framework was previously developed. Our study utilizes this novel framework to develop an approach combining the neuro-fuzzy technique with the System Evaluation and Estimation of Resource-Software Estimation Model (SEER-SEM). Moreover, our study assesses the performance of the proposed model by designing

of the existing components. Although there are various statistical methods exists to find the reusability of the components but soft computing has not been explored for component reusability. The aim of this paper is to formulate, build, evaluate, validate and compare neuro-fuzzy approach in prediction

on different soft computing techniques like Fuzzy Logic and Neural Network and the corresponding average RMSE (Reusability Mean Square Error) was calculated to find out the reusability of those factors. In this paper it takes into account three different factors or variables of reusability of software

of this study is to assess the utility of soft computing tools, namely, neural network, fuzzy and neuro-fuzzy approaches for for landslide susceptibility zonation and risk assessment in a rigorous mountainous terrain in India.

Soft computing is a general term for algorithms that learn from human knowledge and mimic human skills. Example of such algorithms are fuzzy inference systems and neural networks. Many applications, especially in control engineering, have demonstrated their appropriateness in building intelligent

using higher order path and regression analyses, and the results con � rmed students ’ perceptions as in � uencing both ‘hard ’ (academic achievement) and ‘soft ’ (satisfaction, development of key skills) learning outcomes, both directly and mediated through their approaches to study. Perceptions

A common approach for determining musical competence is to rely on information about individuals ’ extent of musical training, but relying on musicianship status fails to identify musically untrained individuals with musical skill, as well as those who, despite extensive musical training, may

for the evaluation of soft skills on the Internet. Our approach is based on a consensus measurement approach in social network services based on social graphs as a formalization of personal relationships among users.

and the intrinsic geological complexity, this system follows a great expense in the trial and implemen-tation phases. Utilizing the soft computing methods, this paper proposes a new approach to reduce or to eliminate the cost of the trial phase. Therefore, the Adaptive Neuro Fuzzy Inference System (ANFIS

Universiteit Leuven, Belgium 2005-2010 – PhD thesis: “Understanding Machine Learning Performance with Experiment Databases” On high-throughput, collaborative experimentation systems to map and quickly assess the strengths and weaknesses of data mining techniques, now a reference resource in data mining