Search results for: patronage network

Authors: Ali Hamza

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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: Afifa Hafidh, Fathi Touati, Ahmed Hichem Hamzaoui, Sayda Somrani

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The objective of the present study has been to synthesize and to characterize silica hybrid materials using sol-gel technic and to investigate their properties. Silica materials were successfully fabricated using various bi-functional 1,3,4-thiadiazoles and tetraethoxysilane (TEOS) as co-precursors via a facile one-pot sol-gel pathway. TEOS was introduced at room temperature with 1,3,4-thiadiazole 2,5-difunctiunal adducts, in ethanol as solvent and using HCl acid as catalyst. The sol-gel process lead to the formation of monolithic, coloured and transparent gels. TEOS was used as a principal network forming agent. The incorporation of 1,3,4-thiadiazole molecules was realized by attachment of these later onto a silica matrix. This allowed covalent linkage between organic and inorganic phases and lead to the formation of Si-N and Si-S bonds. The prepared hybrid materials were characterized by Fourier transform infrared, NMR ²⁹Si and ¹³C, scanning electron microscopy and nitrogen absorption-desorption measurements. The optic and magnetic properties of hybrids are studied respectively by ultra violet-visible spectroscopy and electron paramagnetic resonance. It was shown in this work, that heterocyclic moieties were successfully attached in the hybrid skeleton. The formation of the Si-network composed of cyclic units (Q3 structures) connected by oxygen bridges (Q4 structures) was proved by ²⁹Si NMR spectroscopy. The Brunauer-Elmet-Teller nitrogen adsorption-desorption method shows that all the prepared xerogels have isotherms type IV and are mesoporous solids. The specific surface area and pore volume of these materials are important. The obtained results show that all materials are paramagnetic semiconductors. The data obtained by Nuclear magnetic resonance ²⁹Si and Fourier transform infrared spectroscopy, show that Si-OH and Si-NH groups existing in silica hybrids can participate in adsorption interactions. The obtained materials containing reactive centers could exhibit adsorption properties of metal ions due to the presence of OH and NH functionality in the mesoporous frame work. Our design of a simple method to prepare hybrid materials may give interest of the development of mesoporous hybrid systems and their use within the domain of environment in the future.

Keywords: hybrid materials, sol-gel process, 1, 3, 4-thiadaizole, TEOS

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Authors: Kirolos Gerges Yakoub Gerges

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Self-sustaining agricultural machines act in stochastic surroundings and therefore, should be capable of perceive the surroundings in real time. This notion can be done using image sensors blended with superior device learning, mainly Deep mastering. Deep convolutional neural networks excel in labeling and perceiving colour pix and since the fee of RGB-cameras is low, the hardware cost of accurate notion relies upon heavily on memory and computation power. This paper investigates the opportunity of designing lightweight convolutional neural networks for semantic segmentation (pixel clever class) with reduced hardware requirements, to allow for embedded usage in self-reliant agricultural machines. The usage of compression techniques, a lightweight convolutional neural community is designed to carry out actual-time semantic segmentation on an embedded platform. The community is skilled on two big datasets, ImageNet and Pascal Context, to apprehend as much as four hundred man or woman instructions. The 400 training are remapped into agricultural superclasses (e.g. human, animal, sky, road, area, shelterbelt and impediment) and the capacity to provide correct actual-time perception of agricultural environment is studied. The network is carried out to the case of self-sufficient grass mowing the usage of the NVIDIA Tegra X1 embedded platform. Feeding case-unique pics to the community consequences in a fully segmented map of the superclasses within the picture. As the network remains being designed and optimized, handiest a qualitative analysis of the technique is entire on the abstract submission deadline. intending this cut-off date, the finalized layout is quantitatively evaluated on 20 annotated grass mowing pictures. Light-weight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show aggressive performance on the subject of accuracy and speed. It’s miles viable to offer value-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: centrifuge pump, hydraulic energy, agricultural applications, irrigationaxial flux machines, axial flux applications, coreless machines, PM machinesautonomous agricultural machines, deep learning, safety, visual perception

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Authors: Faisel Eltuhami Alzaalik, Omar Imhemed Alramli, Ahmed Mohamed Elaieb

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The IEEE 802.11 defines two modes of MAC, distributed coordination function (DCF) and point coordination function (PCF) mode. The first sub-layer of the MAC is the distributed coordination function (DCF). A contention algorithm is used via DCF to provide access to all traffic. The point coordination function (PCF) is the second sub-layer used to provide contention-free service. PCF is upper DCF and it uses features of DCF to establish guarantee access of its users. Some papers and researches that have been published in this technology were reviewed in this paper, as well as talking briefly about the distributed coordination function (DCF) technology. The simulation of the PCF function have been applied by using a simulation program called network simulator (NS2) and have been found out the throughput of a transmitter system by using this function.

Keywords: DCF, PCF, throughput, NS2

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Authors: Nada Mouchfiq, Ahmed Habbani, Chaimae Benjbara

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The security aspect of the IoT occupies a place of great importance especially after the evolution that has known this field lastly because it must take into account the transformations and the new applications .Blockchain is a new technology dedicated to the data sharing. However, this does not work the same way in the different systems with different operating principles. This article will discuss network security using the Blockchain to facilitate the sending of messages and information, enabling the use of new processes and enabling autonomous coordination of devices. To do this, we will discuss proposed solutions to ensure a high level of security in these networks in the work of other researchers. Finally, our article will propose a method of security more adapted to our needs as a team working in the ad hoc networks, this method is based on the principle of the Blockchain and that we named ”MPR Blockchain”.

Keywords: Ad hocs networks, blockchain, MPR, security

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Authors: F. Lhafiane, A. Elbyed, M. Bouchoum

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Reverse Logistics (RL) Process is considered as complex and dynamic network that involves many stakeholders such as: suppliers, manufactures, warehouse, retails, and costumers, this complexity is inherent in such process due to lack of perfect knowledge or conflicting information. Ontologies, on the other hand, can be considered as an approach to overcome the problem of sharing knowledge and communication among the various reverse logistics partners. In this paper, we propose a semantic representation based on hybrid architecture for building the Ontologies in an ascendant way, this method facilitates the semantic reconciliation between the heterogeneous information systems (ICT) that support reverse logistics Processes and product data.

Keywords: Reverse Logistics, information management, heterogeneity, ontologies, semantic web

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Authors: Ali Kadivar, Kaveh Niayesh

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This report summarizes a simulation-based approach to estimate the current interruption behavior of a fuse element utilized in a DC network protecting battery banks under different stresses. Due to internal resistance of the battries, the short circuit current in very close to the nominal current, and it makes the fuse designation tricky. The base configuration considered in this report consists of five fuse units in parallel. The simulations are performed using a multi-physics software package, COMSOL® 5.6, and the necessary material parameters have been calculated using two other software packages.The first phase of the simulation starts with the heating of the fuse elements resulted from the current flow through the fusing element. In this phase, the heat transfer between the metallic strip and the adjacent materials results in melting and evaporation of the filler and housing before the aluminum strip is evaporated and the current flow in the evaporated strip is cut-off, or an arc is eventually initiated. The initiated arc starts to expand, so the entire metallic strip is ablated, and a long arc of around 20 mm is created within the first 3 milliseconds after arc initiation (v_elongation = 6.6 m/s. The final stage of the simulation is related to the arc simulation and its interaction with the external circuitry. Because of the strong ablation of the filler material and venting of the arc caused by the melting and evaporation of the filler and housing before an arc initiates, the arc is assumed to burn in almost pure ablated material. To be able to precisely model this arc, one more step related to the derivation of the transport coefficients of the plasma in ablated urethane was necessary. The results indicate that an arc current interruption, in this case, will not be achieved within the first tens of milliseconds. In a further study, considering two series elements, the arc was interrupted within few milliseconds. A very important aspect in this context is the potential impact of many broken strips parallel to the one where the arc occurs. The generated arcing voltage is also applied to the other broken strips connected in parallel with arcing path. As the gap between the other strips is very small, a large voltage of a few hundred volts generated during the current interruption may eventually lead to a breakdown of another gap. As two arcs in parallel are not stable, one of the arcs will extinguish, and the total current will be carried by one single arc again. This process may be repeated several times if the generated voltage is very large. The ultimate result would be that the current interruption may be delayed.

Keywords: DC network, high current / low SC fuses, FEM simulation, paralle fuses

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Authors: Abdullah Aljumah, Tariq Ahamad

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The simplest possible human-computer interaction is mobile cloud computing as it emerges and makes the use of all modern-day human-oriented technology. The main aim of this idea is the QoS (quality of service) by using user-friendly and reliable software over the global network in order to make it economical by reducing cost, reliable, and increase the main storage. Since we studied and went through almost all the existing related work in this area and we came up with some challenges that will rise or might be rising for some basic areas in mobile cloud computing and mostly stogie and security area. In this research article, we suggest some recommendation for mobile cloud computing and for its security that will help in building more powerful tools to handle all this pressure.

Keywords: Cloud Computing, MCC, SAAS, computer interaction

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Authors: Joachim F. Sartor

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According to German guidelines, external natural catchments are greater sub-catchments without significant portions of impervious areas, which possess a surface drainage system and empty in a sewer network. Basically, such catchments should be disconnected from sewer networks, particularly from combined systems. If this is not possible due to local conditions, their flow hydrographs have to be considered at the design of sewer systems, because the impact may be significant. Since there is a lack of sufficient measurements of storm-runoff events for such catchments and hence verified simulation methods to analyze their design flows, German standards give only general advices and demands special considerations in such cases. Compared to urban sub-catchments, external natural catchments exhibit greatly different flow characteristics. With increasing area size their hydrological behavior approximates that of rural catchments, e.g. sub-surface flow may prevail and lag times are comparable long. There are few observed peak flow values and simple (mostly empirical) approaches that are offered by literature for Central Europe. Most of them are at least helpful to crosscheck results that are achieved by simulation lacking calibration. Using storm-runoff data from five monitored rural watersheds in the west of Germany with catchment areas between 0.33 and 1.07 km2 , the author investigated by multiple event simulation three different approaches to determine the rainfall excess. These are the modified SCS variable run-off coefficient methods by Lutz and Zaiß as well as the soil moisture model by Ostrowski. Selection criteria for storm events from continuous precipitation data were taken from recommendations of M 165 and the runoff concentration method (parallel cascades of linear reservoirs) from a DWA working report to which the author had contributed. In general, the two run-off coefficient methods showed results that are of sufficient accuracy for most practical purposes. The soil moisture model showed no significant better results, at least not to such a degree that it would justify the additional data collection that its parameter determination requires. Particularly typical convective summer events after long dry periods, that are often decisive for sewer networks (not so much for rivers), showed discrepancies between simulated and measured flow hydrographs.

Keywords: external natural catchments, sewer network design, storm-runoff modelling, urban drainage

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Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Morteza Esfandiari, Shabnam Jabari, Heather MacGrath, David Coleman

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Flood is a severe issue in different places in the world as well as the city of Fredericton, New Brunswick, Canada. The downtown area of Fredericton is close to the Saint John River, which is susceptible to flood around May every year. Recently, the frequency of flooding seems to be increased, especially after the fact that the downtown area and surrounding urban/agricultural lands got flooded in two consecutive years in 2018 and 2019. In order to have an explicit vision of flood span and damage to affected areas, it is necessary to use either flood inundation modelling or satellite data. Due to contingent availability and weather dependency of optical satellites, and limited existing data for the high cost of hydrodynamic models, it is not always feasible to rely on these sources of data to generate quality flood maps after or during the catastrophe. Height Above the Nearest Drainage (HAND), a state-of-the-art topo-hydrological index, normalizes the height of a basin based on the relative elevation along with the stream network and specifies the gravitational or the relative drainage potential of an area. HAND is a relative height difference between the stream network and each cell on a Digital Terrain Model (DTM). The stream layer is provided through a multi-step, time-consuming process which does not always result in an optimal representation of the river centerline depending on the topographic complexity of that region. HAND is used in numerous case studies with quite acceptable and sometimes unexpected results because of natural and human-made features on the surface of the earth. Some of these features might cause a disturbance in the generated model, and consequently, the model might not be able to predict the flow simulation accurately. We propose to include a previously existing stream layer generated by the province of New Brunswick and benefit from culvert maps to improve the water flow simulation and accordingly the accuracy of HAND model. By considering these parameters in our processing, we were able to increase the accuracy of the model from nearly 74% to almost 92%. The improved model can be used for generating highly accurate flood maps, which is necessary for future urban planning and flood damage estimation without any need for satellite imagery or hydrodynamic computations.

Keywords: HAND, DTM, rapid floodplain, simplified conceptual models

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Authors: Saadat Ali, Rashid Abdulla Ahmed Alshehhi

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Cable faults are always probable and common during or after commissioning, causing significant delays and disrupting power distribution or transmission network, which is intolerable for the utilities&service providers being their reliability and business continuity measures. Therefore, the adoption of rapid localization & rectification methodology is the main concern for them. This paper explores the present techniques available for high voltage cable localization & rectification and which is preferable with regards to easier, faster, and also less harmful to cables. It also provides insight experience of high resistance fault locating by utilization of the Very Low Frequency (VLF) method.

Keywords: faults, VLF, real cases, cables

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Authors: Syed W. Hasan, Zhiqun Tian

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Wet spinning provides a facile and economic route to fabricate graphene nanofibers (GFs) on mass scale. Nevertheless, the pristine GFs exhibit significantly low electrical and mechanical properties owing to stacked graphene sheets and weak inter-atomic bonding. In this report, we present highly conductive Ag-decorated-GFs (Ag/GFs). The SEM micrographs show Ag nanoparticles (NPs) (dia ~10 nm) are homogeneously distributed throughout the cross-section of the fiber. The Ag NPs provide a conductive network for the electrons flow raising the conductivity to 1.8(10^4) S/m which is 4 times higher than the pristine GFs. Our results surpass the conductivities of graphene fibers doped with CNTs, Nanocarbon, fullerene, and Cu. The chemical and structural attributes of Ag/GFs are further elucidated through XPS, AFM and Raman spectroscopy.

Keywords: Ag nanoparticles, Conductive fibers, Graphene, Wet spinning

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Authors: Paola Causin, Andrea Aspri, Alessandro Benfenati

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Diffuse Optical Tomography (DOT) is an emergent medical imaging technique which employs NIR light to estimate the spatial distribution of optical coefficients in biological tissues for diagnostic purposes, in a noninvasive and non-ionizing manner. DOT reconstruction is a severely ill-conditioned problem due to prevalent scattering of light in the tissue. In this contribution, we present our research in adopting hybrid knowledgedriven/data-driven approaches which exploit the existence of well assessed physical models and build upon them neural networks integrating the availability of data. Namely, since in this context regularization procedures are mandatory to obtain a reasonable reconstruction [1], we explore the use of neural networks as tools to include prior information on the solution. 2. Materials and Methods The idea underlying our approach is to leverage neural networks to solve PDE-constrained inverse problems of the form 𝒒 ∗ = 𝒂𝒓𝒈 𝒎𝒊𝒏𝒒 𝐃(𝒚, 𝒚̃), (1) where D is a loss function which typically contains a discrepancy measure (or data fidelity) term plus other possible ad-hoc designed terms enforcing specific constraints. In the context of inverse problems like (1), one seeks the optimal set of physical parameters q, given the set of observations y. Moreover, 𝑦̃ is the computable approximation of y, which may be as well obtained from a neural network but also in a classic way via the resolution of a PDE with given input coefficients (forward problem, Fig.1 box ). Due to the severe ill conditioning of the reconstruction problem, we adopt a two-fold approach: i) we restrict the solutions (optical coefficients) to lie in a lower-dimensional subspace generated by auto-decoder type networks. This procedure forms priors of the solution (Fig.1 box ); ii) we use regularization procedures of type 𝒒̂ ∗ = 𝒂𝒓𝒈𝒎𝒊𝒏𝒒 𝐃(𝒚, 𝒚̃)+ 𝑹(𝒒), where 𝑹(𝒒) is a regularization functional depending on regularization parameters which can be fixed a-priori or learned via a neural network in a data-driven modality. To further improve the generalizability of the proposed framework, we also infuse physics knowledge via soft penalty constraints (Fig.1 box ) in the overall optimization procedure (Fig.1 box ). 3. Discussion and Conclusion DOT reconstruction is severely hindered by ill-conditioning. The combined use of data-driven and knowledgedriven elements is beneficial and allows to obtain improved results, especially with a restricted dataset and in presence of variable sources of noise.

Keywords: inverse problem in tomography, deep learning, diffuse optical tomography, regularization

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Authors: Fathi Alwafie

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In this paper we present the simulation of the propagation characteristics of the picocellular propagation channel environment. The first aim has been to find a correct description of the environment for received wave. The result of the first investigations is that the environment of the indoor wave significantly changes as we change the electric parameters of material constructions. A modified 3D ray tracing image method tool has been utilized for the coverage prediction. A detailed analysis of the dependence of the indoor wave on the wide-band characteristics of the channel: Root Mean Square (RMS) delay spread characteristics and mean excess delay, is also investigated.

Keywords: propagation, ray tracing, network, mobile computing

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Authors: Yskak Nabi, Onolkan Umankulova, Ilyas Seitov

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Tempus project about “Central Asian network for quality assurance – CANQA” had been implemented in 2009-2012. As the result of the project, two accreditation agencies were established: the agency for quality assurance in the field of education, “EdNet” in Kyrgyzstan, center of progressive technologies in Tajikistan. The importance of the research studies of the project is supported by the idea that the creation of Central-Asian network for quality assurance in education is still relevant, and results of the International forum “Global in regional: Kazakhstan in Bologna process and EU projects,” that was held in Nur-Sultan in October 2020, proves this. At the same time, the previous experience of the partnership between accreditation agencies of Central Asia shows that recommendations elaborated within the CANQA project were not theoretically justified. But there are a number of facts and arguments that prove the practical appliance of these recommendations. In this respect, joint activities of accreditation agencies of Kyrgyzstan and Kazakhstan are representative. For example, independent Kazakh agency of accreditation and rating successfully conducts accreditation of Kyrgyz universities; based on the memorandum about joint activity between the agency for quality assurance in the field of education “EdNet” (Kyrgyzstan) and Astana accreditation agency (Kazakhstan), the last one provides its experts for accreditation procedures in EdNet. Exchange of experience among the agencies shows an effective approach towards adaptation of European standards to the reality of education systems of Central Asia with consideration of not only a legal framework but also from the point of European practices view. Therefore, the relevance of the research is identified as there is a practical partnership between accreditation agencies of Central Asian countries, but the absence of theoretical justification of integrational processes in the accreditation field. As a result, the following hypothesis was put forward: “if to develop theoretical aspects for harmonization of accreditation standards, then integrational processes would be improved since the implementation of Bologna process principles would be supported with wider possibilities, and particularly, students and academic mobility would be improved.” Indeed, for example, in Kazakhstan, the total share of foreign students was 5,04% in 2020, and most of them are coming from Kyrgyzstan, Tajikistan, and Uzbekistan, and if integrational processes will be improved, then this share can increase.

Keywords: accreditation standards in education, Central Asian countries, pedagogical theory, model

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Authors: Evan Lowhorn, Rocio Alba-Flores

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Traditional classification Convolutional Neural Networks (CNN) attempt to classify an image in its entirety. This becomes problematic when trying to perform classification with a drone’s camera in real-time due to unpredictable backgrounds. Object detectors with bounding boxes can be used to isolate individuals and other items, but the original backgrounds remain within these boxes. These basic detectors have been regularly used to determine what type of object an item is, such as “person” or “dog.” Recent advancement in computer vision, particularly with human imaging, is keypoint detection. Human keypoint detection goes beyond bounding boxes to fully isolate humans and plot points, or Regions of Interest (ROI), on their bodies within an image. ROIs can include shoulders, elbows, knees, heads, etc. These points can then be related to each other and used in deep learning methods such as pose estimation. For drone control based on human motions, poses, or signals using the onboard camera, it is important to have a simple method for pilot identification among multiple individuals while also giving the pilot fine control options for the drone. To achieve this, the OpenPose keypoint detection network was used with body and hand keypoint detection enabled. OpenPose supports the ability to combine multiple keypoint detection methods in real-time with a single network. Body keypoint detection allows simple poses to act as the pilot identifier. The hand keypoint detection with ROIs for each finger can then offer a greater variety of signal options for the pilot once identified. For this work, the individual must raise their non-control arm to be identified as the operator and send commands with the hand on their other arm. The drone ignores all other individuals in the onboard camera feed until the current operator lowers their non-control arm. When another individual wish to operate the drone, they simply raise their arm once the current operator relinquishes control, and then they can begin controlling the drone with their other hand. This is all performed mid-flight with no landing or script editing required. When using a desktop with a discrete NVIDIA GPU, the drone’s 2.4 GHz Wi-Fi connection combined with OpenPose restrictions to only body and hand allows this control method to perform as intended while maintaining the responsiveness required for practical use.

Keywords: computer vision, drone control, keypoint detection, openpose

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Authors: Mohammad A. Al-Madi, Nagham A. Al-Madi, Fanan A. Al-Madi

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The current concept of the technology of cloud computing libraries has been increasing where users can store their data in a virtual space and can be retrieved from anywhere whilst using the network. By using cloud computing technology, industries and individuals save money, time, and space. Moreover, data and information about libraries can be placed in the cloud. This paper discusses the meaning of cloud computing along with its types. Further, the focus has been given to the application of cloud computing in modern libraries. Additionally, the advantages of cloud computing and the areas in which cloud computing be applied with current usage are discussed. Finally, the present situation of the Jordanian libraries is considered and discussed in further detail.

Keywords: cloud computing, community cloud, hybrid cloud, private cloud, public cloud

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Authors: Rowanda Daoud Ahmed, Mansoor Abdulhak, Muhammad Azeem Afzal, Sezer Filiz, Usama Ahmad Mughal

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This paper discusses important aspects of AI in the healthcare domain. The increase of data in healthcare both in size and complexity, opens more room for artificial intelligence applications. Our focus is to review the main AI methods within the scope of the health care domain. The results of the review show that recommendations for diagnosis and recommendations for treatment, patent engagement, and administrative tasks are the key applications of AI in healthcare. Understanding the potential of AI methods in the domain of healthcare would benefit healthcare practitioners and will improve patient outcomes.

Keywords: AI in healthcare, technologies of AI, neural network, future of AI in healthcare

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Authors: Cahit Perkgöz

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Artificial intelligence has brought new approaches to solving problems in almost every research field in recent years. One of these topics is shape design and optimization, which has the possibility of applications in many fields, such as nanotechnology and electronics. A properly constructed cost function can eliminate the need for labeled data required in deep learning and create desired shapes. In this work, the network parameters are optimized differentially, which differs from traditional approaches. The methods are tested for physics-related structures and successful results are obtained. This work is supported by Eskişehir Technical University scientific research project (Project No: 20ADP090)

Keywords: deep learning, shape design, optimization, artificial intelligence

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Authors: Amit R. Bhende, G. K. Awari

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Remaining useful life (RUL) prediction is one of key technologies to realize prognostics and health management that is being widely applied in many industrial systems to ensure high system availability over their life cycles. The present work proposes a data-driven method of RUL prediction based on multiple health state assessment for rolling element bearings. Bearing degradation data at three different conditions from run to failure is used. A RUL prediction model is separately built in each condition. Feed forward back propagation neural network models are developed for prediction modeling.

Keywords: bearing degradation data, remaining useful life (RUL), back propagation, prognosis

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Authors: Luisa Mesquita Oliveira Ribeiro, Alexei Manso Correa Machado

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Handwritten signature is a unique form for recognizing an individual, used to discern documents, carry out investigations in the criminal, legal, banking areas and other applications. Signature verification is based on large amounts of biometric data, as they are simple and easy to acquire, among other characteristics. Given this scenario, signature forgery is a worldwide recurring problem and fast and precise techniques are needed to prevent crimes of this nature from occurring. This article carried out a study on the efficiency of the Capsule Network in analyzing and recognizing signatures. The chosen architecture achieved an accuracy of 98.11% and 80.15% for the CEDAR and GPDS databases, respectively.

Keywords: biometrics, deep learning, handwriting, signature forgery

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Authors: Hany Elsaid Fawaz Abdallah

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This study presents a new artificial neural network(ANN) model to predict the Nusselt Number and pressure drop for the turbulent flow in a triangular corrugated plate heat exchanger for forced air and turbulent water flow. An experimental investigation was performed to create a new dataset for the Nusselt Number and pressure drop values in the following range of dimensionless parameters: The plate corrugation angles (from 0° to 60°), the Reynolds number (from 10000 to 40000), pitch to height ratio (from 1 to 4), and Prandtl number (from 0.7 to 200). Based on the ANN performance graph, the three-layer structure with {12-8-6} hidden neurons has been chosen. The training procedure includes back-propagation with the biases and weight adjustment, the evaluation of the loss function for the training and validation dataset and feed-forward propagation of the input parameters. The linear function was used at the output layer as the activation function, while for the hidden layers, the rectified linear unit activation function was utilized. In order to accelerate the ANN training, the loss function minimization may be achieved by the adaptive moment estimation algorithm (ADAM). The ‘‘MinMax’’ normalization approach was utilized to avoid the increase in the training time due to drastic differences in the loss function gradients with respect to the values of weights. Since the test dataset is not being used for the ANN training, a cross-validation technique is applied to the ANN network using the new data. Such procedure was repeated until loss function convergence was achieved or for 4000 epochs with a batch size of 200 points. The program code was written in Python 3.0 using open-source ANN libraries such as Scikit learn, TensorFlow and Keras libraries. The mean average percent error values of 9.4% for the Nusselt number and 8.2% for pressure drop for the ANN model have been achieved. Therefore, higher accuracy compared to the generalized correlations was achieved. The performance validation of the obtained model was based on a comparison of predicted data with the experimental results yielding excellent accuracy.

Keywords: artificial neural networks, corrugated channel, heat transfer enhancement, Nusselt number, pressure drop, generalized correlations

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Authors: Hammad Khalid

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In this paper, a strategy for computerized arranging of Fiber to the Home (FTTH) get to systems is proposed. We presented an efficient methodology for arranging access organize framework. The GIS information and a lot of calculations were utilized to make the arranging procedure increasingly programmed. The technique clarifies various strides of the arranging process. Considering various situations, various designs can be produced by utilizing the technique. It was likewise conceivable to produce the designs in an extremely brief temporal contrast with the conventional arranging. A contextual investigation is considered to delineate the utilization and abilities of the arranging technique. The technique, be that as it may, doesn't completely robotize the arranging however, make the arranging procedure fundamentally quick. The outcomes and dialog are displayed and end is given at last.

Keywords: FTTH, GIS, robotize, plan

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Authors: Ivanka Valova

Abstract:

This paper is aimed at using a multi-agent approach to monitor and diagnose a biotechnological system in order to validate certain control actions depending on the process development and the operating conditions. A multi-agent system is defined as a network of interacting software modules that collectively solve complex tasks. Remote monitoring and control of biotechnological processes is a necessity when automated and reliable systems operating with no interruption of certain activities are required. The advantage of our approach is in its flexibility, modularity and the possibility of improving by acquiring functionalities through the integration of artificial intelligence.

Keywords: multi-agent approach, artificial intelligence, biotechnological processes, anaerobic biodegradation

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Authors: Pablo M. S. Vallejos

Abstract:

The article aims to analyze the autobiographical practices of users on Instagram, observing the instrumentalization of image resources in the construction of visual narratives that make up that archive and digital diary. Through bibliographical review, discourse exploration and case studies, the research also aims to present a new theoretical perception about everyday records - edited with a collage of filters and aesthetic tools - that permeate that social network, understanding it as a platform fictionalizing and an expansion of life. In this way, therefore, the work reflects on possible futures in the elaboration of representations and identities in the context of digital spaces in the 21st century.

Keywords: visual culture, social media, autobiography, image

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Authors: Mehrnoosh Askarizadeh

Abstract:

Globalization of the economy, e-business, and introduction of new technologies pose new challenges to all organizations. In recent decades, globalization, outsourcing, and information technology have enabled many organizations to successfully operate collaborative supply networks in which each specialized business partner focuses on only a few key strategic activities For this industries supply network can be acknowledged as a new form of organization. We will study about critical success factors (CSFs) for implementation of SCM in companies. It is shown that in different circumstances e- supply chain management has a higher impact on performance.

Keywords: supply chain management, logistics management, critical success factors, information technology, top management support, human resource

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Authors: Rawaa Maher, Ahmed Allam, Haruichi Kanaya, Adel B. Abdelrahman

Abstract:

In this paper, a compact, high-efficiency integrated rectenna is presented to operate in the 2.45 GHz band. A comparison between two rectifier topologies is performed to verify the benefits of removing the matching network from the rectifier. A rectifier high conversion efficiency of 74.1% is achieved. To complete the rectenna system, a novel omnidirectional antenna with high gain (3.72 dB) and compact size (25 mm * 29 mm) is designed and fabricated. The same antenna is used with a reflector for raising the gain to nearly 8.3 dB. The simulation and measurement results of the antenna are in good agreement.

Keywords: internet of things, integrated rectenna, rectenna, RF energy harvesting, wireless sensor networks(WSN)

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Authors: Isabel Freitas, Paula Remoaldo, Olga Matos, Ricardo Goja, Juliana Araujo, Vitor Ribeiro, Miguel Pereira

Abstract:

Several destinations have been experiencing a transition from a massified cultural tourism to a creative tourism approach. In this new segment of tourism, urban territories have been the focus for several decades. Urban studies on creative industries and initiatives have been taking place in big cities marginalizing small towns and more specifically rural areas. This paper envisages evaluating the differences between rural and urban institutions/platforms, mostly certified by the Creative Tourism Network, in what concerns the practices and initiatives in creative tourism worldwide. In the research carried out between March 2017 and March 2018, we had three levels of primary data and qualitative analysis: i) research on Google (web) by using several keywords like 'creative tourism initiatives', 'creative cities', 'best practices in creative tourism' (from March to August 2017). With the help of the certification of institutions/platforms by the Creative Tourism Network, 24 institutions were found and declared to be developing creative initiatives. It was decided to try to unravel the type of activities and some practices and initiatives carried out by these institutions and the analysis of the differences between rural and urban initiatives. A database of 20 items (e.g., institutions in charge of implementing the initiatives, year of implementation, site, activities developed, place of development, country of origin, type of partners chosen) was created for each institution/platform; ii) A deeper analysis was made on the websites’ information on the institutions (from September to December 2017). The type of professionals involved in the activities, the language used in the activities and the type of activity performed were some of the data analysed and iii) To complement these data, semi-structured interviews were done to representatives of the institutions, conducted mainly by Skype from July 2017 to April 2018. The interviews consisted of 17 questions. In the present paper, these interviews are used to complement the analysis of the same items. Some of the qualitative analysis was supported by the narratives of the leaders of the twenty-four institutions that were surveyed. The results indicate that creative tourism is more active and diverse in urban areas. Some more consolidated communication strategies and partnerships are needed for these activities to become economically more sustainable. The findings of this research provide researchers and practitioners with a better understanding of creative tourism and give some information of how creative tourism is developed in rural and urban areas, the gaps and lack of information, and all the possible directions towards the development of the creative tourism industry.

Keywords: creative tourism, practices of creative tourism, rural areas, urban areas

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Authors: Roland Lachmayer, Mahtab Afsari

Abstract:

Research is the cornerstone for advancement of human communities. So that it is one of the indexes for evaluating advancement of countries. Research projects are usually cost and time-consuming and do not end in result in short term. Project scheduling is one of the integral parts of project management. The present article offers a new method by using C# and Matlab software to solve Fuzzy GERT networks for Exclusive-OR kind of nodes to schedule the network. In this article we concentrate on flowcharts that we used in Matlab to show how we apply Matlab to schedule Exclusive-OR nodes.

Keywords: research projects, fuzzy GERT, fuzzy CPM, CPM, α-cuts, scheduling

Procedia PDF Downloads 398