ORIGINAL_ARTICLE
Multi-Task Feature Selection for Speech Emotion Recognition: Common Speaker-Independent Features Among Emotions
Feature selection is the one of the most important steps in designing speech emotion recognition systems. Because there is uncertainty as to which speech feature is related to which emotion, many features must be taken into account and, for this purpose, identifying the most discriminative features is necessary. In the interest of selecting appropriate emotion-related speech features, the current paper focuses on a multi-task approach. For this reason, the study considers each speaker as a task and proposes a multi-task objective function to select features. As a result, the proposed method chooses one set of speaker-independent features of which the selected features are discriminative in all emotion classes. Correspondingly, multi-class classifiers are utilized directly or binary classifications simply perform multi-class classifications. In addition, the present work employs two well-known datasets, the Berlin and Enterface. The experiments also applied the openSmile toolkit to extract more than 6500 features. After feature selection phase, the results illustrated that the proposed method selects the features which is common in the different runs. Also, the runtime of proposed method is the lowest in comparison to other methods. Finally, 7 classifiers are employed and the best achieved performance is 73.76% for the Berlin dataset and 72.17% for the Enterface dataset, in the faced of a new speaker .These experimental results then show that the proposed method is superior to existing state-of-the-art methods.
https://jad.shahroodut.ac.ir/article_2063_e9fd9e160048291a0c98ecb9dfd38358.pdf
2021-07-01
269
282
10.22044/jadm.2021.9800.2118
Speech emotion recognition
Multi-Task Feature Selection
Speaker Independent Features
Cross-Corpus Feature Selection
Affective Processing
E.
Kalhor
e.kalhor333@sadjad.ac.ir
1
Faculty of Computer Engineering and IT, Sadjad University of Technology, Mashhad, Iran.
AUTHOR
B.
Bakhtiari
bakhtiari@sadjad.ac.ir
2
Faculty of Computer Engineering and IT, Sadjad University of Technology, Mashhad, Iran.
LEAD_AUTHOR
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58
ORIGINAL_ARTICLE
A Hybrid Framework for Personality Prediction based on Fuzzy Neural Networks and Deep Neural Networks
In general, humans are very complex organisms, and therefore, research into their various dimensions and aspects, including personality, has become an attractive subject of research. With the advent of technology, the emergence of a new kind of communication in the context of social networks has also given a new form of social communication to humans, and the recognition and categorization of people in this new space have become a hot topic of research that has been challenged by many researchers. In this paper, considering the Big Five personality characteristics of individuals, first, categorization of related work is proposed, and then a hybrid framework based on Fuzzy Neural Networks (FNN), along with, Deep Neural Networks (DNN) has been proposed that improves the accuracy of personality recognition by combining different FNN-classifiers with DNN-classifier in a proposed two-stage decision fusion scheme. Finally, a simulation of the proposed approach is carried out. The proposed approach is using the structural features of Social Networks Analysis (SNA), along with a linguistic analysis (LA) feature extracted from the description of the activities of individuals and comparison with the previous similar researches. The results, well-illustrated the performance improvement of the proposed framework up to 83.2 % of average accuracy on myPersonality dataset.
https://jad.shahroodut.ac.ir/article_2082_bcb5c0d49541ad7886ea649f27f860d6.pdf
2021-07-01
283
294
10.22044/jadm.2021.10583.2197
Personality Prediction
Big Five Model
Fuzzy Neural Networks
Deep Neural Networks
Social Networks Analysis
N.
Taghvaei
taghvaei.training@gmail.com
1
Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
AUTHOR
B.
Masoumi
masoumi.bh@gmail.com
2
Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
LEAD_AUTHOR
M. R.
Keyvanpour
keyvanpour@alzahra.ac.ir
3
Department of Computer Engineering, Alzahra University, Vanak, Tehran, Iran.
AUTHOR
[1] M. M. Tadesse, H. Lin, B. Xu and L. Yang, "Personality Predictions Based on User Behavior on the Facebook Social Media Platform," in IEEE Access, vol. 6, pp. 61959-61969, 2018.
1
[2] G. Farnadi, S. Zoghbi, M. F. Moens, and M. De Cock. "How well do your Facebook status updates express your personality?." In Proceedings of the 22nd edition of the annual Belgian-Dutch conference on machine learning (BENELEARN), p. 88. BNVKI-AIABN, 2013.
2
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[5] N. Majumder, S. Poria, A. Gelbukh and E. Cambria, "Deep learning-based document modeling for personality detection from text." IEEE Intelligent Systems 32, No. 2, pp. 74-79, 2017.
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[8] J. Pletzer, M. Bentvelzen, J. Oostrom and R. de Vries, "A meta-analysis of the relations between personality and workplace deviance: Big Five versus HEXACO." Journal of Vocational Behavior, vol. 112, pp. 369-383, 2019.
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[10] D. C. Osmon, O. Santos, D. Kazakov, M. T. Kassel, Q. R. Mano, and A. Morth. "Big Five personality relationships with general intelligence and specific Cattell-Horn-Carroll factors of intelligence." Personality and Individual Differences, vol. 131, 51-56,2018.
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[11] T. Shimotsukasa, A. Oshio, M. Tani, and M. Yamaki. "Big Five personality traits in inmates and normal adults in Japan." Personality and Individual Differences, vol. 141, pp. 81-85,2019.
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[12] A. Pilarska. "Big-Five personality and aspects of the self-concept: Variable-and person-centered approaches." Personality and Individual Differences, vol.127, pp.107-113, 2018.
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[13] V. Balakrishnan, S. Khan, T. Fernandez, and H. R. Arabnia. "Cyberbullying detection on twitter using Big Five and Dark Triad features." Personality and individual differences, vol. 141, pp. 252-257, 2019.
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[14] G. An, S. I. Levitan, J. Hirschberg, and R. Levitan. "Deep Personality Recognition for Deception Detection." In INTERSPEECH, pp. 421-425. 2018.
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17
[18] L. G. Martínez, J. R. Castro, G. Licea, A. Rodríguez-Díaz, and R. Salas. "Towards a personality fuzzy model based on big five patterns for engineers using an ANFIS learning approach." In Mexican International Conference on Artificial Intelligence, pp. 456-466. Springer, Berlin, Heidelberg, 2012.
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[29] H. Sadr, M. M. Pedram, and M. Teshnehlab. "Convolutional Neural Network Equipped with Attention Mechanism and Transfer Learning for Enhancing Performance of Sentiment Analysis." Journal of AI and Data Mining, 2021, doi: 10.22044/jadm.2021.9618.2100.
29
ORIGINAL_ARTICLE
Camera Arrangement using Geometric Optimization to Minimize Localization Error in Stereo-vision Systems
Stereo machine vision can be used as a Space Sampling technique and the cameras parameters and configuration can effectively change the number of Samples in each Volume of space called Space Sampling Density (SSD). Using the concept of Voxels, this paper presents a method to optimize the geometric configuration of the cameras to maximize the SSD which means minimizing the Voxel volume and reducing the uncertainty in localizing an object in 3D space. Each pixel’s field of view (FOV) is considered as a skew pyramid. The uncertainty region will be created from the intersection of two pyramids associated with any of the cameras. Then, the mathematical equation of the uncertainty region is developed based on the correspondence field as a criterion for the localization error, including depth error as well as X and Y axes error. This field is completely dependent on the internal and external parameters of the cameras. Given the mathematical equation of localization error, the camera’s configuration optimization is addressed in a stereo vision system. Finally, the validity of the proposed method is examined by simulation and empirical results. These results show that the localization error will be significantly decreased in the optimized camera configuration.
https://jad.shahroodut.ac.ir/article_2084_16dc0a8da395a446959c42ca6d67b6f9.pdf
2021-07-01
295
307
10.22044/jadm.2021.9855.2117
Computer Vision
Camera Arrangement
Correspondence Field
Geometric optimization
H.
Kamali Ardakani
h.k.ardakani@gmail.com
1
Electrical and Computer Engineering Faculty, K.N.Toosi University of Technology, Tehran, Iran.
LEAD_AUTHOR
Seyed A.
Mousavinia
moosavie@eetd.kntu.ac.ir
2
Electrical and Computer Engineering Faculty, K.N.Toosi University of Technology, Tehran, Iran.
AUTHOR
F.
Safaei
farzad@uow.edu.au
3
Faculty of Engineering and Information Sciences, University of Wollongong, New South Wales, Australia.
AUTHOR
[1] Wu, J., Sharma, R., and Huang, T. (1998). Analysis of uncertainty bounds due to quantization for three-dimensional position estimation using multiple cameras. Optical Engineering journal, Vol. 37, No. 1, pp. 280–292.
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[4] Ardakani, H., K., Mousavinia, A., and Safaei, F. (2020). Four points: one-pass geometrical camera calibration algorithm. Visual Computer, Vol. 36, pp. 413-424.
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[5] Aliakbarpour, H. and Dias, J. (2012). Three-dimensional reconstruction based on multiple virtual planes by using fusion-based camera network. IET Computer Vision, Vol. 6, No. 4, pp. 355 – 369.
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[6] Mandun, Z., Lichao, Q., Guodong, C., and Ming, Y. (2009). A triangulation method in 3D reconstruction from image sequences. Second International Conference on Intelligent Networks and Intelligent Systems, Tianjin, China.
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[8] Weilharter, R. and Fraundorfer, F. (2021). HighRes-MVSNet: A Fast Multi-View Stereo Network for Dense 3D Reconstruction from High-Resolution Images. IEEE Access, Vol. 9, pp. 11306-11315.
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[10] Zhang, C. (2019). CuFusion2: Accurate and Denoised Volumetric 3D Object Reconstruction Using Depth Cameras. IEEE Access, Vol. 7, pp. 49882-49893.
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[11] Liu, Z. -N., Cao, Y., Kuang, Z., Kobbelt, L., and Hu, S. (2021). High-Quality Textured 3D Shape Reconstruction with Cascaded Fully Convolutional Networks. IEEE Transactions on Visualization and Computer Graphics, Vol. 27, No. 1, pp. 83-97.
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23
ORIGINAL_ARTICLE
An Efficient Approach to Solve Software-defined Networks based Virtual Machines Placement Problem using Moth-Flame Optimization in the Cloud Computing Environment
Generally, the issue of quality assurance is a specific assurance in computer networks. The conventional computer networks with hierarchical structures that are used in organizations are formed using some nodes of Ethernet switches within a tree structure. Open Flow is one of the main fundamental protocols of Software-defined networks (SDNs) and provides the direct access to and change in program of sending network equipment such as switches and routers, physically and virtually. Lack of an open interface in data sending program has led to advent of integrated and close equipment that are similar to CPU in current networks. This study proposes a solution to reduce traffic using a correct placement of virtual machines while their security is maintained. The proposed solution is based on the moth-flame optimization, which has been evaluated. The obtained results indicate the priority of the proposed method.
https://jad.shahroodut.ac.ir/article_2085_c62ea5c37e7e0c416aba831db6ebbcb0.pdf
2021-07-01
309
320
10.22044/jadm.2021.9737.2106
Cloud Computin
Virtual Machine Placement
Software-Defined Networks
Moth-Flame Algorithm
A. H
Safari-Bavil
ah.safaribavil@gmail.com
1
Department of Electrical and Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
S.
Jabbehdari
sjabbehdari@gmail.com
2
Department of Electrical and Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran.
LEAD_AUTHOR
M.
Ghobaei-Arani
mostafaghobaye@yahoo.com
3
Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran.
AUTHOR
[1] A. Beloglazov, R. Buyya, Y. C. Lee, and A. Zomaya (2011). “A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems”, pp. 47–111.
1
[2] Tajamolian, M., Ghasemzadeh, M. (2019). Analytical evaluation of an innovative decision-making algorithm for VM live migration. Journal of AI and Data Mining, 7(4), 589-596. doi: 10.22044/jadm.2018.7178.1847.
2
[3] Mabhoot, N., Momeni, H. (2021). An Energy-aware Real-time Task Scheduling Approach in a Cloud Computing Environment. Journal of AI and Data Mining, (), -. doi: 10.22044/jadm.2021.10344.2171.
3
[4] Donyagard Vahed, N., Ghobaei‐Arani, M., & Souri, A. (2019). Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review. International Journal of Communication Systems, 32(14), e4068.
4
[5] Masdari, M., Gharehpasha, S., Ghobaei-Arani, M., & Ghasemi, V. (2019). Bio-inspired virtual machine placement schemes in cloud computing environment: taxonomy, review, and future research directions. Cluster Computing, 1-31.
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[6] J. Anderson and J.-H. Cho (2017). “Software Defined Network Based Virtual Machine Placement in Cloud Systems,” in MILCOM 2017 IEEE Military Communications Conference (MILCOM), pp. 876–881.
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[7] M.C. Silva Filho, C.C. Monteiro, P.R.M. Inácio, and M. M. Freire (2018). “Approaches for optimizing virtual machine placement and migration in cloud environments: A survey,” J. Parallel Distrib Comput, Vol. 111, pp. 222–250.
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[8] L. Zhang, Y. Zhuang, and W. Zhu (2013). “Constraint Programming based Virtual Cloud Resources Allocation Model,” Int. J. Hybrid Inf. Technol., Vol. 6, No. 6, pp. 333–344.
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[9] C. Dupont, T. Schulze, G. Giuliani, A. Somov, and F. Hermenier (2012). “An energy aware framework for virtual machine placement in cloud federated data centres,” in Proceedings of the 3rd International Conference on Future Energy Systems Where Energy, Computing and Communication Meet- e-Energy’12, pp. 1–10.
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[10] J. Dong, H. Wang, and S. Cheng (2015). “Energy-performance tradeoffs in IaaS cloud with virtual machine scheduling,” China Commun., Vol. 12, No. 2, pp. 155–166.
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[11] W. Song, Z. Xiao, Q. Chen, and H. Luo (2014). “Adaptive Resource Provisioning for the Cloud using Online Bin Packing,” IEEE Trans. Comput., Vol. 63, No. 11, pp. 2647–2660.
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[13] T. Wood, P. Shenoy, A. Venkataramani, and M. Yousif (2009). “Sandpiper: Black-box and gray-box resource management for virtual machines,” Comput. Networks, Vol. 53, No. 17, pp. 2923–2938.
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[14] A. Singh, M. Korupolu, and D. Mohapatra (2008). “Server-storage virtualization: Integration and load balancing in data centers,” in 2008 SC-International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–12.
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[15] N. Bobroff, A. Kochut, and K. Beaty (2007). “Dynamic Placement of Virtual Machines for Managing SLA Violations,” in 2007 10th IFIP/IEEE International Symposium on Integrated Network Management, pp. 119–128.
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[17] Brent Salisbury (2012). The Northbound API- a Big Little Problem, www.networkstatic.net.
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[18] Seyedali Mirjalili (2015). “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm”, Knowledge-based Systems 89, 228–249.
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[19] S. Agrawal, S. Bose, and S. Sundarrajan (2009). “Grouping genetic algorithm for solving the server consolidation problem with conflicts,” Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp. 1-8.
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[20] Ghobaei‐Arani, M., Rahmanian, A. A., Shamsi, M., & Rasouli‐Kenari, A. (2018). A learning‐based approach for virtual machine placement in cloud data centers. International Journal of Communication Systems, 31(8), e3537.
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[21] R.K. Gupta and R. Pateriya (2019). “Survey on virtual machine placement techniques in cloud computing environment,” International Journal on Cloud Computing: Services and Architecture (IJCCSA), Vol. 4, No. 4, pp. 1–7.
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[22] S.-H. Wang, P.P.W. Huang, C.H.P. Wen, and L.-C. Wang (2020). “EQVMP: Energy-efficient and qos-aware virtual machine placement for software defined datacenter networks,” in IEEE International Conference on Information Networking.
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[25] Tie Li; Gang Kou; Yi Peng; Yong Shi (2017). “Classifying With Adaptive Hyper-Spheres: An Incremental Classifier based on Competitive Learning”, IEEE Transactions on Systems, Man, and Cybernetics.
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26
ORIGINAL_ARTICLE
Automatic Grayscale Image Colorization using a Deep Hybrid Model
Image colorization is an interesting yet challenging task due to the descriptive nature of getting a natural-looking color image from any grayscale image. To tackle this challenge and also have a fully automatic procedure, we propose a Convolutional Neural Network (CNN)-based model to benefit from the impressive ability of CNN in the image processing tasks. To this end, we propose a deep-based model for automatic grayscale image colorization. Harnessing from convolutional-based pre-trained models, we fuse three pre-trained models, VGG16, ResNet50, and Inception-v2, to improve the model performance. The average of three model outputs is used to obtain more rich features in the model. The fused features are fed to an encoder-decoder network to obtain a color image from a grayscale input image. We perform a step-by-step analysis of different pre-trained models and fusion methodologies to include a more accurate combination of these models in the proposed model. Results on LFW and ImageNet datasets confirm the effectiveness of our model compared to state-of-the-art alternatives in the field.
https://jad.shahroodut.ac.ir/article_2099_56d99dec486ae27ca1e71ba2853ea374.pdf
2021-07-01
321
328
10.22044/jadm.2021.9957.2131
Grayscale image colorization
deep learning
Convolutional neural network
Inception-v2
Color space
K.
Kiani
kourosh.kiani@semnan.ac.ir
1
Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran.
LEAD_AUTHOR
R.
Hematpour
r.hemmatpour@semnan.ac.ir
2
Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran.
AUTHOR
R.
Rastgoo
rrastgoo@semnan.ac.ir
3
Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran.
AUTHOR
[1] G. Larsson, M. Maire, and G. Shakhnarovich, “Learning representations for automatic colorization,” ECCV, 2016.
1
[2] M.E. Valentinuzzi, “Understanding the human machine: a primer for bioengineering,” World Scientific, Vol. 4, 2004.
2
[3] V.K. Bagaria and K. Tatwawadi, “CS231N Project: Coloring black and white world using Deep Neural Nets”, Stanford University, 2016.
3
[4] X. Gu, M. He, and M. Gu, “Thermal image colorization using Markov decision processes,” Memetic Computing, Vol. 9, pp. 15-22, 2017.
4
[5] I. Virag, L. -Tivadar, and M. Crişan-Vida, “Client-side Medical Image Colorization in a Collaborative Environment,” Studies in health technology and informatics, pp. 904-908, 2017.
5
[6] T. Horiuchi, “Color image coding by colorization approach,” Journal on Image and Video Processing, Vol. 1, pp. 158273, 2018. https://doi.org/10.1155/2008/158273.
6
[7] R. Rastgoo and V. Sattari-Naeini, “A neurofuzzy QoS-aware routing protocol for smart grids” 22nd Iranian Conference on Electrical Engineering (ICEE), pp. 1080-1084, 2014. DOI: 10.1109/IranianCEE.2014.6999696.
7
[8] F. Bordbar, R. Rastgoo, M.A. Askarzadeh, and M.S. Tavallali, “Prediction of Residential Natural Gas Consumption Using Artificial Neural Network,” The 9th International Chemical Engineering Congress & Exhibition (IChEC 2015), pp. 1-4, 2015.
8
[9] R. Rastgoo and V. Sattari-Naeini, “Tuning parameters of the QoS-aware routing protocol for smart grids using genetic algorithm,” Applied Artificial Intelligence, Vol. 30, No. 1, pp. 52-67, 2016.
9
[10] R. Rastgoo and V. Sattari-Naeini, “Multi-Constraint Optimal Path Finding for QoS-Enabled Smart Grids: A Combination Approach of Neural Network and Fuzzy System,” Journal of Computing and Security, Vol. 4, No. 2, pp. 47-61, 2017.
10
[11] R. Rastgoo and V. Sattari-Naeini, “Gsomcr: Multi-constraint genetic-optimized qos-aware routing protocol for smart grids,” Iranian Journal of Science and Technology, Transactions of Electrical Engineering, Vol. 42, No. 2, pp. 185-194, 2018.
11
[12] R. Rastgoo and K. Kiani, “Face recognition using fine-tuning of Deep Convolutional Neural Network and transfer learning,” Journal of Modeling in Engineering, Vol. 17, No, 58, pp. 103-111, 2019.
12
[13] R. Rastgoo, K. Kiani, and S. Escalera, “Hand sign language recognition using multi-view hand skeleton,” Expert Systems with Applications, Vol. 150, No. 113336, 2020. DOI: https://doi.org/10.1016/j.eswa.2020.113336.
13
[14] R. Rastgoo, K. Kiani, and S. Escalera, “Multi-Modal Deep Hand Sign Language Recognition in Still Images using Restricted Boltzmann Machine,” Entropy, Vol. 20, No. 809, 2018.
14
[15] R. Rastgoo, K. Kiani, and S. Escalera, “Video-based isolated hand sign language recognition using a deep cascaded model,” Multimedia Tools and Applications, Vol. 79, pp. 22965–22987, 2020. DOI: https://doi.org/10.1007/s11042-020-09048-5.
15
[16] R. Rastgoo, K. Kiani, and S. Escalera, “Hand pose aware multi-modal isolated sign language recognition,” Multi-media Tools and Applications, Vol. 80, No. 1, pp. 127–163, 2021.
16
[17] R. Rastgoo, K. Kiani, and S. Escalera, “Real-time isolated hand sign language recognition using deep networks and SVD,” Journal of Ambient Intelligence and Humanized Computing, 2021. https://doi.org/10.1007/s12652-021-02920-8.
17
[18] R. Rastgoo, K. Kiani, and S. Escalera, “Sign language recognition: A deep survey,” Expert Systems with Applications, Vol. 164, 2021.
18
[19] M. Kurmanji and F. Ghaderi, “Hand Gesture Recognition from RGB-D Data using 2D and 3D Convolutional Neural Networks: a comparative study,” Journal of AI and Data Mining (JAIDM), Vol. 8, No. 2, pp. 177-188, 2020.
19
[20] M. Asadolahzade-Kermanshahi and M.M. Homayounpour, “Improving Phoneme Sequence Recognition using Phoneme Duration Information in DNN-HSMM,” Journal of AI and Data Mining (JAIDM), Vol. 7, No. 1, pp. 137-147, 2019.
20
[21] A. Torfi, R.A. Shirvani, Y. Keneshloo, N. Tavaf, and E.A. Fox, “Natural Language Processing Advancements by Deep Learning: A Survey,” arXiv: 2003.01200v2, 2020.
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[22] D. Varga and T. Szirányi, “Fully automatic image colorization based on Convolutional Neural Network,” 23rd International Conference in Pattern Recognition (ICPR), 2016.
22
[23] J. An, K.G. Kpeyiton, and Q. Shi, “Grayscale images colorization with convolutional neural networks,” Soft Comput. Vol. 24, pp. 4751–4758, 2020. https://doi.org/10.1007/s00500-020-04711-3.
23
[24] J. Wang and Y. Zhou, “Image Colorization with Deep Convolutional Neural Networks,” Stanford report, 2016. http://cs231n.stanford.edu/reports/2016/pdfs/219_Report.pdf.
24
[25] S. Iizuka, E. Simo-Serra, and H. Ishikawa, “Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification,” ACM Transactions on Graphics (TOG), Vol. 35, No. 4, 2016.
25
[26] S. Titus and J. Rena, “Fast Colorization of Grayscale Images by Convolutional Neural Network,” International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET), 2018.
26
[27] R. Zhang, P. Isola, and A.A. Efros, “Colorful image colorization,” ECCV, 2016.
27
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28
[29] N. Majidi, K. Kiani, and R. Rastgoo, “A Deep Model for Super-resolution Enhancement from a Single Image,” Journal of AI and Data Mining (JAIDM), Vol. 8, No. 4, pp. 451-460, 2020.
29
[30] L. Yatziv and G. Sapiro, “Fast Image and Video Colorization using Chrominance Blending,” IEEE Trans. Image Process, Vol. 15, pp. 1120–1129, 2006.
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[31] B. Li, Y.K. Lai, and P.L. Rosin, “Example-based Image Colorization via Automatic Feature Selection and Fusion,” Neurocomputing, Vol. 266, pp. 687–698, 2017.
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[32] F. Baldassarre, D.G. Morín, and L. Rodés-Guirao, “Deep Koalarization: Image Colorization using CNNs and Inception-ResNet-v2,” arXiv:1712.03400, 2017.
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[33] O. Russakovsky, et al. “ ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision, Vol. 115, pp. 211–252, 2015.
33
[34] G.B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, “Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments,” University of Massachusetts, Technical Report, pp. 7-49, 2008.
34
ORIGINAL_ARTICLE
Hybrid PSO-SA Approach for Feature Weighting in Analogy-Based Software Project Effort Estimation
Software effort estimation plays an important role in software project management, and analogy-based estimation (ABE) is the most common method used for this purpose. ABE estimates the effort required for a new software project based on its similarity to previous projects. A similarity between the projects is evaluated based on a set of project features, each of which has a particular effect on the degree of similarity between projects and the effort feature. The present study examines the hybrid PSO-SA approach for feature weighting in analogy-based software project effort estimation. The proposed approach was implemented and tested on two well-known datasets of software projects. The performance of the proposed model was compared with other optimization algorithms based on MMRE, MDMRE, and PRED(0.25) measures. The results showed that weighted ABE models provide more accurate and better effort estimates relative to unweighted ABE models and that the PSO-SA hybrid approach has led to better and more accurate results compared with the other weighting approaches in both datasets.
https://jad.shahroodut.ac.ir/article_2101_556c984e7298f367abfba417fbde85d9.pdf
2021-07-01
329
340
10.22044/jadm.2021.10119.2152
Software effort estimation
Analogy based estimation
Feature weight optimization
Particle Swarm Optimization
Simulated annealing
Z.
Shahpar
zahrashahpar@yahoo.com
1
Department of Computer engineering, Kerman Branch, Islamic Azad University, Kerman, Iran.
AUTHOR
V.
Khatibi
khatibi78@yahoo.com
2
Department of Computer engineering, Bardsir Branch, Islamic Azad University, Bardsir, Iran.
AUTHOR
A.
Khatibi Bardsiri
khatibi_amid@yahoo.com
3
Department of Computer engineering, Bardsir Branch, Islamic Azad University, Bardsir, Iran.
AUTHOR
[1] M. Shepperd and C. Schofield, “Estimating software project effort using analogies,” IEEE Transactions on Software Engineering, vol. 23, no. 11, pp. 736-743, 1997.
1
[2] I. Guyon and A. Elisseeff “An introduction to variable and feature selection,” Journal of Machine Learning Research, vol. 3, pp. 1157-1182, 2003.
2
[3] D. B. Skalak, “Prototype and feature selection by sampling and random mutation hill climbing algorithms,” In 11th International Machine Learning Conference, ICML-94, Morgan Kau_mann, pp. 293-301, 1994.
3
[4] Z. Shahpar et al., “Improvement of effort estimation accuracy in software projects using a feature selection approach,” Journal of Advances in Computer Engineering and Technology, vol. 2, pp. 31-38, 2016.
4
[5] E. Papatheocharous et al., “Feature Subset Selection for Software Cost Modelling and Estimation,” Engineering Intelligent Systems, vol. 18, 2010.
5
[6] B. B. Sigweni, “An Investigation of Feature Weighting Algorithms and Validation Techniques using Blind Analysis for Analogy-based Estimation,” Ph.D. dissertation, Brunel Univ., London, 2016.
6
[7] A. Kolcz and Y. Wen-tau “Raising the baseline for high-precision text classifiers,” Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 400-409, 2007.
7
[8] Y. Saeys, I. Inza and P. Larranaga, “A review of feature selection techniques in bioinformatics,” Bioinformatics, vol. 23, no. 19, pp. 2507-2517, 2007.
8
[9] D. Koller and M. Sahami “Toward optimal feature selection,” Tech. Rep. TR-1996-77, Stanford InfoLab. 1996.
9
[10] J. W. Keung and B. Kitchenham, “Optimizing Project Feature Weights for Analogy-based Software Cost Estimation using the Mantel Correlation,” IEEE, 14th Asia-Pacific Software Engineering Conference, Aichi, pp. 222-229, 2007.
10
[11] J. W. Keung, A. Kitchenham and D. R. Jeffery, “Analogy-X: Providing Statistical Inference to Analogy-Based Software Cost Estimation,” IEEE Transactions on Software Engineering, vol. 34, no. 4, pp. 471-484, 2008.
11
[12] J. Wen, S. Li. and L. Tang, “Improve Analogy-based Software Effort Estimation using Principal Components Analysis and Correlation Weighting,” 16th Asia-Pacific Software Engineering Conference, pp. 179-186, 2009.
12
[13] E. Khatibi and V. Khatibi, “Model to estimation the software development effort based on in-depth analysis of project attributes,” The Institution of Engineering and Technology, vol. 9, pp. 109-118, 2015.
13
[14] J. Li and G. Ruhe, “Software effort estimation by analogy using attribute selection based on rough set analysis,” International Journal of Software Engineering and Knowledge Engineering, vol. 18, no. 1, pp. 1-23, 2008.
14
[15] J.Li and G. Ruhe, “Analysis of attribute weighting heuristics for analogy based software effort estimation method AQUA+,” Empirical Software Engineering, vol. 13, no. 1, pp. 63-96, 2008.
15
[16] Y. F. Li, M. Xie, and T. N. Goh, “A study of project selection and feature weighting for analogy based software cost estimation,” The Journal of Systems and Software, vol. 82, no. 2, pp. 241-252, 2009.
16
[17] Y. F. Li, M. Xie, and T. N. Goh, “A study of genetic algorithm for project selection for analogy based software cost estimation,” IEEE, International Conference on Industrial Engineering and Engineering Management, Singapore, pp. 1256-1260, 2007.
17
[18] S. J. Huang and N. H. Chiu, “Optimization of Analogy Weights by Genetic Algorithm for Software Effort Estimation,” Information and Software technology, vol. 48, pp. 1034-1045, 2006.
18
[19] Y. F. Li, M. Xie, and T. N. Goh, “Optimization of feature weights and number of neighbors for Analogy based cost Estimation in software project management,” IEEE International Conference on Industrial Engineering and Engineering Management, Singapore, pp. 1542-1546, 2008.
19
[20] A. L. I. Oliveira et al., “GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation,” Information and Software Technology, vol. 52, no. 11, pp. 1155-1166, 2010.
20
[21] V. Khatibi et al., “A hybrid method for increasing the accuracy of software development effort estimation,” Scientific Research and Essays, vol. 6, no. 30, pp. 6382-6382, 2011.
21
[22] P. Reddy, C. Hari and S. Rao “Multi- Objective Particle Swarm Optimization for Software Cost Estimation,” International Journal of Computer Applications, vol. 32, no. 3, pp. 13-17, 2011.
22
[23] M. Azzeh et al., “Pareto efficient multi-objective optimization for local tuning of analogy-based estimation,” Springer, Neural Comput & Applic, November, vol. 27, no. 8, pp. 2241-2265, 2015.
23
[24] R. D. A. Araujo, S. Soares and A. L. I. Oliveria, “Hybrid morphological methodology for software development cost estimation,” Expert Systems with Applications, vol. 39, pp. 6129–6139, 2012.
24
[25] V. Khatibiet et al., “A flexible method to estimate the software development effort based on the classification of projects and localization of comparisons,” Empirical Software Engineering, vol. 19, pp. 857-884, 2014.
25
[26] V. Khatibi et al., “A pso-based model to increase the accuracy of software development effort estimation,” Software Quality Journal, vol. 21, pp. 501-526, 2013.
26
[27] D. Wu, J. Li and C. Bao, “Case-based reasoning with optimized weight derived by particle swarm optimization for software effort estimation,” soft computing, vol. 22, 5299–5310, 2018.
27
[28] T. R. Benala and R. Mall, “DABE: Differential in Analogy-Based Software Development Effort Estimation,” Swarm and Evolutionary Computation, vol. 38, pp. 158-172, 2017.
28
[29] S. Ranichandra “Optimization Non-Orthogonal space distance using ACO in software cost estimation,” Mukt shabd journal, vol. IX, pp. 1592-1604, 2020.
29
[30] A. Khatibi, “An Intelligent Model to Predict the Development Time and Budget of Software Projects,” Int. J. Non-linear Anal. Appl. vol. 11, no. 2, pp. 85-102, 2020.
30
[31] A. M. Shah et al., “Ensembling artificial bee colony with analogy-Based Estimation to Improve software Development Effort Prediction,” IEE Access, vol. 8, pp. 58402-58415, 2020.
31
[32]A. Zakrani, M. Hain and A. Idri, “Improving Software Development effort estimating using Support Vector Regression and Feature Selection,” IAES International Journal of Artificial Intelligence, vol. 8, no. 4, pp. 399-410, 2019.
32
[33] E. Mendes, N. Mosley and S. Counsell, “A replicated assessment of the use of adaptation rules to improve web cost estimation,” IEEE, International Symposium on In Empirical Software Engineering, pp. 100-109, 2003.
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[34] I. Angelis and I. Stamelos, “A Simulation Tool for Efficient Analogy Based Cost Estimation,” Empirical Software Engineering, vol. 5, no. 1, pp. 35-68, 2000.
34
[35] J. Keung, “Software development cost estimation using analogy: a review,” IEEE, Software Engineering Conference, Australian, pp. 327-336, 2009.
35
[36] S. K. Pal and S. C. K. Shiu, Foundations of soft case-based reasoning, John Wiley and Sons, New Jersey, 2004.
36
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37
[38] S. Beiranvand and M. A. Z. Chahooki, “Bridging the semantic gap for software effort estimation by hierarchical feature selection techniques,” Journal of AI and Data Mining, vol. 4, no. 2, pp. 157-168, 2016.
38
ORIGINAL_ARTICLE
Investigating Changes in Household Consumable Market Using Data Mining Techniques
For an economic review of food prices in May 2019 to determine the trend of rising or decreasing prices compared to previous periods, we considered the price of food items at that time. The types of items consumed during specific periods in urban areas and the whole country are selected for our statistical analysis. Among the various methods of modelling and statistical prediction, and in a new approach, we modeled the data using data mining techniques consisting of decision tree methods, associative rules, and Bayesian law. Then, prediction, validation, and standardization of the accuracy of the validation are performed on them. Results of data validation in the urban and national area and the results of the standardization of the accuracy of validation in the urban and national area are presented with the desired accuracy.
https://jad.shahroodut.ac.ir/article_2109_72a2a4abec8bd37df0ba92c94f8b4901.pdf
2021-07-01
341
349
10.22044/jadm.2021.10024.2139
data mining
Bayesian Rule
decision tree
Associative Rule
Households’ Consumer Goods
A.
Hasan-Zadeh
hasanzadeh.a@ut.ac.ir
1
Fouman Faculty of Engineering, College of Engineering, University of Tehran, Fouman, Iran.
LEAD_AUTHOR
F.
Asadi
faezeh.asadi94@ut.ac.ir
2
Fouman Faculty of Engineering, College of Engineering, University of Tehran, Fouman, Iran.
AUTHOR
N.
Garbazkar
najme.garbazkar@ut.ac.ir
3
Fouman Faculty of Engineering, College of Engineering, University of Tehran, Fouman, Iran.
AUTHOR
[1] A. R. De Carvalho, R. S. M. Ribeiro, and A. M. Marques, "Economic development and inflation: a theoretical and empirical analysis," International Review of Applied Economics, vol. 32, no. 4, pp. 546-565, 2018.
1
[2] L. Katusiime, "Private Sector Credit and Inflation Volatility," Economics, vol. 6, no. 2, pp. 1-13, 2017.
2
[3] L. Zhao, J. Mbachu, and Z. Liu, "Identifying Significant Cost-Influencing Factors for Sustainable Development in Construction Industry using Structural Equation Modelling," Mathematical Problems in Engineering, vol. 2020, 4810136, 16 pages, 2020.
3
[4] E. W. T. Ngai, L. Xiu, and D. C. K. Chau, "Application of data mining techniques in customer relationship management: A literature review and classification," Expert Systems with Applications, vol. 36, no. 2, pp. 2592-2602, 2009.
4
[5] A. Zarei, M. Maleki, D. Feiz, and M. A. Siahsarani kojuri, "Competitive Intelligence Text Mining: Words Speak," Journal of AI and Data Mining, vol. 16, no. 1, pp. 79-92, 2018.
5
[6] C. J. Romanowski, and R. Nagi, Analyzing Maintenance Data using Data Mining Methods, Part of the Massive Computing book series (MACO, volume 3): Data Mining for Design and Manufacturing, Kluwer Academic Publishers, pp. 235-254, 2001.
6
[7] B. Grabot, "Rule mining in maintenance: Analyzing large knowledge bases," Computers and Industrial Engineering, vol. 139, 15 pages, 2020.
7
[8] R. Y. Zhong, S. T. Newman, G. O. Huang, and S. Lan, "Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives," Computers and Industrial Engineering, vol. 1021, pp. 572-591, 2016.
8
[9] M. E. Kara, S. Ü. O. Fırat, and A. Ghadge, "A data mining-based framework for supply chain risk management," Computers and Industrial Engineering, vol. 139, 12 pages, 2018.
9
[10] P. Vazan, D. Janikova, P. Tanuska, M. Kebisek, and Z. Cervenanska, "Using data mining methods for manufacturing process control," IFAC-PapersOnLine, vol. 50, no. 1, pp. 6178-6183, 2017.
10
[11] C. Yu, W. Zhang, X. Xu, Y. Ji, and S. Yu, "Data mining based multi-level aggregate service planning for cloud manufacturing," Journal of Intelligent Manufacturing, vol. 29, no. 6, pp. 1351–1361, 2018.
11
[12] Z. Ge, Z. Song, S. X. Ding, and B. Huang, "Data Mining and Analytics in the Process Industry: The Role of Machine Learning," Book: Data-Driven Monitoring, Fault Diagnosis, and Control of Cyber-Physical Systems, IEEE Access, vol. 5, pp. 20590-20616, 2017.
12
[13] B. T. Hazen, J. B. Skipper, C. A. Boone, and R. R. Hill, "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, vol. 270, no. 1-2, pp. 201-211, 2018.
13
[14] R. Ghousi, "Applying a decision support system for accident analysis by using data mining approach: A case study on one of the Iranian manufactures," Journal of Industrial and Systems Engineering, vol. 8, no. 3, pp. 59-76, 2015.
14
[15] S. Shoorabi Sani, "A case study for application of fuzzy inference and data mining in structural health monitoring," Journal of Artificial Intelligence and Data Mining, vol. 6, no. 1, pp. 105-120, 2018.
15
[16] T. Ahmad, and H. Chen, "Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches", Energy and Buildings, vol. 166, no. 1, pp. 460-476, 2018.
16
[17] R. Torkaman, and R. Safabakhsh, "Robust Opponent Modeling in Real-Time Strategy Games using Bayesian Networks," Journal of Artificial Intelligence and Data Mining, vol. 7, no. 1, 149-159, 2019.
17
[18] M. Gul, F. Guneri, F. Yilmaz, and O. Celebi, "Analysis of the relation between the characteristics of workers and occupational accidents using data mining," The Turkish Journal of Occupational/Environmental Medicine and Safety, vol. 1, no. 4, pp. 102-118, 2016.
18
ORIGINAL_ARTICLE
Detecting Breast Cancer through Blood Analysis Data using Classification Algorithms
Breast cancer is the second major cause of death and accounts for 16% of all cancer deaths worldwide. Most of the methods of detecting breast cancer are very expensive and difficult to interpret such as mammography. There are also limitations such as cumulative radiation exposure, over-diagnosis, false positives and negatives in women with a dense breast which pose certain uncertainties in high-risk population. The objective of this study is Detecting Breast Cancer Through Blood Analysis Data Using Classification Algorithms. This will serve as a complement to these expensive methods. High ranking features were extracted from the dataset. The KNN, SVM and J48 algorithms were used as the training platform to classify 116 instances. Furthermore, 10-fold cross validation and holdout procedures were used coupled with changing of random seed. The result showed that KNN algorithm has the highest and best accuracy of 89.99% and 85.21% for cross validation and holdout procedure respectively. This is followed by the J48 with 84.65% and 75.65% for the two procedures respectively. SVM had 77.58% and 68.69% respectively. Although it was also discovered that Blood Glucose level is a major determinant in detecting breast cancer, it has to be combined with other attributes to make decision as a result of other health issues like diabetes. With the result obtained, women are advised to do regular check-ups including blood analysis in order to know which of the blood components need to be worked on to prevent breast cancer based on the model generated in this study.
https://jad.shahroodut.ac.ir/article_2112_2cd346dfce98efa9aceec95fd55e31cf.pdf
2021-07-01
351
359
10.22044/jadm.2021.9839.2116
Classification Algorithm
Breast Cancer
data mining
Machine learning
Oladosu
Oladimeji
oladimejioladosu@gmail.com
1
Department of Computer Science, University of Ibadan, Ibadan, Nigeria
LEAD_AUTHOR
Olayanju
Oladimeji
oladimejiolayanju@gmail.com
2
Department of Computer Science and Information Technology, Bowen University, Iwo, Nigeria
AUTHOR
[1] J. Tang, R. M. Rangayyan, J. Xu, I. E. Naqa, and Y. Yang, “Computer-aided detection and diagnosis of breast cancer with mammography: recent advances,” IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 2, pp. 236-251, 2009.
1
[2] M. F. Aslan, Y. Celik, K. Sabanci, and A. Durdu, “Breast Cancer Diagnosis by Different Machine Learning Methods using Blood Analysis Data,” International Journal of Intelligent Systems and Applications in Engineering, vol. 6, no. 4, 2018.
2
[3] Z. Ahmad, A. Khurshid, A. Qureshi, R. Idress, N. Asghar, and N. Kayani, “Breast carcinoma grading, estimation of tumor size, axillary lymph node status, staging, and Nottingham prognostic index scoring on mastectomy specimens,” Indian Journal of Pathology and Microbiology, vol. 52, no. 4, pp. 477, 2009.
3
[4] U. R. Acharya, E. Y. Ng, J. H. Tan, and S. V. Sree, “Thermography-based breast cancer detection using texture features and support vector machine,” Journal of medical systems, vol. 36, no. 3, pp. 1503-1510, 2012.
4
[5] K. Ganesan, U. R. Acharya, C. K. Chua, L. C. Min, K. T. Abraham, and K.H. Ng, “Computer-aided breast cancer detection using mammograms: a review,” IEEE Reviews in biomedical engineering, vol. 6, pp. 77-98, 2013.
5
[6] http://www.who.int/cancer/detection/breastcancer/en/index1.html.
6
[7] U. Raghavendra, A. Gudigar, N. T. Rao, E. J. Ciaccio, E. Y. Ng, and U. R. Acharya, “Computer-aided diagnosis for the identification of breast cancer using thermogram images: A comprehensive review,” Infrared Physics and Technology, vol. 102, 2019
7
[8] https://www.cdc.gov/cancer/breast/basic_info/risk_factor.htm.
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[9] I. Schreer and J. Lüttges, “Breast cancer: early detection,” In Radiologic-Pathologic Correlations from Head to Toe, Germany, pp. 767-784, 2005.
9
[10] J. Melnikow, J. J. Fenton, E. P. Whitlock, D. L. Miglioretti, M. S Weyrich, and J. H. Thompson, “Supplemental screening for breast cancer in women with dense breasts: a systematic review for the U.S. Preventive Services Task Force,” Ann Intern Med. Vol. 164, 2016.
10
[11] A. B Miller, C. Wall, C. J. Baines, P. Sun, T. To, and S. A. Narod, Twenty-five-year follow-up for breast cancer incidence and mortality of the Canadian National Breast Screening Study: randomized screening trial,” BMJ, 2014.
11
[12] J. Crisóstomo, P. Matafome, D. Santos-Silva, A. Gomes, M. Gomes, M. Patricio, L. Letra, A. Sarmento-Ribeiro, L. Santos, R. Seica, “Hyperresistinemia and metabolic dysregulation: the close crosstalk in obese breast cancer,” Endocrine, vol. 53, no. 2, 2016.
12
[13] S.B. Kotsiantis, “Supervised Machine Learning: A Review of Classification Techniques,” Informatica, vol. 31, pp 249-268, 2007.
13
[14] E. Ahishakiye, E. O. Omulo, D. Taremwa, and I. Niyonzima, “Crime Prediction Using Decision Tree (J48) Classification Algorithm,” International Journal of Computer and Information Technology, vol. 6, no. 3, 2017.
14
[15] L. Rokach and O. Maimon, “Top – Down Induction of Decision Trees Classifiers–A Survey,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 35, no. 4, pp. 476-487, 2005.
15
[16] H. Jiawei and M. Kamber, “Data Mining: Concepts and Techniques,” Morgan Kaufman, 2011.
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[17] M. Pal and P. M. Mather, “An assessment of the effectiveness of decision tree methods for land cover classification,” Remote Sensing of Environment, vol. 86, pp. 554-565, 2003.
17
[18] O. O. Oladimeji and O. O. Oladimeji, “Exploring Data Mining Research in West Africa: A Bibliometric Analysis,” SLIS Connecting, vol. 9, no. 2, 2020.
18
[19] M. Abdar, W. Ksiazek, U. R. Acharya, R. Tan, V. Makarenkov, and P. A. Plawiak, “A New Machine Learning Technique for an Accurate Diagnosis of Coronary Artery Disease,” Computer Methods and Programs in Biomedicine, vol. 179, 2019.
19
[20] S. Maniraj, A. Saini, S. D. Sarka, and S. Ahmed, “Credit Card Fraud Detection using Machine Learning and Data Science,” International Journal of Engineering Research and Technology, vol. 8, no. 9, 2019.
20
[21] M.U. Ghani, T.M. Alam, and F.H. Jaskani, “Comparison of Classification Models for Early Prediction of Breast Cancer,” In 2019 International Conference on Innovative Computing (ICIC), pp. 1-6, 2019.
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[22] Y. Li and Z. Chen, “Performance Evaluation of Machine Learning Methods for Breast Cancer Prediction,” Applied and Computational Mathematics, vol. 7, no. 4, pp. 212 -216, 2018.
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30
ORIGINAL_ARTICLE
A Heuristic Algorithm for Multi-layer Network Optimization in Cloud Computing
Background: One of the most important concepts in cloud computing is modeling the problem as a multi-layer optimization problem which leads to cost savings in designing and operating the networks. Previous researchers have modeled the two-layer network operating problem as an Integer Linear Programming (ILP) problem, and due to the computational complexity of solving it jointly, they suggested a two-stage procedure for solving it by considering one layer at each stage.Aim: In this paper, considering the ILP model and using some properties of it, we propose a heuristic algorithm for solving the model jointly, considering unicast, multicast, and anycast flows simultaneously. Method: We first sort demands in decreasing order and use a greedy method to realize demands in order. Due to the high computational complexity of ILP model, the proposed heuristic algorithm is suitable for networks with a large number of nodes; In this regard, various examples are solved by CPLEX and MATLAB soft wares. Results: Our simulation results show that for small values of M and N CPLEX fails to find the optimal solution, while AGA finds a near-optimal solution quickly.Conclusion: The proposed greedy algorithm could solve the large-scale networks approximately in polynomial time and its approximation is reasonable.
https://jad.shahroodut.ac.ir/article_2064_bb364166274e0e51a3cf2d2ab3f3199b.pdf
2021-07-01
361
367
10.22044/jadm.2021.9955.2133
Model-driven development
MPLS
Cloud Computing
A.
Hadian
ahg.info2003@gmail.com
1
Department of Applied Mathematics, University campus 2, University of Guilan, Rasht, Iran
AUTHOR
M.
Bagherian
mbagherian@guilan.ac.ir
2
Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran
LEAD_AUTHOR
B.
Fathi Vajargah
fathi@guilan.ac.ir
3
Department of Statistics, Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran
AUTHOR
[1]M. Pióro, and D. Medhi, Routing, flow, and capacity design in communication and computer networks. Elsevier, 2004.
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[2] K. Walkowiak, Modeling and optimization of cloud-ready and content-oriented networks, Springer International Publishing, vol 56, 2016.
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[3] G. Carofiglio, G. Morabito, L. Muscariello, I. Solis, and M. Varvello, From content delivery today to information centric networking. Computer Networks, vol 57(16), pp. 3116-3127, 2013.
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[4] G. Tyson, E. Bodanese, J. Bigham, and A. Mauthe, Beyond content delivery: Can icns help emergency scenarios? IEEE Network, 28(3), pp. 44-49, 2014.
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[5] J.M. Simmons, Optical network design and planning. Springer, 2014.
5
[6] I. Tomkos, S. Azodolmolky, J. Sole-Pareta, D. Careglio, and E. Palkopoulou, A tutorial on the flexible optical networking paradigm: State of the art, trends, and research challenges. Proceedings of the IEEE, 102(9), pp. 1317-1337, 2014.
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[7] Y. Li, D. King, F. Zhang, and A. Farrel, Generalized Labels for the Flexi-Grid in Lambda Switch Capable (LSC) Label Switching Routers. IEEE, 2015.
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[8] B. Mukherjee, WDM optical communication networks: progress and challenges. IEEE Journal on Selected Areas in communications, 18(10), pp. 1810-1824, 2000.
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[10] O. Nevzorova, O. Lemeshko, A. Mersni, A.M. Hailan, A.S. Ali, and S. Harkusha, July. Improved Two-Level Method of Multicast Routing in MPLS-TE Network. IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON) (pp. 846-850). IEEE, 2019.
10
[11] I. Zhukovyts’kyy, V. Pakhomova, H. Domanskay, and A. Nechaiev, Distribution of information flows in the advanced network of MPLS of railway transport by means of a neural model. In MATEC Web of Conferences (Vol. 294, p. 04007). EDP Sciences. 2019.
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[12] M. Masood, M.M. Fouad, R. Kamal, I. Glesk, and I.U. Khan, An improved particle swarm algorithm for multi-objective-based optimization in MPLS/GMPLS networks. IEEE Access, 7, pp. 137147-137162, 2019.
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[13] M. Masood, M. Mosta Foyad, R. Kamak, I. Glesk, and I. Ullahkhan, An Improved Particle Swarm Algorithm for Multi-Objective-based Optimization in MPLS/GMPLS Networks, IEEE Access, 2019.
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[15] MABHOOT, Nahid; MOMENI, Hossein. An Energy-aware Real-time Task Scheduling Approach in a Cloud Computing Environment. Journal of AI and Data Mining, 2021.
15
ORIGINAL_ARTICLE
Sequential Multi-objective Genetic Algorithm
Many of the real-world issues have multiple conflicting objectives that the optimization between contradictory objectives is very difficult. In recent years, the Multi-objective Evolutionary Algorithms (MOEAs) have shown great performance to optimize such problems. So, the development of MOEAs will always lead to the advancement of science. The Non-dominated Sorting Genetic Algorithm II (NSGAII) is considered as one of the most used evolutionary algorithms, and many MOEAs have emerged to resolve NSGAII problems, such as the Sequential Multi-Objective Algorithm (SEQ-MOGA). SEQ-MOGA presents a new survival selection that arranges individuals systematically, and the chromosomes can cover the entire Pareto Front region. In this study, the Archive Sequential Multi-Objective Algorithm (ASMOGA) is proposed to develop and improve SEQ-MOGA. ASMOGA uses the archive technique to save the history of the search procedure, so that the maintenance of the diversity in the decision space is satisfied adequately. To demonstrate the performance of ASMOGA, it is used and compared with several state-of-the-art MOEAs for optimizing benchmark functions and designing the I-Beam problem. The optimization results are evaluated by Performance Metrics such as hypervolume, Generational Distance, Spacing, and the t-test (a statistical test); based on the results, the superiority of the proposed algorithm is identified clearly.
https://jad.shahroodut.ac.ir/article_2083_b52b50309cff632ba8008d54cae0a02c.pdf
2021-07-01
369
381
10.22044/jadm.2021.9598.2092
Multi-Objective Evolutionary Algorithms (MOEAs)
Non-dominated Sorting Genetic Algorithm II (NSGAII)
Sequential Multi-Objective Algorithm (SEQ-MOGA)
Benchmark functions
t-test
L.
Falahiazar
lfastd@gmail.com
1
Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
V.
Seydi
leilalfa@gmail.com
2
Department of Computer Engineering, South Tehran Branch, Islamic Azad University Tehran, Iran.
LEAD_AUTHOR
M.
Mirzarezaee
mirzarezaee@srbiau.ac.ir
3
Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
[1] B. Huang, B. Buckley, and T.-M. Kechadi, "Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications," Expert Systems with Applications, vol. 37, no. 5, pp. 3638-3646, 2010.
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[2] M. R. Nikoo, I. Varjavand, R. Kerachian, M. D. Pirooz, and A. Karimi, "Multi-objective optimumA design of double-layer perforated-wall breakwaters: Application of NSGA-II and bargaining models," Applied Ocean Research, vol. 47, pp. 47-52, 2014.
2
[3] A. Nourbakhsh, H. Safikhani, and S. Derakhshan, "The comparison of multi-objective particle swarm optimization and NSGA II algorithm: applications in centrifugal pumps," Engineering Optimization, vol. 43, no. 10, pp. 1095-1113, 2011.
3
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[5] J. Zeng, X. Zhang, and X. Guan, "Path Planning for General Aircrafts Under Complex Scenarios Using an Improved NSGA-II Algorithm⋆," Journal of Computational Information Systems, vol. 9, no. 16, pp. 6545-6553, 2013.
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ORIGINAL_ARTICLE
A Mobile Charger based on Wireless Power Transfer Technologies: A Survey of Concepts, Techniques, Challenges, and Applications on Rechargeable Wireless Sensor Networks
Battery power limitation of sensor nodes (SNs) is a major challenge for wireless sensor networks (WSNs) which affects network survival. Thus, optimizing the energy consumption of the SNs as well as increasing the lifetime of the SNs and thus, extending the lifetime of WSNs are of crucial importance in these types of networks. Mobile chargers (MCs) and wireless power transfer (WPT) technologies have played an important long role in WSNs, and much research has been done on how to use the MC to enhance the performance of WSNs in recent decades. In this paper, we first review the application of MCs and WPT technologies in WSNs. Then, forwarding issues the MC has been considered in the role of power transmitter in WSNs and the existing approaches are categorized, with the purposes and limitations of MC dispatching studied. Then an overview of the existing articles is presented and to better understand the contents, tables and figures are offered that summarize the existing methods. We examine them in different dimensions such as advantages and disadvantages etc. Finally, the future prospects of MC are discussed.
https://jad.shahroodut.ac.ir/article_2096_b76dd73754b0d83509e63c2e53993d54.pdf
2021-07-01
383
402
10.22044/jadm.2021.9936.2127
Wireless Charging Technology
Mobile charger vehicle
Rechargeable Wireless Sensor Networks
Wireless Power Transfer
N.
Nowrozian
n.nowrozian@yahoo.com
1
Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
AUTHOR
F.
Tashtarian
tashtarian@yahoo.com
2
Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
LEAD_AUTHOR
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79
ORIGINAL_ARTICLE
DTEC-MAC: Diverse Traffic with Guarantee Energy Consumption for MAC in Wireless Body Area Networks
Wireless body area networks (WBAN) are innovative technologies that have been the anticipation greatly promote healthcare monitoring systems. All WBAN included biomedical sensors that can be worn on or implanted in the body. Sensors are monitoring vital signs and then processing the data and transmitting to the central server. Biomedical sensors are limited in energy resources and need an improved design for managing energy consumption. Therefore, DTEC-MAC (Diverse Traffic with Energy Consumption-MAC) is proposed based on the priority of data classification in the cluster nodes and provides medical data based on energy management. The proposed method uses fuzzy logic based on the distance to sink and the remaining energy and length of data to select the cluster head. MATLAB software was used to simulate the method. This method compared with similar methods called iM-SIMPLE and M-ATTEMPT, ERP. Results of the simulations indicate that it works better to extend the lifetime and guarantee minimum energy and packet delivery rates, maximizing the throughput.
https://jad.shahroodut.ac.ir/article_2118_e3ebd70efefc121a646dd6cb37a5b7ab.pdf
2021-07-01
403
414
10.22044/jadm.2021.10117.2149
Wireless Body Area Networks
data classification
MAC protocol
fuzzy logic
Energy
F.
Rismanian Yazdi
f_rismanian_yazdi@iau-tnb.ac.ir
1
Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
AUTHOR
M.
Hosseinzadeh
hosseinzadeh.m@iums.ac.ir
2
Mental Health Research Center, Psychosocial Health Research Institute, Iran University of Medical Sciences, Tehran, Iran
LEAD_AUTHOR
S.
Jabbehdari
s_jabbehdari@iau-tnb.ac.ir
3
Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
AUTHOR
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