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Can a machine Learn Through Customer Sentiment? A Cost-Aware Approach to Predict Support Ticket Escalations

IEEE SOFTWARE 2019年第5期36卷 38-45页

作者： Werner, Colin Li, Ze Shi Damian, Daniela Univ Victoria Victoria BC Canada Univ Victoria Researching Privacy Nonfunct Requirements & Conti Victoria BC Canada Univ Victoria Dept Comp Sci Software Engn Global InterAct Lab Victoria BC Canada

Given the connection between customer happiness and support ticket escalation, we describe an approach that 1) analyzes the emotions in conversations between a customer and a support analyst and 2) provides organizations with a cost-based mechanism to evaluate machine-learning algorithms trained on emotion-related features to predict support ticket escalations.

关键词： Customer Services learning Artificial Intelligence Customer Sentiment Cost Aware Approach Customer Happiness Cost Based Mechanism machine learning algorithms Support Ticket Escalations Emotion Related Features Training Data Software Engineering Sentiment Analysis machine learning algorithms Data Mining Emotion Recognition Sentiment Analysis Natural Language Affective Computing machine learning Modeling And Prediction Cost Sensitive learning Data Mining Defect Escalation machine learning Software Defect Escalation Prediction

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Linear and Nonlinear Fault Location in Smart Distribution Network Under Line Parameter Uncertainty

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第12期17卷 8308-8318页

作者： Mirshekali, Hamid Dashti, Rahman Shaker, Hamid Reza Samsami, Reza Torabi, Amin Jahromi Persian Gulf Univ Clin Lab Ctr Power Syst & Protect Fac Intelligent Syst Engn & Data Sci Bushehr *** Iran Persian Gulf Univ Engn Coll Elect Engn Grp Bushehr *** Iran Univ Southern Denmark Ctr Energy Informat DK-5230 Odense Denmark

The line parameters of the distribution network (DN) may change because of the atmospheric, structural, and operational conditions. The uncertainty of line parameters can compromise the accuracy of the automatic fault location methods. Besides, arc faults (AFs) may happen in the DN. These faults are difficult to locate in the faulty section because of the nonlinear, asymmetric, and random nature of AF current. In this article, we present a new time-domain fault location (TDFL) method to determine the location of the fault in smart power DN under the line parameters uncertainty. In the suggested method, line parameters' accurate values are determined using a mixed gradient descent particle swarm optimization algorithm. The suggested method's performance is investigated with the help of an IEEE 123-node test feeder in MATLAB (R2018b). The effects of parameter uncertainty, distributed generations operation conditions and modes, different types of AF, various fault distances, resistances, and the fault inception angles are studied. For further evaluation of the proposed method's robustness, two practical tests in the laboratory are carried out. The results confirm that the proposed TDFL method has high accuracy.

关键词： Fault location Time-domain analysis Mathematical model Distribution networks Uncertain systems machine learning algorithms State estimation Distributed generations (DGs) operation modes gradient descent (GD) method optimization problem time-domain fault location (TDFL) method

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Distributed Random Reshuffling Over Networks

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 1143-1158页

作者： Huang, Kun Li, Xiao Milzarek, Andre Pu, Shi Qiu, Junwen Chinese Univ Hong Kong Shenzhen Res Inst Big Data Sch Data Sci Shenzhen 518172 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518129 Peoples R China Chinese Univ Hong Kong Sch Data Sci Shenzhen 518172 Peoples R China

In this paper, we consider distributed optimization problems where n agents, each possessing a local cost function, collaboratively minimize the average of the local cost functions over a connected network. To solve the problem, we propose a distributed random reshuffling (D-RR) algorithm that invokes the random reshuffling (RR) update in each agent. We show that D-RR inherits favorable characteristics of RR for both smooth strongly convex and smooth nonconvex objective functions. In particular, for smooth strongly convex objective functions, D-RR achieves O(1/T-2) rate of convergence (where T counts the epoch number) in terms of the squared distance between the iterate and the global minimizer. When the objective function is assumed to be smooth nonconvex, we show that D-RR drives the squared norm of the gradient to 0 at a rate of O(1/T-2/3). These convergence results match those of centralized RR (up to constant factors) and outperform the distributed stochastic gradient descent (DSGD) algorithm if we run a relatively large number of epochs. Finally, we conduct a set of numerical experiments to illustrate the efficiency of the proposed D-RR method on both strongly convex and nonconvex distributed optimization problems.

关键词： Convergence Linear programming Signal processing algorithms Gradient methods Distributed databases Big Data machine learning algorithms Distributed optimization random reshuffling stochastic gradient methods

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Design on Early Warning System for Renal Cancer Recurrence Based on CNN-Based Internet of Things

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IEEE ACCESS 2022年 10卷 34835-34845页

作者： Liu, Dong Shao, Jinkai Liu, Honggang Cheng, Wei Shanxi Prov Peoples Hosp Dept Urol Taiyuan 030012 Peoples R China Shanxi Med Univ Shanxi Bethune Hosp Dept Urol Hosp 3 Taiyuan 030032 Peoples R China Huazhong Univ Sci & Technol Tongji Hosp Dept Urol Tongji Med Coll Wuhan 430030 Peoples R China

Kidney cancer is a type of urinary system tumor. The incidence of kidney cancer, which is second only to bladder cancer, has shown an overall upward trend in recent years. However, the early judgment of kidney cancer is still in the imaging biomarker discovery stage. Early detection and treatment cannot be achieved. Based on the natural advantages of the Internet of Things in the medical field, we focused on an intelligent early warning model of renal cancer recurrence and built a renal cancer early warning system integrated with the Internet of Things. We integrated the influencing factors of renal cell carcinoma, constructed a sample set, conducted data analysis and optimized the dataset. Aiming at the instability of renal cancer recurrence, five supervised learning prediction algorithms, including multiple linear regression, Bayesian ridge regression, gradient boosting tree, support vector regression, and convolutional neural network were used to develop a renal cancer recurrence prediction model. The predictive performance of these five algorithms were compared and discussed. Finally, the best renal cancer recurrence prediction model was established by combining a convolutional neural network with an Internet of Things medical framework. This design provided an intelligent early warning system to predict the recurrence time of renal cancer patients. In addition, the warning prompts provided in accordance with the model results can assist doctors in making preliminary judgments of the patient's condition and has a certain auxiliary effect on the clinical diagnosis and treatment of cancer and kidney cancers.

关键词： Cancer Medical diagnostic imaging Analytical models machine learning algorithms Licenses Data mining Psychology Kidney cancer recurrence time recurrence warning system convolutional neural network

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Push-Sum on Random Graphs: Almost Sure Convergence and Convergence Rate

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第3期65卷 1295-1302页

作者： Rezaienia, Pouya Gharesifard, Bahman Linder, Tamas Touri, Behrouz Univ British Columbia Sauder Sch Business Vancouver BC V6T 1Z4 Canada Queens Univ Dept Math & Stat Kingston ON K7L 3N6 Canada Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

In this paper, we study the problem of achieving average consensus over a random time-varying sequence of directed graphs by extending the class of so-called push-sum algorithms to such random scenarios. Provided that an ergodicity notion, which we term the directed infinite flow property, holds and the auxiliary states of agents are uniformly bounded away from zero infinitely often, we prove the almost sure convergence of the evolutions of this class of algorithms to the average of initial states. Moreover, for a random sequence of graphs generated using a so-called time-varying -irreducible probability matrix, we establish convergence rates for the proposed push-sum algorithm.

关键词： Convergence Heuristic algorithms Random sequences Protocols Optimization machine learning algorithms Communication networks Distributed optimization optimization algorithms random networks

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Edge-Cloud Continuum Solutions for Urban Mobility Prediction and Planning

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IEEE ACCESS 2023年 11卷 38864-38874页

作者： Belcastro, Loris Marozzo, Fabrizio Orsino, Alessio Talia, Domenico Trunfio, Paolo Univ Calabria Dept Informat Modeling Elect & Syst DIMES I-87036 Arcavacata Di Rende Italy

In recent years, there has been an increase in the use of edge-cloud continuum solutions to efficiently collect and analyze data generated by IoT devices. In this paper, we investigate to what extent these solutions can manage tasks related to urban mobility, by combining real-time and low latency analysis offered by the edge with large computing and storage resources provided by the cloud. Our proposal is organized into three parts. The first part focuses on defining three application scenarios in which geotagged data generated by IoT objects, such as taxis, cars, and smartphones, are collected and analyzed through machine learning-based algorithms (i.e., next location prediction, location-based advertising, and points of interest recommendation). The second part is dedicated to modeling an edge-cloud continuum architecture capable of managing a large number of IoT devices and executing machine learning algorithms to analyze the data they generate. The third part analyzes the experimental results in which different design choices were evaluated, such as the number of devices and orchestration policies, to improve the performance of machine learning algorithms in terms of processing time, network delay, task failure, and computational resource utilization. The results highlight the potential benefits of edge and cloud cooperation in the three application scenarios, demonstrating that it significantly improves resource utilization and reduces the task failure rate compared to other widely adopted architectures, such as edge- or cloud-only architectures.

关键词： Internet of Things Cloud computing Computer architecture Task analysis Public transportation machine learning algorithms Data models Edge-cloud architecture IoT infrastructure edge computing urban computing smart cities urban mobility

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Comparative study of ML models for IIoT intrusion detection: impact of data preprocessing and balancing

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NEURAL COMPUTING & APPLICATIONS 2024年第13期36卷 6955-6972页

作者： Eid, Abdulrahman Mahmoud Soudan, Bassel Nasif, Ali Bou Injadat, Mohammadnoor Univ Sharjah Coll Comp & Informat Dept Comp Engn Sharjah U Arab Emirates Zarqa Univ Fac Informat Technol Dept Data Sci & Al Zarqa Jordan

This study investigates the effectiveness of six prominent machine learning models-random forest, decision trees, K-nearest neighbor, logistic regression, support vector machines, and Naive Bayes-for intrusion detection systems in industrial Internet of Things environments. The evaluation encompasses the effects of data preprocessing techniques, including feature engineering, data normalization, recoding, and missing data mitigation. Furthermore, the research delves into dataset balancing, examining the effects of six different techniques on model performance. The investigations are conducted using the domain-specific WUSTL-IIOT-2021 dataset, which captures the unique characteristics of IIoT data. The study also investigates multi-class attack identification utilizing an innovative SMOTE-based multi-class balancing approach to tackle dataset imbalances. The results indicate that data preprocessing and intelligent dataset balancing produce consistent enhancements in the classification performance of the selected models across binary and multi-classification tasks. Random forest emerges as the standout algorithm, delivering consistently high performance with computational efficiency.

关键词： Industrial Internet of Things Intrusion detection machine learning algorithms Data normalization Dataset balancing

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Real World Object Detection Dataset for Quadcopter Unmanned Aerial Vehicle Detection

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IEEE ACCESS 2020年 8卷 174394-174409页

作者： Pawelczyk, Maciej L. Wojtyra, Marek Warsaw Univ Technol Inst Aeronaut & Appl Mech PL-00665 Warsaw Poland

Recent years have shown a noticeable rise in the number of incidents with drones, related to both civilian and military installations. While drone neutralization techniques have become increasingly effective, detection most often relies on professional equipment, which is too expensive to be used for all critical nodes and applications. Therefore, there is a need for drone detection systems that could work on low performance hardware. Its critical component consists of an object detection system. In this article, we introduce a new object detection dataset, built entirely to train computer vision based object detection machine learning algorithms for a task of binary object detection to enable automated, industrial camera based detection of multiple drone objects using camera feed. The dataset expands existing multiclass image classification and object detection datasets (ImageNet, MS-COCO, PASCAL VOC, anti-UAV) with a diversified dataset of drone images. In order to maximize the effectiveness of the model, real world footage was utilized, transformed into images and hand-labelled to create a custom set of 56821 images and 55539 bounding boxes. Additionally, semi-automated labelling was proposed, tested and proved to be very useful for object detection applications. The dataset was divided into train and test subsets for further processing and used to generate 603 easily deployable Haar Cascades as well as 819 high performing Deep Neural Networks based models. They were used to test different object detection methods to determine the long term feasibility of a large scale drone detection system utilizing machine learning algorithms. The study has shown that Haar Cascade can be used as the Minimum Viable Product model for mediocre performance but fails to scale up effectively for a larger dataset compared to the Deep Neural Network model.

关键词： Drones Object detection Task analysis Computational modeling machine learning machine learning algorithms Aerospace engineering aerospace safety artificial intelligence computer vision databases image processing unmanned aerial vehicles

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Maximal Correlation Regression

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IEEE ACCESS 2020年 8卷 26591-26601页

作者： Xu, Xiangxiang Huang, Shao-Lun Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Berkeley Shenzhen Inst DSIT Res Ctr Shenzhen 518055 Peoples R China

In this paper, we propose a novel regression analysis approach, called maximal correlation regression, by exploiting the ideas from the Hirschfeld-Gebelein-Renyi (HGR) maximal correlation. We show that in supervised learning problems, the optimal weights in maximal correlation regression can be expressed analytically with the relationships to the HGR maximal correlation functions, which reveals theoretical insights for our approach. In addition, we apply the maximal correlation regression to deep learning, in which efficient training algorithms are proposed for learning the weights in hidden layers. Furthermore, we illustrate that the maximal correlation regression is deeply connected to several existing approaches in information theory and machine learning, including the universal feature selection problem, linear discriminant analysis, and the softmax regression. Finally, experiments on real datasets demonstrate that our approach can obtain performance comparable to the widely used softmax regression based-method.

关键词： Artificial neural networks HGR maximal correlation linear discriminant analysis machine learning algorithms regression analysis softmax regression

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Sensing Coverage-Based Cooperative Spectrum Detection in Cognitive Radio Networks

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IEEE SENSORS JOURNAL 2019年第13期19卷 5325-5332页

作者： Kim, Junhee Choi, Jihwan P. Daegu Gyeongbuk Inst Sci & Technol Dept Informat & Commun Engn Daegu 42988 South Korea

With the advancement of the Internet of Things (IoT), conventional principles of spectrum allocation cannot mitigate spectrum depletion so that a cognitive radio technology is proposed as a solution. The hidden primary user (PU) problem, however, is a critical issue in cognitive radio networks, because spectrum sensing nodes (SNs) can misclassify spectrum occupancy. To cope with this, machine learning-based cooperative spectrum sensing schemes (CSSs) have been proposed. The CSSs without considering the node placement, however, are still faced with the hidden PU problem. In this paper, we present how to place SNs to guarantee the performance of machine learning-based CSSs. We verify that the hidden PU problem makes the overlap of data distribution, which deteriorates the spectrum sensing ability. Based on the Kullback-Leibler divergence, analytical expressions for the spectrum sensing coverage of a single SN are derived. Then, we propose a strategy on how to place a few SNs to cover the whole area of the PU and prove the feasibility of our proposal by the experiment results.

关键词： Cognitive radio networks spectrum sensing coverage opportunistic spectrum access machine learning algorithms

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