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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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Applying blockchain technology for improvement of the educational process in terms of data processing 11

Applying blockchain technology for improvement of the educat...

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11th Majorov international conference on Software engineering and Computer Systems, MICSECS 2019

作者： Zimina, Dina Mouromtsev, Dmitry ITMO University Saint-Petersburg197101 Russia

the paper considers some new methods of using blockchain technology as a tool of improving educational process management. Two e-learning problems are described. the first one is low ratings of completing MOOCs. the second one is the need for more detailed and accurate reports. Blockchain technology and its applications in education is considered. Blockchain's ability of documenting events is reviewed particularly. Two cases of using blockchain in this way are performed. the first one is making educational reports more detailed and easier to design. Another one is using blockchain for creating any student model in adaptive education to make electronic educational courses more effective and to increase ratings of completing MOOCs. A blockchain's option of documenting timestamps and users' addresses automatically is specially mentioned. Both cases are described mathematically and united in one model. Using Ethereum blockchain for the evaluation because of its smart-contract's mechanism is justified. Sequence diagram showing application workflow, is presented. Input data from the real e-learning course are processed with using statistics to get the most frequently used system events. Application tools for working with the Ethereum blockchain are presented. the process of developing, deploying and mining smart-contract is shown. Output report is presented. Implementation is described, the ways of future learn are reported. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Smart contract

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Investigating the use of Machine learning for Smart Parking Applications 11

Investigating the use of Machine Learning for Smart Parking ...

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11th international conference on Knowledge and Systems engineering (KSE)

作者： Barker, Jonathan Rehman, Sabih Ur Charles Sturt Univ Sch Comp & Math Bathurst NSW Australia

ISBN: (纸本)9781728130033

Traffic congestion caused by greater competition for limited parking spaces in the world's major cities is a growing problem. To overcome this challenge, a study has been carried out to use a smart parking application that utilises machine learning algorithms to help predict future car parking occupancy rates at Port Macquarie campus of Charles Sturt University (CSU), Australia. Parking data was collected over a five-week period and the WEKA Machine learning Workbench was used to identify high-performing algorithms for predicting future parking occupancy rates. In the initial phase, some well known algorithms were used to investigate occupancy rates. In the next phase of the study, student class timetable data was used to enhance prediction accuracy and investigate parking occupancy trends. While most algorithms proved to be accurate in stable conditions, the KStar algorithm appeared to produce better results during highly variable conditions.

关键词： Traffic congestion

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Computational Analysis of Machine learning Algorithms to Predict Heart Disease

Computational Analysis of Machine Learning Algorithms to Pre...

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international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Kunal Rao Pawan Raj Gopal Kusum Lata University School of Management and Entrepreneurship Delhi Technological University Delhi India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

Heart disease is one of the most common problems and also a disease whose rate of increase has been higher in recent years. the complex task associated is exploitation of hidden patterns for effective and accurate prediction. there is plenty of data generated every year from various health institutes. this research focuses on development of supervised machine learning models for prediction of target heart disease. We have used publicly available heart disease datasets from University of California, Irvine (UCI) data repository for Cleveland, Switzerland, Hungarian and Long Beach. Various forms of preprocessing steps such as handling the missing values and null values, removal of duplicate entries is employed on these datasets in order to use it for developing effective models. the correlation between the features set and the target variable is studied. the prediction models are developed using effective machine learning techniques like Logistic Regression, Decision Trees, Naive Bayes, K-nearest neighbors, ensembles AdaBoost and XGBoost. the predictive performance of the developed models is compared with the help of stable accuracy measures like accuracy, precision, recall, F1-seore, Cohen's kappa and Area Under the Curve score. K- nearest neighbors was the best model for the Cleveland dataset with 86.81% accuracy. AdaBoost algorithm gave us the highest accuracy of 98% for Switzerland dataset. Bernoulli Naive Bayes predicted the heart diseases for Hungarian dataset with 84.26% accuracy. XGBoost gave better accuracy of 82.20% for VA Long Beach dataset. the results of the study advocate the applicability of machine learning techniques to predict heart disease.

关键词： Heart Machine learning algorithms Machine learning Predictive models Task analysis Regression tree analysis Diseases

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Software-Defined Networking Application with Deep Deterministic Policy Gradient 19

Software-Defined Networking Application with Deep Determinis...

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11th international conference on Computer Modeling and Simulation (ICCMS) / 8th international conference on intelligent Computing and Applications (ICICA)

作者： Witanto, Joseph Nathanael Lim, Hyotaek Dongseo Univ Dept Ubiquitous IT Busan South Korea Dongseo Univ Div Comp Engn Busan South Korea

ISBN: (纸本)9781450366199

Software-Defined Networking (SDN) simplifies hardware-centric network architecture by employing forwarding devices (switches), SDN controller, and SDN applications. Depending on the SDN application that manages the controller, controller can turn a switch to act as a switch, router, firewall, etc. SDN also enables switch to access more network information through controller. Our proposed SDN application consists of traffic monitoring module and routing module to optimize the network. Traffic monitoring module will monitor switches' port utilization and routing module uses deep reinforcement learning agent trained with deep deterministic policy gradient to manage switch forwarding. Relevant flow entries are issued to OpenFlow switches to manage switch forwarding. Agent changes the routing according to the switches' port utilization and learn the best routing to minimize packet loss. the proposed method will be implemented and evaluated in the future work using Mininet to emulate data layer (hosts, switches) and open source SDN controller Ryu.

关键词： software-defined networking deep deterministic policy gradient deep reinforcement learning packet loss minimization

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Case Study on Model-based Application of Machine learning using Small CAD databases for Cost Estimation 11

Case Study on Model-based Application of Machine Learning us...

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11th international Joint conference on Knowledge Discovery, Knowledge engineering and Knowledge Management (IC3K) / 11th international conference on Knowledge Discovery and Information Retrieval (KDIR)

作者： Boerzel, Stefan Frochte, Joerg Breuckmann GmbH & Co KG Dieselstr 26-28 D-42579 Heiligenhaus Germany Bochum Univ Appl Sci Dept Elect Engn & Comp Sci Kettwiger Str 20 D-42579 Heiligenhaus Germany

ISBN: (纸本)9789897583827

In many industries, the development is aimed towards Industry 4.0, which is accompanied by a movement from large to small quantities of individually adapted products in a multitude of variants. In this scenario, it is essential to be able to provide the price for these small batches fast and without additional costs to the customer. this is a big challenge in technical applications in which this price calculation is in general performed by local experts. From the age of expert systems, one knows how hard it is to achieve a formalised model-based on expert knowledge. So it makes sense to use today's machine learning techniques. Unfortunately, the small batches combined with typically small and midsize production enterprises (SMEs) lead to smaller databases to rely on. this comes along with data which is often based on 3D data or other sources that lead in the first step to a lot of features. In this paper, we present an approach for such use cases that combines the advantages of model-based approaches with modern machine learning techniques, as well as a discussion on feature generation from CAD data and reduction to a low-dimensional representation of the customer requests.

关键词： Cost Estimation Small databases Model-based Approach Feature Generation from CAD data

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Evaluation of Nephrology dataset through Deep learning Technique 5th

Evaluation of Nephrology Dataset Through Deep Learning Techn...

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5th international conference on Recent Developments in Science, engineering and Technology, REDSET 2019

作者： Dohare, Neha Sachdeva, Shelly Maharaja Surajmal Institute of Technology New Delhi India National Institute of Technology Delhi New Delhi India

ISBN: (纸本)9789811558269

Multiple parameters need to be analyzed for making diagnosis in patients with renal disorders. An automatic system that would analyze these parameters and provide diagnosis without the help of a specialist would decrease the burden on already overburdened clinicians. Deep learning has already made a huge impact on medical fields like cancer diagnosis, precision medicine, and can be applied to build this automated system which can diagnose renal disorders. this study has been carried out using the data of nephrology related diseases extracted from a standard based healthcare dataset based on openEHR standard. A feed forward neural network has been used for the creation of the model that would predict the kidney disease present in the patient based on several parameters (such as abdominal ultrasonography, enrolled for transplantation, vascular access, HbsAg, venous fistula amount, urea reduction rate, HIC antibodies and HIV). An accuracy of 98.7% has been achieved through the experiments. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Diagnosis

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Image Classification using small Convolutional Neural Network 9

Image Classification using small Convolutional Neural Networ...

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9th international conference on Cloud Computing, data Science and engineering (CONFLUENCE)

作者： Tripathi, Shyava Kumar, Rishi Amity Univ Dept Comp Sci Engn Amity Sch Engn & Technol Noida Uttar Pradesh India

ISBN: (纸本)9781538659335

With an. upsurge in the rate of data production., pervasive usage of cameras for automation and surveillance and the requirement of visual input for artificially intelligent devices all across the globe, there has been a rapid increase in the mass of image data being generated today. this gives rise to the essentiality of automated image processing required to simplify image related tasks. automated image processing bridges the gap between the human visual system and the pixel level data of images. Deep Convolution Neural Networks are being deployed expansively to analyze detect and classify images for a diverse number of tasks. these neural networks, similar to the human neural network, contain neurons with learnable weights and biases, which are trained to identify and classify different objects or features across the image. this paper presents a functional implementation of image recognition using a small convolutional neural network, proposing less complexity and yielding good classification accuracy for all tested data sets.

关键词： Image Classification Object Detection Deep learning Convolutional Neural Networks

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Use Case Prediction Using Product Reviews Text Classification 20th

Use Case Prediction Using Product Reviews Text Classificatio...

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20th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Wamambo, Tinashe Luca, Cristina Fatima, Arooj Anglia Ruskin Univ East Rd Cambridge CB1 1PT England

ISBN: (纸本)9783030336073;9783030336066

Research into sentiment analysis and its capabilities at analysing product reviews has increased tremendously in recent years. In this paper, we propose an approach to classify product reviews and identify use cases. Several iterations showing the application of natural language processing techniques and machine learning classifications are depicted. A number of machine learning classifiers are trained/tested in various iterations, their performance and accuracy at predicting the existence of use cases in product reviews is evaluated.

关键词： Machine learning Natural language processing Sentiment analysis Ensemble methods Text classification

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A comparison of machine learning methods of sentiment analysis based on Russian language twitter data 11

A comparison of machine learning methods of sentiment analys...

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11th Majorov international conference on Software engineering and Computer Systems, MICSECS 2019

作者： Zvonarev, Andrey Bilyi, Andrey ITMO University 49 Kronverksky Pr. St. Petersburg197101 Russia

the paper is focused on comparing the performance of different techniques of text tonality analysis, a widely used approach in business to conduct social listening research. However, there are still debates on what type of models perform better for NLP classification tasks. On a corpus of Russian language tweets three models were tested to solve binary classification problem: Logistic regression (LR), XGBoost classifier and Convolutional Neural Network (CNN). the paper descriptively overviews main techniques useful for data cleaning and preprocessing for these methods and covers its possible fitfalls. Based on the study CNN showed best results among chosen models, which goes in line with several articles in the field for other than Russian languages. Together with high predictive results, neural networks exhibit a computational drawback - their performance is poor in terms of timings. Besides this, all methods showed high sensitivity to the way data is preprocessed, which is a product of Russian language variability. this leads to a conclusion that there is still a room for improvement - in future research more emphasis will be put on hyper parameter tuning for"boosting type" models and extending list of applied methods. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Sentiment analysis

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Examinations on the Performance of Classification Models for thai News Articles 11

Examinations on the Performance of Classification Models for...

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11th international conference on Information Technology and Electrical engineering (ICITEE)

作者： Noppakaow, Arisara Uchida, Osamu Tokai Univ Grduate Sch Engn Dept Elect & Elect Engn Hiratsuka Kanagawa Japan Tokai Univ Sch Informat Sci & Technol Dept Human & Informat Sci Hiratsuka Kanagawa Japan

ISBN: (纸本)9781728140551

this research aims to examine automatic models to classify thai online news articles. the data set is six thousands of news articles from three mainstream websites. the news articles are classified into four categories-crime news, politic news, sport news, and entertainment news. Examinations on the classification algorithms of Decision Tree, Support Vector Machine (SVM), and Deep learning are conducted. the performance is measured by the accuracy, the recall, the precision, and the F-Measure. the results show that the accuracies of Decision Tree, SVM, and Deep learning models are 86%, 94%, and 95%, respectively.

关键词： text classification machine learning Decision Tree Support Vector Machine Deep learning

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