Sizable human population of the world is associated directly or indirectly with the agriculture and natural resources found on the earth and is important to the study of sustainability. Despite of difficulties in practical agriculture, new opportunities have been created by sophisticated technology-driven change to address judicious use and management related problems of resources and thereby improving human wellbeing. In agriculture, Intelligent Decision Support Systems (IDSS) have been used for optimization of number of planning and decision making challenges under variable number constraints based on noisy data. This research describes an IDSS to implement and optimize pest and disease protection decision making processes within temperate regions of India; develops hybrid algorithm using Case Based Reasoning and Database Technology and implement the same using web based client server architecture. The accuracy of decision making process provided by the system has been 90.20% and it can provide significant support to the apple farmers in decision making towards Eco-friendly pest management practices. © Springer International Publishing AG 2017.

Shantharajah S P

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Padma T

Mir S.A

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

&nbsp;

&nbsp;

VIT University

Intelligent Decision Support System for an Integrated Pest Management in Apple Orchard

Intelligent Decision Support Systems for Sustainable Computing Studies in Computational Intelligence

Heart disease diagnosis is found to be a challenging issue which can offer a computerized estimate about the level of heart disease so that supplementary action can be made easy. Thus, heart disease diagnosis has expected massive attention worldwide among the healthcare environment. Optimization algorithms played a significant role in heart disease diagnosis with good efficiency. The objective of this paper is to propose an optimization function on the basis of support vector machine (SVM). This objective function is used in the genetic algorithm (GA) for selecting the more significant features to get heart disease. The experimental results of the GA–SVM are compared with the various existing feature selection algorithms such as Relief, CFS, Filtered subset, Info gain, Consistency subset, Chi squared, One attribute based, Filtered attribute, Gain ratio, and GA. The receiver operating characteristic analysis is performed to evaluate the good performance of SVM classifier. The proposed framework is demonstrated in the MATLAB environment with a dataset collected from Cleveland heart disease database. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Cluster Computing

An optimized feature selection based on genetic approach and support vector machine for heart disease

Increase in body mass index value is a serious health problem across the world. Humans with high body mass index value have the higher probability of getting the cardiovascular disease and high blood pressure. The proposed work is concerned in predicting the probability of CVD and high blood pressure in India. The disease has been predicted with body mass index value; from the health reports of India, the pervasiveness of CVD and HBP is identified. The demographic health survey 2016 of India is used in this work. Bayesian model is used to find the pervasiveness of CVD and HBP based on the gender and the place of living. Out of 432 articles studied, 34 articles suggested the pervasiveness of CVD and HBP. Pervasiveness of CVD increased (95% of interval) rapidly between 1992 and 2016 from 3.0% (0.3–5.7) to 16.4% (9.4–16.2) for men, and from 5.2% (2.3–8.6) to 15.4% (3.3–18.6) for women. Pervasiveness of high blood pressure increased rapidly between 1992 and 2016 from 11.0% (8.6–17.4) to 21.4% (19.4–23.6) for men and from 14.0% (4.3–16.7) to 20.4% (5.6–25.6) for women. The pervasiveness of CVD in 2030 is predicted as 24.6% (13.6–37.8), and pervasiveness of high blood pressure is predicted to be 21.7% (19.6–27.8). The annual average of pervasiveness of CVD is high for women in village areas, and pervasiveness of high blood pressure is found high for men in city regions. The cutoff point for pervasiveness of CVD for overall population is 23.02&nbsp;kg/m2. © 2018, The Natural Computing Applications Forum.

Neural Computing and Applications

Optimal body mass index cutoff point for cardiovascular disease and high blood pressure

Heart failure (HF) has been considered as one of the deadliest human diseases worldwide and the accurate prediction of HF risks would be vital for HF prevention and treatment. To predict HF risks, decision support systems based on artificial neural networks (ANN) have been widely proposed in previous studies. Generally, these existing ANN-based systems usually assumed that HF attributes have equal risk contribution to the HF diagnosis. However, several previous investigations have shown that the risk contributions of the attributes would be different. Thus the equal risk assumption concept associated with existing ANN methods would not properly reflect the diagnosis status of HF patients. In this study, the commonly used 13 HF attributes were considered and their contributions were determined by an experienced cardiac clinician. And Fuzzy analytic hierarchy process (Fuzzy_AHP) technique was used to compute the global weights for the attributes based on their individual contribution. Then the global weights that represent the contributions of the attributes were applied to train an ANN classifier for the prediction of HF risks in patients. The performance of the newly proposed decision support system based on the integration of ANN and Fuzzy_AHP methods was evaluated by using online clinical dataset of 297 HF patients and compared with that of the conventional ANN method. Our result shows that the proposed method could achieve an average prediction accuracy of 91.10%, which is 4.40% higher in comparison to that of the conventional ANN method. In addition, the newly proposed method also had better performance than seven previous methods that reported prediction accuracies in the range of 57.85–89.01%. The improvement of the HF risk prediction in the current study might be due to both the various contributions of the HF attributes and the proposed hybrid method. These findings suggest that the proposed method could be used to accurately predict HF risks in the clinic. © 2016 Elsevier Ltd

Expert Systems with Applications

An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction

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. © 2015 Elsevier B.V. All rights reserved.

Applied Soft Computing

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

The offshore/on-site teams' service climate is one of the key predictors of attaining the global software development (GSD) project outcome from the software service outsourcing perspective. This study aims at proposing a comprehensive methodology for measuring offshore/on-site team-level service climate (TSC) in the context of the GSD project outcome. For the evaluation of TSC in GSD projects, synthesizing literature reviews, extensive investigations are taken into account and to extend our earlier works (Sangaiah and Thangavelu in Int Rev Comput Softw 7(5):2159-2172, 2012; Sangaiah and Thangavelu in Proceedings of the IEEE international conference on computer communication and informatics (ICCCI), 2013; Sangaiah and Thangavelu in Cent Eur J Eng 3(3):419-435, 2013; Sangaiah and Thangavelu in Aust J Basic Appl Sci 7(8):374-385, 2013). Therefore, consistent with earlier studies on IT service climate, we have classified TSC characteristics of offshore/on-site teams' into three main dimensions: managerial practices (deliver quality of service), global service climate (measure overall perceptions), and service leadership (goal setting, work planning, and coordination) which covers twenty-five offshore/on-site teams' service climate attributes. For evaluating the TSC, we have considered adaptive neuro-fuzzy inference system, which is more suitable to find interrelationship between service climate criteria. Finally, this study empirically assesses with 338 experts in India to explore offshore/on-site team-level service climate and GSD project outcome relationship. © 2013 Springer-Verlag London.

Journal	Data powered by TypesetIntelligent Decision Support Systems for Sustainable Computing Studies in Computational Intelligence
Publisher	Data powered by TypesetSpringer International Publishing
ISSN	1860-949X
Open Access	0