Search results for: wavelet networks

Authors: Riyadh Alsultani, Ali Majdi

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It is necessary to develop a design methodology that includes the possibility of seismic events occurring in a region, the vulnerability of the civil hydraulic structure, and the effects of the occurrence hazard on society, environment, and economy in order to evaluate the flood and earthquake risks of coastal bridge networks. This paper presents a design approach for the assessment of the risk and sustainability of coastal bridge networks under time-variant flood-earthquake conditions. The social, environmental, and economic indicators of the network are used to measure its sustainability. These consist of anticipated loss, downtime, energy waste, and carbon dioxide emissions. The design process takes into account the possibility of happening in a set of flood and earthquake scenarios that represent the local seismic activity. Based on the performance of each bridge as determined by fragility assessments, network linkages are measured. The network's connections and bridges' damage statuses after an earthquake scenario determine the network's sustainability and danger. The sustainability measures' temporal volatility and the danger of structural degradation are both highlighted. The method is shown using a transportation network in Baghdad, Iraq.

Keywords: sustainability, Coastal bridge networks, flood-earthquake risk, structural design

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Authors: RAM PAL SINGH, VIKASH CHAUDHARY, MONIKA VERMA

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In this paper, we proposed the implementation of optimization based Extreme Learning Machine (ELM) for watermarking of B-channel of color image in discrete wavelet transform (DWT) domain. ELM, a regularization algorithm, works based on generalized single-hidden-layer feed-forward neural networks (SLFNs). However, hidden layer parameters, generally called feature mapping in context of ELM need not to be tuned every time. This paper shows the embedding and extraction processes of watermark with the help of ELM and results are compared with already used machine learning models for watermarking.Here, a cover image is divide into suitable numbers of non-overlapping blocks of required size and DWT is applied to each block to be transformed in low frequency sub-band domain. Basically, ELM gives a unified leaning platform with a feature mapping, that is, mapping between hidden layer and output layer of SLFNs, is tried for watermark embedding and extraction purpose in a cover image. Although ELM has widespread application right from binary classification, multiclass classification to regression and function estimation etc. Unlike SVM based algorithm which achieve suboptimal solution with high computational complexity, ELM can provide better generalization performance results with very small complexity. Efficacy of optimization method based ELM algorithm is measured by using quantitative and qualitative parameters on a watermarked image even though image is subjected to different types of geometrical and conventional attacks.

Keywords: BER, DWT, extreme leaning machine (ELM), PSNR

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Authors: Juan Sebastián Hernández

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The Nevado del Ruíz volcano, located between the limits of the Departments of Caldas and Tolima in Colombia, presented an unstable behaviour in the course of the year 2020, this volcanic activity led to secondary effects on the crust, which is why the prediction of deformations becomes the task of geoscientists. In the course of this article, the use of tropospheric variables such as evapotranspiration, UV aerosol index, carbon monoxide, nitrogen dioxide, methane, surface temperature, among others, is used to train a set of neural networks that can predict the behaviour of the resulting phase of an unrolled interferogram with the DInSAR technique, whose main objective is to identify and characterise the behaviour of the crust based on the environmental conditions. For this purpose, variables were collected, a generalised linear model was created, and a set of neural networks was created. After the training of the network, validation was carried out with the test data, giving an MSE of 0.17598 and an associated r-squared of approximately 0.88454. The resulting model provided a dataset with good thematic accuracy, reflecting the behaviour of the volcano in 2020, given a set of environmental characteristics.

Keywords: crustal deformation, Tropomi, neural networks (ANN), volcanic activity, DInSAR

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Authors: Shiyin He, Zheng Huang

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In this paper, deep learning methods are applied in bio-medical field to detect and count different types of cells in an automatic way instead of manual work in medical practice, specifically in bone marrow examination. The process is mainly composed of two steps, detection and recognition. Mask-Region-Convolutional Neural Networks (Mask-RCNN) was used for detection and image segmentation to extract cells and then Convolutional Neural Networks (CNN), as well as Deep Residual Network (ResNet) was used to classify. Result of cell detection network shows high efficiency to meet application requirements. For the cell recognition network, two networks are compared and the final system is fully applicable.

Keywords: cell detection, cell recognition, deep learning, Mask-RCNN, ResNet

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Authors: Sima Najafzadehkhoei, George Vega Yon

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This paper presents an approach for calibrating Agent-Based Models (ABMs) efficiently, utilizing Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. These machine learning techniques are applied to Susceptible-Infected-Recovered (SIR) models, which are a core framework in the study of epidemiology. Our method replicates parameter values from observed trajectory curves, enhancing the accuracy of predictions when compared to traditional calibration techniques. Through the use of simulated data, we train the models to predict epidemiological parameters more accurately. Two primary approaches were explored: one where the number of susceptible, infected, and recovered individuals is fully known, and another using only the number of infected individuals. Our method shows promise for application in other ABMs where calibration is computationally intensive and expensive.

Keywords: ABM, calibration, CNN, LSTM, epidemiology

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Authors: Mohammad Mehdizadeh

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COVID-19 forced marketers to change their marketing strategies, focusing less on reactive approaches and more on proactive approaches, primarily social media. The next few years will be dominated by employee engagement and customer experience, leading to businesses focusing more on "long-term customer relationships." A large number of marketing strategies need to be employed in an ever-evolving online environment, which is both filled with opportunities and dangers, as well as being an intimidating platform to use, incorporating new and exciting opportunities for businesses and organizations as it constantly evolves. In this article, we examine the effect of social networks on marketing in post-pandemic environments. A descriptive survey is used as the research method. The results show that social networks have a positive and significant impact on marketing in a post-pandemic environment. Among the social networks studied, Instagram, Facebook, and Twitter have the most positive effect on marketing advancement.

Keywords: COVID-19, customers, marketing, post-pandemic

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Authors: Umair Shafique, Hafiz Usman Zia, Fiaz Majeed, Samina Naz, Javeria Ahmed, Maleeha Zainab

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The Internet of Things (IoT) has become a hot topic for the last couple of years. This innovative technology has shown promising progress in various areas, and the world has witnessed exponential growth in multiple application domains. Researchers are working to investigate its aptitudes to get the best from it by harnessing its true potential. But at the same time, IoT networks open up a new aspect of vulnerability and physical threats to data integrity, privacy, and confidentiality. It's is due to centralized control, data silos approach for handling information, and a lack of standardization in the IoT networks. As we know, blockchain is a new technology that involves creating secure distributed ledgers to store and communicate data. Some of the benefits include resiliency, integrity, anonymity, decentralization, and autonomous control. The potential for blockchain technology to provide the key to managing and controlling IoT has created a new wave of excitement around the idea of putting that data back into the hands of the end-users. In this manuscript, we have proposed a model that combines blockchain and IoT networks to address potential security and privacy issues in the healthcare domain. Then we try to describe various application areas, challenges, and future directions in the healthcare sector where blockchain platforms merge with IoT networks.

Keywords: IoT, blockchain, cryptocurrency, healthcare, consensus, data

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Authors: Chen Xi, Lao Xuerui, Li Junjie, Jiang Yike, Wang Hanwei, Zeng Zihao

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To further promote the development of smart cities, the microscopic "nerve endings" of the City Intelligent Model (CIM) are extended to be more sensitive. In this paper, we develop a pedestrian trajectory twin terminal based on the CIM and CNN technology. It also uses 5G networks, architectural and geoinformatics technologies, convolutional neural networks, combined with deep learning networks for human behaviour recognition models, to provide empirical data such as 'pedestrian flow data and human behavioural characteristics data', and ultimately form spatial performance evaluation criteria and spatial performance warning systems, to make the empirical data accurate and intelligent for prediction and decision making.

Keywords: urban planning, urban governance, CIM, artificial intelligence, convolutional neural network

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Authors: Radhika Ranjan Roy

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Deployment of machine learning (ML)/deep learning (DL) algorithms for cyberattack detection in operational communications networks (wireless and/or wire-line) is being delayed because of low-performance parameters (e.g., recall, precision, and f₁-score). If datasets become imbalanced, which is the usual case for communications networks, the performance tends to become worse. Complexities in handling reducing dimensions of the feature sets for increasing performance are also a huge problem. Mahalanobis algorithms have been widely applied in scientific research because Mahalanobis distance metric learning is a successful framework. In this paper, we have investigated the Mahalanobis binary classifier algorithm for increasing cyberattack detection performance over communications networks as a proof of concept. We have also found that high-dimensional information in intermediate features that are not utilized as much for classification tasks in ML/DL algorithms are the main contributor to the state-of-the-art of improved performance of the Mahalanobis method, even for imbalanced and sparse datasets. With no feature reduction, MD offers uniform results for precision, recall, and f₁-score for unbalanced and sparse NSL-KDD datasets.

Keywords: Mahalanobis distance, machine learning, deep learning, NS-KDD, local intrinsic dimensionality, chi-square, positive semi-definite, area under the curve

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Authors: Shiuh-Kuang Yang, Sheam-Chyun Lin, Jyin-Wen Cheng, Deng-Guei Hsu

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The torsional mode of guided wave, T(0,1), has been applied to detect characteristics and defects in pipelines, especially in the cases of coated, elevated and buried pipes. The signals of minor corrosions would be covered by the noise, unfortunately, because the coated material and buried medium always induce a strong attenuation of the guided wave. Furthermore, the guided wave would be attenuated more seriously and make the signals hard to be identified when setting the array ring of the transducers on a general corrosion area of the pipe. The objective of this study is then to discuss the effects of the above-mentioned general corrosion on guided wave tests by experiments and signal processing techniques, based on the use of the finite element method, the two-dimensional Fourier transform and the continuous wavelet transform. Results show that the excitation energy would be reduced when the array ring set on the pipe surface having general corrosion. The non-uniformed contact surface also produces the unwanted asymmetric modes of the propagating guided wave. Some of them are even mixing together with T(0,1) mode and increase the difficulty of measurements, especially when a defect or local corrosion merged in the general corrosion area. It is also showed that the guided waves attenuation are increasing with the increasing corrosion depth or the rising inspection frequency. However, the coherent signals caused by the general corrosion would be decayed with increasing frequency. The results obtained from this research should be able to provide detectors to understand the impact when the array ring set on the area of general corrosion and the way to distinguish the localized corrosion which is inside the area of general corrosion.

Keywords: guided wave, finite element method, two-dimensional fourier transform, wavelet transform, general corrosion, localized corrosion

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Authors: Nicole Pearcy, Jonathan J. Crofts, Nadia Chuzhanova

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In this paper, we employ a directed hypergraph model to investigate the extent to which environmental variability influences the set of available biochemical reactions within a living cell. Such an approach avoids the limitations of the usual complex network formalism by allowing for the multilateral relationships (i.e. connections involving more than two nodes) that naturally occur within many biological processes. More specifically, we extend the concept of network reciprocity to complex hyper-networks, thus enabling us to characterize a network in terms of the existence of mutual hyper-connections, which may be considered a proxy for metabolic network complexity. To demonstrate these ideas, we study 115 metabolic hyper-networks of bacteria, each of which can be classified into one of 6 increasingly varied habitats. In particular, we found that reciprocity increases significantly with increased environmental variability, supporting the view that organism adaptability leads to increased complexities in the resultant biochemical networks.

Keywords: complexity, hypergraphs, reciprocity, metabolism

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Authors: P. Prasanna Murali Krishna, M. V. Subramanyam, K. Satya Prasad

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File sharing in networks are generally achieved using Peer-to-Peer (P2P) applications. Structured P2P approaches are widely used in adhoc networks due to its distributed and scalability features. Efficient mechanisms are required to handle the huge amount of data distributed to all peers. The intrinsic characteristics of P2P system makes for easier content distribution when compared to client-server architecture. All the nodes in a P2P network act as both client and server, thus, distributing data takes lesser time when compared to the client-server method. CHORD protocol is a resource routing based where nodes and data items are structured into a 1- dimensional ring. The structured lookup algorithm of Chord is advantageous for distributed P2P networking applications. Though, structured approach improves lookup performance in a high bandwidth wired network it could contribute to unnecessary overhead in overlay networks leading to degradation of network performance. In this paper, the performance of existing CHORD protocol on Wireless Mesh Network (WMN) when nodes are static and dynamic is investigated.

Keywords: wireless mesh network (WMN), structured P2P networks, peer to peer resource sharing, CHORD Protocol, DHT

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Authors: Rahul Malhotra

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Fiber based access networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This paper is targeted to show the simultaneous delivery of triple play service (data, voice and video). The comparative investigation and suitability of various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be accommodated decreases due to increase in bit error rate.

Keywords: BER, PON, TDMPON, GPON, CWDM, OLT, ONT

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Authors: Mbida Mohamed, Ezzati Abdellah

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A strong challenge in the wireless sensor networks (WSN) is to save the energy and have a long life time in the network without having a high rate of loss information. However, topology control (TC) protocols are designed in a way that the network is divided and having a standard system of exchange packets between nodes. In this article, we will propose a clustering based and centralized routing table protocol of TC (CBCRT) which delegates a leader node that will encapsulate a single routing table in every cluster nodes. Hence, if a node wants to send packets to the sink, it requests the information's routing table of the current cluster from the node leader in order to root the packet.

Keywords: mobile wireless sensor networks, routing, topology of control, protocols

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Authors: Rashi Jain

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The Wireless Body Area Networks (WBANs) is an emerging interdisciplinary area where small sensors are placed on/within the human body. These sensors monitor the physiological activities and vital statistics of the body. The data from these sensors is aggregated and communicated to a remote doctor for immediate attention or to a database for records. On 6 Feb 2012, the IEEE 802.15.6 task group approved the standard for Body Area Network (BAN) technologies. The standard proposes the physical and MAC layer for the WBANs. The work provides an introduction to WBANs and overview of the physical and MAC layers of the standard. The physical layer specifications have been covered. A comparison of different protocols used at MAC layer is drawn. An introduction to the network layer and security aspects of the WBANs is made. The WBANs suffer certain limitations such as regulation of frequency bands, minimizing the effect of transmission and reception of electromagnetic signals on the human body, maintaining the energy efficiency among others. This has slowed down their implementation.

Keywords: vehicular networks, sensors, MicroController 8085, LTE

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Authors: N. Meddeb, A. M. Makhlouf, M. A. Ben Ayed

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Due to the real-time requirement of message in Vehicular Ad hoc NETworks (VANET), it is necessary to authenticate vehicles to achieve security, efficiency, and conditional privacy-preserving. Privacy is of utmost relevance in VANETs. For this reason, we have proposed a new protocol called ‘Multilevel Authentication Protocol’ (MAP) that considers different vehicle categories. The proposed protocol is based on our Multilevel Authentication protocol for Vehicular networks (MAVnet). But the MAP leads to human safety, where the priority is given to the ambulance vehicles. For evaluation, we used the Java language to develop a demo application and deployed it on the Network Security Simulation (Nessi2). Compared with existing authentication protocols, MAP markedly enhance the communication overhead and decreases the delay of exchanging messages while preserving conditional privacy.

Keywords: Vehicular Ad hoc NETworks (VANET), vehicle categories, safety, databases, privacy, authentication, throughput, delay

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Authors: Debneil Saha Roy

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Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

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Authors: Rachid Dehini, Brahim Berbaoui

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The main objectives of shunt active power filter (SAPF) is to preserve the power system from unwanted harmonic currents produced by nonlinear loads, as well as to compensate the reactive power. The aim of this paper is to present a (PAPF) supplied by the Photovoltaic cells ,in such a way that the (PAPF) feeds the linear and nonlinear loads by harmonics currents and the excess of the energy is injected into the power system. In order to improve the performances of conventional (PAPF) This paper also proposes artificial neural networks (ANN) for harmonics identification and DC link voltage control. The simulation study results of the new (SAPF) identification technique are found quite satisfactory by assuring good filtering characteristics and high system stability.

Keywords: SAPF, harmonics current, photovoltaic cells, MPPT, artificial neural networks (ANN)

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Authors: Rania Khadim, Mohammed Erritali, Abdelhakim Maaden

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Nowadays Wireless Sensor Networks have attracted worldwide research and industrial interest, because they can be applied in various areas. Geographic routing protocols are very suitable to those networks because they use location information when they need to route packets. Obviously, location information is maintained by Location-Based Services provided by network nodes in a distributed way. In this paper we choose to evaluate the performance of two hierarchical rendezvous location based-services, GLS (Grid Location Service) and HLS (Hierarchical Location Service) coupled to the GPSR routing protocol (Greedy Perimeter Stateless Routing) for Wireless Sensor Network. The simulations were performed using NS2 simulator to evaluate the performance and power of the two services in term of location overhead, the request travel time (RTT) and the query Success ratio (QSR). This work presents also a new scalability performance study of both GLS and HLS, specifically, what happens if the number of nodes N increases. The study will focus on three qualitative metrics: The location maintenance cost, the location query cost and the storage cost.

Keywords: location based-services, routing protocols, scalability, wireless sensor networks

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Authors: Biljana Marković

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Social networks are an integral part of our everyday lives, becoming an indispensable medium for communication in personal and business environments. New forms and ways of communication change the general mindset and significantly affect the quality of life of individuals. Quality of life is perceived as an abstract term, but often people are not aware that they directly affect the quality of their own lives, making minor but significant everyday choices and decisions. Quality of life can be defined broadly, but in the widest sense, it involves a subjective sense of satisfaction with one's life. Scientific knowledge about the impact of social networks on self-assessment of the quality of life of individuals is only just beginning to be researched. Available research indicates potential benefits as well as a number of disadvantages. In the context of the previous claims, the focus of the study conducted by the authors of this paper focuses on analyzing the impact of social networks on individual’s self-assessment of quality of life and the correlation between time spent on social networks, and the choice of content that individuals choose to share to present themselves. Moreover, it is aimed to explain how much and in what ways they critically judge the lives of others online. The research aspires to show the positive as well as negative aspects that social networks, primarily Facebook and Instagram, have on creating a picture of individuals and how they compare themselves with others. The topic of this paper is based on quantitative research conducted on a representative sample. An analysis of the results of the survey conducted online has elaborated a hypothesis which claims that content shared by individuals on social networks influences the image they create about themselves. A comparative analysis of the results obtained with the results of similar research has led to the conclusion about the synergistic influence of social networks on the feeling of the quality of life of respondents. The originality of this work is reflected in the approach of conducting research by examining attitudes about an individual's life satisfaction, the way he or she creates a picture of himself/herself through social networks, the extent to which he/she compares herself/himself with others, and what social media applications he/she uses. At the cognitive level, scientific contributions were made through the development of information concepts on quality of life, and at the methodological level through the development of an original methodology for qualitative alignment of respondents' attitudes using statistical analysis. Furthermore, at the practical level through the application of concepts in assessing the creation of self-image and the image of others through social networks.

Keywords: quality of life, social media, self image, influence of social media

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Authors: Farideh Halakou, Emel Sen, Attila Gursoy, Ozlem Keskin

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Protein–Protein Interactions (PPIs) mediate major biological processes in living cells. The study of PPIs as networks and analyze the network properties contribute to the identification of genes and proteins associated with diseases. In this study, we have created the sub-networks of brain and lung metastasis from primary tumor in breast cancer. To do so, we used seed genes known to cause metastasis, and produced their interactions through a network-topology based prioritization method named GUILDify. In order to have the experimental support for the sub-networks, we further curated them using STRING database. We proceeded by modeling structures for the interactions lacking complex forms in Protein Data Bank (PDB). The functional enrichment analysis shows that KEGG pathways associated with the immune system and infectious diseases, particularly the chemokine signaling pathway, are important for lung metastasis. On the other hand, pathways related to genetic information processing are more involved in brain metastasis. The structural analyses of the sub-networks vividly demonstrated their difference in terms of using specific interfaces in lung and brain metastasis. Furthermore, the topological analysis identified genes such as RPL5, MMP2, CCR5 and DPP4, which are already known to be associated with lung or brain metastasis. Additionally, we found 6 and 9 putative genes that are specific for lung and brain metastasis, respectively. Our analysis suggests that variations in genes and pathways contributing to these different breast metastasis types may arise due to change in tissue microenvironment. To show the benefits of using structural PPI networks instead of traditional node and edge presentation, we inspect two case studies showing the mutual exclusiveness of interactions and effects of mutations on protein conformation which lead to different signaling.

Keywords: breast cancer, metastasis, PPI networks, protein conformational changes

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Authors: Chen Xi, Liu Xuebing, Lao Xueru, Kuan Sinman, Jiang Yike, Wang Hanwei, Yang Xiaolang, Zhou Junjie, Xie Jinpeng

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To further promote the development of smart cities, the microscopic "nerve endings" of the City Intelligent Model (CIM) are extended to be more sensitive. In this paper, we develop a pedestrian trajectory twin terminal based on the CIM and CNN technology. It also uses 5G networks, architectural and geoinformatics technologies, convolutional neural networks, combined with deep learning networks for human behavior recognition models, to provide empirical data such as 'pedestrian flow data and human behavioral characteristics data', and ultimately form spatial performance evaluation criteria and spatial performance warning systems, to make the empirical data accurate and intelligent for prediction and decision making.

Keywords: urban planning, urban governance, CIM, artificial intelligence, sustainable development

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Authors: A. Moawad, K. C. Yao, A. Mansour, R. Gautier

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The detecting of an occupied frequency band is a major issue in cognitive radio systems. The detection process becomes difficult if the signal occupying the band of interest has faded amplitude due to multipath effects. These effects make it hard for an occupying user to be detected. This work mitigates the missed-detection problem in the context of cognitive radio in frequency-selective fading channel by proposing blind channel estimation method that is based on scattering transform. By initially applying conventional energy detection, the missed-detection probability is evaluated, and if it is greater than or equal to 50%, channel estimation is applied on the received signal followed by channel equalization to reduce the channel effects. In the proposed channel estimator, we modify the Morlet wavelet by using its first derivative for better frequency resolution. A mathematical description of the modified function and its frequency resolution is formulated in this work. The improved frequency resolution is required to follow the spectral variation of the channel. The channel estimation error is evaluated in the mean-square sense for different channel settings, and energy detection is applied to the equalized received signal. The simulation results show improvement in reducing the missed-detection probability as compared to the detection based on principal component analysis. This improvement is achieved at the expense of increased estimator complexity, which depends on the number of wavelet filters as related to the channel taps. Also, the detection performance shows an improvement in detection probability for low signal-to-noise scenarios over principal component analysis- based energy detection.

Keywords: channel estimation, cognitive radio, scattering transform, spectrum sensing

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Authors: Apoorva Vinod, Archana Mathur, Snehanshu Saha

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Though there exists a plethora of Activation Functions (AFs) used in single and multiple hidden layer Neural Networks (NN), their behavior always raised curiosity, whether used in combination or singly. The popular AFs –Sigmoid, ReLU, and Tanh–have performed prominently well for shallow and deep architectures. Most of the time, AFs are used singly in multi-layered NN, and, to the best of our knowledge, their performance is never studied and analyzed deeply when used in combination. In this manuscript, we experiment with multi-layered NN architecture (both on shallow and deep architectures; Convolutional NN and VGG16) and investigate how well the network responds to using two different AFs (Sigmoid-Tanh, Tanh-ReLU, ReLU-Sigmoid) used alternately against a traditional, single (Sigmoid-Sigmoid, Tanh-Tanh, ReLUReLU) combination. Our results show that using two different AFs, the network achieves better accuracy, substantially lower loss, and faster convergence on 4 computer vision (CV) and 15 Non-CV (NCV) datasets. When using different AFs, not only was the accuracy greater by 6-7%, but we also accomplished convergence twice as fast. We present a case study to investigate the probability of networks suffering vanishing and exploding gradients when using two different AFs. Additionally, we theoretically showed that a composition of two or more AFs satisfies Universal Approximation Theorem (UAT).

Keywords: activation function, universal approximation function, neural networks, convergence

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Authors: Lindy Van Vliet, Saloni Phadke, Anthea Nelson, Ann Gallant

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Comprehensive and compassionate palliative care and support requires an integrated system of care that draws on formal health and social service providers working together with community and informal networks to ensure that patients and families have access to the care they need. The objective of this study is to further explore and understand the community supports, services, and informal networks that health care professionals and social service providers rely on to allow their patients to die in their homes and communities. Drawing on an interpretivist, exploratory, qualitative design, our multidisciplinary research team (medicine, nursing and social work) conducted interviews with 15 health care and social service providers in the Ottawa region. Interview data was audio-recorded, transcribed and analyzed using a reflexive thematic analysis approach. The data deepens our understandings of the facilitators and barriers that arise as health care and social service providers attempt to build networks of care for patients with life limiting illnesses and families. Three main findings emerged: First, the variability that arises due to systemic barriers in accessing and providing care; second, the exceptionally challenging workload that providers are facing as they work to address complex social care needs (housing, disability, food security), along with escalating palliative care needs; and, finally, the lack of structural support that providers and informal care networks receive. Conclusion: These findings will facilitate and build stronger person-centred/relationship-centred principles and practices between providers, patients, community, and informal care networks by highlighting the systemic barriers to accessing and providing person-centred care. Further, they will have important implications for future partnerships in integrated care delivery programs and initiatives, community policies, education programs, and provincial and national palliative care strategies.

Keywords: public health palliative care, palliative care nursing, care networks, informal care, integrated health care

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Authors: Ameen Alshugaa, Abul Mansur Masih

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This paper sheds light on the economic impacts of political uncertainty caused by the civil uprisings that swept the Arab World and have been collectively known as the Arab Spring. Measuring documented effects of political uncertainty on regional stock market indices, we examine the impact of the Arab Spring on the volatility of stock markets in eight countries in the Middle East and North Africa (MENA) region: Egypt, Lebanon, Jordon, United Arab Emirate, Qatar, Bahrain, Oman and Kuwait. This analysis also permits testing the existence of financial contagion among equity markets in the MENA region during the Arab Spring. To capture the time-varying and multi-horizon nature of the evidence of volatility and contagion in the eight MENA stock markets, we apply two robust methodologies on consecutive data from November 2008 to March 2014: MGARCH-DCC, Continuous Wavelet Transforms (CWT). Our results indicate two key findings. First, the discrepancies between volatile stock markets of countries directly impacted by the Arab Spring and countries that were not directly impacted indicate that international investors may still enjoy portfolio diversification and investment in MENA markets. Second, the lack of financial contagion during the Arab Spring suggests that there is little evidence of cointegration among MENA markets. Providing a general analysis of the economic situation and the investment climate in the MENA region during and after the Arab Spring, this study bear significant importance for policy makers, local and international investors, and market regulators.

Keywords: Portfolio Diversification , MENA Region , Stock Market Indices, MGARCH-DCC, Wavelet Analysis, CWT

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Authors: Shaik Jilani Basha, Inavolu Avinash, Alla Venu Sai Reddy, Bitragunta Taraka Ramu

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We can use the neural style transfer technique to build a picture with the same "content" as the beginning image but the "style" of the picture we've chosen. Neural style transfer is a technique for merging the style of one image into another while retaining its original information. The only change is how the image is formatted to give it an additional artistic sense. The content image depicts the plan or drawing, as well as the colors of the drawing or paintings used to portray the style. It is a computer vision programme that learns and processes images through deep convolutional neural networks. To implement software, we used to train deep learning models with the train data, and whenever a user takes an image and a styled image, the output will be as the style gets transferred to the original image, and it will be shown as the output.

Keywords: neural networks, computer vision, deep learning, convolutional neural networks

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Authors: Xuewen Yu, Danhui Dan

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The identification of impact load and some hard-to-obtain system parameters is crucial for the activities of analysis, validation, and evaluation in the engineering field. This paper proposes a method that utilizes neural networks based on 1D-CNN to identify the impact load and partial system parameters from measured responses. To this end, forward computations are conducted to provide datasets consisting of the triples (parameter θ, input u, output y). Then neural networks are trained to learn the mapping from input to output, fu|{θ} : y → u, as well as from input and output to parameter, fθ : (u, y) → θ. Afterward, feeding the trained neural networks the measured output response, the input impact load and system parameter can be calculated, respectively. The method is tested on two simulated examples and shows sound accuracy in estimating the impact load (waveform and location) and system parameters.

Keywords: convolutional neural network, impact load identification, system parameter identification, inverse problem

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Authors: Arthur D. Wiedemann, Christopher Fuller, Kyle A. Pascioni

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With the introduction of electric motors, small unmanned aerial vehicle designers have to consider trade-offs between acoustic noise and thrust generated. Currently, there are few low-computational tools available for predicting acoustic noise emitted by a propeller into the far-field. Artificial neural networks offer a highly non-linear and adaptive model for predicting isolated and interactive tonal noise. But neural networks require large data sets, exceeding practical considerations in modeling experimental results. A methodology known as physics guided machine learning has been applied in this study to reduce the required data set to train the network. After building and evaluating several neural networks, the best model is investigated to determine how the network successfully predicts the acoustic waveform. Lastly, a post-network transfer function is developed to remove discontinuity from the predicted waveform. Overall, methodologies from physics guided machine learning show a notable improvement in prediction performance, but additional loss functions are necessary for constructing predictive networks on small datasets.

Keywords: aeroacoustics, machine learning, propeller, rotor, neural network, physics guided machine learning

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Authors: A. Kardi, R. Zagrouba, M. Alqahtani

Abstract:

The Internet of Everything (IoE) presents today a very attractive and motivating field of research. It is basically based on Wireless Sensor Networks (WSNs) in which the routing task is the major analysis topic. In fact, it directly affects the effectiveness and the lifetime of the network. This paper, developed from recent works and based on extensive researches, proposes a taxonomy of routing protocols in WSNs. Our main contribution is that we propose a classification model based on nine classes namely application type, delivery mode, initiator of communication, network architecture, path establishment (route discovery), network topology (structure), protocol operation, next hop selection and latency-awareness and energy-efficient routing protocols. In order to provide a total classification pattern to serve as reference for network designers, each class is subdivided into possible subclasses, presented, and discussed using different parameters such as purposes and characteristics.

Keywords: routing, sensor, survey, wireless sensor networks, WSNs

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