In recent years, artificial intelligence and bio-inspired computing methodologies have risen rapidly and have been successfully applied to many fields. Bio-inspired network systems are a field of biology and computer science, it has the high relation to the bio-inspired computing and bio-inspired system. It has the self-organizing and self-healing characteristics that help them in achieving complex tasks with much ease in the network environment. Software-defined networking provides a breakthrough in network transformation. However, increasing network requirement and focus on the controller for determining the network functionality and resources allocations aims at self-management capabilities. More recently, the artificial bee colony (ABC) algorithm has been used to solve the issues of parameter optimization. In this paper, a discretized food source for an artificial bee colony (DfABC) optimization algorithm is proposed and applied to optimize the kernel parameters of a support vector machine (SVM) model, creating a new hybrid. In order to further improve prediction accuracy, the proposed DfABC algorithm is applied to six popular UCI datasets. We also compare the DfABC algorithm to particle swarm optimization (PSO), the genetic algorithm (GA), and the original ABC algorithm. The experimental results show that the proposed DfABC-SVM model achieves better classification accuracy with a shorter convergence time, outperforming the other hybrid artificial intelligence models. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Arun Kumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Chiang H.-S

Chen M.-Y

Liu J.-Y.

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.

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VIT University

International Journal of Parallel Programming

Effective prioritization of software project risks play an important role in determining whether the project will be successful in terms of performance characteristics or not. In this context, this study aims at hybridizing fuzzy multi criteria decision making approaches for the development of an assessment framework that can be used to efficiently identify and rank notable software project risks will aid decision making during the lifecycle of a software product. Specifically, for the assessment of project risk, we have integrated fuzzy Decision Making Trial and Evaluation Laboratory, Fuzzy Multi-Criteria Decision Making and TODIM (an acronym in Portuguese for Interactive and Multiple Attribute Decision Making) approaches. The proposed methodology is used for providing an effective rating mechanism to measure software risk factors. The effectiveness of the proposed approach results have been compared with earlier methods for the assessment of software project risks. The comparison was done based on the OMRON data set of 40 software projects. Evaluation results reveal that the integration of fuzzy approaches could be effective and as well accurate compared to existing approaches for evaluating software project risks. © 2017 Elsevier Ltd

Computers & Electrical Engineering

Towards an efficient risk assessment in software projects–Fuzzy reinforcement paradigm

Heart attack, a complex health problem in which the electrical activity of the heart becomes chaotic due to extreme heart failure conditions, has been ranked the deadliest human diseases. Recent studies have reported that remote monitoring of patients with heart failure disease could help quantify their level of risks and provide useful information for efficient therapy. Additionally, such platforms could help increase accessibility to health care delivery at a relatively lower cost. Therefore this study proposed a context aware clinical decision support model using support vector machine (SVM) for heart failure risk prediction. The proposed model's performance was evaluated using dataset of potential heart failure patients with metrics including prediction accuracy, sensitive, specificity, and receiving operating characteristic (ROC). An average prediction accuracy of 87.9% and 82.0%, were respectively achieved for the training and testing sessions of the built Radial basis function (RBF) based SVM classifier with a sensitivity value of 76.9%. The results obtained from this study might aid the development of an efficient context aware clinical decision support systems in smart cities and the society at large. © 2017 Elsevier Ltd

Sustainable Cities and Society

Adaptive context aware decision computing paradigm for intensive health care delivery in smart cities—A case analysis

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

Journal of Medical Diagnostic Methods

Enhanced Neuro-Fuzzy System Based on Genetic Algorithm for Medical Diagnosis

Applied Soft Computing

A SVM controller for the stable walking of biped robots based on small sample sizes

A Novel Artificial Bee Colony Optimization Algorithm with SVM for Bio-inspired Software-Defined Networking

Journal	Data powered by TypesetInternational Journal of Parallel Programming
Publisher	Data powered by TypesetSpringer Science and Business Media LLC
ISSN	0885-7458
Open Access	0