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Fachtagung des GI-Fachbereichs Softwaretechnik, Software Engineering 2020 - Conference of the GI Special Interest Group on Software Engineering, Software Engineering 2020

作者： Ghofrani, Javad Kozegar, Ehsan Bozorgmehr, Arezoo Soorati, Mohammad Divband HTW Dresden Faculty of Mathematic/Informatics Friedrich-List-Platz 1 Dresden01069 Germany University of East Guilan Department of computer science Guilan Iran General Practice and family medicine University Hospital Bonn Bonn Germany School of Electronics and Computer Science University of Southampton United Kingdom

ISBN: (纸本)9783885796947

This manuscript summarizes our empirical study on reuse in the context of Artificial Neural Networks (ANNs) extensively described in [Gh19b] as a paper with the same title, presented in 2nd International Workshop on Experiences and Empirical Studies on Software Reuse (WEESR 2019). © 2020 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Reusability

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Comparative Analysis of Efficient Image Segmentation Technique for Text Recognition and Human Skin Recognition 7

Comparative Analysis of Efficient Image Segmentation Techniq...

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7th International Conference on Global Optimization and Its Application 2018, ICoGOIA 2018

作者： Cahyadi, Septian Damatraseta, Febri Sugara, Victor Ilyas Faculty of Mathematic and Natural Science Pakuan University Bogor Indonesia Department of Computer Science Faculty of Master Budi Luhur Universit Jakarta Indonesia

computer Vision and Pattern Recognition is one of the most interesting research subject on computer science, especially in case of reading or recognition of objects in real-time from the camera device. Object detection has wide range of segments, in this study we will try to find where the better methodologies for detecting a text and human skin. This study aims to develop a computer vision technology that will be used to help people with disabilities, especially illiterate (tuna aksara) and deaf (penyandang tuli) to recognize and learn the letters of the alphabet (A-Z). Based on our research, it is found that the best method and technique used for text recognition is Convolutional Neural Network with achievement accuracy reaches 93%, the next best achievement obtained OCR method, which reached 98% on the reading plate number. And also OCR method are 88% with stable image reading and good lighting conditions as well as the standard font type of a book. Mean while, best method and technique to detect human skin is by using Skin Color Segmentation: CIELab color space with accuracy of 96.87%. While the algorithm for classification using Convolutional Neural Network (CNN), the accuracy rate of 98%. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Image segmentation

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A hybrid data mining model for Indonesian telematics SMEs empowerment 3

A hybrid data mining model for Indonesian telematics SMEs em...

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3rd Indonesian Operations Research Association - International Conference on Operations Research 2018, IORA-ICOR 2018

作者： Tosida, E.T. Andria, F. Wahyudin, I. Widianto, R. Ganda, M. Lathif, R.R. Computer Science Department Faculty of Mathematic and Natural Science Universitas Pakuan Bogor Indonesia Management Department Faculty of Economy Universitas Pakuan Bogor Indonesia

The power of information technology and communication (telematics) is one of the vital forces for every country. In the Industrial Revolution 4.0 era, the development of telematics was one of the priorities of the Indonesian government nawacitas. The development of the field of telematics in Indonesia for a decade is inseparable from the role of SMEs. The role of telematics SMEs in the strength of national development can be mapped through the optimization of National Economic Census data (Susenas). The detailed 2016 Susenas data has not been released by BPS. Therefore, this research still uses 2006 Susenas data. The 2016 Susenas recapitalization shows that Indonesian telematics has a very large power, consisting of 2.6 million players. This great strength needs to be optimized to have high competitiveness so as to be able to support Indonesia's development. The purpose of this study was to conduct hybrid data mining modeling to be used as a decision model in mapping the classification of Indonesian telematics SMEs. The classification map includes the feasibility of assistance for the empowerment of Indonesian telematics SMEs, business prospects and development plans for Indonesian telematics SMEs. The hybrid data mining model with K-Medoids & C4.5 technique shows better performance compared to other models, with an average accuracy rate of 71.87%. This model validation test also involves K-fold cross validations. © Published under licence by IOP Publishing Ltd.

关键词： Telematics

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A normalized cross-correlation convolutional neural network (CNN-NCC) for exemplar-based object detection

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AIP Conference Proceedings 2023年第1期2734卷

作者： Yaya Wihardi Winda M. Kristy Erlangga Arjon Turnip Intan N. Yulita Endroyono Computer Science Education Department Faculty of Mathematic and Natural Science Education Universitas Pendidikan Indonesia Bandung Indonesia Artificial Intelligence and Big Data Research Center Padjadjaran University Sumedang Indonesia Department of Electrical Engineering Sepuluh Nopember Institute of Technology Surabaya Indonesia

Object detection in dynamic environments, such as CCTV surveillance systems and robotics, requires a special approach. New objects that have not been previously trained to the model are often captured by sensors, and it is very important to recognize them in the future. One approach that can be used to solve this problem is an exemplar-based approach. On this study, we try to build an exemplar-based object detector based on Convolutional Neural Network with Normalized Cross Correlation matching. Our proposed method did not need to re-train the model to identify unknown objects that had not been acknowledged by the model during the training stage. The results show that the model achieves 0.449 mAP on the COCO dataset.

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Combined group method of data handling models using artificial bee colony algorithm in time series forecasting 16

Combined group method of data handling models using artifici...

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16th International Learning and Technology Conference, L and T 2019

作者： Yahya, Nurhaziyatul A. Samsudin, Ruhaidah Shabri, Ani Saeed, Faisal School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Johor81310 Malaysia School of Mathematic Faculty of Science Universiti Teknologi Malaysia Johor Bahru Johor81310 Malaysia Colleague of Computer Science and Engineering Taibah University Medina Saudi Arabia

Time series forecasting is an important area of forecasting which has gained many attentions from various research areas. In line with its popularity, various models have been introduced for the purpose of producing accurate time series forecasts. Nevertheless, it is difficult to find an ideal model as there is no model that can perform best for all types of data. Recently, combination of forecasts has gained immense popularity in the time series forecasting area. Among the well-known combination technique is the weighted-based approach, where appropriate weights are given based on the performance of each individual model. In this paper, a robust methodology for accurate time series forecasting based on Artificial Bee Colony (ABC) algorithm and four Group Method of Data Handling (GMDH) models, namely GMDH-Polynomial, GMDH-Radial Basis Function, GMDH-Sigmoid and GMDH-Tangent are proposed. In this methodology, the first step was to develop forecasts using individual GMDH models. In the second step, weights for each individual GMDH were calculated heuristically using ABC algorithm. In the final step, the results were aggregated to form a new forecast. For the purpose of evaluating the performance of the proposed model, the model was applied to a real time series data which is the monthly tourist arrivals from Singapore to Malaysia from year 2000 to 2017. Based on the empirical results, the application of nonlinear transfer function such as Tangent function has the potential to improve the performance of GMDH model, while the other functions such as Sigmoid function produced a forecast which is worse than the conventional GMDH-Polynomial. However, the combination of GMDH models using ABC managed to outperform all the individual models. The empirical finding demonstrated the efficiency of Combined GMDH model in time series forecasting, as well as the applicability of implementing optimization algorithm to find appropriate weights for the individual models. © 2019 The Aut

关键词： Time series

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Develop learning methods imr (integrated material realistic) alternative mathematics learning during a pandemic

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Journal of Physics: Conference Series 2021年第4期1918卷

作者： I.M. Rusmana Zainuri Wardono A.L Nulhakim Doctoral Program Universitas Negeri Semarang Indonesia Department of Mathematic Education Faculty of Mathematics and Natural Science Universitas Indraprasta PGRI Jakarta Indonesia Department of Mathematic Education Faculty of Mathematics and Natural Science Universitas Negeri Semarang Indonesia Department of Informatics Engineering Faculty of Engineering and Computer Science Universitas Indraprasta PGRI Jakarta Indonesia

The learning methods used by teachers in schools in Serang City are very ineffective, especially during the pandemic. This is because not integrated content of material in one subject with other subjects. Never mind between subjects, sometimes the material is not well integrated. The purpose of this study is to provide an alternative learning method during a pandemic, namely a method that integrates material with the realities of life which is abbreviated as IMR (Integrated Realistic Material). The method in this research is Research and Development (RnD) with the ADDIE model. In making this learning method according to the stages from the ADDIE study. The results of this study are in the form of learning methods that are integrated with the realities of life during the pandemic.

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Design mathematical model for studying properties thin layers

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Journal of Physics: Conference Series 2020年第1期1530卷

作者： Enas Y Abdullah Ali M Abdulameer Department of Mathematic Faculty of Education Kufa University Department of Mathematic Faculty of Computer Science & Mathematics Kufa University

In this paper, highlighted the study of properties thin films of squeeze film characteristics The Reynolds equation a commutating for speed molecules and squeeze action is mathematically derived. The Reynolds equation is disband for the hydrodynamic pressure and the bearing characteristics, such as load carrying capacity and time approach. It is found increases speed molecules for first layers lead to decrease hydrodynamic pressure and load compared to final layers case.

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Design of an optical biosensor based on photonic crystal at THz frequency

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Journal of Physics: Conference Series 2020年第2期1567卷

作者： T P Negara Computer Science Division Faculty of Mathematic and Natural Science Universitas Pakuan Bogor

We discuss the transmission characteristics of photonic crystal with two defect illuminated by a continuous wave of Transverse Electric (TE) mode at low terahertz frequency. To study its performance we solve numerically the corresponding Maxwell equations by means of finite difference frequency domain method. By varying the angle of incident, width of defect layer and refractive index in the second defect layer, it is found that the device exhibits a significant transmission enhancement at two wavelengths with typical changes. We also demonstrate its potential applications for biosensor platform

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Combined group method of data handling models using artificial bee colony algorithm in time series forecasting

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Procedia computer science 2019年 163卷 319-329页

作者： Nurhaziyatul A. Yahya Ruhaidah Samsudin Ani Shabri Faisal Saeed School of Mathematic Faculty of Science Universiti Teknologi Malaysia 81310 Johor Bahru Johor Malaysia Colleague of Computer Science and Engineering Taibah University Medina Saudi Arabia

关键词： Artificial bee colony combined models forecasting group method of data handling time series weighted-based

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Multi prime numbers principle to expand implementation of CRT method on RSA algorithm

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AIP Conference Proceedings 2021年第1期2331卷

作者： Nanang Triagung Edi Hermawan Edi Winarko Ahmad Ashari 1Indonesian Nuclear Energy Regulatory Agency Jakarta Indonesia 2Department of Computer Science and Electronic Faculty of Mathematic and Natural Science Gadjah Mada University Yogyakarta Indonesia

One of the uses of the Chinese Remainder Theorem (CRT) is to shorten the time in the RSA Cryptosystem decryption process. However, in applying a combination of the CRT method and the RSA Algorithm with Python programming modules, recursion errors often occurred when determining the inverse modulus to establish the private key. The error was caused by limitation iterations of the related algorithm. Some Python library needs more iteration processes to calculate the inverse modulus for larger key size. This is a pure computational problem. The standard RSA algorithm uses two prime numbers to generate their key pairs. The multi-prime RSA modification based on more than two prime numbers can be applied to solve the problem. A modified RSA was applied in Python programming that generates keys based on 2, 4, 8, 16, and 32 prime numbers. Regarding on our experiments, the combination of the CRT method and RSA algorithm based on four prime numbers can be applied without recursion error up to a key length of 6280 bits, with eight primes up to 12,560 bits, with sixteen primes up to 25,120 bits, and with thirty-two primes up to 50,240 bits. Implementation of more multi-prime numbers can expand to combine the CRT method and RSA Algorithm for longer key sizes by approximation relation y = 1567,8x + 63,333, which y is the key size and x is the number of primes numbers. The method also can increase key generation and decryption rate significantly.

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