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Predicting Churn: How Multilayer Perceptron Method Can Help with Customer Retention in Telecom Industry

IOP Conference Series: Materials Science and engineering 2020年第1期864卷

作者： NNA Sjarif NF Azmi HM Sarkan SM Sam MZ Osman Advanced Informatics Department Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Jalan Sultan Yahya Petra 54100 Kuala Lumpur Malaysia Soft Computing Research & Intelligent System Group (SPINT) Faculty of Systems Computer & Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak 26300 Gambang Pahang Malaysia

Customer churn prediction has been used widely in various kind of domain especially subscription-basis industries. With the rapid growth of telecommunication industry over the last decade, this industry not only focuses on providing numerous products, but also satisfying the customers as it is one of the key solutions to remain competitive. This research proposed MultiLayer Perceptron Method for churn prediction. The evaluation is compared with three classifiers which includes are Support Vector Machine, Naïve Bayes and Decision Tree in term of several aspects. In preprocessing phase, we employed Principal Component Analysis and normalization to find the correlation among all the variables. For the postprocessing, InfoGainAttribute is used to identify the highest factor attribute that leads to customer retention. It is found that MultiLayer Perceptron outperforms other classifiers and international plan plays important role to retain customer from leaving organization.

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A Direct Proof of Improved Biased Random Walk with Gastric Cancer Dataset

A Direct Proof of Improved Biased Random Walk with Gastric C...

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International Conference on Applied Mathematics & computer Science

作者： Choon Sen Seah Shahreen Kasim Mohd Farhan Md Fudzee Mohd Saberi Mohamad Rd Rohmat Saedudin Deden Witarsyah Rohayanti Hassan Mohd Arfian Ismail Rodziah Atan Soft Computing and Data Mining Centre Faculty of Computer Sciences and Information Technology Universiti Tun Hussein Onn Malaysia Faculty of Creative Technology and Heritage Universiti Malaysia Kelantan School of Industrial Engineering Telkom University Laboratory of Biodiversity and Bioinformatics Universiti Teknologi Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Department of Software Engineering & Information System Faculty of Computer Science and Information Technology University Putra Malaysia (UPM)

ISBN: (数字)9781728132587

ISBN: (纸本)9781728132594

The relationship between the weight of a single genes and the connection between the genes had been studied. Chemistry and physical sciences have proved the attraction between molecules. Molecules are attracted to each other by bond. This paper is presented to disclose the relationship between weight and connectivity of nodes with biased random walk. An equation of biased random walk which named as significant directed random walk is formed to enhance the connectivity of nodes in directed graph via weigh. To be completely biased to the random walk, references data is implement as directed graph. Weight of genes will be used as one of the parameter in the formula. While the adjacency matrix is further enhanced by Warshall's algorithm to increases the accessibility of nodes via vector. The evolution of random walk is disclosed in this paper as well. Significant directed random walk will be used to prove the importance of weight in this paper. Comparison of the result between biased random walk is presented to prove the enhancement of random walk.

关键词： Biased random walk Cancer classification Gene expression dataset Random walk cancer classification Biased Ganglia one-parameter Mentoring Physical science

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A simulation model for crowd evacuation of fire emergency scenario 8

A simulation model for crowd evacuation of fire emergency sc...

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8th International Conference on Information Technology, ICIT 2017

作者： Bakar, Noor Akma Abu Adam, Khalid Majid, Mazlina Abdul Allegra, Mario Faculty of Computer System and Software Engineering Universiti Malaysia Pahang Malaysia Via Ugo Malfa 153 Palermo90146 Italy

ISBN: (纸本)9781509063321

Human safety is an important issue when handling the crowd evacuation in order to avoid death and injuries. Human behavior changes from normal to panic behavior when they are in an emergency scenario such as building on fire. Modelling panic behavior on evacuee is a critical challenge as the human movement is unpredictable. Simulation (example: Discrete Event Simulation and Agent-Based Simulation) is a well-known method to investigate crowd evacuation. But the question here is: Which simulation method is suitable to model a realistic representation of crowd evacuation? Thus, in this paper, a simulation model based on Agent-Based Simulation (ABS) and Social Force Simulation (SFS) Model is proposed to improve on modelling fire evacuation in a closed space (building). To achieve the above research aim, three simulation models (ABS Model, SFS Model and a hybrid of ABS and SFS model) will be developed and compared. This paper will discuss on the development of conceptual model of these three simulation models. Later, the conceptual models will be used to represent the investigated simulation models. © 2017 IEEE.

关键词： Discrete event simulation

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Preface

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CEUR Workshop Proceedings 2022年 3091卷

作者： Kovalev, Igor Semenkin, Eugene Schwenker, Friedhelm Shaidurov, Vladimir Ganchev, Todor Škraba, Andrej Stanimirović, Predrag Kolehmainen, Mikko Kronberger, Gabriel Affenzeller, Michael Brester, Christina Ryzhikov, Ivan Kazakovtsev, Lev Sopov, Evgenii Semenkina, Maria Akhmedova, Shakhnaz Stanovov, Vladimir Krasnoyarsk Regional Science and Technology City Hall Department of Computer Science of Siberian Federal University Krasnoyarsk Russia Siberian Institute of Applied System Analysis named after A.N. Antamoshkin The Department ofsystem analysis and operations research of Reshetnev University Krasnoyarsk Russia Institute of Neural Information Processing Ulm University Ulm Germany The Institute of Computational Modeling SB RAS Krasnoyarsk Russia Technical University of Varna Varna Bulgaria University of Maribor UM Faculty of Organizational Sciences Cybernetics and Decision Support Systems Laboratory Slovenia Department of Computer Science Niš Serbia Department of Environmental and Biological Sciences Environmental Informatics University of Eastern Finland Kuopio Finland University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria University of Eastern Finland UEF · Department of Environmental Science Kuopio Finland The Department of Systems Analysis Reshetnev University of Science and Technology Krasnoyarsk Russia Siberian Institute of Applied System Analysis Krasnoyarsk Russia University of Applied Sciences Upper Austria and Softwarepark Hagenberg Austria Reshetnev Siberian States University of Science and Technology Siberian Institute of Applied System Analysis Krasnoyarsk Russia SibSAU · Institute of computer science and telecommunications Krasnoyarsk Russia

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Appropriation of social media for fostering effective tacit knowledge sharing: Developing conceptual model 6

Appropriation of social media for fostering effective tacit ...

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6th International Conference on computer Science and Computational Mathematics, ICCSCM 2017

作者： Amidi, A. Jabar, M. Jusoh, Y.Y. Abdullah, R. Department of Software Engineering and Information System Faculty of Computer Science and Information Technology University Putra Malaysia UPM Serdang43400 Malaysia

With the rising popularity of social media in the past few years, several researches ratiocinate that this type of interactive and collaborative technology could be a beneficial tool for the sharing of tacit knowledge. Nevertheless, very few literatures have tackled the subject of how social media could facilitate tacit knowledge sharing among medical practitioners, and what are its contributions in the area. Thus, the factors that drive individuals to share tacit knowledge need to be investigated further and included in literature. Through a systematic literature review, this study proposes seven enabling conditions which could potentially facilitate the sharing of tacit knowledge. TAM was applied as a novelty in this study in investigating the factors influencing knowledge sharing via social media, whilst taking into account the mediation effects of Attitude in social media usage. This study uncovered an important correlation between virtual settings and the conversion of tacit knowledge, which affects organizational members who are not co-located physically but have a crucial need for sharing information. © Published under licence by IOP Publishing Ltd.

关键词： Social networking (online)

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Lemma functions for frama-C: C programs as proofs

arXiv

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arXiv 2018年

作者： Volkov, Grigoriy Mandrykin, Mikhail Efremov, Denis Faculty of Computer Science National Research University Higher School of Economics Moscow Russia Software Engineering Department Ivannikov Institute for System Programming Russian Academy of Sciences Moscow Russia

This paper describes the development of an auto-active verification technique in the Frama-C framework. We outline the lemma functions method and present the corresponding ACSL extension, its implementation in Frama-C, and evaluation on a set of string-manipulating functions from the Linux kernel. We illustrate the benefits our approach can bring concerning the effort required to prove lemmas, compared to the approach based on interactive provers such as Coq. Current limitations of the method and its implementation are discussed. Copyright © 2018, The Authors. All rights reserved.

关键词： Formal verification

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A Focal load balancer based algorithm for task assignment in cloud environment

A Focal load balancer based algorithm for task assignment in...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Mostafa Abdulghfoor Mohammed Raed Abdulkareem Hasan Munef Abdullah Ahmed Nicolae Tapus Marwan Ali Shanan Mohammad Khamees Khaleel Ahmed.H. Ali Faculty of Automatic Control and Computers University Polytechnic of Bucharest Independentei Romania Northern Technical University Mosul Iraq Universiti Malaysia Pahang Kuantan Pahang MY Faculty of Computer System and software Engineering University Malaysia Pahang (UMP) Kuantan Pahang Malaysia

ISBN: (纸本)9781538649022;9781538649015

A new trend rising in IT environs is the Mobile cloud computing with colossal prerequisites of infrastructure along with resources. In cloud computing environment, load balancing a vital aspect. Cloud load balancing way toward disseminating workloads across numerous computing resources. Proficient load balancing plan guarantees effective resource usage by the supply of resources to cloud user's on-demand premise and it might even help organizing clients by applying fitting planning criteria the current paper discusses and implements the concept of load balancers, which are the lifeblood of any cloud computing network. In this paper, a new load balancing system is presented Focal Load Balancer (F-LB), which has been developed to reduce the traffic in the Cloud, whilst assuring a smooth flow of data in the cloud network. The proposed algorithm takes advantage of the dynamic load balancing characteristics over static balancing, and avoids the damage that a static load balancer causes if it fails. Simulation results show that the proposed algorithm operates efficiently and effectively, and it provides a significantly improved performance over existing algorithms. Comparisons with the krill-LB and agent-based algorithms show that the new system provides a reduction in average wait time, a significant increase in throughput, and a dramatic reduction in CPU time consumption.

关键词： Load management Cloud computing Heuristic algorithms Servers Throughput Task analysis software

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Utilizing Information from Task-Independent Aspects via GAN-Assisted Knowledge Transfer

Utilizing Information from Task-Independent Aspects via GAN-...

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International Joint Conference on Neural Networks

作者： Lunkai Fu Jun Li Langxiong Zhou Zhenyuan Ma Shaopeng Liu Zhiyong Lin Mukesh Prasad School of Computer Science Guangdong Polytechnic Normal University Guangzhou China School of Software and Centre for Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia School of Mathematics and System Sciences Guangdong Polytechnic Normal University Guangzhou China

Observed data often have multiple labels with respect to different aspects. For example, a picture can have one label specifying the contents in terms of the object category such as aeroplane, building, cat, etc. and in the meanwhile have another label describing the image style such as photo-realistic or artistic. The central idea of this work is that any annotation of the data contains precious knowledge and is not to be foregone: an analytic task focusing on one aspect of the data can benefit from the knowledge transferred from the other aspects. We propose a passive knowledge transfer scheme for deep neural network training based on the generative adversarial nets (GANs). The adversarial training scheme encourages the nets to encode data into representations that are both discriminative for the target aspect and invariant with respect to the irrelevant aspects. We show that the scheme mixes the conditional distributions of the encoded data on the irrelevant aspects, by the theory on the link between the GAN framework and the Wasserstein metric in distribution spaces. Moreover, we empirically verified the method by i) classifying images despite influence by geometric transform and ii) recognizing the movements (geometric transform) regardless the image contents.

关键词： Task analysis Neural networks Knowledge transfer Gallium nitride Generators Training Measurement

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GAN2C: Information Completion GAN with Dual Consistency Constraints

GAN2C: Information Completion GAN with Dual Consistency Cons...

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International Joint Conference on Neural Networks (IJCNN)

作者： Lujuan Zhang Jun Li Tao Huang Zhenyuan Ma Zhiyong Lin Mukesh Prasad School of Mathematics and System Sciences Guangdong Polytechnic Normal University Guangzhou China School of Software and Center for Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia School of Computer Science Guangdong Polytechnic Normal University Guangzhou China

This paper proposes an information completion technique, GAN2C, by imposing dual consistency constraints (2C) to a closed loop encoder-decoder architecture based on the generative adversarial nets (GAN). When adopting deep neural networks as function approximators, GAN2C enables highly effective multi-modality image conversion with sparse observation in the target modes. For empirical demonstration and model evaluation, we show that trained deep neural networks in GAN2C can infer colors for grayscale images, as well as estimate rich 3D information of a scene by densely predicting the depths. The results of the experiments show that in both tasks GAN2C as a generic framework has been comparable to or advanced the state-of-the-art performance which are achieved by highly specialized systems. Code is available at https://***/AdalinZhang/GAN2C.

关键词： Image color analysis Gallium nitride Task analysis Neural networks Gray-scale Generative adversarial networks Probabilistic logic

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MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

arXiv

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

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