jist-202605192320260519232534CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagJournal of Information Systems and Telecommunication (JIST) jist2322-14377232021935RRathnaLMary GladenceJSybi CynthiaVMaria Anu723202120721710.52547/jist.9.35.207http://jist.ir/en/Article/15479http://jist.ir/en/Article/Download/15479http://jist.ir/en/Article/Download/15479http://jist.ir/en/Article/Download/15479http://jist.ir/en/Article/Download/15479http://jist.ir/en/Article/Download/15479http://jist.ir/en/Article/Download/15479http://jist.ir/en/Article/Download/15479[1] Jennifer Yick, Biswanath Mukherjee and Dipak Ghosal, “Wireless Sensor Network Survey” in Computer Networks, Elsevier, 2008, 52(12), 2292-2230. [2] Wendi Rabiner Heinzelman, Anantha Chandrakasan, Hari Balakrishnan, “Energy Efficient Communication Protocol for Wireless Microsensor Networks” , Poceedings of the 33rd Annual Hawaii International Conference on System Sciences, Maui, Hi, USA, 2000, Vol.2. pp.10. [3] Al-Jemeli, Marwan, and F. Hussin, “An energy efficient cross-layer network operation model for IEEE 802.15. 4-based mobile Wireless sensor networks” in Sensors Journal, 2015, 15 (2) 684-692. [4] Khan, RanaAzeem M., and Holger Karl, “Mac protocols for cooperative diversity in wireless lans and wireless sensor networks” in Communications Surveys & Tutorials, IEEE, 2015, 16(1), 46-63. [5] Huang, Pei, et al., “The evolution of MAC protocols in wireless sensor networks: A survey” in Communications Surveys & Tutorials, IEEE. 2014, 15(1), 101-120. [6] Doudou, Messaoud, DjamelDjenouri, and NadjibBadache, “Survey on latency issues of asynchronous mac protocols in delay-sensitive wireless sensor networks” in Communications Surveys & Tutorials, IEEE, 2013,15(2), 528-550. [7] Akhlaq, Muhammad, and Tarek R. Sheltami, “RTSP: An accurate and energy-efficient protocol for clock synchronization in WSNs” in IEEE Transactions on Instrumentation and Measurement, 2013, 62(3), 578-589. [8] Anastasi, Giuseppe, Marco Conti, and Mario Di Francesco, “A comprehensive analysis of the MAC unreliability problem in IEEE 802.15. 4 wireless sensor networks” in IEEE Transactions on Industrial Informatics, 2011, 7(1), 52-65. [9] Di Francesco, Mario, et al. “Reliability and Energy-Efficiency inIEEE 802.15. 4/ZigBee Sensor Networks: An Adaptive and Cross-Layer Approach” in IEEE Journal on Selected Areas in Communications, 2011, 29(8), 1508-1524. [10] Liu, Yuhua, et al., “Multi-layer clustering routing algorithm for wireless vehicular sensor networks” in IET communications, 2010, 4(7), 810-816. [11] Shanti, Chilukuri, and AnirudhaSahoo, “DGRAM: a delay guaranteed routing and MAC protocol for wireless sensor networks” in IEEE Transactions on Mobile Computing, 2010, 9(10), 1407-1423. [12] Chin, Kwan-Wu “Pairwise: a time hopping medium access control protocol for wireless sensor networks” in IEEE Transactions on Consumer Electronics, 2009, 55(4), 1898-1906. [13] Choi, Sung-Chan, Seong-Lyong Gong, and Jang-Won Lee, “An average velocity-based routing protocol with low end-to-end delay for wireless sensor networks” in Communications Letters, 2009, IEEE. 13(8), 621-623. [14] Hong, Sung-Hwa, and Hoon-ki Kim, “A multi-hop reservation method for end-to-end latency performance improvement in asynchronous MAC-based wireless sensor networks” in IEEE Transactions on Consumer Electronics, 2009, 55(3), 1214-1220. [15] Du, Junzhao, and Weisong Shi, “App-MAC: An application-aware event-oriented MAC protocol for multimodality wireless sensor networks” in IEEE Transactions on Vehicular Technology, 2008, 57(6), 3723-3731. [16] Kang, Kyungtae, Yongwoo Cho, and Heonshik Shin, “Energy-efficient MAC-layer error recovery for mobile multimedia applications in 3GPP2 BCMCS” in IEEE Transactions on Broadcasting, 2007, 53(1), 338-349. [17] Chen, Yunxia, and Qing Zhao, “An integrated approach to energy-aware medium access for wireless sensor networks” in IEEE Transactions on Signal Processing, 2007, 55(7), 3429-3444. [18] Song, Liang, and Dimitrios Hatzinakos, “A cross-layer architecture of wireless sensor networks for target tracking” in IEEE/ACM Transactions on Networking, 2007, 15(1), 145-158. [19] Fallahi, Afshin, and EkramHossain, “Distributed and energy-aware MAC for differentiated services, wireless packet networks: a general queuing analytical framework” in IEEE Transactions on Mobile Computing, 2007, 6(4), 381-394. [20] Liu, Jain-Shing, and C-HR Lin, “ECTP: an energy-efficiency label-switching MAC protocol for infrastructure wireless networks” in IEEE Transactions on Vehicular Technology, 2007, 56(3), 1399-1417. [21] Sagduyu, YalinEvren, and Anthony Ephremides, “On joint MAC and network coding in wireless ad hoc networks” in IEEE Transactions on Information Theory, 2007, 53(10), 3697-3713. [22] Amin Shahraki, Amir Taherkordi, Øystein Haugen, and Frank Eliassen, “Clustering objectives in wireless sensor networks: A survey and research direction analysis”,Computer Networks, Volume 180, 2020. [23] August, Nathaniel J., and Dong Sam Ha, “Operation, system architectures, and physical Layer design considerations of distributed MAC protocols for UWB” in IEEE Transactions on Microwave Theory and Techniques, 2006, 54(7), 3001-3012. [24] M. Youssef, J. Farah, C. A. Nour and C. Douillard, "Resource Allocation in NOMA Systems for Centralized and Distributed Antennas With Mixed Traffic Using Matching Theory," in IEEE Transactions on Communications, Jan 2020, vol. 68, no. 1, 414-428. [25] Basnayake, Vishaka; Jayakody, Dushantha N.K.; Sharma, Vishal; Sharma, Nikhil; Muthuchidambaranathan, P. and Mabed, Hakim. 2020. "A New Green Prospective of Non-orthogonal Multiple Access (NOMA) for 5G" Information 11, no. 2: 89. https://doi.org/10.3390/info11020089. [26] Joshua Onyeka Ogbebor, Agbotiname Lucky Imoize, and Aderemi Aaron-Anthony Atayero, “Energy Efficient Design Techniques in Next-Generation Wireless Communication Networks: Emerging Trends and Future Directions”, in Wireless Communications and Mobile Computing, vol. 2020, Article ID 7235362, 19 pages, 2020. [27] T.Bernatin and G.Sundari, “Video compression based on hybrid transform and quantization with Huffman coding for video codec” in ICCICCT-2014,452-456.Mohammad PouyaSalvatiJamshid Bagherzadeh MohasefiSadeghSulaimany723202118319010.52547/jist.9.35.183http://jist.ir/en/Article/15517http://jist.ir/en/Article/Download/15517http://jist.ir/en/Article/Download/15517http://jist.ir/en/Article/Download/15517http://jist.ir/en/Article/Download/15517http://jist.ir/en/Article/Download/15517http://jist.ir/en/Article/Download/15517http://jist.ir/en/Article/Download/15517[1] D. Caiyan, L. Chen, and B. Li, “Link prediction in complex network based on modularity,” Soft Comput., 2016, doi: 10.1007/s00500-016-2030-4. [2] H. Yuan, Y. Ma, F. Zhang, M. Liu, and W. Shen, “A distributed link prediction algorithm based on clustering in dynamic social networks,” in 2015 IEEE International Conference on Systems, Man, and Cybernetics, 2015, pp. 1341–1345. [3] P. Symeonidis and N. Mantas, “Spectral clustering for link prediction in social networks with positive and negative links,” Soc. Netw. Anal. Min., vol. 3, no. 4, pp. 1433–1447, 2013. [4] D. Liben-Nowell and J. Kleinberg, “The link-prediction problem for social networks,” J. Am. Soc. Inf. Sci. Technol., vol. 58, no. 7, pp. 1019–1031, 2007. [5] G. SALTON and M. J. MCGILL, “Introduction to Modern Information Retrieval (pp. paginas 400).” 1986. [6] M. E. J. Newman, “Clustering and preferential attachment in growing networks,” Phys. Rev. E, vol. 64, no. 2, p. 25102, 2001. [7] P. Wang, B. W. Xu, Y. R. Wu, and X. Y. Zhou, “Link prediction in social networks: the state-of-the-art,” Sci. China Inf. Sci., vol. 58, no. 1, pp. 1–38, 2014, doi: 10.1007/s11432-014-5237-y. [8] M. K. Khouzani and S. Sulaimany, “Identification of the effects of the existing network properties on the performance of current community detection methods,” J. King Saud Univ. - Comput. Inf. Sci., Apr. 2020, doi: 10.1016/j.jksuci.2020.04.007. [9] R. Guimerà, M. Sales-Pardo, and L. A. N. Amaral, “A network-based method for target selection in metabolic networks,” Bioinformatics, vol. 23, no. 13, pp. 1616–1622, 2007. [10] N. Benchettara, R. Kanawati, and C. Rouveirol, “A supervised machine learning link prediction approach for academic collaboration recommendation,” in Proceedings of the fourth ACM conference on Recommender systems, 2010, pp. 253–256. [11] G. W. Flake, S. Lawrence, C. L. Giles, and F. M. Coetzee, “Self-organization and identification of web communities,” Computer (Long. Beach. Calif)., vol. 35, no. 3, pp. 66–70, 2002. [12] A. Clauset, C. Moore, and M. E. J. Newman, “Hierarchical structure and the prediction of missing links in networks,” Nature, vol. 453, no. 7191, pp. 98–101, 2008. [13] Z. Liu, Q.-M. Zhang, L. Lü, and T. Zhou, “Link prediction in complex networks,” EPL (Europhysics Lett., vol. 96, no. 4, p. 48007, 2011. [14] E. M. Airoldi, D. M. Blei, S. E. Fienberg, E. P. Xing, and T. Jaakkola, “Mixed membership stochastic block models for relational data with application to protein-protein interactions,” in Proceedings of the international biometrics society annual meeting, 2006, vol. 15. [15] J. H. S Soundarajan, “Using community information to improve the precision of link prediction methods,” WWW (Companion Vol., vol. 2012, pp. 607–608, 2012. [16] S. Yokoi, H. Kajino, and H. Kashima, “Link prediction in sparse networks by incidence matrix factorization,” J. Inf. Process., vol. 25, pp. 477–485, 2017. [17] K. Shang, T. Li, M. Small, D. Burton, and Y. Wang, “Link prediction for tree-like networks,” Chaos An Interdiscip. J. Nonlinear Sci., vol. 29, no. 6, p. 61103, 2019. [18] J. Zhang, J. Chen, S. Zhi, Y. Chang, S. Y. Philip, and J. Han, “Link prediction across aligned networks with sparse and low rank matrix estimation,” in 2017 IEEE 33rd International Conference on Data Engineering (ICDE), 2017, pp. 971–982. [19] C. H. Nguyen and H. Mamitsuka, “Latent feature kernels for link prediction on sparse graphs,” IEEE Trans. neural networks Learn. Syst., vol. 23, no. 11, pp. 1793–1804, 2012. [20] X. Feng, J. C. Zhao, and K. Xu, “Link prediction in complex networks: a clustering perspective,” Eur. Phys. J. B, vol. 85, no. 1, p. 3, 2012. [21] H. Liu, Z. Hu, H. Haddadi, and H. Tian, “Hidden link prediction based on node centrality and weak ties,” EPL (Europhysics Lett., vol. 101, no. 1, p. 18004, Jan. 2013, doi: 10.1209/0295-5075/101/18004. [22] V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, “Fast unfolding of communities in large networks,” J. Stat. Mech. theory Exp., vol. 2008, no. 10, p. P10008, 2008. [23] Y.-Y. Ahn, J. P. Bagrow, and S. Lehmann, “Link communities reveal multiscale complexity in networks,” Nature, vol. 466, no. 7307, pp. 761–764, 2010. [24] A. Lancichinetti and S. Fortunato, “Community detection algorithms: a comparative analysis,” Phys. Rev. E, vol. 80, no. 5, p. 56117, 2009. [25] M. Rosvall and C. T. Bergstrom, “Maps of random walks on complex networks reveal community structure,” Proc. Natl. Acad. Sci., vol. 105, no. 4, pp. 1118–1123, 2008. [26] K. Esders, “Link Prediction in Large-scale Complex Networks,” Bachelor’s Thesis Karlsruhe Inst. Technol., no. February, 2015, [Online]. Available: https://hackernoon.com/link-prediction-in-large-scale-networks-f836fcb05c88. [27] M. Hajiabadi, H. Zare, and H. Bobarshad, “IEDC: An integrated approach for overlapping and non-overlapping community detection,” Knowledge-Based Syst., vol. 123, pp. 188–199, 2017. [28] H. Zare, M. Hajiabadi, and M. Jalili, “Detection of community structures in networks with nodal features based on generative probabilistic approach,” IEEE Trans. Knowl. Data Eng., 2019. [29] H. Zare, M. A. N. Pour, and P. Moradi, “Enhanced recommender system using predictive network approach,” Phys. A Stat. Mech. its Appl., vol. 520, pp. 322–337, 2019.NafeesaBashirRaeesaBashirJP SinghJoorelTariq Rashid JanJan723202115116010.52547/jist.9.35.151http://jist.ir/en/Article/15555http://jist.ir/en/Article/Download/15555http://jist.ir/en/Article/Download/15555http://jist.ir/en/Article/Download/15555http://jist.ir/en/Article/Download/15555http://jist.ir/en/Article/Download/15555http://jist.ir/en/Article/Download/15555http://jist.ir/en/Article/Download/15555[1] A. Kumar and S. C. Malik, “Reliability Analysis of a Redundant System with ‘FCFS’ Repair Policy Subject to Weather Conditions”, International Journal of Advanced Science and Technology ,Vol. 29, No. 03, 2020, pp. 7568 – 7578. [2] A. Kumar, D. Pawar, and S. C. Malik, ”Profit analysis of a warm standby non-identical unit system with single server performing in normal/abnormal environment,” Life Cycle Reliab Saf Eng., doi 10.1007/s41872-019-00083-2. 2019. [3] Chib, Rakesh, J.P. S. Joorel, and V. Sharma “Profit Analysis of a Two Unit Priority System with Two Types of Repair Facility”, International Journal of Statistics and Reliability Engineering, Vol. 1, No. 1, 2014, pp. 69-81. [4] I. kumar, A. Kumar, and M. Saina, ”Analysis of performance measures of computer systems with priority and maximum operation time,” Advances in intelligent systems and computing,Springer Singapore, vol 933, 2019. [5] J.K. Sureria and S.C. Malik “Reliability Measures of a Computer System with Arrival Time of the Server and Priority to H/w Repair over S/w Up-gradation”, International Journal of Computer Applications, Vol. 65, No.13, 2013, pp. 44-48. [6] M.S. Kadyan, S.C.Malik and J.Kumar, “Cost-Analysis of a System under Priority to Repair and Degradation” International Journal of Statistics and Systems, Vol. 5, No. 1, 2010, pp. 1-10. [7] N. Bashir, R. Bashir and T R Jan, “Effectiveness Analysis of Two Non-Identical Unit System Model with Priority Unit Subject to Degradation and Inspection Facility”, IEEE International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), 2019, pp. 40-45. ISBN 978-1-7281-3778-0. [8] N. Gorjian Jolfaei , B. Jin , L. van der Linden , I. Gunawan and N. Gorjian , “Reliability modelling with redundancy—A case study of power generation engines in a wastewater treatment plant”, Qual Reliab Engng Int. 2019, pp. 1–13. https://doi.org/10.1002/qre.2573. [9] R. Rathee and S. Chander “A Parallel System with Priority to Repair over Preventive Maintenance Subject to Maximum Operation and Repair Times”, International Journal of Statistics and Reliability Engineering, Vol. 1, No. 1, 2014, pp. 57-68. [10] R. Sharma and J.P.S. Joorel, “Reliability modelling and cost benefit analysis of fruit juice manufacturing system”. Mathematics in Engineering, Science and Aerospace, Vol. 6, No. 2, 2015, pp. 281-293. [11] S.Chander “Reliability models with priority for operation and repair with arrival time of server”, Pure and Applied Mathematika Sciences, Vol. XI, No. 1-2, 2005, pp. 9-22. [12] S.C.Malik, M.S. Kadian, and J. Kumar , “Cost-Analysis of a System under Priority to Repair and Degradation”, International Journal of Statistics and Systems, Vol. 5,No.1, 2010, pp. 1 – 10. [13] S.C. Malik, “Reliability Modeling of a computer System with Preventive Maintenance and Priority Subject to Maximum Operation and Repair Times”, International Journal of System Assurance Engineering and Management, Vol. 4(1), 2013, pp. 94-100. [14] V.J. Munday and S.C. Malik, “Reliability Measures of a Computer System with Priority for Repair and Hardware Redundancy” , International Journal of Computer Applications, Vol. 125, No.8, 2015, pp. 32-37. [15] Y.L.Zhang and G.J. Wang, “A Geometric Process Repair Model for a Repairable Cold Standby System with Priority in Use and Repair”, Journal of Reliability Engineering and System Safety, Vol. 94, 2009, pp. 90-100. [16] M.S. Barak , Neeraj, and S.K. Barak “Proft analysis of a two-unit cold standby system model operating under different weather conditions”, Life Cycle Reliability and Safety Engineering, Springer, 2018, https://doi.org/10.1007/s4187 2-018-0048-6. [17] O. Dahiya, A. Kumar, and M. Saini, “Modeling and analysis of concrete mixture plant subject to coverage factor and profust reliability approach”, Life Cycle Reliability and Safety Engineering, Springer, 9, 2020, pp. 273–281, https://doi.org/10.1007/s41872-019-00104-0. [18] D. Goyal, A. Kumar, M. Saini, and H. Joshi. “Reliability, maintainability and sensitivity analysis of physical processing unit of sewage treatment plant”, SN Applied Sciences, Springer, 2020. https://doi.org/10.1007/s42452-019-1544-7. [19] N. Gupta, M. Saini, and A. Kumar, “Operational availability analysis of generators in steam turbine power plants” SN Applied Sciences, Springer, 2020. https://doi.org/10.1007/s42452-020-2520-y.Adeep BiswasDebayan BhattacharyaKakelliAnil Kumar723202116116810.52547/jist.9.35.161http://jist.ir/en/Article/15558http://jist.ir/en/Article/Download/15558http://jist.ir/en/Article/Download/15558http://jist.ir/en/Article/Download/15558http://jist.ir/en/Article/Download/15558http://jist.ir/en/Article/Download/15558http://jist.ir/en/Article/Download/15558http://jist.ir/en/Article/Download/15558[1] Kumar, P., Vatsa, M., & Singh, R. Detecting face2face facial reenactment in videos. In The IEEE Winter Conference on Applications of Computer Vision, IEEE, 2020, pp. 2589-2597. [2] Sabir, E., Cheng, J., Jaiswal, A., AbdAlmageed, W., Masi, I., & Natarajan, P. Recurrent convolutional strategies for face manipulation detection in videos. Interfaces (GUI), 2019, vol 3(1). [3] Nguyen, T. T., Nguyen, C. M., Nguyen, D. T., Nguyen, D. T., & Nahavandi, S. Deep learning for deepfakes creation and detection, 2019, arXiv preprint arXiv:1909.11573. [4] Lyu, S. Deepfake detection: Current challenges and next steps. In 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), IEEE, 2019, pp. 1-6. [5] Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. Deepfakes and beyond: A survey of face manipulation and fake detection. 2020, arXiv preprint arXiv:2001.00179. [6] Bitouk, D., Kumar, N., Dhillon, S., Belhumeur, P., & Nayar, S. K. Face swapping: automatically replacing faces in photographs. In ACM SIGGRAPH 2008 papers, 2008, pp. 1-8. [7] Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., & Nießner, M. Face2face: Real-time face capture and reenactment of rgb videos. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016 pp. 2387-2395. [8] Tolosana, R., Romero-Tapiador, S., Fierrez, J., & Vera-Rodriguez, R. DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance, 2020, arXiv preprint arXiv:2004.07532. [9] Li, Y., Yang, X., Sun, P., Qi, H., & Lyu, S. Celeb-df: A new dataset for deepfake forensics, 2019, arXiv preprint arXiv:1909.12962. [10] Chollet, F. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1251-1258. [11] Huang, Y., Juefei-Xu, F., Wang, R., Xie, X., Ma, L., Li, J., ... & Pu, G. FakeLocator: Robust Localization of GAN-Based Face Manipulations via Semantic Segmentation Networks with Bells and Whistles, 2020, arXiv preprint arXiv:2001.09598. [12] Nirkin, Y., Keller, Y., & Hassner, T. FSGAN: Subject agnostic face swapping and reenactment. In Proceedings of the IEEE international conference on computer vision, 2019, pp. 7184-7193. [13] McCloskey, S., & Albright, M. Detecting gan-generated imagery using color cues, 2018, arXiv preprint arXiv:1812.08247. [14] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., & Guo, B. Face x-ray for more general face forgery detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 5001-5010. [15] Amerini, I., Galteri, L., Caldelli, R., & Del Bimbo, A. Deepfake video detection through optical flow based cnn. In Proceedings of the IEEE International Conference on Computer Vision Workshops, 2019. [16] Fernando, T., Fookes, C., Denman, S., & Sridharan, S. Exploiting human social cognition for the detection of fake and fraudulent faces via memory networks. 2019, arXiv preprint arXiv:1911.07844. [17] Venkatesh, S., Ramachandra, R., Raja, K., Spreeuwers, L., Veldhuis, R., & Busch, C. Detecting morphed face attacks using residual noise from deep multi-scale context aggregation network. In The IEEE Winter Conference on Applications of Computer Vision, 2020, pp. 280-289. [18] Jeon, H., Bang, Y., & Woo, S. S. FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning Network, 2020, arXiv preprint arXiv:2001.01265. [19] Zhang, W., Zhao, C., & Li, Y. A Novel Counterfeit Feature Extraction Technique for Exposing Face-Swap Images Based on Deep Learning and Error Level Analysis. Entropy, 2020, vol 22(2), no. 249. [20] Dang, L. M., Min, K., Lee, S., Han, D., & Moon, H. Tampered and computer-generated face images identification based on deep learning. Applied Sciences, 2020, vol 10(2), no. 505. [21] Rössler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Nießner, M. Faceforensics: A large-scale video dataset for forgery detection in human faces, 2018, arXiv preprint arXiv:1803.09179. [22] Jiang, L., Li, R., Wu, W., Qian, C., & Loy, C. C. DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 2889-2898. [23] de Lima, O., Franklin, S., Basu, S., Karwoski, B., & George, A. Deepfake Detection using Spatiotemporal Convolutional Networks, 2020, arXiv preprint arXiv:2006.14749. [24] Li, Y., Yang, X., Sun, P., Qi, H., & Lyu, S. Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 3207-3216. [25] Chollet, F. Xception: deep learning with depthwise separable convolutions, in: 2017 IEEE conference on computer vision and pattern recognition CVPR, 2017. [26] Kaiser, L., Gomez, A. N., & Chollet, F. Depthwise separable convolutions for neural machine translation, 2017, arXiv preprint arXiv:1706.03059. [27] Rahimian, E., Zabihi, S., Atashzar, S. F., Asif, A., & Mohammadi, A. XceptionTime: Independent Time-Window Xceptiontime Architecture for Hand Gesture Classification. In ICASSP IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2020, pp. 1304-1308. [28] Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. Deepfakes and beyond: A survey of face manipulation and fake detection, 2020, arXiv preprint arXiv:2001.00179. [29] Guarnera, L., Giudice, O., & Battiato, S. DeepFake Detection by Analyzing Convolutional Traces. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020, pp. 666-667. [30] Zhang, J., Salehizadeh, M., & Diller, E. Parallel pick and place using two independent untethered mobile magnetic microgrippers in IEEE International Conference on Robotics and Automation, 2018. [31] Bau, D., Zhu, J. Y., Strobelt, H., Zhou, B., Tenenbaum, J. B., Freeman, W. T., & Torralba, A. Visualizing and understanding generative adversarial networks, 2019, arXiv preprint arXiv:1901.09887. [32] Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. Generative adversarial networks: An overview. IEEE Signal Processing Magazine, vol 35(1), 2018, pp 53-65. [33] Kietzmann, J., Lee, L. W., McCarthy, I. P., & Kietzmann, T. C. Deepfakes: Trick or treat?. Business Horizons, 2020, vol 63(2), pp 135-146. [34] Wang, J., Liu, A., & Xiao, J. Video-Based Pig Recognition with Feature-Integrated Transfer Learning. In Chinese Conference on Biometric Recognition, Springer, Cham, 2018, pp 620-631. [35] Güera, D., & Delp, E. J. Deepfake video detection using recurrent neural networks. In 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE,2018, pp. 1-6. [36] Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. Mesonet: a compact facial video forgery detection network. In IEEE International Workshop on Information Forensics and Security (WIFS), IEEE, 2018, pp. 1-7. [37] Sohrawardi, S. J., Chintha, A., Thai, B., Seng, S., Hickerson, A., Ptucha, R., & Wright, M. Poster: Towards robust open-world detection of deepfakes. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019, pp. 2613-2615. [38] Albahar, M., & Almalki, J. Deepfakes: Threats and countermeasures systematic review. Journal of Theoretical and Applied Information Technology, vol 97(22), 2019, pp 3242-3250. [39] Maksutov, A. A., Morozov, V. O., Lavrenov, A. A., & Smirnov, A. S. Methods of Deepfake Detection Based on Machine Learning. In 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), IEEE, 2020, pp. 408-411. [40] Korshunov, P., & Marcel, S. Deepfakes: a new threat to face recognition assessment and detection, 2018, arXiv preprint arXiv:1812.08685.ElhamGholamiSeyed RezaKamel TabbakhMaryamkhairabadi723202119119610.52547/jist.9.35.191http://jist.ir/en/Article/15576http://jist.ir/en/Article/Download/15576http://jist.ir/en/Article/Download/15576http://jist.ir/en/Article/Download/15576http://jist.ir/en/Article/Download/15576http://jist.ir/en/Article/Download/15576http://jist.ir/en/Article/Download/15576http://jist.ir/en/Article/Download/15576[1] B. R. Bistrian, “Modern Nutrition in Health and Disease (Tenth Edition),” Crit. Care Med., vol. 34, no. 9, p. 2514, Sep. 2006, doi: 10.1097/01.CCM.0000236502.51400.9F. [2] R. A. Smith et al., “American Cancer Society Guidelines for the Early Detection of Cancer,” CA. Cancer J. Clin., vol. 52, no. 1, pp. 8–22, Jan. 2002, doi: 10.3322/canjclin.52.1.8. [3] G. Murphy, R. Pfeiffer, M. C. Camargo, and C. S. Rabkin, “Meta-analysis Shows That Prevalence of Epstein–Barr Virus-Positive Gastric Cancer Differs Based on Sex and Anatomic Location,” Gastroenterology, vol. 137, no. 3, pp. 824–833, 2009, doi: https://doi.org/10.1053/j.gastro.2009.05.001. [4] H. H. & J. M. Azizi F, “Epidemiology and control of common diseases in Iran,” 3th ed. Tehran Khosravi Publ., pp. 45–7, 2010. [5] P. Bertuccio et al., “Recent patterns in gastric cancer: A global overview,” International Journal of Cancer, vol. 125, no. 3. Wiley-Liss Inc., pp. 666–673, Aug. 01, 2009, doi: 10.1002/ijc.24290. [6] A. Baranovsky and M. H. Myers, “Cancer Incidence and Survival in Patients 65 Years of Age and Older,” CA. Cancer J. Clin., vol. 36, no. 1, pp. 26–41, Jan. 1986, doi: 10.3322/canjclin.36.1.26. [7] J. D. Emerson and G. A. Colditz, “ Use of Statistical Analysis in The New England Journal of Medicine ,” N. Engl. J. Med., vol. 309, no. 12, pp. 709–713, Sep. 1983, doi: 10.1056/nejm198309223091206. [8] et al. Hasanzadeh, J., “No TitleGender differences in esophagus, stomach, colon and rectum cancers in fars, Iran, during 2009-2010: an epidemiological population based study,” J. Rafsanjan Univ. Med. Sci., vol. 12.5, pp. 333-342., 2013. [9] N. Zhang, J. Donahue, R. Girshick, and T. Darrell, “Part-Based R-CNNs for Fine-Grained Category Detection BT - Computer Vision – ECCV 2014,” 2014, pp. 834–849. [10] H. H. Hartgrink, E. P. M. Jansen, N. C. T. van Grieken, and C. J. H. van de Velde, “Gastric cancer,” Lancet, vol. 374, no. 9688, pp. 477–490, 2009, doi: https://doi.org/10.1016/S0140-6736(09)60617-6. [11] S. Sarebanha, A. Hooman Kazemi, P. Sadrolsadat, and N. Xin, “75 Comparison of Traditional Chinese Medicine and Traditional Iranian Medicine in Diagnostic Aspect,” Mar. 2016. Accessed: Nov. 19, 2020. [Online]. Available: http://jtim.tums.ac.ir. [12] J. Hu, S. Han, Y. Chen, and Z. Ji, “Variations of Tongue Coating Microbiota in Patients with Gastric Cancer,” Biomed Res. Int., vol. 2015, 2015, doi: 10.1155/2015/173729. [13] T. Ma, C. Tan, H. Zhang, M. Wang, W. Ding, and S. Li, “Bridging the gap between traditional Chinese medicine and systems biology: The connection of Cold Syndrome and NEI network,” Molecular BioSystems, vol. 6, no. 4. The Royal Society of Chemistry, pp. 613–619, Mar. 17, 2010, doi: 10.1039/b914024g. [14] X. Liu et al., “The Metabonomic Studies of Tongue Coating in H. pylori Positive Chronic Gastritis Patients,” Evidence-Based Complement. Altern. Med., vol. 2015, p. 804085, 2015, doi: 10.1155/2015/804085. [15] C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, “The caltech-ucsd birds-200-2011 dataset,” 2011. [16] X. Liu et al., “The Metabonomic Studies of Tongue Coating in H. pylori Positive Chronic Gastritis Patients,” Evidence-based Complement. Altern. Med., vol. 2015, 2015, doi: 10.1155/2015/804085. [17] C.-C. Chiu, “A novel approach based on computerized image analysis for traditional Chinese medical diagnosis of the tongue,” Comput. Methods Programs Biomed., vol. 61, no. 2, pp. 77–89, 2000, doi: https://doi.org/10.1016/S0169-2607(99)00031-0. [18] S. Branson et al., “Visual Recognition with Humans in the Loop BT - Computer Vision – ECCV 2010,” 2010, pp. 438–451. [19] M. Moghimi, “Using color for object recognition,” Calif. Inst. Technol. Tech. Rep, 2011. [20] E. Gavves, B. Fernando, C. G. M. Snoek, A. W. M. Smeulders, and T. Tuytelaars, “Fine-Grained Categorization by Alignments,” pp. 1713–1720, 2013, doi: 10.1109/ICCV.2013.215. [21] X. Zhang, H. Xiong, W. Zhou, and Q. Tian, “Fused One-vs-All Features with Semantic Alignments for Fine-Grained Visual Categorization,” IEEE Trans. Image Process., vol. 25, no. 2, pp. 878–892, Feb. 2016, doi: 10.1109/TIP.2015.2509425. [22] B. Yao, A. Khosla, and L. Fei-Fei, “Combining randomization and discrimination for fine-grained image categorization,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, pp. 1577–1584, doi: 10.1109/CVPR.2011.5995368. [23] B. Yao, G. Bradski, and L. Fei-Fei, “A codebook-free and annotation-free approach for fine-grained image categorization,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012, pp. 3466–3473, doi: 10.1109/CVPR.2012.6248088. [24] N. Zhang, R. Farrell, and T. Darrell, “Pose pooling kernels for sub-category recognition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012, pp. 3665–3672, doi: 10.1109/CVPR.2012.6248364. [25] Y. J. Lee, A. A. Efros, and M. Hebert, “Style-aware mid-level representation for discovering visual connections in space and time,” in Proceedings of the IEEE international conference on computer vision, 2013, pp. 1857–1864. [26] T. Berg and P. N. Belhumeur, “Poof: Part-based one-vs.-one features for fine-grained categorization, face verification, and attribute estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 955–962. [27] A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “CNN features off-the-shelf: an astounding baseline for recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2014, pp. 806–813. [28] N. Zhang, R. Farrell, F. Iandola, and T. Darrell, “Deformable part descriptors for fine-grained recognition and attribute prediction,” in Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 729–736. [29] J. Donahue et al., “Decaf: A deep convolutional activation feature for generic visual recognition,” in International conference on machine learning, 2014, pp. 647–655.RameshGPrasanna G BSantosh V BhatChandrashekar NaikChampa H N723202116918210.52547/jist.9.35.169http://jist.ir/en/Article/15608http://jist.ir/en/Article/Download/15608http://jist.ir/en/Article/Download/15608http://jist.ir/en/Article/Download/15608http://jist.ir/en/Article/Download/15608http://jist.ir/en/Article/Download/15608http://jist.ir/en/Article/Download/15608http://jist.ir/en/Article/Download/15608[1] R. R. Kunte and R. Samuel, “170wavelet features based on-line recognition of handwritten,” Journal of the Visualization Society of Japan, vol. 20, no. 1, pp. 417–420, 2000. [2] G. Rajput, H. Rajeswari, and C. Sidramappa, “Printed and handwritten kannada numeral recognition using crack codes and fourier descriptors plate,” International Journal of Computer Application (IJCA) on Recent Trends in Image Processing and Pattern Recognition (RTIPPR)}, pp. 53-58, 2010. [3] C. Chiang, R.-H. Wang, and B.-R. Chen, “Recognizing arbitrarily connected and superimposed handwritten numerals in intangible writing interfaces,” Pattern Recognition, {Elsevier} vol. 61, pp. 15--28, 2017. [4] M. H. Ramappa and S. Krishnamurthy, “A comparative study of different feature extraction and classification methods for recognition of hand- written kannada numerals,” International Journal of Database Theory & Application, vol. 6, no. 4, pp. 71–90, 2013. [5] B.V.Dhandra, G. Mukarambi, and M. Hangarge, “Zone based features for handwritten and printed mixed kannada digits recognition,” IJCA Proceedings on International Conference on VLSI, Communications and Instrumentation (ICVCI), no. 7, pp. 5–8, 2011. [6] S. Karthik and K. Murthy, “Handwritten kannada numerals recognition using histogram of oriented gradient descriptors and support vector machines,” Advances in Intelligent Systems and Computing, vol.2, pp. 51–57, 2015. [7] S. V. Rajashekararadhya and P. Vanaja Ranjan, “Neural network based handwritten numeral recognition of kannada and telugu scripts,” in TENCON 2008 - 2008 IEEE Region 10 Conference, pp. 1–5, 2008. [8] G. Rajput, Horakeri, Rajeswari, and C. Sidramappa, “Printed and handwritten mixed kannada numerals recognition using svm,” International Journal on Computer Science and Engineering, vol. 2, pp. 1622- 1626, 2010. [9] V. Hallur and R. Hegadi, “Offline kannada handwritten numeral recognition: Holistic approach,” Proceeding of Second International Conference on Emerging Research in Computing, Information, Communication and Applications, vol. 3, pp. 632-637, 2014. [10] U. Pal, N. Sharma, T. Wakabayashi, and F. Kimura, “Handwritten numeral recognition of six popular indian scripts,” in Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), vol. 2, pp. 749–753, 2007. [11] F. Bovolo, L. Bruzzone, and L. Carlin, “A novel technique for subpixel image classification based on support vector machine,” IEEE Transactions on Image Processing, vol. 19, no. 11, pp. 2983–2999, 2010. [12] M. Maloo and K. Kale, “Support vector machine based gujarati numeral recognition,” International Journal of Computer Science Engineering (IJCSE), ISSN 0975-3397, vol. 3, pp. 2595–2600, 07 2011. [13] H. Sajedi, “Handwriting recognition of digits, signs, and numerical strings in persian,” Computers Electrical Engineering, vol. 49, pp. 52– 65, 01 2016. [14] W. Lu, “Handwritten digits’ recognition using pca of histogram of oriented gradient,” in 2017 IEEE Pacific Rim Conference on Communi- cations, Computers and Signal Processing (PACRIM), pp. 1–5, 2017. [15] E. S. GATI, B. D. NIMO, and E. K. ASIAMAH, “Kannada-mnist classification using skip cnn,” in 2019 16th International Computer Conference on Wavelet Active Media Technology and Information Pro- cessing, pp. 245–248, 2019. [16] G. Jha and H. Cecotti, “Data augmentation for handwritten digit recognition using generative adversarial networks,” Multimedia Tools and Applications, pp. 1–14, 2020. [17] S. Aly and S. Almotairi, “Deep convolutional self-organizing map network for robust handwritten digit recognition,” IEEE Access, vol. 8, pp. 107035–107045, 2020. [18] V. U. Prabhu, “Kannada-mnist: A new handwritten digit’s dataset for the kannada language,” arXiv preprint arXiv:1908.01242, 2019. [19] H. Cocotte, “Active graph based semi-supervised learning using image matching: application to handwritten digit recognition”, Pattern Recognition Letters, vol. 73, pp. 76--82, 2016. [20] Hallur, Vishweshwrayya C., and R. S. Hegadi. "Handwritten Kannada numerals recognition using deep learning convolution neural network (DCNN) classifier." CSI Transactions on ICT, vol. 8, pp. 295-309, 2020. [21] Aly, Saleh, and Ahmed Mohamed. "Unknown-length handwritten numeral string recognition using cascade of pca-svmnet classifiers." IEEE Access vol. 7, pp. 52024-52034. 2019. [22] UÇAR, Emine, and Murat UÇAR. "Applying Capsule Network on Kannada-MNIST Handwritten Digit Dataset." Natural and Engineering Sciences (2019). [23] Gonzalez, Rafael C., and Richard E. Woods. "Digital image processing." (2002).AliGhaffariSeyyed Keyvan Mousavi723202119720610.52547/jist.9.35.197http://jist.ir/en/Article/15656http://jist.ir/en/Article/Download/15656http://jist.ir/en/Article/Download/15656http://jist.ir/en/Article/Download/15656http://jist.ir/en/Article/Download/15656http://jist.ir/en/Article/Download/15656http://jist.ir/en/Article/Download/15656http://jist.ir/en/Article/Download/15656[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "Wireless sensor networks: a survey," Computer networks, vol. 38, no. 4, pp. 393-422, 2002. [2] D. KeyKhosravi, A. Ghaffari, A. Hosseinalipour, and B. A. Khasragi, "New Clustering Protocol to Decrease Probability Failure Nodes and Increasing the Lifetime in WSNs," Int. J. Adv. Comp. Techn., vol. 2, no. 2, pp. 117-121, 2010. [3] A. Ghaffari, "Designing a wireless sensor network for ocean status notification system," Indian Journal of Science and Technology, vol. 7, no. 6, pp. 809-814, 2014. [4] A. Ghaffari, "Congestion control mechanisms in wireless sensor networks: A survey," Journal of network and computer applications, vol. 52, pp. 101-115, 2015. [5] A. Seyfollahi and A. Ghaffari, "A lightweight load balancing and route minimizing solution for routing protocol for low-power and lossy networks," Computer Networks, vol. 179, p. 107368, 2020. [6] A. Seyfollahi and A. Ghaffari, "Reliable data dissemination for the Internet of Things using Harris hawks optimization," Peer-to-Peer Networking and Applications, vol. 13, no. 6, pp. 1886-1902, 2020. [7] J. A. Torkestani, "An adaptive energy-efficient area coverage algorithm for wireless sensor networks," Ad hoc networks, vol. 11, no. 6, pp. 1655-1666, 2013. [8] S. M. Mohamed, H. S. Hamza, and I. A. Saroit, "Coverage in mobile wireless sensor networks (M-WSN): A survey," Computer Communications, vol. 110, pp. 133-150, 2017. [9] A. Ghaffari and A. Rahmani, "Fault tolerant model for data dissemination in wireless sensor networks," in Information Technology, 2008. ITSim 2008. International Symposium on, 2008, vol. 4, pp. 1-8: IEEE. [10] H. D. Nikokheslat and A. Ghaffari, "Protocol for Controlling Congestion in Wireless Sensor Networks," Wireless Personal Communications, vol. 95, no. 3, pp. 3233-3251, 2017. [11] P. Natarajan and L. Parthiban, "k-coverage m-connected node placement using shuffled frog leaping: Nelder–Mead algorithm in WSN," Journal of Ambient Intelligence and Humanized Computing, pp. 1-16, 2020. [12] C. Shivalingegowda and P. Jayasree, "Hybrid gravitational search algorithm based model for optimizing coverage and connectivity in wireless sensor networks," Journal of Ambient Intelligence and Humanized Computing, pp. 1-14, 2020. [13] M. El-Hosseini, H. ZainEldin, H. Arafat, and M. Badawy, "A fire detection model based on power-aware scheduling for IoT-sensors in smart cities with partial coverage," Journal of Ambient Intelligence and Humanized Computing, pp. 1-20, 2020. [14] R. Yarinezhad and S. N. Hashemi, "A sensor deployment approach for target coverage problem in wireless sensor networks," Journal of Ambient Intelligence and Humanized Computing, pp. 1-16, 2020. [15] A. Pakmehr and A. Ghaffari, "Coverage Improving with Energy Efficient in Wireless Sensor Networks," Information Systems & Telecommunication, p. 57. [16] M. A. Benatia, M. h. Sahnoun, D. Baudry, A. Louis, A. El-Hami, and B. Mazari, "Multi-Objective WSN Deployment Using Genetic Algorithms Under Cost, Coverage, and Connectivity Constraints," Wireless Personal Communications, vol. 94, no. 4, pp. 2739-2768, 2017. [17] A. Xenakis, F. Foukalas, G. Stamoulis, and I. Katsavounidis, "Topology control with coverage and lifetime optimization of wireless sensor networks with unequal energy distribution," Computers & Electrical Engineering, 2017. [18] J. Roselin, P. Latha, and S. Benitta, "Maximizing the wireless sensor networks lifetime through energy efficient connected coverage," Ad Hoc Networks, vol. 62, pp. 1-10, 2017. [19] D. Thomas, R. Shankaran, M. Orgun, and S. Mukhopadhyay, "A secure barrier coverage scheduling framework for WSN-based IoT applications," IEEE Transactions on Green Communications and Networking, 2021. [20] C. Shivalingegowda and P. Jayasree, "Hybrid gravitational search algorithm based model for optimizing coverage and connectivity in wireless sensor networks," Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 2, pp. 2835-2848, 2021. [21] J. Yu, Y. Chen, L. Ma, B. Huang, and X. Cheng, "On connected target k-coverage in heterogeneous wireless sensor networks," Sensors, vol. 16, no. 1, p. 104, 2016. [22] N.-T. Le and Y. M. Jang, "Energy-efficient coverage guarantees scheduling and routing strategy for wireless sensor networks," International Journal of Distributed Sensor Networks, vol. 11, no. 8, p. 612383, 2015. [23] S. S. Dhillon, K. Chakrabarty, and S. S. Iyengar, "Sensor placement for grid coverage under imprecise detections," in Information Fusion, 2002. Proceedings of the Fifth International Conference on, 2002, vol. 2, pp. 1581-1587: IEEE. [24] K.-P. Shih, Y.-D. Chen, C.-W. Chiang, and B.-J. Liu, "A distributed active sensor selection scheme for wireless sensor networks," in Computers and Communications, 2006. ISCC'06. Proceedings. 11th IEEE Symposium on, 2006, pp. 923-928: IEEE. [25] S. Soro and W. B. Heinzelman, "Cluster head election techniques for coverage preservation in wireless sensor networks," Ad Hoc Networks, vol. 7, no. 5, pp. 955-972, 2009. [26] H. Mostafaei, A. Montieri, V. Persico, and A. Pescapé, "A sleep scheduling approach based on learning automata for WSN partial coverage," Journal of Network and Computer Applications, vol. 80, pp. 67-78, 2017. [27] M. Akhlaq, T. R. Sheltami, and E. M. Shakshuki, "C3: an energy-efficient protocol for coverage, connectivity and communication in WSNs," Personal and Ubiquitous Computing, vol. 18, no. 5, pp. 1117-1133, 2014. [28] Z. Sun, Y. Zhang, Y. Nie, W. Wei, J. Lloret, and H. Song, "CASMOC: a novel complex alliance strategy with multi-objective optimization of coverage in wireless sensor networks," wireless Networks, vol. 23, no. 4, pp. 1201-1222, 2017. [29] A. Khelil and R. Beghdad, "SPMI: Single Phase Multiple Initiator Protocol for Coverage in Wireless Sensor Networks," Wireless Personal Communications, vol. 96, no. 2, pp. 3159-3178, 2017. [30] T. G. Nguyen, C. So-In, N. G. Nguyen, and S. Phoemphon, "A novel energy-efficient clustering protocol with area coverage awareness for wireless sensor networks," Peer-to-Peer Networking and Applications, vol. 10, no. 3, pp. 519-536, 2017. [31] Y. Hu, Y. Niu, J. Lam, and Z. Shu, "An energy-efficient adaptive overlapping clustering method for dynamic continuous monitoring in WSNs," IEEE Sensors Journal, vol. 17, no. 3, pp. 834-847, 2016.