International Journal of Networking and Virtual Organisations (20 papers in press)
Analysing the challenges in stakeholder relationship management in the healthcare process: A social network perspective
by Farooq Ali, Harri Haapasalo, Kari-Pekka Tampio, Henriikka Haapasalo
Abstract: We investigate stakeholder relationship management and identify challenges that impact relationships at the healthcare process level using the inductive research approach. We adopt an inductive approach and grounded theory method since there is a need for detailed descriptions on stakeholder network, especially on relationship management. The emergent grounded theoretical model explains the challenges that impact stakeholder relationship management, i.e. gaps in the healthcare network, challenges in articulating a healthcare vision, triggers of challenges, contextual challenges, healthcare landscape, challenges in trust-building, and collaboration. Additionally, our findings show how the network structure and stakeholders position in the network, based on their interactions patterns, influence stakeholder relationship management. The grounded theory that emerged from our study confirms several themes and their interrelationships, which constitute our main contribution.
Keywords: Stakeholder relationship management; Healthcare process; Social network; Stakeholder network; Network structure; Healthcare network; Healthcare management; Grounded theory; Stakeholder identification and mapping.
Should I Accommodate You? Cross-Cultural Code-Switching Behaviours of Global Virtual Team Members during Swift Trust Formation
by NURSAKIRAH AB RAHMAN MUTON, NORHAYATI ZAKARIA, ASMAT-NIZAM ABDUL-TALIB
Abstract: This conceptual paper explores the process of cross-cultural code-switching (C3S) between high-context (HC) and low context (LC) global virtual team members during the knowledge-sharing and social network exchanges. We will introduce a cross-cultural code-switching framework in a virtual setting and develop propositions to explain how GVT members attempt to switch their communicative behaviour based on two theoretical lenses: Giles Communication Accommodation Theory (1973) and Hall (1976) high context and low context theory. This paper offers several propositions to illuminate the process of code-switching behaviours among GVT members during the socialisation process and explores how these behaviours help develop swift trust. It considers whether developing swift trust is possible and, if so, how? We will provide future research directions in our concluding remarks.
Keywords: Cross-cultural code-switching behaviour; knowledge sharing; high-context and low-context cultures; communication accommodation theory; global virtual teams; communication styles; communicative behaviour; Malaysia.
Special Issue on: ISCV 2020 Methods and Applications of Computer Science and Information Technology
A Word Alignment Study to Improve the Reliability of the Statistical and Neural Translation System
by Safae Berrichi, Azzeddine Mazroui
Abstract: Word alignment is an essential task for numerous natural language processing applications, including machine translation. The performance of the statistical machine translation systems is directly impacted by the performance of their alignment modules. However, such alignment models perform worse and induce low machine translation performance when translating morphological rich or low resource languages, such as Arabic. The first objective of this paper is to examine the impact of incorporating some morphosyntactic features, like stem, lemma, root, and part of speech tag, on the statistical alignment models and on the associated translation systems for the (Arabic, English) language pair, and to identify which of these features is most suitable. We also evaluate, for each morphological representation, the impact of the training corpus enrichment on the alignment and the translation qualities. Although the standard neural machine translation system does not directly include a concept of word alignment, the attention mechanism plays an implicit alignment role in these systems. In the second part of this work, we propose a method of adjusting the attention mechanism by the statistical alignments, and we analyze the effect of this adjustment on neural machine translation systems. We also study the impact of different morphological representations on the performance of these supervised systems. The various performed tests show a substantial improvement in the alignment and the translation performances of the proposed approaches.
Keywords: Morphosyntactic Representation; Statistical Word Alignment; Attention Mechanism; Statistical Translation; Neural Translation; Arabic language.
Deep learning based distributed denial-of-service detection
by Hanene Mennour, Sihem Mostefai
Abstract: The nuisance of distributed denial-of-service (DDoS) attacks has
extended unremittingly nowadays. Thus, guaranteeing system availability in this
open-ended pandemic is a crucial task. In this work, we propose three different
deep learning strategies as network anomaly-based intrusion detection system
(N-IDS) for a DDoS multiclassification task. We built a deep Convolutional
Neural Network (CNN), a Stacked Long short-term memory (S-LSTM) neural
network which is a distinct artificial Recurrent Neural Network (RNN), the third
model is a hybridization between CNN and LSTM. Then, we evaluated them on
three up to date flow-based datasets: CICIDS2017, CICDDoS2019 and BoT-IoT
benchmarks. The outcomes demonstrate that hybrid CNN-LSTM outperforms
the existing state-of-the-art schemes in almost all the validation metrics.
Keywords: Deep Learning; Network Intrusion Detection System; Anomaly-
Based; Distributed Denial-Of-Service; Multiclassification; Flow-Based; CNN;
A Cluster Workload Forecasting Strategy Using A Higher Order Statistics Based ARMA Model For IaaS Cloud Services.
by Zohra AMEKRAZ, Moulay Youssef Hadi
Abstract: With the cloud computing services becoming more popular among Internet users, cloud providers are facing a challenge in allocating resources to users according to demand instantly. The delay caused by the Virtual Machines (VMs) start up time makes the reactive techniques, which allocate new resources only when a given load threshold is attained, not effective for the allocation process. An interesting alternative to the reactive technique is the proactive technique. This latter consists of predicting the future demand known as workload and allocating or releasing resources in advance to prevent any overload to occur and also to reduce any related costs. In this paper, we introduce an adaptive workload prediction method based on the use of Higher Order Statistics (HOS) and Autoregressive Moving Average (ARMA) model. The proposed method uses the HOS to make a Gaussianity checking test of the cloud workload and then decides the suitable identification method of the ARMA model to be used to forecast the workload. Furthermore, the proposed method updates the parameters of the ARMA model constantly whenever new workload data are available. We evaluate our proposal with two real workload traces extracted from cluster workloads. The results show that the proposed method has an average of 34% higher accuracy than the baseline ARMA model and presents a low overhead for forecasting incoming workload (<2 s).
Keywords: IaaS Cloud Services; Workload Prediction; Cluster Workload;
Autoregressive Moving Average; Higher Order Statistics.
Cloud Spot Price Prediction Approach Using Adaptive Neural Fuzzy Inference System With Chaos Theory
by Zohra AMEKRAZ, Moulay Youssef Hadi
Abstract: The dynamic pricing of cloud computing is a major challenge for cloud
users all over the world. This challenge was first addressed by Amazon under the
name of Amazon Spot Instance Market. Cloud users can bid for a spot instance
using this market and obtain the requested spot if their bids exceed a dynamically
changing spot price. Amazon publicizes the spot price but does not reveal how
it is determined. In this paper, we perform chaotic time series analysis over the
spot price trace.We also develop a chaos based Adaptive Neural Fuzzy Inference
System (ANFIS) model based on phase-space vectors obtained during the phase
of chaotic analysis. Next, we study the effect of chaos existence on the prediction
accuracy of the spot price by comparing the proposed chaos-ANFIS model with
the baseline ANFIS model (non-chaotic approach). Evaluation results show that
the proposed chaos-ANFIS model yields better predictions of spot price compared
to the baseline ANFIS model in terms of Root Mean Square Error (RMSE) and
Mean Absolute Percentage Error (MAPE).
Keywords: dynamic pricing; cloud computing; spot instance; spot price; chaotic time series analysis; ANFIS.
Stock Market Manipulation Detection using Feature Modelling with Hybrid Recurrent Neural Networks
by Sashank Sridhar, Siddartha Mootha
Abstract: A stock market is a potent platform which handles a large of number of transactions within a second. Keeping track of every single transaction is a daunting task for regulatory bodies. The objective of a regulatory body is to ensure a fair trading environment and to verify that the price of a stock is not being manipulated. This paper proposes a hybrid stacked artificial neural network and recurrent neural network to model the static and dynamic features of stock data. Based on the manipulated stocks, affidavits provided by the Securities and Exchange Board of India (SEBI), a daily trading dataset is created by scraping the Bombay Stock Exchange (BSE) website. The system is capable of identifying three types of manipulation scenarios. The proposed hybrid system is compared to various supervised algorithms, and various ensemble models and the system outperforms all with an accuracy of 96.06%.
Keywords: Manipulation Detection; Hybrid Neural Networks; Ensemble Learning; Recurrent Neural Networks; Fraud Detection; Long Short Term Memory; Bidirectional Long Short Term Memory; Stacked Generalization; Artificial Neural Networks; Feature Engineering;.
Special Issue on: ICICCT 2020 Sustainable Computing and Wireless Networks
Optimal Feature Selection in Intrusion Detection using SVM-CA
by Shinly Swarna Sugi, Raja Ratna S
Abstract: Feature selection plays a vital role in toning down the effects of the curse of dimensionality in the humungous datasets seen in Intrusion Detection. Feature Selection Algorithms are used to pick the relevant features and averts the extraneous and repeated features from the dataset to improve the efficiency. It can reduce processing time, dimension of data and enhance the performance of the system in terms of precision and training time. This paper proposes a variant of SVM, known as SVM Correlation Algorithm (SVM-CA) to choose the relevant features. The combination of Support Vector Machine with correlation algorithm enhances the classification accuracy. Our proposed SVM-CA algorithm deals with the problems faced by the existing algorithm like low accuracy and high detection time. The performance of the algorithm is appraised by five parameters including the modeling time, true positive rate (TPR), false positive rate (FPR), false negative rate (FNR) and accuracy. The experimental results show that our proposed technique decreases the false positive rate and processing time.
Keywords: Correlation Algorithm Feature Selection; Intrusion detection; support vector machine; Machine Learning.
Sustainable Data Analysis Framework of Smart City Based on Wireless Sensor Network
by Hua Wang
Abstract: Under the background of the new era, the construction of smart society has been promoted to the national level. Smart city is the foundation of smart society, which is highly valued. The development of the new generation of information and communication technology and its in-depth application in various fields of the city form a new urban development model, which is called smart city. In terms of technology, smart city applies the new generation of information and communication technology to all walks of life in the city, and transmits information through communication mechanisms, including communication network and Internet. With the continuous development of wireless sensor network technology, smart phone communication technology and Internet information technology, remote wireless mobile home monitoring system will become one of the mainstream of smart home network. Smart home technology is the use of wireless communication technology, sensor network technology, Internet technology, embedded technology and computer technology to connect all kinds of home electrical equipment through the network. Wireless sensor network and wireless communication technology monitor various devices and monitoring targets in the form of self-organizing multi hop. Therefore, this paper studies the sustainable data analysis model of intelligent city based on wireless sensor network. The performance of the model is validated throug the public datbase, and the results are satisfactory.
Keywords: Wireless Sensor Network; Sustainable Development; Smart City; Energy Saving Measures; Data Analysis; Smart home; Monitoring.
Framework Design of Urban Traffic Planning Based on Wireless Network Optimization and Cognitive Sustainable Data Retrieval
by Gang Zhang, Tiechun Li
Abstract: With the acceleration of urban development in China, the development of urban traffic system should be improved. Therefore, there are many problems such as traffic congestion, resource shortage and environmental pollution. Transportation is one of most basic needs for the survival and development of human society. Urban traffic planning is of great significance to the sustainable development of modern cities. It should be noted that urban traffic planning is only a part of urban planning, but there is a very close relationship between them. If in the process of urban traffic planning, the value of urban traffic planning can not be brought into full play if the coordination relationship between them is not clarified. In order to avoid the above problems, traffic management departments have established various traffic management information systems with the help of wireless network optimization technology. The data structure and supported data types of each system are also different, which results in the operation of "island" management information system, therefore, this paper constructs a framework of urban traffic planning based on wireless network optimization model and cognitive sustainable data retrieval. We optimize the communication model to construct the efficient system.
Keywords: Wireless Network Optimization; Cognitive Model; Data Retrieval; Sustainable Development; Urban Traffic Planning.
Painting Image Quality on Visual Art Analysis for Information Transmission of Wireless Networks
by Hanxiao Li
Abstract: As a treasure of human civilization, painting art has immeasurable artistic value. However, due to the influence of human factors, accidents and natural environment, there are different degrees of damage. Traditional image acquisition is based on RGB three channel chroma information acquisition, which can not objectively represent the color information of painting works. In the research of computational aesthetics, it is necessary to analyze the high-resolution images of paintings. However, most of the actual paintings are low resolution, so this paper studies the influence of the quality of painting images on the visual art analysis effect. Nowadays, wireless network is widely used and has become the main way of user terminal access to the network. With the rapid development of wireless network services, especially the development of mobile services, the performance requirements of data transmission under wireless networks are becoming higher and higher. Based on the development of wireless network transmission technology, this paper studies the image quality analysis system of visual art painting. The designed system is validated through different scenarios. The experimental results have proven the effectiveness.
Keywords: Wireless Network Transmission; Visual Art; Painting Image; Quality Analysis; Embedded System.
Wireless Image Transmission Network: 3D Modeling Based on 2D Hand Drawing with Sustainable Computing
by Hanxiao Li
Abstract: Nowadays, we can describe the world in a variety of ways, such as the original text description form, and then to the voice, image, video and other multimedia forms. All these make the methods of describing the world more and more abundant. The appearance of 3D model and 3D scene extends the way of describing the world to 3D space. Because it is closer to the real world, our perception is more rich and more realistic. In the two-dimensional art creation, it is the basic operation mode for art personnel to input and draw two-dimensional images by the handwriting pad. This way is close to manual drawing, in line with the operating habits of artists. In recent years, the emerging research on computational sustainability has become an effective way and a new research hotspot of image wireless transmission. The advent of big data era brings opportunities for the research of computational sustainability, as well as new challenges such as problem complexity, computational efficiency, and method scalability. Based on this background, this paper designs the 3D modeling of 2D hand drawn wireless image transmission network based on sustainable computing model.
Keywords: Sustainable Computing; Big Data; 2D Image; Hand-Drawn Graph; Wireless Transmission Network; 3D Modeling.
Wireless Network for Computer Puzzle Online Software Cloud Platform Based on CBIR and Sustainable Computing
by Bing Sun
Abstract: Wireless network for the computer puzzle online software cloud platform based on the CBIR and sustainable computing is proposed in this paper. In order to improve the ability to resist shortcut attacks, which is generally speaking, encryption algorithms, the higher the number of rounds, the better of the overall performance, because the attack from the crack encryption algorithm. On the other hand, the efficiency of shortcut attack is obviously more than that of exhaustive key search attack high. Hence, we propose two novelty aspects. (1) The CBIR is optimized using the machine learning models to enhance the computational robusness. (2) The WSN and computing models are combined to improve the robustness of the proposed methodology. Our research fins the optimal combination of the wireless systems with the CBIR system that will improve the efficiency. The experimental results have shown that the performance of the model is validated both from accuracy level and robustness level.
Keywords: CBIR Technology; Wireless Network; Sustainable Computing; Online Software; Cloud Platform.
Special Issue on: The Impact and Importance of Networking and Virtualisation in a Post-COVID-19 Scenario
Analysis of low-cost electronic device for diagnosis of Covid-19
by Akshaya Nidhi Bhati, Himanshu Maharshi, Arun Kumar
Abstract: This paper presents the design of a low-cost electronic device that can be used to diagnose Corona Virus Disease 2019 (COVID-19) at home with the help of symptoms. The device will check whether the patient has a fever or not with the help of a thermal sensor, oxygen saturation in the blood (Spo2) with the help of a Pulse-Oximeter, and cough through artificial intelligence. The remaining symptoms will be diagnosed using a survey-based system, where respondents will be invited to self-report various symptoms. The estimate, conception and development of this device can greatly contribute to the creation, and assist in breaking off the spread of the disease, getting the timely treatments and potentially save lives.
Keywords: COVID-19; Oximeter; OLED Screen; Thermal Sensor; Spo2.
PlasmaBlock: A Plasma donation Blockchain system in COVID-19
by Riya Sapra, Parneeta Dhaliwal
Abstract: COVID-19 has brought the whole world to a still. The extensive spread of the disease has adversely affected the life of the common man and the nation as a whole. Various vaccine trials for the disease are being done by scientists and researchers in different countries. Plasma therapy is one of the treatments, medical professionals are using for treating critical patients. This treatment requires a valid plasma donor who can donate plasma after its recovery from COVID-19. In this paper, a decentralized platform for plasma donation registration and plasma matching has been proposed using blockchain technology to speed up the process of convalescent plasma therapy. It uses smart contract to find appropriate plasma matches for the critical patients to locate the plasma of required blood group. It will help in tracking the donations and the procedures afterward.
Keywords: Convalescent plasma; Plasma therapy; Blockchain; donor-recipient; plasma donors; smart contract; corona virus; COVID-19.
COVID 19 and its Impact on Global Virtual Teams: Exploring the Unexplored
by Archana Shrivastava, Pooja Misra
Abstract: Based on our understanding, most of the research related to the outbreak of pandemic focus on health sector and economy. Our research article examines how internationally disruptive events like COVID 19 pandemic influence global virtual teams, particularly those in which team members have never met in person. Research also explored participants perception of the higher education institutions towards online learning and attitude of corporate organisations towards remote working in the coming years. Results of the study are promising in throwing light on the insight created via narratives.
Keywords: COVID19; corona virus; virtual teams; higher education; online learning; remote working.
An IoT and Artificial Intelligence based Patient Care System focused on COVID-19 Pandemic
by Vishal Kumar Goar, Nagendra Singh Yadav, Chiranji Lal Chowdhary, Kumaresan P, Mohit Mittal
Abstract: World Health Organization has declared COVID-19 a pandemic. The spread rate of COVID-19 outbreak is much faster over past outbreaks of the Ebola and SARS-COV. Deaths and illnesses started to increase exponentially, and nations around the globe struggled to control the spread of the COVID-19 virus. Like many other epidemic outbreaks, COVID-19 faces significant challenges, like the identification of the epidemic's source of disease, control the spread rate, and adequate healthcare for all patients. Digital technology, particularly the IoT, could be used as an essential tool to combat and control the spread of these pandemics to minimize the economic loss and disruption. The digital technology allowed health care professionals in identification and isolation to the source of the infection to prevent community transmission of the virus by remotely monitor the COVID-19 infected patients. We proposed a machine learning prediction model using the Orange Canvas Program by creating a local instance dataset of eight suspected individuals measured body parameters. The body parameter values are extracted by the caretaker in a clinical system. Furthermore, six machine learning classifiers such as KNN, DT, SVM, Random Forest, Neural Network and Na
Keywords: Internet of Things; Artificial Intelligence; COVID-19; Pandemic; Wearablernsensors; Cloud interface; Machine learning.
AHP-based evaluation of Critical Barriers for Social Distancing in India during COVID-19
by Hemant Upadhyay, Abhinav Juneja, Sapna Juneja, Deepak Gupta
Abstract: COVID-19 has made the world realize, the uncertainty of our existence on the planet. There have been unprecedented consequences of the spread of this deadly pandemic. New methods and theories are being proposed by research community to save the humans from being affected by this disease. The aim of the presented research work is to analyse the critical barriers in social distancing in India during COVID-19 using AHP technique. Experts opinions are utilized to identify critical barriers in self-distancing in India during COVID-19. Twelve critical barriers have been compared and evaluated using Analytical Hierarchical Process Method to get ranked in terms of priorities. The results may be utilized by the policy manufacturers for nurturing adequate reforms and policies to effectively deal with current pandemic considering the relative importance of these critical barriers in social distancing management.
Keywords: COVID-19; Analytic Hierarchy Process; AHP; Critical Barriers; Social Distancing.
Students' Perspective on Online teaching in higher institutions during COVID 19
by Roseline Oluwaseun Ogundokun, Muhammad Daniyal, Sanjay Misra, Joseph Bamidele Awotunde
Abstract: Learning and teaching online is not a modern concept. It has been seen primarily as a part of face-to-face teaching and learning purposes for the past few years. Evaluation is an important aspect of teaching and learning since it determines the students' accomplishment of course learning outcomes. Today due to the coronavirus outbreak, there's a peculiar issue facing the global education community. The government and educational policymakers have to find other ways of teaching and learning to help learners during this isolation period at all locations; thus, learners need to be literate and be able to use online technologies effectively and consider ever-changing dynamics. Therefore, the study aims to explore whether students of the developing countries are contented with the innovation of online education introduced by all higher establishments worldwide as a result of the new coronavirus called COVID-19. This study evaluates the effect of online teaching during COVID-19 lockdown on students in the developing country like Nigeria. An online survey was conducted between 1st October and 8th October 2020 to achieve this evaluation. A 'Google form' structural questionnaire was forwarded to scholars via WhatsApp and e-mail. A total of 1419 students submitted complete survey information. These are evaluated to discover their thoughts on online education during the COVID-19 outbreak in a developing country as Nigeria. The study findings highlighted that an overwhelmingly greater number of the scholars are incapable of connecting to the net owing to technological and fiscal problems, thereby made online learning not to be able to yield desired results. In a group of few additional concerns emphasized by university scholars is the absence of one-on-one contact with the teacher, reaction period and nonappearance of typical teaching space socialization.
Keywords: COVID-19; Online teaching and learning; Higher institution; Internet; Developing countries.
Stock Movement Prediction Using Neuro Genetic Hybrid Approach and Impact on Growth Trend due to COVID-19
by Pradeepta Kumar Sarangi, Kalpna Guleria, Devendra Prasad, Deepak Kumar Verma
Abstract: Knowing the future perspective is a matter of great concern for every business organization. Its importance increases when it comes to the matter of financial data related to the stock market. Researchers apply various methods to predict the stock trend but still there is no method that can guarantee the accurate stock movement whereas a nearly accurate approach could be achieved in case of short-term stock movements. Artificial Neural Network (ANN) is one of the most popular methods to analyze the stock trend and forecast the future direction of the stock market and the most challenging task in using the neural network is the selection of a proper architecture, input parameters and training of the network. This can be overcome by analyzing the relationship between independent and dependent factors and also by implementing hybrid models such as ANN with Genetic Algorithm (GA) for network training. This work has addressed this issue for short term stock prediction and is divided into two phases. In phase-I, experiments have been done to implement a hybrid ANN-GA model for short term stock prediction and in phase-II, a study has been carried out to analyze the impact of the COVID-19 on the share prices of selective major banks in India.
Keywords: Time Series Analysis; Neural Network(NN); Genetic Algorithm(GA); Financial Forecasting; COVID impact on banking sector.