ARTIFICIAL INTELLIGENCE : CHARACTERISTICS, SUBFIELDS, TECHNIQUESAND FUTURE PREDICTIONS

Authors:

B. Swathi,S. Shoban Babu,Monelli Ayyavaraiah,

DOI NO:

https://doi.org/10.26782/jmcms.2019.12.00010

Keywords:

AI,ML,Characteristics,

Abstract

The term intelligence refers to the ability to acquire and apply different skills and knowledge to solve a given problem. The current wave of technological change based on advancements in artificial intelligence (AI) has created widespread fear of job losses and further rises in inequality. Artificial intelligence in the last two decades has greatly improved performance of the manufacturing and service systems. Study in the area of artificial intelligence has given rise to the rapidly growing technology known as expert system. This paper will explore the future predictions for artificial intelligence and based on which potential solution will be recommended to solve it within next decades.

Refference:

I. Anusha Medavaka,P. Shireesha, “Optimal framework to Wireless
RechargeableSensor Network based Joint Spatial of theMobile Node” in
“Journal of Advances in Science and Technology”, Vol. XI, Issue No. XXII,
May-2016 [ISSN : 2230-9659]
II. Anusha Medavaka,“Enhanced Classification Framework on SocialNetworks” in
“Journal of Advances in Science and Technology”, Vol. IX, Issue No. XIX,
May-2015 [ISSN : 2230-9659]

III. Anusha Medavaka,P. Shireesha, “A Survey on TraffiCop Android Application”
in “Journal of Advances in Science and Technology”, Vol. 14, Issue No. 2,
September-2017 [ISSN : 2230-9659]
IV. B. M. Lake, T. D. Ullman, J. B. Tenenbaum, and S. J. Gershman, “Building
machines that learn and think like people,” Behavioral and Brain Sciences, vol.
40, 2017.
V. Charles Weddle, Graduate Student, Florida State University “Artificial
Intelligence and Computer Games”, unpublished.
VI. C.Sampada,, et al, “Adaptive Neuro-Fuzzy Intrusion Detection Systems”,
Proceedings: International Conference on Information Technology: Coding and
Computing (ITCC‟04),2004.
VII. E. Ohn-Bar and M. M. Trivedi, “Looking at humans in the age of self-driving
and highly automated vehicles,” IEEE Transactions on Intelligent Vehicles, vol.
1, no. 1, pp. 90–104, 2016.
VIII. H. Cuaya´ huitl, S. Keizer, and O. Lemon, “Strategic dialogue
management via deep reinforcement learning,” arXiv preprint
arXiv:1511.08099, 2015.
IX. J. Wei, H. Liu, G. Yan, and F. Sun, “Robotic grasping recognition using
multi-modal deep extreme learning machine,” Multidimen- sional Systems and
Signal Processing, vol. 28, no. 3, pp. 817–833, 2017.
X. Pramod Kumar P, Thirupathi V, Monica D, “Enhancements in Mobility
Management for Future Wireless Networks”, International Journal of Advanced
Research in Computer and Communication Engineering, Vol. 2, Issue 2,
February 2013
XI. Pramod Kumar P, CH Sandeep, Naresh Kumar S, “An Overview of the Factors
Affecting Handovers and Effective Highlights of Handover Techniques for
Next Generation Wireless Networks”, Indian Journal of Public Health
Research & Development, November 2018, Vol.9, No. 11
XII. Pramod Kumar P and Sagar K, “A Relative Survey on Handover Techniques in
Mobility Management”, IOP Conf. Series: Materials Science and Engineering
594 (2019) 012027, IOP Publishing, doi:10.1088/1757-899X/594/1/012027

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