Search results for: © Learning Network

Authors: Simran Ballani

Abstract:

The purpose of this paper is to call education professionals to implement etiquette and public speaking skills for preschoolers, primary, middle and higher school students. In this paper the author aims to present importance of etiquette learning and public speaking curriculum for preschoolers, reflect on experiences from implementation of the curriculum and discuss the effect of the said implementation on higher education/global job market. Author’s aim to introduce this curriculum was to provide children with innovative learning and all around development. This training of soft skills at kindergarten level can have a long term effect on their social behaviors which in turn can contribute to professional success once they are ready for campus recruitment/global job markets. Additionally, if preschoolers learn polite, appropriate behavior at early age, it will enable them to become more socially attentive and display good manners as an adult. It is easier to nurture these skills in a child rather than changing bad manners at adulthood. Preschool/Kindergarten education can provide the platform for children to learn these crucial soft skills irrespective of the ethnicity, economic or social background they come from. These skills developed at such early years can go a long way to shape them into better and confident individuals. Unfortunately, accessibility of the etiquette learning and public speaking skill education is not standardized in pre-primary or primary level and most of the time embedding into the kindergarten curriculum is next to nil. All young children should be provided with equal opportunity to learn these soft skills which are essential for finding their place in job market.

Keywords: Etiquette learning, public speaking, preschoolers, overall child development, early childhood interventions, soft skills.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 996

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

Abstract:

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

Keywords: Reverse Logistics, information management, heterogeneity, Ontologies, semantic web.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2966

Authors: P. Cermak

Abstract:

This paper shows possibility of extraction Social, Group and Individual Mind from Multiple Agents Rule Bases. Types those Rule bases are selected as two fuzzy systems, namely Mambdani and Takagi-Sugeno fuzzy system. Their rule bases are describing (modeling) agent behavior. Modifying of agent behavior in the time varying environment will be provided by learning fuzzyneural networks and optimization of their parameters with using genetic algorithms in development system FUZNET. Finally, extraction Social, Group and Individual Mind from Multiple Agents Rule Bases are provided by Cognitive analysis and Matching criterion.

Keywords: Mind, Multi-agent system, Cognitive analysis, Fuzzy system, Neural network, Genetic algorithm, Rule base.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1253

Authors: Patrice Obinna Umenne, Marcel O. Odhiambo

Abstract:

WiMAX and Wi-Fi are considered as the promising broadband access solutions for wireless MAN’s and LANs, respectively. In the recent works WiMAX is considered suitable as a backhaul service to connect multiple dispersed Wi-Fi ‘hotspots’. Hence a new integrated WiMAX/Wi-Fi architecture has been proposed in literatures. In this paper the performance of an integrated WiMAX/Wi-Fi network has been investigated by streaming a video conference application. The difference in performance between the two protocols is compared with respect to video conferencing. The Heterogeneous network was simulated in the OPNET simulator.

Keywords: Throughput, delay, delay variance, packet loss, Quality of Service (QoS).

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2649

Authors: Mohammad Ali Jabraeil Jamali, Ahmad Khademzadeh

Abstract:

The success of an electronic system in a System-on- Chip is highly dependent on the efficiency of its interconnection network, which is constructed from routers and channels (the routers move data across the channels between nodes). Since neither classical bus based nor point to point architectures can provide scalable solutions and satisfy the tight power and performance requirements of future applications, the Network-on-Chip (NoC) approach has recently been proposed as a promising solution. Indeed, in contrast to the traditional solutions, the NoC approach can provide large bandwidth with moderate area overhead. The selected topology of the components interconnects plays prime rule in the performance of NoC architecture as well as routing and switching techniques that can be used. In this paper, we present two generic NoC architectures that can be customized to the specific communication needs of an application in order to reduce the area with minimal degradation of the latency of the system. An experimental study is performed to compare these structures with basic NoC topologies represented by 2D mesh, Butterfly-Fat Tree (BFT) and SPIN. It is shown that Cluster mesh (CMesh) and MinRoot schemes achieves significant improvements in network latency and energy consumption with only negligible area overhead and complexity over existing architectures. In fact, in the case of basic NoC topologies, CMesh and MinRoot schemes provides substantial savings in area as well, because they requires fewer routers. The simulation results show that CMesh and MinRoot networks outperforms MESH, BFT and SPIN in main performance metrics.

Keywords: MinRoot, CMesh, NoC, Topology, Performance Evaluation

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2127

Authors: El Mahdi Barrah, Said Safi, Abdessamad Malaoui

Abstract:

In this paper, we present an algorithm for reconstruction from incomplete 2D scans for tifinagh characters. This algorithm is based on using correlation between the lost block and its neighbors. This system proposed contains three main parts: pre-processing, features extraction and recognition. In the first step, we construct a database of tifinagh characters. In the second step, we will apply “shape analysis algorithm”. In classification part, we will use Neural Network. The simulation results demonstrate that the proposed method give good results.

Keywords: Tifinagh character recognition, Neural networks, Local cost computation.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1286

Authors: Wen-Jye Shyr, Chia-Ming Lin, Hung-Yun Feng

Abstract:

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 APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1137

Authors: K. Krishna Naik, M. N. Giri Prasad

Abstract:

These days wireless local area networks has become very popular, when the initial IEEE802.11 is the standard for providing wireless connectivity to automatic machinery, equipment and stations that require rapid deployment, which may be portable, handheld or which may be mounted on moving vehicles within a local area. IEEE802.11 Wireless local area network is a sharedmedium communication network that transmits information over wireless links for all IEEE802.11 stations in its transmission range to receive. When a user is moving from one location to another, how the other user knows about the required station inside WLAN. For that we designed and implemented a system to locate a mobile user inside the wireless local area network based on RSSI with the help of four specially designed architectures. These architectures are based on statistical or we can say manual configuration of mapping and radio map of indoor and outdoor location with the help of available Sniffer based and cluster based techniques. We found a better location of a mobile user in WLAN. We tested this work in indoor and outdoor environments with different locations with the help of Pamvotis, a simulator for WLAN.

Keywords: AP, RSSI, RPM, WLAN.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1315

Authors: Kentaro Miyoko, Yoshihiko Mori, Yugo Ikeda, Yoshihiro Nishino, Yong-Bok Choi, Hiromi Okada

Abstract:

Optical burst switching (OBS) has been proposed to realize the next generation Internet based on the wavelength division multiplexing (WDM) network technologies. In the OBS, the burst contention is one of the major problems. The deflection routing has been designed for resolving the problem. However, the deflection routing becomes difficult to prevent from the burst contentions as the network load becomes high. In this paper, we introduce a flow rate control methods to reduce burst contentions. We propose new flow rate control methods based on the leaky bucket algorithm and deflection routing, i.e. separate leaky bucket deflection method, and dynamic leaky bucket deflection method. In proposed methods, edge nodes which generate data bursts carry out the flow rate control protocols. In order to verify the effectiveness of the flow rate control in OBS networks, we show that the proposed methods improve the network utilization and reduce the burst loss probability through computer simulations.

Keywords: Optical burst switching, OBS, flow rate control.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1706

Authors: Yuanqing Li, Changyi Lei, Zhaopeng Xue, Zhuo Zheng, Yanbo Long

Abstract:

In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.

Keywords: Agricultural object detection, Deep learning, machine vision, YOLO family.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1099

Authors: Dong-Young Yoo, Jong-Whoi Shin, Gang Shin Lee, Jae-Il Lee

Abstract:

As the disfunctions of the information society and social development progress, intrusion problems such as malicious replies, spam mail, private information leakage, phishing, and pharming, and side effects such as the spread of unwholesome information and privacy invasion are becoming serious social problems. Illegal access to information is also becoming a problem as the exchange and sharing of information increases on the basis of the extension of the communication network. On the other hand, as the communication network has been constructed as an international, global system, the legal response against invasion and cyber-attack from abroad is facing its limit. In addition, in an environment where the important infrastructures are managed and controlled on the basis of the information communication network, such problems pose a threat to national security. Countermeasures to such threats are developed and implemented on a yearly basis to protect the major infrastructures of information communication. As a part of such measures, we have developed a methodology for assessing the information protection level which can be used to establish the quantitative object setting method required for the improvement of the information protection level.

Keywords: Information Security Evaluation Methodology, Critical Information Infrastructure Protection.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1660

Authors: A. Safari, R. Jahani, H. A. Shayanfar, J. Olamaei

Abstract:

This paper shows the results obtained in the analysis of the impact of distributed generation (DG) on distribution losses and presents a new algorithm to the optimal allocation of distributed generation resources in distribution networks. The optimization is based on a Hybrid Genetic Algorithm and Particle Swarm Optimization (HGAPSO) aiming to optimal DG allocation in distribution network. Through this algorithm a significant improvement in the optimization goal is achieved. With a numerical example the superiority of the proposed algorithm is demonstrated in comparison with the simple genetic algorithm.

Keywords: Distributed Generation, Distribution Networks, Genetic Algorithm, Particle Swarm Optimization.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2704

Authors: Angelina A. Tzacheva, Jaishree Ranganathan

Abstract:

Interest in (STEM) Science Technology Engineering Mathematics education especially Computer Science education has seen a drastic increase across the country. This fuels effort towards recruiting and admitting a diverse population of students. Thus the changing conditions in terms of the student population, diversity and the expected teaching and learning outcomes give the platform for use of Innovative Teaching models and technologies. It is necessary that these methods adapted should also concentrate on raising quality of such innovations and have positive impact on student learning. Light-Weight Team is an Active Learning Pedagogy, which is considered to be low-stake activity and has very little or no direct impact on student grades. Emotion plays a major role in student’s motivation to learning. In this work we use the student feedback data with emotion classification using surveys at a public research institution in the United States. We use Actionable Pattern Discovery method for this purpose. Actionable patterns are patterns that provide suggestions in the form of rules to help the user achieve better outcomes. The proposed method provides meaningful insight in terms of changes that can be incorporated in the Light-Weight team activities, resources utilized in the course. The results suggest how to enhance student emotions to a more positive state, in particular focuses on the emotions ‘Trust’ and ‘Joy’.

Keywords: Actionable pattern discovery, education, emotion, data mining.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 526

Authors: Emad Alenany, M. Adel El-Baz

Abstract:

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 APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1529

Authors: Shamsuddin Ahmed, M. G. M. Khan, Biman Prasad, Avlin Prasad

Abstract:

The focus of this paper is to construct daily time series exchange rate forecast models of Samoan Tala/USD and Tala/AUD during the year 2008 to 2012 with neural network The performance of the models was measured by using varies error functions such as Root Square mean error (RSME), Mean absolute error (MAE), and Mean absolute percentage error (MAPE). Our empirical findings suggest that AR (1) model is an effective tool to forecast the Tala/USD and Tala/AUD.

Keywords: Neural Network Forecasting Model, Autoregressive time series, Exchange rate, Tala/AUD, winters model.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2433

Authors: Suraiya Jabin, Kamal K. Bharadwaj

Abstract:

In the recent past Learning Classifier Systems have been successfully used for data mining. Learning Classifier System (LCS) is basically a machine learning technique which combines evolutionary computing, reinforcement learning, supervised or unsupervised learning and heuristics to produce adaptive systems. A LCS learns by interacting with an environment from which it receives feedback in the form of numerical reward. Learning is achieved by trying to maximize the amount of reward received. All LCSs models more or less, comprise four main components; a finite population of condition–action rules, called classifiers; the performance component, which governs the interaction with the environment; the credit assignment component, which distributes the reward received from the environment to the classifiers accountable for the rewards obtained; the discovery component, which is responsible for discovering better rules and improving existing ones through a genetic algorithm. The concatenate of the production rules in the LCS form the genotype, and therefore the GA should operate on a population of classifier systems. This approach is known as the 'Pittsburgh' Classifier Systems. Other LCS that perform their GA at the rule level within a population are known as 'Mitchigan' Classifier Systems. The most predominant representation of the discovered knowledge is the standard production rules (PRs) in the form of IF P THEN D. The PRs, however, are unable to handle exceptions and do not exhibit variable precision. The Censored Production Rules (CPRs), an extension of PRs, were proposed by Michalski and Winston that exhibit variable precision and supports an efficient mechanism for handling exceptions. A CPR is an augmented production rule of the form: IF P THEN D UNLESS C, where Censor C is an exception to the rule. Such rules are employed in situations, in which conditional statement IF P THEN D holds frequently and the assertion C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence are tight or there is simply no information available as to whether it holds or not. Thus, the IF P THEN D part of CPR expresses important information, while the UNLESS C part acts only as a switch and changes the polarity of D to ~D. In this paper Pittsburgh style LCSs approach is used for automated discovery of CPRs. An appropriate encoding scheme is suggested to represent a chromosome consisting of fixed size set of CPRs. Suitable genetic operators are designed for the set of CPRs and individual CPRs and also appropriate fitness function is proposed that incorporates basic constraints on CPR. Experimental results are presented to demonstrate the performance of the proposed learning classifier system.

Keywords: Censored Production Rule, Data Mining, GeneticAlgorithm, Learning Classifier System, Machine Learning, PittsburgApproach, , Reinforcement learning.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1530

Authors: Rim Messaoudi, Nogaye-Gueye Gning, François Azelart

Abstract:

Automatic text classification applies mostly natural language processing (NLP) and other artificial intelligence (AI)-guided techniques to automatically classify text in a faster and more accurate manner. This paper discusses the subject of using predictive maintenance to manage incident tickets inside the sociality. It focuses on proposing a tool that treats and analyses comments and notes written by administrators after resolving an incident ticket. The goal here is to increase the quality of these comments. Additionally, this tool is based on NLP and machine learning techniques to realize the textual analytics of the extracted data. This approach was tested using real data taken from the French National Railways (SNCF) company and was given a high-quality result.

Keywords: Machine learning, text classification, NLP techniques, semantic representation.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 240

Authors: Galeano R. Katherine, Rincon L. David, Luengas C. Lely

Abstract:

This paper describes the development of an electronic instrument that looks like a flute, which is able to sense the basic musical notes being executed by a specific user. The principal function of the instrument is to teach how to play a flute. This device will generate a significant academic impact, in a field of virtual reality interactive that combine art and technology. With this example is expected to contribute in research and implementation of teaching devices around the world.

Keywords: Flute, Hardware, Learning, Virtual Reality.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1664

Authors: Hae-Soon Ahn, Eun-Jun Yoon

Abstract:

Three-party password authenticated key exchange (3PAKE) protocols are widely deployed on lots of remote user authentication system due to its simplicity and convenience of maintaining a human-memorable password at client side to achieve secure communication within a hostile network. Recently, an improvement of 3PAKE protocol by processing a built-in data attached to other party for identity authentication to individual data was proposed by some researchers. However, this paper points out that the improved 3PAKE protocol is still vulnerable to undetectable on-line dictionary attack and off-line dictionary attack.

Keywords: Three-party key exchange, 3PAKE, Passwordauthenticated key exchange, Network security, Dictionary attack

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2122

Authors: Victor Krebss

Abstract:

Task of object localization is one of the major challenges in creating intelligent transportation. Unfortunately, in densely built-up urban areas, localization based on GPS only produces a large error, or simply becomes impossible. New opportunities arise for the localization due to the rapidly emerging concept of a wireless ad-hoc network. Such network, allows estimating potential distance between these objects measuring received signal level and construct a graph of distances in which nodes are the localization objects, and edges - estimates of the distances between pairs of nodes. Due to the known coordinates of individual nodes (anchors), it is possible to determine the location of all (or part) of the remaining nodes of the graph. Moreover, road map, available in digital format can provide localization routines with valuable additional information to narrow node location search. However, despite abundance of well-known algorithms for solving the problem of localization and significant research efforts, there are still many issues that currently are addressed only partially. In this paper, we propose localization approach based on the graph mapped distances on the digital road map data basis. In fact, problem is reduced to distance graph embedding into the graph representing area geo location data. It makes possible to localize objects, in some cases even if only one reference point is available. We propose simple embedding algorithm and sample implementation as spatial queries over sensor network data stored in spatial database, allowing employing effectively spatial indexing, optimized spatial search routines and geometry functions.

Keywords: Intelligent Transportation System, Sensor Network, Localization, Spatial Query, GIS, Graph Embedding.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1535

Authors: Barry Ardley, Abi Hunt, Nick Taylor

Abstract:

As a theoretical and practical framework this study uses the student as producer approach to learning in higher education, as adopted by the Lincoln International Business School, University of Lincoln, UK. Student as producer positions learners as skilled and capable agents, able to participate as partners with tutors in live research projects. To illuminate the nature of this approach to learning and to highlight its critical issues, the authors report on two guided student consultancy projects. These were set up with the assistance of two local organisations in the city of Lincoln UK. Using the student as producer model to deliver the projects enabled learners to acquire and develop a range of key skills and knowledge, not easily accessible in more traditional educational settings. This paper presents a systematic case study analysis of the eight organising principles of the student as producer model, as adopted by university tutors. The experience of tutors implementing student as producer suggests that the model can be widely applied to benefit not only the learning and teaching experiences of higher education students, and staff, but additionally, a university’s research programme and its community partners.

Keywords: Experiential learning, consultancy clients, student as producer.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 242

Authors: R. R. Chen, K. Khorasani

Abstract:

In this paper, a robust decentralized congestion control strategy is developed for a large scale network with Differentiated Services (Diff-Serv) traffic. The network is modeled by a nonlinear fluid flow model corresponding to two classes of traffic, namely the premium traffic and the ordinary traffic. The proposed congestion controller does take into account the associated physical network resource limitations and is shown to be robust to the unknown and time-varying delays. Our proposed decentralized congestion control strategy is developed on the basis of Diff-Serv architecture by utilizing a robust adaptive technique. A Linear Matrix Inequality (LMI) condition is obtained to guarantee the ultimate boundedness of the closed-loop system. Numerical simulation implementations are presented by utilizing the QualNet and Matlab software tools to illustrate the effectiveness and capabilities of our proposed decentralized congestion control strategy.

Keywords: Congestion control, Large scale networks, Decentralized control, Differentiated services traffic, Time-delay systems.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1989

Authors: Raquel M. de Sousa, Sofiane Labidi, Allan Kardec D. Barros, Alex O. Barradas Filho, Aldalea L. B. Marques

Abstract:

Several parameters are established in order to measure biodiesel quality. One of them is the iodine value, which is an important parameter that measures the total unsaturation within a mixture of fatty acids. Limitation of unsaturated fatty acids is necessary since warming of higher quantity of these ones ends in either formation of deposits inside the motor or damage of lubricant. Determination of iodine value by official procedure tends to be very laborious, with high costs and toxicity of the reagents, this study uses artificial neural network (ANN) in order to predict the iodine value property as an alternative to these problems. The methodology of development of networks used 13 esters of fatty acids in the input with convergence algorithms of back propagation of back propagation type were optimized in order to get an architecture of prediction of iodine value. This study allowed us to demonstrate the neural networks’ ability to learn the correlation between biodiesel quality properties, in this caseiodine value, and the molecular structures that make it up. The model developed in the study reached a correlation coefficient (R) of 0.99 for both network validation and network simulation, with Levenberg-Maquardt algorithm.

Keywords: Artificial Neural Networks, Biodiesel, Iodine Value, Prediction.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2380

Authors: Tarifa S. Almulhim, Ludmil Mikhailov, Dong-Ling Xu

Abstract:

In this paper, a method for deriving a group priority vector in the Fuzzy Analytic Network Process (FANP) is proposed. By introducing importance weights of multiple decision makers (DMs) based on their experiences, the Fuzzy Preferences Programming Method (FPP) is extended to a fuzzy group prioritization problem in the FANP. Additionally, fuzzy pair-wise comparison judgments are presented rather than exact numerical assessments in order to model the uncertainty and imprecision in the DMs- judgments and then transform the fuzzy group prioritization problem into a fuzzy non-linear programming optimization problem which maximize the group satisfaction. Unlike the known fuzzy prioritization techniques, the new method proposed in this paper can easily derive crisp weights from incomplete and inconsistency fuzzy set of comparison judgments and does not require additional aggregation producers. Detailed numerical examples are used to illustrate the implement of our approach and compare with the latest fuzzy prioritization method.

Keywords: Fuzzy Analytic Network Process (FANP), Fuzzy Non-linear Programming, Fuzzy Preferences Programming Method (FPP), Multiple Criteria Decision-Making (MCDM), Triangular Fuzzy Number.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2386

Authors: Kavitha Thamadharan, Nurazean Maarop

Abstract:

The implementation of e-assessment as tool to support the process of teaching and learning in university has become a popular technological means in universities. E-Assessment provides many advantages to the users especially the flexibility in teaching and learning. The e-assessment system has the capability to improve its quality of delivering education. However, there still exists a drawback in terms of security which limits the user acceptance of the online learning system. Even though there are studies providing solutions for identified security threats in e-learning usage, there is no particular model which addresses the factors that influences the acceptance of e-assessment system by lecturers from security perspective. The aim of this study is to explore security aspects of eassessment in regard to the acceptance of the technology. As a result a conceptual model of secure acceptance of e-assessment is proposed. Both human and security factors are considered in formulation of this conceptual model. In order to increase understanding of critical issues related to the subject of this study, interpretive approach involving convergent mixed method research method is proposed to be used to execute the research. This study will be useful in providing more insightful understanding regarding the factors that influence the user acceptance of e-assessment system from security perspective.

Keywords: Secure Technology Acceptance, E-Assessment Security, E-Assessment, Education Technology.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2438

Authors: Y. Harold Robinson, M. Rajaram, E. Golden Julie, S. Balaji

Abstract:

Mobile ad hoc network (MANET) is a self-configuring network of mobile node connected without wires. A Fuzzy Logic Based Collaborative watchdog approach is used to reduce the detection time of misbehaved nodes and increase the overall truthfulness. This methodology will increase the secure efficient routing by detecting the Black Holes attacks. The simulation results proved that this method improved the energy, reduced the delay and also improved the overall performance of the detecting black hole attacks in MANET.

Keywords: MANET, collaborative watchdog, fuzzy logic, AODV.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1351

Authors: Taiwo. M. Akinmuyisitan, John Cosmas

Abstract:

This paper practically demonstrates the theories and concepts of an Advanced Convolutional Neural Network in the design and development of a scalable artificial intelligence model for the detection of criminal masterminds. The technique uses machine vision algorithms to compute the facial characteristics of suspects and classify actors as criminal or non-criminal faces. The paper proceeds further to compare the results of the error accuracy of two popular custom convolutional pre-trained networks, VGG16 and Resnet50. The result shows that VGG16 is probably more efficient than ResNet50 for the dataset we used.

Keywords: Artificial intelligence, convolutional neural networks, Resnet50, VGG16.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 284

Authors: S. Studente, S. Ellis, S. F. Garivaldis

Abstract:

We report upon a study introducing a chatbot to develop learning communities at a London University, with a largely international student base. The focus of the chatbot was twofold; to ease the transition for students into their first year of university study, and to increase study engagement. Four learning communities were created using the chatbot; level 3 foundation, level 4 undergraduate, level 6 undergraduate and level 7 post-graduate. Students and programme leaders were provided with access to the chat bot via mobile app prior to their study induction and throughout the autumn term of 2019. At the end of the term, data were collected via questionnaires and focus groups with students and teaching staff to allow for identification of benefits and challenges. Findings indicated a positive correlation between study engagement and engagement with peers. Students reported that the chatbot enabled them to obtain support and connect to their programme leader. Both staff and students also made recommendation on how engagement could be further enhanced using the bot in terms of; clearly specified purpose, integration with existing university systems, leading by example and connectivity. Extending upon these recommendations, a second pilot study is planned for September 2020, for which the focus will be upon improving attendance rates, student satisfaction and module pass rates.

Keywords: Chatbot, e-learning, learning communities, student engagement.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1715

Authors: Zhong Ma, Zhuping Wang, Congxin Liu, Xiangzeng Liu

Abstract:

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 APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2346

Authors: Raid R. O. Al-Nima, S. S. Dlay, W. L. Woo

Abstract:

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 APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1684