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Predicting Dropout in Higher Education based on Secondary School Performance

Predicting Dropout in Higher Education based on Secondary Sc...

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IEEE international conference on intelligent engineering Systems

作者： Marcell Nagy Roland Molontay MTA-BME Stochastics Research Group Hungary

Predicting student performance, preventing failure and identifying the factors influencing student dropout are issues that have attracted a great deal of research interest recently. In this study, we employ and evaluate several machine learning algorithms to identify students at-risk and predict student dropout of university programs based on the data available at the time of enrollment (secondary school performance, personal details). We also present a data-driven decision support platform for education directorate and stakeholders. The models are built on data of 15,825 undergraduate students from Budapest University of Technology and Economies enrolled between 2010 and 2017 and finished their undergraduate studies either by graduation or dropping out. We handle the problem of missing data by imputation. After performing feature extraction and feature selection, a wide range of classifiers have been trained including Decision Tree-based algorithms, Naive Bayes, k-NN, Linear Models and Deep learning with different input settings. The methods were tested using 10-fold cross-validation and the AUC of the best models, Gradient Boosted Trees and Deep learning, were 0.808 and 0.811 respectively.

关键词： Education Biological system modeling data models Economics Decision trees Predictive models Mathematics

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A predictive learning framework for monitoring aggregated performance indicators over business process events 18

A predictive learning framework for monitoring aggregated pe...

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22nd international database engineering and Applications Symposium, IDEAS 2018

作者： Cuzzocrea, Alfredo Folino, Francesco Guarascio, Massimo Pontieri, Luigi Dept. DIA University of Trieste Trieste Italy Rende Italy

ISBN: (纸本)9781450365277

In many application contexts, a business process’ executions are subject to performance constraints expressed in an aggregated form, usually over predefined time windows, and detecting a likely violation to such a constraint in advance could help undertake corrective measures for preventing it. This paper illustrates a prediction-aware event processing framework that addresses the problem of estimating whether the process instances of a given (unfinished) window w will violate an aggregate performance constraint, based on the continuous learning and application of an ensemble of models, capable each of making and integrating two kinds of predictions: Single-instance predictions concerning the ongoing process instances of w, and time-series predictions concerning the "future" process instances of w (i.e. those that have not started yet, but will start by the end of w). Notably, the framework can continuously update the ensemble, fully exploiting the raw event data produced by the process under monitoring, suitably lifted to an adequate level of abstraction. The framework has been validated against historical event data coming from real-life business processes, showing promising results in terms of both accuracy and efficiency. © 2018 Association for Computing Machinery.

关键词： Forecasting

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A Management Method of the Corporate Brand Image Based on Customers' Perception 22

A Management Method of the Corporate Brand Image Based on Cu...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems, KES 2018

作者： Kato, Takumi Tsuda, Kazuhiko Business Analytics Section Honda Motor Co. Ltd. 2-1-1 Minami Aoyama Minato Tokyo107-0062 Japan Graduate School of Business Sciences University of Tsukuba 3-29-1 Otsuka Bunkyo Tokyo112-0012 Japan

In order to acquire target brand images, companies develop various activities to manage brands as assetS. However, in fact, since companies cannot grasp the necessary elements for the image, there are companies that make inconsistent products and promotions into the world. Therefore, in this reSearch, we verified the factors that form »quality» brand image, which is said to be ambiguous and complicated. Quality is said to include not only objective value (Functional value) such as performance and durability but also subjective value (Emotional value) such as beauty and perceived quality. In recent years, companies such as Apple and Samsung, which have excellent emotional value, are emerging, so the value is considered as a major source of competition in the manufacturing industry. We believe that this reSearch will enable companies to undertake effective decision-making without obscuring the elements necessary to acquire the target brand image. © 2018 The Author(s).

关键词： Machine learning

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A Multivariate Fuzzy Time Series Resource Forecast Model for Clouds using LSTM and data Correlation Analysis 22

A Multivariate Fuzzy Time Series Resource Forecast Model for...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Nhuan Tran Thang Nguyen Binh Minh Nguyen Giang Nguyen Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam Slovak Acad Sci Inst Informat Bratislava Slovakia

Today, almost all clouds only offer auto-scaling functions using resource usage thresholds, which are defined by users. Meanwhile, applying prediction-based auto-scaling functions to clouds still faces a problem of inaccurate forecast during operation in practice even though the functions only deal with univariate monitoring data. Up until now, there are still very few efforts to simultaneously process multiple metrics to predict resource utilization. The motivation for this multivariate processing is that there could be some correlations among metrics and they have to be examined in order to increase the model applicability in fact. In this paper, we built a novel forecast model for cloud proactive auto-scaling systems with combining several mechanisms. For preprocessing data phase, to reduce the fluctuation of monitoring data, we exploit fuzzification technique. We evaluate the correlations between different metrics to select suitable data types as inputs for the prediction model. In addition, long-short term memory (LSTM) neural network is employed to predict the resource consumption with multivariate time series data at the same time. Our model thus is called multivariate fuzzy LSTM (MF-LSTM). The proposed system is tested with Google trace data to prove its efficiency and feasibility when applying to clouds. (C) 2018 The Authors. Published by Elseiver Ltd.

关键词： Cloud computing proactive auto-scaling multivariate fuzzy time series long-short term memory LSTM data correlation symmetrical uncertainty measure Google trace dataset

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Classification of Mild Cognitive Impairment Stages Using Machine learning Methods 22

Classification of Mild Cognitive Impairment Stages Using Mac...

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22nd IEEE international conference on intelligent engineering Systems, INES 2018

作者： Cabrera-León, Ylermi Báez, Patricio García Ruiz-Alzola, Juan Suárez-Araujo, Carmen Paz Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Canary Islands35017 Spain Departamento de Ingeniería Informática y de Sistemas Universidad de la Laguna San Cristóbal de La Laguna Canary Islands38200 Spain Instituto Universitario de Investigaciones Biomédicas y Sanitarias Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Canary Islands35016 Spain Instituto Universitario de Ciencias y Tecnologías Cibernéticas Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Canary Islands35017 Spain

ISBN: (纸本)9781538611227

The elderly population in developed countries has augmented in the last decades. This has also entailed an increased prevalence of aging diseases. Mild Cognitive Impairment (MCI) is considered a prodromal stage of Alzheimer's Disease (AD), which is the most common dementia. We present a benchmarking of Machine learning (ML) methods for MCI staging and its early detection via the multiclass classification of Cognitively Normal (CN), Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI) subjects by means of neuropsychological scales. data were obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI). Our study has been aimed towards hybrid neural architectures, such as the Counter-propagation Network (CPN), for their input space quantification capability, and non-neural methods including ensembles. We also analyzed how class balancing affected them. Non-neural ensembles of learners obtained Area Under the Curve (AUC) values in the range 0.721-0.775, whereas 0.650-0.657 with the monolithic architecture CPN. This suggests that there is a weak difference among these cognitive stages, that the used scales do not offer high enough discriminant power, and that neural ensembles can be a more appropriate solution. © 2018 IEEE.

关键词： Neurodegenerative diseases

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Distillation of deep learning ensembles as a regularisation method 6th

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6th international Workshop on Combinations of intelligent Methods and Applications, CIMA 2016 held in conjunction with the 22nd European conference on Artificial Intelligence, ECAI 2016

作者： Mosca, Alan Magoulas, George D. Department of Computer Science and Information Systems Birkbeck University of London London United Kingdom

ISBN: (纸本)9783319667898

Ensemble methods are among the most commonly utilised algorithms that construct a group of models and combine their predictions to provide improved generalisation. They do so by aggregating multiple diverse versions of models learned using machine learning algorithms, and it is this diversity that enables the ensemble to perform better than any of its members taken individually. This approach can be extended to produce ensembles of deep learning methods that combine various good performing models, which are between them very diverse because they have reached different local minima and make different prediction errors. It has been shown that a large, cumbersome deep neural network can be approximated by a smaller network through a process of distillation, and that it is possible to approximate an ensemble of other learning algorithms by using a single neural network, with the help of additional artificially generated pseudo-data. We extend this work to show that an ensemble of deep neural networks can indeed be approximated by a single deep neural network with size and capacity equal to the single ensemble member, and we develop a recipe that shows how this can be achieved without using any artificial training data or any other special provisions, such as using the soft output targets during the distillation process. We also show that, under particular circumstances, the distillation process can be used as a form of regularisation, through its implicit reduction in learning capacity. We corroborate our findings with an experimental analysis on some common benchmark datasets in computer vision and deep learning. © 2018, Springer international Publishing AG.

关键词： Distillation

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A common-sense based system for Geo-IoT 22

A common-sense based system for Geo-IoT

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Cantarero, Ruben Rubio, Ana Trapero, Cristian Santofimia, Maria J. Villanueva, Felix J. Villa, David Lopez, Juan C. Univ Castilla La Mancha Comp Architecture & Networks Grp Paseo Univ 4 Ciudad Real Spain

This paper presents an architecture for smart buildings based on common-sense reasoning using the IndoorGML standard. The main objective is the construction of a knowledge model that allows inferring information that is not explicitly defined in that model. The main challenges addressed are: 1) the modeling of the concepts presented by the standard in the knowledge base used, known as Scone;2) the automation of the introduction of knowledge in Scone with the proposed model;and 3) the implementation of a service-oriented architecture that makes possible to introduce knowledge in a transparent way. The semantic knowledge we present here, which shows more advanced and flexible capabilities, will make it possible in the future to carry out reasonings that not only depend on the structure of a building, but on many other aspects of the IoT such as sensors or actuators that are difficult to contemplate if a common sense approach is not followed. (C) 2018 The Authors. Published by Elseiver Ltd.

关键词： Common sense Smart building Indoor spatial data models OGC IndoorGML Mapping Modelling IoT Geo-IoT Multi-Sensor Information System

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Evaluation of Neural Network-Based Sensing and Perception in Experimental Vehicles

Evaluation of Neural Network-Based Sensing and Perception in...

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IEEE international conference on intelligent engineering Systems (INES)

作者： Ernő Horváth Claudiu Radu Pozna Áron Ballagi Faculty of Computer Engineering Széchenyi István University Győr Hungary Faculty of Electrical Engineering Transylvania University Brasov Romania

As increasing use of neural networks (NN) and deep-learning (DL) can be observed in various fields, such as voice and image recognition, control, translation, data processing, function approximation, etc. These technologies can also be applied in a specific domain of experimental vehicles. This paper presents and summarizes our recent practical experiences gained during the usage of neural network as sensing and perception subsystem in an experimental vehicle. The paper does not targets general NN problems, only focuses on the narrow subdomain. The current paper also reviews and evaluates the available hyper parameter tunings, optimizers and neural network architectures and proposes tested solutions for enhanced performance. All of the used datasets, source codes and additional materials is available online.

关键词： Graphics processing units Training Artificial neural networks Computer architecture Task analysis Robot sensing systems

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A proposal of method to make active learning from class to self-study using active note taking and active textbook system 22

A proposal of method to make active learning from class to s...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Suzuki, Shin-nosuke Akimoto, Yutaro Kobayashi, Yasuhiro Ishihara, Manabu Kameyama, Ryohei Yamaguchi, Masaya Oyama Coll Natl Inst Technol Nakakuki 771 Oyama Tochigi 3230806 Japan Covelline LLC Toshima Ku Nishi Ikebukuro 5-8-9 Tokyo 1710021 Japan

In this paper, we describe a method to make active learning from class to self-study by two proposed methods. One of them is naming active note taking (A-note), which actively makes note taking at class. The other is a system called active textbook system (A-txt) which promotes students' self-study by adding digital contents to a general paper textbook and browsing with a mobile smart device. A-note is aimed at making the head work for taking note itself In this method, in order to reduce passive note taking as much as possible, let students actively write out the information by the students' words and diagrams to handouts distributed in advance. In the class to which this method was applied, the results of the test improved and the introduction effect was confirmed. We also analyzed the materials written by students using image processing technology. When examining the correlation between the analyzing results and the test results, a certain correlation was observed. Therefore, quantitative effects by introducing A-note could be shown. As for A-txt, we expanded iOS application using augmented reality (AR) technology in order to be more easy and effective the conversion of general books into digital educational materials. In this time, we have developed a GUI system that makes it easy to produce the additional content to a textbook. In addition, we added functions that allow students to post questions about the textbook etc. to teachers. We also conducted an application survey cooperating testers and found a smart device specification that can use A-txt without problems. Therefore, by combining A-note and A-txt, we found a possibility that students actively learn from lecture to self-study. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Active learning A-note Handout materials A-txt Marker AR

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A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines

A Knowledge Transfer Platform for Fault Diagnosis of Industr...

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IEEE international conference on intelligent engineering Systems

作者： Y. Zhang G. Jombo A. Latimer School of Engineering University of Lincoln Lincoln U.K. Siemens Industrial Turbomachinery Ltd. Lincoln U.K.

The aim of this paper is to introduce the bases of an intelligent fault diagnostic platform, which assists in detecting mechanical failures of Industrial Gas Turbines (IGTs). This comprises an integration of an expert system and its complementary signal processing techniques. The essential characteristic here is not to exclude humans (experts) from the diagnostic process, hut rather to transfer their knowledge and experience to a computerized platform. The automated process executed by the computerized platform is to ensure the scalability and consistency in fault diagnosis;while the humans are required to corroborate the transparency and liability of the outcomes. In this paper, a Knowledge Transfer Platform (KTP) is proposed for fault diagnosis of industrial systems. It is then designed and tested for combustion fault diagnosis using field data of IGTs. The preliminary results have revealed the feasibility and efficacy of the proposed scheme, which has the potential to be further extended to a large industrial scale and to different engineering diagnostic applications.

关键词： Fault diagnosis Combustion Knowledge transfer Turbines Tools Knowledge based systems Signal processing

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