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Physical Review E 2019年第1期99卷 010201(R)-010201(R)页

作者： Nan Yao Zi-Gang Huang Hai-Peng Ren Celso Grebogi Ying-Cheng Lai Department of Applied Physics Xi'an University of Technology Xi'an 710048 China The Key Laboratory of Biomedical Information Engineering of Ministry of Education National Engineering Research Center of Health Care and Medical Devices The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs and Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an 710049 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China Institute for Complex Systems and Mathematical Biology King's College University of Aberdeen Aberdeen AB24 3UE United Kingdom School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA

Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, and have been shown to be robust against random perturbations. How do chimera states achieve their robustness? We uncover a self-adaptation behavior by which, upon a spatially localized perturbation, the coherent component of the chimera state spontaneously drifts to an optimal location as far away from the perturbation as possible, exposing only its incoherent component to the perturbation to minimize the disturbance. A systematic numerical analysis of the evolution of the spatiotemporal pattern of the chimera state towards the optimal stable state reveals an exponential relaxation process independent of the spatial location of the perturbation, implying that its effects can be modeled as restoring and damping forces in a mechanical system and enabling the articulation of a phenomenological model. Not only is the model able to reproduce the numerical results, it can also predict the trajectory of drifting. Our finding is striking as it reveals that, inherently, chimera states possess a kind of “intelligence” in achieving robustness through self-adaptation. The behavior can be exploited for the controlled generation of chimera states with their coherent component placed in any desired spatial region of the system.

关键词： Chimera states Collective dynamics Coupled oscillators Chaos & nonlinear dynamics

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AClog: Attack Chain Construction Based on Log Correlation

AClog: Attack Chain Construction Based on Log Correlation

引用

IEEE Global Communications Conference

作者： Teng Li Jianfeng Ma Qingqi Pei Yulong Shen Chi Lin Siqi Ma Mohammad S. Obaidat School of Cyber Engineering Xidian University Shaanxi China Shaanxi Key Laboratory of BlockChain and Security Computing Xidian University Shaanxi China School of Computer Science Xidian University Shaanxi China School of Software Technology Dalian University of Technology Dalian China Commonwealth Scientific and Industrial Research Organisation Sydney Australia College of Computing and Informatics University of Sharjah UAE University of Jordan Jordan Nazarbayev University Kazakhstan University of Science and Technology Beijing China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Before the final attack happens, clandestine attackers conduct sequenced stages for being stealthy and elusive. These attacks can leave clues in several different log files. However existing approaches can only detect the anomalies using single type of log and fail to reveal all of the attack steps through log integration and correlation. Such methods can hardly detect the relationships among events and prevent the attack in advance. Additionally, traditional machine learning or data mining in log analysis has a high overhead in computing which is impractically applied in a real product or system. To address these problems, we present AClog, a multiple log correlated analysis system to construct the attack chain. Inspired by penetration testing and social network analysis, we transfer the attack provenance as an event relationship discover problem. We use different logs to form the steps of the system and regard them as the event sequences before the attack. Then, we leverage Fast Linear SVM and Longest Common Subsequences to find out the regular steps before the attack. Finally, we spot the corresponding log sequences to identify the pre-attack steps proactively. We apply our approach in the attack prediction of a cloud computing platform and a university network. The results show that the proposed method can effectively and precisely construct the attack steps and identify the corresponding syslogs.

关键词： Correlation Clustering algorithms Support vector machines Windows

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Learning clinical outcomes from heterogeneous genomic data sources

arXiv

引用

arXiv 2019年

作者： Yousefi, Safoora Shaban, Amirreza Amgad, Mohamed Chandradevan, Ramraj Cooper, Lee A.D. Department of Computer Science Emory University AtlantaGA United States College of Computing Georgia Institute of Technology AtlantaGA United States Department of Biomedical Informatics Emory University School of Medicine AtlantaGA United States Department of Electronic and Telecommunication Engineering University of Moratuwa Moratuwa Sri Lanka

Translating the vast data generated by genomic platforms into reliable predictions of clinical outcomes remains a critical challenge in realizing the promise of genomic medicine largely due to small number of independent samples. In this paper, we show that neural networks can be trained to predict clinical outcomes using heterogeneous genomic data sources via multi-task learning and adversarial representation learning, allowing one to combine multiple cohorts and outcomes in training. We compare our proposed method to two baselines and demonstrate that it can be used to help mitigate the data scarcity and clinical outcome censorship in cancer genomics learning problems. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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DiamondGAN: Unified multi-modal generative adversarial networks for mri sequences synthesis

arXiv

引用

arXiv 2019年

作者： Li, Hongwei Paetzold, Johannes C. Sekuboyina, Anjany Koer, Florian Zhang, Jianguo Kirschke, Jan S. Wiestler, Benedikt Menze, Bjoern Dept. of Informatics Technical University of Munich Germany Dept. of Neuroradiology Klinikum rechts der Isar Germany Dept. of Computer Science and Engineering Southern University of Science and Technology China Shenzhen Institude of Artificial Intelligence and Robotics for Society China Institude for Advanced Study Technical University of Munich

Synthesizing MR imaging sequences is highly relevant in clin-ical practice, as single sequences are often missing or are of poor quality (e.g. due to motion). Naturally, the idea arises that a target modality would benefit from multi-modal input, as proprietary information of in-dividual modalities can be synergistic. However, existing methods fail to scale up to multiple non-aligned imaging modalities, facing common drawbacks of complex imaging sequences. We propose a novel, scalable and multi-modal approach called DiamondGAN. Our model is capable of performing exible non-aligned cross-modality synthesis and data in-fill, when given multiple modalities or any of their arbitrary subsets, learning structured information in an end-to-end fashion. We synthesize two MRI sequences with clinical relevance (i.e., double inversion recovery (DIR) and contrast-enhanced T1 (T1-c)), reconstructed from three com-mon sequences. In addition, we perform a multi-rater visual evaluation experiment and find that trained radiologists are unable to distinguish synthetic DIR images from real ones. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Resolving the black hole sphere of influence in a hyper-luminous obscured quasar at redshift 4.6

arXiv

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arXiv 2025年

作者： Liao, Mai Assef, Roberto J. Tsai, Chao-Wei Aravena, Manuel Aranda, Román Fernández Blain, Andrew W. Díaz-Santos, Tanio Eisenhardt, Peter González-López, Jorge Jun, Hyunsung D. Li, Xiaofeng Li, Guodong Martin, Lee R. Posses, Ana Shobhana, Devika Solimano, Manuel Stern, Daniel Vayner, Andrey Wu, Jingwen Zewdie, Dejene National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Beijing100101 China Chinese Academy of Sciences South America Center for Astronomy Chinese Academy of Sciences 20A Datun Road Beijing100101 China Instituto de Estudios Astrofísicos Facultad de Ingenieŕia y Ciencias Universidad Diego Portales Av. Ejército Libertador 441 Santiago8370191 Chile Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China University of Chinese Academy of Sciences Beijing100049 China Department of Physics University of Crete Heraklion70013 Greece Heraklion70013 Greece Physics & Astronomy University of Leicester LeicesterLE1 7RH United Kingdom School of Sciences European University Cyprus Diogenes street Engomi Nicosia1516 Cyprus Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Instituto de Astrofísica Facultad de Física Pontificia Universidad Católica de Chile 7820436 Chile Las Campanas Observatory Carnegie Institution of Washington Raúl Bitrán La Serena1200 Chile Department of Physics Northwestern College 101 7th St SW Orange CityIA51041 United States School of Physics Korea Institute for Advanced Study 85 Hoegiro Dongdaemun-gu Seoul02455 Korea Republic of School of Computer Science and Information Engineering Changzhou Institute of Technology Changzhou213002 China Kavli Institute for Astronomy and Astrophysics Peking University Beijing100871 China Centre for Space Research North-West University Potchefstroom2520 South Africa

Supermassive black holes (SMBHs) imprint gravitational signatures on the matter within their sphere of influence (SoI). Nuclear gas dynamics can hence be used to accurately measure the mass of an SMBH, yet such measurements remain elusive in the early Universe. We report the first dynamical measurement of an SMBH mass at z > 2, based on high spatial resolution observations of the [C ii] emission line that resolve the SoI in an obscured quasar at z = 4.6. The velocity dispersion rises radially toward the center, requiring the presence of a 6.3 ± 0.14 × 109 M☉ SMBH. We propose that obscured quasars allow [C ii] survivability in the inner regions, and may be ideal targets for increasing dynamical SMBH mass estimates in the early Universe. Copyright © 2025, The Authors. All rights reserved.

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A new coupled PARAFAC2 decomposition for joint processing of somatosensory evoked magnetic fields and somatosensory evoked electrical potentials 52

A new coupled PARAFAC2 decomposition for joint processing of...

引用

52nd Asilomar Conference on Signals, Systems and computers, ACSSC 2018

作者： Cheng, Yao Naskovska, Kristina Haardt, Martin Gotz, Theresa Haueisen, Jens Communications Research Laboratory Ilmenau University of Technology P.O. Box 10 05 65 IlmenauD-98684 Germany Institute of Medical Statistics Computer Sciences and Documentation University Hospital Jena Jena Germany Institute of Biomedical Engineering and Informatics Ilmenau University of Technology IlmenauD-98684 Germany

ISBN: (纸本)9781538692189

The application of tensor-based signal processing in EEG (Electroencephalogram) and MEG (Magnetoencephalogram) data analysis has received growing research interest. In particular, coupled tensor decompositions are regarded as a promising tool for the joint processing of simultaneously recorded EEG and MEG signals. Therefore, we develop a coupled version of the PARAFAC2 decomposition that has been employed for multi-way component analysis of EEG or MEG data separately. Then we discuss the application of this new coupled PARAFAC2 decomposition in the combined analysis of somatosensory evoked magnetic fields (SEFs) and somatosensory evoked electrical potentials (SEPs) to extract the time, frequency, and spatial features of these signals. © 2018 IEEE.

关键词： Electroencephalography

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A Universal Designated Multi-Verifier Transitive Signature Scheme 13th

A Universal Designated Multi-Verifier Transitive Signature S...

引用

13th International Conference on Information Securityand Cryptology, Inscrypt 2017

作者： Zhu, Fei Zhang, Yuexin Lin, Chao Wu, Wei Meng, Ru Fujian Provincial Key Laboratory of Network Security and Cryptology School of Mathematics and Informatics Fujian Normal University Fuzhou China School of Software and Electrical Engineering Swinburne University of Technology HawthornVIC3122 Australia School of Computer Science Shaanxi Normal University Xi’an China

ISBN: (纸本)9783319751597

A Universal Designated Verifier Transitive Signature (UDVTS) scheme is designed for the graph-based big data system. Specifically, it allows a transitive signature holder to convince the designated verifier with a transitive signature. Nevertheless, existing UDVTS schemes cannot be directly employed in the scenarios when multi-verifier are involved. Thus, in this paper, we extend the notion to the Universal Designated Multi-Verifier Transitive Signature (UDMVTS) scheme. Namely, our new scheme allows a transitive signature holder to designate the signature to multi-verifier. Furthermore, for the proposed scheme, we formalize its security notions and prove its security in the random oracle model. We also analyse the performance of our scheme to demonstrate its efficiency. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Graphic methods

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Erratum to: Search for heavy Higgs bosons decaying to a top quark pair in proton-proton collisions at s = 13 TeV

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Journal of High Energy Physics 2022年第3期2022卷 1-21页

作者： Sirunyan, A. M. Tumasyan, A. Adam, W. Ambrogi, F. Bergauer, T. Brandstetter, J. Dragicevic, M. Erö, J. Escalante Del Valle, A. Flechl, M. Frühwirth, R. Jeitler, M. Krammer, N. Krätschmer, I. Liko, D. Madlener, T. Mikulec, I. Rad, N. Schieck, J. Schöfbeck, R. Spanring, M. Spitzbart, D. Waltenberger, W. Wulz, C.-E. Zarucki, M. Drugakov, V. Mossolov, V. Suarez Gonzalez, J. Darwish, M. R. De Wolf, E. A. Di Croce, D. Janssen, X. Lauwers, J. Lelek, A. Pieters, M. Rejeb Sfar, H. Van Haevermaet, H. Van Mechelen, P. Van Putte, S. Van Remortel, N. Blekman, F. Bols, E. S. Chhibra, S. S. D’Hondt, J. De Clercq, J. Lontkovskyi, D. Lowette, S. Marchesini, I. Moortgat, S. Moreels, L. Python, Q. Skovpen, K. Tavernier, S. Van Doninck, W. Van Mulders, P. Van Parijs, I. Beghin, D. Bilin, B. Brun, H. Clerbaux, B. De Lentdecker, G. Delannoy, H. Dorney, B. Favart, L. Grebenyuk, A. Kalsi, A. K. Luetic, J. Popov, A. Postiau, N. Starling, E. Thomas, L. Vander Velde, C. Vanlaer, P. Vannerom, D. Cornelis, T. Dobur, D. Khvastunov, I. Niedziela, M. Roskas, C. Trocino, D. Tytgat, M. Verbeke, W. Vermassen, B. Vit, M. Zaganidis, N. Bondu, O. Bruno, G. Caputo, C. David, P. Delaere, C. Delcourt, M. Giammanco, A. Lemaitre, V. Magitteri, A. Prisciandaro, J. Saggio, A. Vidal Marono, M. Vischia, P. Zobec, J. Alves, F. L. Alves, G. A. Correia Silva, G. Hensel, C. Moraes, A. Rebello Teles, P. Belchior Batista Das Chagas, E. Carvalho, W. Chinellato, J. Coelho, E. Da Costa, E. M. Da Silveira, G. G. De Jesus Damiao, D. De Oliveira Martins, C. Fonseca De Souza, S. Huertas Guativa, L. M. Malbouisson, H. Martins, J. Matos Figueiredo, D. Medina Jaime, M. Melo De Almeida, M. Mora Herrera, C. Mundim, L. Nogima, H. Prado Da Silva, W. L. Sanchez Rosas, L. J. Santoro, A. Sznajder, A. Thiel, M. Tonelli Manganote, E. J. Torres Da Silva De Araujo, F. Vilela Pereira, A. Ahuja, S. Bernardes, C. A. Calligaris, L. Fernandez Perez Tomei, T. R. Gregores, E. M. Lemos, D. S. Mercadante, P. G. Novaes, S. F. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Wien Austria Vienna University of Technology Vienna Austria Institute for Nuclear Problems Minsk Belarus Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium IRFU CEA Université Paris-Saclay Gif-sur-Yvette France Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina Brazil Universidade Federal de Pelotas Pelotas Brazil Universidade Estadual Paulista São Paulo Brazil Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’ Moscow Russia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Joint Institute for Nuclear Research Dubna Russia Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Cairo University Cairo Egypt Academy of Scientific Research and Technolog

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The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting

arXiv

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arXiv 2023年

作者： Kofler, Florian Steinbauer, Felix Meissen, Felix Graf, Robert Ehrlich, Stefan K. Reinke, Annika Oswald, Eva Waldmannstetter, Diana Hoelzl, Florian Horvath, Izabela Turgut, Oezguen Shit, Suprosanna Bukas, Christina Yang, Kaiyuan Paetzold, Johannes C. de da Rosa, Ezequiel Mekki, Isra Vinayahalingam, Shankeeth Kassem, Hasan Zhang, Juexin Chen, Ke Weng, Ying Durrer, Alicia Cattin, Philippe C. Wolleb, Julia Sadique, M.S. Rahman, M.M. Farzana, W. Temtam, A. Iftekharuddin, K.M. Adewole, Maruf Anwar, Syed Muhammad Baid, Ujjwal Janas, Anastasia Kazerooni, Anahita Fathi LaBella, Dominic Bran Li, Hongwei Moawad, Ahmed W. Conte, Gian-Marco Farahani, Keyvan Eddy, James Sheller, Micah Pati, Sarthak Karagyris, Alexandros Aristizabal, Alejandro Bergquist, Timothy Chung, Verena Shinohara, Russell Takeshi Dako, Farouk Wiggins, Walter Reitman, Zachary Wang, Chunhao Liu, Xinyang Jiang, Zhifan Johanson, Elaine Meier, Zeke Familiar, Ariana Davatzikos, Christos Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Colen, Rivka R. Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-André Mahajan, Abhishek Mohan, Suyash Mongan, John Hess, Christopher Cha, Soonmee Villanueva-Meyer, Javier Colak, Errol Crivellaro, Priscila Jakab, Andras Fatade, Abiodun Omidiji, Olubukola Akinola Lagos, Rachel Olatunji, O.O. Khanna, Goldey Kirkpatrick, John Alonso-Basanta, Michelle Rashid, Arif Bornhorst, Miriam Nabavizadeh, Ali Lepore, Natasha Palmer, Joshua Porras, Antonio Albrecht, Jake Anazodo, Udunna Aboian, Mariam Calabrese, Evan Rudie, Jeffrey David Linguraru, Marius George Iglesias, Juan Eugenio Van Leemput, Koen Bakas, Spyridon Wiestler, Benedikt Ezhov, Ivan Piraud, Marie Menze, Bjoern H. Helmholtz AI Germany Technical University of Munich Germany Helmholtz Munich Germany Klinikum Rechts der Isar Munich Germany SETLabs Research GmbH Healthcare Technologies Munich Germany Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Institute of Clinical Neuroimmunology Faculty of Medicine Ludwig Maximilian University Munich Germany University of Zurich Switzerland Helmholtz Research Center Munich Germany Imperial College London United Kingdom Icometrix Leuven Belgium Department of Oral and Maxillofacial Surgery Radboudumc Netherlands Department of Artificial Intelligence Radboud University Netherlands MLCommons University of Nottingham Ningbo China Ningbo315100 China Center for medical Image Analysis and Navigation Department of Biomedical Engineering University of Basel Allschwil Switzerland Old Dominion University NorfolkVA23529 United States Lab Crestview Radiology Lagos Nigeria Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Yale University New HavenCT United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Duke University Medical Center Department of Radiation Oncology United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Department of Neuroradiology Technical University of Munich Munich Germany Mercy Catholic Medical Center DarbyPA United States Department of Radiology Mayo Clinic RochesterMN United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 U

A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with an already pathological scan. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantee for images featuring lesions. Examples include but are not limited to algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS inpainting challenge. Here, the participants explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later, it will be updated to summarize the findings of the challenge. The challenge is organized as part of the ASNR-BraTS MICCAI challenge. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Application of Data Mining Prediction of Electricity Deviation Flow Using Metode Backpropogation at PLN Binjai Area 1

Application of Data Mining Prediction of Electricity Deviati...

引用

1st Workshop on Environmental Science, Society, and technology, WESTECH 2018

作者： Pardede, A.M.H. Andrayani, Febi Wahyu Nurdin Abdullah, Dahlan Erliana, Cut Ita Dana, Raditya Danar Dikananda, Arif Rinaldi Fasya, Firmanda Rahaningsih, Nining Andriani, Meri Subekti, Purwo Elisa, Erlin Sohibun Noor Hasan Siregar, Muhammad Ponidi Winarno, Edy Ketut Sudarsana, I. College of Information and Computer Management Kaputama Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Departement of Information Technology STMIK IKMI Cirebon Indonesia Departement of Accounting Information System STMIK IKMI Cirebon Indonesia Department of Industrial Engineering Universitas Samudra Langsa Indonesia Department of Mechanical Engineering Universitas Pasir Pengaraian Riau Indonesia Department of Information System University of Putera Batam Batam Indonesia Department of Physics Educational Universitas Pasir Pengaraian Riau Indonesia Departement of Management Universitas Graha Nusantara Indonesia Department of Mechanical Engineering Universitas Muhammadiyah Surabaya Surabaya Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia Department of Religious Education Institut Hindu Dharma Negeri Denpasar Indonesia

PLN is the only state-owned electricity company that provides and regulates electricity to the public. However, in the provision of electricity to every community, there are many frauds or irregularities in utilization of electricity by irresponsible parties was found by PLN. Electricity deviation caused by irresponsible parties, like the theft of electricity can have a tremendous impact due to loss of electrical energy. Electricity Deviation, using Electricity Deviation data for 3 years, from 2012 to 2014. Sales data processed by the network, in the form of amount of Kwh deviation per month. The data needed in the process of analysis of this Neural Network is pure database results by taking the input variable in the form of Amount of Kwh Deviation and Amount of Kwh Deviation for the next Month for the following year as output. Electricity for 3 years is processed into Matlab using GUI facilities, to produce information on the predicted results of the Kwh Deviation in November 2014 to March, May, June, August, November, December 2015 experiencing has increase in Kwh, while in April July, September, and Okrober 2015 decreased. © Published under licence by IOP Publishing Ltd.

关键词： Data mining

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