Lecture Notes in Networks and Systems 97 Leonard Barolli Peter Hellinckx Tomoya Enokido Editors Advances on Broad-Band Wireless Computing, Communication and Applications Proceedings of the 14th International Conference on Broad-Band Wireless Computing, Communication and Applications (BWCCA-2019) Lecture Notes in Networks and Systems Volume 97 Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas— UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA; Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada; Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong www.TechnicalBooksPDF.com The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality Original research reported in proceedings and post-proceedings represents the core of LNNS Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them ** Indexing: The books of this series are submitted to ISI Proceedings, SCOPUS, Google Scholar and Springerlink ** More information about this series at http://www.springer.com/series/15179 www.TechnicalBooksPDF.com Leonard Barolli Peter Hellinckx Tomoya Enokido • • Editors Advances on Broad-Band Wireless Computing, Communication and Applications Proceedings of the 14th International Conference on Broad-Band Wireless Computing, Communication and Applications (BWCCA-2019) 123 www.TechnicalBooksPDF.com Editors Leonard Barolli Department of Information and Communication Engineering Fukuoka Institute of Technology Fukuoka, Japan Peter Hellinckx Department of Electronics University of Antwerp Antwerp, Belgium Tomoya Enokido Rissho University Tokyo, Japan ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-030-33505-2 ISBN 978-3-030-33506-9 (eBook) https://doi.org/10.1007/978-3-030-33506-9 © Springer Nature Switzerland AG 2020 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland www.TechnicalBooksPDF.com Welcome Message of BWCCA-2019 International Conference Organizers Welcome to the 14th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA-2019), which will be held in conjunction with the 14th 3PGCIC-2019 International Conference from November to 9, 2019, at University of Antwerp, Antwerp, Belgium This International Conference is a forum for sharing ideas and research work in the emerging areas of broadband and wireless computing Information networks of today are going through a rapid evolution Different kinds of networks with different characteristics are emerging and they are integrating into heterogeneous networks For these reasons, there are many interconnection problems which may occur at different levels of the hardware and software design of communicating entities and communication networks These kinds of networks need to manage an increasing usage demand, provide support for a significant number of services, guarantee their QoS and optimize the network resources The success of all-IP networking and wireless technology has changed the ways of living people around the world The progress of electronic integration and wireless communications is going to pave the way to offer people the access to the wireless networks on the fly, based on which all electronic devices will be able to exchange the information with each other in a ubiquitous way whenever necessary The aim of this conference is to present the innovative research and technologies as well as developments related to broadband networking and mobile and wireless communications This edition BWCCA-2019 received 142 paper submissions, and based on review results, we accepted 41 papers (about 29% acceptance ratio) for presentation in the conference and publication by Springer in Lecture Notes in Networks and Systems Proceedings The organization of an International Conference requires the support and help of many people A lot of people have helped and worked hard to produce a successful BWCCA-2019 technical program and conference proceedings First, we would like to thank all authors for submitting their papers, Program Committee Members and reviewers who carried out the most difficult work by carefully evaluating the submitted papers v www.TechnicalBooksPDF.com vi Welcome Message of BWCCA-2019 International Conference Organizers This year in conjunction with BWCCA-2019, we have seven International Workshops that complemented BWCCA-2019 program with contributions for specific topics We would like to thank the Workshop Co-Chairs and all workshops organizers for organizing these workshops We thank Web Administrators Co-Chairs and Finance Chair for their excellent work We would like to express our gratitude to Honorary Co-Chairs of BWCCA-2019 for their support and help We give special thanks to Keynote Speakers of BWCCA-2019 and Local Arrangement Team of University of Antwerp for making excellent local arrangement for the conference We hope you will enjoy the conference and have a great time in Antwerp, Belgium Peter Hellinckx Leonard Barolli BWCCA-2019 General Co-chairs Maarten Weyn Tomoya Enokido BWCCA-2019 Program Committee Co-chairs www.TechnicalBooksPDF.com Welcome Message from BWCCA-2019 Workshops Co-chairs Welcome to the Workshops of the 14th IEEE International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA-2019), which will be held in conjunction with the 14th 3PGCIC-2019 International Conference from November to 9, 2019, at University of Antwerp, Antwerp, Belgium This year seven workshops will be held in conjunction with BWCCA-2019 International Conference The workshops are very important part of the main conference and they cover specific topics related to next-generation networks, network traffic analysis, sensor technologies, smart environments, complex systems, wireless communication, mobile networks and multimedia networking BWCCA-2019 workshops are listed in following: The 21st International Symposium on Multimedia Network Systems and Applications (MNSA-2019) The 12th International Workshop on Next Generation of Wireless and Mobile Networks (NGWMN-2019) The 10th International Workshop on Methods, Analysis and Protocols for Wireless Communication (MAPWC-2019) The 10th International Workshop on Cloud, Wireless and e-Commerce Security (CWECS-2019) The 8th International Workshop on Robot and Vehicle Interaction, Control, Communication and Cooperation (RVI3C-2019) The 5th International Workshop on Advanced Techniques and Algorithms for Security and Privacy (ATASP-2019) The 2nd International Workshop on Bio-Sensing, Processing, Application and Networking (BioSPAN-2019) These workshops bring to the researchers conducting research in specific themes and the opportunity to learn from this rich multi-disciplinary experience vii www.TechnicalBooksPDF.com viii Welcome Message from BWCCA-2019 Workshops Co-chairs The Workshop Chairs would like to thank the workshop organizers for their great efforts and hard work in proposing the workshop, selecting the papers, the interesting programs and for the arrangements of the workshop during the conference days We hope you enjoy the workshop’s programs and proceedings Bart Lannoo Ben Bellekens Keita Matsuo Farookh Hussain BWCCA-2019 Workshops Co-chairs www.TechnicalBooksPDF.com BWCCA-2019 Organizing Committee Honorary Co-chairs Makoto Takizawa Walter Sevenhans Hosei University, Japan University of Antwerp, Belgium General Co-chairs Peter Hellinckx Leonard Barolli University of Antwerp, Belgium Fukuoka Institute of Technology, Japan Program Committee Co-chairs Maarten Weyn Tomoya Enokido University of Antwerp University, Belgium Rissho University, Japan Workshop Co-chairs Bart Lannoo Ben Bellekens Keita Matsuo Farookh Hussain University of Antwerp, Belgium University of Antwerp, Belgium Fukuoka Institute of Technology, Japan University of Technology Sydney, Australia Finance Chair Makoto Ikeda Fukuoka Institute of Technology, Japan ix www.TechnicalBooksPDF.com 860 3.2 L Zhang and K F Li Principal Component Analysis (PCA) Since there are over 50 attributes in each JSON data frame, we first attempted to use PCA to achieve the unsupervised dimension reduction However, PCA is not able to distinguish whether the movements were performed with two hands or one hand, because the total number of attributes for two hands movements are nearly doubled, comparing with one hand movements Therefore, data had been divided into two data set: one hand movements and two hands movements In each data set, there were six classes in total K-means clustering algorithm had been implemented to classify the data Nevertheless, the classification accuracy only reached around 60% in maximum 3.3 Selected Leap Motion Defined Feature After the above attempts, further investigation into the features and feature selection was necessary Three features were selected for further classification and are shown in Fig Fig Selected features • hands.type: recognizes the detected hand whether it is a right hand or a left hand If both “right” and “left” are detected, this is a two-hands movement; if there is only “right” or “left”, the movement is performed with only one hand • pointables.extended: identifies whether the pointable is straight or bent If “False” appears five time, it means all fingers are bent, and thus, the hand is holding as a fist; if “True” appears once, there is one finger straightened • hands.palmPosition: indicates the center position of the palm For twist movement, the variance on all axes should be relatively small as shown in Fig 7(a) As Fig (b) demonstrates, for vertical movement, the variance on the y axis is larger than that of the other two axes; for horizontal movement, variances on the y axis and z axis are significantly smaller than variance on the x axis, and Fig 7(c) presents this trend 3.4 Decision Tree Using the above features, a decision tree can be constructed as shown in Fig A Feasibility Study on Wrist Rehabilitation Using the Leap Motion Fig Feature “hands.palmPosition” Fig Decision tree 861 862 3.5 L Zhang and K F Li Decision Tree Result Feeding the phase one data to the constructed decision tree, the error rate was approximately 5.21% The total number of errors was out of 96 data To validate the result, both phase one and phase two data were fed to the decision tree, and the error rate was 3.47% There were errors (errors from phase one data) in 144 data, and there were no errors in phase two data All error occurred while detecting how many hands were getting involved in the experiment Hence, one more twist movement had been chosen to test the ability of LMC while identifying the total number of hands The movement, called one palm twist as shown in Fig 9, started with a flat palm facing down Then the hand over was flipped and the palm was facing up After that, the hand returned back to palm facing down Fig Additional selected movement [7] Two people in the previous experiments had performed the new “one palm twist” movement and the decision tree had been changed slightly If the total number of “True” returned by feature “hands.type” is equals to five, the movement will be classified as one palm twist The error rate in this case was zero Hence, although there might be few errors while detecting the total number of hands, the error is not significant Conclusions and Future Works Leap motion is sensitive and reasonably accurate but might have minor errors when detecting the total number of hands that involves in our experiments By plotting the position feature and timestamp into 3-D and 2-D graphs, it is easier to tell the differences between the movements done by the patient and the instructors Hence, an automated system can be set up using Leap Motion to instruct patients on A Feasibility Study on Wrist Rehabilitation Using the Leap Motion 863 how to improve and correct their movement, so that a patient is able to perform the rehabilitation training in a more efficient way For instance, while the patient is exercising the movement “one finger horizontal”, feedbacks regarding how to adjust the speed and position, can be given From Fig 10, an intelligent system would reason that the patient was doing the same movement as the instructor’s, though, the patient was moving faster than the instructor More detailed recommendations, such as the patient should the movement faster and lower, can be seen from the 2-D graphs in Fig 11 Fig 10 A 3-D graph for one finger horizontal (“hands.palmPosition” x axis and y axis versus timestamp) Fig 11 2-D graphs for one finger horizontal 864 L Zhang and K F Li This investigation shows the capability and potential of the Leap Motion We intend to implement such an intelligent advisory system for wrist rehabilitation References Golomb, M.R., McDonald, B.C., Warden, S.J., Yonkman, J., Saykin, A.J., Shirley, B., Huber, M., Rabin, B., AbdelBaky, M., Nwosu, M.E., Barkat-Masih, M.: In-home virtual reality videogame telerehabilitation in adolescents with hemiplegic cerebral palsy Arch Phys Med Rehabil 91(1), 1–8 (2010) Zhang, L., Li, K.F., Lin, J., Ren, J.: Leap motion for telerehabilitation: a feasibility study In: Advances on Broadband and Wireless Computing, Communication and Applications, 13th International Conference on BWCCA, pp 213–223 (2018) https://en.wikipedia.org/wiki/Leap_Motion https://developer-archive.leapmotion.com/documentation/javascript/devguide/Leap_ Overview.html?highlight=palmwidth Golgan, A.: Changing How People Look at Physical Therapy [Blog] Leap Motion (2018) http://blog.leapmotion.com/changing-people-look-physical-therapy/ Accessed 20 Aug 2018 Sathiyanarayanan, M., Rajan, S.: Understanding the Use of Leap Motion Touchless Device in Physiotherapy and Improving the Healthcare System in India (2018) https://ieeexplore.ieee org/stamp/stamp.jsp?tp=&arnumber=7945443&tag=1 Accessed 20 Aug 2018 https://myhealth.alberta.ca/Health/aftercareinformation/pages/conditions.aspx?hwid=bo1655 https://developer-archive.leapmotion.com/documentation/python/index.html Salvador, S., Chan, P.: FastDTW: toward accurate dynamic time warping in linear time and space Intell Data Anal 11(5), 561–580 (2007) Classification of Cotton and Flax Fiber Images Based on Inductive Transfer Learning Yuhan Jiang1, Song Cai1, Chunyan Zeng1(&), and Zhifeng Wang2 Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, China cyzeng@hbut.edu.cn Department of Digital Media Technology, Central China Normal University, Wuhan, China Abstract Aiming at the existing problems of high labor cost, huge training data and long detection period for Identification technology of cotton flax fiber, which is based on textural feature and convolutional neural network (CNN) method In this paper, it proposed a cotton and flax fiber detection method based on transfer learning According to sharing the weight parameters of the convolutional layer and the pooling layer, the model hyperparameters can be adjusted for the new network to achieve high detection accuracy The experimental results show that the detection accuracy of cotton flax fiber obtained by transfer learning is up to 97.3%, the sensitivity is 96.7%, and the specificity is 98.2% Compared with traditional machines, transfer learning method have large increase in the three indicators Furthermore, the transfer learning method has shorter training time and fewer data sets Introduction The most common method of identifying morphological features is microscope observation Researchers mainly observe shapes of the fiber’s cross-sections and fiber ends in the longitudinal view via microscope [1, 2] This method relying on Microscopic features of fiber, demands the knowledge of a specialist in Botany Moreover, the possibility of Subjective biases and mistakes by humans should be considered Besides, it is impractical for a human to analyze and identify every textile for sale With improvement of machine learning and deep learning, the computer makes significant progress in image processing and recognition In 2016, Wang Feng et al proposed Identification Technology of Cotton Flax Fiber Based on Textural Feature (CFFBTF) [3] Firstly, this method used the spiral phase filtering mechanism to enhance the image detail of the fiber, then utilizing the Gray Level Co-occurrence Matrix (GLCM) algorithm to obtain texture features of the fiber image, which will be classified by support vector machine Finally, CFFBTF achieved 94% detection accuracy In 2018, Yunfa Wang et al came up with an Automatic identification system of flax and cotton [4], based on convolutional neural network In this system, a deep convolutional neural network (CNN) trained by 140,000 cotton and flax fiber images made 0.96 detection accuracy Though this method got rid of special need of © Springer Nature Switzerland AG 2020 L Barolli et al (Eds.): BWCCA 2019, LNNS 97, pp 865–871, 2020 https://doi.org/10.1007/978-3-030-33506-9_79 866 Y Jiang et al microscope and simplified the image processing, in order to build a high-performance neural network, it still requires times and effort to recollect and recalibrate huge data sets In this paper, only 20,000 fiber images are used to achieve 97% accuracy, which shows the advantages of transfer learning in the classification of cotton and flax fibers Network 2.1 Deep Learning and Transfer Learning The transfer learning model can automatically learn hierarchical feature representations of data through the pre-training [5] This means that low-level features computed by the first layer, such as color and edge information, are general and can be reused in different problem domains, while high-level Semantic Features computed by the last layer are specific and depend on the chosen dataset and task [6, 7] According to the Categorization of Transfer Learning Techniques proposed by Pan and Yang in 2010 [8], the proposed model belongs to parameter-transfer Therefore, it is unnecessary to reinitialize the pre-trained CNN model parameters for the classification of plant fiber images This paper utilizes two deep convolutional neural networks that have been pretrained in the ImageNet database [9]: AlexNet and VGG 16 as transfer learning architecture for cotton flax The parameter-transfer refers to the method of finding the parameter information shared between them from the source domain and the target domain [10] The assumptions required for this transfer method are: The data in the source domain and the data in the target domain can share the some parameters 2.2 Convolution Neural Networks Convolution neural networks (CNN) are network structures based on artificial neural networks with convolutional layers Its basic structure consists of an input layer, convolution layer, pooling layer, fully connected layer, and an output layer The convolutional layer and the pooling layer are generally taken in a plurality of ways, the convolutional layer and the pooling layer are alternately arranged Since each neuron of the output feature surface in the convolutional layer is locally connected to its input, and the weighted sum is added to the local input by the corresponding connection weight and the offset value, So the input value of the neuron is obtained The process is equivalent to e convolution 2.2.1 VGG16 The outstanding feature of Vgg16 is that using small convolution kernel (3 Â 3) [11], increasing the network depth can effectively improve the effect of the model, and has a good generalization ability for the data set The network structure is shown in Fig Classification of Cotton and Flax Fiber Images 867 Fig Vgg16 network structure 2.2.2 AlexNet ALexNet is a deep convolutional neural network proposed by Alex Krizhevsky et al in 2012 [12] The network incorporates rectified linear unit (RLU), nonlinear activation function, Dropout method, data augmentation method, and a local response Local Response Normalization (LRN) So, AlexNet with multi-GPU training won the t championship at LSVRC in 2012 This paper will extract its bottleneck feature as input of the classifier, and the framework as can ben seen in Fig Fig AlexNet structure 868 Y Jiang et al Result and Discussions In order to verify the performance of the proposed method, this paper uses the cotton and flax fiber images obtained from the laboratory as a data set to compare with traditional machine learning methods, transfer learning models Standardize the hyperparameters in the experiment as follows: (1) (2) (3) (4) (5) 3.1 Optimizer: Adam Learning rate: 0.001 beta1: 0.9 beta2: 0.999 Batch size: Data Set The images used in the experiment are fiber microscope image of cotton and flax as shown (Fig 3): Fig Datasets image samples, (a) Cotton (b) Flax These images are RGB images and saved in PNG format with an image resolution of 1116*164 A total of 20032 images have been collected so far, including 14,445 linen images and 5,598 cotton images In order to adapt to the input size requirements of different networks, the resolution of the image was adjusted by OpenCV to 224*224, 227*227 and 299*299 respectively Classification of Cotton and Flax Fiber Images 3.2 869 Experiment Experiment compares the performance of transfer learning by using the deep CNN with traditional machine learning method with handcraft features In experiment 1, four types of machine learning methods, which are Histogram of Oriented Gradient (HOG), GLCM, Gabor and Local Binary Pattern (LBP) algorithm, would be used to extract texture features of fiber images Furthermore, all handcraft features are trained by support vector machine (SVM) The transfer learning models have AlexNet, VGG-16 and then the SoftMax classifier would be created, the inputs of which are the deep features extracted by above of CNNs Results of experiment are shown in Table Table Results of transfer learning and machine learning Method Model Transfer learning VGG-16 AlexNet Machine learning HOG LBP GLCM Gabor Acc 0.971 0.959 0.841 0.740 0.847 0.795 Sen 0.949 0.921 0.469 0.233 0.622 0.355 Spe 0.979 0.974 0.951 0.960 0.937 0.978 According to Table 1, although the specificity of four different machine learning methods is more than 0.93, their sensitivity is less than 0.62 That is to say, despite the fact that traditional machine method has better recognition ability for flax fiber, it has insufficient recognition ability for cotton fiber which makes the cotton fiber in the blend fabric difficult to be detected In contrast to the machine learning, the sensitivity AlexNet which get the worst performance among above transfer learning methods is still higher than above of machine learning methods Furthermore, the performance of AlexNet is the best, and the accuracy, sensitivity and specificity of the network exceeded 0.94 (Fig 4) Fig Bar chart of the results in classification 870 Y Jiang et al Referring to the bar chart, it can find out that four types of transfer learning methods have good performance on all three evaluation indicators But as for machine learning methods, there is large variance in values of the three indicators In conclusion, the transfer learning methods have better detection ability for cotton and flax images than the traditional machine learning model Conclusion This paper successfully introduces transfer learning into the detection of cotton flax fibers with a small amount of samples, and compares it with the traditional machine learning method with handcraft features The accuracy of cotton-based flax fibers based on transfer learning is up to 97.4% Compared with the traditional machine learning method, it has increased by more than 10% In the case of using small data sets and data imbalance, the experimental results of Wang Yunfa and others have increased by 1.3 percentage points This method has been improved to greatly improve the recognition accuracy However, the problem is that the depth features used in transfer learning are singular, so that the image recognition ability with less data volume is insufficient in the case of data imbalance For this problem, the author will introduce a feature fusion method in the next experiment, and select a suitable network model to solve such problems Acknowledgments This research was supported by National Natural Science Foundation of China (No 61901165, No 61501199), Science and Technology Research Project of Hubei Education Department (No Q20191406), Excellent Young and Middle-aged Science and Technology Innovation Team Project in Higher Education Institutions of Hubei Province (No T201805), Hubei Natural Science Foundation (No 2017CFB683), and self-determined research funds of CCNU from the colleges’ basic research and operation of MOE (No CCNU18QN021) References Jin, Y., Cheng, W.: Practice of identification of textile fibers with similar properties by infrared spectroscopy J China Fiber Inspection 519(11), 78–80 (2018) Ying, L., et al.: Cotton/linen single fiber identification based on longitudinal longitudinal microscopic images J Text J 33(04), 12–18 (2012) Wang, F., et al.: Cotton flax fiber identification technology based on texture features J Cotton Text Technol 44(04), 1–5 (2016) Wang, Y., et al.: Automatic identification system for flax and cotton based on convolutional neural network J Text Test Stand 4(06), 19–23 (2018) Zhang, R., et al.: Automatic detection and classification of colorectal polyps by transferring low-level CNN features from non-medical domain IEEE J Biomed Health Inform 21(1), 41 (2017) Vesal, S., et al.: Classification of Breast Cancer Histology Images Using Transfer Learning Springer, Cham (2018) Shao, L., et al.: Transfer learning for visual categorization: a survey IEEE Trans Neural Netw Learn Syst 26(5), 1019–1034 (2017) Classification of Cotton and Flax Fiber Images 871 Pan, S.J., Yang, Q.: A survey on transfer learning IEEE Trans Knowl Data Eng 22(10), 1345–1359 (2010) Liu, X., et al.: Transfer learning with convolutional neural network for early gastric cancer classification on magnifiying narrow-band imaging images In: 2018 25th IEEE International Conference on Image Processing (ICIP) (2018) 10 Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning J Big Data 3(1), (2016) 11 Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition arXiv:1409.1556 (2014) 12 Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks In: International Conference on Neural Information Processing Systems (2012) Author Index A Abbas, Shahid, 56, 815 Abubaker, Zain, 733 Abukwaik, Hadil, 150 Aburada, Kentaro, 352 Achir, Nadjib, 386 Akhtar, Muhammad Faraz, 56 Aleksy, Markus, 150 Ali, Ishtiaq, 173, 185 An, Hui, 846 Arshad, Muhammad Usman, 56 Ashfaq, Tehreem, 44 Ayoub, Wael, 162 Azeem, Muhammad, 733 B Babbar-Sebens, Meghna, 125 Barolli, Admir, 555 Barolli, Leonard, 3, 22, 32, 105, 555, 609, 711, 721, 747 Basharat, Aliza, 765 Bauwens, Jan, 363 Bista, Bhed Bahadur, 266 Bouras, Christos, 241, 253, 374 Bylykbashi, Kevin, 3, 747 C Cai, Song, 865 Campos, Pablo Payro, 433 Chand, Annas, 521 Chen, Chin-Ling, 670 Chen, Hsing-Chung, 597 Chen, Tzer-Shyong, 327 Chen, Tzu-Ya, 597 Chen, Wei-Sheng, 678 Chen, Xu, 512, 828, 838 Chiang, Dai-Lun, 327 Chiang, Ping-Jui, 698 Chuang, Chun-Yen, 327 Cristobal, Fabricio Landero, 433 Cuka, Miralda, 32 D Dandoush, Abdulhalim, 386 De Poorter, Eli, 363 Delgado, Carmen, 363 Deng, Na, 512, 828, 838 Deng, Yong-Yuan, 670 Dhoska, Klodian, 105 Dhurandher, Sanjay K., 279 Diaz, Gladys, 386 Diles, Georgios, 241 Durresi, Arjan, 125 Durresi, Heidi, 555 E Elkadeem, M R., 409 Elmazi, Donald, 32 Enokido, Tomoya, 81, 93, 114, 137, 471, 483, 494 F Famaey, Jeroen, 363 Fan, Menglin, 570, 630 © Springer Nature Switzerland AG 2020 L Barolli et al (Eds.): BWCCA 2019, LNNS 97, pp 873–875, 2020 https://doi.org/10.1007/978-3-030-33506-9 874 Fan, Pengfei, 570, 630 Farooq, Hassan, 56, 67 Fujisaki, Kiyotaka, 646 G Gao, Tianhan, 315, 535, 846 Ghaffar, Abdul, 815 Ghita, Bogdan, 210 Gima, Kosuke, 494 Gkamas, Apostolos, 374 Groß, Christian, 150 Guo, Yinzhe, 114 Gupta, Geetanshu, 279 Gurmani, Muhammad Usman, 733 H Hameed, Seemab, 765 Haramaki, Toshiyuki, 12 Hayashibara, Naohiro, 199 He, Xiao, 570, 630 Higashinaka, Naoki, 621 Hong, Zhi-Qian, 688 Hsu, Fang-Wei, 670 Hu, Yan-Jing, 657 Huang, Hui-Ling, 698 I Iftikhar, Muhammad Sohaib, 67, 521 Iftikhar, Muhammad Zohaib, 67, 521, 733 Ikeda, Makoto, 32, 711 Ikenoue, Kazuma, 544 Iqbal, Zahid, 44 Ishii, Hazuki, 483 J Jamal, Abid, 815 Javaid, Atia, 173, 185 Javaid, Nadeem, 44, 56, 67, 173, 185, 521, 733, 765, 815 Jawad, Muhammad, 521 Jiang, Lei, 535 Jiang, Mei-He, 597 Jiang, Ningling, 579 Jiang, Yuhan, 865 K Kalogeropoulos, Rafail, 241 Kammoun, Amal, 386 Katsampiris Salgado, Spyridon Aniceto, 374 Kaur, Davinder, 125 Kawauchi, Kiyoto, 338 Kazmi, Hafiza Syeda Zainab, 765 Khan, Abdul Basit Majeed, 521 Author Index Khan, Asad Ullah, 815 Khan, Raja Jalees Ul Hussen, 173, 185 Khan, Zain, 521 Kikuta, Tsubasa, 800 Kim, Myoungsu, 589 Kokkinos, Vasileios, 253, 374 Kolici, Vladi, 105 Koshiba, Rikita, 800 Kulla, Elis, 579, 638 Kumar, Raghav, 279 Kurokawa, Takeru, 199 L Lee, Chin-Feng, 670 Lehmann, Armin, 210 Leu, Fang-Yie, 678, 688, 698 Li, Kin Fun, 855 Li, Yipeng, 838 Lin, Frank Yeong-Sung, 327 Liu, Ching-Cheng, 670 Liu, Shudong, 838 Liu, Yi, Lv, Songnan, 231 M Ma, Peng, 787 Maeda, Hiroshi, 621 Matsumoto, Natsuki, 222 Matsuo, Keita, 32, 721 Mehmood, Shahid, 44 Mi, Qingwei, 315 Michos, Evangelos, 253 Morita, Soushi, 638 Mroue, Mohamad, 162 Mubarak, Sahrish, 765 N Nagatomo, Makoto, 352 Nakamura, Shigenari, 81, 93, 114, 471, 483, 494 Nakasaki, Shogo, 711 Nazeer, Faiza, 765 Nishigaki, Masakatsu, 338 Nishikawa, Hiroki, 338 Nishino, Hiroaki, 12 Noshad, Zainib, 173, 185 Nouvel, Fabienne, 162 O Ogiela, Lidia, 423, 428 Ogiela, Marek R., 428 Ogiela, Urszula, 423 Ohara, Seiji, 22, 555 Author Index Okada, Yoshihiro, 303 Okamoto, Shusuke, 22 Okazaki, Naonobu, 352 Oma, Ryuji, 81, 114, 483, 494 P Park, Mirang, 352 Prévotet, Jean-Christophe, 162 Q Qafzezi, Ermioni, 747 R Ramzan, Muhammad, 67 Rivera, Samuel J., 125 Rizwan, Shahzad, 44, 733 S Sabovic, Adnan, 363 Saito, Takamichi, 800 Saito, Takumi, 471 Sakamoto, Shinji, 22, 555 Samhat, Abed Ellatif, 162 Samuel, Omaji, 67 Sato, Goshi, 445, 504 Shahid, Affaf, 815 Shala, Besfort, 210 Shehzad, Faisal, 67 Shiaeles, Stavros, 210 Shibata, Yoshitaka, 445, 504 Shigeyasu, Tetsuya, 222 Shimano, Kodai, 757 Singh, Jagdeep, 279 Spaho, Evjola, 105, 747 Sugawara, Shinji, 293 Sultana, Tanzeela, 733 Susanto, Heru, 678, 688, 698 T Tabbane, Nabil, 386 Tai, Kuang-Yen, 327 Takata, Toyoo, 266 Takizawa, Makoto, 81, 93, 105, 114, 137, 279, 423, 471, 483, 494 Takumi, Ichi, 757 Tariq, Fatima, 815 875 Tian, Chao, 570, 630 Trick, Ulrich, 210 U Uchida, Kazunori, 609 Uchida, Noriki, 445, 504 Uchiya, Takahiro, 757 Ueda, Kazunori, 544 Uehara, Kota, 338 Uehara, Minoru, 398 Ullah, Zia, 409 Uslu, Suleyman, 125 W Wang, Jiahong, 266 Wang, Shaorong, 409 Wang, Xibao, 535 Wang, Xu An, 657, 777, 787 Wang, Zeyu, 787 Wang, Zhifeng, 231, 865 Watanabe, Kazuki, 352 Wister, Miguel A., 433 Woungang, Isaac, 279 X Xiong, Caiquan, 512, 828 Y Yamamoto, Takumi, 338 Yim, Kangbin, 589 Yoshigai, Yuki, 646 Younis, Muhammad Ahmed, 44 Yuan, Chen, 398 Z Zahid, Bilal, 56 Zahid, Maheen, 173, 185 Zeng, Chunyan, 231, 865 Zhang, Linlin, 855 Zhang, Wei, 787 Zhao, Nan, 570, 630 Zhao, Songyin, 777 Zheng, Yingzhou, 838 Zhong, Deliang, 838 Zhou, Shangli, 231 Zhu, Jin, 455 ... Advances on Broad- Band Wireless Computing, Communication and Applications Proceedings of the 14th International Conference on Broad- Band Wireless Computing, Communication and Applications (BWCCA-2019)... International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA-2019), which will be held in conjunction with the 14th 3PGCIC-2019 International Conference... International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA-2019), which will be held in conjunction with the 14th 3PGCIC-2019 International Conference