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检索条件"任意字段=2nd International Conference on Advanced Data and Information Engineering, DaEng 2015"

共 744 条记录，以下是61-70 订阅

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Vehicle Logo Detection Based on Modified YOLOv2 2nd

Vehicle Logo Detection Based on Modified YOLOv2

引用

2nd EAI international conference on Robotic Sensor Networks, ROSENET 2018

作者： Yang, Shuo Bo, Chunjuan Zhang, Junxing Wang, Meng College of Electromechanical Engineering Dalian Minzu University Dalian China College of Information and Communication Engineering Dalian Minzu University Dalian China Key Laboratory of Intelligent Perception and Advanced Control of State Ethnic Affairs Commission Dalian Minzu University Dalian China

ISBN: (纸本)9783030177621

Vehicle logo detection technology is one of the research directions in the application of intelligent transportation systems. It is an important extension of detection technology based on license plates and motorcycle types. A vehicle logo is characterized by uniqueness, conspicuousness, and diversity. Therefore, thorough research is important in theory and application. Although numerous vehicle logo detection methods exist, most of them cannot achieve real-time detection for different scenes. The YOLOv2 network is improved by constructing the data of a vehicle logo, dimension clustering of the bounding box, reconstructing network pre-training, and multi-scale detection training. This work implements fast and accurate vehicle logo detection. The generalization of the detection model and anti-interference capability in real scenes are optimized by data enrichment. The experimental results show that the accuracy and speed of the detection algorithm are improved. © 2020, Springer Science and Business Media Deutschland GmbH. All rights reserved.

关键词： License plates (automobile)

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Complement Attributes Reduction in Soft Sets for Decision Making 1

引用

2nd international conference on advanced data and information engineering, daeng 2015

作者： Mohammed, Mohammed Adam Taheir Mohd, Wan Maseri Wan Arshah, Ruzaini Abdullah Mungad, M. Sutoyo, Edi Chiroma, Haruna Faculty of Computer System & Software Engineering Universiti Malaysia Pahang Lebuhraya TunRazak KuantanPahang Malaysia Department of Information System University of Malaya Pantai Valley Kuala Lumpur50603 Malaysia Department of Artificial Intelligence University of Malaya Pantai Valley Kuala Lumpur50603 Malaysia Gombe Nigeria

ISBN: (数字)9789811317996

ISBN: (纸本)9789811317972

This paper overcome the false parameters from soft set which focuses on original decision partitions order whereas in some cases the decision partition order not induced original set extensions or the reductions of original set maybe not significant. Based on the priority the decision partition order can be constructed to match whether the sub set is the core of optimal decision or deciding for removing a particular sub set if their complement decision partition order status is match the original classifications. The proposed method has successfully maintained the optimal and sub optimal result. This method overcome the false parameters because it directly forwarded to reduction set. It has been validated that it is effective for parameter reduction even in large sub sets. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Decision making

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Applying Recurrent Neural Network for Passenger Traffic Forecasting 2nd

Applying Recurrent Neural Network for Passenger Traffic Fore...

引用

2nd international conference on Computer Science, engineering and Education Applications, ICCSEEA 2019

作者： Hu, Zhengbing Dychka, Ivan Oleshchenko, Liubov Kukharyev, Sergiy School of Educational Information Technology Central China Normal University Wuhan China National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv Ukraine

ISBN: (纸本)9783030166205

The article represents the analysis of neural networks that can be used to predict passenger traffic between cities. Passenger data nonstationary timetable is considered. A class of recurrent neural networks (RNN) have also been considered, among which the expediency of using the Long Short-Term Memory (LSTM) neural network for analysis and prediction of passenger traffic on the interurban route investigated is selected and substantiated. The stages of the research are represented. The data of the Ukrainian motor transport enterprise for 2007–2015 were used for the experiment. The study uses static methods for predicting the moving average, exponential smoothing of Holt-Winters, linear and logarithmic trends for verification and comparison of forecast accuracy with various methods. © 2020, Springer Nature Switzerland AG.

关键词： Long short-term memory

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Prevalence of machine learning techniques in software defect prediction 2nd

Prevalence of machine learning techniques in software defect...

引用

2nd international conference on Computer Science and Cyber Security, ICONCS 2020

作者： Sohan, Md Fahimuzzman Kabir, Md Alamgir Rahman, Mostafijur Bhuiyan, Touhid Jabiullah, Md Ismail Felix, Ebubeogu Amarachukwu Daffodil International University Dhaka Bangladesh University of Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9783030528553

Software Defect Prediction (SDP) is a popular research area which plays an important role for software quality. It works as an indicator of whether a software module is defect-free or defective. In this study, a review has been conducted from January 2015 to August 2019 and 165 articles are selected in the area of SDP to know the prevalence of Machine Learning (ML) techniques. These articles are collected by searching in Google Scholar, and they are published in various platforms (e.g., IEEE, Springer, Elsevier). Firstly the information has been extracted from the collected particles, and then the information has been pre-processed, categorized, visualized, and finally, the results have been reported. The result shows the most frequently used data sets, classifiers, performance metrics, and techniques in SDP. This investigation will help to find the prevalence of ML techniques in SDP and give a quick view to understand the trends of ML techniques in defect prediction research. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2020.

关键词： Defects

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Gamma-ray burst observation & gravitational wave event follow-up with CALET on the international Space Station 37

Gamma-ray burst observation & gravitational wave event follo...

引用

37th international Cosmic Ray conference, ICRC 2021

作者： Adriani, O. Akaike, Y. Asano, K. Asaoka, Y. Berti, E. Bigongiari, G. Binns, W.R. Bongi, M. Brogi, P. Bruno, A. Buckley, J.H. Cannady, N. Castellini, G. Checchia, C. Cherry, M.L. Collazuol, G. Ebisawa, K. Ficklin, A.W. Fuke, H. Gonzi, S. Guzik, T.G. Hams, T. Hibino, K. Ichimura, M. Ioka, K. Ishizaki, W. Israel, M.H. Kasahara, K. Kataoka, J. Kataoka, R. Katayose, Y. Kato, C. Kawanaka, N. Kawakubo, Yuta Kobayashi, K. Kohri, K. Krawczynski, H.S. Krizmanic, J.F. Maestro, P. Marrocchesi, P.S. Messineo, A.M. Mitchell, J.W. Miyake, S. Moiseev, A.A. Mori, M. Mori, N. Motz, H.M. Munakata, K. Nakahira, S. Nishimura, J. de Nolfo, G.A. Okuno, S. Ormes, J.F. Ospina, N. Ozawa, S. Pacini, L. Papini, P. Rauch, B.F. Ricciarini, S.B. Sakai, K. Sakamoto, T. Sasaki, M. Shimizu, Y. Shiomi, A. Spillantini, P. Stolzi, F. Sugita, S. Sulaj, A. Takita, M. Tamura, T. Terasawa, T. Torii, S. Tsunesada, Y. Uchihori, Y. Vannuccini, E. Wefel, J.P. Yamaoka, K. Yanagita, S. Yoshida, A. Yoshida, K. Zober, W.V. Department of Physics University of Florence Via Sansone 1 Fiorentino Sesto50019 Italy INFN Sezione di Florence Via Sansone 1 Fiorentino Sesto50019 Italy Waseda Research Institute for Science and Engineering Waseda University 17 Kikuicho Shinjuku Tokyo162-0044 Japan JEM Utilization Center Human Spaceflight Technology Directorate Japan Aerospace Exploration Agency 2-1-1 Sengen Ibaraki Tsukuba305-8505 Japan Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwa-no-Ha Chiba Kashiwa277-8582 Japan Department of Physical Sciences Earth and Environment University of Siena via Roma 56 Siena53100 Italy INFN Sezione di Pisa Polo Fibonacci Largo B. Pontecorvo 3 Pisa56127 Italy Department of Physics McDonnell Center for the Space Sciences Washington University One Brookings Drive St. LouisMO63130-4899 United States Heliospheric Physics Laboratory NASA/GSFC GreenbeltMD20771 United States Department of Physics Catholic University of America WashingtonDC20064 United States Center for Space Sciences and Technology University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Astroparticle Physics Laboratory NASA GSFC GreenbeltMD20771 United States Center for Research and Exploration in Space Sciences and Technology NASA GSFC GreenbeltMD20771 United States Via Madonna del Piano 10 Sesto Fiorentino50019 Italy Department of Physics and Astronomy Louisiana State University 202 Nicholson Hall Baton RougeLA70803 United States Department of Physics and Astronomy University of Padova Via Marzolo 8 Padova35131 Italy INFN Sezione di Padova Via Marzolo 8 Padova35131 Italy Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo Kanagawa Sagamihara252-5210 Japan Kanagawa University 3-27-1 Rokkakubashi Kanagawa Kanagawa Yokohama221-8686 Japan Faculty of Science and Technology Graduate School of Science and Technology Hirosaki Unive

The CALorimetric Electron Telescope (CALET) has been observing high-energy cosmic rays and gamma-rays on the international Space Station since October 2015. The CALET gamma-ray burst monitor (CGBM), mounted on CALET to observe prompt emissions of gamma-ray bursts (GRBs) in the hard X-ray and soft gamma-ray band, has been monitoring all-sky with ∼ 60 % duty cycle without any problems since October 2015. As of end May 2021, CGBM has detected 254 GRBs, including 31 short GRBs, thanks to the onboard trigger system. The Calorimeter (CAL), the primary instrument of CALET, has also collected gamma-ray data in the energy range from 1 GeV to 10 TeV while maintaining both instruments in good condition. We continue searching for high-energy gamma-rays from GRBs detected by CGBM, and have found two possible gamma-rays from GRBs. As described above, CALET can detect prompt emissions and high energy gammaray emission of GRBs. Therefore, we also have actively participated in the follow-up campaign for electromagnetic counterparts of the gravitational wave events observed by LIGO/Virgo since the operation start of the CALET. Although we have found no candidates of electromagnetic counterparts of the gravitational wave events, we have derived upper limits of the high-energy gamma-ray flux for 26 events in the LIGO/Virgo third observation run. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 international License (CC BY-NC-nd 4.0)

关键词： Gamma rays

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Concept Based Lattice Mining (CBLM) Using Formal Concept Analysis (FCA) for Text Mining 1

引用

2nd international conference on advanced data and information engineering, daeng 2015

作者： Hassan, Hasni Mohd Saman, Md. Yazid Abdullah, Zailani Mohamad, Mumtazimah Terengganu Malaysia Terengganu Malaysia

ISBN: (数字)9789811317996

ISBN: (纸本)9789811317972

Extracting relevant resources according to a query is imperative due to the factors of time and accuracy. This study proposes a model that enables query matching using output lattices from Formal Concept Analysis (FCA) tool, based on Graph Theory. The deployment of FCA concept lattices ensures that the matching is done based on extracted concepts;not just mere keywords matching hence producing more relevant results. The focus of this study is on the method of Concept Based Lattice Mining (CBLM) where similarities among output lattices will be compared using their normalized adjacency matrices, utilizing a distance measure technique. The corresponding trace values obtained determines the degree of similarities among the lattices. An algorithm for CBLM is proposed and preliminary experimentation demonstrated promising results where lattices that are more similar have smaller trace values while higher trace values indicates greater dissimilarities among the lattices. © Springer Nature Singapore Pte Ltd. 2019.

关键词： data mining

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An Alternative Algorithm for Soft Set Parameter Selection Using Special Order 1

引用

2nd international conference on advanced data and information engineering, daeng 2015

作者： Mohammed, Mohammed Adam Taheir Mohd, Wan Maseri Wan Arshah, Ruzaini Abdullah Mungad, M. Sutoyo, Edi Chiroma, Haruna Faculty of Computer System & Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak KuantanPahang Malaysia Department of Information System University of Malaya Pantai Valley Kuala Lumpur50603 Malaysia Department of Artificial Intelligence University of Malaya Pantai Valley Kuala Lumpur50603 Malaysia Gombe Nigeria

ISBN: (数字)9789811317996

ISBN: (纸本)9789811317972

The outcome of the reduction of soft data is dependent on the quality and discount evidence that increases with optimization analysis. There is a set of techniques that can be used to reduce the data, but the different techniques showed different results as each technique is focused on solving a particular problem. This paper proposed a parameter reduction algorithm, known as 3C algorithm, to circumvent the false frequent object in reduction. Results indicated that the proposed algorithm is easy to implement and perform better than the state-of-the-art parameter reduction algorithm. Also, the proposed algorithm can be used as an effective alternative method for reducing parameters in order to enhance the decision-making process based on decision partition order. Comparative analysis were performed between the proposed algorithm and the state-of-the-art parameter reduction algorithm using several soft set in terms of parameter reduction. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Knowledge management

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Adaptive Priority Scheduling for Downlink WiMAX Networks 1

引用

2nd international conference on advanced data and information engineering, daeng 2015

作者： Shareef, Zayd Ashraf Hussin, Masnida Faculty of Computer Science and Information Technology Department of Communication Technology and Networking University Putra Malaysia Kembangan Malaysia

ISBN: (数字)9789811317996

ISBN: (纸本)9789811317972

Providing quality assurances for various services such as VoIP, FTP and Browsing remain the key interest for IEEE 802.16 WiMaX networks. A scheduling pattern which fulfils QoS constraints becomes more significant for wireless infrastructures. We present a downlink scheduling system titled as adaptive priority scheduling for Downlinks (APSD) that provides QoS assurances in WiMaX networks. APSD embraces two main modules, which are resource management and priority assignment. Diverse types of services mainly rely on its’ QoS desires to modify the priority assignments and fetch the bandwidth properties dynamically. Starvation avoidance and allocating resource are both considered. The Simulation results illustrate that APSD scheme has more substantial performance in terms of average throughput and delay than the original existed scheme. In addition, our APSD has notable advantages over FIFO and SCSA methodologies in Quality of Service satisfaction and sustaining fairness in prevention mode of starvation. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Quality of service

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Multi-dimensional K-Means Algorithm for Student Clustering 1

引用

2nd international conference on advanced data and information engineering, daeng 2015

作者： Mohd, Wan Maseri Wan Beg, A.H. Herawan, Tutut Noraziah, Ahmad Chiroma, Haruna Faculty of Computer Systems & Software Engineering University Malaysia Pahang Lebuhraya Tun Razak Gambang KuantanPahang26300 Malaysia

ISBN: (数字)9789811317996

ISBN: (纸本)9789811317972

K-Means is one of the popular methods for generating clusters. It is very well-known and commonly used for its convenience and fastness. The main disadvantage of these criteria is that user should specify the number of cluster in enhance. As a repetitive clustering strategy, a K-Means criterion is very delicate to the preliminary beginning circumstances. In this paper, has been proposed a clustering strategy known as Multi-dimensional K-Means clustering criteria. This algorithm auto generates preliminary k (the preferred variety of cluster) without asking input from the user. It also used a novel strategy of establishing the preliminary centroids. The experiment of the proposed strategy has been conducted using synthetic data, which is taken form LIyod’s K-means experiments. The algorithm is suited for higher education for calculating the student’s CGPA and extracurricular activities with graphs. © Springer Nature Singapore Pte Ltd. 2019.

关键词： data mining

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Short-Term Time Series Modelling Forecasting Using Genetic Algorithm 1

引用

2nd international conference on advanced data and information engineering, daeng 2015

作者： Haviluddin Alfred, Rayner Department of Informatics Faculty of Computer Science and Information Technology Universitas Mulawarman Samarinda Indonesia Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Kota KinabaluSabah88999 Malaysia

ISBN: (数字)9789811317996

ISBN: (纸本)9789811317972

The prediction analysis of a network traffic time series dataset in order to obtain a reliable forecast is a very important task to any organizations. A time series data can be defined as an ordered sequence of values of a variable at equally spaced time intervals. By analyzing these time series data, one will be able to obtain an understanding of the underlying forces and structure that produced the observed data and apply this knowledge in modelling for forecasting and monitoring. The techniques used to analyze time series data can be categorized into statistical and machine learning techniques. It is easy to apply a statistical technique [e.g., Autoregressive Integrated Moving Average (ARIMA)] in order to analyze time series data. However, applying a genetic algorithm in learning a time series dataset is not an easy and straightforward task. This paper outlines and presents the development of genetic algorithms (GA) that are used for analyzing and predicting short-term network traffic datasets. In this development, the mean squared error (MSE) is taken and computed as the fitness function of the proposed GA based prediction task. The results obtained will be compared with the performance of one of the statistical techniques called ARIMA. This paper is concluded by recommending some future works that can be applied in order to improve the prediction accuracy. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Forecasting

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