﻿<?xml version="1.0" encoding="utf-8"?><doi_batch xmlns="http://www.crossref.org/schema/4.3.7" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.3.7 http://www.crossref.org/schema/deposit/crossref4.3.7.xsd"><head><doi_batch_id>jist-2026051923</doi_batch_id><timestamp>20260519232534</timestamp><depositor><depositor_name>CMV Verlag</depositor_name><email_address>khoffmann@cmv-verlag.com</email_address></depositor><registrant>CMV Verlag</registrant></head><body><journal><journal_metadata language="en"><full_title>Journal of Information Systems and Telecommunication (JIST) </full_title><abbrev_title>jist</abbrev_title><issn media_type="electronic">2322-1437</issn></journal_metadata><journal_issue><publication_date media_type="online"><month>4</month><day>12</day><year>2021</year></publication_date><journal_volume><volume>9</volume></journal_volume><issue>33</issue></journal_issue><journal_article publication_type="full_text"><titles><title> Drone Detection by Neural Network Using GLCM and SURF Features</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Tanzia </given_name><surname>Ahmed</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Tanvir </given_name><surname>Rahman</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Bir </given_name><surname>Ballav Roy</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Jia</given_name><surname>Uddin</surname></person_name></contributors><publication_date media_type="online"><month>4</month><day>12</day><year>2021</year></publication_date><pages><first_page>15</first_page><last_page>24</last_page></pages><doi_data><doi>10.52547/jist.9.33.15</doi><resource>http://jist.ir/en/Article/15424</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jist.ir/en/Article/Download/15424</resource></item><item crawler="google"><resource>http://jist.ir/en/Article/Download/15424</resource></item><item crawler="msn"><resource>http://jist.ir/en/Article/Download/15424</resource></item><item crawler="altavista"><resource>http://jist.ir/en/Article/Download/15424</resource></item><item crawler="yahoo"><resource>http://jist.ir/en/Article/Download/15424</resource></item><item crawler="scirus"><resource>http://jist.ir/en/Article/Download/15424</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jist.ir/en/Article/Download/15424</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	M. Hicks, “Criminal Intent: FBI Details How Drones are used in crime,” Techradar-the source for tech buying advice, May 2018. [online]. https://www.techradar.com/news/criminal-intent-fbi-details-how-drones-are-being-used-for-crime.#</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2]	F. P. George, I. M. Shaikat, P. S. F. Hossain, M. Z. Parvez, and J. Uddin, “Recognition of emotional states using EEG signals based on time-frequency analysis and SVM classifier,” International Journal of Electrical and Computer Engineering, 2019, vol. 9, no. 2, pp. 1012-1020.#</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3]	R. Dong, H. Meng, Z. Long and H. Zhao, “Dimensionality reduction by soft-margin support vector machine,” IEEE International Conference on Agents (ICA), Beijing, China, 2017, pp. 154-156.#</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4]	G. Yan, “Network Anomaly Traffic Detection Method Based on Support Vector Machine,” 2016 International Conference on Smart City and Systems Engineering (ICSCSE), Zhangjiajie, Hunan, China, 2016, pp. 3-6.#</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5]	M. d. Barbosa, C. d. Barbosa, and A. F. Barbosa, “MuSSE: A Tool to Extract Meta-Data from Game Sprite Sheets Using Blob Detection Algorithm,” 14th Brazilian Symposium on Computer Games and Digital Entertainment (SBGAMES), Piauí, Brazil, 2015, pp. 61-69.#</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	F. E. H Tay and L. Lao, “Application of support vector machines in financial time series forecasting,” omega, vol. 29, no. 4, Aug. 2001, pp. 309-317.# </unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	G. Kumar, P. K. Bhatia, “A detailed Review of Feature Extraction in Image Processing Systems,” Fourth International Conference on Advanced Computing and Communication Technologies, IEEE Computer Society, Washington DC, USA, 2014, pp. 5-12.#</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	R. S. Choras, “Image Feature Extraction Techniques and Their Application for CBIR and Biometric Systems,” International Journal of Biology and Biomedical Engineering, 2007, vol. 1, no. 1, pp. 6-16.#</unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9]	Z. Yin et al., “A Deep Normalization and Convolutional Neural Network for Image Smoke Detection,” IEEE Access, vol. 6, 2018, pp. 4287-4296.#  </unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10]	J. Chen et al., “Analysis of the recognition and localization techniques of power transmission lines components in aerial images acquired by drones,” The Institute of Engineering and Technology Journals, IEEE Access, 2017, pp. 29-32.#</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11]	M. A. Abuzneid and A. Mahmood, “Enhanced Human Face Recognition Using LBPH Descriptor, Multi-KNN, and Back-Propagation Neural Network,” IEEE Access, vol. 6, 2018, pp. 20641-2065.#</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12]	R. Hussin, M. R. Juhari, N. W. Kang, R. C. Ismail, A. Kamarudin, “Digital Image Processing Techniques for Object Detection from Complex Background Image,” Procedia Engineering, 2012, pp. 340–344.#</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13]	M. J. Swain and D. H. Ballard, “Color indexing,” International Journal of Computer Vision, vol. 7, no. 11, 1991, pp. 11-32.#</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14]	B. V. Funt and G. D. Finlayson, “Color constant color indexing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 5, May 1995, pp. 522-529.#</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15]	A. Krizhevsky, I. Sutskever, and G. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Neural Information Processing Systems, 2012, pp. 1-9.#</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16]	M. Nakib, R. T. Khan, M. S. Hasan and J. Uddin, “Crime Scene Prediction by Detecting Threatening Objects Using Convolutional Neural Network,” International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, Bangladesh, 2018, pp. 1-4.#</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17]	H. Y. Chae, K. Lee, J. Jang, K. Park, and J. J. Kim, “A Wearable sEMG Pattern-Recognition Integrated Interface Embedding Analog Pseudo-Wavelet Preprocessing,” IEEE Access, 2019, vol. 7, pp. 151320-151328.#</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18]	J. Zupan, “Introduction to Artificial Neural Network (ANN) Methods: What They Are and How to Use Them,” Acta Chimica Slovenica, 1994, vol. 41, no. 3, pp. 327-352.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19]	T. Youtang and W. Jianrning, “Air target fuzzy pattern recognition Threat-judgment model,” Journal of Systems Engineering and Electronics, 2003, vol. 14, no. 1, pp. 41-46.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20]	B. Nikhil, “Ïmage Data Pre-Processing for Neural Networks,” Becoming Human: Artificial Intelligence Magazine, 2017.# </unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21]	rgb2gray-Convert RGB image or colormap to grayscale, MathWorks, v: R2018a, 2018.  [online]. https://www.mathworks.com/help/matlab/ref/rgb2gray.#</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22]	mat2gray-Convert matrix to grayscale image, MathWorks, v: R2018a, 2018. [online]. https://www.mathworks.com/help/images/ref/mat2gray.# </unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23]	R.M. Haralick and L.G. Shapiro, “Computer and Robot Vision,” vol. 1, Addison-Wesley, 1992, pp. 1-459.#</unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24]	A. Xu and G. Namit, “SURF: Speeded-up Robust Features,” 2008, Project Report: McGill University.# </unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25]	T. Das, R. Hasan, M. R. Azam and J. Uddin, “A Robust Method for Detecting Copy-Move Image Forgery Using Stationary Wavelet Transform and Scale Invariant Feature Transform,” International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Bangladesh, 2018, pp. 1-4.#</unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26]	G. S. Rabbani, S. Sultana, M.N. Hasan, S. Q. Fahad, J. Uddin, “Person identification using SURF features of dental radiograph,” 3rd International Conference on Cryptography, Security and Privacy, 2019, pp. 303.#</unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27]	Detect SURF Features-Detect SURF features and return SURF Points object MathWorks, v: R2018a, 2018. [online]. https://www.mathworks.com/help/vision/ref/detectsurffeatures. </unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28]	H. Bay, A. Ess, T. Tuytelaars, L. V. Gool, “SURF: Speeded Up Robust Features,” Computer Vision and Image Understanding, 2008, vol. 110, no. 3, pp. 346–359.# </unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29]	E. Oyallon and J. Rabin, “An Analysis of the SURF Method,” 2015, Image Processing On Line (IPOL), pp.176-218.# </unstructured_citation></citation><citation key="ref30"><unstructured_citation>[30]	B. Fan, Z. Wang, F. Wu, “Local Image Descriptors: Modern Approaches,” Springer, 2015, vol. 12, pp. 1-99.#</unstructured_citation></citation><citation key="ref31"><unstructured_citation>[31]	R. M. Haralick, K. Shanmugan, and I. Dinstein, “Textural Features for Image Classification,” IEEE Transactions on Systems, Man, and Cybernetics, 1973, vol. SMC-3, pp. 610-621.# </unstructured_citation></citation><citation key="ref32"><unstructured_citation>[32]	A. Uppuluri, “GLCM texture features- Calculates texture features from the input GLCMs,” version:1.2.0.0, MathWorks, v: R2018a, 2018. [online]. https://www.mathworks.com/matlabcentral/fileexchange/22187-glcm-texture-features.# </unstructured_citation></citation><citation key="ref33"><unstructured_citation>[33]	P. Cosman, “Gray-Level Co-occurrence Matrices (GLCMs),” [online]. http://www.code.ucsd.edu/pcosman/glcm.pdf.# </unstructured_citation></citation><citation key="ref34"><unstructured_citation>[34]	J. Uddin, R. Islam, J. M. Kim, “Texture Feature Extraction Techniques for Fault Diagnosis of Induction Motors,” Journal of Convergence, 2014, vol. 5, no. 2, pp. 15-20.#</unstructured_citation></citation><citation key="ref35"><unstructured_citation>[35]	Image Recognition- Recognition methods in image processing, MathWorks, v: R2018a, 2018. [online]. https://www.mathworks.com/discovery/pattern-recognition.#</unstructured_citation></citation><citation key="ref36"><unstructured_citation>[36]	“The Machine Learning Dictionary,” available at: www.cse.unsw.edu.au. Retrieved at 4 November 2009.#</unstructured_citation></citation><citation key="ref37"><unstructured_citation>[37]	A. Zell, “chapter 5.2,” Simulation neuronaler Netze [Simulation of Neural Networks] (in German) (1st ed.), Addison-Wesley, 2003.#</unstructured_citation></citation><citation key="ref38"><unstructured_citation>[38]	C. M. Bishop, “Pattern Recognition and Machine Learning,” Springer, 2006, pp. 1-758.#</unstructured_citation></citation><citation key="ref39"><unstructured_citation>[39]	Classify Patterns with a Shallow Neural Network, MathWorks, v: R2018a, 2018. [online]. https://www.mathworks.com/help/nnet/gs/classify-patterns-with-a-neural-network.#</unstructured_citation></citation><citation key="ref40"><unstructured_citation>[40]	M. F. Moller, “A Scale Conjugate Gradient Algorithm for Fast Supervised Learning,” Neural Networks, 1993, vol. 6, no. 4, pp. 525-533.#</unstructured_citation></citation><citation key="ref41"><unstructured_citation>[41]	Crossentropy- Neural network performance, MathWorks, v: R2018a, 2018. [online]. https://www.mathworks.com/help/nnet/ref/crossentropy.#</unstructured_citation></citation><citation key="ref42"><unstructured_citation>[42]	V. Tshitoyan (2021). Simple Neural Network (https://github.com/vtshitoyan/simpleNN), GitHub. Retrieved January 20, 2021.#</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Human Activity Recognition based on Deep Belief Network Classifier and Combination of Local and Global Features</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Azar</given_name><surname>Mahmoodzadeh</surname></person_name></contributors><publication_date media_type="online"><month>4</month><day>12</day><year>2021</year></publication_date><pages><first_page>45</first_page><last_page>54</last_page></pages><doi_data><doi>10.52547/jist.9.33.45</doi><resource>http://jist.ir/en/Article/15439</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jist.ir/en/Article/Download/15439</resource></item><item crawler="google"><resource>http://jist.ir/en/Article/Download/15439</resource></item><item crawler="msn"><resource>http://jist.ir/en/Article/Download/15439</resource></item><item crawler="altavista"><resource>http://jist.ir/en/Article/Download/15439</resource></item><item crawler="yahoo"><resource>http://jist.ir/en/Article/Download/15439</resource></item><item crawler="scirus"><resource>http://jist.ir/en/Article/Download/15439</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jist.ir/en/Article/Download/15439</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	S. Ranasinghe, F. Al Machot, and H.C. Mayr, "A review on applications of activity recognition systems with regard to performance and evaluation," International Journal of Distributed Sensor Networks, vol. 12, no. 8, p. 1550147716665520, 2016.#</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2]	S.S. Agaian, J. Tang, and J. Tan, "Electronic imaging applications in mobile healthcare," 2019.#</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3]	Y. Wang, H. Jiang, M.S. Drew, Z.N. Li, and G. Mori, "Unsupervised discovery of action classes," in Proceedings of CVPR, pp. 17-22.#</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4]	S. Yan, J.S. Smith, W. Lu, and B. Zhang, "Multibranch Attention Networks for Action Recognition in Still Images," IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 4, pp. 1116-1125, 2017.#</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5]	Y. Wang, Y. Li, X. Ji, "Human action recognition based on global gist feature and local patch coding," International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 8, no. 2, pp. 235-246, 2015.#</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	E. Park, X. Han, T.L. Berg, and A.C. Berg, "Combining multiple sources of knowledge in deep cnns for action recognition," in 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1-8, 2016.#</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	H.A. Qazi, U. Jahangir, B.M. Yousuf, and A. Noor, "Human action recognition using SIFT and HOG method," in 2017 International Conference on Information and Communication Technologies (ICICT), pp. 6-10, 2017.#</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	H.F. Nweke, Y.W. Teh, G. Mujtaba, and M. Al-Garadi, "Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions," Information Fusion, vol. 46, pp. 147-170, 2019.#</unstructured_citation></citation><citation key="ref9"><unstructured_citation> [9]	N. Ikizler, R.G. Cinbis, S. Pehlivan, and P. Duygulu, "Recognizing actions from still images," in 2008 19th International Conference on Pattern Recognition, pp. 1-4, 2008.#</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10]	L.J. Li, and L. Fei-Fei, "What, where and who? classifying events by scene and object recognition," In 2007 IEEE 11th international conference on computer vision, pp. 1-8, 2007.#</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11]	C. Thurau and V. Hlavác, "Pose primitive based human action recognition in videos or still images," in 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008.#</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12]	P. Li, J. Ma, and S. Gao, "Actions in still web images: visualization, detection and retrieval," in International Conference on Web-Age Information Management, pp. 302-313, 2011.#</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13]	N. Shapovalova, W. Gong, M. Pedersoli, F.X. Roca, and J. Gonzalez, "On importance of interactions and context in human action recognition," in Iberian conference on pattern recognition and image analysis, pp. 58-66, 2011.#</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14]	V. Delaitre, J. Sivic, and I. Laptev, "Learning person-object interactions for action recognition in still images," in Advances in neural information processing system, pp. 1503-1511, 2011.#</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15]	Y. Zheng, Y.J. Zhang, X. Li, and B.D. Liu, "Action recognition in still images using a combination of human pose and context information," in 2012 19th IEEE International Conference on Image Processing, pp. 785-788, 2012.#</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16]	F. Sener, C. Bas, and N. Ikizler-Cinbis, "On recognizing actions in still images via multiple features," in European Conference on Computer Vision, 2012, pp. 263-272.#</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17]	G. Sharma, F. Jurie, and C. Schmid, "Discriminative spatial saliency for image classification," in 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3506-3513, 2012.#</unstructured_citation></citation><citation key="ref18"><unstructured_citation> [18]	S. Maji, L. Bourdev, and J. Malik, "Action recognition from a distributed representation of pose and appearance," in CVPR 2011, pp. 3177-3184, 2011.#</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19]	B. Yao, X. Jiang, A. Khosla, A.L. Lin, L. Guibas, and L. Fei-Fei, "Human action recognition by learning bases of action attributes and parts," in 2011 International Conference on Computer Vision, pp. 1331-1338, 2011.#</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20]	A. Prest, C. Schmid, and V. Ferrari, "Weakly supervised learning of interactions between humans and objects," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 3, pp. 601-614, 2011.#</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21]	F.S. Khan, R.M. Anwer, J. Van De Weijer, A.D. Bagdanov, and M. Felsberg, "Coloring action recognition in still images," International journal of computer vision, vol. 105, no. 3, pp. 205-221, 2013.#</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22]	F.S. Khan, J. Van De Weijer, R.M. Anwer, M. Felsberg, and C. Gatta, "Semantic pyramids for gender and action recognition," IEEE Transactions on Image Processing, vol. 23, no. 8, pp. 3633-3645, 2014.#</unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23]	F.S. Khan, J. Van De Weijer, R.M. Anwer, A.D. Bagdanov, M. Felsberg, and J. Laaksonen, "Scale coding bag of deep features for human attribute and action recognition," Machine Vision and Applications, vol. 29, no. 1, pp. 55-71, 2018.#</unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24]	T. Watanabe, S. Ito, and K. Yokoi, "Co-occurrence histograms of oriented gradients for pedestrian detection," in Pacific-Rim Symposium on Image and Video Technology, pp. 37-47, 2009.#</unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25]	A. Oliva and A. Torralba, "Modeling the shape of the scene: A holistic representation of the spatial envelope," International journal of computer vision, vol. 42, no. 3, pp. 145-175, 2001.#</unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26]	A. Oliva and A. Torralba, "Building the gist of a scene: The role of global image features in recognition," Progress in brain research, vol. 155, pp. 23-36, 2006.#</unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27]	G. Lowe, "SIFT-The Scale Invariant Feature Transform," Int. J, vol. 2, pp. 91-110, 2004.#</unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28]	D.G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60, pp. 91-110, 2004.#</unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29]	J. Sivic and A. Zisserman, "Video Google: A text retrieval approach to object matching in videos," in null, p. 1470, 2003.#</unstructured_citation></citation><citation key="ref30"><unstructured_citation>[30]	L. Fei-Fei and P. Perona, "A bayesian hierarchical model for learning natural scene categories," in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp. 524-531, 2005.#</unstructured_citation></citation><citation key="ref31"><unstructured_citation>[31]	M.A. Carreira-Perpinan and G.E. Hinton, "On contrastive divergence learning," in Aistats, pp. 33-40, 2005.#</unstructured_citation></citation><citation key="ref32"><unstructured_citation>[32]	G.E. Hinton, "Training products of experts by minimizing contrastive divergence," Neural computation, vol. 14, no.8, pp. 1771-1800, 2002.#</unstructured_citation></citation><citation key="ref33"><unstructured_citation>[33]	N. Le Roux, and Y. Bengio, "Deep belief networks are compact universal approximators," Neural computation, vol. 22, no. 8, pp. 2192-2207, 2010.#</unstructured_citation></citation><citation key="ref34"><unstructured_citation>[34]	R. Salakhutdinov and G. Hinton, "Deep boltzmann machines," in Artificial Intelligence and Statistics, pp. 448-455, 2009.#</unstructured_citation></citation><citation key="ref35"><unstructured_citation>[35]	R. Hecht-Nielsen, "Theory of the backpropagation neural network," in Neural Networks for Perception, ed: Elsevier, pp. 65-93, 1992.#</unstructured_citation></citation><citation key="ref36"><unstructured_citation>[36]	I. Sutskever and G.E. Hinton, "Deep, narrow sigmoid belief networks are universal approximators," Neural computation, vol. 20, no. 11, pp. 2629-2636, 2008.#</unstructured_citation></citation><citation key="ref37"><unstructured_citation>[37]	M. Everingham, L. Van Gool, C.K. Williams, J. Winn, and A. Zisserman, "The pascal visual object classes (voc) challenge," International journal of computer vision, vol. 88, no. 2, pp. 303-338, 2010.#</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Confronting DDoS Attacks in Software-Defined Wireless Sensor Networks based on Evidence Theory</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Nazbanoo</given_name><surname>Farzaneh</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Reyhaneh</given_name><surname>Hoseini</surname></person_name></contributors><publication_date media_type="online"><month>4</month><day>12</day><year>2021</year></publication_date><pages><first_page>25</first_page><last_page>36</last_page></pages><doi_data><doi>10.52547/jist.9.33.25</doi><resource>http://jist.ir/en/Article/15498</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jist.ir/en/Article/Download/15498</resource></item><item crawler="google"><resource>http://jist.ir/en/Article/Download/15498</resource></item><item crawler="msn"><resource>http://jist.ir/en/Article/Download/15498</resource></item><item crawler="altavista"><resource>http://jist.ir/en/Article/Download/15498</resource></item><item crawler="yahoo"><resource>http://jist.ir/en/Article/Download/15498</resource></item><item crawler="scirus"><resource>http://jist.ir/en/Article/Download/15498</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jist.ir/en/Article/Download/15498</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "Wireless sensor networks: a survey," Computer Networks, vol. 38, pp. 393-422, 2002/03/15/ 2002.#</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2]	F. Losilla, C. Vicente-Chicote, B. Álvarez, A. Iborra, and P. Sánchez, "Wireless Sensor Network Application Development: An Architecture-Centric MDE Approach," in Software Architecture, Berlin, Heidelberg, 2007, pp. 179-194.#</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3]	I. Ahmad, S. Namal, M. Ylianttila, and A. Gurtov, "Security in Software Defined Networks: A Survey," IEEE Communications Surveys &amp; Tutorials, vol. 17, pp. 2317-2346, 2015.#</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4]	A. Akhunzada, E. Ahmed, A. Gani, M. K. Khan, M. Imran, and S. Guizani, "Securing software defined networks: taxonomy, requirements, and open issues," IEEE Communications Magazine, vol. 53, pp. 36-44, 2015.#</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5]	I. T. Haque and N. Abu-Ghazaleh, "Wireless Software Defined Networking: A Survey and Taxonomy," IEEE Communications Surveys &amp; Tutorials, vol. 18, pp. 2713-2737, 2016.#</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	D. He, S. Chan, and M. Guizani, "Securing software defined wireless networks," IEEE Communications Magazine, vol. 54, pp. 20-25, 2016.#</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	M. Karakus and A. Durresi, "Quality of Service (QoS) in Software Defined Networking (SDN)," J. Netw. Comput. Appl., vol. 80, pp. 200-218, 2017.#</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	Z.-j. Han and W. Ren, "A Novel Wireless Sensor Networks Structure Based on the SDN," International Journal of Distributed Sensor Networks, vol. 10, p. 874047, 2014/03/01 2014.#</unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9]	T. Kgogo, B. Isong, and A. M. Abu-Mahfouz, "Software defined wireless sensor networks security challenges," in 2017 IEEE AFRICON, 2017, pp. 1508-1513.#</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10]	F. Olivier, G. Carlos, and N. Florent, "SDN Based Architecture for Clustered WSN," in 2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, 2015, pp. 342-347.#</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11]	C. Ioannou, V. Vassiliou, and C. Sergiou, "An Intrusion Detection System for Wireless Sensor Networks," in 2017 24th International Conference on Telecommunications (ICT), 2017, pp. 1-5.#</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12]	A. D. Gante, M. Aslan, and A. Matrawy, "Smart wireless sensor network management based on software-defined networking," in 2014 27th Biennial Symposium on Communications (QBSC), 2014, pp. 71-75.#</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13]	H. I. Kobo, A. M. Abu-Mahfouz, and G. P. Hancke, "A Survey on Software-Defined Wireless Sensor Networks: Challenges and Design Requirements," IEEE Access, vol. 5, pp. 1872-1899, 2017.#</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14]	S. W. Pritchard, G. P. Hancke, and A. M. Abu-Mahfouz, "Security in software-defined wireless sensor networks: Threats, challenges and potential solutions," in 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), 2017, pp. 168-173.#</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15]	K. S. Sahoo, M. Tiwary, and B. Sahoo, "Detection of high rate DDoS attack from flash events using information metrics in software defined networks," in 2018 10th International Conference on Communication Systems &amp; Networks (COMSNETS), 2018, pp. 421-424.#</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16]	S. Shin, L. Xu, S. Hong, and G. Gu, "Enhancing Network Security through Software Defined Networking (SDN)," in 2016 25th International Conference on Computer Communication and Networks (ICCCN), 2016, pp. 1-9.#</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17]	J. Wu, K. Ota, M. Dong, and C. Li, "A Hierarchical Security Framework for Defending Against Sophisticated Attacks on Wireless Sensor Networks in Smart Cities," IEEE Access, vol. 4, pp. 416-424, 2016.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18]	P. Zhang, H. Wang, C. Hu, and C. Lin, "On denial of service attacks in software defined networks," IEEE Network, vol. 30, pp. 28-33, 2016.#</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19]	D. E. P. Alina Madalina Lonea, Huaglory Tianfield, "Detecting DDoS Attacks in Cloud Computing Environment," International Journal of Computers Communications &amp; Control, vol. 8, 2013.#</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20]	Y. Ashok Khimabhai and V. Rohokale, SDN Control Plane Security in Cloud Computing Against DDoS Attack, 2016.#</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21]	S. K. Fayaz, Y. Tobioka, V. Sekar, and M. Bailey, "Bohatei: Flexible and elastic ddos defense," in 24th {USENIX} Security Symposium ({USENIX} Security 15), 2015, pp. 817-832.#</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22]	S. M. Mousavi and M. St-Hilaire, "Early detection of DDoS attacks against SDN controllers," in 2015 International Conference on Computing, Networking and Communications (ICNC), 2015, pp. 77-81.#</unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23]	A. Navaz, V. Sangeetha, and C. Prabhadevi, "Entropy based anomaly detection system to prevent DDoS attacks in cloud," arXiv preprint arXiv:1308.6745, 2013.#</unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24]	R. Vadehra, M. Singh, B. Singh, and N. Chowdhary, "Evaluation of Flow and Average Entropy Based Detection Mecha-nism for DDoS Attacks using NS-2," International Journal of Security and Its Applications, vol. 10, pp. 139-146, 2016.#</unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25]	S. Yu, W. Zhou, R. Doss, and W. Jia, "Traceback of DDoS attacks using entropy variations," IEEE Transactions on Parallel and Distributed Systems, vol. 22, pp. 412-425, 2011.#</unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26]	B. Rashidi, C. Fung, and E. Bertino, "A collaborative DDoS defence framework using network function virtualization," IEEE Transactions on Information Forensics and Security, vol. 12, pp. 2483-2497, 2017.#</unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27]	G. A. N. Segura, S. Skaperas, A. Chorti, L. Mamatas, and C. B. Margi, "Denial of Service Attacks Detection in Software-Defined Wireless Sensor Networks," in 2020 IEEE International Conference on Communications Workshops (ICC Workshops), 2020, pp. 1-7.#</unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28]	A. Wani and S. Revathi, "DDoS Detection and Alleviation in IoT using SDN (SDIoT-DDoS-DA)," Journal of The Institution of Engineers (India): Series B, vol. 101, pp. 117-128, 2020/04/01 2020.#</unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29]	G. A. Nunez Segura, A. Chorti, and C. Borges Margi, "Centralized and Distributed Intrusion Detection for Resource Constrained Wireless SDN Networks," arXiv e-prints, p. arXiv: 2103.01262, 2021.#</unstructured_citation></citation><citation key="ref30"><unstructured_citation>[30]	Á. MacDermott, Q. Shi, and K. Kifayat, "Distributed Attack Prevention Using Dempster-Shafer Theory of Evidence," in Intelligent Computing Methodologies, Cham, 2017, pp. 203-212.#</unstructured_citation></citation><citation key="ref31"><unstructured_citation>[31]	h. tan, M. Ma, H. Labiod, and P. H. J. Chong, "TEDS: A Trusted Entropy and Dempster Shafer Mechanism for Routing in Wireless Mesh Networks," presented at the MOBILITY 2014 The Fourth International Conference on Mobile Services, Resources, and Users, Paris, France, 2014.#</unstructured_citation></citation><citation key="ref32"><unstructured_citation>[32]	M. Ahmed, X. Huang, and D. Sharma, "Dempster-Shafer Theory to Identify Insider Attacker in Wireless Sensor Network," in Network and Parallel Computing, Berlin, Heidelberg, 2012, pp. 94-100.#</unstructured_citation></citation><citation key="ref33"><unstructured_citation>[33]	A. Vassilev and T. A. Hall, "The Importance of Entropy to Information Security," Computer, vol. 47, pp. 78-81, 2014.#</unstructured_citation></citation><citation key="ref34"><unstructured_citation>[34]	R. R. Y. Liu, Classic Works of the Dempster-Shafer Theory of Belief Functions: Springer, Berlin, Heidelberg, 2008.#</unstructured_citation></citation><citation key="ref35"><unstructured_citation>[35]	J. H. Ying-Jin Lu, "Dempster-Shafer Evidence Theory and Study of Some Key Problems," Journal of Electronic Science and vol. 15, pp. 106-112, 2017.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Secured Access Control in Security Information and Event Management Systems</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Leila</given_name><surname>Rikhtechi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Vahid</given_name><surname>Rafeh</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Afshin</given_name><surname>Rezakhani</surname></person_name></contributors><publication_date media_type="online"><month>4</month><day>12</day><year>2021</year></publication_date><pages><first_page>67</first_page><last_page>78</last_page></pages><doi_data><doi>10.52547/jist.9.33.67</doi><resource>http://jist.ir/en/Article/15526</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jist.ir/en/Article/Download/15526</resource></item><item crawler="google"><resource>http://jist.ir/en/Article/Download/15526</resource></item><item crawler="msn"><resource>http://jist.ir/en/Article/Download/15526</resource></item><item crawler="altavista"><resource>http://jist.ir/en/Article/Download/15526</resource></item><item crawler="yahoo"><resource>http://jist.ir/en/Article/Download/15526</resource></item><item crawler="scirus"><resource>http://jist.ir/en/Article/Download/15526</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jist.ir/en/Article/Download/15526</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	D. Godoy and A. Corbellini, "Folksonomy-Based Recommender Systems: A State-of-the-Art Review," Int. J. Intell. Syst., vol. 31, no. 4, pp. 314-346, 2016.#</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2]	Mohammed, N. M., Niazi, M., Alshayeb, M., &amp; Mahmood, S. (2017). Exploring software security approaches in software development lifecycle: A systematic mapping study. Computer Standards &amp; Interfaces, 50, 107-115.#</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3]	DURAIRAJ, S. K. J., &amp; Singla, A. (2017). U.S. Patent Software No. 15/303,771.#</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4]	Detken, K. O., Jahnke, M., Kleiner, C., &amp; Rohde, M. (2017, September). Combining Network Access Control (NAC) and SIEM functionality based on open source. In Proceedings of the 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Software (IDAACS), Bucharest, September 20th till September 23rd.#</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5]	Miller, D. R., Harris, S., Harper, A., VanDyke, S., &amp; Blask, C. (2010). Security Information and Event Management (SIEM) Implementation (Network Pro Library). McGraw Hill.#</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	Layton, T. P. (2016). Information Security: Design, implementation, measurement, and compliance. Auerbach Publications.#</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	Piessens, F., &amp; Verbauwhede, I. (2016, March). Software security: Vulnerabilities and countermeasures for two attacker models. In Proceedings of the 2016 Conference on Design, Automation &amp; Test in Europe (pp. 990-999). EDA Consortium.#</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	Shostack, A. (2014). Threat modeling: Designing for security. John Wiley &amp; Sons.#</unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9]	Aydan, U., Yilmaz, M., Clarke, P. M., &amp; O’Connor, R. V. (2017). Teaching ISO/IEC 12207 software lifecycle processes: a serious game approach. Computer Standards &amp; Interfaces, 54, 129-138.#</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10]	López-Lira Hinojo, F. J. (2014). Agile, CMMI®, RUP®, ISO/ IEC 12207...: is there a method in this madness? ACM SIGSOFT Software Engineering Notes, 39(2), 1-5.#</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11]	Hu, V. C., Kuhn, D. R., &amp; Ferraiolo, D. F. (2015). Attribute-based access control. Computer, 48(2), 85-88.#</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12]	Nazir, A., Alam, M., Malik, S. U., Akhunzada, A., Cheema, M. N., Khan, M. K., ... &amp; Khan, A (October 2016). A high-level domain- specific language for SIEM (design, development, and formal verification). Cluster Computing, 1-15.#</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13]	Di Sarno, C., Garofalo, A., Matteucci, I., &amp; Vallini, M. (2016). A novel security information and event management system for enhancing cybersecurity in a hydroelectric dam. International Journal of Critical Infrastructure Protection, 13, 39-51.#</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14]	Granadillo, G. G., El-Barbori, M., &amp; Debar, H. (2016, November). New Types of Alert Correlation for Security Information and Event Management Systems. In New Technologies, Mobility and Security (NTMS), 2016 8th IFIP International Conference on (pp. 1-7). IEEE.#</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15]	Grambow, G., Oberhauser, R., &amp; Reichert, M. (2016). Context-Aware and Process- Centric Knowledge Provisioning: An Example from the Software Development Domain. Innovations in Knowledge Management (pp. 179-209). Springer Berlin Heidelberg.#</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16]	Rezakhani, A., Shirazi, H., &amp; Modiri, N. (2018). A novel multilayer AAA model for integrated software. Neural Computing and Software, 29(10), 887-901.#</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17]	Grispos, G. (2016). On the enhancement of data quality in security incident response investigations (Doctoral dissertation, University of Glasgow).#</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18]	Betz, L. (2016). An Analysis of the Relationship between Security Information Technology Enhancements and Computer Security Breaches and Incidents. (Doctoral dissertation, Nova Southeastern University).#</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19]	Babu, B. M., &amp; Bhanu, M. S. (2015). Prevention of insider attacks by integrating behavior analysis with risk-based access control model to protect the cloud. Procedia Computer Science, 54, 157-166.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20]	Bhatt, S., Manadhata, P. K., &amp; Zomlot, L. (2014). The operational role of security information and event management systems. IEEE Security &amp; Privacy, (5), 35-41.#</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21]	Boucher, P., Wright, M., Cranny, T., Nault, G., &amp; Smith, M. (2015). U.S. Patent No. 9, 197, 668. Washington, DC: U.S. Patent and Trademark Office.#</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22]	ISO, I. IEC 12207: 2017 Systems and software Engineering-Software life cycle processes., (2017). International Organization for Standardization.#</unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23]	Verbeek, H. M. W., Buijs, J. C., Van Dongen, B. F., &amp; Van Der Aalst, W. M. (2010, June). Xes, xesame, and prom 6. In Forum at the Conference on Advanced Information Systems Engineering (CAiSE) (pp. 60-75). Springer, Berlin, Heidelberg.#</unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24]	IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams, (2016), IEEE Std, pp. 1849-2016.#</unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25]	Kent, K., &amp; Souppaya, M. (2006). Guide to computer security log management: recommendations of the National Institute of Standards and Technology. US Department of Commerce, Technology Administration, National Institute of Standards and Technology.#</unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26]	Erturk, E., &amp; Rajan, A. (2017). Web Vulnerability Scanners: A Case Study. arXiv preprint arXiv:1706.08017.#</unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27]	Hsu, C. L., Chen, W. X., &amp; Le, T. V. (2020). An Autonomous Log Storage Management Protocol with Blockchain Mechanism and Access Control for the Internet of Things. Sensors, 20(22), 6471.#</unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28]	Liang, D. (2020). U.S. Patent No. 10,616,258. Washington, DC: U.S. Patent and Trademark Office.#</unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29]	De Oliveira, M. G., &amp; Jatoba, P. (2020). U.S. Patent No. 10,579,995. Washington, DC: U.S. Patent and Trademark Office.#</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Denoising and Enhancement Speech Signal Using Wavelet</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Meriane</given_name><surname>Brahim</surname></person_name></contributors><publication_date media_type="online"><month>4</month><day>12</day><year>2021</year></publication_date><pages><first_page>37</first_page><last_page>44</last_page></pages><doi_data><doi>10.52547/jist.9.33.37</doi><resource>http://jist.ir/en/Article/15616</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jist.ir/en/Article/Download/15616</resource></item><item crawler="google"><resource>http://jist.ir/en/Article/Download/15616</resource></item><item crawler="msn"><resource>http://jist.ir/en/Article/Download/15616</resource></item><item crawler="altavista"><resource>http://jist.ir/en/Article/Download/15616</resource></item><item crawler="yahoo"><resource>http://jist.ir/en/Article/Download/15616</resource></item><item crawler="scirus"><resource>http://jist.ir/en/Article/Download/15616</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jist.ir/en/Article/Download/15616</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1] Rupali V. Mane, and  Dr.M.T.Kolte,  " Implementation of Adaptive Filtering Algorithm for Speech Signal on FPGA " , International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, Vol. 2, Issue3, March 2014.#</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2] J. Kim, " Time-frequency characterization of hand-transmitted, impulsive vibrations using analytic wavelet transform", Journal of Sound and Vibration, vol.308, pp. 98-111, Nov 2007.#</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3] M. HemaLatha and Dr.S, " Resolution enhancement of low resolution satellite images using Dual tree complex wavelet transform",  International Journal of Scientific &amp; Engineering Research, Volume 8, Issue 5, May-2017.#</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4] I. Daubechies, Ten Lectures on Wavelets. Philadelphia: SIAM, 1992.#</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5] U. Jayakrishnan, G. Dhavale and  P. Khandelwal, Wavelet Denoising Of Discrete-Time Signals, EE678 Wavelets Application Assignment, 2005.#</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6] Y. Hoshino, Wavelet Transform Analysis the Recognizing Brain Activities for Development the Palm-Size and Simplification Near-Infrared Spectroscopy Prototype System by Using Arduino,2018.#</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7] K. Borna, and S. Palizdar,  "Short Time Price Forecasting for Electricity Market Based on Hybrid Fuzzy Wavelet Transform and Bacteria Foraging Algorithm" Journal of Information Systems and Telecommunication, Vol. 4, No. 4, October-December 2016.#</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8] S. Rani, and R. Kaur, "review: audio noise reduction using filters and discrete wavelet transformation", Journal of The International Association of Advanced Technology and Science (JIAATS), ISSN-5563 1682, Vol. 16 , June 2015.#</unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9] C. Gargour, M. Gabrea, V. Ramachandran, and J. Lina, “A Short Introduction to Wavelets and Their Applications”, IEEE Circuits and Systems Magazine, ISSN: 1531-636X, vol. 2, pp. 57-67, 2009.#</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10] R. Torkamani, and Sadeghzadeh, "Wavelet-based Bayesian Algorithm for Distributed Compressed Sensing", Journal of Information Systems and Telecommunication, Vol. 7, No. 2, April-June 2019.#</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11] N. Kaladharan, "Speech Enhancement by Spectral Subtraction Method", International Journal of Computer Applications (0975 – 8887) Volume 96– No.13, June 2014.#</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12] P. Goli, "Speech Intelligibility Improvement in Noisy Environments for Near-End Listening Enhancement", Journal of Information Systems and Telecommunication, Vol. 4, No. 1, January-March 2016.#</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13] M. R. Kahrizi, "Long-Term Spectral Pseudo-Entropy (LTSPE): A New Robust Feature For Speech Activity Detection", Journal of Information Systems and Telecommunication, Vol. 6, No. 4, October-December 2018.#</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14] K. Wang, "Wavelet packet analysis for speaker-independent emotion recognition",  Neurocomputing 398 (2020) 257–264.#</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15] A. Bhattacharyya, " Fourier–Bessel series expansion based empirical wavelet transform for analysis of non-stationary signals ", m5G; v1.232; Prn:7/03/2018; 12:30] P.1 (1-12).#</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16] A. Upadhyay, and R.B. Pachori, "Speech enhancement based on mEMD–VMD method, Electron", ELECTRONICS LETTERS 30th March 2017 Vol. 53 No. 7 pp. 502–504.#</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17] P. Singh, S.D. Joshi, R.K. Patney, and K. Saha, "The Fourier decomposition method for nonlinear and non stationary time series analysis", Proc. R. Soc. A 473 (2017).#</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18] Yaseen, G. Young Son, and Soonil Kwon," Classification of Heart Sound Signal UsingMultiple Features", Appl. Sci. 2018, 8, 2344; doi:10.3390/app8122344.#</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19]M, Pourseiedrezaei, "Prediction of Psychoacoustic Metrics Using Combination of Wavelet Packet Transform and an Optimized Artificial Neural Network ", Archives of Acoustics – Volume 44, Number 3, 2019.#</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Energy Efficient Routing-Based Clustering Protocol Using Computational Intelligence Algorithms in Sensor-Based IoT</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Mohammad</given_name><surname>sedighimanesh</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Hessam </given_name><surname>Zandhessami</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mahmood </given_name><surname>Alborzi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mohammadsadegh </given_name><surname>Khayyatian</surname></person_name></contributors><publication_date media_type="online"><month>4</month><day>12</day><year>2021</year></publication_date><pages><first_page>55</first_page><last_page>66</last_page></pages><doi_data><doi>10.52547/jist.9.33.55</doi><resource>http://jist.ir/en/Article/15641</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jist.ir/en/Article/Download/15641</resource></item><item crawler="google"><resource>http://jist.ir/en/Article/Download/15641</resource></item><item crawler="msn"><resource>http://jist.ir/en/Article/Download/15641</resource></item><item crawler="altavista"><resource>http://jist.ir/en/Article/Download/15641</resource></item><item crawler="yahoo"><resource>http://jist.ir/en/Article/Download/15641</resource></item><item crawler="scirus"><resource>http://jist.ir/en/Article/Download/15641</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jist.ir/en/Article/Download/15641</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	F. Gregorio, G. González, C. Schmidt, and J. Cousseau, “Internet of Things,” in Signals and Communication Technology, 2020.#</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2]	M. Bavaghar, A. Mohajer, and S. T. Motlagh, “Energy Efficient Clustering Algorithm for Wireless Sensor Networks.” Journal of Information Systems and Telecommunication (JIST), pp. 238–247, doi: 10.7508/jist.2019.04.001.#</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3]	S. Rani, R. Talwar, J. Malhotra, S. H. Ahmed, M. Sarkar, and H. Song, “A novel scheme for an energy efficient internet of things based on wireless sensor networks,” Sensors (Switzerland), 2015, doi: 10.3390/s151128603.#</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4]	J. V. V. Sobral, J. J. P. C. Rodrigues, R. A. L. Rabêlo, K. Saleem, and V. Furtado, “LOADng-IoT: An enhanced routing protocol for internet of things applications over low power networks,” Sensors (Switzerland), 2019, doi: 10.3390/s19010150.#</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5]	R. Han, W. Yang, Y. Wang, and K. You, “DCE: A distributed energy-efficient clustering protocol for wireless sensor network based on double-phase cluster-head election,” Sensors (Switzerland), 2017, doi: 10.3390/s17050998.#</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	M. Sedighimanesh* and H. Z. H. and A. Sedighimanesh, “Routing Algorithm based on Clustering for Increasing the Lifetime of Sensor Networks by Using Meta-Heuristic Bee Algorithms,” International Journal of Sensors, Wireless Communications and Control, vol. 10, no. 1. pp. 25–36, 2020, doi: http://dx.doi.org/10.2174/2210327909666190129154802.#</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	K. Thangaramya, K. Kulothungan, R. Logambigai, M. Selvi, S. Ganapathy, and A. Kannan, “Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT,” Comput. Networks, 2019, doi: 10.1016/j.comnet.2019.01.024.#</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	F. Fanian and M. Kuchaki Rafsanjani, “Cluster-based routing protocols in wireless sensor networks: A survey based on methodology,” J. Netw. Comput. Appl., vol. 142, pp. 111–142, Sep. 2019, doi: 10.1016/J.JNCA.2019.04.021.#</unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9]	B. Bhushan and G. Sahoo, “Routing protocols in wireless sensor networks,” in Studies in Computational Intelligence, 2019.#</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10]	J. Bhola, S. Soni, and G. K. Cheema, “Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks,” J. Ambient Intell. Humaniz. Comput., vol. 11, no. 3, pp. 1281–1288, 2020.#</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11]	J. H. Lee, “Energy-efficient clustering scheme in wireless sensor network,” Int. J. Grid Distrib. Comput., 2018, doi: 10.14257/ijgdc.2018.11.10.09.#</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12]	A. Kochhar, P. Kaur, P. Singh, and S. Sharma, “Protocols for wireless sensor networks: A survey,” Journal of Telecommunications and Information Technology. 2018, doi: 10.26636/jtit.2018.117417.#</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13]	B. Bhushan and G. Sahoo, “Routing protocols in wireless sensor networks,” in Studies in Computational Intelligence, 2019.#</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14]	G. Smaragdakis, I. Matta, and A. Bestavros, “SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks *,” 2nd Int. Work. Sens. Actor Netw. Protoc. Appl., 2004, doi: 10.3923/jmcomm.2010.38.42.#</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15]	L. Qing, Q. Zhu, and M. Wang, “Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks,” Comput. Commun., 2006, doi: 10.1016/j.comcom.2006.02.017.#</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16]	M. Sedighimanesh, H. Zandhesami, and A. Sedighimanesh, “Presenting the Hybrid Algorithm of Honeybee - Harmony in Clustering and Routing of Wireless Sensor Networks,” Int. J. Sensors, Wirel. Commun. Control, 2018, doi: 10.2174/2210327908666181029094346.#</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17]	M. Sugeno and G. T. Kang, “Structure identification of fuzzy model,” Fuzzy Sets Syst., 1988, doi: 10.1016/0165-0114(88)90113-3.#</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18]	A. Kousar, N. Mittal, and P. Singh, “An Improved Hierarchical Clustering Method for Mobile Wireless Sensor Network Using Type-2 Fuzzy Logic,” in Lecture Notes in Electrical Engineering, 2020, doi: 10.1007/978-3-030-30577-2_11.#</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Phase Transition in the Social Impact Model of Opinion Formation in Log-Normal Networks</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Alireza</given_name><surname>Mansouri</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Fattaneh</given_name><surname>Taghiyareh</surname></person_name></contributors><publication_date media_type="online"><month>4</month><day>12</day><year>2021</year></publication_date><pages><first_page>1</first_page><last_page>14</last_page></pages><doi_data><doi>10.52547/jist.9.33.1</doi><resource>http://jist.ir/en/Article/15891</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jist.ir/en/Article/Download/15891</resource></item><item crawler="google"><resource>http://jist.ir/en/Article/Download/15891</resource></item><item crawler="msn"><resource>http://jist.ir/en/Article/Download/15891</resource></item><item crawler="altavista"><resource>http://jist.ir/en/Article/Download/15891</resource></item><item crawler="yahoo"><resource>http://jist.ir/en/Article/Download/15891</resource></item><item crawler="scirus"><resource>http://jist.ir/en/Article/Download/15891</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jist.ir/en/Article/Download/15891</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	P. Hedström, and P. Bearman, “What is analytical sociology all about? An introductory essay,” The Oxford handbook of analytical sociology, pp. 3-24, 2009.#</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2]	M. Keuschnigg, N. Lovsjö, and P. Hedström, “Analytical sociology and computational social science,” Journal of Computational Social Science, vol. 1, no. 1, pp. 3-14, 2018.#</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3]	L. Mastroeni, P. Vellucci, and M. Naldi, “Agent-based models for opinion formation: A bibliographic survey,” IEEE Access, vol. 7, pp. 58836-58848, 2019.#</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4]	B. D. Anderson, and M. Ye, “Recent advances in the modelling and analysis of opinion dynamics on influence networks,” International Journal of Automation and Computing, vol. 16, no. 2, pp. 129-149, 2019.#</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5]	C. Castellano, S. Fortunato, and V. Loreto, “Statistical physics of social dynamics,” Reviews of modern physics, vol. 81, no. 2, pp. 591, 2009.#</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	J. R. French Jr, “A formal theory of social power,” Psychological review, vol. 63, no. 3, pp. 181, 1956.#</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	J. A. Hołyst, K. Kacperski, and F. Schweitzer, “Social impact models of opinion dynamics,” Annual reviews of computational physics, vol. 9, pp. 253-273, 2001.#</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	B. Latané, “The psychology of social impact,” American psychologist, vol. 36, no. 4, pp. 343-356, 1981.#</unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9]	S. Hobolt, T. J. Leeper, and J. Tilley, “Divided by the vote: affective polarization in the wake of the Brexit referendum,” British Journal of Political Science, 2020.#</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10]	M. Pineda, R. Toral, and E. Hernandez-Garcia, “Noisy continuous-opinion dynamics,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2009, no. 08, pp. P08001, 2009.#</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11]	L. P. Kadanoff, “More is the same; phase transitions and mean field theories,” Journal of Statistical Physics, vol. 137, no. 5-6, pp. 777, 2009.#</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12]	A. Mansouri, and F. Taghiyareh, “Phase Transition in the Social Impact Model of Opinion Formation in Scale-Free Networks: The Social Power Effect,” Journal of Artificial Societies and Social Simulation, vol. 23, no. 2, pp. 3, 2020.#</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13]	J. A. Hołyst, K. Kacperski, and F. Schweitzer, “Phase transitions in social impact models of opinion formation,” Physica A: Statistical Mechanics and its Applications, vol. 285, no. 1-2, pp. 199-210, 2000.#</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14]	G. Jaeger, “The Ehrenfest classification of phase transitions: introduction and evolution,” Archive for history of exact sciences, vol. 53, no. 1, pp. 51-81, 1998.#</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15]	M. Li, and H. Dankowicz, “Impact of temporal network structures on the speed of consensus formation in opinion dynamics,” Physica A: Statistical Mechanics and its Applications, vol. 523, pp. 1355-1370, 2019.#</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16]	A.-L. Barabási, and R. Albert, “Emergence of scaling in random networks,” science, vol. 286, no. 5439, pp. 509-512, 1999.#</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17]	T. Johansson, “Generating artificial social networks,” The Quantitative Methods for Psychology, vol. 15, no. 2, pp. 56-74, 2019.#</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18]	A. D. Broido, and A. Clauset, “Scale-free networks are rare,” Nature communications, vol. 10, no. 1, pp. 1-10, 2019.#</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19]	K. Sun, “Explanation of log-normal distributions and power-law distributions in biology and social science,” Tech. Report, Department of Physics, 2004.#</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20]	C. Cioffi-Revilla, “Computational social science,” Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2, no. 3, pp. 259-271, 2010.#</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21]	P. Y.-z. Wan, “Analytical sociology: A Bungean appreciation,” Science &amp; Education, vol. 21, no. 10, pp. 1545-1565, 2012.#</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22]	N. Gilbert, and K. Troitzsch, Simulation for the social scientist: McGraw-Hill Education (UK), 2005.#</unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23]	J. Hauke, I. Lorscheid, and M. Meyer, “Recent development of social simulation as reflected in JASSS between 2008 and 2014: A citation and co-citation analysis,” Journal of artificial societies and social simulation, vol. 20, no. 1, 2017.#</unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24]	E. Chattoe-Brown, “Why sociology should use agent based modelling,” Sociological Research Online, vol. 18, no. 3, pp. 1-11, 2013.#</unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25]	F. Bianchi, and F. Squazzoni, “Agent-based models in sociology,” Wiley Interdisciplinary Reviews: Computational Statistics, vol. 7, no. 4, pp. 284-306, 2015.#</unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26]	A. Jędrzejewski, and K. Sznajd-Weron, “Statistical physics of opinion formation: is it a spoof?,” Comptes Rendus Physique, 2019.#</unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27]	R. P. Abelson, “Mathematical models of the distribution of attitudes under controversy,” Contributions to mathematical psychology, vol. 14, pp. 1-160, 1964.#</unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28]	M. H. DeGroot, “Reaching a consensus,” Journal of the American Statistical Association, vol. 69, no. 345, pp. 118-121, 1974.#</unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29]	R. A. Holley, and T. M. Liggett, “Ergodic theorems for weakly interacting infinite systems and the voter model,” The annals of probability, pp. 643-663, 1975.#</unstructured_citation></citation><citation key="ref30"><unstructured_citation>[30]	N. E. Friedkin, and E. C. Johnsen, “Social influence and opinions,” Journal of Mathematical Sociology, vol. 15, no. 3-4, pp. 193-206, 1990.#</unstructured_citation></citation><citation key="ref31"><unstructured_citation>[31]	N. E. Friedkin, and E. C. Johnsen, “Social influence networks and opinion change,” Advances in Group Processes, vol. 16, pp. 1-29, 1999.#</unstructured_citation></citation><citation key="ref32"><unstructured_citation>[32]	R. Axelrod, “The dissemination of culture: A model with local convergence and global polarization,” Journal of conflict resolution, vol. 41, no. 2, pp. 203-226, 1997.#</unstructured_citation></citation><citation key="ref33"><unstructured_citation>[33]	K. Sznajd-Weron, and J. Sznajd, “Opinion evolution in closed community,” International Journal of Modern Physics C, vol. 11, no. 06, pp. 1157-1165, 2000.#</unstructured_citation></citation><citation key="ref34"><unstructured_citation>[34]	D. Stauffer, A. O. Sousa, and S. M. De Oliveira, “Generalization to square lattice of Sznajd sociophysics model,” International Journal of Modern Physics C, vol. 11, no. 06, pp. 1239-1245, 2000.#</unstructured_citation></citation><citation key="ref35"><unstructured_citation>[35]	G. Deffuant, D. Neau, F. Amblard, and G. Weisbuch, “Mixing beliefs among interacting agents,” Advances in Complex Systems, vol. 3, no. 01n04, pp. 87-98, 2000.#</unstructured_citation></citation><citation key="ref36"><unstructured_citation>[36]	G. Deffuant, F. Amblard, G. Weisbuch, and T. Faure, “How can extremism prevail? A study based on the relative agreement interaction model,” Journal of artificial societies and social simulation, vol. 5, no. 4, 2002.#</unstructured_citation></citation><citation key="ref37"><unstructured_citation>[37]	G. Deffuant, F. Amblard, and G. Weisbuch, “Modelling group opinion shift to extreme: the smooth bounded confidence model,” arXiv preprint cond-mat/0410199, 2004.#</unstructured_citation></citation><citation key="ref38"><unstructured_citation>[38]	R. Hegselmann, and U. Krause, “Opinion dynamics and bounded confidence models, analysis, and simulation,” Journal of Artificial Societies and Social Simulation, vol. 5, no. 3, 2002.#</unstructured_citation></citation><citation key="ref39"><unstructured_citation>[39]	S. Galam, “Minority opinion spreading in random geometry,” The European Physical Journal B-Condensed Matter and Complex Systems, vol. 25, no. 4, pp. 403-406, 2002.#</unstructured_citation></citation><citation key="ref40"><unstructured_citation>[40]	C. Altafini, “Dynamics of opinion forming in structurally balanced social networks,” PloS one, vol. 7, no. 6, pp. e38135, 2012.#</unstructured_citation></citation><citation key="ref41"><unstructured_citation>[41]	C. Altafini, “Consensus problems on networks with antagonistic interactions,” IEEE transactions on automatic control, vol. 58, no. 4, pp. 935-946, 2013.#</unstructured_citation></citation><citation key="ref42"><unstructured_citation>[42]	C. Altafini, and G. Lini, “Predictable dynamics of opinion forming for networks with antagonistic interactions,” IEEE Transactions on Automatic Control, vol. 60, no. 2, pp. 342-357, 2015.#</unstructured_citation></citation><citation key="ref43"><unstructured_citation>[43]	A. Nowak, J. Szamrej, and B. Latané, “From private attitude to public opinion: A dynamic theory of social impact,” Psychological review, vol. 97, no. 3, pp. 362, 1990.#</unstructured_citation></citation><citation key="ref44"><unstructured_citation>[44]	A. Mansouri, F. Taghiyareh, and J. Hatami, “Improving Opinion Formation Models on Social Media Through Emotions,” in 5th International Conference on Web Research (ICWR), 2019.#</unstructured_citation></citation><citation key="ref45"><unstructured_citation>[45]	A. Mansouri, F. Taghiyareh, and J. Hatami, “Post-Based Prediction of Users' Opinions Employing the Social Impact Model Improved by Emotion,” International Journal of Web Research, vol. 1, no. 2, pp. 34-42, 2018.#</unstructured_citation></citation><citation key="ref46"><unstructured_citation>[46]	M. Golosovsky, “Power-law citation distributions are not scale-free,” Physical Review E, vol. 96, no. 3, pp. 032306, 2017.#</unstructured_citation></citation><citation key="ref47"><unstructured_citation>[47]	A. Clauset, C. R. Shalizi, and M. E. Newman, “Power-law distributions in empirical data,” SIAM review, vol. 51, no. 4, pp. 661-703, 2009.#</unstructured_citation></citation><citation key="ref48"><unstructured_citation>[48]	K. Binder, “Theory of first-order phase transitions,” Reports on progress in physics, vol. 50, no. 7, pp. 783, 1987.#</unstructured_citation></citation><citation key="ref49"><unstructured_citation>[49]	A. Barrat, M. Barthelemy, and A. Vespignani, Dynamical processes on complex networks: Cambridge university press, 2008.#</unstructured_citation></citation><citation key="ref50"><unstructured_citation>[50]	P. Fronczak, A. Fronczak, and J. A. Hołyst, “Phase transitions in social networks,” The European Physical Journal B, vol. 59, no. 1, pp. 133-139, 2007.#</unstructured_citation></citation><citation key="ref51"><unstructured_citation>[51]	M. Perc, “Phase transitions in models of human cooperation,” Physics Letters A, vol. 380, no. 36, pp. 2803-2808, 2016.#</unstructured_citation></citation><citation key="ref52"><unstructured_citation>[52]	M. Bojanowski, and R. Corten, “Measuring segregation in social networks,” Social Networks, vol. 39, pp. 14-32, 2014.#</unstructured_citation></citation><citation key="ref53"><unstructured_citation>[53]	A. Kowalska-Styczeń, and K. Malarz, “Noise induced unanimity and disorder in opinion formation,” Plos one, vol. 15, no. 7, pp. e0235313, 2020.#</unstructured_citation></citation><citation key="ref54"><unstructured_citation>[54]	S. Grauwin, and P. Jensen, “Opinion group formation and dynamics: Structures that last from nonlasting entities,” Physical Review E, vol. 85, no. 6, pp. 066113, 2012.#</unstructured_citation></citation><citation key="ref55"><unstructured_citation>[55]	M. Pineda, R. Toral, and E. Hernández-García, “Diffusing opinions in bounded confidence processes,” The European Physical Journal D, vol. 62, no. 1, pp. 109-117, 2011.#</unstructured_citation></citation><citation key="ref56"><unstructured_citation>[56]	A. Carro, R. Toral, and M. San Miguel, “The role of noise and initial conditions in the asymptotic solution of a bounded confidence, continuous-opinion model,” Journal of Statistical Physics, vol. 151, no. 1-2, pp. 131-149, 2013.#</unstructured_citation></citation><citation key="ref57"><unstructured_citation>[57]	J. Zhang, and Y. Zhao, “The robust consensus of a noisy deffuant-weisbuch model,” Mathematical Problems in Engineering, vol. 2018, 2018.#</unstructured_citation></citation><citation key="ref58"><unstructured_citation>[58]	L. Sabatelli, and P. Richmond, “Non-monotonic spontaneous magnetization in a Sznajd-like consensus model,” Physica A: Statistical Mechanics and its Applications, vol. 334, no. 1-2, pp. 274-280, 2004.#</unstructured_citation></citation><citation key="ref59"><unstructured_citation>[59]	K. Sznajd-Weron, “Sznajd model and its applications,” arXiv preprint physics/0503239, 2005.#</unstructured_citation></citation><citation key="ref60"><unstructured_citation>[60]	W. Su, G. Chen, and Y. Hong, “Noise leads to quasi-consensus of Hegselmann–Krause opinion dynamics,” Automatica, vol. 85, pp. 448-454, 2017.#</unstructured_citation></citation><citation key="ref61"><unstructured_citation>[61]	G. Chen, W. Su, S. Ding, and Y. Hong, “Heterogeneous hegselmann-krause dynamics with environment and communication noise,” IEEE Transactions on Automatic Control, 2019.#</unstructured_citation></citation><citation key="ref62"><unstructured_citation>[62]	M. Pineda, R. Toral, and E. Hernández-García, “The noisy Hegselmann-Krause model for opinion dynamics,” The European Physical Journal B, vol. 86, no. 12, pp. 490, 2013.#</unstructured_citation></citation><citation key="ref63"><unstructured_citation>[63]	G. Bianconi, and A.-L. Barabási, “Competition and multiscaling in evolving networks,” EPL (Europhysics Letters), vol. 54, no. 4, pp. 436, 2001.#</unstructured_citation></citation><citation key="ref64"><unstructured_citation>[64]	T. Pham, P. Sheridan, H. Shimodaira, M. T. Pham, and I. Rcpp, “Package ‘PAFit’,” 2020.#</unstructured_citation></citation></citation_list></journal_article></journal></body></doi_batch>