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Deep Convolutional Neural Networks Based on Image data Augmentation for Visual Object Recognition 20th

Deep Convolutional Neural Networks Based on Image Data Augme...

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20th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Jayech, Khaoula Univ Sousse Ecole Natl Ingn Sousse LATIS Lab Adv Technol & Intelligent Syst Sousse 4023 Tunisia

ISBN: (纸本)9783030336073;9783030336066

Deep Neural Networks (DNNs) have achieved a great success in machine learning. Among a lot of DNN structures, Deep Convolutional Neural Networks (DCNNs) are currently the main tool in the state-of-the-art variety of classification tasks like visual object recognition and handwriting and speech recognition. Despite wide perspectives, DCNNs have still some challenges to deal with. In previous work, we demonstrated the effectiveness of using some regularization techniques such as the dropout to enhance the performance of DCNNs. However, DCNNs need enough training data or even a class balance within datasets to conduct better results. To resolve this problem, some researchers have evoked different data augmentation approaches. this paper presents an extension of a later study. In this work, we conducted and compared the results of many experiments on CIFAR-10, STL-10 and SVHN using variant techniques of data augmentation combined with regularization techniques. the analysis results show that with the right use of data augmentation approaches, it is possible to achieve good results and outperform the state-of-the-art in this field.

关键词： Deep learning DCNN Image data augmentation Object recognition

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System identification and damage detection framework using simulated experiments and machine learning techniques 11

System identification and damage detection framework using s...

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11th international conference on Structural Dynamics, EURODYN 2020

作者： Seventekidis, P. Giagopoulos, D. Arailopoulos, A. MArkogiannaki, O. Department of Mechanical Engineering University of Western Macedonia KozaniGR-50100 Greece

ISBN: (纸本)9786188507203

the present study focuses on the implementation of a methodology to bridge the gap between SHM models with numerically generated data and correspondence with measurements from the real structure to provide reliable damage predictions. In the proposed novel methodology, numerically generated data from simulation models are integrated with measurements from the corresponding real structure to achieve high accuracy in identifying and predicting potential structural damages. A truss structure consisting of composite carbon fiber tubes, aluminum elements and steel bolts for the connections is used for the application of the proposed approach. the process begins with the three-dimensional finite element (FE) models of the examined cylindrical parts, developed in robust finite element analysis software simulating each carbon fiber ply and resin matrix. the real structure and FE models are analyzed in dynamic loading to identify their response. After, the complete assembly FE models are updated based on the data from experimental tests that correspond to the conducted analysis tests on composite cylindrical parts. the potential damage of the structure, set as loose bolts defining a multiclass damage identification problem, is then simulated with the optimal models through a series of stochastic FE load cases for different excitation characteristics. the simulated acceleration time series are then be fed in for the training of a supervised Convolutional Neural Network (CNN) classifier. the trained CNN is finally validated on experimentally measured structural states of the truss. Reliable results prove that optimal FE modeling may be used with machine learning techniques to synthesize a damage identification tool despite the uncertainties, which are tackled by the inherent advantage of numerical generated results to simulate arbitrary number of load cases in small amount of type and minimal effort. © 2020 European Association for Structural Dynamics. All rights reserved.

关键词： Structural health monitoring

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IoT-based irrigation management for smallholder farmers in rural Sub-Saharan Africa 11

IoT-based irrigation management for smallholder farmers in r...

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11th international conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2020

作者： Nigussie, Ethiopia Olwal, thomas Musumba, George Tegegne, Tesfa Lemma, Atli Mekuria, Fisseha Department of Future Technologies Turku Finland Department of Electrical Engineering Tshwane University of Technology Pretoria South Africa School of Computer Science and Information Technology Dedan Kimathi University of Technology Nyeri Kenya Center for ICT for Development Bahir Dar University Bahir Dar Ethiopia School of Electircal Engineering Haramaya University Dire Dawa Ethiopia Council for Scientific and Industrial Research Pretoria South Africa

Ensuring food security has become a challenge in Sub-Saharan Africa (SSA) due to combined effects of climate change, high population growth, and relying on rainfed farming. Governments are establishing shared irrigation infrastructure for smallholder farmers as part of the solutions for food security. However, the irrigated farms often failed to achieve the expected crop yield. this is partly due to lack of water management system in the irrigation infrastructure. In this work, IoT-based irrigation management system is proposed after investigating problems of irrigated farmlands in three SSA countries, Ethiopia, Kenya, and South Africa as case studies. Resource-efficient IoT architecture is developed that monitors soil, microclimate and water parameters and performs appropriate irrigation management. Indigenous farming and expert knowledge, regional weather information, crop and soil specific characteristics are also provided to the system for informed-decision making and efficient operation of the irrigation management system. In SSA, broadband connectivity and cloud services are either unavailable or expensive. To tackle these limitations, data processing, network management, irrigation decisions and communication to the farmers are carried out locally, without the involvement of any back-end servers. Furthermore, the use of green energy sources and resource-aware intelligent data analysis algorithm is studied. It is expected that an intelligent data analysis will help to discover new knowledge that support further development of agricultural expertise. the proposed IoT-based irrigation management system is expected to contribute towards long term and sustainable high crop yield with minimum resource consumption and impact to the biodiversity around the case farmlands. © 2020 the Authors. Published by Elsevier B.V.

关键词： Internet of things

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Transfer Knowledge Between Sub-regions for Traffic Prediction Using Deep learning Method 20th

Transfer Knowledge Between Sub-regions for Traffic Predictio...

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20th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Ren, Yi Xie, Kunqing Peking Univ Sch Elect Engn & Comp Sci Key Lab Machine Percept Minist Educ Beijing 100871 Peoples R China

ISBN: (纸本)9783030336073;9783030336066

In modern society, traffic is a very important aspect of our social lives, but a lot of problems arise such as traffic accident, congestion, air pollution, etc. the emergence of ITS (intelligent Transportation System) brings a range of scientific ways to tackle these problems, among which there is a crucial need to study prediction methods in modern traffic systems. Fortunately, big data in ITS and advanced machine learning technologies motivate us to implement extensive researches in this area. In this paper, we introduce an innovative method for traffic prediction. We split the whole traffic system into separate sub-regions. then, each of them would be modeled by deep graph neural networks, which could extract unique characters of the region as well as preserve the topology property of it. Moreover, we transfer the useful knowledge between these regions and share the basic information in order to improve the performance of our model. Finally, we conduct experiment on real world data set and achieve the best performance, which proves the effectiveness of our method.

关键词： data mining Deep learning Big data Traffic prediction intelligent Transportation System Smart city

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Sports Analytics: Maximizing Precision in Predicting MLB Base Hits 11

Sports Analytics: Maximizing Precision in Predicting MLB Bas...

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11th international Joint conference on Knowledge Discovery, Knowledge engineering and Knowledge Management (IC3K) / 11th international conference on Knowledge Discovery and Information Retrieval (KDIR)

作者： Alceo, Pedro Henriques, Roberto NOVA Informat Management Sch Campus Campolide Lisbon Portugal

ISBN: (纸本)9789897583827

As the world of sports expands to never seen levels, so does the necessity for tools which provided material advantages for organizations and other stakeholders. the main objective of this paper is to build a predictive model capable of predicting what are the odds of a baseball player getting a base hit on a given day, with the intention of both winning the game Beat the Streak and to provide valuable information for the coaching staff. Using baseball statistics, weather forecasts and ballpark characteristics several models were built with the CRISP-DM architecture. the main constraints considered when building the models were balancing, outliers, dimensionality reduction, variable selection and the type of algorithm - Logistic Regression, Multi-layer Perceptron, Random Forest and Stochastic Gradient Descent. the results obtained were positive, in which the best model was a Multi-layer Perceptron with an 85% correct pick ratio.

关键词： Machine learning data Mining Predictive Analysis Classification Model Baseball MLB

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the Use of Unified Activity Records to Predict Requests Made by Applications for External Services 20th

The Use of Unified Activity Records to Predict Requests Made...

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20th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Grzenda, Maciej Kunicki, Robert Legierski, Jaroslaw Warsaw Univ Technol Fac Math & Informat Sci Ul Koszykowa 75 PL-00662 Warsaw Poland City Warsaw Digitalisat Dept Pl Bankowy 2 PL-00095 Warsaw Poland IoT & Adv Technol Ul Wolumen 11 PL-01912 Warsaw Poland

ISBN: (纸本)9783030336172;9783030336165

Many modern applications use services and data made available by provisioning platforms of third parties. the question arises if the use of individual services and data resources such as open data by novel applications can be predicted. In particular, whether initial software development efforts such as application development during hackathons can be monitored to provide data for the models predicting requests submitted to open data platforms and possibly other platforms is not clear. In this work, we propose an iterative method of transforming request streams into activity records. By activity records, vectors containing aggregated representation of the requests for external services made by individual applications over growing periods of software development are meant. the approach we propose extends previous works on the development of network flows aggregating network traffic and makes it possible to predict future requests made to web services with high accuracy.

关键词： data aggregation Prediction Network flow Web service

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Predicting Shear Strength at the Joint of a Beam and a Column Using Supervised learning and Multivariate Neural Networks 11th

Predicting Shear Strength at the Joint of a Beam and a...

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11th Annual international conference on Information Management and Big data, SIMBig 2024

作者： Huaman, Yhon Fuentes Ramos, Yessenia Yari Ramos, Raymundo Aguirre Computer Science Department UNSA Calle San Agustin 107 - Cercado Arequipa04001 Peru Department of Engineering UNAMBA Av. Inca Garcilazo de la Vega Tamburco03001 Peru

ISBN: (纸本)9783031914270

Shear Strength analysis in beam-column joint is important to avoid failure and measure the performance of reinforced concrete in structures. this analysis is important because failures in the column beam joints can cause partial damage or collapse of the structures. Two types of failures are analyzed: brittle failure, which is by shear before the creep of the beam, and ductile failure by shear after the creep of the beam, the creep of the beam without joint failure. In this article, 598 rows × 14 columns were studied, which are the input and one output characteristics (Shear Strength), 80% of the data was partitioned in training and 20% for testing. Supervised learning models (Decision Tree, Random Forest, Adaboost, GBRT and XGBoost) and Artificial Neural Networks and MLP were applied in the prediction of Shear Strength. In the case of supervised learning techniques, the metrics of MAE, MSE, RMSE, R2, SMAPE were used;of which XGBoost gave better results in MAE = 0.3920, MSE = 0.3382, RMSE = 0.5816 and Random Forest in R2=0.8138, XGBoost SMAPE = 0.4017;on the other hand, applying the Neural Networks SMAPE = 0.118, Acurracy = 72.12% and MLP with SMAPE = 0.116, Acurracy = 73.82%. One of the best models the Neural Networks with 500 neurons because it gave a better fit in SMAPE and a larger Acurracy. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Shear strength

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Delay-Sensitive Task Offloading for Internet of things in Nonorthogonal Multiple Access MEC Networks 11

Delay-Sensitive Task Offloading for Internet of Things in No...

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11th international conference on Information and Communication Technology Convergence, ICTC 2020

作者： Dat Tuong, Van Phung Truong, thanh Tran, Anh-Tien Masood, Arooj Shumeye Lakew, Demeke Lee, Chunghyun Lee, Yunseong Cho, Sungrae Chung-Ang University School of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9781728167589

With the rapid development of the Internet of things (IoTs), the fifth-generation (5G) networks need to serve massive connection and accommodate ultra-low delay. In response to these challenges, mobile edge computing (MEC) and nonorthogonal multiple access (NOMA) have been considered as the promising solutions. In this paper, we investigate the joint optimization problem of computation offloading and resource allocation in NOMA MEC networks to minimize the delay to complete tasks of all users. Different from the conventional optimization approach, we propose and develop an online solution based on deep reinforcement learning (DRL) algorithm, which can fit with dynamic networks with time-varying channels. In particular, we employ deep neural networks (DNNs) to process the raw state inputs and then output the computation offloading decision and resource allocation at different times. the weights of DNNs are continuously trained with the observed data via interactions with the environment. Simulation results reveal that our proposed algorithm achieves higher delay reduction compared to the existing strategies. © 2020 IEEE.

关键词： Resource allocation

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Using Multimodal Information to Enhance Addressee Detection in Multiparty Interaction 11

Using Multimodal Information to Enhance Addressee Detection ...

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11th international conference on Agents and Artificial Intelligence (ICAART)

作者： Malik, Usman Barange, Mukesh Saunier, Julien Pauchet, Alexandre Normandie Univ INSA Rouen LITIS F-76000 Rouen France

ISBN: (纸本)9789897583506

Addressee detection is an important challenge to tackle in order to improve dialogical interactions between humans and agents. this detection, essential for turn-taking models, is a hard task in multiparty conditions. Rule based as well as statistical approaches have been explored. Statistical approaches, particularly deep learning approaches, require a huge amount of data to train. However, smart feature selection can help improve addressee detection on small datasets, particularly if multimodal information is available. In this article, we propose a statistical approach based on smart feature selection that exploits contextual and multimodal information for addressee detection. the results show that our model outperforms an existing baseline.

关键词： Human-Computer Interaction intelligent Agents Machine learning

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A Study on Feature Values as a Speaker Identification Method

A Study on Feature Values as a Speaker Identification Method

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Etsuro Nakamura Yoichi Kageyama Motonari Shirasu Graduate School of Engineering Science Akita University Akita Japan Service and Support Division Japan Business Systems Inc. Tokyo Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

Automatic speaker assignment can enhance the efficiency of automated systems used for creating meeting minutes. therefore, various methods of speaker identification have been widely utilized; however, in such methods, it is necessary to optimally arrange the equipment. In this paper, we propose a method for identifying speakers using an omnidirectional camera and a microphone. In this process, tens of minutes of image and voice data were used for model training, and the speaker was successfully identified. In the speaker discrimination experiments, the proposed method was able to identify the speaker with an average success rate of more than 70.0% using the lip height and product of the lip height feature and lip width feature as the lip movement features.

关键词： Lips Training Videos Training data Microphones Feature extraction Business

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