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Analysis of classification and Naïve Bayes algorithm k-nearest neighbor in data mining

IOP Conference Series: Materials science and Engineering 2020年第1期725卷

作者： Lotar Mateus Sinaga Sawaluddin Saib Suwilo Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

Naïve Bayes is a prediction method that contains a simple probabilistic that is based on the application of the Bayes theorem (Bayes rule) with the assumption that the dependence is strong. K-Nearest Neighbor (K-NN) is a group of instance-based learning, K-NN is also a lazy learning technique by searching groups of k objects in training data that are closest (similar) to objects on new data or testing data. Classification is a technique in Data mining to form a model from a predetermined data set. Data mining techniques are the choices that can be overcome in solving this problem. The results of the two different classification algorithms result in the discovery of better and more efficient algorithms for future use. It is recommended to use different datasets to analyze comparisons of naïve bayes and K-NN algorithms. the writer formulates the problem so that the research becomes more directed. The formulation of the problem in this study is to find the value of accuracy in the Naïve Bayes and KNN algorithms in classifying data.

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Improving the accuracy of old and young face detection in the template matching method with Fuzzy Associative Memory (FAM)

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IOP Conference Series: Materials science and Engineering 2020年第1期725卷

作者： Rafika Sari Sembiring Syahril Efendi Saib Suwilo Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

Every human has a face pattern and certain characteristics even though identical twins, but the human face pattern still has its own distinctiveness as well as old face patterns and young face patterns even though the human face pattern is very diverse but for young and old face patterns will be a difference between one face and the other face. Face detection (face detection) is one of the initial stages that very important in face recognition that is used in biometric identification. Face detection can also be used to search or index face data from images or videos that contain faces of various sizes, positions, and backgrounds. Face detection (face detection) automatically with the help of a computer is a problem that is not easy because the human face has a high level of variability both intra-personal and extra-personal variability. This study shows that systems with template matching methods combined with FAM can successfully detect differences in human faces, 80% accuracy, 10% better by using ordinary template matching.

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Strawberry Optimization for Multi-Objective Knapsack Problem

Strawberry Optimization for Multi-Objective Knapsack Problem

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International Symposium on Computing and Networking Workshops (CANDARW)

作者： Yamato Mizobe Kei Ohnishi Akihiro Fujiwara Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Fukuoka Japan

In the present paper, we consider multi-objective knapsack problem, which is one of well-known multi-objective optimization problems, and propose an optimization algorithm based on a strawberry algorithm (SBA). The experimental results show that the proposed algorithm obtains a better set of Pareto solutions than the existing algorithm.

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On using Euler's Factorization Algorithm to Factor RSA Modulus

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Journal of Physics: Conference Series 2020年第1期1566卷

作者： M A Budiman M Zarlis O S Sitompul H Mawengkang Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

The RSA public key cryptosystem was among the first algorithms to implement the Diffie-Hellman key exchange protocol. At the core of RSA's security is the problem of factoring its modulus, a very large integer, into its prime factors. In this study, we show a step-by-step tutorial on how to factor the RSA modulus using Euler's factorization algorithm, an algorithm that belongs to the class of exact algorithms. The Euler's factorization algorithm is implemented in Python programming language. In this experiment, we also record the relation between the length of the RSA moduli and its factorization time. As a result, this study shows that the Euler's factorization algorithm can be used to factor small modulus of RSA, the correlation between the factoring time and the size of RSA modulus is directly proportional, and better selection of some Euler's parameters may lead to lower factoring time.

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Adaptive Moment Estimation To Minimize Square Error In Backpropagation Algorithm

Adaptive Moment Estimation To Minimize Square Error In Backp...

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International Conference of computer science and Information technology

作者： Roy Nuary Singarimbun Erna Budhiarti Nababan Opim Salim Sitompul Graduate School of Computer Science and Department of Information Technology Universitas Sumatera Utara Medan Indonesia

Backpropagation (BP) has long training time and slow convergence, i.e. in minimizing Mean Square Error (MSE). Adaptive Moment Estimation (ADAM) was employed to speed up training and to obtain rate of BP. Result shown ... 详细信息

ISBN: (数字)9781728154237

ISBN: (纸本)9781728154244

关键词： Artificial Neural Network (ANN) Gradient Descent Backpropagation MSE minimization Adaptive Moment Estimation (ADAM) back propagation Backpropagation algorithms quadratic errors Mean square error Moment of force Self tuning Training

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Enhanced solar photothermal effect of PANi fabrics with plasmonic nanostructures

arXiv

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arXiv 2020年

作者： Nga, Do T. Phan, Anh D. Lam, Vu D. Woods, Lilia M. Wakabayashi, Katsunori Institute of Physics Vietnam Academy of Science and Technology 10 Dao Tan Ba Dinh Hanoi10000 Viet Nam Phenikaa Institute for Advanced Study Artificial Intelligence Laboratory Faculty of Computer Science Materials Science and Engineering Phenikaa University Hanoi12116 Viet Nam Department of Nanotechnology for Sustainable Energy School of Science and Technology Kwansei Gakuin University Sanda Hyogo669-1337 Japan Graduate University of Science and Technology Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Hanoi Viet Nam Department of Physics University of South Florida TampaFL33620 United States

The photothermal energy conversion in hanging and floating polyaniline (PANi)-cotton fabrics is investigated using a model based on the heat diffusion equation. Perfect absorption and antireflection of wet hanging PANi-cotton fabrics cause quick transfer of total incident light into water confining nearly 100 % of the sunlight. As a result, a hanging membrane is found to have more attractive properties than a floating above water fabric. We find, however, that the photothermal properties of a floating PANi-cotton membrane can greatly be enhanced by dispersing TiN nanoparticles in the water below the fabric. The calculated temperature gradients for TiN nanoparticle solutions show that the absorbed energy grows with increasing the nanoparticle density and that the photothermal process occurs mostly near the surface. The collective heating effects depend on the size and density of nanoparticles, which can further be used to modulate the photothermal process. Copyright © 2020, The Authors. All rights reserved.

关键词： Cotton

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Effect of milling techniques on the particle characteristics of conductive Pr-substituted YBa2Cu3O7-y compound 5

Effect of milling techniques on the particle characteristics...

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5th Southeast Asia Conference on Thermoelectrics, SACT 2018

作者： Prayoonphokkharat, P. Wannasut, P. Sriprachuabwong, C. Tuantranont, A. Watcharapasorn, A. Department of Physics and Materials Science Faculty of Science Chiang Mai University Chiang Mai50200 Thailand Graduate School Chiang Mai University Chiang Mai50200 Thailand Department of Science Takpittayakhom School Tak63000 Thailand Center of Advanced Materials for Printed Electronics and Sensors Materials Science Research Center Faculty of Science Chiang Mai University Chiang Mai50200 Thailand Thailand Organic and Printed Electronics Innovation Center National Electronics and Computer Technology Center National Science and Technology Development Agency Klong LuangPathumthani12120 Thailand Center of Excellence in Materials Science and Technology Materials Science Research Center Chiang Mai University Chiang Mai50200 Thailand

In this work, the effects of milling techniques on Pr-substituted YBa2Cu3O7-y (YPrBCO) particles were investigated. The Pr-substituted YBa2Cu3O7-y powders were prepared by solid-state reaction method. The stoichiometric mixtures of Y2O3, BaCO3, CuO and Pr6O11 starting powders were calcined at 880 °C for 12 h in air to form respective compounds. The resulting products were milled for 4-12 h using the conventional ball milling technique and for 4 h using the high-energy planetary ball milling method. The phase and structure identification of powders were characterized by X-ray diffraction (XRD) technique. The microstructure and chemical composition were studied using scanning electron microscopy (SEM) with energy dispersive X-ray analysis (EDS). The XRD patterns indicated that the pure phase of YPrBCO powders was obtained. For this material system, the conventional ball mill technique gave particles having a relatively wide particle size distribution with a maximum size of ∼2 μm regardless of milling time. In contrast, the narrower particle size distribution was observed for the YPrBCO powder obtained from the high-speed ball milling method and the largest particle size did not exceed 100 nm. These results showed that the powder produced by the high-speed ball milling technique could have a potential use in colloidal solution for printed thermoelectric film. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Barium compounds

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Performance Evaluation of WMNs Using WMN-PSOHC-DGA Considering LDVM and RDVM Replacement Methods 34th

Performance Evaluation of WMNs Using WMN-PSOHC-DGA Consideri...

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34th International Conference on Advanced Information Networking and Applications, AINA 2020

作者： Barolli, Admir Ohara, Seiji Sakamoto, Shinji Takizawa, Makoto Barolli, Leonard Department of Information Technology Aleksander Moisiu University of Durres L.1 Rruga e Currilave Durres Albania Graduate School of Engineering Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Higashi-Ku Fukuoka811-0295 Japan Department of Computer and Information Science Seikei University 3-3-1 Kichijoji-Kitamachi Tokyo Musashino-shi180-8633 Japan Department of Advanced Sciences Faculty of Science and Engineering Hosei University Kajino-Machi Tokyo Koganei-Shi184-8584 Japan Department of Information and Communication Engineering Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Higashi-Ku Fukuoka811-0295 Japan

ISBN: (纸本)9783030440404

The Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure because they have many advantages such as low cost and increased high speed wireless Internet connectivity. In our previous work, we implemented a simulation system considering Particle Swarm Optimization (PSO), Hill Climbing (HC) and Distributed GA (DGA), called WMN-PSOHC-DGA. In this paper, we present the performance of WMNs using WMN-PSOHC-DGA simulation system considering Linearly Decreasing Vmax Method (LDVM) and Rational Decrement of Vmax Method (RDVM). Simulation results show that a good performance is achieved for RDVM compared with the case of LDVM. © 2020, Springer Nature Switzerland AG.

关键词： Particle swarm optimization (PSO)

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Input optics systems of the KAGRA detector during O3GK (vol 2023, 023F01, 2023)

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PROGRESS OF THEORETICAL AND EXPERIMENTAL PHYSICS 2023年第5期2023卷

作者： Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Bae, Y. Baiotti, L. Bajpai, R. Barton, M. A. Cannon, K. Cao, Z. Capocasa, E. Chan, M. Chen, C. Chen, K. Chen, Y. Chiang, C-I Chu, H. Chu, Y-K Eguchi, S. Enomoto, Y. Flaminio, R. Fujii, Y. Fujikawa, Y. Fukunaga, M. Fukushima, M. Furuhata, T. Gao, D. Ge, G-G Ha, S. Hagiwara, A. Haino, S. Han, W-B Hasegawa, K. Hattori, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hiranuma, Y. Hirata, N. Hirose, E. Hong, Z. Hsieh, B-H Huang, G-Z Huang, H-Y Huang, P. Huang, Y-C Huang, Y-J Hui, D. C. Y. Ide, S. Ikenoue, B. Imam, S. Inayoshi, K. Inoue, Y. Ioka, K. Ito, K. Itoh, Y. Izumi, K. Jeon, C. Jin, H-B Jung, K. Jung, P. Kaihotsu, K. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanbara, S. Kanda, N. Kang, G. Kataoka, Y. Kawaguchi, K. Kawai, N. Kawasaki, T. Kim, C. Kim, J. Kim, J. C. Kim, Ws Kim, Y-M Kimura, N. Kita, N. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A. K. H. Kotake, K. Kozakai, C. Kozu, R. Kumar, R. Kume, J. Kuo, C. Kuo, H-S Kuromiya, Y. Kuroyanagi, S. Kusayanagi, K. Kwak, K. Lee, H. K. Lee, H. W. Lee, R. Leonardi, M. Li, K. L. Lin, L. C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H. L. Liu, G. C. Luo, L-W Majorana, E. Marchio, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyazaki, Y. Miyo, K. Miyoki, S. Mori, Y. Morisaki, S. Moriwaki, Y. Nagano, K. Nagano, S. Nakamura, K. Nakano, H. Nakano, M. Nakashima, R. Nakayama, Y. Narikawa, T. Naticchioni, L. Negishi, R. Quynh, L. Nguyen Ni, W-T Nishizawa, A. Nozaki, S. Obuchi, Y. Ogaki, W. Oh, J. J. Oh, S. H. Ohashi, M. Ohishi, N. Ohkawa, M. Ohta, H. Okutani, Y. Okutomi, K. Oohara, K. Ooi, C. Oshino, S. Otabe, S. Pan, K-C Pang, H. Parisi, A. Park, J. Arellano, F. E. Pena Pinto, I. Sago, N. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sakuno, Y. Sato, S. Sato, T. Sawada, T. Sekiguchi, T. Sekiguchi, Y. Shao, L. Shibagaki, S. Shimizu, R. Shimoda, T. Shimode, K. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Son, E. J. Sotani, H. Sugimoto, R. Suresh, J. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Graduate School of Science and Technology Hirosaki University 3 Bunkyo-cho Hirosaki Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Republic of Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Road Danshui Dist. New Taipei City 25137 Taiwan Department of Physics

KAGRA, the underground and cryogenic gravitational-wave detector, was operated for its solo observation from February 25 to March 10, 2020, and its first joint observation with the GEO 600 detector from April 7 to April 21, 2020 (O3GK). This study presents an overview of the input optics systems of the KAGRA detector, which consist of various optical systems, such as a laser source, its intensity and frequency stabilization systems, modulators, a Faraday isolator, mode-matching telescopes, and a high-power beam dump. These optics were successfully delivered to the KAGRA interferometer and operated stably during the observations. The laser frequency noise was observed to limit the detector sensitivity above a few kilohertz, whereas the laser intensity did not significantly limit the detector sensitivity.

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Performance Improvement of Clustering Affinity Propagation Method using Principal Component Analysis

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Journal of Physics: Conference Series 2020年第1期1566卷

作者： Jasael Simanullang Muhammad Zarlis Elviawaty Muisa Zamzami Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

Affinity Propagation Method it is necessary to modify the algorithm by using Principal Component Analysis (PCA). PCA method is used to reduce the attributes or characteristics that are less influential on the data so that the most influential attributes are obtained to then be carried out the clustering process with Affinity Propagation. The comparison results of the PCA + AP grouping model have better performance than the conventional AP grouping model. This is justified because the number of iterations and clusters produced by the PCA + AP clustering model does not change and converges when there are 8 optimal cluster clusters. While the performance of conventional clustering models produces an optimal number of clusters from 14 clusters with a significant number of iterations. So it can be concluded that the PCA + AP grouping model is suitable for the Air Quality dataset because it produces an optimal number of clusters and iterations of 8 clusters. The comparison results of the PCA + AP grouping model have better performance than the conventional AP grouping model. This is justified because the number of iterations and clusters produced by the PCA + AP clustering model does not change and converges when the optimal number of clusters is 5 clusters. While the performance of conventional clustering models produces a suboptimal number of 10 clusters with a significant number of iterations. So it can be concluded that the PCA + AP grouping model is suitable for the Water Quality Status dataset because it produces an optimal number of clusters and 5 cluster repetitions.

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