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Using the Model of a Functional Rationalizer of Consumer Behavior in Recommendation Systems for Managing the Transport Activity of the Urban Population 23

Using the Model of a Functional Rationalizer of Consumer Beh...

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23rd International Conference on Soft Computing and Measurements, SCM 2020

作者： Seliverstov, Yaroslav A. Seliverstov, Svytoslav A. Podoprigora, Nikolay V. Starichenkov, Alexey L. Naryshkin, Roman S. Solomenko Institute of Transport Problems Russian Academy of Sciences Institute of Computer Science and Technology Peter the Great St.Petersburg Polytechnic University Laboratory of Intelligent Transport Systems St. Petersburg Russia Saint Petersburg State University of Architecture and Civil Engineering Department of Ground Transportation Technological Machines St. Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Department of Ship Control Systems St. Petersburg Russia Saint Petersburg State University Department of Programming Technology St. Petersburg Russia

ISBN: (纸本)9781728196923

An overview of existing urban software mobile applications of the transport and economic direction is given. A model of a functional rationalizer of consumer behavior is being built. The software model of the functional rationalizer of consumer behavior (SM FRCB) was developed for the first time. PMFRCB allows the user to expand the number of his preferred needs (occupations, activities) during the daily cycle through intelligent recommendations. Also, SM FRCB allows you to optimize needs (activities, activities) at the cost and rate of their use. The SM FRCB is made in Python_3 using the libraries matplotlib, collections, numpy, pyplot, mplot3d. The analysis of consumer behavior before and after using the functional rationalizer model is carried out. The graphical results of a structural analysis of the costs of old and new consumer behavior, analysis of consumer behavior within the boundaries of the theory of possibilities and the model of transport behavior of a resident before and after applying the functional rationalizer model are presented. The prospects of further improving the SM FRCB are considered. © 2020 IEEE.

关键词： Smart city

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A Model of Deep Neural Network for Iris Classification With Different Activation Functions

A Model of Deep Neural Network for Iris Classification With ...

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2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018

作者： Eldem, Ayşe Eldem, Hüseyin Üstün, Deniz Department of Computer Engineering Karamanoǧlu Mehmetbey University Karaman Turkey Department of Technology and Computer Programming Karamanoǧlu Mehmetbey University Karaman Turkey

ISBN: (纸本)9781538668788

In recent years, deep neural network (DNN) has been frequently used for classification. In this study, iris flowers having 3 different types are classified by using DNN which are utilized the width and length of petal and sepal features as input. Some experiments are made for the iris dataset by using different activation functions and different epoch numbers. Then, the activation function which gives the best result is determined. The classification success of the developed model is achieved 96% for the iris dataset. © 2018 IEEE.

关键词： Deep neural networks

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Monitoring System for Prediction and Detection of Epilepsy Seizure

Monitoring System for Prediction and Detection of Epilepsy S...

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International Conference on Advances in Computational Tools for engineering Applications, ACTEA

作者： Nashaat el Halabi Roy Abi Zeid Daou Roger Achkar Ali Hayek Josef Börcsök Department of Computer and Communications Engineering American University of Science and Technology Beirut Lebanon MART Learning Education and Research Center Chananir Lebanon Department of Computer Architecture and System Programming University of Kassel Germany

Epilepsy is a neurological disorder associated with abnormal electrical activity in the brain, which causes seizures. The occurrence of seizure is not predictable; the duration between seizures, as well as the symptoms, varies from patient to another. Since the seizures are not predictable, and most of epileptic patients suffer from physical risky symptoms during the seizure, such patients are not able to perform daily work activities. The objective of this project is to design and implement a monitoring system for epileptic patients; the system should continuously check some vital signs, analyze the measurements, and decide whether the patient is nearly to have a seizure or not. Whenever a seizure is predicted, the system initiates an alarm. In addition, a notification should be sent to the health care responsible, as well as one preferred contact. By implementing the monitoring system, people who suffer from epilepsy will have more chance to work and live a normal life. Thus, this paper presents the concept of the overall system and shows results of the implemented systems: EEG, ECG and Fall Detection system. Results have shown that the fall detection accuracy reached 99.89% whereas the accuracy of the prediction using the ANN was about 97.34%.

关键词： Electroencephalography Epilepsy Feature extraction Tools Monitoring Artificial neural networks Support vector machines

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Piquasso: A Photonic Quantum computer Simulation Software Platform

arXiv

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arXiv 2024年

作者： Kolarovszki, Zoltán Rybotycki, Tomasz Rakyta, Péter Kaposi, Ágoston Poór, Boldizsár Jóczik, Szabolcs Nagy, Dániel T.R. Varga, Henrik El-Safty, Kareem H. Morse, Gregory Oszmaniec, Michal Kozsik, Tamás Zimborás, Zoltán Quantum Computing and Quantum Information Research Group HUN-REN Wigner Research Centre for Physics Konkoly-Thege Miklós út 29-33 BudapestH-1525 Hungary Department of Programming Languages and Compilers Eötvös Loránd University Pázmány Péter sétány 1/a BudapestH-1117 Hungary Center for Theoretical Physics Polish Academy of Sciences Al. Lotników 32/46 Warszawa02-668 Poland Department of Physics of Complex Systems Eötvös Loránd University Pázmány Péter sétány 1/a BudapestH-1117 Hungary Quantinuum 17 Beaumont Street OxfordOX1 2NA United Kingdom Robert Bosch Kft. Gyömrői út 104. BudapestH-1103 Hungary Department of Computer Engineering Technical University of Munich Arcisstraße 21 München80333 Germany Algorithmiq Ltd. Kanavakatu 3C Helsinki00160 Finland

We introduce the Piquasso quantum programming framework, a full-stack open-source software platform for the simulation and programming of photonic quantum computers. Piquasso can be programmed via a high-level Python programming interface enabling users to perform efficient quantum computing with discrete and continuous variables. Via optional high-performance C++ backends, Piquasso provides state-of-the-art performance in the simulation of photonic quantum computers. The Piquasso framework is supported by an intuitive web-based graphical user interface where the users can design quantum circuits, run computations, and visualize the results. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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Structure and Functioning of Information Systems of Background Monitoring of Landscape Elements of Gorgany Nature Reserve

Structure and Functioning of Information Systems of Backgrou...

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International Conference on Advanced computer Information Technologies (ACIT)

作者： Yaroslav Nykolaychuk Yaroslav Petrashchuk Olena Slobodian Ihor Pitukh Taras Grynchyshyn Lyubov Nykolaychuk Volodymyr Hryha Department of Specialized Computer Systems Ternopil National Economic University Ternopil Ukraine Gorgany nature reserve Nadvirna Ukraine Department of Programming and Information Technologies Nadvirna College by National Transport University Nadvirna Ukraine Department of Social Communication and Law Ivano-Frankivsk National Technical University of Oil and Gas Ivano-Frankivsk Ukraine Department of Computer Engineering and Electronics Vasyl Stefanyk Precarpathian National University Ivano-Frankivsk Ukraine

ISBN: (数字)9781728167602

ISBN: (纸本)9781728167619

This paper discusses the structural organization of the background monitoring system (BMS) of the transboundary UNESCO World Heritage Site “Beech forests of the Carpathians and ancient beech forests of Germany” using actual data and information from Gorgany Nature Reserve in Ivano-Frankivsk, Ukraine. The features of the structure and functions of background monitoring of the protected natural resources, forests and wildlife of Gorgany Nature Reserve is based on modern methods of collecting and processing of digital data, implementation of the principles of Internet of Things and cloud technologies, combined with GPS-systems, sensor networks with the use of optical and fiber-optic telecommunication channels. The information technology of the background monitoring system is based on figurative cluster models of monitoring objects according to theoretical principles of statistical, correlatory, spectral, cluster, logic-statistical and entropy analysis.

关键词： Monitoring Forestry Correlation Optical fiber sensors Optical fibers Biological system modeling

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Classification of a bank data set on various data mining platforms 4

Classification of a bank data set on various data mining pla...

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4th Electric Electronics, computer Science, Biomedical engineerings' Meeting, EBBT 2018

作者： Basarslan, Muhammet Sinan Argun, Irem Duzdar Computer Programming School of Advanced Vocational Studies Doǧuş University Istanbul Turkey Industry Engineering Department Engineering Faculty Düzce University Düzce Turkey

ISBN: (纸本)9781538651353

The process of extracting meaningful rules from big and complex data is called data mining. Data mining has an increasing popularity in every field today. Data units are established in customer-oriented industries such as marketing, finance and telecommunication to work on the customer churn and acquisition, in particular. Among the data mining methods, classification algorithms are used in studies conducted for customer acquisition to predict the potential customers of the company in question in the related industry. In this study, bank marketing data set in UCI Machine Learning Data Set was used by creating models with the same classification algorithms in different data mining programs. Accuracy, precision and f- measure criteria were used to test performances of the classification models. When creating the classification models, the test and training data sets were randomly divided by the holdout method to evaluate the performance of the data set. The data set was divided into training and test data sets with the 60-40%, 75, 25% and 80-20% separation ratios. Data mining programs used for these processes are the R, Knime, RapidMiner and WEKA. And, classification algorithms commonly used in these platforms are the k-nearest neighbor (k-nn), Naive Bayes, and C4.5 decision tree. © 2018 IEEE.

关键词： Data mining

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Identification of the computer System State Based on Multidimensional Discriminant Analysis

Identification of the Computer System State Based on Multidi...

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International Scientific Symposium on Metrology and Metrology Assurance (MMA)

作者： Svetlana Gavrylenko Viktor Chelak Oleksii Hornostal Stella Gornostal Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine Department of Fire Prevention in Settlements National University of Civil Defence of Ukraine Kharkiv Ukraine

ISBN: (纸本)9781728122144

This paper describes the process of identifying the state of a computer system based on tracking changes in the file system under the condition that there is no reliable information about the state of objects in the external environment and the state of the system itself. In this case, the performance indicators of a computer system are random variables with unknown distribution laws. A method for identifying the state of the computer system based on tracking changes in the file system using classical discriminative analysis, taking into account the uncertainty of the input data, is proposed. For this purpose, a special method was developed for solving a fuzzy system of linear algebraic equations. The software was developed for tracking changes in the computer file system: creating, modifying, renaming, and deleting files and directories. The program also performs state classification based on a fuzzy discriminant classifier.

关键词： Identification computer system Malware Anomalous state Discriminant analysis Fuzzy variable computer systems Spyware File systems discriminant analysis Fuzzy systems MULTIDIMENSIONAL computers

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Forecasting Measles Cases in Ethiopia using Neuro-Fuzzy Systems

Forecasting Measles Cases in Ethiopia using Neuro-Fuzzy Syst...

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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Kaan Uyar Umit Ilhan Erkut Inan Iseri Ahmet Ilhan Dept. of Computer Engineering Near East University Mersin 10 Turkey Dept. of Software Engineering Near East University Mersin 10 Turkey Dept. of Biophysics Near East University Mersin 10 Turkey Dept. of Computer Programming Near East University Mersin 10 Turkey

Measles is still one of the deadly diseases that terrify the world. Making accurate decisions is necessary in organizing an effective fight against the disease. Forecasting is a very important tool for planning future needs of the health services including the vaccine stocks. Forecasting methods will help to watch over the critical stock levels of the medication as well as their expiration dates and perhaps take the necessary action to ensure a health stock flow. Governments and health institutions estimate the measles vaccine requirements using certain equations generally based on the size of the target population and the past consumption records. There are several studies that have examined the measles forecasting and conducted a vaccine requirement assessment. In this study genetic algorithm (GA) based trained recurrent fuzzy neural network (RFNN) and Adaptive neuro-fuzzy inference system (ANFIS) used to forecast the monthly measles cases in Ethiopia. The Ethiopia measles data was extracted from the World Health Organization Measles and Rubella Surveillance Data, which covers the period from January 2011 to December 2017. Out of total monthly measles cases, 80% were used for training, and 20% were chosen for testing. GA based trained RFNN shows better performance compared to ANFIS results.

关键词： Forecasting Testing Tools Organizations Diseases Vaccines Sociology

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Visualizing regions with a new split-screen view for the online tool travis

Visualizing regions with a new split-screen view for the onl...

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2017 Joint of the Workshops at Modellierung, MOD-WS 2018

作者： Meis, Benjamin Bergenthum, Robin Fern Universität in Hagen Software Engineering and Theory of Programming Universitätsstraße 1 Hagen58097 Germany FernUniversität in Hagen Faculty of Mathematics and Computer Science Universitätsstraße 1 Hagen58097 Germany

The online tool travis can synthesize a k-bounded Petri net model from a reachability graph and unfold a k-bounded Petri net to its reachability graph. Synthesis is built on the theory of regions, but where travis and other existing tools only show the synthesized model, we want to present the synthesized model as well as the calculated regions after a user has performed the synthesis procedure. In this paper, we present a new split-screen module of travis which is able to visualize regions, calculated during the synthesis procedure, as well as markings of places related to single states of the reachability graph, calculated during the unfolding procedure. Both sets, i.e. regions and markings, help to really understand the relations between the behavioral and the synthesized models at hand as well as the underlying fundamental concepts of state-based synthesis and reachability analysis. With these new features, travis is tailored to be used in a teaching environment to help students understand the concepts and notions of state-based synthesis. © 2018 CEUR-WS. All rights reserved.

关键词： Petri nets

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Application of Interval Type-2 Fuzzy Logic to polypropylene business policy in a petrochemical plant in India

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Journal of the Saudi Society of Agricultural Sciences 2018年第1期17卷 24-42页

作者： Jana, Dipak Kumar Castillo, Oscar Pramanik, Sutapa Maiti, Manoranjan Department of Engineering Science Haldia Institute of Technology Haldia Purba Midnapur 721 657 West Bengal India Tijuana Institute of Technology Tijuana Mexico Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Midnapore 721 102 West Bengal India

This paper presents a new approach to predict the quality of polypropylene in petrochemical plants. The proposed approach constructs four different models, based on a large number of data collected from a renowned petrochemical plant in India and uses it to predict the polypropylene quality. The quality of polypropylene depends on the indices such as melt flow index and the xylene solubility of the product and the parameters controlling these two indices are hydrogen flow, donor flow, pressure and temperature of polymerization reactors. Mamdani Interval Type-2 Fuzzy Logic inference systems are formed for first time. The model outcomes are compared with the collected plant data and a sequence of sensitivity analysis elects the most suitable model from them. Some sensitivity analyses are provided using Fuzzy C-Means Clustering to the models. © 2015 The Authors

关键词： Fuzzy C-Means Clustering Fuzzy inference system Interval Type-2 Fuzzy Logic Polypropylene Product quality

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