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Present state and future application of smart technologies in manufacturing processes

(1), 15883–15889. Monostori, L., 2014. C yber-physical Production Systems: Roots , Expectations and R&D Challenges. Procedia CIRP, 17, 9-13. Pan, Y., 2016. Heading toward Artificial Intelligence 2.0. Engineering , 2(4), 409-413. Scalability of intelligent automation technologies directly linked to financial performance, finds KPMG survey—KPMG Global. (2019, May 21). Retrieved 9 September 2019, from KPMG website: https

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Qualitative and quantitative Analysis of consumers perception regarding anthropomorphic AI designs


Business intelligence and analytics are nowadays being integrated into diverse industries, from healthcare to customer relationship management and behavioral profiling, due to the competitive advantages that they offer. Nevertheless, most companies try to integrate as many forms of business intelligence systems as possible into different internal processes. This overall digitization applied to more and more business departments is being analyzed with both curiosity and reluctance. The decision regarding the implementation of innovative forms of automation is taken in an attempt to discover and solve business challenges. However, there are several issues involved, which need to be addressed. One of the risks that are being discussed in the research environment refers to the level of acceptance of artificial intelligence systems. The tolerance and overall readiness of the consumers towards innovation and technology is one of the critical factors which need to be determined before implementing disruptive business intelligence systems. Moreover, in an effort to make devices friendlier to consumers, some developers chose to assign anthropomorphic appearances and even create individual identities for each artificial intelligence system. In this context, it is important for most companies investing in intelligent automation systems to determine to which extend the use of anthropomorphic designs impacts the customer’s perception. The objective of this research paper is to analyze the unconscious reaction of consumers towards two opposite designs of artificial intelligence systems: a robotic-like form and a human-like design. Based on this difference, a photo collage was created figuring two pictures: one with a metallic robot having a conversation with a human being and one with a robot with a strong anthropomorphic figure found in the same situation. For the analysis, an eye tracking device was used, in order to measure the point of gaze, the unconscious motion of the eyes, along with the time spent on each fixation and the order in which different elements were fixated upon by the respondents. As the eye-tracking device can generate data in various forms, this research includes both qualitative and quantitative analyses of the results, which confirm the same hypothesis, regarding the consumer’s preference towards artificial intelligence systems with robotic designs.

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Research on the Image Denoising Method Based on Partial Differential Equations

. Tebbini, E. B. Braiek. Smart Real Time Adaptive Gaussian Filter Supervised Neural Network for Efficient Gray Scale and RGB Image De-Noising. - Intelligent Automation & Soft Computing, 2014, pp. 203-211. 8. Fang, J., Q. Cao. Total Variation Image De-Noising Bases on the Improved Sobel Operator. - Journal of Multimedia, 2013, pp. 84-91. 9. Yin, L., D. Chen, C. Li. Two-Dimensional Wavelet Transform De-Noising Algorithm in Collecting Intelligent Agriculture Image. - Journal of Software, 2013, pp. 84-89.

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Ship course stabilization by feedback linearization with adaptive object model

References 1. Borkowski P.: Data fusion in a navigational decision support system on a sea-going vessel. Polish Maritime Research vol. 19, no. 4(76), 78, 2012 2. Borkowski P., Zwierzewicz Z.: Ship course-keeping algorithm based on knowledge base. Intelligent Automation and Soft Computing vol. 17, no. 2, 149, 2011 3. De Wit C., Oppe J.: Optimal collision avoidance in unconfined waters. Journal of the Institute of Navigation, vol. 26, no. 4, 296, 1979 4. Fabri S., Kadirkamanathan V.: Functional

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Pareto Based Virtual Machine Selection with Load Balancing in Cloud Data Centre

-246. 11. Devi, D. C., V. R. Uthariaraj. Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks. – The Scientific World Journal, Vol. 2016 , 2016. 12. Feng, Y., et al. A Novel Cloud Load Balancing Mechanism in Premise of Ensuring QoS. – Intelligent Automation & Soft Computing, Vol. 19 , 2013, No 2, pp. 151-163. 13. Bhatt, H., H. A. Bheda. Enhance Load Balancing Using Flexible Load Sharing in Cloud Computing. – 2015, No September, pp. 4-5. 14. Liu, C. A Load Balancing Aware Virtual

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GPFIS-Control: A Genetic Fuzzy System For Control Tasks

Transactions on Evolutionary Computation, Vol.7, No.4, 2003, pp.397-415. [11] E. Tunstel, and M. Jamshidi, On genetic programming of fuzzy rule-based systems for intelligent control, International Journal of Intelligent Automation and Soft Computing, Vol. 2, No. 3, 1996, pp.271-284. [12] A. Tsakonas, Local and global optimization for Takagi–Sugeno fuzzy system by memetic genetic programming, Expert Systems with Applications, Vol.40, No.8, 2013, pp.3282-3298. [13] N. Kasabov, and Q. Song, DENFIS: dynamic evolving neural-fuzzy inference system and its

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Mechatronic Approaches for Functional Structural Synthesis of Mechanical Systems of Industrial Robots

v, V. Michailo v, Z. Sotiro v, J. Deanov. Type- Dimensional Synthesis of Mechanisms for Robot Manipulators. - In: Proceedings of 6th International Machine Design and Production Conference (UMTIC’94), Ankara, 1994, 159-170. 15. Galabov, V., I. Avramo v, V. Michailo v, R. Bote v. Concurrent Formulation of Basic Problems in Synthesis and Control of Manipulating Mechanisms Used in Task-Specific Robots. - In: Proceedings of 2nd ECPD International Conference on Advanced Robotics, Intelligent Automation and Active Systems, Vol. 1, Wienna, 1996, 247

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Generalization of Pawlak’s Approximations in Hypermodules by Set-Valued Homomorphisms

, Inform. Sci 180 (2010) 1759-1768. [37] X.-P. Yang, T.-J. Li, The minimization of axiom sets characterizing generalized approximation operators , Inform. Sci. 176 (2006) 887-899. [38] Y. Y. Yao, T. Y. Lin, Generalization of rough sets using model logic , Intelligent Automation and Soft Computing 2 (1996) 103-120. [39] Y. Y. Yao, S. K. M. Wong, T. Y. Lin, A review of rough set models, in: T.Y. Lin, N. Cercone (Eds.), Rough Sets and Data Mining: Analysis for Imprecise Data , Kluwer Academic Publishers., Boston, 1997, pp. 47-75. [40] Y. Yao

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A multivariable multiobjective predictive controller

45 (12): 2823-2830. Ben Abdennour, R., Ksouri, M. and Favier, G. (1998). Application of fuzzy logic to the on-line adjustment of the parameters of a generalized predictive controller, Intelligent Automation and Soft Computing 4 (3): 197-214. Berro, A. (2001). Optimisation multiobjectif et strat’egies d”evolution en environment dynamique , Ph.D. thesis, Université des Sciences Sociales Toulouse I, Toulouse. Boussaid, B., Aubrun, C., Abdelkrim, M.N. and Ben Gayed, M.K. (2011). Performance evaluation based fault

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Slime Mould Games Based on Rough Set Theory

in Computer Science, Vol. 4, Oxford University Press, Oxford, pp. 1-148. Yao, Y. and Lin, T. (1996). Generalization of rough sets using modal logics, Intelligent Automation and Soft Computing 2(2): 103-120. Yao, Y.Y., Wong, S.K.M. and Lin, T.Y. (1997). A review of rough set models, in T.Y. Lin and N. Cercone (Eds.), Rough Sets and Data Mining: Analysis of Imprecise Data, Kluwer, Dordrecht, pp. 47-75. Ziarko, W. (1993). Variable precision rough set model, Journal of Computer and System Sciences 46(1): 39-59.

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