M.A. Saleem Durai

An ANFIS approach for evaluation of team-level service climate in GSD projects using Taguchi-genetic learning algorithm

ANFIS architecture for a multi-inputs and single output Sugeno model with fuzzy n rules. The GSD team-level service climate is one of the key determinants to achieve the outcome of global software development (GSD) projects.To evaluate the GSD team-level service climate and GSD project outcome relationship based on Adaptive Neuro-Fuzzy Inference System (ANFIS) with the Hybrid Taguchi-Genetic Learning Algorithm (HTGLA).The applicability and capability of HTGLA-based ANFIS approach is investigated through the real data sets obtained from Indian software industries. The GSD team-level service climate is one of the key determinants to achieve the outcome of global software development (GSD) projects from the software service outsourcing perspective. The main aim of this study is to evaluate the GSD team-level service climate and GSD project outcome relationship based on adaptive neuro-fuzzy inference system (ANFIS) with the genetic learning algorithm. For measuring the team-level service climate, the Hybrid Taguchi-Genetic Learning Algorithm (HTGLA) is adopted in the ANFIS, which is more appropriate to determine the optimal premise and consequent constructs by reducing the root-mean-square-error (RMSE) of service climate criteria. For measuring the GSD team-level service climate, synthesizing the literature reviews and consistent with the earlier studies on IT service climate which is classified into three main criterion: managerial practices (deliver quality of service), global service climate (measure overall perceptions), service leadership (goal setting, work planning, and coordination) which comprises 25 GSD team-level service climate attributes. The experimental results show that the optimal prediction error is obtained by the HTGLA-based ANFIS approach is 3.26%, which outperforms the earlier result that is the optimal prediction errors 4.41% and 5.75% determined, respectively, by ANFIS and statistical methods.

An intelligent knowledge mining model for kidney cancer using rough set theory

Medical diagnosis processes vary in the degree to which they attempt to deal with different complicating aspects of diagnosis such as relative importance of symptoms, varied symptom pattern and the relation between diseases themselves. Rough set approach has two major advantages over the other methods. First, it can handle different types of data such as categorical, numerical etc. Secondly, it does not make any assumption like probability distribution function in stochastic modeling or membership grade function in fuzzy set theory. It involves pattern recognition through logical computational rules rather than approximating them through smooth mathematical functional forms. In this paper we use rough set theory as a data mining tool to derive useful patterns and rules for kidney cancer faulty diagnosis. In particular, the historical data of twenty five research hospitals and medical college is used for validation and the results show the practical viability of the proposed approach.

Secure Medical Diagnosis Using Rule Based Mining

Security is the governing dynamics of all walks of life. Here we propose a secured medical diagnosis system. Certain specific rules are specified implicitly by the designer of the expert system and then symptoms for the diseases are obtained from the users and by using the pre defined confidence and support values we extract a threshold value which is used to conclude on a particular disease and the stage using Rule Mining. “THINK” CAPTCHA mechanism is used to distinguish between the human and the robots thereby eliminating the robots and preventing them from creating fake accounts and spam’s. A novel image encryption mechanism is designed using genetic algorithm to encrypt the medical images thereby storing and sending the image data in a secured manner.

Prediction of Protein Folding Kinetic States Using Fuzzy Back Propagation Method

Protein folding process is extremely vital in major the molecular function. The kinetic order of protein folding chooses whether the molecule reaches its native structure through intermediates or not. They can either fold without stable intermediates (2State/2S) and with stable intermediates (3State/3S). This is generally determined using equilibrium denaturation research and is often time consuming and tedious. Moreover, the unfolding appliance of large number of Proteins available in the PDB are found unknown. Therefore, it created interest and directed us to predict and classify the folding mechanism as two state or three state (Multiple State). We developed the classification models using Fuzzy Back Propagation Network (FBPN) with the known attributes (Protein length (PL), hydrophobicity, hydrophilicity, secondary structural components). The models performed fairly well for predicting two state and three state folding using the well-known variables. The FBPN model produced accuracy of 83 % for cross validation. This method thus can intensely assist as a outline for predicted monomer and dimer structures with unknown folding appliance for further confirmation through investigational studies.

A Hybrid Model for Rice Disease Diagnosis Using Entropy Based Neuro Genetic Algorithm

Disease prediction is often characterized by a high degree of fuzziness and uncertainty. This may reside in the imperfect and complex nature of symptoms that aids in diagnosis.. For precise rice disease diagnosis, domain knowledge of expertise pathologists along with clinically screened database of crop symptoms is considered as knowledge base. The hybrid method pre treats the crop symptoms for removal of noise and redundancy. It forms as target data for rice disease diagnostic model. The Entropy assisted GEANN algorithm reduces the n- dimensionality of diagnostic symptoms and optimizes the target data search space for higher accuracy. Finally the neuro fuzzy system make way for prediction of diseases based on the rules derived from qualitative interpretation of crop symptoms uniqueness. The algorithm is tested for real time case studies of Vellore district, Tamilnadu, India and the results evolved consistent performance against regression, back propagation algorithm and fuzzy network in disease prediction.

A Survey of Mobile Cloud Computing for Extending Energy of Mobile Devices

Mobile cloud computing will wear down gaining quality among users, the researchers predicts these troubles by execution of mobile applications on application suppliers external to the mobile device. During this paper, we have a tendency to gift a wide survey of mobile cloud computing, whereas prominence the particular considerations in mobile cloud computing square measure as follows. (a) Highlights the present state in Application of cloud computing usage in real time world. (b) Identifies the problems in testing bandwidth and (c) provides a optimizing of the offloading that saves energy