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共 265 条记录，以下是251-260 订阅

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AEG: An Adversarial Attack Algorithm Based on Structured data and threshold Constraint

AEG: An Adversarial Attack Algorithm Based on Structured Dat...

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IEEE international conference on Cloud Computing and Big data Analysis (ICCCBDA)

作者： Xu Zhai College of Automation& College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China

Although machine learning models are very effective in practical applications, many researches have shown that most algorithms are vulnerable to attack with well-designed adversarial examples and adversarial attack algorithms. But the current studies are mainly based on images, which are not suitable for models trained on structured data. In this paper, to expand the research on adversarial examples applicable to structured data, a new adversarial attack algorithm called AEG is proposed with a modified gradient descent method. Real-world structured data and trained load prediction models are applied for attack experiments. Experiments show that the algorithm has achieved significant attack performance while evading the anomaly detection mechanism.

关键词： Cloud computing machine learning algorithms conferences Computational modeling machine learning Predictive models Big data

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A Predictive analytics Framework to Anomaly Detection 6

A Predictive Analytics Framework to Anomaly Detection

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6th IEEE international conference on Big data Computing Service and Applications (IEEE BigdataService)

作者： Wang, Junzhang de Moraes, Rafael Martins Bari, Anasse NYU Dept Comp Sci CIMS New York NY 10003 USA

ISBN: (纸本)9781728170220

Online fraudulent activities cost the global economy billions of dollars each year. In the world of big data from large number of transactions, detecting and preventing anomaly cases like fraudulent activities is an essential ingredient to success not only to big banks, insurance companies, small businesses, but also to consumers. Forms and levels of sophistication of online fraud are constantly changing, hence there is a need for a flexible framework that can predict and adjust to new fraudulent cases. One major problem in building a predictive model for anomaly detection, is the scarcity of fraudulent data records in comparison to non-fraudulent, which makes the training data imbalanced. In this paper, we present a comprehensive predictive analytics framework that aims at detecting anomaly cases and most importantly mitigating the problem of imbalanced datasets in training anomaly detection models. the framework is developed through experimentation of an ensemble of sampling algorithms, feature engineering methods, and an array of machine learning algorithms. the framework presented in this study provides a data-driven approach applied to financial credit card data and can be adapted to other domains where deviations from what is normal occur in large datasets.

关键词： anomaly detection data science predictive analytics artificial intelligence feature selection imbalanced dataset fraud detection finance credit card data

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machine learning-based study of eye features under the emotion of anger 22

Machine learning-based study of eye features under the emoti...

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Proceedings of the 2022 6th international conference on Electronic Information Technology and Computer Engineering

作者： Zhuoluo Huang Yafang Li Key Laboratory of Traditional Chinese Medicine Psychology and Brain Science Jiangxi Provincial Administration of Traditional Chinese Medicine China

ISBN: (纸本)9781450397148

Based on the theory of "the liver is open to the eyes", this study uses a machine learning approach to explore methods and models that can be applied to the identification of eye features for angry emotions, in the hope of providing some ideas for objective identification of angry emotions. We used the angry emotion faces in the China Affective Picture System (CAPS) as the research object. Using multiple feature point inspection, we use openface software to obtain the 3D coordinate point coordinate information of the eye features of the angry emotion images. the aggregated data were then statistically analyzed using matlab software to calculate the eye feature values to be used, and then continued to process them using the classifier function to build a face eye feature point model based on Support Vector machine (SVM), Linear Discriminant Analysis(LDA), K-Neares Neighbor, Ensemble Subspace Discriminant (ESD), Decision Tree(DT) and other classifiers, and the model was comprehensively evaluated in terms of accuracy, area under the ROC curve and other evaluation metrics. For the results of the measured classification tests, the correct rate obtained by DT classification reached 83.1%, KNN reached 84.7%, ESD reached 88.1%, SVM reached 84.7% correct rate and LDA reached 88.1%. the effect of the eye feature description parameters on the classification effect of anger emotion was verified through experiments, and the best performance of ESD and LDA classification was achieved. Based on the above results we can draw the following conclusions. "the eye is the heart's ambassador", and the classifier constructed based on the eye diagnosis information can play the role of classification, which has certain scientific value and can provide objective diagnostic guidance for clinically relevant psychosomatic problems.

关键词： machine learning Anger emotions Eye features Image processing

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Prediction Algorithm for Drugstore Consumption Members

Prediction Algorithm for Drugstore Consumption Members

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IEEE international conference on Big data Analysis (ICBDA)

作者： Yuan Chang Tao Li College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

It is an important part of precision marketing to identify potential consumer members of specific activities in pharmacies based on the big data formed by members' historical consumption information and participation information. the problem of consumer member prediction is abstracted as the binary classification problem of whether the members will use the discount to consume in the drugstore after the drugstore gives the discount to the members. In order to solve the limitations of the traditional marketing model applied to the drugstore and the low accuracy of predicting consumer members, this paper proposes a multi angle feature expression and prediction model for drugstore members. According to the characteristics of pharmacies, such as the particularity of pharmacies and the association between drugs and members' diseases, this paper puts forward a variety of new features from the three perspectives of members' attributes, dynamics and consumption, and constructs a feature set by combining the new features with traditional consumption features. through a variety of machine learning algorithms to build pharmacy consumer member prediction model and comparison, the results show that the ensemble learning algorithm based on XGBoost has high accuracy in consumer member prediction.

关键词： Drugs machine learning algorithms Heuristic algorithms conferences Predictive models Big data Prediction algorithms

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AN INTERACTIVE PLATFORM for ENVIRONMENTAL SENSORS data ANALYSES 6

AN INTERACTIVE PLATFORM for ENVIRONMENTAL SENSORS DATA ANALY...

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6th international conference on Smart data and Smart Cities, SDSC 2021

作者： Harbola, S. Coors, V. University of Stuttgart Stuttgart Germany University of Applied Sciences Stuttgart Germany

the increased usage of the environmental monitoring system and sensors, installed on a day-to-day basis to explore information and monitor the cities' environment and pollution conditions, are in demand. Sensor networking advancement with quality and quantity of environmental data has given rise to increasing techniques and methodologies supporting spatiotemporal data interactive visualisation analyses. Moreover, Visualisation (Vis) and Visual analytics (VA) of spatiotemporal data have become essential for research, policymakers, and industries to improve energy efficiency, environmental management, and cities' air pollution planning. A platform covering Vis and VA of spatiotemporal data collected from a city helps to portray such techniques' potential in exploring crucial environmental inside, which is still required. therefore, this work presents Vis and VA interface for the spatiotemporal data represented in terms of location, including time, and several measured attributes like Particular Matter (PM) PM2.5 and PM10, along with humidity, and wind (speed and direction) to assess the detailed temporal patterns of these parameters in Stuttgart, Germany. the time series are analysed using the unsupervised HDBSCAN clustering on a series of (above mentioned) parameters. Furthermore, with the in-depth sensors nature understanding and trends, machine learning (ML) approach called Transformers Network predictor model is integrated, that takes successive time values of parameters as input with sensors' locations and predict the future dominant (highly measured) values with location in time as the output. the selected parameters variations are compared and analysed in the spatiotemporal frame to provide detailed estimations on how average conditions would change in a region over the time. this work would help to get a better insight into the urban system and enable the sustainable development of cities by improving human interaction with the spatiotemporal data. Hence, the

关键词： Visualization

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machine learning Application to Family Business Status Classification 6th

Machine Learning Application to Family Business Status Class...

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6th international conference on machine learning, Optimization, and data Science, LOD 2020

作者： Gnecco, Giorgio Amato, Stefano Patuelli, Alessia Lattanzi, Nicola IMT - School for Advanced Studies Piazza S. Francesco 19 Lucca Italy

ISBN: (纸本)9783030645823

According to a recent trend of research, there is a growing interest in applications of machine learning techniques to business analytics. In this work, both supervised and unsupervised machine learning techniques are applied to the analysis of a dataset made of both family and non-family firms. this is worth investigating, because the two kinds of firms typically differ in some aspects related to performance, which can be reflected in balance sheet data. First, binary classification techniques are applied to discriminate the two kinds of firms, by combining an unlabeled dataset with the labels provided by a survey. the most important features for performing such binary classification are identified. then, clustering is applied to highlight why supervised learning can be effective in the previous task, by showing that most of the largest clusters found are quite unequally populated by the two classes. © 2020, Springer Nature Switzerland AG.

关键词： Supervised learning

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Performance Evaluation for Four Types of machine learning Algorithms Using Educational Open data 6th

Performance Evaluation for Four Types of Machine Learning Al...

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6th KES Annual international conference on Smart Education and e-learning (KES SEEL) / 2nd KES international Symposium on Smart Transportation Systems (KES-STS)

作者： Terawaki, Yuki Unoki, Teruhiko Kato, Toshiyasu Kodama, Yasushi Hollywood Grad Sch Beauty Business Tokyo Japan Hosei Univ Tokyo Japan Photron Ltd Tokyo Japan Nippon Inst Technol Saitama Japan

ISBN: (纸本)9789811382604;9789811382598

Based on the educational data published under Creative Commons License, this study describes about the performance prediction experiment applied with four types of machine learning algorithms, including the deep learning algorithm, and examines how the prediction accuracy is affected depending on the selected feature quantities. the aim of this paper is to compare method selection and feature selection in terms of their ability to improve the prediction results. In data analysis by machine learning or deep learning, the determinant of result is often unclear. In the field of learning analytics, analysis can be performed even if the amount of data is small compared to the field of image recognition. therefore, it is meaningful to compare analysis accuracy using machine learning and deep learning and to examine which method is most effective for prediction academic performance. In this research, we revealed that Deep learning has the best method for learning analytics. Also, the results of this study indicate that feature selection is more important for improvement to prediction rather than method selection.

关键词： Deep learning machine learning Student performance prediction educational mining dataset learning analytics

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Uncovering Los Angeles Tourists' Patterns Using Geospatial Analysis and Supervised machine learning with Random Forest Predictors 6

Uncovering Los Angeles Tourists' Patterns Using Geospatial A...

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6th Annual conference on Computational Science and Computational Intelligence (CSCI)

作者： Lee, Yuan-Yuan Chang, Yi Ling Claremont Grad Univ Sch Social Sci Policy & Evaluat Claremont CA 91711 USA

ISBN: (纸本)9781728155845

Consumer behavior analytics is at the epicenter of a Big data revolution. In this paper we propose to analyze intra-regional spatial patterns mining tourists' behaviors and characteristics based on traveling group size with data collected from Airbnb open source focused on Los Angeles neighborhood in 2016. Random Forest Classification (RF) technique, an ensemble approach, is applied to identify the key drivers according to relevant traveler groups and presented patterns using Hotspot Analysis on Geographic Information System (GIS). Our empirical result highlights driving factors within Airbnb listings, providing valuable insights to better plan, monitor and manage tourism activity.

关键词： Pattern Classification Computational Complexity machine learning Random Forest Hotspot Analysis

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Malware detection using machine learning and deep learning 6th

Malware detection using machine learning and deep learning

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6th international conference on Big data analytics, BDA 2018

作者： Rathore, Hemant Agarwal, Swati Sahay, Sanjay K. Sewak, Mohit Department of CS and IS BITS Pilani Goa Campus Goa India

ISBN: (纸本)9783030047795

Research shows that over the last decade, malware have been growing exponentially, causing substantial financial losses to various organizations. Different anti-malware companies have been proposing solutions to defend attacks from these malware. the velocity, volume, and the complexity of malware are posing new challenges to the anti-malware community. Current state-of-the-art research shows that recently, researchers and anti-virus organizations started applying machine learning and deep learning methods for malware analysis and detection. We have used opcode frequency as a feature vector and applied unsupervised learning in addition to supervised learning for malware classification. the focus of this tutorial is to present our work on detecting malware with (1) various machine learning algorithms and (2) deep learning models. Our results show that the Random Forest outperforms Deep Neural Network with opcode frequency as a feature. Also in feature reduction, Deep Auto-Encoders are overkill for the dataset, and elementary function like Variance threshold perform better than others. In addition to the proposed methodologies, we will also discuss the additional issues and the unique challenges in the domain, open research problems, limitations, and future directions. © Springer Nature Switzerland AG 2018.

关键词： Malware

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An integrated approach incorporating nonlinear dynamics and machine learning for predictive analytics and delving causal interaction 6th

An integrated approach incorporating nonlinear dynamics and ...

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6th international conference on Frontiers of Intelligent Computing: theory and Applications, FICTA-2017

作者： Ghosh, Indranil Sanyal, Manas K. Jana, R.K. Department of Operations Management Calcutta Business School Kolkata India Department of Business Administration University of Kalyani Kalyani India Indian Institute of Management Raipur RaipurCG492015 India

ISBN: (纸本)9789811075650

Development of predictive modeling framework for observational data, exhibiting nonlinear and random characteristics, is a challenging task. In this study, a neoteric framework comprising tools of nonlinear dynamics and machine learning has been presented to carry out predictive modeling and assessing causal interrelationships of financial markets. Fractal analysis and recurrent quantification analysis are two components of nonlinear dynamics that have been applied to comprehend the evolutional dynamics of the markets in order to distinguish between a perfect random series and a biased one. Subsequently, three machine learning algorithms, namely random forest, boosting and group method of data handling, have been adopted for forecasting the future figures. Apart from proper identification of nature of the pattern and performing predictive modeling, effort has been made to discover long-rung interactions or co-movements among the said markets through Bayesian belief network as well. We have considered daily data of price of crude oil and natural gas, NIFTY energy index, and US dollar-Rupee rate for empirical analyses. Results justify the usage of presented research framework in effective forecasting and modeling causal influence. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Dynamics

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