Performance Analysis of Software Effort Estimation Models Using Neural Networks | Zendy

E. Praynlin | Zendy; P. Latha | Zendy

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Performance Analysis of Software Effort Estimation Models Using Neural Networks

Author(s) -

E. Praynlin,

P. Latha

Publication year - 2013

Publication title -

international journal of information technology and computer science

Language(s) - English

Resource type - Journals

eISSN - 2074-9015

pISSN - 2074-9007

DOI - 10.5815/ijitcs.2013.09.10

Subject(s) - computer science , artificial neural network , schedule , software , backpropagation , estimation , software development , machine learning , artificial intelligence , data mining , systems engineering , engineering , programming language , operating system

This paper presents a constrained finite-state model to represent the morphotactic rule of Manipuri adjective word forms. There is no adjective word category in Manipuri language. By rule this category is derived from verb roots with the help of some selected affixes applicable only to verb roots. The affixes meant for the purpose and the different rules for adjective word category formation are identified. Rules are composed for describing the simple agglutinative morphology of this category. These rules are combined to describe the more complex morphotactic structures. Finite-state machine is used to describe the concatenation rules and corresponding non-deterministic and deterministic automaton are developed for ease of computerization. A root lexicon of verb category words is used along with an affix dictionary in a database. The system is capable to analyze and recognize a certain word as adjective by observing the morpheme concatenation rule defined with the help of finite-state networks

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