Search results for: fuzzy neural network

Authors: Lenka Matulova

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Geopolymers are alumosilicate materials that have long been studied. Despite this fact, little is known about the long-term stability of geopolymer mechanical and structural properties, so crucial for their successful industrial application. To improve understanding, we investigated the effect of four different types of environments on the mechanical and structural properties of a metakaolin-based geopolymer (MK GP). The MK GP samples were stored in laboratory conditions (control samples), in water at 20 °C, in water at 80 °C, and outside exposed to the weather. Compressive and tensile strengths were measured after 28, 56, 90, and 360 days. In parallel, structural properties were analyzed using XRD, SEM, and mercury intrusion porosimetry. Whereas the mechanical properties of the samples in laboratory conditions and in 20 °C water were stable, the mechanical properties of the outdoor samples and the samples 80 °C water decreased noticeably after 360 days. Structural analyses were focused on changes in sample microstructure (developing microcrack network, porosity) and identifying zeolites, the presence of which would indicate detrimental processes in the structure that can change it from amorphous to crystalline. No zeolites were found during the 360-day period in MK GP samples, but the reduction in mechanical properties coincided with a developing network of microcracks and changes in pore size distribution.

Keywords: geopolymer, long-term properties, mechanical properties, metakaolin, structural properties

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of Artificial Intelligence (AI), specifically Deep Learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our pioneering approach introduces a hybrid model, amalgamating the strengths of two renowned Convolutional Neural Networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging

Procedia PDF Downloads 87

Authors: Rabbia Idrees, Ananda Maiti, Saurabh Garg, Muhammad Bilal Amin

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IoT devices are the basic building blocks of IoT network that generate enormous volume of real-time and high-speed data to help organizations and companies to take intelligent decisions. To integrate this enormous data from multisource and transfer it to the appropriate client is the fundamental of IoT development. The handling of this huge quantity of devices along with the huge volume of data is very challenging. The IoT devices are battery-powered and resource-constrained and to provide energy efficient communication, these IoT devices go sleep or online/wakeup periodically and a-periodically depending on the traffic loads to reduce energy consumption. Sometime these devices get disconnected due to device battery depletion. If the node is not available in the network, then the IoT network provides incomplete, missing, and inaccurate data. Moreover, many IoT applications, like vehicle tracking and patient tracking require the IoT devices to be mobile. Due to this mobility, If the distance of the device from the sink node become greater than required, the connection is lost. Due to this disconnection other devices join the network for replacing the broken-down and left devices. This make IoT devices dynamic in nature which brings uncertainty and unreliability in the IoT network and hence produce bad quality of data. Due to this dynamic nature of IoT devices we do not know the actual reason of abnormal data. If data are of poor-quality decisions are likely to be unsound. It is highly important to process data and estimate data quality before bringing it to use in IoT applications. In the past many researchers tried to estimate data quality and provided several Machine Learning (ML), stochastic and statistical methods to perform analysis on stored data in the data processing layer, without focusing the challenges and issues arises from the dynamic nature of IoT devices and how it is impacting data quality. A comprehensive review on determining the impact of dynamic nature of IoT devices on data quality is done in this research and presented a data quality model that can deal with this challenge and produce good quality of data. This research presents the data quality model for the sensors monitoring water quality. DBSCAN clustering and weather sensors are used in this research to make data quality model for the sensors monitoring water quality. An extensive study has been done in this research on finding the relationship between the data of weather sensors and sensors monitoring water quality of the lakes and beaches. The detailed theoretical analysis has been presented in this research mentioning correlation between independent data streams of the two sets of sensors. With the help of the analysis and DBSCAN, a data quality model is prepared. This model encompasses five dimensions of data quality: outliers’ detection and removal, completeness, patterns of missing values and checks the accuracy of the data with the help of cluster’s position. At the end, the statistical analysis has been done on the clusters formed as the result of DBSCAN, and consistency is evaluated through Coefficient of Variation (CoV).

Keywords: clustering, data quality, DBSCAN, and Internet of things (IoT)

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Authors: Rozo D. Juan Felipe, Ramírez L. Leonardo Juan, Puerta A. Gabriel Alberto

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The telemedicine services require correct computing resource management to guarantee productivity and efficiency for medical and non-medical staff. The aim of this study was to examine web management strategies to ensure the availability of resources and services in telemedicine so as to provide medical information management with an accessible strategy. In addition, to evaluate the quality-of-service parameters, the followings were measured: delays, throughput, jitter, latency, available bandwidth, percent of access and denial of services based of web management performance map with profiles permissions and database management. Through 24 different test scenarios, the results show 100% in availability of medical information, in relation to access of medical staff to web services, and quality of service (QoS) of 99% because of network delay and performance of computer network. The findings of this study suggest that the proposed strategy of web management is an ideal solution to guarantee the availability, reliability, and accessibility of medical information. Finally, this strategy offers seven user profile used at telemedicine center of Bogota-Colombia keeping QoS parameters suitable to telemedicine services.

Keywords: availability, medical information, QoS, strategy, telemedicine

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Authors: Xiaoqing Wang, Jianjian Zong, Li Li, Yanxing Zheng, Jinrong Tong, Mao Zhan

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With the rapid development of network and communication technology, a great deal of data has been generated in different domains of a network. These data show a trend of increasing scale and more complex structure. Therefore, an effective and flexible cross-domain data-sharing system is needed. The Cross-domain Data Sharing System(CDSS) in a net-centric environment is composed of three sub-systems. The data distribution sub-system provides data exchange service through publish-subscribe technology that supports asynchronism and multi-to-multi communication, which adapts to the needs of the dynamic and large-scale distributed computing environment. The access control sub-system adopts Attribute-Based Access Control(ABAC) technology to uniformly model various data attributes such as subject, object, permission and environment, which effectively monitors the activities of users accessing resources and ensures that legitimate users get effective access control rights within a legal time. The cross-domain access security negotiation subsystem automatically determines the access rights between different security domains in the process of interactive disclosure of digital certificates and access control policies through trust policy management and negotiation algorithms, which provides an effective means for cross-domain trust relationship establishment and access control in a distributed environment. The CDSS’s asynchronous,multi-to-multi and loosely-coupled communication features can adapt well to data exchange and sharing in dynamic, distributed and large-scale network environments. Next, we will give CDSS new features to support the mobile computing environment.

Keywords: data sharing, cross-domain, data exchange, publish-subscribe

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Authors: Jaypalsinh Parmar, Pintu Nakrani, Bhaumik Shah

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Digital Elevation Model (DEM) is elevation data of the virtual grid on the ground. DEM can be used in application in GIS such as hydrological modelling, flood forecasting, morphometrical analysis and surveying etc.. For morphometrical analysis the stream flow network plays a very important role. DEM lacks accuracy and cannot match field data as it should for accurate results of morphometrical analysis. The present study focuses on comparing the Agree method and the conventional Shortest path method for finding out morphometric parameters in the flat region of the Lower Tapi Basin which is located in the western India. For the present study, open source SRTM (Shuttle Radar Topography Mission with 1 arc resolution) and toposheets issued by Survey of India (SOI) were used to determine the morphometric linear aspect such as stream order, number of stream, stream length, bifurcation ratio, mean stream length, mean bifurcation ratio, stream length ratio, length of overland flow, constant of channel maintenance and aerial aspect such as drainage density, stream frequency, drainage texture, form factor, circularity ratio, elongation ratio, shape factor and relief aspect such as relief ratio, gradient ratio and basin relief for 53 catchments of Lower Tapi Basin. Stream network was digitized from the available toposheets. Agree DEM was created by using the SRTM and stream network from the toposheets. The results obtained were used to demonstrate a comparison between the two methods in the flat areas.

Keywords: agree method, morphometric analysis, lower Tapi basin, shortest path method

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Authors: Kherbache Mehdi

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Within the framework of the validation of the Master in business administration marketing and sales in INSIM institute international in management Blida, we get the opportunity to do a professional internship in Sonelgaz Enterprise and a thesis. The thesis deals with the integration of social networking in the communication strategy of a company. The problematic is: How communicate with social network can be a solution for companies? The challenges stressed by this thesis were to suggest limits and recommendations to Sonelgaz Enterprise concerning social networks. The whole social networks represent more than a billion people as a potential target for the companies. Thanks to research and a qualitative approach, we have identified tree valid hypothesis. The first hypothesis allows confirming that using social networks cannot be ignored by any company in its communication strategy. However, the second hypothesis demonstrates that it’s necessary to prepare a strategy that integrates social networks in the communication plan of the company. The risk of this strategy is very limited because failure on social networks is not a restraint for the enterprise, social networking is not expensive and, a bad image which could result from it is not as important in the long-term. Furthermore, the return on investment is difficult to evaluate. Finally, the last hypothesis shows that firms establish a new relation between consumers and brands thanks to the proximity allowed by social networks. After the validation of the hypothesis, we suggested some recommendations to Sonelgaz Enterprise regarding the communication through social networks. Firstly, the company must use the interactivity of social network in order to have fruitful exchanges with the community. We also recommended having a strategy to treat negative comments. The company must also suggest delivering resources to the community thanks to a community manager, in order to have a good relation with the community. Furthermore, we advised using social networks to do business intelligence. Sonelgaz Enterprise can have some creative and interactive contents with some amazing applications on Facebook for example. Finally, we recommended to the company to be not intrusive with “fans” or “followers” and to be open to all the platforms: Twitter, Facebook, Linked-In for example.

Keywords: social network, buzz, communication, consumer, return on investment, internet users, web 2.0, Facebook, Twitter, interaction

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Authors: Krystian Chachula, Robert Nowak

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Manufacturing home-made explosives and synthetic drugs is an increasing problem in Europe. To combat that, a data fusion system is proposed for the detection and localization of production sites in urban environments. The data consists of measurements of properties of wastewater performed by various sensors installed in a sewage network. A four-stage fusion strategy allows detecting sources of waste products from known chemical reactions. First, suspicious measurements are used to compute the amount and position of discharged compounds. Then, this information is propagated through the sewage network to account for missing sensors. The next step is clustering and the formation of tracks. Eventually, tracks are used to reconstruct discharge events. Sensor measurements are simulated by a subsystem based on real-world data. In this paper, different discharge scenarios are considered to show how the parameters of used algorithms affect the effectiveness of the proposed system. This research is a part of the SYSTEM project (SYnergy of integrated Sensors and Technologies for urban sEcured environMent).

Keywords: continuous monitoring, information fusion and sensors, internet of things, multisensor fusion

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Authors: Sachini Wickramanayaka

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The occurrence of natural disasters has increased in an alarming rate in recent times due to escalating effects of climate change. One such natural disaster that has continued to grow in frequency and intensity is ‘flooding’, adversely affecting communities around the globe. This is an exploration on how architecture can intervene and facilitate in preserving communities in the face of disaster, specifically in battling floods. ‘Resilience’ is one of the concepts that have been brought forward to be instilled in vulnerable communities to lower the impact from such disasters as a preventative and coping mechanism. While there are number of ways to achieve resilience in the built environment, this paper aims to create a synthesis between resilience and ‘urban acupuncture’. It will consider strengthening communities from within, by layering a network of relatively small-scale, fast phased interventions on pre-existing conventional flood preventative large-scale engineering infrastructure.By investigating ‘The Woodlands’, a planned neighborhood as a case study, this paper will argue that large-scale water management solutions while extremely important will not suffice as a single solution particularly during a time of frequent and extreme weather events. The different projects will try to synthesize non-architectural aspects such as neighborhood aspirations, requirements, potential and awareness into a network of architectural forms that would collectively increase neighborhood resiliency to floods. A mapping study of the selected study area will identify the problematic areas that flood in the neighborhood while the empirical data from previously implemented case studies will assess the success of each solution.If successful the different solutions for each of the identified problem areas will exhibithow flooding and water management can be integrated as part and parcel of daily life.

Keywords: acupuncture, architecture, resiliency, micro-interventions, neighborhood

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Authors: Medha Basu, Kumardeb Banerjee, Dipak Ghosh

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After the massive blow of the COVID-19 pandemic, several health hazards have been reported all over the world. Serious cases of Major Depressive Disorder (MDD) are seen to be common in about 15% of the global population, making depression one of the leading mental health diseases, as reported by the World Health Organization. Various psychological and pharmacological treatment techniques are regularly being reported. Music, a globally accepted mode of entertainment, is often used as a therapeutic measure to treat various health conditions. We have tried to understand how Indian Classical Music can affect the overall well-being of the human brain. A case study has been reported here, where a Flute-rendition has been chosen from a detailed audience response survey, and the effects of that clip on human brain conditions have been studied from both psychological and neural perspectives. Taking help from internationally-accepted depression-rating scales, two questionnaires have been designed to understand both the prolonged and immediate effect of music on various emotional states of human lives. Thereafter, from EEG experiments on 5 participants using the same clip, the parameter ‘ALAY’, alpha frontal asymmetry (alpha power difference of right and left frontal hemispheres), has been calculated. Works of Richard Davidson show that an increase in the ‘ALAY’ value indicates a decrease in depressive symptoms. Using the non-linear technique of MFDFA on EEG analysis, we have also calculated frontal asymmetry using the complexity values of alpha-waves in both hemispheres. The results show a positive correlation between both the psychological survey and the EEG findings, revealing the prominent role of music on the human brain, leading to a decrease in mental unrest and an increase in overall well-being. In this study, we plan to propose the scientific foundation of music therapy, especially from a neurocognition perspective, with appropriate neural bio-markers to understand the positive and remedial effects of music on the human brain.

Keywords: music therapy, EEG, psychological survey, frontal alpha asymmetry, wellbeing

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Authors: Craig M. Allen

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Twenty two African entrepreneurs with Small and Medium Enterprises (SMEs) in a single social network centered around a non-Muslim population in a smaller African country, selected an Islamic financing structure, a form of Murabaha, based solely on market rationale. These entrepreneurs had all won procurement contracts from major purchasers of goods within their country and faced difficulty arranging traditional bank financing to support their supply-chain needs. The Murabaha-based structure satisfied their market-driven demand and provided an attractive alternative to the traditional bank-offered lending products. The Murabaha-styled trade-financing structure was not promoted with any religious implications, but solely as a market solution to the existing problems associated with bank-related financing. This indicates the strong market forces that draw SMEs to financing structures that are traditionally considered within the framework of Islamic finance.

Keywords: Africa, entrepreneurs, Islamic finance, market acceptance, Murabaha, SMEs

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Authors: M. Rahimi, F. Corman

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This paper study the travel behavior of passengers in a public transport disruption under information provision strategies. We develop a within-day approach for multi-agent simulation to evaluate the behavior of the agents, under comprehensive scenarios through various information exposure, equilibrium, and non-equilibrium scenarios. In particular, we quantify the effects of information strategies in disruption situation on passengers’ satisfaction, number of involved agents, and the caused delay. An agent-based micro-simulation model (MATSim) is applied for the city of Zürich, Switzerland, for the purpose of activity-based simulation in a multimodal network. Statistic outcome is analysed for all the agents who may be involved in the disruption. Agents’ movement in the public transport network illustrates agents’ adaptations to available information about the disruption. Agents’ delays and utility reveal that information significantly affects agents’ satisfaction and delay in public transport disruption. Besides, while the earlier availability of the information causes the fewer consequent delay for the involved agents, however, it also leads to more amount of affected agents.

Keywords: agent-based simulation, disruption management, passengers’ behavior simulation, public transport

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Authors: Sahar Al-Qahtani, Rabeeh Ayaz Abbasi, Naif Radi Aljohani

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Many universities use social media platforms as new communication channels to disseminate information and promptly communicate with their audience. As Twitter is one of the widely used social media platforms, this research aims to explore the adaption and utilization of Twitter by universities. We propose a framework called 'Social Network Analysis for Universities on Twitter' (SNAUT) to analyze the usage of Twitter by universities and to measure their interaction with public. The study includes a sample of around 110,000 tweets from 36 Saudi universities, including both public and private universities. Using SNAUT, we can (1) investigate the purpose of using Twitter by universities, (2) determine the broad topics discussed by them, and (3) identify the groups closely associated with the universities. The results show that most of the Saudi universities (whether public or private) actively use Twitter. Results also reveal that public universities respond to public queries more frequently, but private universities stand out more in terms of information dissemination using retweets and diverse hashtags. Finally, we develop a ranking mechanism in SNAUT for ranking universities based on their social interaction with the public on Twitter.

Keywords: social media, twitter, social network analysis, universities, higher education, Saudi Arabia

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Authors: Vahid Bairami Rad

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Wireless communications offer organizations and users many benefits such as portability and flexibility, increased productivity, and lower installation costs. Wireless networks serve as the transport mechanism between devices and among devices and the traditional wired networks (enterprise networks and the internet). Wireless networks are many and diverse but are frequently categorized into three groups based on their coverage range: WWAN, WLAN, and WPAN. WWAN, representing wireless wide area networks, includes wide coverage area technologies such as 2G cellular, Cellular Digital Packet Data (CDPD), Global System for Mobile Communications (GSM), and Mobitex. WLAN, representing wireless local area networks, includes 802.11, Hyper lan, and several others. WPAN, represents wireless personal area network technologies such as Bluetooth and Infrared. The security services are provided largely by the WEP (Wired Equivalent Privacy) protocol to protect link-level data during wireless transmission between clients and access points. That is, WEP does not provide end-to-end security but only for the wireless portion of the connection.

Keywords: wireless lan, wired equivalent privacy, wireless network security, wlan security

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Authors: Gyeongbeom Yi

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The purpose of this study is to introduce the analytic solution for determining the optimal capacity (lot-size) of a multiproduct, multistage production and inventory system to meet the finished product demand. Reasonable decision-making about the capacity of processes and storage units is an important subject for industry. The industrial solution for this subject is to use the classical economic lot sizing method, EOQ/EPQ (Economic Order Quantity/Economic Production Quantity) model, incorporated with practical experience. However, the unrealistic material flow assumption of the EOQ/EPQ model is not suitable for chemical plant design with highly interlinked processes and storage units. This study overcomes the limitation of the classical lot sizing method developed on the basis of the single product and single stage assumption. The superstructure of the plant considered consists of a network of serially and/or parallelly interlinked processes and storage units. The processes involve chemical reactions with multiple feedstock materials and multiple products as well as mixing, splitting or transportation of materials. The objective function for optimization is minimizing the total cost composed of setup and inventory holding costs as well as the capital costs of constructing processes and storage units. A novel production and inventory analysis method, PSW (Periodic Square Wave) model, is applied. The advantage of the PSW model comes from the fact that the model provides a set of simple analytic solutions in spite of a realistic description of the material flow between processes and storage units. The resulting simple analytic solution can greatly enhance the proper and quick investment decision for plant design and operation problem confronted in diverse economic situations.

Keywords: analytic solution, optimal design, process-storage network

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Authors: Mohammad Mahdi Paydar, Armin Cheraghalipour, Mostafa Hajiaghaei-Keshteli

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Nowadays, in advanced countries, agriculture as one of the most significant sectors of the economy, plays an important role in its political and economic independence. Due to farmers' lack of information about products' demand and lack of proper planning for harvest time, annually the considerable amount of products is corrupted. Besides, in this paper, we attempt to improve these unfavorable conditions via designing an effective supply chain network that tries to minimize total costs of agricultural products along with minimizing shortage in demand points. To validate the proposed model, a stochastic optimization approach by using a branch and bound solver of the LINGO software is utilized. Furthermore, to accumulate the data of parameters, a case study in Mazandaran province placed in the north of Iran has been applied. Finally, using ɛ-constraint approach, a Pareto front is obtained and one of its Pareto solutions as best solution is selected. Then, related results of this solution are explained. Finally, conclusions and suggestions for the future research are presented.

Keywords: perishable products, stochastic optimization, agricultural supply chain, ɛ-constraint

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Authors: Shan Zhang, Peter Suechting

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Predicting trade patterns is critical to decision-making in public and private domains, especially in the current context of trade disputes among major economies. In the past, U.S. recycling has relied heavily on strong demand for recyclable materials overseas. However, starting in 2017, a series of new recycling policies (bans and higher inspection standards) was enacted by multiple countries that were the primary importers of recyclables from the U.S. prior to that point. As the global trade flow of recycling shifts, some new importers, mostly developing countries in South and Southeast Asia, have been overwhelmed by the sheer quantities of scrap materials they have received. As the leading exporter of recyclable materials, the U.S. now has a pressing need to build its recycling industry domestically. With respect to the global trade in scrap materials used for recycling, the interest in this paper is (1) predicting how the export of recyclable materials from the U.S. might vary over time, and (2) predicting how international trade flows for recyclables might change in the future. Focusing on three major recyclable materials with a history of trade, this study uses data-driven and machine learning (ML) algorithms---supervised (shrinkage and tree methods) and unsupervised (neural network method)---to decipher the international trade pattern of recycling. Forecasting the potential trade values of recyclables in the future could help importing countries, to which those materials will shift next, to prepare related trade policies. Such policies can assist policymakers in minimizing negative environmental externalities and in finding the optimal amount of recyclables needed by each country. Such forecasts can also help exporting countries, like the U.S understand the importance of healthy domestic recycling industry. The preliminary result suggests that gravity models---in addition to particular selection macroeconomic predictor variables--are appropriate predictors of the total export value of recyclables. With the inclusion of variables measuring aspects of the political conditions (trade tariffs and bans), predictions show that recyclable materials are shifting from more policy-restricted countries to less policy-restricted countries in international recycling trade. Those countries also tend to have high manufacturing activities as a percentage of their GDP.

Keywords: environmental economics, machine learning, recycling, international trade

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Authors: Nurhak Altın, Ayhan Kucukmanisa, Oguzhan Urhan

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It is important that highways are in good condition for traffic safety. Road crashes (road cracks, erosion of lane markings, etc.) can cause accidents by affecting driving. Image processing based methods for detecting road cracks are available in the literature. In this paper, a deep learning based road crack detection approach is proposed. YOLO (You Look Only Once) is adopted as core component of the road crack detection approach presented. The YOLO network structure, which is developed for object detection, is trained with road crack images as a new class that is not previously used in YOLO. The performance of the proposed method is compared using different training methods: using randomly generated weights and training their own pre-trained weights (transfer learning). A similar training approach is applied to the simplified version of the YOLO network model (tiny yolo) and the results of the performance are examined. The developed system is able to process 8 fps on NVIDIA Jetson TX1 development kit.

Keywords: deep learning, embedded platform, real-time processing, road crack detection

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Authors: Areti Angeliki Veroniki, Sofia Tsokani, Evangelos Paraskevaidis, Dimitris Mavridis

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The plethora of diagnostic test accuracy (DTA) studies has led to the increased use of systematic reviews and meta-analysis of DTA studies. Clinicians and healthcare professionals often consult DTA meta-analyses to make informed decisions regarding the optimum test to choose and use for a given setting. For example, the human papilloma virus (HPV) DNA, mRNA, and cytology can be used for the cervical intraepithelial neoplasia grade 2+ (CIN2+) diagnosis. But which test is the most accurate? Studies directly comparing test accuracy are not always available, and comparisons between multiple tests create a network of DTA studies that can be synthesized through a network meta-analysis of diagnostic tests (DTA-NMA). The aim is to summarize the DTA-NMA methods for at least three index tests presented in the methodological literature. We illustrate the application of the methods using a real data set for the comparative accuracy of HPV DNA, HPV mRNA, and cytology tests for cervical cancer. A search was conducted in PubMed, Web of Science, and Scopus from inception until the end of July 2019 to identify full-text research articles that describe a DTA-NMA method for three or more index tests. Since the joint classification of the results from one index against the results of another index test amongst those with the target condition and amongst those without the target condition are rarely reported in DTA studies, only methods requiring the 2x2 tables of the results of each index test against the reference standard were included. Studies of any design published in English were eligible for inclusion. Relevant unpublished material was also included. Ten relevant studies were finally included to evaluate their methodology. DTA-NMA methods that have been presented in the literature together with their advantages and disadvantages are described. In addition, using 37 studies for cervical cancer obtained from a published Cochrane review as a case study, an application of the identified DTA-NMA methods to determine the most promising test (in terms of sensitivity and specificity) for use as the best screening test to detect CIN2+ is presented. As a conclusion, different approaches for the comparative DTA meta-analysis of multiple tests may conclude to different results and hence may influence decision-making. Acknowledgment: This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning 2014-2020» in the context of the project “Extension of Network Meta-Analysis for the Comparison of Diagnostic Tests ” (MIS 5047640).

Keywords: colposcopy, diagnostic test, HPV, network meta-analysis

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Authors: Hamed Mohamed Haggag

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Prior to 12 November 1955, no earthquakes have been reported from the Abu Dabbab area in the International Seismological Center catalogue (ISC). The largest earthquake in Abu Dabbab area occurred on November 12, 1955 with magnitude Mb 6.0. The closest station from the epicenter was at Helwan (about 700 km to the north), so the depth of this event is not constrained and no foreshocks or aftershocks were recorded. Two other earthquakes of magnitude Mb 4.5 and 5.2 took place in the same area on March 02, 1982 and July 02, 1984, respectively. Since the installation of Aswan Seismic Network stations in 1982, (250-300 km to the south-west of Abu Dabbab area) then the Egyptian Natoinal Seismic Network stations, it was possible to record some activity from Abu Dabbab area. The recorded earthquakes at Abu Dabbab area as recorded from 1982 to 2014 shows that the earthquake epicenters are distributed in the same direction of the main trends of the faults in the area, which is parallel to the Red Sea coast. The spectral analysis was made for some earthquakes. The source parameters, seismic moment (Mo), source dimension (r), stress drop (Δδ), and apparent stress (δ) are determined for these events. The spectral analysis technique was completed using MAG software program.

Keywords: Abu Dabbab, seismicity, seismic moment, source parameter

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Authors: Dong-Hyun Ha, Young-Min Ko, Chang-Bin Ha, Hyoung-Kyu Song

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In Heterogeneous network (HetNet) can provide high quality of a service in a wireless communication system by composition of small cell networks. The composition of small cell networks improves cell coverage and capacity to the mobile users.Recently, various techniques using small cell networks have been researched in the wireless communication system. In this paper, the cooperative scheme obtaining high reliability is proposed in the small cell networks. The proposed scheme suggests a cooperative small cell system and the new signal transmission technique in the proposed system model. The new signal transmission technique applies a cyclic delay diversity (CDD) scheme based on the multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system to obtain improved performance. The improved performance of the proposed scheme is confirmed by the simulation results.

Keywords: adaptive transmission, cooperative communication, diversity gain, OFDM

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Authors: I. Grigoratos, R. Monteiro

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Regions with a significant percentage of non-seismically designed buildings and reduced urban planning are particularly vulnerable to natural hazards. In this context, the project ‘Improved Tools for Disaster Risk Mitigation in Algeria’ (ITERATE) aims at seismic risk mitigation in Algeria. Past earthquakes in North Algeria caused extensive damages, e.g. the El Asnam 1980 moment magnitude (Mw) 7.1 and Boumerdes 2003 Mw 6.8 earthquakes. This paper will address a number of proposed developments and considerations made towards a further improvement of the component of seismic hazard. In specific, an updated earthquake catalog (until year 2018) is compiled, and new conversion equations to moment magnitude are introduced. Furthermore, a network-based method for the estimation of the spatial and temporal distribution of the minimum magnitude of completeness is applied. We found relatively large values for M_c, due to the sparse network, and a nonlinear trend between M_w and body wave (m_b) or local magnitude (M_L), which are the most common scales reported in the region. Lastly, the resulting b-value of the Gutenberg-Richter distribution is sensitive to the declustering method.

Keywords: conversion equation, magnitude of completeness, seismic events, seismic hazard

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Authors: Hafida Ferfera-Harrar, Nacera Aiouaz, Nassima Dairi

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Super absorbents polymers received much attention and are used in many fields because of their superior characters to traditional absorbents, e.g., sponge and cotton. So, it is very important but challenging to prepare highly and fast-swelling super absorbents. A reliable, efficient and low-cost technique for removing heavy metal ions from waste water is the adsorption using bio-adsorbents obtained from biological materials, such as polysaccharides-based hydrogels super absorbents. In this study, novel multi-functional super absorbent composites type semi-interpenetrating polymer networks (Semi-IPNs) were prepared via graft polymerization of acrylamide onto chitosan backbone in presence of gelatin, CTS-g-PAAm/Ge, using potassium persulfate and N,N’ -methylenebisacrylamide as initiator and cross linker, respectively. These hydrogels were also partially hydrolyzed to achieve superabsorbents with ampholytic properties and uppermost swelling capacity. The formation of the grafted network was evidenced by Fourier Transform Infrared Spectroscopy (ATR-FTIR) and thermo gravimetric Analysis (TGA). The porous structures were observed by Scanning Electron Microscope (SEM). From TGA analysis, it was concluded that the incorporation of the Ge in the CTS-g-PAAm network has marginally affected its thermal stability. The effect of gelatin content on the swelling capacities of these super absorbent composites was examined in various media (distilled water, saline and pH-solutions).The water absorbency was enhanced by adding Ge in the network, where the optimum value was reached at 2 wt. % of Ge. Their hydrolysis has not only greatly optimized their absorption capacity but also improved the swelling kinetic. These materials have also showed reswelling ability. We believe that these super-absorbing materials would be very effective for the adsorption of harmful metal ions from waste water.

Keywords: chitosan, gelatin, superabsorbent, water absorbency

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Authors: Zachary Huffman, Joana Rocha

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Wall-pressure fluctuations induced by the turbulent boundary layer (TBL) developed over aircraft are a significant source of aircraft cabin noise. Since the power spectral density (PSD) of these pressure fluctuations is directly correlated with the amount of sound radiated into the cabin, the development of accurate empirical models that predict the PSD has been an important ongoing research topic. The sound emitted can be represented from the pressure fluctuations term in the Reynoldsaveraged Navier-Stokes equations (RANS). Therefore, early TBL empirical models (including those from Lowson, Robertson, Chase, and Howe) were primarily derived by simplifying and solving the RANS for pressure fluctuation and adding appropriate scales. Most subsequent models (including Goody, Efimtsov, Laganelli, Smol’yakov, and Rackl and Weston models) were derived by making modifications to these early models or by physical principles. Overall, these models have had varying levels of accuracy, but, in general, they are most accurate under the specific Reynolds and Mach numbers they were developed for, while being less accurate under other flow conditions. Despite this, recent research into the possibility of using alternative methods for deriving the models has been rather limited. More recent studies have demonstrated that an artificial neural network model was more accurate than traditional models and could be applied more generally, but the accuracy of other machine learning techniques has not been explored. In the current study, an original model is derived using a stepwise regression algorithm in the statistical programming language R, and TBL wall-pressure fluctuations PSD data gathered at the Carleton University wind tunnel. The theoretical advantage of a stepwise regression approach is that it will automatically filter out redundant or uncorrelated input variables (through the process of feature selection), and it is computationally faster than machine learning. The main disadvantage is the potential risk of overfitting. The accuracy of the developed model is assessed by comparing it to independently sourced datasets.

Keywords: aircraft noise, machine learning, power spectral density models, regression models, turbulent boundary layer wall-pressure fluctuations

Procedia PDF Downloads 135

Authors: Abdulah Fajar

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Electronic Procurement System has been implemented at government institution in Indonesia. This system has been developed centrally at Institution of National Procurement Policy (LKPP) and implemented autonomously at either local or national government institution. The lack of competency at many institution on Information Technology Management arise several major problems. The main concern of LKPP to local administrator is assured that the system is running normally and always be able to serve the needs of its users. Monitoring system has been identified as the one of solution to prevent the problems appeared. Monitoring system is developed using Simple Network Management Protocol (SNMP) and implemented at LKPP. There are two modules; Main Dashboard and Local Agent. Main Dashboard is intended for LKPP and Local Agent is intended to implement at local autonomous e-procurement system (LPSE). There are several resources that must be monitored such as computation, memory and network traffic. Agile paradigm is applied to this project to assure user and system requirement is met. The length of project is the one of reason why agile paradigm has been chosen. The system has been successfully delivered to LKPP.

Keywords: procurement system, SNMP, LKPP, LPSE

Procedia PDF Downloads 427

Authors: Serhat Tüzün, Tufan Demirel

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Decision Support Systems (DSS) have been investigated by researchers and technologists for more than 35 years. This paper analyses the developments in the architecture and software of these systems, provides a systematic analysis for different Web-based DSS approaches and Intelligent Decision-making Technologies (IDT), with the suggestion for future studies. Decision Support Systems literature begins with building model-oriented DSS in the late 1960s, theory developments in the 1970s, and the implementation of financial planning systems and Group DSS in the early and mid-80s. Then it documents the origins of Executive Information Systems, online analytic processing (OLAP) and Business Intelligence. The implementation of Web-based DSS occurred in the mid-1990s. With the beginning of the new millennia, intelligence is the main focus on DSS studies. Web-based technologies are having a major impact on design, development and implementation processes for all types of DSS. Web technologies are being utilized for the development of DSS tools by leading developers of decision support technologies. Major companies are encouraging its customers to port their DSS applications, such as data mining, customer relationship management (CRM) and OLAP systems, to a web-based environment. Similarly, real-time data fed from manufacturing plants are now helping floor managers make decisions regarding production adjustment to ensure that high-quality products are produced and delivered. Web-based DSS are being employed by organizations as decision aids for employees as well as customers. A common usage of Web-based DSS has been to assist customers configure product and service according to their needs. These systems allow individual customers to design their own products by choosing from a menu of attributes, components, prices and delivery options. The Intelligent Decision-making Technologies (IDT) domain is a fast growing area of research that integrates various aspects of computer science and information systems. This includes intelligent systems, intelligent technology, intelligent agents, artificial intelligence, fuzzy logic, neural networks, machine learning, knowledge discovery, computational intelligence, data science, big data analytics, inference engines, recommender systems or engines, and a variety of related disciplines. Innovative applications that emerge using IDT often have a significant impact on decision-making processes in government, industry, business, and academia in general. This is particularly pronounced in finance, accounting, healthcare, computer networks, real-time safety monitoring and crisis response systems. Similarly, IDT is commonly used in military decision-making systems, security, marketing, stock market prediction, and robotics. Even though lots of research studies have been conducted on Decision Support Systems, a systematic analysis on the subject is still missing. Because of this necessity, this paper has been prepared to search recent articles about the DSS. The literature has been deeply reviewed and by classifying previous studies according to their preferences, taxonomy for DSS has been prepared. With the aid of the taxonomic review and the recent developments over the subject, this study aims to analyze the future trends in decision support systems.

Keywords: decision support systems, intelligent decision-making, systematic analysis, taxonomic review

Procedia PDF Downloads 279

Authors: R. Rajabpour, N. Talebbeydokhti, M. H. Ahmadi

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Carefully scheduling the operations of pumps can be resulted to significant energy savings. Schedules can be defined either implicit, in terms of other elements of the network such as tank levels, or explicit by specifying the time during which each pump is on/off. In this study, two new explicit representations based on time-controlled triggers were analyzed, where the maximum number of pump switches was established beforehand, and the schedule may contain fewer switches than the maximum. The optimal operation of pumping stations was determined using a Jumping Particle Swarm Optimization (JPSO) algorithm to achieve the minimum energy cost. The model integrates JPSO optimizer and EPANET hydraulic network solver. The optimal pump operation schedule of VanZyl water distribution system was determined using the proposed model and compared with those from Genetic and Ant Colony algorithms. The results indicate that the proposed model utilizing the JPSP algorithm outperformed the others and is a versatile management model for the operation of real-world water distribution system.

Keywords: JPSO, operation, optimization, water distribution system

Procedia PDF Downloads 245

Authors: Sofia Dokuka, Liz Lockhart, Alex Furman

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Organizational hierarchy, traditionally composed of individual contributors, middle management, and executives, is enhanced by the understanding of informal social roles. These roles, identified with organizational network analysis (ONA), might have an important effect on organizational functioning. In this paper, we identify three social roles – influencers, bridges, and soloists, and provide empirical analysis based on real-world organizational networks. Influencers are employees with broad networks and whose contacts also have rich networks. Influence is calculated using PageRank, initially proposed for measuring website importance, but now applied in various network settings, including social networks. Influencers, having high PageRank, become key players in shaping opinions and behaviors within an organization. Bridges serve as links between loosely connected groups within the organization. Bridges are identified using betweenness and Burt’s constraint. Betweenness quantifies a node's control over information flows by evaluating its role in the control over the shortest paths within the network. Burt's constraint measures the extent of interconnection among an individual's contacts. A high constraint value suggests fewer structural holes and lesser control over information flows, whereas a low value suggests the contrary. Soloists are individuals with fewer than 5 stable social contacts, potentially facing challenges due to reduced social interaction and hypothetical lack of feedback and communication. We considered social roles in the analysis of real-world organizations (N=1,060). Based on data from digital traces (Slack, corporate email and calendar) we reconstructed an organizational communication network and identified influencers, bridges and soloists. We also collected employee engagement data through an online survey. Among the top-5% of influencers, 10% are members of the Executive Team. 56% of the Executive Team members are part of the top influencers group. The same proportion of top influencers (10%) is individual contributors, accounting for just 0.6% of all individual contributors in the company. The majority of influencers (80%) are at the middle management level. Out of all middle managers, 19% hold the role of influencers. However, individual contributors represent a small proportion of influencers, and having information about these individuals who hold influential roles can be crucial for management in identifying high-potential talents. Among the bridges, 4% are members of the Executive Team, 16% are individual contributors, and 80% are middle management. Predominantly middle management acts as a bridge. Bridge positions of some members of the executive team might indicate potential micromanagement on the leader's part. Recognizing the individuals serving as bridges in an organization uncovers potential communication problems. The majority of soloists are individual contributors (96%), and 4% of soloists are from middle management. These managers might face communication difficulties. We found an association between being an influencer and attitude toward a company's direction. There is a statistically significant 20% higher perception that the company is headed in the right direction among influencers compared to non-influencers (p < 0.05, Mann-Whitney test). Taken together, we demonstrate that considering social roles in the company might indicate both positive and negative aspects of organizational functioning that should be considered in data-driven decision-making.

Keywords: organizational network analysis, social roles, influencer, bridge, soloist

Procedia PDF Downloads 105

Authors: Lyamine Guezouli, Kamel Barka, Zineb Seghir

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Wireless sensor and actor networks (WSANs) present a research challenge for different practice areas. Researchers are trying to optimize the use of such networks through their research work. This optimization is done on certain criteria, such as improving energy efficiency, exploiting node heterogeneity, self-adaptability and self-configuration. In this article, we present our proposal for BIFSA (Biologically-Inspired Framework for Wireless Sensor and Actor networks). Indeed, BIFSA is a middleware that addresses the key issues of wireless sensor and actor networks. BIFSA consists of two types of agents: sensor agents (SA) that operate at the sensor level to collect and transport data to actors and actor agents (AA) that operate at the actor level to transport data to base stations. Once the sensor agent arrives at the actor, it becomes an actor agent, which can exploit the resources of the actors and vice versa. BIFSA allows agents to evolve their genetic structures and adapt to the current network conditions. The simulation results show that BIFSA allows the agents to make better use of all the resources available in each type of node, which improves the performance of the network.

Keywords: wireless sensor and actor networks, self-management, genetic algorithm, agent.

Procedia PDF Downloads 89

Authors: Long Cheng, Tong He, Iraj Mantegh, Wen-Fang Xie

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Path planning is essential for UAVs (Unmanned Aerial Vehicle) with autonomous navigation in unknown environments. In this paper, an adaptive probabilistic sampling algorithm is proposed for the GPS-denied environment, which can be utilized for autonomous navigation system of multiple UAVs in a dynamically-changing structured environment. This method can be used for Unmanned Aircraft Systems Traffic Management (UTM) solutions and in autonomous urban aerial mobility, where a number of platforms are expected to share the airspace. A path network is initially built off line based on available environment map, and on-board sensors systems on the flying UAVs are used for continuous situational awareness and to inform the changes in the path network. Simulation results based on MATLAB and Gazebo in different scenarios and algorithms performance measurement show the high efficiency and accuracy of the proposed technique in unknown environments.

Keywords: path planning, adaptive probabilistic sampling, obstacle avoidance, multiple unmanned aerial vehicles, unknown environments

Procedia PDF Downloads 156