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检索条件"任意字段=2020 International Conference on Data Processing Algorithms and Models, ICDPAM 2020"

共 5726 条记录，以下是491-500 订阅

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Prediction of Consumers' Characteristics in Multichannel Selection Based on Hidden Markov Model: Taking Malaysian Consumers as an Example

Prediction of Consumers' Characteristics in Multichannel Sel...

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Advances in Electrical Engineering and Computer Applications( AEECA), 2020 IEEE international conference on

作者： Zhaopeng Zhu Bing Xue Jinan Engineering Polytechnic Jinan China New Era University College Kuala Lumpur Malaysia Jinan Engineering Polytechnic Jinan China HELP University Kuala Lumpur Malaysia

ISBN: (纸本)9798350355260

Traditional static algorithmic models often struggle to effectively capture the dynamic and evolving nature of consumer behavior in multichannel shopping environments, where consumers frequently switch between various platforms, such as online stores, physical locations, and mobile apps. This study applies Hidden Markov models (HMM) to provide a more accurate prediction and analysis of consumer multichannel choices. By gathering and analyzing detailed shopping data from Malaysian consumers, HMM models consumer behavior transitions across different channels, estimating critical parameters like state transition probabilities and observation probabilities. The results demonstrate high predictive accuracy, with both accuracy and F1 scores ranging from 0.97 to 0.99. These findings show that HMM can offer a more nuanced and comprehensive understanding of consumer behavior in multichannel settings. This improved modeling helps businesses optimize their omnichannel strategies, allowing them to better forecast consumer actions and align their operations with evolving customer preferences in diverse shopping environments.

关键词： Industries Consumer behavior Accuracy Heuristic algorithms Hidden Markov models Switches Predictive models Prediction algorithms data models Mobile applications

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An Experimental Analysis for Credit Card Fraud Detection with Imbalanced and Machine Learning Techniques 26th

An Experimental Analysis for Credit Card Fraud Detection wi...

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26th annual international conference on Advanced Computing and Communications, ADCOM 2020

作者： Anirudh, G. Talukdar, Upasana Department of Data Science and Analytics Central University of Rajasthan Ajmer India

ISBN: (纸本)9789811900181

Credit card fraud is a criminal misdemeanour and causes harm to banks, persons as well as financial institutions. Hence, it is substantial to detect and prevent fraudulent activities. Several significant approaches have been put forward in the literature to detect different types of fraud cases. However, most of the existing approaches face different challenges. Among them, the class imbalance problem is the most common. This problem consists of unequal distribution of observations across the classes. In this study, the ratio fraudulent:non-fraudulent is very small, and this poses a challenge for traditional classification algorithms to detect fraudulent activities. This paper presents a rigorous experimental analysis to tackle the class imbalance problem. The experiments were carried out in three categories: (a) experiments with traditional machine learning algorithms, (b) experiments with ensemble methods, and (c) experiments with traditional machine learning algorithms after imbalanced data pre-processing techniques. The imbalanced data pre-processing techniques include undersampling, oversampling, and a combination of both. The performance of the solutions has been evaluated using five different metrics: F1-score, precision, recall, area under precision–recall curve (AUPR), and area under receiver operating characteristic curve (AUROC). The experimental results show improved performance in detecting fraudulent cases. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Offline Imitation Learning Using Reward-free Exploratory data 5

Offline Imitation Learning Using Reward-free Exploratory Dat...

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5th international conference on algorithms, Computing and Artificial Intelligence, ACAI 2022

作者： Wang, Hao Feng, Dawei Ding, Bo Li, Wei College of Computer National University of Defense Technology Changsha Hunan China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Independent Researcher GuangZhou China

ISBN: (纸本)9781450398343

Offline imitative learning(OIL) is often used to solve complex continuous decision-making tasks. For these tasks such as robot control, automatic driving and etc., it is either difficult to design an effective reward for learning or very expensive and time-consuming for agents to collect data interactively with the environment. However, the data used in previous OIL methods are all gathered by reinforcement learning algorithms guided by task-specific rewards, which is not a true reward-free premise and still suffers from the problem of designing an effective reward function in real tasks. To this end, we propose the reward-free exploratory data driven offline imitation learning (ExDOIL) framework. ExDOIL first trains an unsupervised reinforcement learning agent by interacting with the environment, and collects enough unsupervised exploration data during training;Then, a task independent yet simple and efficient reward function is used to relabel the collected data;Finally, an agent is trained to imitate the expert to complete the task through a conventional RL algorithm such as TD3. Extensive experiments on continuous control tasks demonstrate that the proposed framework can achieve better imitation performance(28% higher episode returns on average) comparing with previous SOTA method(ORIL) without any task-specific rewards. © 2022 ACM.

关键词： Reinforcement learning

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Bosch's Industry 4.0 Advanced data Analytics: Historical and Predictive data Integration for Decision Support 16th

Bosch's Industry 4.0 Advanced Data Analytics: Historical and...

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16th international conference on Research Challenges in Information Sciences (RCIS)

作者： Galvao, Joao Ribeiro, Diogo Machado, Ines Ferreira, Filipa Goncalves, Julio Faria, Rui Moreira, Guilherme Costa, Carlos Cortez, Paulo Santos, Maribel Yasmina Univ Minho ALGORITMI Res Ctr Guimaraes Portugal Bosch Car Multimedia Braga Portugal

ISBN: (纸本)9783031057601;9783031057595

Industry 4.0, characterized by the development of automation and data exchanging technologies, has contributed to an increase in the volume of data, generated from various data sources, with great speed and variety. Organizations need to collect, store, process, and analyse this data in order to extract meaningful insights from these vast amounts of data. By overcoming these challenges imposed by what is currently known as Big data, organizations take a step towards optimizing business processes. This paper proposes a Big data Analytics architecture as an artefact for the integration of historical data - from the organizational business processes - and predictive data - obtained by the use of Machine Learning models -, providing an advanced data analytics environment for decision support. To support data integration in a Big data Warehouse, a data modelling method is also proposed. These proposals were implemented and validated with a demonstration case in a multinational organization, Bosch Car Multimedia in Braga. The obtained results highlight the ability to take advantage of large amounts of historical data enhanced with predictions that support complex decision support scenarios.

关键词： Big data Warehousing Advanced analytics Machine Learning Industry 4.0

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Efficient and Secure data Handling in IoT Networks: Robust Lightweight Approach

Efficient and Secure Data Handling in IoT Networks: Robust L...

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Innovations in High Speed Communication and Signal processing (IHCSP), international conference on

作者： Pooja Sharma Nishanthi Nishanthi Noopur Pandey Krishna Reddy B N Bhandari Mahesh Ashok S. Prabagaran Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of CSE Prince Shri Venkateshwara Padmavathy Engineering College Chennai Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka IT Vishwakarma Institute of Technology Pune India Department of Mechanical Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

A network of physical things that are outfitted with sensors, smart networking, and RFID technology is referred to as the "Internet of Things" (IoT), which is an abbreviation for the phrase. The items in question are able to communicate with one another and share data with other systems and devices respectively. Information security is of the utmost importance since by the year 2020, there will be more than fifty billion devices connected to the internet. Traditional cryptography, such as the Advanced Encryption Standard (DES) and the Advanced Encryption Standard (AES), does not take into account the necessary hardware and software, the amount of power consumed, the size of the code, or the amount of memory overhead. NLBSIT, an Energy Efficient and Low-Pooled Memory Cryptographic Algorithm, and a Robust Lightweight Algorithm are the three lightweight encryption methods that are described in this thesis. NLBSIT is an acronym for natural language based secure information transfer. These algorithms are intended for use with devices connected to the Internet of Things. In order to solve the problems that Internet of Things devices encounter, such as excessive memory usage, power consumption, and energy consumption, these algorithms have been developed.

关键词： Power demand data handling Memory management Software algorithms Signal processing algorithms Energy efficiency Encryption Complexity theory Internet of Things Standards

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A Nonlinear Model Based on data-Driven Control for Cascading Failure in Power Grids 5

A Nonlinear Model Based on Data-Driven Control for Cascading...

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5th international conference on Mechatronics, Robotics and Automation, ICMRA 2022

作者： Li, Beibei Song, Kefan Chen, Linfeng Jia, Aohui Northwestern Polytechnical University Cyberspace Security Institute Xi'an China

ISBN: (纸本)9781665472654

People's ability to manipulate complex networks depends on the design of efficient control algorithms, as well as the availability of accurate and operable models of network dynamics. Although the design of network system control algorithms has made significant progress in the past few years, most of them are based on static network research, which is difficult to satisfy in practice. Finding the key nodes in the network plays a decisive role in network cascading failure and fault repair. This paper aims to establish a method for controlling cascade failures driven by nonlinear data in complex power grids. The focus is on minimizing the input nodes of the control system. The model is applied to the simulation network ER, SF and the real network topology of the US Powergrid to verify that it achieves the largest cascading failure of the network under the least node control, so as to effectively predict the cascading failure of the reverse protection network, and effectively quickly repair cascading failures before the system completely crashes. © 2022 IEEE.

关键词： Smart power grids

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Active transfer learning for data-driven manufacturing process modelling 16

Active transfer learning for data-driven manufacturing proce...

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16th CIRP conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2022

作者： Chen, Gengxiang Li, Yingguang Mehdi-Souzani, Charyar Liu, Xu Nanjing University of Aeronautics and Astronautics College of Mechanical and Electrical Engineering Nanjing210016 China Université Paris-Saclay ENS Pars-Saclay LURPA Université Sorbonne Paris Nord Gif-Sur-Yvette91190 France Nanjing Tech University School of Mechanical and Power Engineering Nanjing211816 China

Manufacturing process modelling (MPM) aims to construct high-fidelity digital predictive models of the concerned properties of products, processes or manufacturing systems for the further optimisation and improvement of manufacturing activities. data-driven modelling methods, including machine learning and deep learning, have drawn immense attention to MPM problems because of their powerful representative ability. However, the labelled data of concerning properties in the manufacturing process is often insufficient and sparse because of the expensive and time-consuming experiments or simulations. The scarcity of labelled data hinders the further development of data-driven models in MPM problems. This paper proposes an active transfer learning framework by integrating active generation of labelled data and the processing of relevant data to reduce the requirements of labelled data. Firstly, the initial active labelling module introduces the generation of a more representative and informative labelled dataset rather than a randomly generated one. Then, the transfer learning model can extract the general information from the relevant data to address the information scarcity for the target task. Besides, the iterative active labelling module can determine to query promising new labelled data according to the performance of the current model. The effectiveness of the proposed framework is verified in a tool wear prediction case. The experimental outcomes demonstrate that the three modules of the framework can reduce the labelled data requirements and enhance the performance of the data-drive model under limited labelled data. © 2023 Elsevier B.V.. All rights reserved.

关键词： Iterative methods

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Using Bayesian Optimization and Wavelet Decomposition in GPU for Arterial Blood Pressure Estimation 44

Using Bayesian Optimization and Wavelet Decomposition in GPU...

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44th Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Gonzalez-Novoa, Jose A. Busto, Laura Santana, Pablo Farina, Jose Rodriguez-Andina, Juan J. Juan-Salvadores, Pablo Jimenez, Victor Iniguez, Andres Veiga, Cesar Galicia Sur Hlth Res Inst IIS Galicia Sur Cardiovasc Res Grp Vigo 36213 Spain Univ Vigo Dept Elect Technol Vigo 36310 Spain Hosp Alvaro Cunqueiro SERGAS Cardiol Dept Vigo 36213 Spain

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Continuous monitoring of arterial blood pressure (ABP) of patients in hospital is currently carried out in an invasive way, which could represent a risk for them. In this paper, a noninvasive methodology to optimize ABP estimators using electrocardiogram and photoplethysmography signals is proposed. For this, the XGBoost machine learning model, optimized with Bayesian techniques, is executed in a Graphics processing Unit, which drastically reduces execution time. The methodology is evaluated using the MIMIC-III Waveform database. Systolic and diastolic pressures are estimated with mean absolute error values of 15.85 and 11.59 mmHg, respectively, similar to those of the state of the art. The main advantage of the proposed methodology with respect to others of the current state of the art is that it allows the optimization of the estimator model to be performed automatically and more efficiently at the computational level for the data available.

关键词： Computational modeling MIMICs Graphics processing units Arterial blood pressure Electrocardiography Feature extraction data models

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Handling incomplete data using Radial basis Kernelized Intuitionistic Fuzzy C-Means 2

Handling incomplete data using Radial basis Kernelized Intui...

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2nd international conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Sethia, Kavita Singh, Jaspreeti Gosain, Anjana University School of Information Communication and Technology Guru Gobind Singh Indraprastha University Sector-16C Dwarka New Delhi110078 India

Missing data imputation is a critical task in the data pre-processing stage to ensure the quality, stability, and reliability of machine learning models. If missing values are imputed incorrectly, it can result in erroneous predictions and inconsistent model performance. Traditional imputation methods often struggle with complex data patterns having non-linearity and uncertainties. By integrating soft clustering principles, the proposed work provides a flexible framework for imputing missing data by taking into account the underlying inherent data pattern. To address the missing data challenges, this paper presents a novel imputation technique called Radial Basis Kernel Intuitionistic Fuzzy C-Means Imputation (KIFCMI), which builds upon the standard Intuitionistic Fuzzy C-Means (IFCM) technique. KIFCMI explores the application of centroid-based imputation using IFCM by incorporating the RBF kernel-induced metric in the data space as a replacement for the original Euclidean norm metric. By employing a kernel function, KIFCMI enables the clustering of data that lacks linear separability within the original space, enabling the formation of homogeneous clusters in a space of higher dimensionality. The effectiveness of the proposed imputation technique is validated on ten diverse real-world datasets obtained from the Public Library UCI with 10% and 20% missing data. The comparative analyses of the proposed technique KIFCMI, is carried out with three conventional techniques, namely fuzzy c-means imputation (FCMI), kernelized fuzzy c-means imputation (KFCMI), and intuitionistic fuzzy c-means imputation (IFCMI). The experimental results using two performance measures, namely RMSE and MAE, showcase the robustness and versatility of the proposed technique across other imputation outcomes. This research paper contributes to the evolving landscape of missing data imputation, offering insights into the practical applications of fuzzy clustering techniques. © 2024 Elsevier B.V..

关键词： data mining

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SGNN: A New Method for Learning Representations on Signed Networks 32nd

SGNN: A New Method for Learning Representations on Signed Ne...

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32nd international conference on Artificial Neural Networks (ICANN)

作者： Jin, Ji Bi, Jialin Qu, Cunquan Shandong Univ Data Sci Inst Jinan 250100 Peoples R China Shandong Univ Sch Math Jinan 250100 Peoples R China

ISBN: (纸本)9783031441912;9783031441929

Graph Convolutional Neural Networks (GCNNs) have emerged as a powerful tool for processing graph-structured data and achieving outstanding performance in various scenarios, including node classification, link prediction, and graph visualization. However, in signed networks, which contain links with logically opposite relations, two types of associations can more accurately capture relationships in the real world. In this paper, we propose a novel optimization objective for learning representations in signed networks by capitalizing on the structural balance theory. Additionally, we introduce a new message propagation framework and develop a scalable signed graph neural network model, SGNN, which significantly outperforms existing signed graph embedding models when applied to the link classification task on four empirical networks. Moreover, our message propagation framework enables the construction of deeper GCNNs. Overall, our study presents a new approach for learning meaningful representations on signed networks, utilizing a novel message propagation framework and optimization objective that captures the complex relationships between nodes in a signed graph.

关键词： Graph neural networks Signed networks Embedding learning

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