Main Article Content

Abstract

Artificial Intelligence (AI) is a part of computer science concerned with designing intelligent computer systems that exhibit the characteristics used to associate with intelligence in human behavior. Basically, it define as a field that study and design of intelligent agents. Traditional AI approach deals with cognitive and biological models that imitate and describe human information processing skills. This processing skills help to perceive and interact with their environment. But in modern era developers can build system that assemble superior information processing needs of government and industry by choosing from large areas of mature technologies. Soft Computing (SC) is an added area of AI. It focused on the design of intelligent systems that process uncertain, imprecise and incomplete information. It applied in real world problems frequently to offer more robust, tractable and less costly solutions than those obtained by more conventional mathematical techniques. This paper reviews correlation of artificial intelligence techniques with soft computing in various areas.

Keywords

Artificial intelligence Soft computing Correlation Engineering and science

Article Details

How to Cite
Kumar, A., Kumar, A., & Burnwal, A. P. (2017). CORRELATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES WITH SOFT COMPUTING IN VARIOUS AREAS. International Journal of Students’ Research in Technology & Management, 5(4), 58-65. https://doi.org/10.18510/ijsrtm.2017.548

References

    [1] A. Burnwal, A. Kumar, and S. K. Das, “Survey on application of artificial intelligence techniques,” International Journal of Engineering Research & Management, 2014, vol. 1, no. 5, pp. 215–219.
    [2] J. M. Ali, M. Hussain, M. O. Tade, and J. Zhang, “Artificial intelligence techniques applied as estimator in chemical process systems–a literature survey,” Expert Systems with Applications, 2015, vol. 42, no. 14, pp. 5915–5931.
    [3] S. K. Das, A. Kumar, B. Das, and A. Burnwal, “On soft computing techniques in various areas,” Computer Science & Information Technology (CS & IT), 2013, vol. 3, p. 59-68, DOI : 10.5121/csit.2013.3206.
    [4] S. K. Das, S. Tripathi, and A. Burnwal, “Some relevance fields of soft computing methodology,” International Journal of Research in Computer Applications and Robotics, 2014, vol. 2, pp. 1-6.
    [5] R. Maestre-Martınez, A. Hernando, and E. Roanes-Lozano, “An algebraic approach for detecting nearly dangerous situations in expert systems,” Mathematics and Computers in Simulation, 2016.
    [6] E. Caballero-Ruiz, G. Garc´ıa-Saez,´ M. Rigla, M. Villaplana, B. Pons, and M. E. Hernando, “Automatic classification of glycaemia measurements to enhance data interpretation in an expert system for gestational diabetes,” Expert Systems with Applications, 2016, vol. 63, pp. 386–396.
    [7] K.-S. Kim and M.-I. Roh, “A submarine arrangement de-sign program based on the expert system and the multistage optimization,” Advances in Engineering Software, 2016, vol. 98, pp. 97–111.
    [8] J. Seok, J. Kasa-Vubu, M. DiPietro, and A. Girard, “Expert system for automated bone age determination,” Expert Systems with Applications, 2016, vol. 50, pp. 75–88.
    [9] A. J.-P. Tixier, M. R. Hallowell, B. Rajagopalan, and D. Bowman, “Automated content analysis for construction safety: A natural language processing system to extract precursors and outcomes from unstructured injury reports,” Automation in Construction, 2016, vol. 62, pp. 45–56.
    [10] M. Perovsek,ˇ J. Kranjc, T. Erjavec, B. Cestnik, and Lavrac,ˇ “Textflows: A visual programming platform for text mining and natural language processing,” Science of Computer Programming, 2016, vol. 121, pp. 128–152.
    [11] M. Tanana, K. A. Hallgren, Z. E. Imel, D. C. Atkins, and V. Srikumar, “A comparison of natural language processing methods for automated coding of motivational interviewing,” Journal of substance abuse treatment, 2016, vol. 65, pp. 43– 50.
    [12] R. Agerri, X. Artola, Z. Beloki, G. Rigau, and A. Soroa, “Big data for natural language processing: A streaming approach,” Knowledge-Based Systems, 2015, vol. 79, pp. 36–42.
    [13] A. Burnwal, A. Kumar, and S. K. Das, “Assessment of fuzzy set theory in different paradigm,” International Journal of Advanced Technology & Engineering Research, 2013, vol. 3, no. 3, pp. 16–22.
    [14] A. Kumar, R. K. Sharma, and A. Burnwal, “Energy Consumption Model in Wireless Ad-hoc Networks using Fuzzy Set Theory,” Global Journal of Advanced Research, 2015, vol. 2, no. 2, pp. 419-426.
    [15] S. Murmu, S. Jha, A. Burnwal, and V. Kumar, “A proposed fuzzy logic based system for predicting surface roughness when turning hard faced components,” International Journal of Computer Applications, 2015, vol. 125, no. 4.
    [16] S. K. Das, S. Tripathi, and A. Burnwal, “Design of fuzzy based intelligent energy efficient routing protocol for WANET,” in Computer, Communication, Control and Information Technology (C3IT), 2015 Third International Conference on, IEEE, 2015, pp. 1-4, DOI: 10.1109/C3IT.2015.7060201.
    [17] S. K. Das, S. Tripathi, and A. Burnwal, “Intelligent energy competency multipath routing in wanet,” in Information Systems Design and Intelligent Applications, Springer, 2015, pp. 535–543, DOI: 10.1007/978-81-322-2250-7_53.
    [18] S. K. Das, S. Tripathi, and A. Burnwal, “Fuzzy based energy efficient multicast routing for ad-hoc network,” in Computer, Communication, Control and Information Technology (C3IT), 2015 Third International Conference on, IEEE, 2015, pp. 1-5, DOI: 10.1109/C3IT.2015.7060126.
    [19] S. K. Das, A. Kumar, B. Das, and A. Burnwal, “Ethics of reducing power consumption in wireless sensor networks using soft computing techniques,” International Journal of Advanced Computer Research, 2013, vol. 3, no. 1, pp. 301-304.
    [20] S. K. Das, B. Das, and A. Burnwal, “Intelligent energy competency routing scheme for wireless sensor networks”, International Journal of Research in Computer Applications and Robotics (IJRCAR), 2014, vol. 2, no. 3, pp. 79–84.
    [21] S. K. Das and S. Tripathi, “Energy efficient routing protocol for manet based on vague set measurement technique,” Procedia Computer Science, 2015, vol. 58, pp. 348-355, doi:10.1016/j.procs.2015.08.030.
    [22] S. K. Das and S. Tripathi, “Energy Efficient Routing Protocol for MANET Using Vague Set,” in Proceedings of Fifth International Conference on Soft Computing for Problem Solving, Springer, 2016, pp. 235-245, DOI: 10.1007/978-981-10-0448-3_19.
    [23] P. Muthukumar and G. S. S. Krishnan, “A similarity measure of intuitionistic fuzzy soft sets and its application in medical diagnosis,” Applied Soft Computing, 2016, vol. 41, pp. 148–156.
    [24] P. Gupta, M. K. Mehlawat, and N. Grover, “Intuitionistic fuzzy multi-attribute group decision-making with an application to plant location selection based on a new extended vikor method,” Information Sciences, 2016, vol. 370, pp. 184-203.
    [25] H. Dadgostar and F. Afsari, “Image steganography based on interval-valued intuitionistic fuzzy edge detection and modified lsb,” Journal of Information Security and Applications, 2016.
    [26] S. K. Das, A. Kumar, B. Das, and A. Burnwal, “Ethics of E-Commerce in Information and Communications Technologies,” International Journal of Advanced Computer Research, 2013, vol. 3, no. 1, pp. 122-124, doi=10.1.1.300.9397.
    [27] G. Cosma and G. Acampora, “A computational intelligence approach to efficiently predicting review ratings in e-commerce,” Applied Soft Computing, 2016, vol. 44, pp. 153–162.
    [28] M. d. M. R. Garcıa, J. Garcıa-Nieto, and J. F. Aldana-Montes, “An ontology-based data integration approach for web analytics in e-commerce,” Expert Systems with Applications, 2016, vol. 63, pp. 20–34.
    [29] N. Gordini and V. Veglio, “Customers churn prediction and marketing retention strategies. an application of support vector machines based on the auc parameter-selection technique in b2b e-commerce industry,” Industrial Marketing Management, 2016.
    [30] G.-Y. Chan, C.-S. Lee, and S.-H. Heng, “Defending against xml-related attacks in e-commerce applications with predictive fuzzy associative rules,” Applied Soft Computing, 2014, vol. 24, pp. 142–157.
    [31] A. Burnwal, A. Kumar, and S. K. Das, “Assessment of mathematical modeling in different areas,” International Journal of Advanced Technology & Engineering Research, 2013, vol. 3, no. 3, pp. 23–26.
    [32] G. Kang, C. Marquez,´ A. Barat, A. T. Byrne, J. H. Prehn, J. Sorribes, and E. Cesar,´ “Colorectal tumour simulation using agent based modelling and high performance computing,” Future Generation Computer Systems, 2016.
    [33] B. Behera, “Woven fabric engineering by mathematical modeling and soft computing methods,” Soft Computing in Textile Engineering, 2010, p. 181.
    [34] H. Tamimi and D. Soffker, “Modeling of elastic robotic arm using a soft-computing algorithm,” IFAC-PapersOnLine, 2015, vol. 48, no. 1, pp. 655–656.
    [35] D. Magalhaes, R. N. Calheiros, R. Buyya, and D. G. Gomes, “Workload modeling for resource usage analysis and simulation in cloud computing,” Computers & Electrical Engineering, 2015, vol. 47, pp. 69–81.
    [36] A. Criminisi, “Machine learning for medical images analysis,” Medical Image Analysis, 2016, vol. 33, pp. 91–93.
    [37] A. Cohen, N. Nissim, L. Rokach, and Y. Elovici, “Sfem: Structural feature extraction methodology for the detection of malicious office documents using machine learning methods,”
    [38] J. Sanchez´-Oro, A. Duarte, and S. Salcedo-Sanz, “Robust total energy demand estimation with a hybrid variable neighborhood search–extreme learning machine algorithm,” Energy Conversion and Management, 2016, vol. 123, pp. 445– 452.
    [39] J.-S. Chou and N.-T. Ngo, “Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns,” Applied Energy, 2016, vol. 177, pp. 751–770.
    [40] E. P. Ijjina and K. M. Chalavadi, “Human action recognition using genetic algorithms and convolutional neural networks,” Pattern Recognition, 2016.
    [41] J. Wang and J. Guo, “Research on the base station calibration of multi-station and time-sharing measurement based on hybrid genetic algorithm,” Measurement, 2016, vol. 94, pp. 139–148.
    [42] F. Guo, H. Peng, and J. Tang, “Genetic algorithm-based parameter selection approach to single image defogging,” Information Processing Letters, 2016.
    [43] Y. Ar and E. Bostanci, “A genetic algorithm solution to the collaborative filtering problem,” Expert Systems with Applications, 2016, vol. 61, pp. 122–128.
    [44] X. Miao, J.-S. Chen, and C.-H. Ko, “A neural network based on the generalized fb function for nonlinear convex programs with second-order cone constraints,” Neurocomputing, 2016, vol. 203, pp. 62–72.
    [45] Y.-M. Li and D. Wei, “Delayed lagrange neural network for sparse signal reconstruction under compressive sampling,” Optik-International Journal for Light and Electron Optics, 2016, vol. 127, no. 18, pp. 7077–7082.
    [46] G. Dudek, “Neural networks for pattern-based short-term load forecasting: A comparative study,” Neurocomputing, 2016, vol. 205, pp. 64–74.
    [47] D. Dabrowski, “Condition monitoring of planetary gearbox by hardware implementation of artificial neural networks,” Measurement, 2016.
    [48] A. Crax`ı, C. F. Perno, M. Vigano,` F. Ceccherini-Silberstein, S. Petta, et al., “From current status to optimization of hcv treatment: Recommendations from an expert panel,” Digestive and Liver Disease, 2016, vol. 48, no. 9, pp. 995–1005.
    [49] B. Bhattacharyya and R. Babu, “Teaching learning based optimization algorithm for reactive power planning,” Inter-national Journal of Electrical Power & Energy Systems, 2016, vol. 81, pp. 248–253.
    [50] K.-F. Seitz and J. Grabe, “Three-dimensional topology optimization for geotechnical foundations in granular soil,” Computers and Geotechnics, 2016, vol. 80, pp. 41–48.
    [51] G. U. Kaya, O. Erkaymaz, and Z. Sarac, “Optimization of digital holographic setup by a fuzzy logic prediction sys-tem,” Expert Systems with Applications, 2016, vol. 56, pp. 177– 185.
    [52] S. K. Das, A. K. Yadav and S. Tripathi, “IE2M: Design of intellectual energy efficient multicast routing protocol for ad-hoc network,” Peer-to-Peer Networking and Applications, 2016, pp. 1-18, DOI 10.1007/s12083-016-0532-6.
    [53] S. K. Das and S. Tripathi, “Intelligent energy-aware efficient routing for MANET,” Wireless Networks, 2016, pp. 1-21, DOI 10.1007/s11276-016-1388-7