Comput

This paper describes an approach introducing location intelligence using open-source software components as the solution for planning and construction of the airport infrastructure. As a case study, the spatial information system of the International Airport in Sarajevo is selected. Due to the frequent construction work on new terminals and the increase of existing airport capacities, as one of the measures for more efficient management of airport infrastructures, the development team has suggested to airport management to introduce location intelligence, meaning to upgrade the existing information system with a functional WebGIS solution. This solution is based on OpenGeo architecture that includes a set of spatial data management technologies used to create an online internet map and build a location intelligence infrastructure.

The use of digital technology as the only communication and relationship channel in work, school and social contexts is bringing out dynamics that are sometimes in contrast with each other. The purpose of this article is to investigate the impact of digital technology on teachers’ school practices in the context of COVID-19. This impact was studied in relation to the constructs of motivation, perceived stress, sense of self-efficacy and resistance to/acceptance of technologies. This study examined the role played by the massive and coercive use of digital technologies (and the relationship with innovation and change) in predicting motivation and perceived stress among teachers. To this end, the impact of digital technologies on motivation and perceived stress were explored in the sample. A questionnaire consisting of three scales was administered to 688 Italian school teachers of all educational levels (from childhood to upper-secondary school), who completed a socio-demographic sec...

The use of digital technology as the only communication and relationship channel in work, school and social contexts is bringing out dynamics that are sometimes in contrast with each other. The purpose of this article is to investigate the impact of digital technology on teachers’ school practices in the context of COVID-19. This impact was studied in relation to the constructs of motivation, perceived stress, sense of self-efficacy and resistance to/acceptance of technologies. This study examined the role played by the massive and coercive use of digital technologies (and the relationship with innovation and change) in predicting motivation and perceived stress among teachers. To this end, the impact of digital technologies on motivation and perceived stress were explored in the sample. A questionnaire consisting of three scales was administered to 688 Italian school teachers of all educational levels (from childhood to upper-secondary school), who completed a socio-demographic sec...

Ransomware is a relatively new type of intrusion attack, and is made with the objective of extorting a ransom from its victim. There are several types of ransomware attacks, but the present paper focuses only upon the crypto-ransomware, because it makes data unrecoverable once the victim’s files have been encrypted. Therefore, in this research, it was proposed that machine learning is used to detect crypto-ransomware before it starts its encryption function, or at the pre-encryption stage. Successful detection at this stage is crucial to enable the attack to be stopped from achieving its objective. Once the victim was aware of the presence of crypto-ransomware, valuable data and files can be backed up to another location, and then an attempt can be made to clean the ransomware with minimum risk. Therefore we proposed a pre-encryption detection algorithm (PEDA) that consisted of two phases. In, PEDA-Phase-I, a Windows application programming interface (API) generated by a suspicious ...

Video streaming is one of the challenging issues in vehicular ad-hoc networks (VANETs) due to their highly dynamic topology and frequent connectivity disruptions. Recent developments in the routing protocol methods used in VANETs have contributed to improvements in the quality of experience (QoE) of the received video. One of these methods is the selection of the next-hop relay vehicle. In this paper, a QoE-aware geographic protocol for video streaming over VANETs is proposed. The selection process of the next relay vehicle is based on a correlated formula of QoE and quality of service (QoS) factors to enhance the users’ QoE. The simulation results show that the proposed GeoQoE-Vanet outperforms both GPSR and GPSR-2P protocols in providing the best end-user QoE of video streaming service.

Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets and apply a machine learning pipeline to: (i) perform device profiling, and (ii) predict the inter-arrival of IoT packets. This latter analysis is very related to the channel and network usage and can be leveraged in the future for system performance enhancements. Our analysis mainly focuses on the...

Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets and apply a machine learning pipeline to: (i) perform device profiling, and (ii) predict the inter-arrival of IoT packets. This latter analysis is very related to the channel and network usage and can be leveraged in the future for system performance enhancements. Our analysis mainly focuses on the...

Cognitive disorders remain a major cause of disability in Multiple Sclerosis (MS). They lead to unemployment, the need for daily assistance, and a poor quality of life. The understanding of the origin, factors, processes, and consequences of cognitive disfunction is key to its prevention, early diagnosis, and rehabilitation. The neuropsychological testing and continuous monitoring of cognitive status as part of the overall evaluation of patients with MS in parallel with clinical and paraclinical examinations are highly recommended. In order to improve health and disease understanding, a close linkage between fundamental, clinical, epidemiological, and socio-economic research is required. The effective sharing of data, standardized data processing, and the linkage of such data with large-scale cohort studies is a prerequisite for the translation of research findings into the clinical setting. In this context, this paper proposes a software platform for the cognitive assessment and re...

In this paper, we construct novel numerical algorithms to solve the heat or diffusion equation. We start with 105 different leapfrog-hopscotch algorithm combinations and narrow this selection down to five during subsequent tests. We demonstrate the performance of these top five methods in the case of large systems with random parameters and discontinuous initial conditions, by comparing them with other methods. We verify the methods by reproducing an analytical solution using a non-equidistant mesh. Then, we construct a new nontrivial analytical solution containing the Kummer functions for the heat equation with time-dependent coefficients, and also reproduce this solution. The new methods are then applied to the nonlinear Fisher equation. Finally, we analytically prove that the order of accuracy of the methods is two, and present evidence that they are unconditionally stable.

Data security plays a crucial role in healthcare monitoring systems, since critical patient information is transacted over the Internet, especially through wireless devices, wireless routes such as optical wireless channels, or optical transport networks related to optical fibers. Many hospitals are acquiring their own metro dark fiber networks for collaborating with other institutes as a way to maximize their capacity to meet patient needs, as sharing scarce and expensive assets, such as scanners, allows them to optimize their efficiency. The primary goal of this article is to develop of an attack detection model suitable for healthcare monitoring systems that uses internet protocol (IP) virtual private networks (VPNs) over optical transport networks. To this end, this article presents the vulnerabilities in healthcare monitoring system networks, which employ VPNs over optical transport layer architecture. Furthermore, a multilayer network architecture for closer integration of the...

This paper presents a new depth-integrated non-hydrostatic finite element model for simulating wave propagation, breaking and runup using a combination of discontinuous and continuous Galerkin methods. The formulation decomposes the depth-integrated non-hydrostatic equations into hydrostatic and non-hydrostatic parts. The hydrostatic part is solved with a discontinuous Galerkin finite element method to allow the simulation of discontinuous flows, wave breaking and runup. The non-hydrostatic part led to a Poisson type equation, where the non-hydrostatic pressure is solved using a continuous Galerkin method to allow the modeling of wave propagation and transformation. The model uses linear quadrilateral finite elements for horizontal velocities, water surface elevations and non-hydrostatic pressures approximations. A new slope limiter for quadrilateral elements is developed. The model is verified and validated by a series of analytical solutions and laboratory experiments.

Cellular beams are an attractive option for the steel construction industry due to their versatility in terms of strength, size, and weight. Further benefits are the integration of services thereby reducing ceiling-to-floor depth (thus, building’s height), which has a great economic impact. Moreover, the complex localised and global failures characterizing those members have led several scientists to focus their research on the development of more efficient design guidelines. This paper aims to propose an artificial neural network (ANN)-based formula to precisely compute the critical elastic buckling load of simply supported cellular beams under uniformly distributed vertical loads. The 3645-point dataset used in ANN design was obtained from an extensive parametric finite element analysis performed in ABAQUS. The independent variables adopted as ANN inputs are the following: beam’s length, opening diameter, web-post width, cross-section height, web thickness, flange width, flange th...

The integration of a P300-based brain–computer interface (BCI) into virtual reality (VR) environments is promising for the video games industry. However, it faces several limitations, mainly due to hardware constraints and limitations engendered by the stimulation needed by the BCI. The main restriction is still the low transfer rate that can be achieved by current BCI technology, preventing movement while using VR. The goal of this paper is to review current limitations and to provide application creators with design recommendations to overcome them, thus significantly reducing the development time and making the domain of BCI more accessible to developers. We review the design of video games from the perspective of BCI and VR with the objective of enhancing the user experience. An essential recommendation is to use the BCI only for non-complex and non-critical tasks in the game. Also, the BCI should be used to control actions that are naturally integrated into the virtual world. F...

In this paper, a combination of graph-based design and simulation-based engineering (SBE) into a new concept called Executable Integrative Product-Production Model (EIPPM) is elaborated. Today, the first collaborative process in engineering for all mechatronic disciplines is the virtual commissioning phase. The authors see a hitherto untapped potential for the earlier, integrated and iterative use of SBE for the development of production systems (PS). Seamless generation of and exchange between Model-, Software- and Hardware-in-the-Loop simulations is necessary. Feedback from simulation results will go into the design decisions after each iteration. The presented approach combines knowledge of the domain “PSs” together with the knowledge of the corresponding “product” using a so called Graph-based Design Language (GBDL). Its central data model, which represents the entire life cycle of product and PS, results of an automatic translation step in a compiler. Since the execution of the...

Buruli ulcer caused by Mycobacterium ulcerans (M. ulcerans) is identified by a pain-free cyst or edema which develops into a massive skin ulcer if left untreated. There are reports of chemoresistance, toxicity, noncompliance, and poor efficacy of current therapeutic options. Previously, we used cheminformatics approaches to identify potential antimycobacterial compounds targeting major receptors in M. ulcerans. In this paper, we sought to identify potential bioactive compounds by targeting Cystathionine gamma-synthase (CGS) MetB, a key receptor involved in methionine synthesis. Inhibition of methionine synthesis restricts the growth of M. ulcerans. Two potent inhibitors Juglone (IC50 0.7 +/− 0.7 µmol/L) and 9-hydroxy-alpha-lapachone (IC50 0.9 +/− 0.1 µmol/L) were used to generate 3D chemical feature pharmacophore model via LigandScout with a score of 0.9719. The validated model was screened against a pre-filtered library of 2530 African natural products. Compounds with fit scores ab...

Wind turbine gearboxes are known to be among the weakest components in the system and the possibility to study and understand the behavior of geared transmissions when subject to several types of faults might be useful to plan maintenance and eventually reduce the costs by preventing further damage. The aim of this work is to develop a high-fidelity numerical model of a single-stage planetary gearbox selected as representative and to evaluate its behavior in the presence of surface fatigue and tooth-root bending damage, i.e., pits and cracks. The planetary gearbox is almost entirely modelled, including shafts, gears as well as bearings with all the rolling elements. Stresses and strains in the most critical areas are analyzed to better evaluate if the presence of such damage can be somehow detected using strain gauges and where to place them to maximize the sensitivity of the measures to the damage. Several simulations with different levels, types and positions of the damage were pe...

A computer vision system for automatic recognition and classification of five varieties of plant leaves under controlled laboratory imaging conditions, comprising: 1–Cydonia oblonga (quince), 2–Eucalyptus camaldulensis dehn (river red gum), 3–Malus pumila (apple), 4–Pistacia atlantica (mt. Atlas mastic tree) and 5–Prunus armeniaca (apricot), is proposed. 516 tree leaves images were taken and 285 features computed from each object including shape features, color features, texture features based on the gray level co-occurrence matrix, texture descriptors based on histogram and moment invariants. Seven discriminant features were selected and input for classification purposes using three classifiers: hybrid artificial neural network–ant bee colony (ANN–ABC), hybrid artificial neural network–biogeography based optimization (ANN–BBO) and Fisher linear discriminant analysis (LDA). Mean correct classification rates (CCR), resulted in 94.04%, 89.23%, and 93.99%, for hybrid ANN–ABC; hybrid AN...

A modified power-law (MPL) viscosity model of non-Newtonian fluid flow has been used for the multiple-relaxation-time (MRT) lattice Boltzmann methods (LBM) and then validated with the benchmark problems using the graphics process unit (GPU) parallel computing via Compute Unified Device Architecture (CUDA) C platform. The MPL model for characterizing the non-Newtonian behavior is an empirical correlation that considers the Newtonian behavior of a non-Newtonian fluid at a very low and high shear rate. A new time unit parameter (λ) governing the flow has been identified, and this parameter is the consequence of the induced length scale introduced by the power law. The MPL model is free from any singularities due to the very low or even zero shear-rate. The proposed MPL model was first validated for the benchmark study of the lid-driven cavity and channel flows. The model was then applied for shear-thinning and shear-thickening fluid flows through a backward-facing step with relatively ...

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was found to be a severe threat to global public health in late 2019. Nevertheless, no approved medicines have been found to inhibit the virus effectively. Anti-malarial and antiviral medicines have been reported to target the SARS-CoV-2 virus. This paper chose eight natural eucalyptus compounds to study their binding interactions with the SARS-CoV-2 main protease (Mpro) to assess their potential for becoming herbal drugs for the new SARS-CoV-2 infection virus. In-silico methods such as molecular docking, molecular dynamics (MD) simulations, and Molecular Mechanics Poisson Boltzmann Surface Area (MM/PBSA) analysis were used to examine interactions at the atomistic level. The results of molecular docking indicate that Mpro has good binding energy for all compounds studied. Three docked compounds, α-gurjunene, aromadendrene, and allo-aromadendrene, with highest binding energies of −7.34 kcal/mol (−30.75 kJ/mol), −7.23 kcal/m...

In many developed countries, the usage of artificial intelligence (AI) and machine learning (ML) has become important in paving the future path in how data is managed and secured in the small and medium enterprises (SMEs) sector. SMEs in these developed countries have created their own cyber regimes around AI and ML. This knowledge is tested daily in how these countries’ SMEs run their businesses and identify threats and attacks, based on the support structure of the individual country. Based on recent changes to the UK General Data Protection Regulation (GDPR), Brexit, and ISO standards requirements, machine learning cybersecurity (MLCS) adoption in the UK SME market has become prevalent and a good example to lean on, amongst other developed nations. Whilst MLCS has been successfully applied in many applications, including network intrusion detection systems (NIDs) worldwide, there is still a gap in the rate of adoption of MLCS techniques for UK SMEs. Other developed countries such...

The platform economy denotes a subset of economic activities enabled by platforms such as Amazon, Alibaba, and Uber. Due to their tremendous success, more and more offerings concentrate around platforms increasing platforms’ positional-power, hence leading towards a de-facto centralization of previously decentralized online markets. Furthermore, platform models work well for individual products and services or predefined combinations of these. However, they fall short in supporting complex products (personalized combinations of individual products and services), the combination of which is required to fulfill a particular consumer need, consequently increasing transaction costs for consumers looking for such products. To address these issues, we envision a “post-platform economy”—an economy facilitated by decentralized and self-organized online structures named Distributed Market Spaces. This work proposes a comprehensive model to serve as a guiding framework for the analysis, desig...

We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches de...

Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) signals is a key signal processing problem. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new SWD method with a low computational complexity that can be easily trained with data from standard medical protocols. Precisely, EEG signals are divided into time segments for which the Morlet 1-D decomposition is applied. The generalized Gaussian distribution (GGD) statistical model is fitted to the resulting wavelet coefficients. A k-nearest neighbors (k-NN) self-supervised classifier is trained using the GGD parameters to detect the spike-and-wave pattern. Experiments were conducted using 106 spike-and-wave signals and 106 non-spike-and-wave signals for training and another 96 annotated EEG segments from six human subjects for testing. The proposed SWD classification methodology achieved 95 % sensitivit...

In this study, an effective local minima detection and definition algorithm is introduced for a mobile robot navigating through unknown static environments. Furthermore, five approaches are presented and compared with the popular approach wall-following to pull the robot out of the local minima enclosure namely; Random Virtual Target, Reflected Virtual Target, Global Path Backtracking, Half Path Backtracking, and Local Path Backtracking. The proposed approaches mainly depend on changing the target location temporarily to avoid the original target’s attraction force effect on the robot. Moreover, to avoid getting trapped in the same location, a virtual obstacle is placed to cover the local minima enclosure. To include the most common shapes of deadlock situations, the proposed approaches were evaluated in four different environments; V-shaped, double U-shaped, C-shaped, and cluttered environments. The results reveal that the robot, using any of the proposed approaches, requires fewer...

IT investment is a crucial issue as it does not only influence the performance in Small-Medium Enterprises (SMEs) but it also helps executives to align business strategy with organizational performance. Admittedly, though, there is ineffective use of Information Systems (IS) due to a lack of strategic planning and of formal processes resulting in executives’ failure to develop IS plans and achieve long-term sustainability. Therefore, the purpose of this paper is to examine the phases of Strategic Information Systems Planning (SISP) process that contribute to a greater extent of success so that guidelines regarding the implementation of the process in SMEs can be provided. Data was collected by 160 IS executives in Greek SMEs during February and May 2017. Multivariate Regression Analysis was applied on the detailed items of the SISP process and success constructs. The results of this survey present that managers should be aware of the strategic use of IS planning so as to increase co...

Conjugated singlet ground state diradicals have received remarkable attention owing to their potential applications in optoelectronic devices. A distinctive character of these systems is the location of the double exciton state, a low lying excited state dominated by the doubly excited H,H→L,L configuration, which may influence optical and other photophysical properties. In this contribution we investigate this specific excited state, for a series of recently synthesized conjugated diradicals, employing time dependent density functional theory based on the unrestricted parallel spin reference configuration in the spin-flip formulation (SF-TDDFT) and standard TD calculations based on the unrestricted antiparallel spin reference configuration (TDUDFT). The quality of the computed results is assessed considering diradical and multiradical descriptors and the excited state wavefunction composition.

Using molecular dynamics, a comparative study was performed of two pairs of glassy polymers, low permeability polyetherimides (PEIs) and highly permeable Si-containing polytricyclononenes. All calculations were made with 32 independent models for each polymer. In both cases, the accessible free volume (AFV) increases with decreasing probe size. However, for a zero-size probe, the curves for both types of polymers cross the ordinate in the vicinity of 40%. The size distribution of free volume in PEI and highly permeable polymers differ significantly. In the former case, they are represented by relatively narrow peaks, with the maxima in the range of 0.5–1.0 Å for all the probes from H2 to Xe. In the case of highly permeable Si-containing polymers, much broader peaks are observed to extend up to 7–8 Å for all the gaseous probes. The obtained size distributions of free volume and accessible volume explain the differences in the selectivity of the studied polymers. The surface area of A...

Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results of the proposed method offered high speed and efficiency, which could be a prominent aspect in designing image-processing systems based on Type 2 fuzzy rules characterization for identifying fixed and moving images. The proposed fuzzy method obtained an accuracy rate for identifying and recognizing moving objects of 98% and a mean square error of 0.1183464 less than other studies. It also achi...

IT investment is a crucial issue as it does not only influence the performance in Small-Medium Enterprises (SMEs) but it also helps executives to align business strategy with organizational performance. Admittedly, though, there is ineffective use of Information Systems (IS) due to a lack of strategic planning and of formal processes resulting in executives’ failure to develop IS plans and achieve long-term sustainability. Therefore, the purpose of this paper is to examine the phases of Strategic Information Systems Planning (SISP) process that contribute to a greater extent of success so that guidelines regarding the implementation of the process in SMEs can be provided. Data was collected by 160 IS executives in Greek SMEs during February and May 2017. Multivariate Regression Analysis was applied on the detailed items of the SISP process and success constructs. The results of this survey present that managers should be aware of the strategic use of IS planning so as to increase co...

Business Improvement Districts (BIDs) are a contemporary urban revitalization policy that has been set in motion through international policymaking circuits. They have been presented as a panacea to the economic and social challenges facing many cities and traditional shopping districts. However, a comprehensive overview of the academic literature on this form of local governance remains to be conducted. Drawing on bibliometric methods and bibliometrix R-tool, this paper maps and examines the state-of-the-art of academic knowledge on BIDs published between 1979 and 2021. Findings suggest that (i) scientific production has increased since the early 2000s, has crossed US borders but remains highly Anglo-Saxon-centered; (ii) academic knowledge on BIDs is multidisciplinary and has been published in high-impact journals; (iii) influential documents on BIDs have centered on three issues: urban governance/politics, policy mobilities–mutation and impacts assessment and criticisms; (iv) whil...

The study of biofilm formation is undoubtedly important due to micro-organisms forming a protected mode from the host defense mechanism, which may result in alteration in the host gene transcription and growth rate. A mathematical model of the nonlinear advection–diffusion–reaction equation has been studied for biofilm formation. In this paper, we present two novel non-standard finite difference schemes to obtain an approximate solution to the mathematical model of biofilm formation. One explicit non-standard finite difference scheme is proposed for biomass density equation and one property-conserving scheme for a coupled substrate–biomass system of equations. The nonlinear term in the mathematical model has been handled efficiently. The proposed schemes maintain dynamical consistency (positivity, boundedness, merging of colonies, biofilm annihilation), which is revealed through experimental observation. In order to verify the accuracy and effectiveness of our proposed schemes, we c...

Identifying human face shape and eye attributes is the first and most vital process before applying for the right hairstyle and eyelashes extension. The aim of this research work includes the development of a decision support program to constitute an aid system that analyses eye and face features automatically based on the image taken from a user. The system suggests a suitable recommendation of eyelashes type and hairstyle based on the automatic reported users’ eye and face features. To achieve the aim, we develop a multi-model system comprising three separate models; each model targeted a different task, including; face shape classification, eye attribute identification and gender detection model. Face shape classification system has been designed based on the development of a hybrid framework of handcrafting and learned feature. Eye attributes have been identified by exploiting the geometrical eye measurements using the detected eye landmarks. Gender identification system has bee...

In this study, we present a thorough investigation of a compressible kinetic model for non-ideal fluids [DOI:10.1103/PhysRevE.102.020103]. The model imposes the local thermodynamic pressure through appropriate rescaling of the particle's velocities, which accounts for both long- and short-range effects and hence full thermodynamic consistency. The model is fully Galilean invariant and treats mass, momentum, and energy as local conservation laws. After detailed analysis and derivation of the hydrodynamic limit, the model's accuracy and robustness is assessed for various benchmark simulation ranging from Joule-Thompson effect, phase-change and high-speed flows. We show that our model can operate in entire phase diagram, including super- as well as sub-critical regimes and inherently captures phase-change phenomena.

Data security plays a crucial role in healthcare monitoring systems, since critical patient information is transacted over the Internet, especially through wireless devices, wireless routes such as optical wireless channels, or optical transport networks related to optical fibers. Many hospitals are acquiring their own metro dark fiber networks for collaborating with other institutes as a way to maximize their capacity to meet patient needs, as sharing scarce and expensive assets, such as scanners, allows them to optimize their efficiency. The primary goal of this article is to develop of an attack detection model suitable for healthcare monitoring systems that uses internet protocol (IP) virtual private networks (VPNs) over optical transport networks. To this end, this article presents the vulnerabilities in healthcare monitoring system networks, which employ VPNs over optical transport layer architecture. Furthermore, a multilayer network architecture for closer integration of the...