Arun Kumar Sangaiah

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

This study proposed a hybrid decision support method (ANN and Fuzzy_AHP) for heart failure prediction.The performance of the proposed method was examined using three performance metrics.From the evaluations results, the proposed method performed better than the conventional ANN approachThe proposed method would provide improved and realistic result for efficient therapy administration. 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.

Anonymous mutual authentication and key agreement scheme for wearable sensors in wireless body area networks

Abstract Wireless body area networks (WBANs) are used to collect and exchange vital and sensitive information about the physical conditions of patients. Due to the openness and mobility of such networks, even without knowing the context of the exchanged data or linking traffic to the identities of involved sensors, criminals are able to gain useful information about the severe conditions of patients and carry effective undetectable physical attacks. Therefore, confidentiality and mutual authentication services are essential for WBANs, and the transmission must be anonymous and unlinkable as well. Given the limitations of the resources available for these sensors, a lightweight anonymous mutual authentication and key agreement scheme for centralized two-hop WBANs is proposed in this paper, which allows sensor nodes attached to the patient’s body to authenticate with the local server/hub node and establish a session key in an anonymous and unlinkable manner. The security of our scheme is proved by rigorous formal proof using BAN logic and also through informal analysis. Besides, the security of our scheme is evaluated by using the Automated Validation of Internet Security Protocols and Applications (AVISPA) as well. Finally, we compare our proposed scheme with other related schemes and the comparison results show that our scheme outperforms previously related schemes.

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

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.

Medical JPEG image steganography based on preserving inter-block dependencies☆

Abstract With the development of computer and biomedical technologies, medical JPEG images contain the patients’ personal information and the security of the private information attracts great attention. Steganography is utilized to conceal the private information, so as to provide privacy protection of medical images. Most of existing JPEG steganographic schemes embed messages by modifying discrete cosine transform (DCT) coefficients, but the dependencies among DCT coefficients would be disrupted. In this paper, we propose a new medical JPEG image steganographic scheme based on the dependencies of inter-block coefficients. The basic strategy is to preserve the differences among DCT coefficients at the same position in adjacent DCT blocks as much as possible. The cost values are allocated dynamically according to the modifications of inter-block neighbors in the embedding process. Experimental results show that the proposed scheme can cluster the inter-block embedding changes and perform better than the state-of-the-art steganographic method.

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

Abstract 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.

A three-factor anonymous authentication scheme for wireless sensor networks in internet of things environments

Internet of Things (IoT) is an emerging technology, which makes the remote sensing and control across heterogeneous network a reality, and has good prospects in industrial applications. As an important infrastructure, Wireless Sensor Networks (WSNs) play a crucial role in industrial IoT. Due to the resource constrained feature of sensor nodes, the design of security and efficiency balanced authentication scheme for WSNs becomes a big challenge in IoT applications. First, a two-factor authentication scheme for WSNs proposed by Jiang et al. is reviewed, and the functional and security flaws of their scheme are analyzed. Then, we proposed a three-factor anonymous authentication scheme for WSNs in Internet of Things environments, where fuzzy commitment scheme is adopted to handle the users biometric information. Analysis and comparison results show that the proposed scheme keeps computational efficiency, and also achieves more security and functional features. Compared with other related work, the proposed scheme is more suitable for Internet of Things environments.

Pattern recognition of electromyography signals based on novel time domain features for amputees' limb motion classification☆

Abstract Feature extraction is essential in Electromyography pattern recognition (EMG-PR) based prostheses control method. Time-domain features have been shown to have good performance in upper limb movement classification. However, the performance of EMG-PR prostheses driven by the existing time-domain features is still unsatisfactory. Hence, this study proposed three new time-domain features to improve the performance of EMG-PR based strategy in arm movement classification. EMG signals were recorded from the residual arms of eight amputees while performing different upper limb movements. Then, the newly proposed features were extracted and used to classify their limb movements. Experimental results showed that the proposed features could achieved an average classification accuracy of 92.00% ± 3.11% which was 6.49% higher than that of the commonly used time-domain features (p

A Robust Time Synchronization Scheme for Industrial Internet of Things

Energy-efficient and robust-time synchronization is crucial for industrial Internet of things (IIoT). Some energy-efficient time synchronization schemes that achieve high accuracy have been proposed recently. However, some unsynchronized nodes namely isolated nodes exist in the schemes. To deal with the problem, this paper presents R-Sync, a robust time synchronization scheme for IIoT. We use a pulling timer to pull isolated nodes into synchronized networks whose initial value is set according to level of spanning tree. Then, another timer is set up to select backbone node and its initial value is related to the distance to parent node. Moreover, we do experiments based on simulation tool NS-2 and testbed based on wireless hardware nodes. The experimental results show that our approach makes all the nodes get synchronized and gets the better performance in terms of accuracy and energy consumption, compared with three existing time synchronization algorithms TPSN, GPA, STETS.

Medical image classification based on multi-scale non-negative sparse coding

With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance.