Top Fuzzy Logic Systems Research Articles of 2020 - International Journal of Fuzzy Logic Systems

Top Fuzzy Logic Systems Research Articles of 2020 International Journal of Fuzzy Logic Systems (IJFLS)

ISSN : 1839 – 6283

https://wireilla.com/ijfls/index.html

FILTER-BASED ACTIVE SUSPENSION SYSTEM WITH ADAPTED REFERENCE INPUT Adel Djellal1 and Rabah Lakel2 1Department of Second Cycle, Higher School for Industrial Technologies, Annaba, Algeria 2Department of Electronics, Badji Mokhtar University, Annaba, Algeria ABSTRACT In this paper, the Active Suspension system is controlled using a PID controller with an adapted reference point. After the derivation of the quarter car suspension model. Three approaches were applied: passive suspension system, Active Suspension system with constant reference and with adapted reference. The proposed approach was focusing on system life span; how to reduce brutal controller actions, that can cause car body damage, and assure a certain ride comfort? Simulation of three approaches has been done using the quarter car system and Matlab simulation model to implement the proposed technique and compare performance variation in different cases: road bump and other road disturbances.

KEYWORDS Active Suspension system, PID controller, Quarter car model, Passive Suspension system FULL TEXT : https://aircconline.com/csit/papers/vol10/csit100112.pdf VOLUME LINK : http://airccse.org/csit/V10N01.html

7th International Conference on Computer Science and Information Technology (CoSIT 2020)

REFERENCES [1] A. Chamseddine, H. Noura, and T. Raharijaona, “Control of linear full vehicle active suspension system using sliding mode techniques,” In IEEE International Conference on Control Applications, 2006. DOI: 10.1109/CACSD-CCA-ISIC.2006.4776831.

[2] M. Sunwoo, “Model reference adaptive control for vehicle active suspension systems,” IEEE Transactions on Industrial Electronics, 1991. DOI: 10.1109/ICIEA.2006.257242.

[3] Alleyne and K. J. Hedrick, “Nonlinear adaptive control of active suspensions,” IEEE Trans. On Control Systems Technology, 1995. DOI: 10.1109/87.370714.

[4] A. Kruczek and A. Stribrsky, “H_ control of automotive active suspension with linear motor,” IFAC Mechatronic Systems, 2004. DOI: 10.1016/S1474-6670(17)31131-X. [5] C. Rossi and Gianluca Lucente, “H_ control of automotive semi-active suspensions,” IFAC Advances in Automotive Control, 2003. DOI: 10.1016/S1474-6670(17)30402-0.

[6] Y. M. Sam and K. Hudha, “Modelling and force tracking control of hydraulic actuator for an active suspension system,” In ICIEA 2006, 2006. DOI: 10.1109/ICIEA.2006.257242.

[7] I. Eski and S. Yildirim, “Vibration control of vehicle active suspension system using a new robust neural network control system,” Simulation Modelling Practice and Theory, 2009.

DOI:10.1016/j.simpat.2009.01.004.

[8] K. Hyniova, A. Stribrsky, J. Honcu, and A. Kruczek, “Active suspension system-energy control,” In IFAC, 2009. DOI: 10.3182/20090921-3-TR-3005.00027.

[9] E. C. Conde and F. B. Carbajal, “Generalized PI control of active vehicle suspension systems with MATLAB,” Applications of MATLAB in Science and Engineering, 2011. DOI: 10.5772/23746.

[10]S. F. Choudhury and R. M. A. Sarkar, “An approach on performance comparison between automotive passive suspension and active suspension system (PID controller) using MATLAB/SIMULINK,” Journal of Theoretical and Applied Information Technology, 2012.

DOI:10.9790/1684-1304010106.

[11]H. Li, J. Yu, C. Hilton, and H. Liu, “Adaptive sliding-mode control for nonlinear active suspension vehicle systems using T-S fuzzy approach,” IEEE Transactions on Industrial Electronics, 2013. DOI: 10.1109/TIE.2012.2202354.

[12]F. M. Jamil, M.A. Abdullah, M.R. Ridzuan, M. Ibrahim, and F. Ahmad, “Multi-order PID control for a simple suspension system,” In Innovative Research and Industrial Dialogue'16, 2017.

[13]D. A. Crolla, “Vehicle dynamics: theory into practice,” In International Mech.Engrs, Part D: J. Automobile Engineering, 1996. DOI: 10.1243/PIME PROC 1996 210 250 02.

[14]D. A. Wilson, R. S. Sharp, and S. A. Hassan, “The application of linear optimal control theory to the design of active automotive suspension,” Vehicle System, 1986. DOI: 10.1080/00423118608968846.

[15]M. Mailah and G. Priyandoko, “Simulation of a suspension system with adaptive fuzzy active force control,” International Journal of Simulation Modeling, 2005. DOI: 10.2507/IJSIMM06(1)3.079.

[16] A. Djellal and R. Lakel, “Adapted reference input to control PID-based active suspension system,”European Journal of Automated Systems, 51(1-3):7-23, June 2018. DOI: 10.3166/JESA.51.7-23

BUILDING A BI-OBJECTIVE QUADRATIC PROGRAMMING MODEL FOR THE SUPPORT VECTOR MACHINE Mohammed Zakaria Moustafa1, Mohammed Rizk Mohammed2, HatemAwad Khater3, Hager Ali Yahia4 1Department of Electrical Engineering (Power and Machines Section) Alexandria University, Alexandria, Egypt 2Department of Communication and Electronics Engineering, Alexandria University, Alexandria, Egypt 3Department of Mechatronics, Faculty of Engineering, HORAS University, Damietta, Egypt 4Department of Communication and Electronics Engineering, Alexandria University, Alexandria, Egypt ABSTRACT A support vector machine (SVM) learns the decision surface from two different classes of the input points, in many applications there are misclassifications in some of the input points. In this paper a biobjective quadratic programming model is utilized and different feature quality measures are optimized simultaneously using the weighting method for solving our biobjective quadratic programming problem. An important contribution will be added for the proposed bi-objective quadratic programming model by getting different efficient support vectors due to changing the weighting values. The experimental results, give evidence of the effectiveness of the weighting parameters on reducing the misclassification between two classes of the input points.

KEYWORDS Support vector machine (SVMs), Classification, Multi-objective problems, weighting method, Quadratic programming.

FULL TEXT : https://aircconline.com/csit/papers/vol10/csit100208.pdf VOLUME LINK : http://airccse.org/csit/V10N02.html

8th International Conference on Signal, Image Processing and Pattern Recognition (SIPP 2020)

REFERENCES [1] Cortes, Corinna; Vapnik, Vladimir N (1995) "Support vector networks" (PDF). Machine learning. 20 (3):273297. CiteSeerX 10.1.1.15.9362. DOI:10.1007/BF00994018.

[2] Asmaa Hamad1,3(B), Essam H. Houssein1,3, Aboul Ella Hassanien2,3, and Aly A. Fahmy2:Hybrid Grasshopper Optimization Algorithm and Support Vector Machines for Automatic Seizure Detection in EEG Signals. Faculty of Computers and Information, Minia University, Minya, Egypt. January 2018. DOI: 10.1007/978-3-319-74690-6_9.

[3] Alaa Tharwat1;_, Thomas Gabel1, Aboul Ella Hassanien2;_ Parameter Optimization of Support Vector Machine using Dragon_y Algorithm. Faculty of Computer Science and Engineering, Frankfurt University of Applied Sciences, Frankfurt am Main, Germany ,Faculty of Computers and Information, Cairo University, Egypt. January 2018 DOI: 10.1007/978-3-319-64861-3_29.

[4] Gray, D., Bowes, D., Davey, N., Sun, Y., Christianson, B.: Using the Support Vector Machine as a Classification Method for Software Defect Prediction with Static Code Metrics.

In: Palmer Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds.) EANN 2009.

Communications in Computer and Information Science, vol. 43, pp. 223–234.

Springer,Heidelberg (2009).

[5] Chun-Fu Lin and Sheng-De Wang: Fuzzy Support Vector Machines. Article in IEEE Transaction on neural networks March 2002. DOI:10.1109/72.991432.

[6] C. Burges, ”A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol.2, no.2,1998.

[7] C. Cortes and V. N. Vapnik, “Support vector networks,” Machine Learning, vol.20,pp.273-297,1995.

[8] Chankong V. and Haimes Y.Y., Multi-objective Decision-Making: Theory and Methodology (North Holland Series in System Science and Engineering, 1983).

[9] Yaochu Jin (Ed.), Multi-objective Machine Learning Studies in Computational Intelligence, Vol. 16, pp. 199-220, Springer-Verlag, 2006.

[10] Shounak Datta and Swagatam Das, Multiobjective Support Vector Machines: Handling Class Imbalance With Pareto Optimality, IEEE Transactions on Neural Networks and Learning Systems, 2019.DOI:10.1109/TNNLS.2018.2869298.

ENHANCING NETWORK FORENSICS with PARTICLE SWARM and DEEP LEARNING: THE PARTICLE DEEP FRAMEWORK Nickolaos Koroniotis1 and Nour Moustafa1 School of Engineering and Information Technology, University of New South Wales Canberra, Canberra, Australia ABSTRACT The popularity of IoT smart things is rising, due to the automation they provide and its effects on productivity. However, it has been proven that IoT devices are vulnerable to both well established and new IoT-specific attack vectors. In this paper, we propose the Particle Deep Framework, a new network forensic framework for IoT networks that utilised Particle Swarm Optimisation to tune the hyperparameters of a deep MLP model and improve its performance.

The PDF is trained and validated using Bot-IoT dataset, a contemporary network-traffic dataset that combines normal IoT and non-IoT traffic, with well known botnet-related attacks.

Through experimentation, we show that the performance of a deep MLP model is vastly improved, achieving an accuracy of 99.9% and false alarm rate of close to 0%.

KEYWORDS Network forensics, Particle swarm optimization, Deep Learning, IoT, Botnets FULL TEXT : https://aircconline.com/csit/papers/vol10/csit100304.pdf VOLUME LINK : http://airccse.org/csit/V10N03.html

7th International Conference on Artificial Intelligence and Applications (AIAP-2020)

REFERENCES 1. S. Cook, “60+ iot statistics and facts,” Comparitech, 2019. [Online]. Available: https://www.comparitech.com/internet-providers/iot-statistics/ 2. S. Corporation, “Internet security threat report, volume 24,” Symantec, Tech. Rep. 24, 2019. [Online]. Available: https://www.symantec.com/content/dam/symantec/docs/reports/ istr-24-2019-en.pdf 3. N. Koroniotis, N. Moustafa, and E. Sitnikova, “Forensics and deep learning mechanisms for botnets in internet of things: A survey of challenges and solutions,” IEEE Access, vol. 7, pp. 61 764–61 785, 2019.

4. B. Ali and A. Awad, “Cyber and physical security vulnerability assessment for iot-based smart homes,” Sensors, vol. 18, no. 3, p. 817, 2018.

5. C. Robberts and J. Toft, “Finding vulnerabilities in iot devices: Ethical hacking of electronic locks,” 2019.

6. M. Conti, A. Dehghantanha, K. Franke, and S. Watson, “Internet of things security and forensics: Challenges and opportunities,” Future Generation Computer Systems, vol. 78, pp.

544–546, 2018.

7. E. Ronen, A. Shamir, A.-O. Weingarten, and C. O’Flynn, “Iot goes nuclear: Creating a zigbee chain reaction,” in 2017 IEEE Symposium on Security and Privacy (SP). IEEE, 2017, pp. 195–212.

8. C. Meffert, D. Clark, I. Baggili, and F. Breitinger, “Forensic state acquisition from internet of things (fsaiot): A general framework and practical approach for iot forensics through iot device state acquisition,” in Proceedings of the 12th International Conference on Availability, Reliability and Security. ACM, 2017, p. 56.

9. S. Al-Sarawi, M. Anbar, K. Alieyan, and M. Alzubaidi, “Internet of things (iot) communication protocols,” in 2017 8th International conference on information technology (ICIT). IEEE, 2017, pp. 685–690.

10. R. Kaur and A. Kaur, “Digital forensics,” International Journal of Computer Applications, vol. 50, no. 5, 2012.

11. M. Hossain, Y. Karim, and R. Hasan, “Fif-iot: A forensic investigation framework for iot using a public digital ledger,” in 2018 IEEE International Congress on Internet of Things (ICIOT). IEEE, 2018, pp. 33–40.

12. M. M. Hossain, R. Hasan, and S. Zawoad, “Probe-iot: A public digital ledger based forensic investigation framework for iot.” in INFOCOM Workshops, 2018, pp. 1–2.

13. M. Cebe, E. Erdin, K. Akkaya, H. Aksu, and S. Uluagac, “Block4forensic: An integrated lightweight blockchain framework for forensics applications of connected vehicles,” IEEE Communications Magazine, vol. 56, no. 10, pp. 50–57, OCTOBER 2018.

14. N. Shone, T. N. Ngoc, V. D. Phai, and Q. Shi, “A deep learning approach to network intrusion detection,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 2, no. 1, pp. 41–50, 2018.

15. S. Prabakaran and S. Mitra, “Survey of analysis of crime detection techniques using data mining and machine learning,” in Journal of Physics: Conference Series, vol. 1000, no. 1.

IOP Publishing, 2018, p. 012046.

16. Z. Wang, “The applications of deep learning on traffic identification,” BlackHat USA, vol. 24, 2015.

17. G. Zhao, C. Zhang, and L. Zheng, “Intrusion detection using deep belief network and probabilistic neural network,” in 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), vol. 1. IEEE, 2017, pp. 639–642.

18. A. Zela, A. Klein, S. Falkner, and F. Hutter, “Towards automated deep learning: Efficient joint neural architecture and hyperparameter search,” in ICML 2018 AutoML Workshop, Jul. 2018.

19. B. Wang and N. Z. Gong, “Stealing hyperparameters in machine learning,” in 2018 IEEE Symposium on Security and Privacy (SP). IEEE, 2018, pp. 36–52.

20. T. Chen, L. Zheng, E. Yan, Z. Jiang, T. Moreau, L. Ceze, C. Guestrin, and A. Krishnamurthy, “Learning to optimize tensor programs,” in Advances in Neural Information Processing Systems, 2018, pp. 3389–3400.

21. J. Wang, J. Xu, and X. Wang, “Combination of hyperband and bayesian optimization for hyperparameter optimization in deep learning,” arXiv preprint arXiv:1801.01596, 2018.

22. D. Stamoulis, E. Cai, D.-C. Juan, and D. Marculescu, “Hyperpower: Power-and memoryconstrained hyper-parameter optimization for neural networks,” in 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2018, pp. 19–24.

23. S. Watson and A. Dehghantanha, “Digital forensics: the missing piece of the internet of things promise,” Computer Fraud & Security, vol. 2016, no. 6, pp. 5–8, 2016.

24. M. Chernyshev, S. Zeadally, Z. Baig, and A. Woodward, “Internet of things forensics: The need, process models, and open issues,” IT Professional, vol. 20, no. 3, pp. 40–49, 2018.

25. G. L. Palmer, “A road map for digital forensics research-report from the first digital forensics research workshop (dfrws)(technical report dtr-t001-01 final),” Air Force Research Laboratory, Rome Research Site, Utica, pp. 1–48, 2001.

26. D. P. Joseph and J. Norman, “An analysis of digital forensics in cyber security,” in First International Conference on Artificial Intelligence and Cognitive Computing. Springer, 2019, pp. 701–708.

27. D.-P. Le, H. Meng, L. Su, S. L. Yeo, and V. Thing, “Biff: A blockchain-based iot forensics framework with identity privacy,” in TENCON 2018-2018 IEEE Region 10 Conference. IEEE, 2018, pp. 2372–2377.

28. A. Valjarevic and H. S. Venter, “A comprehensive and harmonized digital forensic investigation process model,” Journal of forensic sciences, vol. 60, no. 6, pp. 1467–1483, 2015.

29. L. Caviglione, S. Wendzel, and W. Mazurczyk, “The future of digital forensics: Challenges and the road ahead,” IEEE Security & Privacy, vol. 15, no. 6, pp. 12–17, 2017.

30. S.-W. Han, H. Kwon, C. Hahn, D. Koo, and J. Hur, “A survey on mitm and its countermeasures in the tls handshake protocol,” in 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, 2016, pp. 724–729.

31. N. Liao, S. Tian, and T. Wang, “Network forensics based on fuzzy logic and expert system,” Computer Communications, vol. 32, no. 17, pp. 1881–1892, 2009.

32. A. A. Ahmed and M. F. Mohammed, “Sairf: A similarity approach for attack intention recognition using fuzzy min-max neural network,” Journal of Computational Science, vol.

25, pp. 467–473, 2018.

33. A. Yudhana, I. Riadi, and F. Ridho, “Ddos classification using neural network and na¨ıve bayes methods for network forensics,” INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, vol. 9, no. 11, pp. 177–183, 2018.

34. K. Nguyen, D. Tran, W. Ma, and D. Sharma, “An approach to detect network attacks applied for network forensics,” in 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2014, pp. 655–660.

35. K. Alrawashdeh and C. Purdy, “Toward an online anomaly intrusion detection system based on deep learning,” in 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2016, pp. 195–200.

36. A. Azmoodeh, A. Dehghantanha, and K.-K. R. Choo, “Robust malware detection for internet of (battlefield) things devices using deep eigenspace learning,” IEEE Transactions on Sustainable Computing, vol. 4, no. 1, pp. 88–95, 2018.

37. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521, no. 7553, p. 436, 2015.

38. O. Brun, Y. Yin, and E. Gelenbe, “Deep learning with dense random neural network for detecting attacks against iot-connected home environments,” Procedia computer science, vol.

134, pp. 458–463, 2018.

39. A. Pekta¸s and T. Acarman, “Botnet detection based on network flow summary and deep learning,” International Journal of Network Management, p. e2039, 2018.

40. R. Cheng and G. Watson, “D2pi: Identifying malware through deep packet inspection with deep learning,” 2018.

41. I. Alrashdi, A. Alqazzaz, E. Aloufi, R. Alharthi, M. Zohdy, and H. Ming, “Ad-iot: anomaly detection of iot cyberattacks in smart city using machine learning,” in 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2019, pp. 0305–0310.

42. L. Babun, A. K. Sikder, A. Acar, and A. S. Uluagac, “Iotdots: A digital forensics framework for smart environments,” arXiv preprint arXiv:1809.00745, 2018.

43. J. Kennedy and R. Eberhart, “Particle swarm optimization (pso),” in Proc. IEEE International Conference on Neural Networks, Perth, Australia, 1995, pp. 1942–1948.

44. F. Marini and B. Walczak, “Particle swarm optimization (pso). a tutorial,” Chemometrics and Intelligent Laboratory Systems, vol. 149, pp. 153–165, 2015.

45. D. Wang, D. Tan, and L. Liu, “Particle swarm optimization algorithm: an overview,” Soft Computing, vol. 22, no. 2, pp. 387–408, 2018.

46. Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360). IEEE, 1998, pp. 69–73.

47. R. C. Eberhart and Y. Shi, “Tracking and optimizing dynamic systems with particle swarms,” in Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546), vol. 1. IEEE, 2001, pp. 94–100.

48. R. Eberhart, P. Simpson, and R. Dobbins, Computational intelligence PC tools. Academic Press Professional, Inc., 1996.

49. M. Clerc, “The swarm and the queen: towards a deterministic and adaptive particle swarm optimization,” in Proceedings of the 1999 congress on evolutionary computationCEC99 (Cat.No. 99TH8406), vol. 3. IEEE, 1999, pp. 1951–1957.

50. R. C. Eberhart and Y. Shi, “Comparing inertia weights and constriction factors in particle swarm optimization,” in Proceedings of the 2000 congress on evolutionary computation.

CEC00 (Cat. No. 00TH8512), vol. 1. IEEE, 2000, pp. 84–88.

51. J. Kennedy and R. C. Eberhart, “A discrete binary version of the particle swarm algorithm,” in 1997 IEEE International conference on systems, man, and cybernetics.

Computational cybernetics and simulation, vol. 5. IEEE, 1997, pp. 4104–4108.

52. F. Van den Bergh and A. P. Engelbrecht, “A cooperative approach to particle swarm optimization,” IEEE transactions on evolutionary computation, vol. 8, no. 3, pp. 225–239, 2004.

53. R. Mendes, J. Kennedy, and J. Neves, “The fully informed particle swarm: simpler, maybe better,” IEEE transactions on evolutionary computation, vol. 8, no. 3, pp. 204–210, 2004.

54. Wireshark tool. [Online]. Available: https://www.wireshark.org/ 55. Tcpdump tool. [Online]. Available: https://www.tcpdump.org 56. Ettercap tool. [Online]. Available: https://www.ettercap-project.org/ 57. N. Koroniotis, N. Moustafa, E. Sitnikova, and B. Turnbull, “Towards the development of

realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset,” Future Generation Computer Systems, 2019.

58. Tshark network analysis tool. [Online]. Available: https://www.wireshark.org/ 59. F. Armknecht and A. Dewald, “Privacy-preserving email forensics,” Digital Investigation, vol. 14, pp. S127–S136, 2015.

60. Argus tool. [Online]. Available: https://qosient.com/argus/index.shtml 61. Bro tool. [Online]. Available: https://www.zeek.org/ 62. X. Lu and M. Liu, “A fuzzy logic controller tuned with pso for delta robot trajectory control,” in IECON 2015-41st Annual Conference of the IEEE Industrial Electronics Society.

IEEE, 2015, pp. 004 345–004 351.

63. M. Couceiro and P. Ghamisi, Particle Swarm Optimization. Cham: Springer International Publishing, 2016, pp. 1–10.

64. M. R. Bonyadi and Z. Michalewicz, “Particle swarm optimization for single objective continuous space problems: a review,” 2017.

65. M. N. Ab Wahab, S. Nefti-Meziani, and A. Atyabi, “A comprehensive review of swarm optimization algorithms,” PloS one, vol. 10, no. 5, p. e0122827, 2015.

66. Z. Zhang and M. Sabuncu, “Generalized cross entropy loss for training deep neural networks with noisy labels,” in Advances in neural information processing systems, 2018, pp.

8778–8788.

67. Node-red tool. [Online]. Available: https://nodered.org/ 68. M. Claesen, J. Simm, D. Popovic, and B. Moor, “Hyperparameter tuning in python using optunity,” in Proceedings of the International Workshop on Technical Computing for Machine Learning and Mathematical Engineering, vol. 1, 2014, p. 3.

69. O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. Mohamed, and H. Arshad, “State-of-the-art in artificial neural network applications: A survey,” Heliyon, vol. 4, no. 11, p. e00938, 2018.

70. M. S. Mahdavinejad, M. Rezvan, M. Barekatain, P. Adibi, P. Barnaghi, and A. P. Sheth, “Machine learning for internet of things data analysis: A survey,” Digital Communications and Networks, vol. 4, no. 3, pp. 161–175, 2018.

A SELF-ATTENTIONAL AUTO ENCODER BASED INTRUSION DETECTION SYSTEM Bingzhang Hu and Yu Guan School of Computing, Newcastle University, Newcastle upon Tyne, UK ABSTRACT Intrusion detection systems (IDSs) have received increasing attention in recent years due to the rapid development of Internet applications and Internet of Things. Anomaly based IDSs are preferred in many situations due to their capabilities of detecting novel unseen attacks.

However, existing works have neither considered the intrinsic relationships within the network traffic data nor the correlations shared among the sub features (i.e. content feature, host-based feature, etc.). In this paper, we propose a self-attentional auto-encoder based intrusion detection system, namely the STAR-IDS, to effectively explore the intrinsic structures of network traffic data and evaluated it on the NSL-KDD dataset. The experimental results show that the proposed STAR-IDS has achieved state-of-the-art performances.

KEYWORDS Intrusion Detection System, Auto Encoder, Anomaly Detection, Self-attentional FULL TEXT : https://aircconline.com/csit/papers/vol10/csit100704.pdf VOLUME LINK : http://airccse.org/csit/V10N07.html

6th International Conference on Image and Signal Processing (ISPR 2020)

REFERENCES [1] C. Yin, Y. Zhu, J. Fei and X. He, “A deep learning approach for intrusion detection using recurrent neural networks,” Ieee Access, vol. 5, p. 21954–21961, 2017.

[2] M. Gharib, B. Mohammadi, S. H. Dastgerdi and M. Sabokrou, “AutoIDS: Auto-encoder Based Method for Intrusion Detection System,” arXiv preprint arXiv:1911.03306, 2019.

[3] J. Kevric, S. Jukic and A. Subasi, “An effective combining classifier approach using tree algorithms for network intrusion detection,” Neural Computing and Applications, vol. 28, p.

1051–1058, 2017.

[4] S. Naseer, Y. Saleem, S. Khalid, M. K. Bashir, J. Han, M. M. Iqbal and K. Han, “Enhanced network anomaly detection based on deep neural networks,” IEEE Access, vol. 6, p. 48231–48246, 2018.

[5] S. Aljawarneh, M. Aldwairi and M. B. Yassein, “Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model,” Journal of Computational Science, vol. 25, p. 152–160, 2018.

[6] M. Tavallaee, E. Bagheri, W. Lu and A. A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set,” in 2009 IEEE symposium on computational intelligence for security and defense applications, 2009.

[7] M. H. Bhuyan, D. K. Bhattacharyya and J. K. Kalita, “Network anomaly detection: methods, systems and tools,” Ieee communications surveys & tutorials, vol. 16, p. 303–336, 2013.

[8] H.-J. Liao, C.-H. R. Lin, Y.-C. Lin and K.-Y. Tung, “Intrusion detection system: A comprehensive review,” Journal of Network and Computer Applications, vol. 36, p. 16–24, 2013.

[9] P. Kushwaha, H. Buckchash and B. Raman, “Anomaly based intrusion detection using filter based feature selection on KDD-CUP 99,” in TENCON 2017 - 2017 IEEE Region 10 Conference, 2017.

[10] Y. Liao and V. R. Vemuri, “Use of k-nearest neighbor classifier for intrusion detection,” Computers & security, vol. 21, p. 439–448, 2002.

[11] D. Papamartzivanos, F. G. Mármol and G. Kambourakis, “Introducing deep learning self-adaptive misuse network intrusion detection systems,” IEEE Access, vol. 7, p. 13546– 13560, 2019.

[12] D. E. Rumelhart, G. E. Hinton and R. J. Williams, “Learning internal representations by errorpropagation,” in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, Cambridge, MA, 1986, pp. 318-362.

[13] A. Ng and others, “Sparse autoencoder”.

[14] P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio and P.-A. Manzagol, “Stacked denoising

autoencoders: Learning useful representations in a deep network with a local denoising criterion,” Journal of machine learning research, vol. 11, p. 3371–3408, 2010.

[15] J. Masci, U. Meier, D. Cireşan and J. Schmidhuber, “Stacked convolutional autoencoders for hierarchical feature extraction,” in International conference on artificial neural networks, 2011.

[16] M. Jaderberg, K. Simonyan, A. Zisserman and others, “Spatial transformer networks,” in Advances in neural information processing systems, 2015.

[17] Aygun, R. Can and Y. A. Gokhan, “Network anomaly detection with stochastically improved autoencoder based models,” in IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud, 2017.

[18] A. a. N. Q. a. S. W. a. A. M. Javaid, “A deep learning approach for network intrusion detection system,” in Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies, 2016.

[19] X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. Ng, B. Liu, S. Y. Philip and others, “Top 10 algorithms in data mining,” Knowledge and information systems, vol. 14, p. 1–37, 2008.

[20] N. Sultana, N. Chilamkurti, W. Peng and R. Alhadad, “Survey on SDN based network intrusion detection system using machine learning approaches,” Peer-to-Peer Networking and Applications, vol. 12, p. 493–501, 2019.

[21] S. Mukkamala, G. Janoski and A. Sung, “Intrusion detection using neural networks and support vector machines,” in Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No. 02CH37290), 2002.

[22] S. Mukkamala and A. H. Sung, “Detecting denial of service attacks using support vector machines,” in The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ'03., 2003.

[23] P. Mishra, V. Varadharajan, U. Tupakula and E. S. Pilli, “A detailed investigation and analysis of using machine learning techniques for intrusion detection,” IEEE Communications Surveys & Tutorials, vol. 21, p. 686–728, 2018.

[24] E. Hodo, X. Bellekens, A. Hamilton, C. Tachtatzis and R. Atkinson, “Shallow and deep networks intrusion detection system: A taxonomy and survey,” arXiv preprint arXiv:1701.02145, 2017.

[25] D. Heckerman, “Bayesian networks for data mining,” Data mining and knowledge discovery, vol. 1, p. 79–119, 1997.

[26] N. Chand, P. Mishra, C. R. Krishna, E. S. Pilli and M. C. Govil, “A comparative analysis of SVM and its stacking with other classification algorithm for intrusion detection,” in 2016 International Conference on Advances in Computing, Communication, & Automation (ICACCA)(Spring), 2016.

[27] D. H. Ballard, “Modular Learning in Neural Networks.”.

[28] D. E. Rumelhart, G. E. Hinton and R. J. Williams, “Learning internal representations by errorpropagation,� in Parallel Distributed Processing: Explorations in the Microstructure of Cognition., vol. 1, MIT Press, Cambridge, MA, 1986, pp. 318-362.

MULTIPLE LAYERS OF FUZZY LOGIC TO QUANTIFY VULNERABILITIES IN IOT Mohammad Shojaeshafiei1, Letha Etzkorn1and Michael Anderson2 1Department of Computer Science, The University of Alabama in Huntsville, Huntsville, USA 2 Department of Civil and Environmental Engineering, The University of Alabama in Huntsville, Huntsville, USA

ABSTRACT Quantifying vulnerabilities of network systems has been a highly controversial issue in the fields of network security and IoT. Much research has been conducted on this purpose; however, these have many ambiguities and uncertainties. In this paper, we investigate the quantification of vulnerability in the Department of Transportation (DOT) as our proof of concept. We initiate the analysis of security requirements, using Security Quality Requirements Engineering (SQUARE) for security requirements elicitation. Then we apply published security standards such as NIST SP-800 and ISO 27001 to map our security factors and sub-factors. Finally, we propose our Multi-layered Fuzzy Logic (MFL) approach based on Goal question Metrics (GQM) to quantify network security and IoT (Mobile Devices) vulnerability in DOT. KEYWORDS Computer Network, Network Security, Mobile Devices, Fuzzy Logic, Vulnerability, Cyber Security FULL TEXT : https://aircconline.com/csit/papers/vol10/csit100914.pdf VOLUME LINK : http://airccse.org/csit/V10N09.html

9th International Conference on Advanced Information Technologies and Applications (ICAITA 2020)

REFERENCES [1] B. Madan, K. Gogeva-Popstojanova, K. Vaidyanathan, and K. Trivedi, “Modeling and quantification of security attributes of software systems,” Proceedings International Conference on Dependable Systems and Networks, 2002.

[2] M. U. A. Khan and M. Zulkernine, “Quantifying Security in Secure Software Development Phases,” 2008 32nd Annual IEEE International Computer Software and Applications Conference, 2008.

[3] P. Ralston, J. Graham, and J. Hieb, “Cyber security risk assessment for SCADA and DCS networks,” ISA Transactions, vol. 46, no. 4, pp. 583–594, 2007.

[4] C. J. Alberts and A. J. Dorofee, Managing Information Security Risks: The OCTAVESM Approach. Addison-Wesley Professional, 2002.

[5] P.-C. Cheng, P. Rohatgi, C. Keser, P. A. Karger, G. M. Wagner, and A. S. Reninger, “Fuzzy MultiLevel Security: An Experiment on Quantified Risk-Adaptive Access Control,” 2007 IEEE Symposium on Security and Privacy (SP 07), 2007.

[6] V. R. Basili and S. Green, “Software Process Evolution at the SEL,” Foundations of Empirical Software Engineering, pp. 142–154.

[7] L. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.

[8] J. Zhao and B. Bose, “Evaluation of membership functions for fuzzy logic controlled induction motor drive,” IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02.

[9] H.-J. Zimmermann, “Fuzzy Sets, Decision Making, and Expert Systems,” 1987.

[10] W. Pedrycz, “Why triangular membership functions?,” Fuzzy Sets and Systems, vol. 64, no. 1, pp. 21–30, 1994.

[11] N. Karnik, J. Mendel, and Q. Liang, “Type-2 fuzzy logic systems,” IEEE Transactions on Fuzzy Systems, vol. 7, no. 6, pp. 643–658, 1999. 186 Computer Science & Information Technology (CS & IT) [12] W. V. Leekwijck and E. E. Kerre, “Defuzzification: criteria and classification,” Fuzzy Sets and Systems, vol. 108, no. 2, pp. 159–178, 1999.

[13] E. Mamdani, “Advances in the linguistic synthesis of fuzzy controllers,” International Journal of Man-Machine Studies, vol. 8, no. 6, pp. 669–678, 1976.

[14] E. Erturk and E. A. Sezer, “Software fault prediction using Mamdani type fuzzy inference system,” International Journal of Data Analysis Techniques and Strategies, vol. 8, no. 1, p. 14, 2016.

[15] A. S. Sodiya, S. A. Onashoga, and B. Oladunjoye, “Threat Modeling Using Fuzzy Logic Paradigm,” Proceedings of the 2007 InSITE Conference, 2007.

[16] P. Kamongi, S. Kotikela, M. Gomathisankaran, and K. Kavi, “A methodology for ranking cloud system vulnerabilities,” 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), 2013.

[17] I. V. Anikin, “Information security risk assessment and management method in computer networks,” 2015 International Siberian Conference on Control and Communications (SIBCON), 2015.

[18] “Common Vulnerability Scoring System SIG,” FIRST. [Online]. Available: https://www.first.org/cvss/. [Accessed: 03-Feb-2020].

[19] G. Kuang, X. Wang, and L. Yin, “A fuzzy forecast method for network security situation based on Markov,” 2012 International Conference on Computer Science and Information Processing (CSIP), 2012.

[20] KDD Cup 1999 Data. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.

[Online].

Available:

[21] “2000 DARPA Intrusion Detection Scenario Specific Datasets,” MIT Lincoln Laboratory. [Online]. Available: https://www.ll.mit.edu/r-d/datasets/2000-darpa-intrusiondetection-scenario-specificdatasets.

[22] A. Broring, S. Schmid, C.-K. Schindhelm, A. Khelil, S. Kabisch, D. Kramer, D. L. Phuoc, J. Mitic, D. Anicic, and E. Teniente, “Enabling IoT Ecosystems through Platform Interoperability,” IEEE Software, vol. 34, no. 1, pp. 54–61, 2017.

[23] R. Williams, E. Mcmahon, S. Samtani, M. Patton, and H. Chen, “Identifying vulnerabilities of consumer Internet of Things (IoT) devices: A scalable approach,” 2017 IEEE International Conference on Intelligence and Security Informatics (ISI), 2017.

[24] “Introducing Nessus,” Nessus Network Auditing, pp. 27–43, 2004.

[25] M. Patton, E. Gross, R. Chinn, S. Forbis, L. Walker, and H. Chen, “Uninvited Connections: A Study of Vulnerable Devices on the Internet of Things (IoT),” 2014 IEEE Joint Intelligence and Security Informatics Conference, 2014.

[26] “Leading the IoT https://www.gartner.com/imagesrv/books/iot/iotEbook_digital.pdf.

gartner.com.”

[27] Boehm, B. W. & Papaccio, P. N. “Understanding and Controlling Software Costs.”

IEEE Transactions on Software Engineering SE-4, 10 (October 1988): 1462-77.

[28] Ahl V (2005) An experimental comparison of five prioritization methods. Master’s Thesis, School of Engineering, Blekinge Institute of Technology, Ronneby, Sweden.

[29] V. R. Basili and S. Green, “Software Process Evolution at the SEL,” Foundations of Empirical Software Engineering, pp. 142–154.

[30] M. Shepperd, “Practical software metrics for project management and process. improvement,” Information and Software Technology, vol. 35, no. 11-12, p. 701, 1993.

[31] M. Shepperd, “Practical software metrics for project management and process improvement,” Information and Software Technology, vol. 35, no. 11-12, p. 701, 1993.

[32] M. Shojaeshafiei, L. Etzkorn, and M. Anderson, “Cybersecurity Framework Requirements to Quantify Vulnerabilities Based on GQM,” SpringerLink, 04-Jun-2019.

[Online]. Available: https://link.springer.com/chapter/10.1007/978-3-030-31239-8_20.

[33] “The Iso 27001 Risk Assessment,” Information Security Risk Management for ISO 27001/ISO 27002, third edition, pp. 87–93, 2019.

[34] H. Susanto and M. N. Almunawar, “Information Security Management Systems,” 2018.

[35] J. T. Force, “Security and Privacy Controls for Information Systems and Organizations,” CSRC, 15- Aug-2017. [Online]. Available: https://csrc.nist.gov/publications/detail/sp/80053/rev-5/draft.

[36] L. Pokoradi and B. Szamosi, “Fuzzy failure modes and effects analysis with summarized center of gravity defuzzification,” 2015 16th IEEE International Symposium on Computational Intelligence and Informatics (CINTI), 2015.

[37] E. Ngai and F. Wat, “Fuzzy decision support system for risk analysis in e-commerce development,” Decision Support Systems, vol. 40, no. 2, pp. 235–255, 2005.

[38] J. Kacprzyk, “Group decision making with a fuzzy linguistic majority,” Fuzzy Sets and Systems, vol. 18, no. 2, pp. 105–118, 1986.

[39] A. Lotfi and A. Tsoi, “Learning fuzzy inference systems using an adaptive membership function scheme,” IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol. 26, no. 2, pp. 326–331, 1996.

A NEW INTELLIGENT POWER FACTOR CORRECTOR FOR CONVERTER APPLICATIONS Hussain Attia Electrical, Electronics and Communications Engineering Department, School of Engineering, American University of Ras Al Khaimah, Ras Al Khaimah, UAE ABSTRACT This paper presents a new design of a unity power factor corrector for DC-DC converter applications based on an Artificial Neural Network algorithm. The controller firstly calculates the system power factor by measuring the phase shift between the grid voltage and the grid current. Secondly, the controller receives the absolute value of the grid voltage and the measured phase shift through the designed ANN, which predicts the duty cycle of the pulse width modulation (PWM) drive pulses, these PWM pulses are used to drive the Boost DCDC converter to enforce the drawn current to be fully in phase with the grid voltage as well as to improve the level of Total Harmonics Distortion (THD) of the grid current.

MATLAB/Simulink software is adopted to simulate the presented design. The analysis of the simulation results indicates the high performance of the proposed controller in terms of correcting the power factor, and improving the grid current THD. KEYWORDS Power factor corrector, artificial neural network, Boost DC-DC converter, Total Harmonic Distortion, MATLAB/Simulink.

FULL TEXT : https://aircconline.com/csit/papers/vol10/csit100915.pdf VOLUME LINK : http://airccse.org/csit/V10N09.html

9th International Conference on Advanced Information Technologies and Applications (ICAITA 2020)

REFERENCES [1] Saravanan D, Gopinath M, (2016), “A Novel Power Factor Correction Modified Bridge Less-CUK Converter for LED Lamp Applications” IJPEDS, Vol. 7, No. 3, pp. 880-891.

[2] Mallisetti R. K., Duraisamy L., Ch Sai B., (2011), “A Variable Switching Frequency with Boost Power Factor Correction Converter”, TELKOMNIKA, Vol. 9, No. 1, pp. 47-54.

[3] R. A. Rani, S. Saat, Y. Yusop, H. Husin, F. K. Abdul Rahman, A. A. Isa, (2016), “The Effects of Total Harmonics Distortion for Power Factor Correction at Non-Linear Load”, IJPEDS, Vol. 7, No. 2, pp. 543-550.

[4] Nurul H. I., Muhammad N. Z., Nur A. S., Faizal M. T. T., Aini H. M. S., (2015), “A design of an Automatic Single Phase Power Factor Controller by using Arduino Uno Rev-3”, Applied Mechanics and Materials, Vol. 785, pp 419-423.

[5] Kartikesh K. J., Bidyut M., Prem P., Kartick C. J., (2016), “Hardware Implementation of Single Phase Power Factor Correction System using Micro-Controller”, IJPEDS, Vol. 7, No. 3, pp. 790-799.

[6] Ahmet G., Ö. Fatih K., Hakan A., Ceyhun Y., Mustafa Ş.i, (2016), “Simulation Study on Power Factor Correction Controlling Excitation Current of Synchronous Motor with Fuzzy Logic Controller”, IJISAE, 4(Special Issue), pp. 229–233.

[7] A. Bhakthavachala, S. Tara kalyani, K. Anuradha, Nukala Vengaiah, (2017) “Fuzzy Logic Controller based Unity Power Factor Correction of Boost Converter”, IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), Volume 12, Issue 6 Ver. II, PP 52-58.

[8] Abdelhalim Kessal, (2014), “Combined Fuzzy and Predictive Controller for Unity Power Factor Converter”, International Journal of Electrical and Computer Engineering, Vol:8, No:9, 2014, pp. 1541-1546.

[9] S. Sagiroglu, I. Colak B, R. Bayindir, (2006) “Power factor correction technique based on artificial neural networks”, Energy Conversion and Management, Vol. 47, pp. 3204–3215.

[10] Afaf A. A., (2018), “Improved Power Factor of Electrical Generation by using Clustering Neural Network”, IJAER, Vol. 13, No. 7, pp. 4633-4636.

[11] Carl John O. Salaan and Noel R. Estoperez, (2011), “An Artificial Neural Network Based Real-time Reactive Power Controller”, Proceedings of the World Congress on Engineering and Computer Science, Vol. I, WCECS 2011, October 19-21, 2011, San Francisco, USA. [12] N. Ramchandra, M. Kalyanchakravarthi, (2012), “Neural Network Based Unified Power Quality Conditioner”, International Journal of Modern Engineering Research (IJMER), Vol.2, Issue.1, pp359-365.

[13] Masashi O., Hirofumi M. (2003), “An AC/DC Converter with High Power Factor”, IEEE Trans. On Industrial Electronics, Vol. 50, No. 2, pp. 356–361.

[14] M. Malinowski, M. Jasinski, M.P. Kazmierkowski, (2004) “Simple direct power control of threephase PWM rectifier using space-vector modulation (DPCSVM)”, IEEE Trans. on Industrial Electronics, pp. 447–454.

[15] Hussain A. Attia, (2019), “High performance PV system based on artificial neural network MPPT with PI controller for direct current water pump applications, IJPEDS, Vol. 10, No. 3, pp. 1329-1338.

[16] Hussain Attia, (2019), “Supplying DC Electricity to the Isolated Dwellings through MPP Tracked PV System Based on Artificial Neural Network”, 6th International Conference of Control, Dynamic Systems, and Robotics (CDSR'19), Ottawa, Canada.

[17] Hussain Attia, (2018), “Artificial Neural Networks Based Maximum Power Point Tracking Photovoltaic System for Remote Parks LED Lighting Applications”, International Review on Modelling and Simulations, Vol. 11, Iss. 6, pp. 396-405.

[18] H. A. Attia, T. K. S. Freddy, H. S. Che, W. P. Hew, A. El Khateb, (2017), "Confined Band Variable Switching Frequency Pulse Width Modulation (CB-VSF PWM) for SinglePhase Inverter with LCL Filter," IEEE Trans. on Power Electronics, Vol. 32, No. 11, pp.

8593–8605.

[19] Hussain Attia, (2018), “A Stand-alone Solar PV System with MPPT Based on Fuzzy Logic Control for Direct Current Portable House Applications”, IREMOS, Vol. 11, Iss. 6, pp.

377-385.

[20] Hussain Attia, (2018), “Fuzzy Logic Controller Effectiveness Evaluation through Comparative Memberships for Photovoltaic Maximum Power Point Tracking Function”, IJPEDS, Vol. 9, No. 3, pp. 1147-1156.

[21] Attia Hussain, Del Ama G. F., (2019), “Stand-alone PV System with MPPT Function Based on Fuzzy Logic Control for Remote Building Applications”, IJPEDS, Vol. 10, Iss. 2.

[22] Hussain A Attia, (2017), “Comparative design of fuzzy logic controller for photovoltaic panel maximum power point tracking”, ICECTA2017, Ras Al Khaimah-UAE. [23] Hussain Attia, Amjad Omar, Maen Takruri, Halah Y. Ali, (2017), "Pulse Width Modulation Based Decentralized Street LED Light," International Journal of Power Electronics and Drive System (IJPEDS), vol. 8(3), pp. 1238-1247.

[24] Hussain A. Attia, Y. I. Al-Mashhadany, Beza N. G., (2014), "Design and Simulation of a High Performance Standalone Photovoltaic System," ICREGA’14 – Renewable Energy: Generation and Applications, Springer Proceedings in Energy 2014.

A REVIEW OF BEHAVIOR ANALYSIS OF COLLEGE STUDENTS Wei-hong WANG, Hong-yan LV, Yu-hui CAO, Lei SUN and Qian FENG School of Information Technology, Hebei University of Economics and Business, Shijiazhuang, China ABSTRACT Student behavior analysis plays an increasingly important role in education data mining research, but it lacks systematic analysis and summary. Based on reading a large amount of literature, this paper has carried out the overall framework, methods and applications of its research. Comprehensive combing and elaboration. Firstly, statistical analysis and knowledge map analysis of the relevant literature on student behavior analysis in the CNKI database are carried out, and then the research trends and research hot spots are obtained. Then, from the different perspectives of the overall process and technical support of student behavior analysis, the overall framework of the research is constructed, and the student behavior evaluation indicators, student portraits and used tools and methods are highlighted. Finally, it summarizes the principal applications of student behavior analysis and points out the future research direction.

KEYWORDS Student Behavior; Knowledge Graph; Behavior Analysis; Student Portraits; Data Mining FULL TEXT : https://aircconline.com/csit/papers/vol10/csit101007.pdf VOLUME LINK : http://airccse.org/csit/V10N10.html

10th International Conference on Computer Science, Engineering and Applications (CCSEA 2020)

REFERENCES [1] Ouyang X , Zhong J , Liu J . Study on the Construction of Wisdom Campus Based on University Resource Planning (URP)[C]// International Conference on Information Technology in Medicine & Education. IEEE, 2017.

[2] Shen Xin-yi, Wu Jian-wei, Zhang Yan-xia, Ma Yu-chun. Study on Online Learning Behavior and Learning Effect Evaluation Model of MOOCAP Learners[J].Distance Education in China,2019(07):38-46+93.

[3] Wang Yuan-ying, Yin de-zhi.The Evaluation System and Mathematical Model of the Comprehensive Quality of College Students[J].Journal of Southwest University for Nationalities (Humanities and Social Sciences Edition),2003(12):191-193.

[4] Jianting L , Haoming W . An Evaluation Model of E-learning Behavior Analysis[C]// Proceedings of the 2011 Third International Workshop on Education Technology and Computer Science – Volume 02. 2011.

[5] Li You-zeng, Zeng Hao. Big Data Application Research of Intelligent Campus Education in Colleges and Universities Based on Student Behavior Analysis Model[J].China Educational Technology,2018(07):33-38.

[6] Wang Zheng. Design and Implementation of Academic Warning and Social Analysis System Based on Student Campus Data[D].Beijing University of Posts and Telecommunications,2019.

[7] RE. At-Risk Students: Portraits, Policies, Programs, and Practices.[J]. 1993.

[8] Han Feng-xia.Exploration and Research on Early-warning Mechanism of College Enrollment in the Era of Big Data[J].The Chinese Journal of ICT in Education,2015(19):4649.

[9] Liu Xuan. Research and Application of Performance Prediction Model Based on Student Behavior[D].University of Electronic Science and Technology,2017.

[10] Dong Xiao-xiao, Hu Yan, Chen Yan-ping.Analysis and Research of College Students' Performance Portrait Based on Campus Data[J].Computer & Digital Engineering,2018,46(06):1200-1204+1262.

[11] Chen Hai-jian, Dai Yong-hui, Han Dong-mei, Feng Yan-jie, Huang Hei-xiao. Discussion on Learners' Portrait and Individualized Teaching Under Open Teaching[J].Open Education Research,2017,23(03):105-112.

[12] Zhang Hong-xin, Cheng Feng-fan, Xu Pei-yuan, Tang Ying. Visualizing User Characteristics Based on Mobile Device Log Data[J].Journal of Software,2016,27(05):11741187.

[13] Zhang Xue, Tan Yue-ying, Luo Heng. A Group Study of Non-native Language Learners in Online Learning: Category Portraits and Behavioral Characteristics Analysis[J]. Modern Distance Education, 2019, 181(01):19-27.

[14] Bresfelean V P . Analysis and Predictions on Students' Behavior Using Decision Trees in Weka Environment[C]// International Conference on Information Technology Interfaces.

IEEE, 2007.

[15] Ramesh V , Parkavi P , Ramar K . Predicting Student Performance: A Statistical and Data Mining Approach[J]. International Journal of Computer Applications, 2013, 63(8):3539.

[16] Zhang Jia-ting, Zhou Qin, Zhu Zhi-ting. Application of Online Learning Intervention Model from the Perspective of Learning Analysis[J].Modern Distance Education Research,2017(04):88-96.

[17] Meng Ling-ling, Gu Xiao-qing, Li Ze. Study the Comparative Study of Analytical Tools[J].Open Education Research,2014,20(04):66-75.

[18] Ding D , Li J , Wang H , et al. Student Behavior Clustering Method Based on Campus Big Data[C]// International Conference on Computational Intelligence & Security. IEEE Computer Society, 2017.

[19] Wang Fa-yu, Jiang Yan. Learning Interest Analysis of Users in Campus Wireless Network Based on Self-Organizing Neural Network and Fuzzy C-means Clustering Algorithm[J].Application Research of Computers,2018,35(01):186-189.

[20] Hu Y H , Lo C L , Shih S P . Developing early warning systems to predict students’ online learning performance[J]. Computers in Human Behavior, 2014, 36:469-478.

[21] Guo Peng, Cai Cheng. Data Mining and Analysis of Students’ Score Based on Clustering and Association Algorithm[J/OL].Computer Engineering and Applications:112[2019-08-30].http://kns.cnki.net/kcms/detail/11.2127.TP.20190604.0952.014.html.

[22] Liu Bo-peng, Fan Tie-cheng, Yang Hong. Research on Application of Early Warning of Students’ Achievement Based on Sata Mining[J].Journal of Sichuan University(Natural Science Edition),2019,56(02):267-272.

[23] Cantabella M, Martínez-España R, Ayuso B, et al. Analysis of student behavior in learning management systems through a Big Data framework[J]. Future Generation Computer Systems, 2019,90: 262-272.

[24] He Chu, Song Jian, Zhuo Tong. Curriculum Association Model and Student Performance Prediction Based on Spectral Clustering of Frequent Pattern[J].Application Research of Computers,2015,32(10):2930-2933.

[25] Deng Feng-guang, Zhang Zi-shi. Research on the Construction of Students' Campus Behavior Analysis and Warning Management Platform Based on Big Data[J].China Educational Technology,2017(11):60-64.

[26] Su-Hui G, Cheng-Jie B, Quan W. Hadoop-based college student behavior warning decision system[C]//2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA).

IEEE, 2018: 217-221.

[27] Liu Min, Zheng Ming-yue. Learning Analytics and Learning Resources Personalized Recommendation in Smart Education[J].China Educational Technology,2019(09):38-47.

[28] Su Y S , Ding T J , Lue J H , et al. Applying big data analysis technique to students' learning behaviour and learning resource recommendation in a MOOCs course[C]// International Conference on Applied System Innovation. IEEE, 2017.

[29] Gui Zhong-yan, Zhang Yan-ming, Li Wei-wei. Research on Learning Resource Recommendation Algorithm Based on Behavior Sequence Analysis[J/OL].Application Research of Computers:15[2019-09-17].https://doi.org/10.19734/j.issn.10013695.2018.12.0930.

[30] Zhou Lu. The Problems and Improvement of the College Students’ Comprehensive Quality Assessment[D].Hunan University,2013.

[31] He Yi. Research and Realization of Comprehensive Quality Evaluation System for College Students in SiChuan Vocational and Technical College Based on Analytic Hierarchy Process[D].University of Electronic Science and Technology,2012.

[32] Su Yu, Zang Dan, Liu Qing-wen, Zhang Qing-wen, Chen Yu-ying, Ding Hong-qiang.

Student score prediction: A knowledge-aware auto-encoder model[J].Journal of University of Science and Technology of China,2019,49(01):21-30.

[33] Zhai Yu, Xu Meng, Huang Bin. Personalized Learning Resource Recommendation Based on Knowledge State[J].Journal of Jishou University(Natural Sciences Edition),2019,40(03):23-27.

[34] Balakrishnan G, Coetzee D. Predicting student retention in massive open online courses using hidden markov models[J]. Electrical Engineering and Computer Sciences University of California at Berkeley, 2013, 53: 57-58.

[35] Kizilcec R F, Piech C, Schneider E. Deconstructing disengagement: analyzing learner subpopulations in massive open online courses[C]//Proceedings of the third international conference on learning analytics and knowledge. ACM, 2013: 170-179.

[36] Miao C, Zhu X, Miao J. The analysis of student grades based on collected data of their Wi-Fi behaviors on campus[C]//2016 2nd IEEE International Conference on Computer and Communications (ICCC). IEEE, 2016: 130-134.

CODING WITH LOGISTIC SOFTMAX SPARSE UNITS Gustavo A. Lado and Enrique C. Segura Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina ABSTRACT This paper presents a new technique for efficient coding of highly dimensional vectors, overcoming the typical drawbacks of classical approaches, both, the type of local representations and those of distributed codifications. The main advantages and disadvantages of these classical approaches are revised and a novel, fully parameterized strategy, is introduced to obtain representations of intermediate levels of locality and sparsity, according to the necessities of the particular problem to deal with. The proposed method, called COLOSSUS (COding with LOgistic Softmax Sparse UnitS) is based on an algorithm that permits a smooth transition between both extreme behaviours -local, distributed- via a parameter that regulates the sparsity of the representation. The activation function is of the logistic type. We propose an appropriate cost function and derive a learning rule which happens to be similar to the Oja's Hebbian learning rule. Experiments are reported showing the efficiency of the proposed technique.

KEYWORDS Neural Networks, Sparse Coding, Autoencoders FULL TEXT : https://aircconline.com/csit/papers/vol10/csit101019.pdf VOLUME LINK : http://airccse.org/csit/V10N10.html

10th International Conference on Computer Science, Engineering and Applications (CCSEA 2020)

REFERENCES [1] Vincent, Pascal and Larochelle, Hugo and Lajoie, Isabelle and Bengio, Yoshua and Manzagol, Pierre-Antoine, "Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion", Journal of machine learning research (2010), 3371-- 3408.

[2] Mikolov, Tomas and Sutskever, Ilya and Chen, Kai and Corrado, Greg S and Dean, Jeff, "Distributed Representations of Words and Phrases and their Compositionality", Curran Associates, Inc. (2013), 3111--3119.

[3] Sutskever, Ilya and Hinton, Geoffrey, "Learning multilevel distributed representations for highdimensional sequences" (2007), 548--555.

[4] Samuel Kaski and Teuvo Kohonen, "Winner-take-all networks for physiological models of competitive learning", Neural Networks (1994), 973 - 984.

[5] Rolls, Edmund T and Treves, Alessandro, "The relative advantages of sparse versus distributed encoding for associative neuronal networks in the brain", Network: computation in neural systems (1990), 407--421.

[6] Rehn, Martin and Sommer, Friedrich T, "A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields.", J Comput Neurosci (2007), 135- 46.

[7] Bridle, John S., "Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recogni...", Springer Berlin Heidelberg (1990), 227-- 236.

[8] Donoho, David L. and Elad, Michael, "Optimally sparse representation in general (nonorthogonal) dictionaries via L1 minimization", Proceedings of the National Academy of Sciences (2003), 2197--2202.

[9] Gregor, Karol and LeCun, Yann, "Learning Fast Approximations of Sparse Coding" (2010), 399- 406.

[10] Lee, Honglak and Battle, Alexis and Raina, Rajat and Ng, Andrew Y, "Efficient sparse coding algorithms" (2007), 801--808.

[11] Coates, Adam and Ng, Andrew Y, "The importance of encoding versus training with sparse coding and vector quantization" (2011), 921--928.

[12] Hinton, Geoffrey E, "Learning multiple layers of representation.", Trends Cogn. Sci. (Regul. Ed.) (2007), 428-34.

[13] Rumelhart, David E. and Hinton, Geoffrey E. and Williams, Ronald J., "Learning representations by back-propagating errors", nature (1986), 533.

[14] Oja, Erkki, "Principal components, minor components, and linear neural networks", Neural networks (1992), 927--935.

[15] Olshausen, Bruno A and Field, David J, "Sparse coding with an overcomplete basis set: A strategy employed by V1?", Vision research (1997), 3311--3325.

[16] Porrill, John and Stone, James V, "Undercomplete independent component analysis for signal separation and dimension reduction", Citeseer (1998).

APPROACHES TO FRAUD DETECTION ON CREDIT CARD TRANSACTIONS USING ARTIFICIAL INTELLIGENCE METHODS Yusuf Yazici Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey ABSTRACT Credit card fraud is an ongoing problem for almost all industries in the world, and it raises millions of dollars to the global economy each year. Therefore, there is a number of research either completed or proceeding in order to detect these kinds of frauds in the industry. These researches generally use rule-based or novel artificial intelligence approaches to find eligible solutions. The ultimate goal of this paper is to summarize state-of-the-art approaches to fraud detection using artificial intelligence and machine learning techniques. While summarizing, we will categorize the common problems such as imbalanced dataset, real time working scenarios, and feature engineering challenges that almost all research works encounter, and identify general approaches to solve them. The imbalanced dataset problem occurs because the number of legitimate transactions is much higher than the fraudulent ones whereas applying the right feature engineering is substantial as the features obtained from the industries are limited, and applying feature engineering methods and reforming the dataset is crucial. Also, adapting the detection system to real time scenarios is a challenge since the number of credit card transactions in a limited time period is very high. In addition, we will discuss how evaluation metrics and machine learning methods differentiate among each research.

KEYWORDS Credit Card, Fraud Detection, Machine Learning, Survey, Artificial Intelligence FULL TEXT : https://aircconline.com/csit/papers/vol10/csit101018.pdf VOLUME LINK : http://airccse.org/csit/V10N10.html

10th International Conference on Computer Science, Engineering and Applications (CCSEA 2020)

REFERENCES [1] S. Akila and U. Srinivasulu Reddy, “Cost-sensitive Risk Induced Bayesian Inference Bagging (RIBIB) for credit card fraud detection,” Journal of Computational Science, vol. 27, pp. 247–254, Jul. 2018, doi: 10.1016/j.jocs.2018.06.009.

[2] A. M. Ozbayoglu, M. U. Gudelek, and O. B. Sezer, “Deep learning for financial applications : A survey,” Applied Soft Computing, vol. 93, p. 106384, Aug. 2020, doi: 10.1016/j.asoc.2020.106384.

[3] Y. Jin, R. M. Rejesus *, and B. B. Little, “Binary choice models for rare events data: a crop insurance fraud application,” Applied Economics, vol. 37, no. 7, pp. 841–848, Apr. 2005, doi: 10.1080/0003684042000337433.

[4] S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, “Data mining for credit card fraud: A comparative study,” Decision Support Systems, vol. 50, no. 3, pp. 602–613, Feb. 2011, doi: 10.1016/j.dss.2010.08.008.

[5] A. G. C. de Sá, A. C. M. Pereira, and G. L. Pappa, “A customized classification algorithm for creditcard fraud detection,” Engineering Applications of Artificial Intelligence, vol. 72, pp. 21–29, Jun. 2018, doi: 10.1016/j.engappai.2018.03.011.

[6] F. Carcillo, Y.-A. Le Borgne, O. Caelen, Y. Kessaci, F. Oblé, and G. Bontempi, “Combining unsupervised and supervised learning in credit card fraud detection,” Information Sciences, May 2019, doi: 10.1016/j.ins.2019.05.042.

[7] E. Kim et al., “Champion-challenger analysis for credit card fraud detection: Hybrid ensemble and deep learning,” Expert Systems with Applications, vol. 128, pp. 214–224, Aug. 2019, doi: 10.1016/j.eswa.2019.03.042.

[8] S. M. S. Askari and M. A. Hussain, “IFDTC4.5: Intuitionistic fuzzy logic based decision tree for Etransactional fraud detection,” Journal of Information Security and Applications, vol. 52, p. 102469, Jun. 2020, doi: 10.1016/j.jisa.2020.102469.

[9] T. Pourhabibi, K.-L. Ong, B. H. Kam, and Y. L. Boo, “Fraud detection: A systematic literature review of graph-based anomaly detection approaches,” Decision Support Systems, vol. 133, p. 113303, Jun. 2020, doi: 10.1016/j.dss.2020.113303.

[10] Y. Lucas et al., “Towards automated feature engineering for credit card fraud detection using multiperspective HMMs,” Future Generation Computer Systems, vol. 102, pp. 393– 402, Jan. 2020, doi: 10.1016/j.future.2019.08.029.

[11] S. Misra, S. Thakur, M. Ghosh, and S. K. Saha, “An Autoencoder Based Model for Detecting Fraudulent Credit Card Transaction,” Procedia Computer Science, vol. 167, pp.

254–262, 2020, doi: 10.1016/j.procs.2020.03.219.

[12] V. N. Dornadula and S. Geetha, “Credit Card Fraud Detection using Machine Learning Algorithms,” Procedia Computer Science, vol. 165, pp. 631–641, 2019, doi: 10.1016/j.procs.2020.01.057.

[13] S. Carta, G. Fenu, D. Reforgiato Recupero, and R. Saia, “Fraud detection for Ecommerce transactions by employing a prudential Multiple Consensus model,” Journal of Information Security and Applications, vol. 46, pp. 13–22, Jun. 2019, doi: 10.1016/j.jisa.2019.02.007.

[14] S. Nami and M. Shajari, “Cost-sensitive payment card fraud detection based on dynamic random forest and k -nearest neighbors,” Expert Systems with Applications, vol. 110, pp.

381–392, Nov. 2018, doi: 10.1016/j.eswa.2018.06.011.

[15] A. C. Bahnsen, A. Stojanovic, D. Aouada and B. Ottersten, "Cost Sensitive Credit Card Fraud Detection Using Bayes Minimum Risk," 2013 12th International Conference on Machine Learning and Applications, Miami, FL, 2013, pp. 333-338, doi: 10.1109/ICMLA.2013.68.

[16] U. Fiore, A. De Santis, F. Perla, P. Zanetti, and F. Palmieri, “Using generative adversarial networks for improving classification effectiveness in credit card fraud detection,” Information Sciences, vol. 479, pp. 448–455, Apr. 2019, doi: 10.1016/j.ins.2017.12.030.

[17] N. F. Ryman-Tubb, P. Krause, and W. Garn, “How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark,” Engineering Applications of Artificial Intelligence, vol. 76, pp. 130–157, Nov. 2018.

[18] N. Japkowicz and S. Stephen, “The class imbalance problem: A systematic study,” IDA, vol. 6, no. 5, pp. 429–449, Nov. 2002, doi: 10.3233/IDA-2002-6504.

[19] X. Zhang, Y. Han, W. Xu, and Q. Wang, “HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture,” Information Sciences, May 2019, doi: 10.1016/j.ins.2019.05.023.

[20] Roy, Abhimanyu & Sun, Jingyi & Mahoney, Robert & Alonzi, Loreto & Adams, Stephen & Beling, Peter. (2018). Deep learning detecting fraud in credit card transactions.

129-134. 10.1109/SIEDS.2018.8374722.

[21] Chouiekh, Alae & Haj, EL. (2018). ConvNets for Fraud Detection analysis. Procedia Computer Science. 127. 133-138. 10.1016/j.procs.2018.01.107.

[22] Wang, Deshen & Chen, Bintong & Chen, Jing. (2018). Credit Card Fraud Detection Strategies with Consumer Incentives. Omega. 88. 10.1016/j.omega.2018.07.001.

[23] F. Carcillo, A. Dal Pozzolo, Y.-A. Le Borgne, O. Caelen, Y. Mazzer, and G. Bontempi, “SCARFF : A scalable framework for streaming credit card fraud detection with spark,” Information Fusion, vol.41, pp. 182–194, May 2018, doi: 10.1016/j.inffus.2017.09.005.

[24] Rtayli, Naoufal & Enneya, Nourddine. (2020). Selection Features and Support Vector Machine for Credit Card Risk Identification. Procedia Manufacturing. 46. 941-948.

10.1016/j.promfg.2020.05.012.

[25] Y. Wu, Y. Xu, and J. Li, “Feature construction for fraudulent credit card cash-out detection,” Decision Support Systems, vol. 127, p. 113155, Dec. 2019.

[26] M. F. Zeager, A. Sridhar, N. Fogal, S. Adams, D. E. Brown and P. A. Beling, "Adversarial learning in credit card fraud detection," 2017 Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, VA, 2017, pp. 112-116, doi: 10.1109/SIEDS.2017.7937699.

[27] J. Jurgovsky et al., “Sequence classification for credit-card fraud detection,” Expert Systems with Applications, vol.

100, pp.

234–245, Jun. 2018, doi: 10.1016/j.eswa.2018.01.037.

[28] H. Zhu, G. Liu, M. Zhou, Y. Xie, A. Abusorrah, and Q. Kang, “Optimizing Weighted Extreme Learning Machines for imbalanced classification and application to credit card fraud detection,” Neurocomputing, vol.

407, pp.

50–62, Sep. 2020, doi: 10.1016/j.neucom.2020.04.078.

[29] S. Patil, V. Nemade, and P. K. Soni, “Predictive Modelling For Credit Card Fraud Detection Using Data Analytics,” Procedia Computer Science, vol. 132, pp. 385–395, 2018.

[30] A. Dal Pozzolo, G. Boracchi, O. Caelen, C. Alippi and G. Bontempi, "Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy," in IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 8, pp. 3784-3797, Aug. 2018, doi: 10.1109/TNNLS.2017.2736643.

ANALYSIS OF THE DISPLACEMENT OF TERRESTRIAL MOBILE ROBOTS IN CORRIDORS USING PARACONSISTENT ANNOTATED EVIDENTIAL LOGIC EĎ„ Flavio Amadeu Bernardini1, Marcia Terra da Silva1, Jair Minoro Abe1,Luiz Antonio de Lima1and Kanstantsin Miatluk2 1Graduate Program in Production Engineering Paulista University, Sao Paulo, Brazil 2Bialystok University of Technology, Bialystok, Poland ABSTRACT This article proposes an algorithm for a servo motor that controls the movement of an autonomous terrestrial mobile robot using Paraconsistent Logic. The design process of mechatronic systems guided the robot construction phases. The project intends to monitor the robot through its sensors that send positioning signals to the microcontroller. The signals are adjusted by an embedded technology interface maintained in the concepts of Paraconsistent Annotated Logic acting directly on the servo steering motor. The electric signals sent to the servo motor were analyzed, and it indicates that the algorithm paraconsistent can contribute to the increase of precision of movements of servo motors.

KEYWORDS Paraconsistent annotated logic, Servo motor, Autonomous terrestrial mobile robot, Robotics FULL TEXT : https://aircconline.com/csit/papers/vol10/csit101115.pdf VOLUME LINK : http://airccse.org/csit/V10N11.html

7th International Conference on Computer Science, Engineering and Information Technology (CSEIT 2020)

REFERENCES [1] Ruggero, S. M., dos Santos, N. A., Sacomano, J. B., & da Silva, M. T. (2019).

Investments in the Automotive Sector and Industry 4.0. Brazilian Case. In IFIP International Conference on Advances in Production Management Systems (pp. 650-657). Springer, Cham.

[2] Hexmoor, Henry. (2013). “Essential Principles for Autonomous Robotics.” Synthesis Lectures on Artificial Intelligence and Machine Learning 7(2): 1–155. https://doi.org/ 10.2200/S00506ED1V01Y201305AIM021.

[3] Abe, J. M., Akama, S., & Nakamatsu, K. (2015). Introduction to annotated logics: foundations for paracomplete and paraconsistent reasoning Vol. 88. Springer.

[4] Torres, C. R., Abe, J. M., Lambert-Torres, G., da Silva Filho, J. I., & Martins, H. G. (2009). Autonomous mobile robot emmy iii. In New Advances in Intelligent Decision Technologies (pp. 317- 327). Springer, Berlin, Heidelberg.

[5] Carvalho, Fábio R.; Abe, Jair M. (2018). A Paraconsistent Decision-Making Method, Smart Innovation, Systems and Technologies Vol. 87, Springer International Publishing. https://doi.org/10.1007/978-3-319-74110-9, Library of Congress Control Number: 2018933003.

[6] Bayindir, Ramazan, Ersan Kabalci, Orhan Kaplan, e Yunus Emre Oz. (2012).

“Microcontroller based electrical machines training set.” In 2012 15th International Power Electronics and Motion Control Conference (EPE/PEMC), Novi Sad, Serbia: IEEE, S3e.121-DS3e.12-4. https://doi.org/ 10.1109/EPEPEMC.2012.6397366.

[7] Miatliuk, K. (2015). Conceptual model in the formal basis of hierarchical systems for mechatronic design. Cybernetics and Systems 46 (8), 666-680.

[8] Miatliuk, K. (2017). Conceptual Design of Mechatronic Systems. WPB, Bialystok, available online: https://pb.edu.pl/oficyna-wydawnicza/wpcontent/uploads/sites/4/2018/02/Miatluk_publikacja.pdf [9] Miatliuk, K., Kim ,Y.H., Kim, K., Siemieniako, F. (2010). Use of hierarchical system technology in mechatronic design. Mechatronics 20 (2), 335-339.

[10] Miatluk, K., Nawrocka, A., Holewa, K., Moulianitis, V. (2020) Conceptual design of BCI for mobile robot control. Applied Sciences 10 (7), 2557.

[11] Abe, J. M. (2010). Paraconsistent logics and applications. In 4th International Workshop on Soft Computing Applications. p. 11-18, IEEE. [12] Abe, J. M. (2015). Paraconsistent intelligent-based systems: New trends in the applications of paraconsistency. (Eds.), Vol. 94. Springer.

STABILITY ANALYSIS OF QUATERNIONVALUED NEURAL NETWORKS WITH LEAKAGE DELAY AND ADDITIVE TIME-VARYING DELAYS Qun Huang and Jinde Cao School of Mathematics, Southeast University, Nanjing, China ABSTRACT In this paper, the stability analysis of quaternion-valued neural networks (QVNNs) with both leakage delay and additive time-varying delays is proposed. By employing the Lyapunov Krasovskii functional method and fully considering the relationship between time-varying delays and upper bounds of delays, some sufficient criteria are derived based on reciprocally convex method and several inequality techniques. The stability criteria are established in two forms: quaternion-valued linear matrix inequalities (QVLMIs) and complex-valued linear matrix inequalities (CVLMIs),in which CVLMIs can be directly resolved by the Yalmip toolbox in MATLAB. Finally, an illustrative example is presented to demonstrate the validity of the theoretical results.

KEYWORDS Quaternion-valued Neural Networks, Stability Analysis, Lyapunov-KrasovskiiFunctional, Leakage Delay, Additive Time-varying Delays FULL TEXT : https://aircconline.com/csit/papers/vol10/csit101305.pdf VOLUME LINK : http://airccse.org/csit/V10N13.html

International Conference on Big Data, IOT and Blockchain (BIBC 2020)

REFERENCES [1] H. Bao, Ju H. Park, and J. Cao, (2016) “Exponential synchronization of coupled stochastic memristor-based neural networks with time-varying probabilistic delay coupling and impulsive delay”,IEEE Transactions on Neural Networks,Vol. 27, No. 1, pp. 190–201.

[2] J. Cao and R. Li, (2017) “Fixed-time synchronization of delayed memristor-based recurrent neural networks”,Science China: Information Sciences, Vol. 60, No. 3, pp. 108– 122.

[3] X. Li and J. Cao, (2017) “An impulsive delay inequality involving unbounded timevarying delay and applications”,IEEE Transactions on Automatic Control, Vol. 62, No. 7, pp.

3618–3625.

[4] J. Wang, H. Wu, and T. Huang, (2015) “Passivity-based synchronization of a class of complex dynamical networks with time-varying delay”,Automatica, Vol. 56, pp. 105–112.

[5] R. Yang, B. Wu, and Y. Liu, (2015) “A halanay-type inequality approach to the stability analysis of discrete time neural networks with delays”, Applied Mathematics and Computation, Vol. 265, pp. 696–707.

[6] X. Yang and J. Lu, (2016) “Finite-time synchronization of coupled networks with markovian topology and impulsive effects”, IEEE Transactions on Automatic Control, Vol. 61, No. 8, pp. 2256– 2261.

[7] S. Jankowski, A. Lozowski, and J.M. Zurada, (1996) “Complex-valued multistate neural associative memory”, IEEE Transactions on Neural Networks, Vol. 7, No. 6, pp. 1491–1496.

[8] H. Bao, Ju H. Park, and J. Cao, (2016) “Synchronization of fractional-order complexvalued neural networks with time delay”, Neural Networks, Vol. 81, pp. 16–28.

[9] W. Gong, J. Liang, and J. Cao, (2015) “Matrix measure method for global exponential stability of complex-valued recurrent neural networks with time-varying delays”,Neural Networks, Vol. 70, pp. 81–89.

[10] A. Hirose and S. Yoshida, (2012) “Generalization characteristics of complex-valued feedforward neural networks in relationto signal coherence”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 23, No. 4, pp. 541–551.

[11] R. Rakkiyappan, G. Velmurugan, X. Li, and D. O’Regan, (2016) “Global dissipativity of memristorbased complex-valued neural networks with time-varying delays”, Neural Computing and Applications, Vol. 27, No. 3, pp. 629–649.

[12] N. Matsui, T. Isokawa, H. Kusamichi, F. Peper, and H. Nishimura, (2004) “Quaternion neural network with geometrical operators”, Journal of Intelligent and Fuzzy Systems, Vol. 15, No. 3, pp. 149–164.

[13] B.C. Ujang, C.C. Took, and D.P. Mandic, (2011) “Quaternion-valued nonlinear adaptive filtering”,IEEE Transactions onNeural Networks, Vol. 22, No. 8, pp. 1193–1206.

[14] Y. Liu, D. Zhang, J. Lu, and J. Cao, (2016) “Global μ-stability criteria for quaternionvalued neural networks with unbounded time-varying delays”,Information Sciences, Vol. 360, pp. 273–288.

[15] H. Shu, Q. Song, Y. Liu, Z. Zhao, and F.E. Alsaadi, (2017) “Global μ-stability of quaternion-valued neural networks withnon-differentiable time-varying delays” ,Neurocomputing, Vol. 247, pp. 202–212.

[16] Y. Liu, D. Zhang, and J. Lu, (2016) “Global exponential stability for quaternion-valued recurrent neural networks with time-varying delays”, Nonlinear Dynamics, Vol. 87, No. 1, pp. 1–13.

[17] X. Chen, Z. Li, Q. Song, J. Hu, and Y. Tan, (2017) “Robust stability analysis of quaternion-valued neural networks with time delays and parameter uncertainties”, Neural Networks, Vol. 91, pp. 55–65.

[18] X. Chen, Q. Song, Z. Li, Z. Zhao, and Y. Liu, (2018) “Stability analysis of continuoustime and discrete-time quaternion-valued neural networks with linear threshold neurons”, IEEE Transactions on Neural Networks, Vol. 29, No. 7, pp. 2769–2781.

[19] Z. Tu, J. Cao, A. Alsaedi, and T. Hayat, (2017) “Global dissipativity analysis for delayed quaternionvalued neural networks”, Neural Networks, Vol. 89, pp. 97–104.

[20] Z. Yu, H. Gao, and S. Mou, (2008) “Asymptotic stability analysis of neural networks with successive time delay components”, Neurocomputing, Vol. 71, No. 13-15, pp. 2848– 2856.

[21] H. Shao and Q. Han, (2011) “New delay-dependent stability criteria for neural networks with two additive time-varying delay components”, IEEE Transactions on Neural Networks, Vol. 22, No. 5, pp. 812–818.

[22] J. Tian and S. Zhong, (2012) “Improved delay-dependent stability criteria for neural networks with two additive time-varying delay components”, Neurocomputing, Vol. 77, pp.

114–119.

[23] P.G. Park, J.W. Ko, and C. Jeong, (2011) “Reciprocally convex approach to stability of systems with time-varying delays”, Automatica, Vol. 47, No. 1, pp. 235–238.

[24] J. Liang, K. Li, Q. Song, Z. Zhao, Y. Liu, and F.E. Alsaadi, (2018) “State estimation of complex valued neural networks with two additive time-varying delays” ,Neurocomputing, Vol. 309, pp. 54–61.