﻿<?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>ijece-2026051923</doi_batch_id><timestamp>20260519231853</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="fa"><full_title>Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran</full_title><abbrev_title>ijece</abbrev_title><issn media_type="electronic">16823745</issn></journal_metadata><journal_issue><publication_date media_type="online"><month>10</month><day>18</day><year>2025</year></publication_date><journal_volume><volume>23</volume></journal_volume><issue>2</issue></journal_issue><journal_article publication_type="full_text"><titles><title>A Hybrid Method for Heart Disease Diagnosis Using Integrated Feature Selection and Optimized Classification Approaches</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Maral</given_name><surname>Kolahkaj</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Marjan</given_name><surname>Motiee Zadeh</surname></person_name></contributors><publication_date media_type="online"><month>10</month><day>18</day><year>2025</year></publication_date><pages><first_page>121</first_page><last_page>131</last_page></pages><doi_data><doi>10.66224/ijece.33191.23.2.121</doi><resource>http://ijece.org/en/Article/33191</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://ijece.org/en/Article/Download/33191</resource></item><item crawler="google"><resource>http://ijece.org/en/Article/Download/33191</resource></item><item crawler="msn"><resource>http://ijece.org/en/Article/Download/33191</resource></item><item crawler="altavista"><resource>http://ijece.org/en/Article/Download/33191</resource></item><item crawler="yahoo"><resource>http://ijece.org/en/Article/Download/33191</resource></item><item crawler="scirus"><resource>http://ijece.org/en/Article/Download/33191</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://ijece.org/en/Article/Download/33191</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	E. J. Nelwan, E. Widjajanto, S. Andarini, and M. S. Djati, "Modified risk factors for coronary heart disease (CHD) in Minahasa ethnic group from Manado city Indonesia," J. of Experimental Life Science, vol. 6, no. 2, pp. 88-94, Apr. 2017.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
[2]	C. J. Taylor and J. Moore, "NICE chronic heart failure update guideline 2018," Primary Care Cardiovascular J., vol. 3, no. 9, pp. 1-3, Apr. 2019.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
[3]	S. Citlik-Saritas, S. Saritas, R. Cevik-Akyil, and K. Isik, "The effects of Turkish classical music on physiological parameters, pain and analgesic use in patients with myocardial infarction: a non-randomized controlled study," Eur. J. of Integrative Medicine, vol. 22, pp. 50-53, Sept. 2018.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>
[4]	M. Adam, et al., "Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals," Comput. Methods and Programs in Biomedicine, vol. 161, pp. 133-143, Jul. 2018.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>
[5]	M. Kolahkaj, A. Harounabadi, and M. Sadeghzade, "A recommender system for web mining using neural network and fuzzy algorithm," Int. J. of Computer Applications, vol. 78, no. 8, pp. 20-24, Sept. 2013.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
[6]	م. کلاه¬کج، "ارائه سیستم بازیابی تصاویر مبتنی بر محتوا با بهره‌گیری از یادگیری  نیمه¬نظارت‌شده و کاوش الگوهای مکرر انجمنی،" نشریه مهندسی برق و مهندسی کامپیوتر ایران، ب- مهندسی کامپیوتر، سال 20، شماره 3، صص. 252-245، پاییز 1401.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
[7]	K. Oh, Z. Li, B. S. Oh, and K. A. Toh, "Optimizing between data transformation and parametric weighting for stable binary classification," J. of the Franklin Institute, vol. 355, no. 4, pp. 1614-1637, Mar. 2018.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>
[8]	M. Kolahkaj, "An image retrieval approach based on feature extraction and self-supervised learning," in Proc. 2nd Int. Conf. on Distributed Computing and High-Performance Computing, pp. 46-51, Qom, Iran, 2-3 Mar. 2022.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>
[9]	C. Berry, D. R. Murdoch, and J. J. McMurray, "Economics of chronic heart failure," Eur. J. of Heart Failure, vol. 3, no. 3, pp. 283-291, Jun. 2001.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
[10]	J. C. de la Torre, "Hemodynamic instability in heart failure intensifies age-dependent cognitive decline," J. of Alzheimer's Disease, vol. 76, no. 1, pp. 63–84, May 2020.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>
[11]	H. Linusson, U. Johansson, H. Boström, and T. Löfström, "Classification with reject option using conformal prediction," in Proc. Pacific-Asia Conf. on Knowledge Discovery and Data Mining, pp. 94-105, Melbourne, Australia, 3-6 Jun. 2018.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
[12]	J. Qiu, J. Xie, D. Zhang, and R. Zhang, "A robust twin support vector machine based on fuzzy systems," Int. J. of Intelligent Computing and Cybernetics, vol. 17, no. 1, pp. 101-125, Feb. 2024.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
[13]	B. Sahmadi and D. Boughaci, "Hybrid genetic algorithm with SVM for medical data classification," in Proc. Int. Conf. on Applied Smart Systems, 6 pp., Medea, Algeria, 24-25 Nov. 2018.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
[14]	S. Chen, J. Cao, F. Chen, and B. Liu, "Entropy-based fuzzy least squares twin support vector machine for pattern classification," Neural Processing Letters, vol. 51, no. 1, pp. 41-66, Feb. 2020.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
[15]	Z. Zainuddin, K. H. Lai, and P. Ong, "An enhanced harmony searches-based algorithm for feature selection: applications in epileptic seizure detection and prediction," Computers &amp; Electrical Engineering, vol. 53, pp. 143-162, Jul. 2016.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>
[16]	M. Nekkaa and D. Boughaci, "Hybrid harmony search combined with stochastic local search for feature selection," Neural Processing Letters, vol. 44, no. 1, pp. 199-220, Aug. 2016.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
[17]	D. Karaboga and C. Ozturk, "A novel clustering approach: artificial bee colony (ABC) algorithm," Appl. Soft Computing, vol. 11, no. 1, pp. 652-657, Jan. 2011.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
[18]	P. Tapkan, L. Özbakır, S. Kulluk, and A. Baykasoğlu, "A cost-sensitive classification algorithm: BEE-Miner," Knowledge-Based Systems, vol. 95, pp. 99-113, Mar. 2016.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
[19]	X. Lai, Z. Zhang, H. Chen, L. Zhang, Z. Li, and W. Lu, "Tracking-removed neural network with graph information for classification 
of incomplete data," Appl. Intelligence, vol. 55, no. 3, pp. 1-20, Feb. 2025.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>
[20]	J. Wu, S. Pan, X. Zhu, P. Zhang, and C. Zhang, "Sode: self-adaptive one-dependence estimators for classification," Pattern Recognition, vol. 51, pp. 358-377, Mar. 2016.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>
[21]	X. Zhu, et al., "Confidence guided semi-supervised cross-modality person re-identification," Pattern Recognition, vol. 165, Article ID: 111669, Sept. 2025.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>
[22]	X. Wang, G. Wu, G. Hao, and Z. Zhang, "A novel fuzzy twin support vector machine using mass-based dissimilarity measure," Knowledge and Information Systems, vol. 55, no. 5, pp. 4233-4300, Jan. 2025.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>
[23]	B. Aydïlek, "Examining effects of the support vector machines kernel types on biomedical data classification," in Proc. Int. Conf. on Artificial Intelligence and Data Processing, 4 pp., Maltaya, Turkey, 28-30 Sept. 2018.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>
[24]	S. Chen, J. Cao, and Z. Huang, "Weighted linear loss projection twin support vector machine for pattern classification," IEEE Access, vol. 7, pp. 57349-57360, 2019.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>
[25]	S. Lee and C. H. Jun, "Fast incremental learning of logistic model tree using least angle regression," Expert Systems with Applications, vol. 97, pp. 137-145, May 2018.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>
[26]	H. Wang, P. Li, Y. Zheng, K. Jiang, and Y. Xu, "Sparse pinball universum nonparallel support vector machine and its safe screening rule," Appl. Intelligence, vol. 55, no. 6, pp. 563-580, Apr. 2025.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>
[27]	C. T. Tran, M. Zhang, P. Andreae, B. Xue, and L. T. Bui, "An effective and efficient approach to classification with incomplete data," Knowledge-Based Systems, vol. 154, pp. 1-16, Aug. 2018.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>
[28]	A. K. Jović, K. Brkić, and N. Bogunović, "A review of feature selection methods with applications," in Proc. 38th Int. Convention on Information and Communication Technology, Electronics and Microelectronics, pp. 1200-1205, Opatija, Croatia, 25-29 May 2015.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>
[29]	J. Hamidzadeh, Z. Mehravaran, and A. Harati, "Feature selection by utilizing kernel-based fuzzy rough set and entropy-based non-dominated sorting genetic algorithm in multi-label data," Knowledge and Information Systems, vol. 67, no. 4, pp. 3789–3819, Apr. 2025.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>
[30]	S. Narayanamoorthy, S. Geetha, R. Rakkiyappan, and Y. H. Joo, "Interval-valued intuitionistic hesitant fuzzy entropy based VIKOR method for industrial robots' selection," Expert Systems with Applications, vol. 121, pp. 28-37, May 2019.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>
[31]	I. Kadhim Ajlan, H. Murad, A. A. Salim, and A. Fadhil Bin Yousif, "Extreme learning machine algorithm for breast cancer diagnosis," Multimedia Tools and Applications, vol. 84, pp. 14739-14758, 2024.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>
[32]	X. Zhang, X. Hu, G. Cui, Y. Wang, and Y. Niu, "An improved shuffled frog leaping algorithm with cognitive behavior," in Proc. 7th World Congress on Intelligent Control and Automation, pp. 6197-6202, Chongqing, China, 25-27, Jun. 2008.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>
[33]	UCI Machine Learning Repository, Heart Disease, 1988, available at https://www.archive.ics.uci.edu/ml/datasets/Heart+Disease</unstructured_citation></citation><citation key="ref34"><unstructured_citation>
[34]	G. Moody and R. Mark, MIT-BIH Arrhythmia Database, 2025, available at https://physionet.org/content/mitdb/1.0.0/</unstructured_citation></citation><citation key="ref35"><unstructured_citation>
[35]	J. Wu, S. Pan, X. Zhu, Z. Cai, P. Zhang, and C. Zhang, "Self-adaptive attribute weighting for Naive Bayes classification," Expert Systems with Applications, vol. 42, no. 3, pp. 1487-1502, Feb. 2015.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>
[36]	P. Shunmugapriya and S. Kanmani, "A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC Hybrid)," Swarm and Evolutionary Computation, vol. 36, pp. 27-36, Oct. 2017.</unstructured_citation></citation><citation key="ref37"><unstructured_citation>
[37]	X. J. Shen, Y. Dong, J. P. Gou, Y. Z. Zhan, and J. Fan, "Least squares kernel ensemble regression in reproducing kernel Hilbert space," Neurocomputing, vol. 311, pp. 235-244, Oct. 2018.</unstructured_citation></citation><citation key="ref38"><unstructured_citation>
[38]	C. Yang and X. C. Yin, "Diversity-based random forests with sample weight learning," Cognitive Computation, vol. 11, no. 5, pp. 685-696, Oct. 2019.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Automation of Software Test Data Generation Based on Path Coverage Criteria and Using Coati Optimization Algorithm and Q Learning</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Marzieh</given_name><surname>sepahvand</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mojtaba </given_name><surname>Salehi</surname></person_name></contributors><publication_date media_type="online"><month>10</month><day>18</day><year>2025</year></publication_date><pages><first_page>99</first_page><last_page>110</last_page></pages><doi_data><doi>10.66224/ijece.46471.23.2.99</doi><resource>http://ijece.org/en/Article/46471</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://ijece.org/en/Article/Download/46471</resource></item><item crawler="google"><resource>http://ijece.org/en/Article/Download/46471</resource></item><item crawler="msn"><resource>http://ijece.org/en/Article/Download/46471</resource></item><item crawler="altavista"><resource>http://ijece.org/en/Article/Download/46471</resource></item><item crawler="yahoo"><resource>http://ijece.org/en/Article/Download/46471</resource></item><item crawler="scirus"><resource>http://ijece.org/en/Article/Download/46471</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://ijece.org/en/Article/Download/46471</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	P. Ammann and J. Offutt, Introduction to Software Testing, Cambridge University Press, 2016.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
[2]	F. Lonetti and E. Marchetti, "Emerging software testing technologies," Advances in Computers, Elsevier, vol. 108, pp. 91-143, 2018.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
[3]	S. Parsa, "Automatic test data generation symbolic and concolic executions," In S. Parsa (ed.), Software Testing Automation: Testability Evaluation, Refactoring, Test Data Generation and Fault Localization, pp. 503-542, Springer, 2023.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>
[4]	G. Zhang, et al.,"FDSE: enhance symbolic execution by fuzzing-based pre-analysis (competition contribution)," in Proc. 27th Int. Conf. on Fundamental Approaches to Software Engineering, pp. 304-308, Luxembourg City, Luxembourg, 6-11 Apr. 2024.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>
[5]	O. Sahin and B. Akay, "Comparisons of metaheuristic algorithms and fitness functions on software test data generation," Applied Soft Computing, vol. 49, pp. 1202-1214, Dec. 2016.
[6]	M. Harman and P. McMinn, "A theoretical and empirical study of search-based testing: local, global, and hybrid search," IEEE Trans. on Software Engineering, vol. 36, no. 2, pp. 226-247, Mar./Apr. 2009.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
[7]	M. Dehghani, Z. Montazeri, E. Trojovská, and P. Trojovský, "Coati optimization algorithm: a new bio-inspired metaheuristic algorithm for solving optimization problems," Knowledge-Based Systems, vol. 259, Article ID: pp. 110011, Jan. 2023.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
[8]	N. Soffair and S. Mannor, \Textit {MinMaxMin} $Q$-Learning, arXiv preprint arXiv:2402.05951, 2024.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>
[9]	N. Khoshniat, A. Jamarani, A. Ahmadzadeh, M. Haghi Kashani, and E. Mahdipour, "Nature-inspired metaheuristic methods in software testing," Soft Computing, vol. 28, no. 2, pp. 1503-1544, 2024.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>
[10]	P. Ashwini, B. Rajani, and B. Vijitha, "An efficient early software reliability prediction using particle swarm optimization (PSO)," In K. Venkata Murali Mohan, M. Suresh Babu (eds). Disruptive Technologies in Computing and Communication Systems, pp. 52-58, CRC Press, 2024.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
[11]	T. Avdeenko and K. Serdyukov, "Automated test data generation based on a genetic algorithm with maximum code coverage and population diversity," Applied Sciences, vol. 11, no. 10, Article ID: 4673, May-2 2021.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>
[12]	M. Nosrati, H. Haghighi, and M. V. Asl, "Test data generation using genetic programming," Information and Software Technology, vol. 130, Article ID: 106446, 2021.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
[13]	M. Angelova, K. Atanassov, and T. Pencheva, "Multi-population genetic algorithm quality assessment implementing intuitionistic fuzzy logic," in Proc. Federated Conf. on Computer Science and Information Systems, pp. 365-370, Wroclaw, Poland, 9-12 Sept. 2012.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
[14]	M. Alshraideh, B. A. Mahafzah, and S. Al-Sharaeh, "A multiple-population genetic algorithm for branch coverage test data generation," Software Quality J., vol. 19, pp. 489-513, 2011.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
[15]	N. Zhang, B. Wu, and X. Bao, "Automatic generation of test cases based on multi-population genetic algorithm," Int. J. Multimedia Ubiquitous Eng., vol. 10, no. 6, pp. 113-122, 2015.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
[16]	X. Bao, Z. Xiong, N. Zhang, J. Qian, B. Wu, and W. Zhang, "Path-oriented test cases generation based adaptive genetic algorithm," PloS One, vol. 12, no. 11, Article ID: e0187471, 2017.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>
[17]	A. Damia, M. Esnaashari, and M. Parvizimosaed, "Software testing using an adaptive genetic algorithm," J. of AI and Data Mining, vol. 9, no. 4, pp. 465-474, Oct. 2021.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
[18]	M. Mann, P. Tomar, and O. P. Sangwan, "Test data generation using optimization algorithm: an empirical evaluation," in Proc. of Soft Computing: Theories and Applications, vol. 2, pp. 679-686, Jaipur, India, 28-30 Dec. 2018.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
[19]	M. Mann, O. P. Sangwan, P. Tomar, and S. Singh, "Automatic goal-oriented test data generation using a genetic algorithm and simulated annealing," in Proc. 6th Int. Conf.-Cloud System and Big Data Engineering, pp. 83-87, Noida, India, 14-15 Jan. 2016.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
[20]	X. M. Zhu and X. F. Yang, "Software test data generation automatically based on improved adaptive particle swarm optimizer," in Proc. Int. Conf. on Computational and Information Sciences, pp. 1300-1303, Chengdu, China, 17-19 Dec. 2010.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>
[21]	S. Singla, D. Kumar, H. Rai, and P. Singla, "A hybrid PSO approach to automate test data generation for data flow coverage with dominance concepts," International J. of Advanced Science and Technology, vol. 37, no. 11, pp. 15-26, 2011.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>
[22]	S. Jiang, J. Shi, Y. Zhang, and H. Han, "Automatic test data generation based on reduced adaptive particle swarm optimization algorithm," Neurocomputing, vol. 158, pp. 109-116, Jun. 2015.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>
[23]	H. Sharifipour, M. Shakeri, and H. Haghighi, "Structural test data generation using a memetic ant colony optimization based on evolution strategies," Swarm and Evolutionary Computation, vol. 40, no. 40, pp. 76-91, Jun. 2018.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>
[24]	A. H. Damia and M. M. Esnaashari, "Automated test data generation using a combination of firefly algorithm and asexual reproduction optimization algorithm," International J. of Web Research, vol. 3, no. 1, pp. 19-28, Jun. 2020.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>
[25]	A. Damia, M. Esnaashari, and M. Parvizimosaed, "Automatic web-based software structural testing using an adaptive particle swarm optimization algorithm for test data generation," in Proc. 7th Int. Conf. on Web Research, pp. 282-286, Tehran, Iran, 19-20 May 2021.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>
[26]	O. Al-Masri and W. A. Al-Sorori, "Object-oriented test case generation using teaching learning-based optimization (TLBO) algorithm," IEEE Access, vol. 10, pp. 110879-110888, 2022.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>
[27]	M. Saadtjoo and S. Babamir, "Optimizing cost function in imperialist competitive algorithm for path coverage problem in software testing," J. of AI and Data Mining, vol. 6, no. 2, pp. 375-385, Jul. 2018.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>
[28]	X. Dai, W. Gong, and Q. Gu, "Automated test case generation based on differential evolution with node branch archive," Computers &amp; Industrial Engineering, vol. 156, Article ID: 107290, Jun. 2021.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>
[29]	R. R. Sahoo and M. Ray, "Forest optimization-based test case generation for multiple paths with metamorphic relations," International J. of Applied Metaheuristic Computing, 
vol. 13, no. 1, pp. 1-18, Jan. 2022.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>
[30]	F. Feyzi and S. Parsa, "Bayes‐TDG: effective test data generation using Bayesian belief network: toward failure‐detection effectiveness and maximum coverage," IET Software, vol. 12, no. 3, pp. 225-235, Jun. 2018.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>
[31]	M. Esnaashari and A. H. Damia, "Automation of software test data generation using genetic algorithm and reinforcement learning," Expert Systems with Applications, vol. 183, Article ID: 115446, Nov. 2021.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>
[32]	M. Malkauthekar, "Analysis of euclidean distance and manhattan distance measure in face recognition," in 3rd Int. Conf. on Computational Intelligence and Information Technology, pp. 503-507, Chennai, India, 27 Jul. 2013.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>
[33]	Y. Duan, et al., "CAPSO: chaos adaptive particle swarm optimization algorithm," IEEE Access, vol. 10, pp. 29393-29405, 2022.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>
[34]	P. Trojovský and M. Dehghani, "Pelican optimization algorithm: a novel nature-inspired algorithm for engineering applications," Sensors, vol. 22, no. 3, Article ID: 855, 2022.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>
[35]	S. H. S. Moosavi and V. K. Bardsiri, "Poor and rich optimization algorithm: a new human-based and multi populations algorithm," Engineering Applications of Artificial Intelligence, vol. 86, pp. 165-181, Nov. 2019.</unstructured_citation></citation><citation key="ref35"><unstructured_citation>
[36]	S. Zhao, T. Zhang, S. Ma, and M. Chen, "Dandelion optimizer: a nature-inspired metaheuristic algorithm for engineering applications," Engineering Applications of Artificial Intelligence, vol. 114, Article ID: 105075, Sept. 2022.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>
[37]	I. Fister, I. Fister Jr, X. S. Yang, and J. Brest, "A comprehensive review of firefly algorithms," Swarm and Evolutionary Computation, vol. 13, pp. 34-46, Dec. 2013.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Safe Offloading Based on Federated Learning in the Fog Computing Environment Using Software-Defined Networks</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>M. R.</given_name><surname>Sharafi Hoyavada</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mohammad Reza</given_name><surname>Mollahosseini</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Vahid </given_name><surname>Ayatollahitafti </surname></person_name></contributors><publication_date media_type="online"><month>10</month><day>18</day><year>2025</year></publication_date><pages><first_page>111</first_page><last_page>120</last_page></pages><doi_data><doi>10.66224/ijece.48486.23.2.111</doi><resource>http://ijece.org/en/Article/48486</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://ijece.org/en/Article/Download/48486</resource></item><item crawler="google"><resource>http://ijece.org/en/Article/Download/48486</resource></item><item crawler="msn"><resource>http://ijece.org/en/Article/Download/48486</resource></item><item crawler="altavista"><resource>http://ijece.org/en/Article/Download/48486</resource></item><item crawler="yahoo"><resource>http://ijece.org/en/Article/Download/48486</resource></item><item crawler="scirus"><resource>http://ijece.org/en/Article/Download/48486</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://ijece.org/en/Article/Download/48486</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	M. Chiang and T. Zhang, "Fog and IoT: an over view of research opportunities," IEEE Internet of Things J., vol. 3, no. 6, pp. 854-864, Dec. 2016.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
[2]	J. Wen, et al., "A survey on federated learning: challenges and opportunities," International Journal of Machine Learning and Cybernetics, vol. 14, pp. 513-535, 2023.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
[3]	Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, "A survey on mobile edge computing: the communication," IEEE Communications Surveys Tutorials, vol. 9, no. 4, pp. 2322-2358, Fourthquarter 2017.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>
[4]	P. Macha and Z. Becvar, "Mobile edge computing: a survey on architecture and computation ofﬂoading," IEEE Communications Surveys Tutorials, vol. 19, no. 3, pp. 1628-1656, Thirdquarter 2017.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>
[5]	A, Kumari and P. K. Jana, "Communication efficient federated learning with data offloading in fog-based IoT environment," Future Generation Computer Systems, vol. 158, pp. 158-166, Sept. 2024.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
[6]	J. Singh, P, Singh, M. Hedabou, and N. Kumar, "An efficient machine learning-based resource allocation scheme for SDN-enabled fog computing environment," IEEE Trans. on on Vehicular Technology, vol. 72, no. 6, pp. 8004-8017, Jun. 2023.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
[7]	A. Zaman, A. Jarray, and A. Karmouch, " Software-defined network-based edge cloud resource allocation framework," IEEE Access, vol. 7, 10672–10690, 2019.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>
[8]	R. W. Cottle and T. N. Mukund, Linear and Nonlinear Optimization, 2nd Editon, vol. 253, New York, NY, USA: Springer, 2017.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>
[9]	P. Muts, I. Nowak, and E. M. T. Hendrix, "The decomposition-based outer approximation algorithm for convex mixed-integer nonlinear programming," J. of Global Optimization, vol. 77, no. 1, pp. 75-96, May 2020.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
[10]	P. P. Liang, et al., Thinklocally, Actglobally: Federated Learning with Local and Global Representations, 2020, arXiv: 2001.01523.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>
[11]	H. H. Zhuo, W. Feng, Y. Lin, Q. Xu, and Q. Yang, Federated Deeprein-Forcement Learning, Feb. 2020, arXiv: 1901.08277. </unstructured_citation></citation><citation key="ref12"><unstructured_citation>
[12]	H. Yu, et al., "A fairness-aware incentive scheme for federated learning," in Proc. AAAI/ACM Conf. AI, Ethics, and Society, pp. 393-399, New York, NY, USA, 7-9 Feb. 2020.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
[13]	S. Truex, et al., "A hybrid approach to privacy-preserving federated learning," in Proc. 12th ACM Workshop on Artificial Intelligence and Security, pp. 1-11, London, UK, 15-15 Nov. 2019.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
[14]	A. A. Süzen and M. A. Simsek, "A novel approach to machine learning application to protection privacy data in health care: federated learning," Namlk Kemal Tip Dergisi, vol. 8, no. 1, pp. 22-30, 2020.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
[15]	S. Lin, G. Yang, and J. Zhang, Real-Time Edge Intelligence Inthemaking: A Collaborative Learning Framework via Federated Meta-Learning, 2020, arXiv: 2001.03229.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>
[16]	S. R. Pandey, N. H. Tran, M. Bennis, Y. K. Tun, A. Manzoor, and C. S. Hong, "Acrowd sourcing framework for on-device federated learning," IEEE Trans. on Wireless Communications, vol. 19, no. 5, pp. 3241-3256, May 2020.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
[17]	M. G. R. Alam, Y. K. Tun, and C. S. Hong, "Multi-agent and reinforcement learning based code offloading in mobile fog," in Proc. Int. Conf. on Information Networking, Kota Kinabalu, Malaysia, pp. 334-338, 13-15 Jan. 2016.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
[18]	T. N. Dang and C. S. Hong, "A distributed ADMM approach for data offloading in fog computing," ¬in Proc. of the Int. Conf. on Advanced Technologies for Communications, Hanoi, Vietnam, pp. 286-291, Atlanta, GA, USA, 12-14 Oct. 2016.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
[19]	C. Fricker, F. Guillemin, P. Robert, and G. Thompson, "Analysis of an offloading scheme for data centers in the framework of fog computing," ACM Trans. on Modeling and Performance Evaluation of Computing Systems, vol. 1, no. 4, pp. 1-18, Jul. 2016.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>
[20]	Q. Zhu, B. Si, F. Yang, and Y. Ma, "Task offloading decision in fog computing system," China Communications, vol. 14, no. 11, pp. 59-68, Nov. 2017.
[21]	L. Liu, Z. Chang, and X. Guo, "Socially-aware dynamic computation offloading scheme for fog computing system with energy harvesting devices," IEEE Internet of Things J., vol. 5, no. 3, pp. 1869-1879, Jun. 2018.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>
[22]	L. Phan, D. Nguyen, M. Lee, D. Park, and T. Kim, "Dynamic fog-to-fog offloading in SDN-based fog computing systems," Future Generation Computer Systems, vol. 117, pp. 486-497, Apr. 2020.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>
[23]	J. Baek and G. Kaddoum, "Heterogeneous task offoading and resource allocations via deep recurrent reinforcement learning in partial observable multi-fog networks," IEEE Internet of Things J., vol. 8, no. 2, pp. 1041-1056, Jan. 2017.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>
[24]	H. Kim, J. Park, M. Bennis, and S. Kim, "Blockchained on-device federated learning," IEEE Communications Letters, vol. 24, no. 6, pp. 1279-1283, Jun. 2019.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>
[25]	L. Liu, J. Zhang, S. Song, and K. Letaief, "Client-edge-cloud hierarchical federated learning," in Proc. of IEEE Int. Conf. on Communications, 6 pp., Dublin, Ireland, 7-11 Jun. 2020.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>
[26]	M. Abad, E. Ozfatura, D. Gunduz, and O. Ercetin, "Hierarchical federated learning across heterogeneous cellular networks," in Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, pp. 8866-8870, Virtual Barcelona, 4-8 May 2020.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>
[27]	J. Ren, H. Wang, T. Hou, S. Zheng, and C. Tang, "Federated learning-based computation offloading optimization in edge computing-supported internet of things," IEEE Internet of Things J., vol. 7, no. 8, pp. 6914-6921, Aug. 2020.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>
[28]	Y. Yunfan, L. Shen, L. Fang, T. Yonghao, and H. Wanting, "EdgeFed: optimized federated learning based on edge computing," IEEE Access, vol. 8, pp. 209191-209198, 2020.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>
[29]	R. Saha, S. Misra, and P. K. Deb, "FogFL: fog assisted federated learning for resource-constrained IoT devices," IEEE Internet of Things J., vol. 8, no. 10, pp. 8456-8463, 15 May 2020.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Automatic Classification of Breast Cancer Images Using Transfer Learning on Enhanced Mammography Images</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Zahra</given_name><surname>Amiri</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Zahra</given_name><surname>Mortezaie</surname></person_name></contributors><publication_date media_type="online"><month>10</month><day>18</day><year>2025</year></publication_date><pages><first_page>132</first_page><last_page>138</last_page></pages><doi_data><doi>10.66224/ijece.48640.23.2.132</doi><resource>http://ijece.org/en/Article/48640</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://ijece.org/en/Article/Download/48640</resource></item><item crawler="google"><resource>http://ijece.org/en/Article/Download/48640</resource></item><item crawler="msn"><resource>http://ijece.org/en/Article/Download/48640</resource></item><item crawler="altavista"><resource>http://ijece.org/en/Article/Download/48640</resource></item><item crawler="yahoo"><resource>http://ijece.org/en/Article/Download/48640</resource></item><item crawler="scirus"><resource>http://ijece.org/en/Article/Download/48640</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://ijece.org/en/Article/Download/48640</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>

[1] 	S. Łukasiewicz, M. Czeczelewski, A. Forma, J. Baj, R. Sitarz and A. Stanisławek, "Breast Cancer—Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies—an updated review.," Cancers, vol. 13, no. 17, p. 4287, 2021.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
[2] 	C. Cruz-Ramos, O. García-Avila, J. Almaraz-Damian, V. Ponomaryov, R. Reyes-Reyes and S. Sadovnychiy, "Benign and Malignant Breast Tumor Classification in Ultrasound and Mammography Images via Fusion of Deep Learning and Handcraft Feature," Entropy, vol. 25, no. 7, p. 991, 2023.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
[3] 	K. Korhonen, S. Weinstein, E. McDonald and E. Conant, "Strategies to Increase Cancer Detection: Review of True-positive and False-negative Results at Digital Breast Tomosynthesis Screening," Radiographics, vol. 36, no. 7, pp. 1954-1965, 2016. </unstructured_citation></citation><citation key="ref4"><unstructured_citation>
[4] 	E. Ekpo, M. Alakhras and P. Brennan, "Errors in Mammography Cannot be sSolved Through Technology Alone," Asian Pacific journal of cancer prevention, vol. 19, no. 2, p. 291, 2018.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>
[5] 	L. Nicosia, G. Gnocchi, I. Gorini, M. Venturini, F. Fontana, F. Pesapane, I. Abiuso, A. Bozzini, M. Pizzamiglio, A. Latronico and F. Abbate, "History of Mammography: Analysis of Breast Imaging Diagnostic Achievements over the Last Century.," Healthcare, vol. 11, no. 11, p. 1596, 2023.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
[6] 	F. Shahidi, S. Daud, H. Abas, N. Ahmad and N. Maarop, "Breast Cancer Classification using Deep Learning Approaches and Histopathology Image: A Comparison Study," IEEE Access, vol. 8, pp. 187531-187552, 2020.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
[7] 	B. Bektaş, İ. Emre, E. Kartal and S. Gulsecen, "Classification of Mammography Images by Machine Learning Techniques," in 3rd International Conference on Computer Science and Engineering (UBMK) (pp. 580-585), 2018.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>
[8] 	J. Arevalo, F. González, R. Ramos-Pollán, J. Oliveira and M. Lopez, "Convolutional Neural Networks for Mammography Mass Lesion Classification," in 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 797-800). IEEE, 2015.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>
[9] 	H. Li, S. L. Zhuang, Z. D.A. and Y. J. Ma, "Benign and Malignant Classification of Mammogram Images based on Deep Learning," Biomedical Signal Processing and Control, vol. 51, pp. 347-354, 2019.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
[10] 	P. Hepsağ, S. Özel and A. Yazıcı, "Using Deep Learning for Mammography Classification," in International Conference on Computer Science and Engineering (UBMK) (pp. 418-423). IEEE, 2017.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>
[11] 	W. Salama and M. Aly, "Deep Learning in Mammography Images Segmentation and Classification: Automated CNN Approach," Alexandria Engineering Journal, vol. 60, no. 5, pp. 4701-4709, 2021.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
[12] 	U. Albalawi, S. Manimurugan and R. Varatharajan, "Classification of Breast Cancer Mammogram Images using Convolution Neural Network," Concurrency and Computation: Practice and Experience, vol. 34, no. 13, p. 5803, 2022.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
[13] 	T. Mahmood, J. Li, Y. Pei, F. Akhtar, M. Rehman and S. Wasti, "Breast Lesions Classifications of Mammographic Images Using a Deep Convolutional Neural Network-based Approach," Plos One, vol. 17, no. 1, 2022.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
[14] 	S. Agnes, J. Anitha, S. Pandian and J. Peter, "Classification of Mammogram Images using Multiscale All Nonvolutional Neural Network (MA-CNN)," Journal of Medical Systems, vol. 44, no. 1, p. 30, 2019.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
[15] 	U. K. Sajid, R.A., S. S.M. and S. Arif, "Breast Cancer Classification using Deep Learned Features Boosted with Handcrafted Features," Biomedical Signal Processing and Control, , vol. 86, 2023.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>
[16] 	A. Joseph, M. Abdullahi, S. Junaidu, H. Ibrahim and H. Chiroma, "Improved Multi-classification of Breast Cancer Histopathological Images using Handcrafted Features and Deep Neural Network (dense layer)," Intelligent Systems with Applications, vol. 14, 2022.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
[17] 	D. Muduli, R. Dash and B. Majhi, "Automated Diagnosis of Breast Cancer using Multi-modal Datasets: A Deep Convolution Neural Network based Approach," Biomedical. Signal Processing and Control, vol. 71, 2022.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
[18] 	L. Falconí, M. Pérez, W. Aguilar and A. Conci, "Transfer Learning and Fine Tuning in Mammogram Bi-rads Classification," in 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, USA., 2020.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
[19] 	C. Szegedy, S. Ioffe, V. Vanhoucke and A. A. Alemi, "Inception-v4, Inception-Resnet and the Impact of Residual Connections on Learning," in Thirty-first AAAI Conference on Artificial Intelligence, San Francisco, 2017.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>
[20] 	B. Jena, G. K. Nayak and S. Saxena, "Convolutional Neural Network and Its Pretrained Models for Image Classification and Object Detection: A Survey‏," Concurrency and Computation: Practice and Experience, vol. 34, no. 6, 2022.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>
[21] 	A. Caroppo, A. Leone and P. Siciliano, "Deep Transfer Learning Approaches for Bleeding Detection in Endoscopy Images," Computerized Medical Imaging and Graphics, vol. 88, p. 101852, 2021.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>
[22] 	C. Ding and P. H., "Minimum Redundancy Feature Selection from Microarray Gene Expression Data," Journal of Bioinformatics and Computational Biology, vol. 3, no. 2, p. 185–205, 2005.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>
[23] 	M. M. AlyanNezhadi, H. Dabbaghan, S. Moghani and M. Forghani, A Painting Artist Recognition System Based on Image Processing and Hierarchical SVM, Tehran, Iran: 5th Conference on Knowledge Based Engineering and Innovation (KBEI), 2019.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>
[24] 	P. S. Sundaram and N. Santhiyakumari, "An Enhancement of Computer Aided Approach for Colon Cancer Detection in WCE Images Using ROI Based Color Histogram and SVM2‏," Journal of medical systems, vol. 43, no. 2, p. 29, 2019.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>
[25] 	Z. Amiri, H. Hassanpour and A. Beghdadi, "Abnormalities detection in wireless capsule endoscopy images using EM algorithm‏," The Visual Computer, vol. 39, no. 7, pp. 2999-3010, 2023. </unstructured_citation></citation><citation key="ref26"><unstructured_citation>
[26] 	Z. Amiri, H. Hassanpour and A. Beghdadi, "Combining deep features and hand-crafted features for abnormality detection in WCE images‏," Multimedia Tools and Applications, vol. 83, no. 2, pp. 5837-5870, 2024. </unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Enhancing Text Image Super-Resolution by Intentionally Weakening OCR Loss to Impose Stricter Reconstruction Constraints on the SR Network</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>K.</given_name><surname>Mehrgan</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>A.</given_name><surname>Ebrahimi moghadam</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>M.</given_name><surname>Khademi Doroh</surname></person_name></contributors><publication_date media_type="online"><month>10</month><day>18</day><year>2025</year></publication_date><pages><first_page>139</first_page><last_page>145</last_page></pages><doi_data><doi>10.66224/ijece.48961.23.2.139</doi><resource>http://ijece.org/en/Article/48961</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://ijece.org/en/Article/Download/48961</resource></item><item crawler="google"><resource>http://ijece.org/en/Article/Download/48961</resource></item><item crawler="msn"><resource>http://ijece.org/en/Article/Download/48961</resource></item><item crawler="altavista"><resource>http://ijece.org/en/Article/Download/48961</resource></item><item crawler="yahoo"><resource>http://ijece.org/en/Article/Download/48961</resource></item><item crawler="scirus"><resource>http://ijece.org/en/Article/Download/48961</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://ijece.org/en/Article/Download/48961</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	R. Shu, C. Zhao, S. Feng, L. Zhu, and D. Miao, "Text-enhanced scene image super-resolution via stroke mask and orthogonal attention," IEEE Trans. on Circuits and Systems for Video Technology, vol. 33, no. 11, pp. 6317-6330, Nov. 2023.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
[2]	J. Ma, S. Guo, and L. Zhang, "Text prior guided scene text image super-resolution," IEEE Trans. on Image Processing, vol. 32, pp. 1341-1353, 2023.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
[3]	J. Ma, Z. Liang, and L. Zhang, "A text attention network for spatial deformation robust scene text image super-resolution," in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, pp. 5911-5920, New Orleans, LA, USA, 19-24 Jun. 2022.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>
[4]	ع. عابدی و ا. کبیر، "فراتفکیک‌پذیری مبتنی بر نمونه تک‌تصویر متن با روش نزول گرادیان ناهمزمان ترتیبی،" نشریه مهندسی برق و مهندسی کامپیوتر ایران، ب- مهندسی کامپیوتر، سال 14، شماره 3، صص. 192-177، پاییز 1395.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>
[5]	K. Mehrgan, A. R. Ahmadyfard, and H. Khosravi, "Super-resolution of license-plates using weighted interpolation of neighboring pixels from video frames," International J. of Engineering, Trans. B: Applications, vol. 33, no. 5, pp. 992-999, May 2020.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
[6]	C. Dong, C. C. Loy, K. He, and X. Tang, "Learning a deep convolutional network for image super-resolution," in Proc. 13th European Conf, Computer Vision, pp. 184-199, Zurich, Switzerland, 6-12 Sept. 2014.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
[7]	A. Kappeler, S. Yoo, Q. Dai, and A. K. Katsaggelos, "Video super-resolution with convolutional neural networks," IEEE Trans. Comput Imaging, vol. 2, no. 2, pp. 109-122, Jun. 2016.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>
[8]	M. Hradiš, J. Kotera, P. Zemcık, and F. Šroubek, "Convolutional neural networks for direct text deblurring," in Proc. of the British Machine Vision Conf., 13 pp., Swansea, UK, 7-10 Dec. 2015.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>
[9]	C. Dong, C. C. Loy, K. He, and X. Tang, "Image super-resolution using deep convolutional networks," IEEE Trans. Pattern Anal Mach Intell, vol. 38, no. 2, pp. 295-307, Feb. 2015.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
[10]	D. Gudivada and P. K. Rangarajan, "Enhancing PROBA-V satellite imagery for vegetation monitoring using FSRCNN-based super-resolution," in Proc. Int. Conf. on Next Generation Electronics, 6 pp., Vellore, India, 14-16 Dec. 2023.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>
[11]	J. Zhang, M. Liu, X. Wang, and C. Cao, "Residual net use on FSRCNN for image super-resolution," in Proc. 40th Chinese Control Conf., pp. 8077-8083, Shanghai, China, 26-28 Jul. 2021.
[12]	T. Khachatryan, D. Galstyan, and E. Harutyunyan, "A comprehensive approach for enhancing deep learning datasets quality using combined SSIM algorithm and FSRCNN," in Proc. IEEE East-West Design &amp; Test Symp., 4 pp., 22-25 Sept. 2023.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
[13]	Y. Zhu, X. Sun, W. Diao, H. Li, and K. Fu, "RFA-Net: reconstructed feature alignment network for domain adaptation object detection in remote sensing imagery," IEEE J. Sel Top Appl Earth Obs Remote Sens, vol. 15, pp. 5689-5703, 2022.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
[14]	Z. Wang, D. Liu, J. Yang, W. Han, and T. Huang, "Deep networks for image super-resolution with sparse prior," in Proc. of the IEEE Int. Conf. on Computer Vision, pp. 370-378, Santiago, Chile, 7-13 Dec. 2015.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
[15]	M. Chen, et al., "RFA-Net: residual feature attention network for fine-grained image inpainting," Engineering Applications of Artificial Intelligence, vol. 119, Article ID: 105814, Mar. 2023.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
[16]	Z. Wang and J. Tang, "Advancing quality and detail: enhanced-lapSRN for chip socket image super-resolution," in Proc. Int. Conf. on Image Processing, Computer Vision and Machine Learning, pp. 153-159, Chengdu, China, 3-5 Nov. 2023.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>
[17]	R. Tang, et al., "Medical image super-resolution with Laplacian dense network," Multimedia Tools and Applications, vol. 81, no. 3, pp. 3131-3144, Jan. 2022.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
[18]	K. Wu, C. K. Lee, and K. Ma, "Memsr: training memory-efficient lightweight model for image super-resolution," in Proc. 39th Int. Conf. on Machine Learning, pp. 24076-24092, Baltimore, MD, USA, 17-23 Jul. 2022.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
[19]	Z. Du, et al., "Fast and memory-efficient network towards efficient image super-resolution," in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, pp. 853-862, New Orleans, LA, USA, 19-20 Jun. 2022.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
[20]	K. H. Liu, B. Y. Lin, and T. J. Liu, "MADnet: a multiple attention decoder network for segmentation of remote sensing images," in Proc. Int. Conf. on Consumer Electronics-Taiwan  pp. 835-836, PingTung, Taiwan, 17-19 Jul. 2023.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>
[21]	D. Zhang, W. Zhang, W. Lei, and X. Chen, "Diverse branch feature refinement network for efficient multi‐scale super‐resolution," IET Image Process, vol. 18, no. 6, pp. 1475-1490, May 2024.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>
[22]	T. Tong, G. Li, X. Liu, and Q. Gao, "Image super-resolution using dense skip connections," in Proc. of the IEEE Int. Conf. on Computer Vision, pp. 4799-4807, Venice, Italy, 22-29 Oct. 2017.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>
[23]	K. Zhang, W. Zuo, and L. Zhang, "Learning a single convolutional super-resolution network for multiple degradations," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 3262-3271, Salt Lake City, UT, USA, 18-22 Jun. 2018.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>
[24]	W. Zhang, Y. Liu, C. Dong, and Y. Qiao, "Ranksrgan: super resolution generative adversarial networks with learning to rank," IEEE Trans Pattern Anal Mach Intell, vol. 44, no. 10, pp. 7149-7166, Oct. 2021.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>
[25]	C. Ledig, et al., "Photo-realistic single image super-resolution using a generative adversarial network," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 4681-4690, Honolulu, HI, USA, 21-26 Jul. 2017.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>
[26]	B. K. Xie, S. B. Liu, and L. Li, "Large-scale microscope with improved resolution using SRGAN," Optics &amp; Laser Technology, vol. 179, Article ID: 111291, Dec. 2024.
[27]	I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>
[28]	J. Baek, et al., "What is wrong with scene text recognition model comparisons? dataset and model analysis," in Proc. of the IEEE/CVF Int. Conf. on Computer Vision, pp. 4715-4723, Seoul, South, Korea, 27 Oct.-2 Nov. 2019.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>
[29]	W. Wang, et al., "Scene text image super-resolution in the wild," in Proc. 16th European Conf. on Computer Vision, pp. 650-666, Glasgow, UK, 20-28 Aug. 2020.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>
[30]	D. Karatzas, et al., "ICDAR 2015 competition on robust reading," in Proc. 13th Int. Conf. on Document Analysis and Recognition, pp. 1156-1160, Tunis, Tunisia, 23-26 Aug. 2015.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>
[31]	K. Wang, B. Babenko, and S. Belongie, "End-to-end scene text recognition," in Proc. Int. Conf. on Computer Vision. pp. 1457-1464, Barcelona, Spain, 6-13 Nov. 2011.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>
[32]	H. Zhao, X. Kong, J. He, Y. Qiao, and C. Dong, "Efficient image super-resolution using pixel attention," in Proc., Computer Vision-ECCV Workshops, pp. 56-72, Glasgow, UK, 23-28 Aug. 2020.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>
[33]	S. Anwar and N. Barnes, "Densely residual laplacian super-resolution," IEEE Trans Pattern Anal Mach Intell, vol. 44, no. 3, pp. 1192-1204, Mar. 2022.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>
[34]	[34]	H. Chen, J. Gu, and Z. Zhang, Attention in Attention Network for Image Super-Resolution, arXiv Preprint, arXiv:2104.09497, 2021.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>
[35]	X. Chen, X. Wang, J. Zhou, and C. Dong, "Activating more pixels in image super-resolution transformer," in Proc. IEEE/CVF Conf. on Computer Vision and Pattern Recognition, pp. 22367-22377, Vancouver, Canada, 18-22 Jun, 2023.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>
[36]	Z. Chen, Y. Zhang, J. Gu, L. Kong, X. Yang, and F. Yu, "Dual aggregation transformer for image super-resolution," in Proc. IEEE/CVF Int. Conf. on Computer Vision, pp. 12278-12287, Vancouver, Canada, 18-22 Jun, 2023.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Performance Evaluation of Apache and Nginx Web Servers on Docker, Podman, and LXC Containers</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>A.</given_name><surname>Farhadian</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mostafa</given_name><surname>Bastam</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>E.</given_name><surname>Ataei</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>M.</given_name><surname>Babagoli</surname></person_name></contributors><publication_date media_type="online"><month>10</month><day>18</day><year>2025</year></publication_date><pages><first_page>79</first_page><last_page>98</last_page></pages><doi_data><doi>10.66224/ijece.49036.23.2.79</doi><resource>http://ijece.org/en/Article/49036</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://ijece.org/en/Article/Download/49036</resource></item><item crawler="google"><resource>http://ijece.org/en/Article/Download/49036</resource></item><item crawler="msn"><resource>http://ijece.org/en/Article/Download/49036</resource></item><item crawler="altavista"><resource>http://ijece.org/en/Article/Download/49036</resource></item><item crawler="yahoo"><resource>http://ijece.org/en/Article/Download/49036</resource></item><item crawler="scirus"><resource>http://ijece.org/en/Article/Download/49036</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://ijece.org/en/Article/Download/49036</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	N. Fazuludeen, S. S. Banu, A. Gupta, and V. Swathi, "Challenges and issues of managing the virtualization environment through Vmware Vsphere," Nanotechnology Perceptions, vol. 20, no. S1, pp. 281-292, 2024.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
[2]	A. M. Potdar, D. Narayan, S. Kengond, and M. M. Mulla, "Performance evaluation of docker container and virtual machine," Procedia Computer Science, vol. 171, pp. 1419-1428, 2020.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
[3]	S. Lozano, T. Lugo, and J. Carretero, "A comprehensive survey on the use of hypervisors in safety-critical systems," IEEE Access, vol. 11, pp. 36244-36263, 2023.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>
[4]	A. Bhardwaj and C. R. Krishna, "Virtualization in cloud computing: moving from hypervisor to containerization-a survey," Arabian J. for Science and Engineering, vol. 46, pp. 8585-8601, 2021.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>
[5]	R. Ranjan, I. S. Thakur, G. S. Aujla, N. Kumar, and A. Y. Zomaya, "Energy-efficient workflow scheduling using container-based virtualization in software-defined data centers," IEEE Trans. on Industrial Informatics, vol. 16, no. 12, pp. 7646-7657, Dec. 2020.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
[6]	K. Wang, et al., "Characterizing and optimizing Kernel resource isolation for containers," Future Generation Computer Systems, vol. 141, pp. 218-229, Apr. 2023.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
[7]	G. Rodriguez, et al., "Understanding and addressing the allocation of microservices into containers: a review," IETE J. of Research, vol. 70, no. 4, pp. 3887-3900, 2024.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>
[8]	A. Ganne, "Cloud data security methods: Kubernetes vs Docker swarm," International Research J. of Modernization in Engineering Technology, vol. 4, no. 11, pp. 1-6, 2022.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>
[9]	G. Li, et al., "The convergence of container and traditional virtualization: strengths and limitations," SN Computer Science, vol. 4, no. 4, Article ID: 387, 2023.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
[10]	M. Sobieraj and D. Kotyński, "Docker performance evaluation across operating systems," Applied Sciences, vol. 14, no. 15, Article ID: 6672, 2024.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>
[11]	W. Shen, et al., "Towards understanding and defeating abstract resource attacks for container platforms," IEEE Trans. on Dependable and Secure Computing, vol. 22, no. 1, pp. 474-490, Jan./Feb. 2024.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
[12]	D. Pennino and M. Pizzonia, Toward Scalable Docker-Based Emulations of Blockchain Networks for Research and Development, arXiv preprint arXiv:2402.14610, 2024.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
[13]	S. A. Baker, H. H. Mohammed, and O. I. Alsaif, "Docker container security analysis based on virtualization technologies," International J. for Computers &amp; Their Applications, vol. 31, no. 1, pp. 69-78, 2024.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
[14]	N. Zhou, H. Zhou, and D. Hoppe, "Containerization for high performance computing systems: survey and prospects," IEEE Trans. on Software Engineering, vol. 49, no. 4, pp. 2722-2740, Apr. 2022.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
[15]	N. Singh, et al., "Load balancing and service discovery using Docker Swarm for microservice based big data applications," J. of Cloud Computing, vol. 12, Article ID: 4, 2023.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>
[16]	S. T. Arzo, et al., "Softwarized and containerized microservices-based network management analysis with MSN," Computer Networks, vol. 254, Article ID: 110750, Dec. 2024.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
[17]	O. I. Alqaisi, A. Ş. Tosun, and T. Korkmaz, "Performance analysis of container technologies for computer vision applications on edge devices," IEEE Access, vol. 12, pp. 41852-41869, 2024.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
[18]	D. Silva, J. Rafael, and A. Fonte, "Toward optimal virtualization: an updated comparative analysis of docker and LXD container technologies," Computers, vol. 13, no. 4, Article ID: 94, Apr. 2024.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
[19]	S. Tarasiuk, D. Traczuk, K. Szczepaniuk, P. Stoń, and J. Smołka, "Performance evaluation of designated containerization and virtualization solutions using a synthetic benchmark," J. of Computer Sciences Institute, vol. 32, pp. 157-162, 2024.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>
[20]	D. P. VS, S. C. Sethuraman, and M. K. Khan, "Container security: precaution levels, mitigation strategies, and research perspectives," Computers &amp; Security, vol. 135, Article ID: 103490, Dec. 2023.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>
[21]	K. Senjab, S. Abbas, N. Ahmed, and A. U. R. Khan, "A survey of Kubernetes scheduling algorithms," J. of Cloud Computing, vol. 12, Article ID: 87, 2023.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>
[22]	E. Truyen, H. Xie, and W. Joosen, "Vendor-agnostic reconfiguration of kubernetes clusters in cloud federations," Future Internet, vol. 15, no. 2, Article ID: 63, Feb. 2023.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>
[23]	R. Queiroz, T. Cruz, J. Mendes, P. Sousa, and P. Simões, "Container-based virtualization for real-time industrial systems-a systematic review," ACM Computing Surveys, vol. 56, no. 3, Article ID: 59, Mar. 2023.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>
[24]	A. Alamoush and H. Eichelberger, "Open source container orchestration for industry 4.0-requirements and systematic feature analysis," International J. on Software Tools for Technology Transfer, vol. 26, pp. 527-550, 2024.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>
[25]	O. Flauzac, F. Mauhourat, and F. Nolot, "A review of native container security for running applications," Procedia Computer Science, vol. 175, pp. 157-164, 2020.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>
[26]	S. Kaiser, M. S. Haq, A. Ş. Tosun, and T. Korkmaz, "Container technologies for arm architecture: a comprehensive survey of the state-of-the-art," IEEE Access, vol. 10, pp. 84853-84881, 2022.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>
[27]	J. P. Martin, A. Kandasamy, and K. Chandrasekaran, "Exploring the support for high performance applications in the container runtime environment," Human-Centric Computing and Information Sciences, vol. 8, Article ID: 63, 15 pp., 2018.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>
[28]	E. Casalicchio and S. Iannucci, "The state‐of‐the‐art in container technologies: application, orchestration and security," Concurrency and Computation: Practice and Experience, vol. 32, no. 17, Article ID: e5668, Sept. 2020.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>
[29]	D. Moreau, K. Wiebels, and C. Boettiger, "Containers for computational reproducibility," Nature Reviews Methods Primers, vol. 3, no. 1, p. 50, 2023.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>
[30]	V. P. M. John, "A study on cloud container technology," i-Manager's J. on Cloud Computing, vol. 10, no. 1, pp. 7-16, Jan./Jun. 2023.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>
[31]	H. Liu, W. Zhu, S. Fu, and Y. Lu, "A trend detection-based auto-scaling method for containers in high-concurrency scenarios," IEEE Access, vol. 12, pp. 71821-71834, 2024.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>
[32]	Z. P. Putro and R. A. Supono, "Comparison analysis of apache and Nginx webserver load balancing on proxmox VE in supporting server performance," International Research J. of Advanced Engineering and Science, vol. 7, no. 3, pp. 144-151, 2022.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>
[33]	C. T. Yeh, T. M. Chen, and Z. J. Liu, "Flexible IoT cloud application for ornamental fish recognition using YOLOv3 model," Sensors &amp; Materials, vol. 34, no. 3, pp. 1229-1240, 2022.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>
[34]	M. Kwon, et al., "Deterministic I/O and resource isolation for OS-level virtualization in server computing." in Proc. 12th Annual Non-Volatile Memories Workshop, 2 pp., 7-21 Mar. 2021.</unstructured_citation></citation><citation key="ref35"><unstructured_citation>
[35]	D. Šandor and M. Bagić Babac, "Designing scalable event-driven systems with message-oriented architecture," Distributed Intelligent Circuits and Systems, Ch. 2, pp. World Scientific, 2024.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>
[36]	D. DeJonghe, Nginx Cookbook, O'Reilly Media, 2020.</unstructured_citation></citation></citation_list></journal_article></journal></body></doi_batch>