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检索条件"任意字段=2021 13th International Conference on Machine Learning and Computing, ICMLC 2021"

共 633 条记录，以下是101-110 订阅

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Comparison of RNN and Embeddings Methods for Next-item and Last-basket Session-based Recommendations 2021

Comparison of RNN and Embeddings Methods for Next-item and L...

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2021 13th international conference on machine learning and computing, icmlc 2021

作者： Salampasis, Michail Siomos, theodosios Katsalis, Alkiviadis Diamantaras, Konstantinos Christantonis, Konstantinos Delianidi, Marina Karaveli, Iphigenia International Hellenic University Thessaloniki Greece

ISBN: (纸本)9781450389310

Recurrent Neural Networks (RNNs) have been shown to perform very effectively in session-based recommendation settings, when compared to other commonly used methods that consider the last viewed item of the user and precomputed item-to-item similarities. However, there is little systematic study on how RNNs perform in comparison to methods that use embeddings for item representation for Collaborative Filtering. In this paper we use two industry datasets to compare RNNs with other sequential recommenders that use various embedding methods to represent items. the first dataset corresponds to a typical e-commerce session-based scenario demanding effective next-item recommendation. the second dataset represents a last-basket prediction setting. Results show that although the RNN greatly outperforms embedding methods in the next-item scenario, the dynamic nature of the RNNs has not the same positive impact in the last-basket prediction task. We also present and test a framework that enables the hybrid utilization of text content and item sequences using embeddings. Finally, we report on experiments with reranking methods that demonstrate the effectiveness of simple and practical methods, using item categories, to improve the results. © 2021 ACM.

关键词： Recurrent neural networks

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Controlled Environment for Spinach Cultured Plant with Health Analysis using machine learning 13

Controlled Environment for Spinach Cultured Plant with Healt...

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2021 IEEE 13th international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021

作者： Araneta, Mdas Angelo M. Asenjo, Daniel V. Lamprea, Carl Jeremiah L. Reyes, Argilyn Mae L. Medina, Oliver A. Sigue, Anna-Liza F. Cabal, Marielle M. Soriano, Aldrin J. Beano, Mary Grace P. Pamantasan Ng Cabuyao College of Computing and Engineering Laguna Cabuyao Philippines

ISBN: (纸本)9781665401678

the combination of mobile application and machine learning with environmental sensing and image processing device in monitoring the health and growth of the plant in real-time. the goal of the study is to develop a controlled environment for spinach by monitoring its health condition using image processing and supervised machine learning. the device collects real-time data for humidity, temperature, and soil conditions using different sensors. Diagnosis of spinach health status is done by capturing the images of spinach using image processing techniques. Two classifiers were used in detecting spinach health conditions, Green for healthy spinach or no damages on leaves and Green, Yellow, Brown for unhealthy or with holes, damages;this classifier is also called datasets. Spinach health status is monitored using a mobile application called Spinach Monitoring Application (SPIMON) and all collected data are stored in the cloud. the result spinach health status showed that its best to culture spinach in a controlled environment. © 2021 IEEE.

关键词： Image processing

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Distributed Deep Reinforcement learning-Based Content Caching in Edge computing-Enabled Blockchain Networks 13

Distributed Deep Reinforcement Learning-Based Content Cachin...

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13th international conference on Wireless Communications and Signal Processing, WCSP 2021

作者： Chen, Mengqi Wu, Guangming Zhang, Yuhuang Lin, Yan Zhang, Yijin Li, Jun School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9781665407854

As an emerging technology, blockchain possesses the decentralized trust establishment, preservation and delivery capabilities, but its application is limited by users' computing and storage capabilities. With the aid of Mobile edge computing (MEC) technology, frequently used block data of blockchain network can be cached in the edge servers to improve communication efficiency and reduce data transmission delay. In this paper, we investigate the problem of content caching in MEC-enabled blockchain network. In order to encourage edge caching and reduce transmission delay with high efficiency, we adopt distributed deep reinforcement learning (DRL) in solving the high-dimensional and large-scale decision-making problem. the simulation results show that the proposed scheme based on distributed proximal policy optimization (DPPO) outperforms the schemes based on other existing DRL algorithms. © 2021 IEEE.

关键词： Blockchain

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Local Navigation and Docking of an Autonomous Robot Mower Using Reinforcement learning and Computer Vision 13

Local Navigation and Docking of an Autonomous Robot Mower Us...

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13th international conference on Computer and Automation Engineering (ICCAE)

作者： Taghibakhshi, Ali Ogden, Nathan West, Matthew Univ Illinois Dept Mech Sci & Engn Urbana IL 61801 USA John Deere Technol Innovat Ctr Champaign IL USA

ISBN: (纸本)9781665412957

We demonstrate a successful navigation and docking control system for the John Deere Tango autonomous mower, using only a single camera as the input. this vision-only system is of interest because it is inexpensive, simple for production, and requires no external sensing. this is in contrast to existing systems that rely on integrated position sensors and global positioning system (GPS) technologies. To produce our system we combined a state-of-the-art object detection architecture, You Look Only Once (YOLO), with a reinforcement learning (RL) architecture, Double Deep Q-Networks (Double DQN). the object detection network identifies features on the mower and passes its output to the RL network, providing it with a low-dimensional representation that enables rapid and robust training. Finally, the RL network learns how to navigate the machine to the desired spot in a custom simulation environment. When tested on mower hardware, the system is able to dock with centimeter-level accuracy from arbitrary initial locations and orientations.

关键词： Reinforcement learning Deep Q-learning Object Detection YOLO Mower

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Automated Classification of Criminal and Violent Activities in thailand from Online News Articles 13

Automated Classification of Criminal and Violent Activities ...

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13th international conference on Knowledge and Smart Technology (KST)

作者： thaipisutikul, Tipajin Tuarob, Suppawong Pongpaichet, Siripen Amornvatcharapong, Amornsri Shih, Timothy K. Mahidol Univ Fac Informat & Commun Technol Salaya Nakhon Pathom Thailand Natl Cent Univ Comp Sci & Informat Engn Taoyuan Taiwan Mahidol Wittayanusorn Sch Salaya Nakhon Pathom Thailand

ISBN: (纸本)9781728176024

Criminal and violent activities are a universal concern that affects a society's nature of life and economic dynamics. With dramatically increasing crime rates, law enforcement agencies have begun to show attention in utilizing machine learning approaches to analyze crime patterns to protect their communities. However, there are only a few studies that carried out experiments to classify thai crime news articles into their proper categories. Also, the comparison of various machine learning algorithms toward this task has still been under-investigated. therefore, in this paper, we aim to develop a framework to automate the classification and visualization of criminal and violent activities from online thai news articles. Six classifiers are employed to classify crime news articles into one of the five crime categories including Burglary, Drug, Murder, Accident, and Corruption. the results have shown that Support Vector machine and Logistic Regression approaches outperform other classifiers in terms of Accuracy, Precision, Recall, and F-Measure metrics.

关键词： Crime and Violence Classification News Articles Text Mining Information Extraction Supervised machine learning

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Curriculum Reinforcement learning-Based Computation Offloading Approach in Space-Air-Ground Integrated Network 13

Curriculum Reinforcement Learning-Based Computation Offloadi...

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13th international conference on Wireless Communications and Signal Processing, WCSP 2021

作者： Wang, Zheng Yu, Hui Zhu, Shichao Yang, Bo Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China

ISBN: (纸本)9781665407854

Space-air-ground integrated network (SAGIN) is emerging as a prominent framework supporting the ever-growing Internet of things (IoT) applications in the areas without infrastructures. In this paper, we investigate the problem of IoT task offloading under the SAGIN scenario where multiple IoT devices cooperatively use computing resources. We formulate the task offloading problem of minimizing the processing delay of all tasks, taking into account the dynamics of tasks generated by each IoT device, the mobility of unmanned aerial vehicle (UAV), and the difference in computing power between the UAV and the low earth orbit (LEO) satellite. then the problem is formulated as a Markov decision process (MDP). To cope with the dynamics and complexity of the system, as well as the training difficulties caused by the large number of agents, we propose a curriculum learning-multi-agent deep deterministic policy gradient (CL-MADDPG) approach to learn the near-optimal offloading strategy. Simulation results show that the proposed method has satisfactory convergency and can significantly reduce the average task processing delay. © 2021 IEEE.

关键词： Markov processes

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A Cybertwin-Driven Task Offloading Scheme Based on Deep Reinforcement learning and Graph Attention Networks 13

A Cybertwin-Driven Task Offloading Scheme Based on Deep Rein...

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13th international conference on Wireless Communications and Signal Processing, WCSP 2021

作者： Zhong, Xiaoxu He, Yejun Shenzhen University Guangdong Engineering Research Center of Base Station Antennas and Propagation Shenzhen Key Laboratory of Antennas and Propagation College of Electronics and Information Engineering 518060 China

ISBN: (纸本)9781665407854

With the improvement of mobile computing capability, the service demands of user terminals are also increasing. therefore, task offloading in mobile edge computing (MEC) has always been a research focus. However, most of task offloading schemes are confined to current network paradigms. During the shaping of 6G, it is of value to explore novel task offloading architectures and methods which fit future communication system. In this paper, we present a cybertwin-based system that allows flexible resource deployment and coordination. In addition, by targeting at the graph structure of this system, we propose an energy-efficient and security-enhanced partial-offloading scheme based on a combination of deep reinforcement learning (DRL) and modified graph attention network (GAT). Numerical results demonstrate that the proposed training method converges better and the trained GAT model achieves utility improvement. © 2021 IEEE.

关键词： Reinforcement learning

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KST 2021 - 2021 13th international conference Knowledge and Smart Technology

KST 2021 - 2021 13th International Conference Knowledge and ...

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13th international conference Knowledge and Smart Technology, KST 2021

ISBN: (纸本)9781728176024

the proceedings contain 44 papers. the topics discussed include: K-mean index learning for multimedia datasets;design and analysis of 5GHz microwave absorber;improved step detection with smartphone handheld mode recognition;a deep learning approach to digital filter parameter estimation based on amplitude responses;the spatio-temporal distribution of residential real estate price monitoring system;the impulse outlier suppression techniques using ROAD and VMF for color portraits;analysis of dust exposure impact on cardiovascular diseases risk prediction in Bangkok, thailand;CPS-based automation model prediction for inspection;machine learning for explosive detection from electronic nose datasets;an RSSI-based weighting with accelerometers for real-time indoor positioning;and dual-step nearest neighborhood prediction method of individual childhood growth.

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Predicting the Progress of Vehicle Development Projects: An Approach for the Identification of Input Features 13

Predicting the Progress of Vehicle Development Projects: An ...

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13th international conference on Agents and Artificial Intelligence (ICAART)

作者： Boehme, Oliver Meisen, Tobias Berg Univ Wuppertal Chair Technol & Management Digital Transformat Rainer Gruenter Str 21 Wuppertal Germany Berg Univ Wuppertal Dept Elect Engn Informat Technol & Media Technol Rainer Gruenter Str 21 Wuppertal Germany

ISBN: (纸本)9789897584848

Today project managers estimate time and other project relevant key performance indicators by using project management tools e.g. milestone trend analysis. We believe that predicting the project's progress with traditional methods will soon reach its limitations due to the increasing complexity in vehicle development. machine learning methods provide one possible solution. the vision is to predict the progress of development projects in the early stages of the project. In order to make this vision come true, we need to define measurable input features for machine learning models. In this paper, we focus on representing an approach to identify parameters that exert influence on the progress of development projects.

关键词： machine learning Input Features Automotive R&D Project Success Indicators Critical Success Factors Employee Survey

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Customer Visit Prediction Using Purchase Behavior and Tendency 13th

Customer Visit Prediction Using Purchase Behavior and Tenden...

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13th international conference on Social computing and Social Media (SCSM) part of the 23rd international conference on Human-Computer Interaction (HCI)

作者： Saito, Retsuya Otake, Kohei Namatame, Takashi Chuo Univ Grad Sch Sci & Engn 1-13-27 KasugaBunkyo Ku Tokyo 1128551 Japan Tokai Univ Sch Informat & Telecommun Engn 2-3-23 TakanawaMinato Ku Tokyo 1088619 Japan Chuo Univ Fac Sci & Engn 1-13-27 KasugaBunkyo Ku Tokyo 1128551 Japan

ISBN: (纸本)9783030776848;9783030776855

Over the last few decades, consumers' preferences and lifestyles have changed significantly. In suchmarket, the number of products and services aims to mass marketing have become less effective. therefore, personalization tailored to individual characteristics, certain segments, and one-to-one marketing focusing on individuals are becoming important. therefore, customer relationship management, retaining existing customers and costs for acquiring new customers are important for both of academic and business field. However, for that purpose, it is essential to grasp customer behavior in more detail and use it for analysis. therefore, in this study, we estimate the potential clusters of customers and customers' purchase behavior. Concretely, we use pLSA and XGBoost which become popular machine learning methodologies. In this study, we set the number of store visits per month for objective variable and show a prediction model of it. then we compare the model that incorporates the result of predicting the probability of the latent cluster as an explanatory variable with the model that incorporates a general explanatory variable.

关键词： Customer Behavior pLSA XGBoost

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