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A machine-learning-based approach to online transaction analysis for personalized customer services

A machine-learning-based approach to online transaction anal...

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The international conference on Forthcoming Networks and Sustainability (FoNeS 2022)

作者： H. Du R. Ye University of Rochester Rochester NY 14642 America Shanghai University Shanghai 200444 People's Republic of China

With the amount of online transactions surging, online marketplaces are generating millions of transaction data and customer profiles. Providing personalized customer service is becoming a rewarding yet challenging task for online sellers and stores as online customers grow both in number and in diversity [1]. In this paper, we propose a machine learning based pipeline to analyze and visualize online customer transaction data. We perform data cleaning and feature extraction on a collection of online transaction data. We then implemented dimension reduction and clustering algorithms on the feature data to group customers into segments. Finally, association rules are applied to further analyze transaction data in each customer segment.

关键词： customer profiles customer services data mining electronic commerce feature extraction learning (artificial intelligence) pattern clustering

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Zebrafishtracker: A multi-zebrafish tracking algorithm can effectively solve cross occlusion

Zebrafishtracker: A multi-zebrafish tracking algorithm can e...

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IEEE international conference on robotics and Biomimetics

作者： Zhenhua Fu Yiwen Wang Xin Zhao Mingzhu Sun Institute of Robotics and Automatic Information System (IRAIS) and the Tianjin Key Laboratory of Intelligent Robotic (tjKLIR) Nankai University Tianjin China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen China

ISBN: (纸本)9781665405362

Zebrafish is a model organism, which is widely used in life science and medicine research due to its high homology with human genes. How to handle cross-cases and achieve more comprehensive tracking is always a challenge. Zebrafishtracker proposed in this paper uses the instance segmentation and the skeleton stability analysis strategy to effectively solve the problem of detection error caused by the frequent crossover of multiple zebrafish. Then 2D tracking of zebrafish is realized based on identity information. The results show that in two relatively complex videos, MOTA of our algorithm in the top-view reaches more than 95%.

关键词： Performance evaluation Three-dimensional displays conferences Fish Skeleton Stability analysis Life sciences

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FMECA of NC Machine Tool Manufacturing Stage Based on Fuzzy Mathematics and Data Envelopment analysis

FMECA of NC Machine Tool Manufacturing Stage Based on Fuzzy ...

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international conference on System Reliability and Science (ICSRS)

作者： Lijuan Yu Tianyu Zhang Jianlin Zang Fu Zhang Fuqin Deng Ang Liu Hailong Tian Key Laboratory of CNC Equipment Reliability Ministry of Education School of Mechanical and Aerospace Engineering Jilin University Changchun Jilin Province China A Unit of the Army Equipment Department of the Chinese People's Liberation Army Commercial Vehicle Technology Center of SAIC Shanghai China Commercial Vehicle Technology Center of SAIC Shanghai China School of Intelligent Manufacturing Wuyi University Jiangmen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China 3irobotix Shenzhen China

In order to solve the problem that it is difficult to determine the occurrence probability of FMECA process failure in the manufacturing stage of CNC machine tools and there is no reasonable scientific basis when RPN is obtained, fuzzy mathematics theory and data envelopment analysis DEA are applied to the research of FMECA in the manufacturing stage of CNC machine tools. Considering the technological requirements and fault probability, fuzzy interval method to determine risk factors of degrees, using the data envelopment analysis method to evaluate the potential failure mode to get the efficiency value, to modify the traditional RPN, put forward a new method for calculating risk priority number of RPN, and then to sort of process failure mode, Find defects and weak links in production process and provide reference for improvement of production process. Taking the assembly process of a machining center as an example, the proposed method was applied to analyze the fault mode and its influence, and the effectiveness of the proposed method was verified.

关键词： Production Machining Probability Market research Hazards Manufacturing Machine tools

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Artificial intelligence Based Hierarchical Classification of Frontotemporal Dementia

Artificial Intelligence Based Hierarchical Classification of...

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

作者： Km Poonam Rajlakshmi Guha Partha P Chakrabarti Centre of Excellence in AI IIT Kharagpur West Bengal India Centre for the Science of Happiness IIT Kharagpur West Bengal India Department of Computer Science & Engineering IIT Kharagpur West Bengal India

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Frontotemporal dementia (FTD) is a typical kind of presenile dementia with three main subtypes: behavioral-variant FTD (bvFTD), non-fluent variant primary progressive aphasia (nfvPPA), and semantic variant primary progressive aphasia (svPPA). Our aim is to classify brain images of each subject into one of the spectrums of the FTD in a hierarchical order by applying data-driven techniques. Specifically, we took 300 subjects (30 bvFTD, 41 svPPA, 25 nfvPPA, 80 Alzheimer’s Disease, and 124 cognitively normal) from the Frontotemporal Lobar Degeneration Neuroimaging Initiative archive to validate our approach. The cortical and subcortical measurements, e.g., cortical thickness and volumetric segmentation, were extracted from MRI images for the experiment. Our proposed model yielded classification accuracy of 87.42%, 83.23%, and 82.44% with Support Vector Machine (SVM), Linear Discriminant analysis, and Naive Bayes methods, respectively, for five classes. We observed a significant improvement over the flat multi-class model, which has an accuracy of 82.80%, 81.27%, and 80.45%, respectively, for five classes. We also compared our proposed approach with a Convolutional Neural Networks model and ensemble with multi-layer perception as well. The results showed that the hierarchical approach performs almost equally well here as in the SVM model. Moreover, we also tested our hierarchical approach for three classes (CN, Non-FTD, FTD) to compare with state-of-the-art methods and obtained better *** relevance— This study provides a detailed understanding of the heterogeneity within the FTD subtypes; clinicians can better tailor diagnostic and treatment strategies to match each subtype’s specific characteristics and progression patterns.

关键词： Support vector machines Neuroimaging Accuracy Volume measurement Semantics Aphasia Resonance Naive Bayes methods pattern matching Thickness measurement

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Classification & Detection of Epilepsy Using IEEG Application

Classification & Detection of Epilepsy Using IEEG Applicatio...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： R. Kishore Kanna Kapil Joshi Kamreed Udham Singh Ambali Pacholi Mathar Mohideen Nagoor Thangam X.S. Blessing NimaSajai Department of Biomedical Engineering Jerusalem College of Engineering (Autonomous) Chennai India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India School of Computing Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand School of Nursing Science ITM University Gwalior (MP) India Department of Nursing Faculty of Applied Medical Sciences University of Tabuk Saudi Arabia College of Nursing Khamis MushaytKing Khalid University Kingdom Of Saudi Arabia

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

More than only cognitive loss and impairment, epileptic seizures may seriously harm one’s intellect. Early seizure detection is essential to minimize the harm caused by their possessions. Presently, medical professionals use optical testing of electroencephalographic (EEG) data for project labeling, which may be a labor-intensive and delayed procedure. By resolving EEG data using the best spatial convolutional interconnected system-long temporary thought (1D CNNLSTM) model, this study offers a revolutionary pattern for always identifying seizures. Before the raw EEG dossier is augmented into a 1D CNN that pulls appearance data from the dossier, it is preprocessed and standardized. Hence, worldly faces are extracted using Long Short-Term Memory (LSTM) layers, which are then combined into various suitably linked coatings for capture recognition. The projected model’s representation was evaluated using the UCI epileptic capture acknowledgment dataset. For the twofold and five-class taking acknowledgment tasks, respectively, the outcomes show an exceptional acknowledgment veracity of 99.39 % and 82.00 %. The predicted model achieved better act when compared to existing machine intelligence setups and additional deep education models. These rulings highlight the promise of the proposed method for accurately diagnosing seizures in patients who are experiencing muscular spasms and acknowledge the potential for improved impartial outcomes.

关键词： Optical losses Optical interconnections Predictive models Brain modeling Electroencephalography Task analysis Spatial resolution

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Autonomous Reactive LiDAR-based Mapping for Powerline Inspection

Autonomous Reactive LiDAR-based Mapping for Powerline Inspec...

引用

international conference on Unmanned Aircraft Systems (ICUAS)

作者： J. Paneque V. Valseca J. R. Martí nez-de Dios A. Ollero GRVC Robotics Lab Sevilla Universidad de Sevilla Spain

ISBN: (数字)9781665405935

ISBN: (纸本)9781665405942

There is a strong demand in the automation of powerline inspection, which is currently performed in a two-stage process: 1) data collection by an aerial vehicle equipped with LiDARs, cameras, and other sensors, and 2) offline data analysis using processing and artificial intelligence algorithms. This procedure is very inefficient and in many cases requires repeating the flights if during the offline analysis the collected data is found to be of insufficient quality for the inspection. This paper proposes a reactive quadrotor-based online powerline inspection system. The proposed method: i) builds online an onboard an accurate preliminary 3D map of the environment, ii) performs an online analysis of the quality of the inspection data being obtained, and iii) in case the data has insufficient quality, commands the quadrotor to stop moving so that more data of that part of the environment can be integrated to increase the resolution and precision until the required quality is fulfilled. Hence, the proposed system maximizes the area inspected ensuring that the gathered data fulfils the specified quality metrics. The proposed system has been implemented onboard and aerial robot and validated in powerline inspection experiments conducted in environments with different types conditions and vegetation.

关键词： Measurement Laser radar Three-dimensional displays Vegetation mapping Inspection Software Robustness

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Crowdsensed Data-oriented Distributed and Secure Spatial Query Scheme

Crowdsensed Data-oriented Distributed and Secure Spatial Que...

引用

IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yuxi Li Fucai Zhou Jingjing Chen Dong Ji School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Focusing on the privacy concerns and leakage abuse of sensory data collection in mobile crowdsensing (MCS) environment, we propose a crowdsensed data-oriented distributed and secure spatial query scheme while ensuring data privacy as long as query patterns. To cater to the demands of real-world MCS workloads, we designed a distributed multi-layer architecture and leveraged distributed hash functions(DHT) and broadcast encryption(BE) to achieve load-balancing and enforce access control. Our scheme incorporates a recently developed cryptographic tool–function secret sharing (FSS) to safeguard the query pattern and sensory data from potential compromises at the server *** analysis demonstrates that our scheme achieves affordable query complexity while satisfying adaptive $\mathcal{L}$-semantic security. Encouraging experimental results substantiate the efficacy of our scheme, the growth rates of query cost diminishes as the number of records and participants increases. These findings emphasize the suitability of our scheme for crowdsensing applications with fine-grained access control requirements and establish it as an efficient cryptographic tool that holds promise for diverse MCS applications.

关键词： Access control Data privacy Costs Crowdsensing Focusing Data collection Complexity theory

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Exploring Dynamic Selection of Branch Expansion Orders for Code Generation 59

Exploring Dynamic Selection of Branch Expansion Orders for C...

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Joint conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th international Joint conference on Natural Language Processing (IJCNLP) / 6th Workshop on Representation Learning for NLP (RepL4NLP)

作者： Jiang, Hui Zhou, Chulun Meng, Fandong Zhang, Biao Zhou, Jie Huang, Degen Wu, Qingqiang Su, Jinsong Xiamen Univ Sch Informat Xiamen Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen Peoples R China Tencent Inc WeChat AI Pattern Recognit Ctr Shenzhen Peoples R China Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland Dalian Univ Technol Dalian Peoples R China

ISBN: (纸本)9781954085527

Due to the great potential in facilitating software development, code generation has attracted increasing attention recently. Generally, dominant models are Seq2Tree models, which convert the input natural language description into a sequence of tree-construction actions corresponding to the pre-order traversal of an Abstract Syntax Tree (AST). However, such a traversal order may not be suitable for handling all multi-branch nodes. In this paper, we propose to equip the Seq2Tree model with a context-based Branch Selector, which is able to dynamically determine optimal expansion orders of branches for multibranch nodes. Particularly, since the selection of expansion orders is a non-differentiable multi-step operation, we optimize the selector through reinforcement learning, and formulate the reward function as the difference of model losses obtained through different expansion orders. Experimental results and in-depth analysis on several commonly-used datasets demonstrate the effectiveness and generality of our approach.

关键词： Expansion

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Recognizing boundaries of online professional learning communities in an automated discourse analysis approach

Recognizing boundaries of online professional learning commu...

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Intelligent Education and Intelligent Research (IEIR), international conference on

作者： Si Zhang YuTing Zhang Xinyue He TongYu Guo Yiyao Wang Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China

ISBN: (纸本)9798350345636

Recognizing boundaries of online professional learning communities can help to provide teachers with a meaningful online learning environment that improves their training performance. This study proposed an automated discourse analysis approach for recognizing boundaries of the online learning communities, that combines both Topic Modelling approach (Latent Dirichlet Allocation) and Social Network analysis. The study examined online discourse data of 1843 teachers participating in an online training program. The findings revealed that teachers mainly responded to others’ posts and the pattern of teachers’ response could be mainly divided into four types. The semantic network formed by discourse unit was high-density with low average network distance and high degree centrality, and the cohesion parameter of the semantic network was relatively stable during the whole process of online discourse. The findings of the study also can provide insights into creating online learning communities and teacher education.

关键词： Training Analytical models Social networking (online) Atmospheric measurements Semantics Particle measurements Behavioral sciences

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Sinc-based Multiplication-Convolution Network for Equipment Intelligent Edge Diagnosis under Small Samples

Sinc-based Multiplication-Convolution Network for Equipment ...

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Sensing, Measurement & Data Analytics in the era of Artificial intelligence (ICSMD), international conference on

作者： Rui Liu Xiaoxi Ding Qihang Wu Hao Xiang Huaping Tan Yimin Shao State Key Laboratory of Vehicle NVH and Safety Technology College of Mechanical and Vehicle Engineering Chongqing University Chongqing China The State Key Lab of Mechanical Transmissions Chongqing University Chongqing China College of Mechanical and Vehicle Engineering Chongqing University Chongqing China

Data-driven intelligent diagnosis models need massive monitoring data to train themselves for desired performance. However, in many engineering scenarios, collecting fault data is often expensive and time-consuming, which leads to few-shot learning becoming a valuable research hotspot for intelligent diagnosis. Inspired by mode characteristics and feature enhancement learning, this study propose a Sinc-based multiplication-convolution network (SincMCN) for intelligent fault diagnosis under small samples. It works in frequency domain, and consists of only three layers, including a feature separator, a feature extractor and a classifier. In the feature separator, a series of Sinc-based multiplication filtering kernels (SincMFKs) are designed for improving the utilization of fault information of spectrum samples. The products between SincMFKs and spectrum samples are stacked into activated mode spectrum images (AMSIs) with rich fault-related features retained. Since AMSIs are concise enough, this study employs only a 2D convolutional layer and a fully connected layer as the feature extractor and the classifier for achieving a fast and precise pattern recognition. Experimental results show SincMCN has better diagnosis accuracy and stronger potentials for few-shot diagnosis compared other cutting-edge models. Specially, analytic filtering kernels not only cut down the model parameters for edge diagnosis and provide powerful application potentials and engineering value for online monitoring of rotating machinery.

关键词： Fault diagnosis Training Filtering Particle separators Frequency-domain analysis Feature extraction pattern recognition

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