Rahul Kala

Soft Computing

Do we ever teach a child how to identify its parents? Well, probably we do to an extent. Now what would you say for the children of animals who have so less differences and still are able to make out each other out of the entire lot. Is it that the problem of differentiating people is so small. Probably not, since if it would have been so simple, we wouldn't have required the best of the scholarly community try whole-heartedly to make the machines solve a minute portion of it. Another comment that automatically comes in mind is that we are intelligent beings who are gifted intelligence by the All Mighty as the first Birthday Gift. In case that's true, why doesn't a child recognize things as vast as an adult does? A fact remains that we learn ourselves. This may be from our experiences, understanding, others experience, knowledge sharing practices, or any other means.

The innovation in intelligent systems hence does not necessarily lie in building systems that can carry out tasks, but to build a framework that enables them learn or what we say evolve along with changing needs, issues and complexities. The newly born has an abundant of things to learn and a relatively small time to do that. Imagine the learning as a result of constant reinforcement of vital concepts, short-term and long term learning, on-demand learning and the multiple forms that you may imagine. The systems are no different with they being asked to learn a bulk of important and non-important concepts for different times as per requirements. Hence we find a very strong co-relation with intelligent systems and intelligent humans that was largely due to the biological inspiration of computational intelligence.

Now when a student is asked to design systems to solve problems, he takes a very simple approach. Identify the procedures, methodologies, steps, algorithms, etc. A clear understanding of the problem and the adjoining methodology helps in effective solution that solves everything as far as the underlying assumptions are met. You ask the same student to make systems to solve any of the simplest natural problems and he might be buzzed. The big question is how would you be able to match two faces to be sure that they are actually the same person. Too many inputs, time of execution, tradeoffs, and in short complexity!!! We can solve the hardest of algorithmic problems but not even imagine to solve the simplest of natural problems.

The simple manner in which these are trivially solved motivate the need of alternate ways where any complexity can be cracked in the simplest of ways. This is a complete paradigm shift in the way we perceive problems, model them and ultimately solve them. Here the hardest of problems are solved as if they were a piece of pie cake. This is the world of Soft Computing. I do not claim the hard work of the people working in this field to be a showbiz (which also includes me), but claim the potential to make the impossible possible. Surely their work extracts the best out of the available limitations and restrictions, but the problem is ideally the problem is solved without any effort. The system or the framework does everything. So are the people fools who use complicated system modeling, algorithm design and other techniques to solve their problems? Well probably not. The systems we talk about here have a lot of assumptions regarding the large amount of data availability for experience and learning, continuous reinforcement, large computational access, etc. These may not always be the case with the other methodology of systems. Further the results are always approximate that are known to be correct rather than having an absolute correctness.

Now we know that the assumptions stated may not always hold good. This requires assistance in the various forms that creates a pool of possibilities of hybrid systems that are driven by multiple factors, each factor driving the system and enabling it suited for problem solving in its own way.

Related Publications

• A. Shukla, R. Tiwari, R. Kala (2010) Real Life Applications of Soft Computing, CRC Press, Boca Raton, FL.
• A. Shukla, R. Tiwari, R. Kala (2010) Towards Hybrid and Adaptive Computing: A Perspective, Studies in Computational Intelligence, Springer-Verlag Berlin, Heidelberg.
• R. Kala (2010) Video Lecture of Soft Computing (39 Hour Video Lecture Series), Soft Computing and Expert System Laboratory, IIITM Gwalior, India. Available at: http://rkala.in/softcomputingvideos.php
• R. Kala, A. Shukla, R. Tiwari (2009) Self-Adaptive Parallel Processing Neural Networks with irregular Nodal Processing Powers using Hierarchical Partitioning. Neural Network World, 19(6): 657-680. (Download Paper) (Download PPT)
• S. Kant, R. Kala, R. Tiwari, A. Shukla, S. Kumar (2016) Lip Recognition Using Various Neural Classifiers. International Journal of Electrical, Electronics and Data Communication, 4 (10): 86-94.
• A. Gupta,  S. Bhalla,  S. Dwivedi,  N. Verma, R. Kala (2015) On the Use of Local Search in the Evolution of Neural Networks for the Diagnosis of Breast Cancer, Technologies, 3(3): 162-181.(Download Paper)
• R. R. Janghel, R. Tiwari, R. Kala, A. Shukla (2012) Breast Cancer Data Prediction by Dimensionality Reduction Using PCA and Adaptive Neuro Evolution. International Journal of Information Systems and Social Change, 3(1): 1-9. (Download Paper)
• R. Kala, A. Shukla, R. Tiwari (2011) Modular Symbiotic Adaptive Neural Evolution for High Dimensional Classificatory Problems. Intelligent Decision Technologies, 5(4): 309-319. (Download Paper)
• R. Kala, A. Shukla, R. Tiwari (2011) A Novel Approach to Classificatory problem using Neuro-Fuzzy Architecture. International Journal of Systems, Control and Communications, 3(3): 259-269. (Download Paper)
• R. Kala, R. Tiwari, A. Shukla (2011) Breast Cancer Diagnosis using Optimized Attribute Division in Modular Neural Networks. Journal of Information Technology Research, 4(1): 34-47. (Download Paper)
• R. Kala, R. R. Janghel, R. Tiwari, A. Shukla (2011) Diagnosis of Breast Cancer by Modular Evolutionary Neural Networks. International Journal of Biomedical Engineering and Technology, 7(2): 194 – 211. (Download Paper) (Download PPT)
• A. Tripathi, P. Gupta, A. Trivedi, R. Kala (2011) Wireless Sensor Node Placement using Hybrid Genetic Programming and Genetic Algorithms. International Journal of Intelligent Information Technologies,7(2): 63-83. (Download Paper)
• R. Kala, A. Shukla, R. Tiwari (2010) Clustering Based Hierarchical Genetic Algorithm for Complex Fitness Landscapes. International Journal of Intelligent Systems Technologies and Applications, 9(2): 185-205. (Download Paper)
• R. Kala, A. Shukla, R. Tiwari (2010) Hierarchical Evolutionary Strategy for Complex Fitness Landscapes. Journal of Information Science and Technology,7(2): 36-57. (Download Paper)
• R. Kala, H. Vazirani, N. Khawalkar, M. Bhattacharya (2010) Evolutionary Radial Basis Function Network for Classificatory Problems. International Journal of Computer Science Applications,7(4): 34-49.(Download Paper)
• R. Kala, H. Vazirani, A. Shukla, R. Tiwari (2010) Medical Diagnosis using Incremental Evolution of Neural Network. Journal of Hybrid Computing Research,3(1): 9-17. (Download Paper)
• R. Kala, H. Vazirani, A. Shukla, R. Tiwari (2010) Evolution of Modular Neural Network in Medical Diagnosis. International Journal of Applied Artificial Intelligence in Engineering System, 2(1): 49 -58. (Download Paper)
• R. Kala, H. Vazirani, A. Shukla, R. Tiwari (2010) Offline Handwriting Recognition using Genetic Algorithm. International Journal of Computer Science Issues, 7(2): 16-25. (Download Paper)
• R. Kala, A. Shukla, R. Tiwari (2010) A Novel Approach to Classificatory Problem using Grammatical Evolution based Hybrid Algorithm. International Journal of Computer Applications, 1(28): 61-68. (Download Paper)
• A. Tarsauliya, S. Kant, R. Kala, R. Tiwari, A. Shukla (2010) Analysis of Artificial Neural Network for Financial Time Series Forecasting. International Journal of Computer Applications, 9(5): 16–22. (Download Paper)
• R. Kala, A. Shukla, R. Tiwari (2010) A Novel Approach to Clustering using Genetic Algorithm. International Journal of Engineering Research and Industrial Applications, 3(1): 81-88. (Download Paper)
• H. Vazirani, R. Kala, A. Shukla, R. Tiwari (2010) Use of Modular Neural Network for Heart Disease. International Journal of Computer and Communication Technology, 1(2-4): 88-93. (Download Paper)
• A. Shukla, R. Kala (2008) Multi Neuron Heuristic Search. International Journal of Computer Science and Network Security, 8(6): 344-350. (Download Paper) (Download PPT)
• A. Shukla, R. Kala (2008) Predictive Sort. International Journal of Computer Science and Network Security, 8(6): 314-320. (Download Paper)
• A. Sharma, I. Wadhwa and R. Kala (2015) Monocular camera based object recognition and 3D-localization for robotic grasping. In Proceedings of the 2015 International Conference on Signal Processing, Computing and Control, Waknaghat, pp. 225-229. (Download Paper)
• P. Mohan, S. Srivastava, G. Tiwari, R. Kala (2015) Background and skin colour independent hand region extraction and static gesture recognition. In Proceedings of the 2015 Eighth International Conference on Contemporary Computing, Noida, India, pp.144-149. (Download Paper)
• A. Kumar, R. Kala (2015) Geometric shape drawing using a 3 link planar manipulator. In Proceedings of the 2015 Eighth International Conference on Contemporary Computing, Noida, India, pp. 404-409. (Download Paper)
• N. Joshi, A. Kumar, P. Chakraborty and R. Kala (2015) Speech controlled robotics using Artificial Neural Network, In: Proceedings of the 2015 Third International Conference on Image Information Processing, Waknaghat, pp. 526-530. (Download Paper)
• V. Kumar, G. C. Nandi, R. Kala (2014) Static Hand Gesture Recognition using Stacked Denoising Sparse Autoencoders, In Proceedings of the 2014 Seventh International Conference on Contemporary Computing, Noida, India, pp. 99 - 104. (Download Paper)
• A. Tarsauliya, R. Kala, R. Tiwari, A. Shukla (2011) Financial Time Series Forecast Using Neural Network Ensembles. In Proceedings of the International Conference on Swarm Intelligence, Springer Lecture Notes in Computer Science, Chongqing, China, pp. 480- 488. (Download Paper)
• A. Tarsauliya, R. Kala, R. Tiwari, A. Shukla (2011) Financial Time Series Volatility Forecast Using Evolutionary Hybrid Artificial Neural Network. In Proceedings of the Springer Fourth International Conference on Network Security & Applications, Chennai, India, pp. 463-471. (Download Paper)
• R. R. Janghel, A. Shukla, R. Tiwari, R. Kala (2010) Intelligent Decision Support System for Breast Cancer. In Proceedings of the International Conference on Swarm Intelligence, Springer Lecture Notes in Computer Science, Beijing, China, pp. 351-358. (Download Paper)
• R. Kala, H. Vazirani, A. Shukla, R. Tiwari (2010) Fusion of Speech and Face by Enhanced Modular Neural Network. In Proceedings of the Springer International Conference on Information Systems, Technology and Management, Bankok, Thailand, pp. 363-372. (Download Paper)
• Y. K. Meena, K. V. Arya, R. Kala (2010) Classification using Redundant Mapping in Modular Neural Networks. In Proceedings of the 2010 IEEE World Congress on Nature and Biologically Inspired Computing, Fukuoka, Japan, pp. 554 – 559. (Download Paper)
• R. R. Janghel, A. Shukla, R. Tiwari, R. Kala (2010) Breast Cancer Diagnostic System using Symbiotic Adaptive Neuro-evolution (SANE). In Proceedings of the 2010 IEEE International Conference of Soft Computing and Pattern Recognition, Cercy Pontoise/Paris, France, pp. 326-329. (Download Paper)
• R. R. Janghel, A. Shukla, R. Tiwari, R. Kala (2010) Breast Cancer Diagnosis using Artificial Neural Network Models. In Proceedings of the 3rd IEEE International Conference on Information Sciences and Interaction Sciences,Chengdu, China, pp. 89-94.
• H. Vazirani, R. Kala, A. Shukla, R. Tiwari (2010) Diagnosis of Breast Cancer by Modular Neural Network. In Proceedings of the Third IEEE International Conference on Computer Science and Information Technology,Chengdu, China, pp. 115-119. (Download Paper)
• A. Shukla, R. Tiwari, A. Ranjan, R. Kala (2009) Multi Lingual Character Recognition using Hierarchical Rule Based Classification and Artificial Neural Network. In Proceedings of the Sixth International Symposium on Neural Networks, Springer Verlag Lecture Notes in Computer Science, Wukan, China, pp. 821–830. (Download Paper)
• R. Kala, A. Shukla, R. Tiwari (2009) Comparative analysis of intelligent hybrid systems for detection of PIMA Indian diabetes. In Proceedings of the 2009 IEEE World Congress on Nature & Biologically Inspired Computing, Coimbatote, India, pp. 947 – 952. (Download Paper)
• R. Kala, A. Shukla, R. Tiwari (2009) Optimized Graph Search using Multi Level Graph Clustering. In Proceedings of the Springer International Conference on Contemporary Computing, Noida, India, pp. 103-114.  (Download Paper) (Download PPT)
• R. Kala, A. Shukla, R. Tiwari (2009) Fuzzy Neuro Systems for Machine Learning for Large Data Sets. In Proceedings of the IEEE International Advanced Computing Conference, Patiala, India, pp. 541-545. (Download Paper) (Download PPT)
• R. Kala, A. Shukla, R. Tiwari (2009) Fast Learning Neural Network using modified Corners Algorithm. In Proceedings of the IEEE Global Congress on Intelligent System, Xiamen, China, pp. 367-373. (Download Paper)
• R. Kumar, R. Ranjan, S. K. Singh, R. Kala, A. Shukla, R. Tiwari (2009) Multilingual Speaker Recognition Using Neural Network. In Proceedings of the Frontiers of Research on Speech and Music, Gwalior, India, pp. 1-8. (Download Paper)
• R. Kala, A. Shukla, R. Tiwari (2013) Breast Cancer Diagnosis Using Optimized Attribute Division in Modular Neural Networks. In Interdisciplinary Advances in Information Technology Research, IGI Global, Chapter 3, Hershey, PA, pp. 34-47. (Download Paper)
• R. Kala, A. Shukla, R. Tiwari (2011) Handling Large Medical Data Sets for Disease Detection. In Biomedical Engineering and Information Systems: Technologies, Tools and Applications, IGI Global, Chapter 8, Hershey, PA, pp. 162-176. (Download Paper)
• R. Kala, A. Shukla, R. Tiwari (2010) Hybrid Intelligent Systems for Medical Diagnosis. In Intelligent Medical technologies and Biomedical Engineering: Tools and Applications, IGI Global, Chapter 9, Hershey, PA, pp. 187-202. (Download Paper)
• A. Shukla, R. Tiwari, H. K. Meena, R. Kala (2009) Speaker Identification using Wavelet Analysis and Modular Neural Networks. Journal of Acoustic Society of India,36(1): 14-19. (Download Paper)
• A. Shukla, R. Tiwari, H. K. Meena, R. Kala (2009) Speaker Identification using Wavelet Analysis and Artificial Neural Networks. Journal of Acoustic Society of India, 36(1): 20-25. (Download Paper)
• A. Shukla, R. Tiwari, H. K. Meena, R. Kala (2009) Speaker Identification using Wavelet Analysis and Modular Neural Networks. In Proceedings of the  National Symposium on Acoustics, Vishakhapatnam, India, pp. 125-130. (Download Paper)(Download PPT)