Fuzzy and Neutrosophic Analysis of Periyar's Views on Untouchability (Google eBook)

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Infinite Study, 2005 - Caste - 385 pages
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For the first time, the social problem of untouchability, which is peculiar to India, is being studied mathematically.We have used Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps to analyze the views of the revolutionary Periyar E. V. Ramasamy (17.09.1879 24.12.1973) who relentlessly worked for more than five decades to secure the rights of the oppressed people who were considered untouchables. This thought-provoking book will be of great interest to human rights activists, socio-scientists, historians, and above all, mathematicians.From UNESCO citation: Periyar, The Prophet of the New Age, The Socrates of South East Asia, Father of the Social reform Movement and Arch Enemy of Ignorance, Superstition, Meaningless Customs and Baseless Manners.

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25 Study of Economic Status of Dalits due to untouchability using fuzzy and neutrosophic analysis
26 Analysis of the social problem of untouchability using Fuzzy Relational Maps
32 Translations of the Speeches and Writings of Periyar related to Untouchability
33 Some facts about Untouchability and its Consequences even after 57 years of Independence

22 Analysis of discrimination faced by DalitsSudras in the field of education as untouchables using FCMs and NCMs
23 Social inequality faced by Dalits and some of the most backward classes an analysis using FCM and NCM
24 Problems faced by Dalits in the political arena due to discrimination a FCM and NCM analysis

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Page 14 - The directed edge ey from causal concept Q to concept Cj measures how much Q causes Cj. The time varying concept function Q(t) measures the non negative occurrence of some fuzzy event, perhaps the strength of a political sentiment, historical trend or opinion about some topics like child labor or school dropouts etc. FCMs model the world as a collection of classes and causal relations between them. The edge...
Page 13 - Ek may not be conformable for addition. Combined conflicting opinions tend to cancel out and assisted by the strong law of large numbers, a consensus emerges as the sample opinion approximates the underlying population opinion. This problem will be easily overcome if the FCM entries are only 0 and 1 . We have just briefly recalled the definitions. For more about FCMs please refer Kosko [68].
Page 14 - Ci increases (and or Cj increases as Q decreases). Simple FCMs have edge values in {-1, 0, 1}. Then if causality occurs, it occurs to a maximal positive or negative degree. Simple FCMs provide a quick first approximation to an expert stand or printed causal knowledge. We illustrate this by the following, which gives a simple FCM of a Socio-economic model. A Socio-economic model is constructed with Population, Crime, Economic condition, Poverty and Unemployment as nodes or concept. Here the simple...
Page 15 - Causal feedback loops abound in FCMs in thick tangles. Feedback precludes the graph-search techniques used in artificial-intelligence expert systems. FCMs feedback allows experts to freely draw causal pictures of their problems and allows causal adaptation laws, infer causal links from simple data. FCM feedback forces us to abandon graph search, forward and especially backward chaining. Instead we view the FCM as a dynamical system and take its equilibrium behavior as a forward-evolved inference....
Page 30 - ... n ) is got after thresholding and updating the vector (a b a 2 , ... , a n ) after passing through the neutrosophic adjacency matrix N(E). The following are very important: Note 1: The nodes Ci, C 2 , ..., C n are nodes are not indeterminate nodes because they indicate the concepts which are well known. But the edges connecting Q and Cj may be indeterminate ie an expert may not be in a position to say that Q has some causality on Cj either will he be in a position to state that Q has no relation...

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