Forthcoming and Online First Articles

International Journal of Networking and Virtual Organisations

International Journal of Networking and Virtual Organisations (IJNVO)

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International Journal of Networking and Virtual Organisations (10 papers in press)

Regular Issues

  • A New Analytic Framework for Identifying Tour Routes Service Innovation in E-commerce Platform Using Knowledge Graph   Order a copy of this article
    by Shuang Zhang, Zhu Zhen 
    Abstract: While the growth of the tourism market has created significant market opportunities for tour routes developers to improve service innovation, it has inevitably induced other travel agencies to imitate. However, there has been little rigorous research to identify service innovation from a knowledge graph perspective. This study presents a knowledge graph framework to identify service innovations of tour routes, which can tackle the measurement challenges of knowledge representation in different dimensions. A knowledge graph-based method for service products is employed to obtain the dynamic weight of dimensions, aimed at calculating the importance of graph network nodes through knowledge extraction after division of dimensions. This study shows that the proposed approach can accurately determine the level of service innovation, providing a benchmark for exploring refined knowledge representation in products' unstructured attributes.
    Keywords: Service innovation; E-commerce platform; Tour routes; Analytic framework; Knowledge graph.

  • Salp Swarm Optimization with Deep Transfer Learning Enabled Retinal Fundus Image Classification Model   Order a copy of this article
    by Indresh Gupta, Abha Choubey, Siddhartha Choubey 
    Abstract: Automated screening and diagnostic process in the healthcare sector improves services, reduces cost, and labour. With the developments of machine learning (ML) and deep learning (DL) models, intelligent disease diagnosis models can be designed. Retinal fundus image classification using DL models becomes essential for the identification and classification of distinct retinal diseases. This article develops a salp swarm optimisation with deep transfer learning enabled retinal fundus image classification (SSODTL-RFIC) model. The proposed SSODTL-RFIC model examines the retinal fundus image for the existence of diseases. In addition, a median filtering (MF) approach is employed for the noise removal process and graph cut (GC) segmentation is applied. Besides, MobileNetv1 feature extractor is involved to produce feature vectors. Finally, SSO with cascade forward neural network (CFNN) model is applied for recognition and classification process. A widespread experimentation process is performed on benchmark datasets to examine the enhanced performance of the SSODTL-RFIC model, an extensive comparative examination pointed out the supremacy of the SSODTL-RFIC model over the recent approaches with maximum accuracy of 98.71% and 99.12% on the test ARIA and STARE datasets respectively.
    Keywords: retinal fundus images; image classification; machine learning; deep learning; salp swarm algorithm; cascade forward neural network; CFNN.
    DOI: 10.1504/IJNVO.2022.10050480
  • Evolutionary Optimization with Outlier Detection Based Deep Learning Model for Biomedical Data Classification   Order a copy of this article
    by Raja R, Ashok B 
    Abstract: In recent times, large amount of medical data is being generated by various sources such as test reports, medications, etc. Due to the recent advances of machine learning (ML) and deep learning (DL) models, medical data classification (MDC) remains a crucial process in the healthcare sector. This study introduces a new hyperparameter tuned convolutional neural network-recurrent neural network (HPT-CNN-RNN) model for medical data classification. The proposed HPT-CNN-RNN model includes pre-processing step to transform the actual healthcare data into useful format. Besides, SVM-SMOTE approach was executed to handle the class imbalance problems. In addition, outlier detection process is performed using extreme gradient boosting (XGBoost) model. Moreover, bacterial foraging optimisation algorithm (BFOA) with CNNRNN model is employed to categorise medical data. Furthermore, the BFOA is utilised to optimally choose the hyperparameter values of the CNNRNN model. The experimental outcomes designated the better performance of the HPT-CNN-RNN model over the other methods.
    Keywords: classification; medical data; data mining; outlier detection; class imbalance; deep learning; parameter tuning.
    DOI: 10.1504/IJNVO.2022.10050523
  • Is Google Making us Smart? Health Self-Management for High Performance Employees & Organisations   Order a copy of this article
    by Luuk Simons, Mark A. Neerincx, Catholijn M. Jonker 
    Abstract: Globally, the burden of disease is rising. High performance employees and organisations need to improve their health self-management options and skills. Unfortunately, there are an overwhelming number (>500.000) of new health publications every year. We aim to design a health AI on top of Scholar Google, to support rapid employee DIY (Do-It-Yourself) health improvement. Thus, we analysed user requirements, based on design analyses for two cases: hypertension and T2D (Type 2 Diabetes), two major diseases of affluence in our society, which are reversible with healthy living. We show how a hybrid AI may empower employees instead of medicalising them. To conclude, we propose a next level of Quantified Self for worker health self-management.
    Keywords: Health; Employee Performance; Self-management; AI; Quantified Self; Service Design; Personal Medicine.

  • Joint optimisation techniques for trade-off aware spectrum sensing in cognitive radio network   Order a copy of this article
    by Apurva Daman Katre, T.C. Thanuja 
    Abstract: Cognitive radio (CR) network is considered a promising domain to enhance spectrum efficiency to access underutilised frequency bands. However, due to the influence of channel fading and shadowing, accuracy in primary user (PU) detection by CR gets hampered. This paper designs a joint optimisation technique for spectrum sensing in CR network to optimise energy, delay, and throughput with increased sensing accuracy. Initially, simple energy detection is exhibited for sensing the presence of PU in band. Further, the algorithm is developed to achieve an energy-throughput trade-off, and delay-throughput trade-off. Hence, the optimisation algorithm for detecting energy, reducing delay, and enhancing throughput are developed to optimise complete sensing performance. Furthermore, the joint optimisation model assists in acquiring trade-offs amongst energy, delay, and throughput. The assessment of the technique is performed using delay, energy, and throughput. Moreover, the software-defined radio (SDR) configuration is performed for validating the result.
    Keywords: cognitive radio network; spectrum sensing; energy; throughput; delay.
    DOI: 10.1504/IJNVO.2022.10051032
  • Research on digital logistics parks' horizontal cooperation with Cloud platform: from the perspective of tripartite evolutionary game   Order a copy of this article
    by Yan Feng, Xingjian Zhou, Lihua Cai, Jinshan Dai 
    Abstract: To meet the massive and emergent logistics demand, learning from Europe and the USA, a cooperation mechanism can form a gathered and scaled logistics park network. Based on the current practice in China, the logistics Cloud platform makes the cooperation from a horizontal perspective possible. A tripartite evolutionary game model consisting of one logistics Cloud platform and two digital logistics parks is proposed. The platform has strategy {compensating, no compensating}, and the parks have strategy {sharing, no sharing}. The revenue function of each party is constructed, applying dynamic replication function based on stag hunt game; all equilibrium points are found. With Jacobi matrix, two asymptotically stable strategies are formed. The study shows that the three game players either choose sharing/compensating or no sharing/no compensating strategy. The strategy is affected by the sharing/compensating probability, the compensation and opportunity cost, and the order loss. The managerial insights are also discussed.
    Keywords: digital logistics parks; logistics Cloud platform; resource sharing; horizontal cooperation; evolutionary game.
    DOI: 10.1504/IJNVO.2022.10051033
  • E-commerce platform resources, sequence of digital strategic actions and competitive advantage: an empirical study from online tourism industry   Order a copy of this article
    by Lewei Hu, Jing Zhao, Yi Jiang 
    Abstract: This study examines the impact of the sequence of digital strategic actions initiated by e-commerce platform on competitive advantage in competitive interaction (DSAs). Based on the competitive repertoire theory, the configurational approach is used to analyse the secondary data of Chinese online tourism industry, the research finds out the configurations of DSAs orchestrated by digital technologies, partnership resources and competitor responses. Secondly, dynamic characteristics (including complexity and unpredictability) of DSAs sequence are calculated. Thirdly, panel regression has been applied to explore the relationship between the unpredictability and complexity of DSAs sequence and competitive performance in the competitive interaction, and the inverted U-shaped relationship between them is found. Finally, discussion of the implications for theory and practice is presented. In a nutshell, this study aims to help managers formulate and initiate sequences of competitive actions more effectively in the digital environment.
    Keywords: digital strategic actions; digital technology; sequences of digital strategic actions; complexity; unpredictability; competitive repertoire.
    DOI: 10.1504/IJNVO.2022.10051034
  • Augmented reality in retailing: a systematic review with bibliometric analysis   Order a copy of this article
    by Zhao Du, Jun Liu, Fang Wang 
    Abstract: The emergence and proliferation of augmented reality (AR) technology in retailing has revolutionised consumer shopping and service experience. A body of research on AR in business applications, particularly for retailing, is quickly developing. This research shed light on the current status of the scholarly works on AR in retailing by conducting a systematic literature review using bibliometric analysis and thematic analysis. Specifically, this research examines 51 peer-reviewed journal articles using bibliometric analysis. It provides a detailed view of the literature, including research trends, publication venues, and authorships. Moreover, it classifies and reviews three major themes and summarises the articles in each theme. Finally, this research identifies and discusses the possible directions for future research.
    Keywords: augmented reality; retailing; systematic review; bibliometric analysis; adoption; purchase intention; product presentation; customer experience; design feature; brand attitude; market intelligence.
    DOI: 10.1504/IJNVO.2022.10051036

Special Issue on: Deep Neural Networks and Evolutionary Computation for Biomedical Applications

  • A Deep Learning Model Framework for Diabetic Retinopathy Detection   Order a copy of this article
    by Padmapriya M, Pasupathy S, Sumathi R, Punitha V 
    Abstract: Diabetic retinopathy (DR) is the typical diabetic eye issue and a main reason of blindness around the world. As per the International Diabetes Federation (IDF), the rates of diabetes would rise to 552 million by 2034. Breakthroughs in computer science techniques inclusive of artificial intelligence (AI) and deep studying (DL) have multiplied opportunities for early detection of DR. This indicates that the risk of eyesight loss could be minimized in due course. A deep learning model (ResNet) for medical DR detection was examined in this article. The data set of Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 was used to train and test the DL model. To demonstrate the vitality of the chosen ResNet model, performance measures and testing accuracy like recall, precision, and F1 score were determined. The modified ResNet model reduced the training time and computational complexity and attained a testing accuracy of around 84%.
    Keywords: Diabetic Retinopathy; Convolutional Neural Network; Machine Learning; Deep Learning; ResNet model.

  • Application of Non-linear System Identification for EEG Modeling using VMD based Deep Random Vector Functional Link Network   Order a copy of this article
    by Rakesh Kumar Pattanaik, Rinky Dwivedi, Mihir Narayan Mohanty 
    Abstract: In this paper, the EEG signal is considered for the development of the model. As the signal is nonlinear and non-stationary, the model is designed accordingly that is similar to non-linear dynamic system identification. Initially, the signal is decomposed by a robust variational mode decomposition method for which the basic noise components are eliminated. Based on its clean co-efficient the model is developed using RVFLN for identification. Further the same are utilized in Deep RVFLN to enhance the prediction and detection. The use of Deep RVFLN provides better results as compared to simple RVFLN as explained in the result section. For verification of the system robustness, three different epileptic signals are known as Pre-ictal, Inter-ictal, and Ictal are experienced in this piece of work.
    Keywords: Variational Mode Decomposition; Linear time-invariant System; Random Vector Functional Link Network; Nonlinear system identification; Electroencephalogram.