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

Arun Kumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Samuel O.W

Li X

Abdel-Basset M

Wang H.

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

Computers & Electrical Engineering

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.

International Journal of Parallel Programming

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

The significant role of plants can be observed through the dependency of animals and humans on them. Oxygen, materials, food and the beauty of the world are contributed by plants. Climate change, the decrease in pollinators, and plant diseases are causing a significant decline in both quality and coverage ratio of the plants and crops on a global scale. In developed countries, above 80 percent of rural production is produced by sharecropping. However, due to widespread diseases in plants, yields are reported to have declined by more than a half. These diseases are identified and diagnosed by the agricultural and forestry department. Manual inspection on a large area of fields requires a huge amount of time and effort, thereby reduces the effectiveness significantly. To counter this problem, we propose an automatic disease detection and classification method in radish fields by using a camera attached to an unmanned aerial vehicle (UAV) to capture high quality images from the fields and analyze them by extracting both color and texture features, then we used K-means clustering to filter radish regions and feeds them into a fine-tuned GoogleNet to detect Fusarium wilt of radish efficiently at early stage and allow the authorities to take timely action which ensures the food safety for current and future generations. © 2018 Elsevier Inc.

Fulltext

Sustainable Computing: Informatics and Systems

UAV based wilt detection system via convolutional neural networks

Internet of Things (IoT) is a heterogeneous ubiquitous network based upon modern computational intelligent techniques. A large scale IoT environment composed of thousands distributed entities and a number of multimedia smart devices. In recent years, due to the improvement of popularity and capability of smart mobile devices, Mobile Cloud Computing (MCC) gains a considerable attention in Internet of Things (IoT) environment. As there are variety of clouds that provides same services, it becomes quite difficult for users to choose an ideal cloud from a variety of clouds for migrating computationally intensive applications. So, selecting the optimal cloud among multiple alternatives which saves resource availability and execution time is a Multi-Criteria Decision Making (MCDM) issue. This chapter introduces an assessment model based on Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) which helps the users to select an optimal cloud where uncertainty and subjectivity are parameterized using triangular fuzzy members and is handled by using linguistic values. The proposed computational intelligence decision making model enables decision makers to better understand the whole evaluation process and thus provides more accuracy, systematic and efficient decision support tool. © Springer International Publishing AG 2018.

Cognitive Computing for Big Data Systems Over IoT Lecture Notes on Data Engineering and Communications Technologies

Modelling and Analysis of Multi-objective Service Selection Scheme in IoT-Cloud Environment

Heart failure and other chronic diseases have posed huge challenges to the healthcare sectors across different country. As a result of inadequate tools and techniques to address the challenges, numerous patients have died while several others have experienced a decreased quality of life with various degrees of psychological burdens. With the revivification of artificial intelligence in the 1990s, computational intelligence (CI) tools have since been adopted in developing clinical decision support systems for heart disease diagnosis to ensure qualitative healthcare services. In this chapter, we describe some notable CI techniques and their clinical applications as it relate to heart failure risk prediction. Furthermore, the strength and limitation of these CI techniques in terms of accuracy and computational complexity are also discussed. Lastly, the potential role of CI in healthcare delivery systems is equally described. © 2018 Elsevier Inc. All rights reserved.

Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications

Computational Intelligence Enabling the Development of Efficient Clinical Decision Support Systems: Case Study of Heart Failure

The offshore/on-site teams’ knowledge transfer effectiveness is one of the key predictors for evaluating the outcome of global software development (GSD) project. This study aims at proposing an assessment framework that can be used to evaluate the GSD teams’ (offshore/on-site) knowledge transfer effectiveness in the following criteria: knowledge, team, technology, and organization factors. For evaluating the teams’ knowledge transfer effectiveness in GSD projects, we have integrated the fuzzy decision making trial and evaluation laboratory (DEMATEL) and fuzzy multi-criteria decision making (FMCDM) approaches. Moreover, the proposed DEMATEL-FMCDM methodology is used to determine priority weights of each evaluation criterion and rate knowledge transfer effectiveness on the GSD project outcome from software service outsourcing perspective into two dimensions: product success, (schedule, cost improvement, etc.) and service quality (personal satisfaction, and successful collaboration). The proposed methodology is used for the prediction of a possible rating mechanism for determining the GSD project outcome through knowledge transfer criterions of GSD teams. It also facilitates the determination of knowledge transfer effectiveness of GSD teams’ towards the success of the GSD project outcome and initiates actions to improve the project outcome in GSD environment. This study demonstrates a survey research method that addresses 35 knowledge transfer factors assessed by 70 software professionals at Inowits software organization in India.

International Journal of Machine Learning and Cybernetics

Integration of fuzzy DEMATEL and FMCDM approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome

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 offshore/onsite teams’ knowledge transfer (KT) effectiveness is one of the key determinants for achieving the outcome of global software development (GSD) projects. In this study, the significance of offshore/onsite teams (GSD teams) KT effectiveness in GSD projects is measured through various factors: knowledge, team, technology, and organization factors. Moreover, the assessment framework for the integration of knowledge, team, technology, and organization factors for evaluating KT effectiveness in GSD projects has not been adequately available in the existing literature. For this motivation, the main objective of this study is to propose the assessment framework to evaluate offshore/onsite teams KT effectiveness with reference to GSD project outcome. For evaluating KT effectiveness of GSD teams, we have integrated three Fuzzy Multi-Criteria Decision Making (FMCDM) methodologies: (a) Fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), (b) Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) and (c) Elimination Et Choix Traduisant la REaite (ELECTRE). Further, the hybridization of fuzzy DEMATEL, TOPSIS, and ELECTRE has not available in the existing literature. Based on this research gap, we have integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating KT effectiveness of offshore/onsite teams in the context of GSD project outcome. Subsequently, the applicability and capability of proposed framework has been validated by software experts at Inowits Software Organization in India.

Neural Computing and Applications

An integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome

The theoretical basis for studying the phenomenon of Global Software Development (GSD) draws upon one of the key research streams, that is, the Organizational Behavior (OB) research. The focus of this study has led to two research problems: partnership quality and service climate aspects are addressed which have given an insight into the OB research on GSD teams. Moreover, this study classifies results from the partnership quality and service climate aspects into one integrated framework, which covers 18 attributes to explore the GSD outcome factors perceived by GSD teams in OB research phenomenon. To evaluate partnership quality and team service climate aspects with reference to the GSD project outcome, we have integrated the fuzzy Decision-Making Trial and Evaluation Laboratory Model (DEMATEL) and the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) approach, which is more appropriate to find the significance of criteria. The empirical application of this hybrid approach for evaluating GSD project outcome factors has been tested in Indian software organizations. Consequently, the results of this study provide a vivid picture and facilitate the organization to reveal the importance of OB research on GSD teams. © 2014, The Natural Computing Applications Forum.

A combined fuzzy DEMATEL and fuzzy TOPSIS approach for evaluating GSD project outcome factors

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.

Journal	Data powered by TypesetComputers & Electrical Engineering
Publisher	Data powered by TypesetElsevier BV
ISSN	0045-7906
Open Access	0