related fields capable of improving the automatic understanding of narratives. It includes sessions devoted to research, demo, position papers, work-in-progress, project description, nectar, and negative results papers, keynote talks and space for an informal discussion of the methods, of the challenges and of the future of this research area.

关键词： Information retrieval

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