Search results for: network ties
3800 Hyperspectral Band Selection for Oil Spill Detection Using Deep Neural Network
Authors: Asmau Mukhtar Ahmed, Olga Duran
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Hydrocarbon (HC) spills constitute a significant problem that causes great concern to the environment. With the latest technology (hyperspectral images) and state of the earth techniques (image processing tools), hydrocarbon spills can easily be detected at an early stage to mitigate the effects caused by such menace. In this study; a controlled laboratory experiment was used, and clay soil was mixed and homogenized with different hydrocarbon types (diesel, bio-diesel, and petrol). The different mixtures were scanned with HYSPEX hyperspectral camera under constant illumination to generate the hypersectral datasets used for this experiment. So far, the Short Wave Infrared Region (SWIR) has been exploited in detecting HC spills with excellent accuracy. However, the Near-Infrared Region (NIR) is somewhat unexplored with regards to HC contamination and how it affects the spectrum of soils. In this study, Deep Neural Network (DNN) was applied to the controlled datasets to detect and quantify the amount of HC spills in soils in the Near-Infrared Region. The initial results are extremely encouraging because it indicates that the DNN was able to identify features of HC in the Near-Infrared Region with a good level of accuracy.Keywords: hydrocarbon, Deep Neural Network, short wave infrared region, near-infrared region, hyperspectral image
Procedia PDF Downloads 1053799 Pilot-free Image Transmission System of Joint Source Channel Based on Multi-Level Semantic Information
Authors: Linyu Wang, Liguo Qiao, Jianhong Xiang, Hao Xu
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In semantic communication, the existing joint Source Channel coding (JSCC) wireless communication system without pilot has unstable transmission performance and can not effectively capture the global information and location information of images. In this paper, a pilot-free image transmission system of joint source channel based on multi-level semantic information (Multi-level JSCC) is proposed. The transmitter of the system is composed of two networks. The feature extraction network is used to extract the high-level semantic features of the image, compress the information transmitted by the image, and improve the bandwidth utilization. Feature retention network is used to preserve low-level semantic features and image details to improve communication quality. The receiver also is composed of two networks. The received high-level semantic features are fused with the low-level semantic features after feature enhancement network in the same dimension, and then the image dimension is restored through feature recovery network, and the image location information is effectively used for image reconstruction. This paper verifies that the proposed multi-level JSCC algorithm can effectively transmit and recover image information in both AWGN channel and Rayleigh fading channel, and the peak signal-to-noise ratio (PSNR) is improved by 1~2dB compared with other algorithms under the same simulation conditions.Keywords: deep learning, JSCC, pilot-free picture transmission, multilevel semantic information, robustness
Procedia PDF Downloads 1073798 Computer-Aided Diagnosis of Polycystic Kidney Disease Using ANN
Authors: G. Anjan Babu, G. Sumana, M. Rajasekhar
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Many inherited diseases and non-hereditary disorders are common in the development of renal cystic diseases. Polycystic kidney disease (PKD) is a disorder developed within the kidneys in which grouping of cysts filled with water like fluid. PKD is responsible for 5-10% of end-stage renal failure treated by dialysis or transplantation. New experimental models, application of molecular biology techniques have provided new insights into the pathogenesis of PKD. Researchers are showing keen interest for developing an automated system by applying computer aided techniques for the diagnosis of diseases. In this paper a multi-layered feed forward neural network with one hidden layer is constructed, trained and tested by applying back propagation learning rule for the diagnosis of PKD based on physical symptoms and test results of urinanalysis collected from the individual patients. The data collected from 50 patients are used to train and test the network. Among these samples, 75% of the data used for training and remaining 25% of the data are used for testing purpose. Furthermore, this trained network is used to implement for new samples. The output results in normality and abnormality of the patient.Keywords: dialysis, hereditary, transplantation, polycystic, pathogenesis
Procedia PDF Downloads 3723797 Early Prediction of Disposable Addresses in Ethereum Blockchain
Authors: Ahmad Saleem
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Ethereum is the second largest crypto currency in blockchain ecosystem. Along with standard transactions, it supports smart contracts and NFT’s. Current research trends are focused on analyzing the overall structure of the network its growth and behavior. Ethereum addresses are anonymous and can be created on fly. The nature of Ethereum network and addresses make it hard to predict their behavior. The activity period of an ethereum address is not much analyzed. Using machine learning we can make early prediction about the disposability of the address. In this paper we analyzed the lifetime of the addresses. We also identified and predicted the disposable addresses using machine learning models and compared the results.Keywords: blockchain, Ethereum, cryptocurrency, prediction
Procedia PDF Downloads 883796 Performance Analysis of Scalable Secure Multicasting in Social Networking
Authors: R. Venkatesan, A. Sabari
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Developments of social networking internet scenario are recommended for the requirements of scalable, authentic, secure group communication model like multicasting. Multicasting is an inter network service that offers efficient delivery of data from a source to multiple destinations. Even though multicast has been very successful at providing an efficient and best-effort data delivery service for huge groups, it verified complex process to expand other features to multicast in a scalable way. Separately, the requirement for secure electronic information had become gradually more apparent. Since multicast applications are deployed for mainstream purpose the need to secure multicast communications will become significant.Keywords: multicasting, scalability, security, social network
Procedia PDF Downloads 2843795 Solving the Wireless Mesh Network Design Problem Using Genetic Algorithm and Simulated Annealing Optimization Methods
Authors: Moheb R. Girgis, Tarek M. Mahmoud, Bahgat A. Abdullatif, Ahmed M. Rabie
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Mesh clients, mesh routers and gateways are components of Wireless Mesh Network (WMN). In WMN, gateways connect to Internet using wireline links and supply Internet access services for users. We usually need multiple gateways, which takes time and costs a lot of money set up, due to the limited wireless channel bit rate. WMN is a highly developed technology that offers to end users a wireless broadband access. It offers a high degree of flexibility contrasted to conventional networks; however, this attribute comes at the expense of a more complex construction. Therefore, a challenge is the planning and optimization of WMNs. In this paper, we concentrate on this challenge using a genetic algorithm and simulated annealing. The genetic algorithm and simulated annealing enable searching for a low-cost WMN configuration with constraints and determine the number of used gateways. Experimental results proved that the performance of the genetic algorithm and simulated annealing in minimizing WMN network costs while satisfying quality of service. The proposed models are presented to significantly outperform the existing solutions.Keywords: wireless mesh networks, genetic algorithms, simulated annealing, topology design
Procedia PDF Downloads 4493794 Modelling a Hospital as a Queueing Network: Analysis for Improving Performance
Authors: Emad Alenany, M. Adel El-Baz
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In this paper, the flow of different classes of patients into a hospital is modelled and analyzed by using the queueing network analyzer (QNA) algorithm and discrete event simulation. Input data for QNA are the rate and variability parameters of the arrival and service times in addition to the number of servers in each facility. Patient flows mostly match real flow for a hospital in Egypt. Based on the analysis of the waiting times, two approaches are suggested for improving performance: Separating patients into service groups, and adopting different service policies for sequencing patients through hospital units. The separation of a specific group of patients, with higher performance target, to be served separately from the rest of patients requiring lower performance target, requires the same capacity while improves performance for the selected group of patients with higher target. Besides, it is shown that adopting the shortest processing time and shortest remaining processing time service policies among other tested policies would results in, respectively, 11.47% and 13.75% reduction in average waiting time relative to first come first served policy.Keywords: queueing network, discrete-event simulation, health applications, SPT
Procedia PDF Downloads 1803793 Development of Energy Management System Based on Internet of Things Technique
Authors: Wen-Jye Shyr, Chia-Ming Lin, Hung-Yun Feng
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The purpose of this study was to develop an energy management system for university campuses based on the Internet of Things (IoT) technique. The proposed IoT technique based on WebAccess is used via network browser Internet Explore and applies TCP/IP protocol. The case study of IoT for lighting energy usage management system was proposed. Structure of proposed IoT technique included perception layer, equipment layer, control layer, application layer and network layer.Keywords: energy management, IoT technique, sensor, WebAccess
Procedia PDF Downloads 3233792 Using Two-Mode Network to Access the Connections of Film Festivals
Authors: Qiankun Zhong
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In a global cultural context, film festival awards become authorities to define the aesthetic value of films. To study which genres and producing countries are valued by different film festivals and how those evaluations interact with each other, this research explored the interactions between the film festivals through their selection of movies and the factors that lead to the tendency of film festivals to nominate the same movies. To do this, the author employed a two-mode network on the movies that won the highest awards at five international film festivals with the highest attendance in the past ten years (the Venice Film Festival, the Cannes Film Festival, the Toronto International Film Festival, Sundance Film Festival, and the Berlin International Film Festival) and the film festivals that nominated those movies. The title, genre, producing country and language of 50 movies, and the range (regional, national or international) and organizing country or area of 129 film festivals were collected. These created networks connected by nominating the same films and awarding the same movies. The author then assessed the density and centrality of these networks to answer the question: What are the film festivals that tend to have more shared values with other festivals? Based on the Eigenvector centrality of the two-mode network, Palm Springs, Robert Festival, Toronto, Chicago, and San Sebastian are the festivals that tend to nominate commonly appreciated movies. In contrast, Black Movie Film Festival has the unique value of generally not sharing nominations with other film festivals. A homophily test was applied to access the clustering effects of film and film festivals. The result showed that movie genres (E-I index=0.55) and geographic location (E-I index=0.35) are possible indicators of film festival clustering. A blockmodel was also created to examine the structural roles of the film festivals and their meaning in real-world context. By analyzing the same blocks with film festival attributes, it was identified that film festivals either organized in the same area, with the same history, or with the same attitude on independent films would occupy the same structural roles in the network. Through the interpretation of the blocks, language was identified as an indicator that contributes to the role position of a film festival. Comparing the result of blockmodeling in the different periods, it is seen that international film festivals contrast with the Hollywood industry’s dominant value. The structural role dynamics provide evidence for a multi-value film festival network.Keywords: film festivals, film studies, media industry studies, network analysis
Procedia PDF Downloads 3083791 Automating 2D CAD to 3D Model Generation Process: Wall pop-ups
Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski
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In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection
Procedia PDF Downloads 1383790 Thick Data Analytics for Learning Cataract Severity: A Triplet Loss Siamese Neural Network Model
Authors: Jinan Fiaidhi, Sabah Mohammed
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Diagnosing cataract severity is an important factor in deciding to undertake surgery. It is usually conducted by an ophthalmologist or through taking a variety of fundus photography that needs to be examined by the ophthalmologist. This paper carries out an investigation using a Siamese neural net that can be trained with small anchor samples to score cataract severity. The model used in this paper is based on a triplet loss function that takes the ophthalmologist best experience in rating positive and negative anchors to a specific cataract scaling system. This approach that takes the heuristics of the ophthalmologist is generally called the thick data approach, which is a kind of machine learning approach that learn from a few shots. Clinical Relevance: The lens of the eye is mostly made up of water and proteins. A cataract occurs when these proteins at the eye lens start to clump together and block lights causing impair vision. This research aims at employing thick data machine learning techniques to rate the severity of the cataract using Siamese neural network.Keywords: thick data analytics, siamese neural network, triplet-loss model, few shot learning
Procedia PDF Downloads 1013789 Assessment of Water Quality Network in Karoon River by Dynamic Programming Approach (DPA)
Authors: M. Nasri Nasrabadi, A. A. Hassani
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Karoon is one of the greatest and longest rivers of Iran, which because of the existence of numerous industrial, agricultural centers and drinking usage, has a strategic situation in the west and southwest parts of Iran, and the optimal monitoring of its water quality is an essential and indispensable national issue. Due to financial constraints, water quality monitoring network design is an efficient way to manage water quality. The most crucial part is to find appropriate locations for monitoring stations. Considering the objectives of water usage, we evaluate existing water quality sampling stations of this river. There are several methods for assessment of existing monitoring stations such as Sanders method, multiple criteria decision making and dynamic programming approach (DPA) which DPA opted in this study. The results showed that due to the drinking water quality index out of 20 existing monitoring stations, nine stations should be retained on the river, that include of Gorgor-Band-Ghir of A zone, Dez-Band-Ghir of B zone, Teir, Pole Panjom and Zargan of C zone, Darkhoein, Hafar, Chobade, and Sabonsazi of D zone. In additional, stations of Dez river have the best conditions.Keywords: DPA, karoon river, network monitoring, water quality, sampling site
Procedia PDF Downloads 3663788 Analysis of the IEEE 802.15.4 MAC Parameters to Achive Lower Packet Loss Rates
Authors: Imen Bouazzi
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The IEEE-802.15.4 standard utilizes the CSMA-CA mechanism to control nodes access to the shared wireless communication medium. It is becoming the popular choice for various applications of surveillance and control used in wireless sensor network (WSN). The benefit of this standard is evaluated regarding of the packet loss probability who depends on the configuration of IEEE 802.15.4 MAC parameters and the traffic load. Our exigency is to evaluate the effects of various configurable MAC parameters on the performance of beaconless IEEE 802.15.4 networks under different traffic loads, static values of IEEE 802.15.4 MAC parameters (macMinBE, macMaxCSMABackoffs, and macMaxFrame Retries) will be evaluated. To performance analysis, we use ns-2[2] network simulator.Keywords: WSN, packet loss, CSMA/CA, IEEE-802.15.4
Procedia PDF Downloads 3313787 A Neural Network System for Predicting the Hardness of Titanium Aluminum Nitrite (TiAlN) Coatings
Authors: Omar M. Elmabrouk
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The cutting tool, in the high-speed machining process, is consistently dealing with high localized stress at the tool tip, tip temperature exceeds 800°C and the chip slides along the rake face. These conditions are affecting the tool wear, the cutting tool performances, the quality of the produced parts and the tool life. Therefore, a thin film coating on the cutting tool should be considered to improve the tool surface properties while maintaining its bulks properties. One of the general coating processes in applying thin film for hard coating purpose is PVD magnetron sputtering. In this paper, the prediction of the effects of PVD magnetron sputtering coating process parameters, sputter power in the range of (4.81-7.19 kW), bias voltage in the range of (50.00-300.00 Volts) and substrate temperature in the range of (281.08-600.00 °C), were studied using artificial neural network (ANN). The results were compared with previously published results using RSM model. It was found that the ANN is more accurate in prediction of tool hardness, and hence, it will not only improve the tool life of the tool but also significantly enhances the efficiency of the machining processes.Keywords: artificial neural network, hardness, prediction, titanium aluminium nitrate coating
Procedia PDF Downloads 5463786 A Review of How COVID-19 Has Created an Insider Fraud Pandemic and How to Stop It
Authors: Claire Norman-Maillet
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Insider fraud, including its various synonyms such as occupational, employee or internal fraud, is a major financial crime threat whereby an employee defrauds (or attempts to defraud) their current, prospective, or past employer. ‘Employee’ covers anyone employed by the company, including contractors, directors, and part time staff; they may be a solo bad actor or working in collusion with others, whether internal or external. Insider fraud is even more of a concern given the impacts of the Coronavirus pandemic, which has generated multiple opportunities to commit insider fraud. Insider fraud is something that is not necessarily thought of as a significant financial crime threat; the focus of most academics and practitioners has historically been on that of ‘external fraud’ against businesses or entities where an individual or group has no professional ties. Without the face-to-face, ‘over the shoulder’ capabilities of staff being able to keep an eye on their employees, there is a heightened reliance on trust and transparency. With this, naturally, comes an increased risk of insider fraud perpetration. The objective of the research is to better understand how companies are impacted by insider fraud, and therefore how to stop it. This research will make both an original contribution and stimulate debate within the financial crime field. The financial crime landscape is never static – criminals are always creating new ways to perpetrate financial crime, and new legislation and regulations are implemented as attempts to strengthen controls, in addition to businesses doing what they can internally to detect and prevent it. By focusing on insider fraud specifically, the research will be more specific and will be of greater use to those in the field. To achieve the aims of the research, semi-structured interviews were conducted with 22 individuals who either work in financial services and deal with insider fraud or work within insider fraud perpetration in a recruitment or advisory capacity. This was to enable the sourcing of information from a wide range of individuals in a setting where they were able to elaborate on their answers. The principal recruitment strategy was engaging with the researcher’s network on LinkedIn. The interviews were then transcribed and analysed thematically. Main findings in the research suggest that insider fraud has been ignored owing to the denial of accepting the possibility that colleagues would defraud their employer. Whilst Coronavirus has led to a significant rise in insider fraud, this type of crime has been a major risk to businesses since their inception, however have never been given the financial or strategic backing required to be mitigated, until it's too late. Furthermore, Coronavirus should have led to companies tightening their access rights, controls and policies to mitigate the insider fraud risk. However, in most cases this has not happened. The research concludes that insider fraud needs to be given a platform upon which to be recognised as a threat to any company and given the same level of weighting and attention by Executive Committees and Boards as other types of economic crime.Keywords: fraud, insider fraud, economic crime, coronavirus, Covid-19
Procedia PDF Downloads 623785 Stereotypical Motor Movement Recognition Using Microsoft Kinect with Artificial Neural Network
Authors: M. Jazouli, S. Elhoufi, A. Majda, A. Zarghili, R. Aalouane
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Autism spectrum disorder is a complex developmental disability. It is defined by a certain set of behaviors. Persons with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. The objective of this article is to propose a method to automatically detect this unusual behavior. Our study provides a clinical tool which facilitates for doctors the diagnosis of ASD. We focus on automatic identification of five repetitive gestures among autistic children in real time: body rocking, hand flapping, fingers flapping, hand on the face and hands behind back. In this paper, we present a gesture recognition system for children with autism, which consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using artificial neural network (ANN). The first one uses the Microsoft Kinect sensor, the second one chooses points of interest from the 3D skeleton to characterize the gestures, and the last one proposes a neural connectionist model to perform the supervised classification of data. The experimental results show that our system can achieve above 93.3% recognition rate.Keywords: ASD, artificial neural network, kinect, stereotypical motor movements
Procedia PDF Downloads 3003784 Urban Neighborhood Center Location Evaluating Method Based On UNA the GIS Spatial Analysis Tools: Kerman's Neighborhood in Tehran Case
Authors: Sepideh Jabbari Behnam, Shadabeh Gashtasbi Iraei, Elnaz Mohsenin, MohammadAli Aghajani
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Urban neighborhoods, as important urban forming cells, play a key role in creating urban texture and integrated form. Nowadays, most of neighborhood divisions are based on urban management systems but without considering social issues and the other aspects of urban life. This can cause problems such as providing inappropriate services for city dwellers, the loss of local identity and etc. In this regard for regenerating of such neighborhoods, it is essential to locate neighborhood centers with appropriate access and services for all residents. The main objective of this article is reaching to the location of neighborhood centers in a way that, most of issues relating to the physical features (such as the form of access network and texture permeability and etc.) and other qualities such as land uses, densities and social and economic features can be done simultaneously. This paper attempts to use methods of spatial analysis in order to surveying spatial structure and space syntax of urban textures and Urban Network Analysis Systems. This can be done by one of GIS toolbars which is named UNA (Urban Network Analysis) with the use of its five functions (include: Reach, Betweenness, Gravity, Closeness, Straightness).These functions were written according to space syntax theory and offer its relating output. This paper tries to locate and evaluate the optimal location of neighborhood centers in order to create local centers. This is done through weighing of each of these functions and taking into account of spatial features.Keywords: evaluate optimal location, Local centers, location of neighborhood centers, Spatial analysis, Urban network
Procedia PDF Downloads 4563783 Alternative Key Exchange Algorithm Based on Elliptic Curve Digital Signature Algorithm Certificate and Usage in Applications
Authors: A. Andreasyan, C. Connors
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The Elliptic Curve Digital Signature algorithm-based X509v3 certificates are becoming more popular due to their short public and private key sizes. Moreover, these certificates can be stored in Internet of Things (IoT) devices, with limited resources, using less memory and transmitted in network security protocols, such as Internet Key Exchange (IKE), Transport Layer Security (TLS) and Secure Shell (SSH) with less bandwidth. The proposed method gives another advantage, in that it increases the performance of the above-mentioned protocols in terms of key exchange by saving one scalar multiplication operation.Keywords: cryptography, elliptic curve digital signature algorithm, key exchange, network security protocol
Procedia PDF Downloads 1393782 Artificial Neural Network Based Approach for Estimation of Individual Vehicle Speed under Mixed Traffic Condition
Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh
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Developing speed model is a challenging task particularly under mixed traffic condition where the traffic composition plays a significant role in determining vehicular speed. The present research has been conducted to model individual vehicular speed in the context of mixed traffic on an urban arterial. Traffic speed and volume data have been collected from three midblock arterial road sections in New Delhi. Using the field data, a volume based speed prediction model has been developed adopting the methodology of Artificial Neural Network (ANN). The model developed in this work is capable of estimating speed for individual vehicle category. Validation results show a great deal of agreement between the observed speeds and the predicted values by the model developed. Also, it has been observed that the ANN based model performs better compared to other existing models in terms of accuracy. Finally, the sensitivity analysis has been performed utilizing the model in order to examine the effects of traffic volume and its composition on individual speeds.Keywords: speed model, artificial neural network, arterial, mixed traffic
Procedia PDF Downloads 3793781 Intelligent Rheumatoid Arthritis Identification System Based Image Processing and Neural Classifier
Authors: Abdulkader Helwan
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Rheumatoid joint inflammation is characterized as a perpetual incendiary issue which influences the joints by hurting body tissues Therefore, there is an urgent need for an effective intelligent identification system of knee Rheumatoid arthritis especially in its early stages. This paper is to develop a new intelligent system for the identification of Rheumatoid arthritis of the knee utilizing image processing techniques and neural classifier. The system involves two principle stages. The first one is the image processing stage in which the images are processed using some techniques such as RGB to gryascale conversion, rescaling, median filtering, background extracting, images subtracting, segmentation using canny edge detection, and features extraction using pattern averaging. The extracted features are used then as inputs for the neural network which classifies the X-ray knee images as normal or abnormal (arthritic) based on a backpropagation learning algorithm which involves training of the network on 400 X-ray normal and abnormal knee images. The system was tested on 400 x-ray images and the network shows good performance during that phase, resulting in a good identification rate 97%.Keywords: rheumatoid arthritis, intelligent identification, neural classifier, segmentation, backpropoagation
Procedia PDF Downloads 5243780 Satellite Imagery Classification Based on Deep Convolution Network
Authors: Zhong Ma, Zhuping Wang, Congxin Liu, Xiangzeng Liu
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Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper-parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper-parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper-parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method.Keywords: satellite imagery classification, deep convolution network, genetic algorithm, hyper-parameter optimization
Procedia PDF Downloads 2873779 A New Approach to Predicting Physical Biometrics from Behavioural Biometrics
Authors: Raid R. O. Al-Nima, S. S. Dlay, W. L. Woo
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A relationship between face and signature biometrics is established in this paper. A new approach is developed to predict faces from signatures by using artificial intelligence. A multilayer perceptron (MLP) neural network is used to generate face details from features extracted from signatures, here face is the physical biometric and signatures is the behavioural biometric. The new method establishes a relationship between the two biometrics and regenerates a visible face image from the signature features. Furthermore, the performance efficiencies of our new technique are demonstrated in terms of minimum error rates compared to published work.Keywords: behavioural biometric, face biometric, neural network, physical biometric, signature biometric
Procedia PDF Downloads 4683778 The Pangs of Unemployment and Its Impediment to Nation Building
Authors: Vitalis Okwuchukwu Opara
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The task of nation building primarily consist in welding together, diverse cultural groups into a united nation state, which develops a centripetal political culture that makes its people see themselves as members of one nation linked together by more reliable ties than the coercion offered by the state. Comparatively on the contrary, most countries in the world today are comprised of diverse nationalities, each with its unique set of norms and values, which often come into conflict with others. As such, the task of nation building is in uniting these diverse cultural groups into a united nation state and various human elements that make up its geopolitical zone. The most outstanding impediment to achieving this task is unemployment. Unemployment is like a peril against the nation building. Unemployment is an obstacle for growth of a nation. Often it is said that the wise see obstacles as stepping-stones to advance further. The pangs of unemployment impede nation building such that sometimes it takes very long time to do away with the problem. In recent times, there has been a revolutionary wind blowing across the world. This wind is bound to wake up nations leaders to sit up to their responsibility. Unemployment causes youth restiveness, brings leaders to their knees. It breeds problem. This work is intended to expose the pangs of unemployment and its impending peril to nation building.Keywords: pangs, unemployment, obstacles, nation-building
Procedia PDF Downloads 3483777 Quantum Decision Making with Small Sample for Network Monitoring and Control
Authors: Tatsuya Otoshi, Masayuki Murata
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With the development and diversification of applications on the Internet, applications that require high responsiveness, such as video streaming, are becoming mainstream. Application responsiveness is not only a matter of communication delay but also a matter of time required to grasp changes in network conditions. The tradeoff between accuracy and measurement time is a challenge in network control. We people make countless decisions all the time, and our decisions seem to resolve tradeoffs between time and accuracy. When making decisions, people are known to make appropriate choices based on relatively small samples. Although there have been various studies on models of human decision-making, a model that integrates various cognitive biases, called ”quantum decision-making,” has recently attracted much attention. However, the modeling of small samples has not been examined much so far. In this paper, we extend the model of quantum decision-making to model decision-making with a small sample. In the proposed model, the state is updated by value-based probability amplitude amplification. By analytically obtaining a lower bound on the number of samples required for decision-making, we show that decision-making with a small number of samples is feasible.Keywords: quantum decision making, small sample, MPEG-DASH, Grover's algorithm
Procedia PDF Downloads 683776 Emotion Classification Using Recurrent Neural Network and Scalable Pattern Mining
Authors: Jaishree Ranganathan, MuthuPriya Shanmugakani Velsamy, Shamika Kulkarni, Angelina Tzacheva
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Emotions play an important role in everyday life. An-alyzing these emotions or feelings from social media platforms like Twitter, Facebook, blogs, and forums based on user comments and reviews plays an important role in various factors. Some of them include brand monitoring, marketing strategies, reputation, and competitor analysis. The opinions or sentiments mined from such data helps understand the current state of the user. It does not directly provide intuitive insights on what actions to be taken to benefit the end user or business. Actionable Pattern Mining method provides suggestions or actionable recommendations on what changes or actions need to be taken in order to benefit the end user. In this paper, we propose automatic classification of emotions in Twitter data using Recurrent Neural Network - Gated Recurrent Unit. We achieve training accuracy of 87.58% and validation accuracy of 86.16%. Also, we extract action rules with respect to the user emotion that helps to provide actionable suggestion.Keywords: emotion mining, twitter, recurrent neural network, gated recurrent unit, actionable pattern mining
Procedia PDF Downloads 1573775 Analysis of Subjective Indicators of Quality of Life in Makurdi
Authors: Irene Doosuur Mngutyo
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The preliminary stages in the development of human communities are the formation of a correct understanding of people’s needs. However, perception of human needs is highly subjective and difficult to aggregate. Quality of life measurements are an appropriate means for achieving an understanding of Human needs. Hence this study endeavors to measure quality of life in Makurdi using subjective indices to measure three aspects of subjective wellbeing. A sample of 400 respondents achieved by applying the Taro Yamane formula to Makurdi’s projected population. Questionnaires were randomly distributed to residents of nine wards in Makurdi. Findings from a pilot study( N=100) demonstrated that among the 2 aspects of overall quality of life investigated,22% had a mean low overall assessment of quality of life now being3on the scale and an even poorer assessment for projected quality in the next five years by 17%(3)although an equal percentage are hopeful for a better life(10)in the next five years.60% of the respondents record very rare positive feelings while only 10% have positive feelings always on the eudaimonic scale69%strongly agree that they have a purposeful and meaningful life. Findings indicate good social ties as a strong indicator for perceived good feelings and even though quality of life is perceived as low there is optimism for the future.Keywords: quality of life, subjective indicators, development, urban planning
Procedia PDF Downloads 3893774 Integrated On-Board Diagnostic-II and Direct Controller Area Network Access for Vehicle Monitoring System
Authors: Kavian Khosravinia, Mohd Khair Hassan, Ribhan Zafira Abdul Rahman, Syed Abdul Rahman Al-Haddad
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The CAN (controller area network) bus is introduced as a multi-master, message broadcast system. The messages sent on the CAN are used to communicate state information, referred as a signal between different ECUs, which provides data consistency in every node of the system. OBD-II Dongles that are based on request and response method is the wide-spread solution for extracting sensor data from cars among researchers. Unfortunately, most of the past researches do not consider resolution and quantity of their input data extracted through OBD-II technology. The maximum feasible scan rate is only 9 queries per second which provide 8 data points per second with using ELM327 as well-known OBD-II dongle. This study aims to develop and design a programmable, and latency-sensitive vehicle data acquisition system that improves the modularity and flexibility to extract exact, trustworthy, and fresh car sensor data with higher frequency rates. Furthermore, the researcher must break apart, thoroughly inspect, and observe the internal network of the vehicle, which may cause severe damages to the expensive ECUs of the vehicle due to intrinsic vulnerabilities of the CAN bus during initial research. Desired sensors data were collected from various vehicles utilizing Raspberry Pi3 as computing and processing unit with using OBD (request-response) and direct CAN method at the same time. Two types of data were collected for this study. The first, CAN bus frame data that illustrates data collected for each line of hex data sent from an ECU and the second type is the OBD data that represents some limited data that is requested from ECU under standard condition. The proposed system is reconfigurable, human-readable and multi-task telematics device that can be fitted into any vehicle with minimum effort and minimum time lag in the data extraction process. The standard operational procedure experimental vehicle network test bench is developed and can be used for future vehicle network testing experiment.Keywords: CAN bus, OBD-II, vehicle data acquisition, connected cars, telemetry, Raspberry Pi3
Procedia PDF Downloads 1913773 Distributed Generation Connection to the Network: Obtaining Stability Using Transient Behavior
Authors: A. Hadadi, M. Abdollahi, A. Dustmohammadi
Abstract:
The growing use of DGs in distribution networks provide many advantages and also cause new problems which should be anticipated and be solved with appropriate solutions. One of the problems is transient voltage drop and short circuit in the electrical network, in the presence of distributed generation - which can lead to instability. The appearance of the short circuit will cause loss of generator synchronism, even though if it would be able to recover synchronizing mode after removing faulty generator, it will be stable. In order to increase system reliability and generator lifetime, some strategies should be planned to apply even in some situations which a fault prevent generators from separation. In this paper, one fault current limiter is installed due to prevent DGs separation from the grid when fault occurs. Furthermore, an innovative objective function is applied to determine the impedance optimal amount of fault current limiter in order to improve transient stability of distributed generation. Fault current limiter can prevent generator rotor's sudden acceleration after fault occurrence and thereby improve the network transient stability by reducing the current flow in a fast and effective manner. In fact, by applying created impedance by fault current limiter when a short circuit happens on the path of current injection DG to the fault location, the critical fault clearing time improve remarkably. Therefore, protective relay has more time to clear fault and isolate the fault zone without any instability. Finally, different transient scenarios of connection plan sustainability of small scale synchronous generators to the distribution network are presented.Keywords: critical clearing time, fault current limiter, synchronous generator, transient stability, transient states
Procedia PDF Downloads 1883772 ArcGIS as a Tool for Infrastructure Documentation and Asset Management: Establishing a GIS for Computer Network Documentation
Authors: John Segars
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Built out of a real-world need to have better, more detailed, asset and infrastructure documentation, this project will lay out the case for using the database functionality of ArcGIS as a tool to track and maintain infrastructure location, status, maintenance and serviceability. Workflows and processes will be presented and detailed which may be applied to an organizations’ infrastructure needs that might allow them to make use of the robust tools which surround the ArcGIS platform. The end result is a value-added information system framework with a geographic component e.g., the spatial location of various I.T. assets, a detailed set of records which not only documents location but also captures the maintenance history for assets along with photographs and documentation of these various assets as attachments to the numerous feature class items. In addition to the asset location and documentation benefits, the staff will be able to log into the devices and pull SNMP (Simple Network Management Protocol) based query information from within the user interface. The entire collection of information may be displayed in ArcGIS, via a JavaScript based web application or via queries to the back-end database. The project is applicable to all organizations which maintain an IT infrastructure but specifically targets post-secondary educational institutions where access to ESRI resources is generally already available in house.Keywords: ESRI, GIS, infrastructure, network documentation, PostgreSQL
Procedia PDF Downloads 1723771 Comparison of Different k-NN Models for Speed Prediction in an Urban Traffic Network
Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu
Abstract:
A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.Keywords: big data, k-NN, machine learning, traffic speed prediction
Procedia PDF Downloads 353