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"Agro-Market Prediction by Fuzzy based Neuro-Genetic Algorithm"

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B.Tech CSE's Final-Semester Project to study the rate pricing algorithm for agricultural products.

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"Agro-Market Prediction by Fuzzy based Neuro-Genetic Algorithm"

  1. 1. FINAL SEMSETER PROJECT Guide: Prof. Lavanya K. Submitted by: Tanay Chaudhari (09BCE449) in association with Mritunjay Kumar B.TECH – CSE (2009-13) VIT ,VELLORE
  2. 2.  To analyze data-sets of statistics dealing with the agriculture sector  Collection of cost/capital logs for cost monitoring purposes  Applying hybrid theorems to generate mean cost values of the commodities  Find the best score among the mean cost values to realize a distinct prediction value
  3. 3.  “DataMining: Concepts and Technique” by J. Han, M. Kamber (2006)  Familiarizes with data mining and machine learning statistical approaches for the modern day market data analysis  “Computer Systems that Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems” by S.M. Weiss, C.A. Kulikowski (1991)  Familiarizes and summarizes with concepts of major neural network systems, principles and majorly used theorems
  4. 4.  “An Incremental Approach to Genetic-Algorithms- Based Classification” by S.U. Guan, F. Zhu; IEEE Transactions on Systems, Man and Cybernetics - Part B 35(2), 227–239 (2005)  GA based algorithms defined and described and their use in analytical principles. Clustering concepts discussed in detail.  Research on the data warehouse and data mining techniques applying to decision assistant system” by Lifeng Hou, Tao Li, Jingjun Shen; IEEE Transactions on Data Mining and Warehousing (2011)  The dynamic and enormous info in the current decision-making, the data warehouse technique to build the structure of a decision assistant system.
  5. 5. 1) Genetic Algorithm  A hybrid algorithm search heuristic that mimics the process of natural evolution for optimization and search problem solutions  Method followed is inspired by techniques of natural evolution, viz. – inheritance, mutation, selection, crossover  Applications – bioinformatics, phylogenetics, computational science, engineering, economics, etc.
  6. 6. 2) Fuzzy Logic Algorithm  A hybrid algorithm of many valued logic or probabilistic logic dealing with reasoning that is approximate rather than fixed and exact  Allows approximate values and inferences as well as incomplete or ambiguous data, instead of solely relying on absolute data  Applications – smart computing, seismology, etc.
  7. 7. 3) Neuro (Neural) Algorithm  It combines the concept of artificial neural networks(ANNs) and fuzzy logic  Results in ‘hybrid intelligent systems’, involving the combination of human-like reasoning with the learning structure of neural networks  Applications - robotics, data processing, function approximation, etc.
  8. 8.  A.k.a ‘Backward propagation of errors’  A common method of training ANNs, in addition to the 3 main AI techniques  Concept – from a desired output, the network learns from the main inputs  The standard network is – an input layer, multiple hidden layers and an output layer  Each network weight is updated with the errors that are calculated for each layer, until the termination condition is satisfied for which the algorithm propagates back the ‘square of the error’ and adjusts the weight accordingly  Helps in overcoming the drawbacks of classical GA
  9. 9. Some of the tools used in the implementations of the proposed system, till now:-  Weka Tool: Fuzzy based development tool  GA Fuzzy Clustering Tool: GA based Fuzzy logic application developer  Weka Clusterer Visualize: Clustering developer of the stats input  Microsoft’s ClusPrep: Clustering validation tool  Backpropagation Neuronal Network 0.3: For the neural training set made for prediction NOTE: Statistics derived from the Agritech Portal provided by the Tamil Nadu Agricultural Portal from the year 2007-2011
  10. 10.  Assembled from several years of analytical data logs; to be used in statistics  Clustering of data by available data statistics  Applying the Fuzzy Based GA algorithm  Implementing the training sets obtained by the clustering of data  Re-clustering to improve on clustering of data, thus improving on the available data clusters
  11. 11.  Evaluation of clustering results is also known as clustering validation  Need – • To avoid finding patterns in noise • To compare clustering algorithms • To compare two sets of clusters • To compare two clusters
  12. 12.  Two major types of validation:  Internal Validation • When a clustering result is evaluated based on the data that was clustered itself • To assign the “best score” to the algorithm that produces clusters with high similarity within a cluster and low similarity between clusters • Drawback Internal criteria - high scores on an internal measure do not necessarily result in effective information retrieval applications
  13. 13.  External Validation • clustering results are evaluated based on data that was not used for clustering • Basis on – class labels, external benchmarks, etc. which are created by human experts • Considered as “gold standard” for evaluation
  14. 14.  Determining the clustering tendency of a set of data, i.e. - distinguishing whether non-random structure actually exists in the data.  Comparing the results of a cluster analysis to externally known results, e.g. - to externally given class labels.  Evaluating how well the results of a cluster analysis fit the data without reference to external information.  Comparing the results of two different sets of cluster  Analyses to determine which is better.  Determining the ‘correct’ number of clusters.
  15. 15.  Post cluster validation stage, when another training set is constructed  This set is fed to Bacpropagation algorithm function, to improve on the errors and thus on mean value  The best score of the mean value is considered as the “final prediction value” of the set
  16. 16.  The final prediction value is the calculated value of the price of a commodity  Based on the initial data collected, this value is quite accurate  Subject to vary depending upon the change in attributes  May vary depending upon the amount of data fed initially
  17. 17.  The prediction value is subject to vary each time when a new attributed value is entered  The extremely error-minimized could be high accuracy but not of absolute accuracy  It’s upto administration to implement the moderated prediction value price  The room for enhancements is high based on the grade and the quality of tools employed to generate the prediction value  The outcome of the system could be enhanced by only using attribute suitable data and refined tabulation
  18. 18. Data Set Description – Clustering Data
  • HaderAdel1

    Dec. 12, 2017

B.Tech CSE's Final-Semester Project to study the rate pricing algorithm for agricultural products.

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