Search results for: Bayesian network; structure learning

Authors: Roza Dastres, Mohsen Soori

Abstract:

Networks are vulnerable devices due to their basic feature of facilitating remote access and data communication. The information in the networks needs to be kept secured and safe in order to provide an effective communication and sharing device in the web of data. Due to challenges and threats of the data in networks, the network security is one of the most important considerations in information technology infrastructures. As a result, the security measures are considered in the network in order to decrease the probability of accessing the secured data by the hackers. The purpose of network security is to protect the network and its components from unauthorized access and abuse in order to provide a safe and secured communication device for the users. In the present research work a review in recent development of network threats and security measures is presented and future research works are also suggested. Different attacks to the networks and security measured against them are discussed in order to increase security in the web of data. So, new ideas in the network security systems can be presented by analyzing the published papers in order to move forward the research field.

Keywords: Network threats, network security, security measures, firewalls.

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Authors: Tijani Delleji, Mourad Zribi, Ahmed Ben Hamida

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This paper discusses EM algorithm and Bootstrap approach combination applied for the improvement of the satellite image fusion process. This novel satellite image fusion method based on estimation theory EM algorithm and reinforced by Bootstrap approach was successfully implemented and tested. The sensor images are firstly split by a Bayesian segmentation method to determine a joint region map for the fused image. Then, we use the EM algorithm in conjunction with the Bootstrap approach to develop the bootstrap EM fusion algorithm, hence producing the fused targeted image. We proposed in this research to estimate the statistical parameters from some iterative equations of the EM algorithm relying on a reference of representative Bootstrap samples of images. Sizes of those samples are determined from a new criterion called 'hybrid criterion'. Consequently, the obtained results of our work show that using the Bootstrap EM (BEM) in image fusion improve performances of estimated parameters which involve amelioration of the fused image quality; and reduce the computing time during the fusion process.

Keywords: Satellite image fusion, Bayesian segmentation, Bootstrap approach, EM algorithm.

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Authors: A.Ö. İlkay, C.O. Zeynep

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Information and Communication Technologies (ICT) has changed our life and learn. ICT bares doors to new innovative methods to deliver education. E-learning is a part of ICT and has been endorsed as a tool for developing “21st century skills” in higher education. The aim of this review is to establish the impacts of e-learning in undergraduate nursing education. A systematic literature review was conducted to assess the impacts of e-learning in nursing education by using Akdeniz University electronic databases. According to results, we can decelerate that the nursing faculties cannot treat e-learning methods as a single tool. E-learning should be used with a good understanding of learners’ needs.

Keywords: E-learning, nursing education, systematic literature review.

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Authors: Ahmad Ghayoumi, Mehdi Ghayoumi

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Intelligent schools are those which use IT devices and technologies as media software, hardware and networks to improve learning process. On the other hand management improvement is best described as the process from which managers learn and improve their skills not only to benefit themselves but also their employing organizations Here, we present a model Management improvement System that has been applied on some schools and have made strict improvement.

Keywords: Intelligent school, Management development system, Learning station, Teaching station

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Authors: Khin Sandar Win

Abstract:

In multi hop wireless systems, such as ad hoc and sensor networks, mobile ad hoc network applications are deployed, security emerges as a central requirement. A particularly devastating attack is known as the wormhole attack, where two or more malicious colluding nodes create a higher level virtual tunnel in the network, which is employed to transport packets between the tunnel end points. These tunnels emulate shorter links in the network. In which adversary records transmitted packets at one location in the network, tunnels them to another location, and retransmits them into the network. The wormhole attack is possible even if the attacker has not compromised any hosts and even if all communication provides authenticity and confidentiality. In this paper, we analyze wormhole attack nature in ad hoc and sensor networks and existing methods of the defending mechanism to detect wormhole attacks without require any specialized hardware. This analysis able to provide in establishing a method to reduce the rate of refresh time and the response time to become more faster.

Keywords: Ad hoc network, Sensor network, Wormhole attack, defending mechanism.

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Authors: Rana AlHajri, Salah Al-Sharhan, Ahmed Al-Hunaiyyan

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Mobile learning is a new learning landscape that offers opportunity for collaborative, personal, informal, and students’ centered learning environment. In implementing any learning system such as a mobile learning environment, learners’ expectations should be taken into consideration. However, there is a lack of studies on this aspect, particularly in the context of Kuwait higher education (HE) institutions. This study focused on how students perceive the use of mobile devices in learning. Although m-learning is considered as an effective educational tool in developed countries, it is not yet fully utilized in Kuwait. The study reports on the results of a survey conducted on 623 HE students in Kuwait to a better understand students' perceptions and opinions about the effectiveness of using mobile learning systems. An analysis of quantitative survey data is presented. The findings indicated that Kuwait HE students are very familiar with mobile devices and its applications. The results also reveal that students have positive perceptions of m-learning, and believe that video-based social media applications enhance the teaching and learning process.

Keywords: Higher education, mobile learning, social media, students’ perceptions.

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Authors: S. Chokri

Abstract:

Load modeling is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: Neural network, Load Forecasting, Fuzzy inference, Machine learning, Fuzzy modeling and rule extraction, Support Vector Regression.

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Authors: Mohammad Saleh Vahdatpour, Yanqing Zhang

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Understanding the neural mechanisms underlying motion detection in the human visual system has long been a fascinating challenge in neuroscience and artificial intelligence. This paper presents a spiking neural network model inspired by the processing of motion information in the primate visual system, particularly focusing on the Middle Temporal (MT) area. In our study, we propose a multi-layer spiking neural network model to perform motion detection tasks, leveraging the idea that synaptic delays in neuronal communication are pivotal in motion perception. Synaptic delay, determined by factors like axon length and myelin insulation, affects the temporal order of input spikes, thereby encoding motion direction and speed. Overall, our spiking neural network model demonstrates the feasibility of capturing motion detection principles observed in the primate visual system. The combination of synaptic delays, learning mechanisms, and shared weights and delays in SMD provides a promising framework for motion perception in artificial systems, with potential applications in computer vision and robotics.

Keywords: Neural networks, motion detection, signature detection, convolutional neural network.

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Authors: Khalid Ali, Manar Jammal

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In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity.

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Authors: Hsu-Yung Cheng, Chih-Chang Yu

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In this work, we present an automatic vehicle detection system for airborne videos using combined features. We propose a pixel-wise classification method for vehicle detection using Dynamic Bayesian Networks. In spite of performing pixel-wise classification, relations among neighboring pixels in a region are preserved in the feature extraction process. The main novelty of the detection scheme is that the extracted combined features comprise not only pixel-level information but also region-level information. Afterwards, tracking is performed on the detected vehicles. Tracking is performed using efficient Kalman filter with dynamic particle sampling. Experiments were conducted on a wide variety of airborne videos. We do not assume prior information of camera heights, orientation, and target object sizes in the proposed framework. The results demonstrate flexibility and good generalization abilities of the proposed method on a challenging dataset.

Keywords: Vehicle Detection, Airborne Video, Tracking, Dynamic Bayesian Networks

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Authors: Lina Yan, Jingjing Di, Ke Wang

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A new basis function neural network algorithm is proposed for numerical integration. The main idea is to construct neural network model based on spline basis functions, which is used to approximate the integrand by training neural network weights. The convergence theorem of the neural network algorithm, the theorem for numerical integration and one corollary are presented and proved. The numerical examples, compared with other methods, show that the algorithm is effective and has the characteristics such as high precision and the integrand not required known. Thus, the algorithm presented in this paper can be widely applied in many engineering fields.

Keywords: Numerical integration, Spline basis function, Neural network algorithm

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Authors: Nittaya Kerdprasop, Kittisak Kerdprasop

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The state of the art in instructional design for computer-assisted learning has been strongly influenced by advances in information technology, Internet and Web-based systems. The emphasis of educational systems has shifted from training to learning. The course delivered has also been changed from large inflexible content to sequential small chunks of learning objects. The concepts of learning objects together with the advanced technologies of Web and communications support the reusability, interoperability, and accessibility design criteria currently exploited by most learning systems. These concepts enable just-in-time learning. We propose to extend theses design criteria further to include the learnability concept that will help adapting content to the needs of learners. The learnability concept offers a better personalization leading to the creation and delivery of course content more appropriate to performance and interest of each learner. In this paper we present a new framework of learning environments containing knowledge discovery as a tool to automatically learn patterns of learning behavior from learners' profiles and history.

Keywords: Knowledge mining, Web-based learning, Learning environments.

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Authors: Sasiphan Nitayaprapha

Abstract:

A commercially-supported social network has become an emerging channel for an organization to communicate with and provide services to customers. The success of the commercially-supported social network depends on the ability of the organization to keep the customers in participating in the network. Drawing from the theories of information adoption, information systems continuance, and web usability, the author develops a model to explore how a commercially-supported social network can encourage customers to continue participating and using the information in the network. The theoretical model will be proved through an online survey of customers using the commercially-supported social networking sites of several high technology companies operating in the same sector. The result will be compared with previous studies to learn about the explanatory power of the research model, and to identify the main factors determining users’ intention to continue using a commercially-supported social network. Theoretical and practical implications and limitations are discussed.

Keywords: Social network, Information adoption, Information systems continuance, Web usability, User satisfaction.

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Authors: Hazem M. El-Bakry, Ahmed A. Saleh, Taghreed T. Asfour

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Adaptive e-learning today gives the student a central role in his own learning process. It allows learners to try things out, participate in courses like never before, and get more out of learning than before. In this paper, an adaptive e-learning model for logic design, simplification of Boolean functions and related fields is presented. Such model presents suitable courses for each student in a dynamic and adaptive manner using existing database and workflow technologies. The main objective of this research work is to provide an adaptive e-learning model based learners' personality using explicit and implicit feedback. To recognize the learner-s, we develop dimensions to decide each individual learning style in order to accommodate different abilities of the users and to develop vital skills. Thus, the proposed model becomes more powerful, user friendly and easy to use and interpret. Finally, it suggests a learning strategy and appropriate electronic media that match the learner-s preference.

Keywords: Adaptive learning, Learning styles, Teaching strategies.

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Authors: Jānis Zuters

Abstract:

There are a lot of extensions made to the classic model of multi-layer perceptron (MLP). A notable amount of them has been designed to hasten the learning process without considering the quality of generalization. The paper proposes a new MLP extension based on exploiting topology of the input layer of the network. Experimental results show the extended model to improve upon generalization capability in certain cases. The new model requires additional computational resources to compare to the classic model, nevertheless the loss in efficiency isn-t regarded to be significant.

Keywords: Learning algorithm, multi-layer perceptron, topology.

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Authors: Dalibor Dobrilovic, Borislav Odadžic

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In this paper is being described a possible use of virtualization technology in teaching computer networks. The virtualization can be used as a suitable tool for creating virtual network laboratories, supplementing the real laboratories and network simulation software in teaching networking concepts. It will be given a short description of characteristic projects in the area of virtualization technology usage in network simulation, network experiments and engineering education. A method for implementing laboratory has also been explained, together with possible laboratory usage and design of laboratory exercises. At the end, the laboratory test results of virtual laboratory are presented as well.

Keywords: Computer network simulation software, teaching networking concepts, virtual network laboratory, virtualization technology.

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Authors: Kenichiro Hida, Shin-Ichi Kuribayashi

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In a conventional network, most network devices, such as routers, are dedicated devices that do not have much variation in capacity. In recent years, a new concept of network functions virtualisation (NFV) has come into use. The intention is to implement a variety of network functions with software on general-purpose servers and this allows the network operator to select their capacities and locations without any constraints. This paper focuses on the allocation of NFV-based routing functions which are one of critical network functions, and presents the virtual routing function allocation algorithm that minimizes the total power consumption. In addition, this study presents the useful allocation policy of virtual routing functions, based on an evaluation with a ladder-shaped network model. This policy takes the ratio of the power consumption of a routing function to that of a circuit and traffic distribution between areas into consideration. Furthermore, the present paper shows that there are cases where the use of NFV-based routing functions makes it possible to reduce the total power consumption dramatically, in comparison to a conventional network, in which it is not economically viable to distribute small-capacity routing functions.

Keywords: Virtual routing function, NFV, resource allocation, minimum power consumption.

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Authors: K. Vidya, V.Rhymend Uthariaraj

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Traffic Engineering (TE) is the process of controlling how traffic flows through a network in order to facilitate efficient and reliable network operations while simultaneously optimizing network resource utilization and traffic performance. TE improves the management of data traffic within a network and provides the better utilization of network resources. Many research works considers intra and inter Traffic Engineering separately. But in reality one influences the other. Hence the effective network performances of both inter and intra Autonomous Systems (AS) are not optimized properly. To achieve a better Joint Optimization of both Intra and Inter AS TE, we propose a joint Optimization technique by considering intra-AS features during inter – AS TE and vice versa. This work considers the important criterion say latency within an AS and between ASes. and proposes a Bi-Criteria Latency optimization model. Hence an overall network performance can be improved by considering this jointoptimization technique in terms of Latency.

Keywords: Inter-Domain Routing , Measurement, OptimizationPerformance, Traffic Engineering.

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Authors: S. Sarumathi, M. Vaishnavi, S. Geetha, P. Ranjetha

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Machine learning is a new and exciting area of artificial intelligence nowadays. Machine learning is the most valuable, time, supervised, and cost-effective approach. It is not a narrow learning approach; it also includes a wide range of methods and techniques that can be applied to a wide range of complex realworld problems and time domains. Biological image classification, adaptive testing, computer vision, natural language processing, object detection, cancer detection, face recognition, handwriting recognition, speech recognition, and many other applications of machine learning are widely used in research, industry, and government. Every day, more data are generated, and conventional machine learning techniques are becoming obsolete as users move to distributed and real-time operations. By providing fundamental knowledge of machine learning tools and research opportunities in the field, the aim of this article is to serve as both a comprehensive overview and a guide. A diverse set of machine learning resources is demonstrated and contrasted with the key features in this survey.

Keywords: Artificial intelligence, machine learning, deep learning, machine learning algorithms, machine learning tools.

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Authors: R. Poonkuzhali, M. Y. Sanavullah, M. R. Gurupriya

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Opportunistic routing is used, where the network has the features like dynamic topology changes and intermittent network connectivity. In Delay tolerant network or Disruption tolerant network opportunistic forwarding technique is widely used. The key idea of opportunistic routing is selecting forwarding nodes to forward data packets and coordination among these nodes to avoid duplicate transmissions. This paper gives the analysis of pros and cons of various opportunistic routing techniques used in MANET.

Keywords: Expected Transmission Count (ETX), Opportunistic routing, Proactive Source Routing (PSR), throughput.

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Authors: Srinivasan Chandrasekaran, Thailammai Chithambaram, Shihas A. Khader

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The early-stage damage detection in offshore structures requires continuous structural health monitoring and for the large area the position of sensors will also plays an important role in the efficient damage detection. Determining the dynamic behavior of offshore structures requires dense deployment of sensors. The wired Structural Health Monitoring (SHM) systems are highly expensive and always needs larger installation space to deploy. Wireless sensor networks can enhance the SHM system by deployment of scalable sensor network, which consumes lesser space. This paper presents the results of wireless sensor network based Structural Health Monitoring method applied to a scaled experimental model of offshore structure that underwent wave loading. This method determines the serviceability of the offshore structure which is subjected to various environment loads. Wired and wireless sensors were installed in the model and the response of the scaled BLSRP model under wave loading was recorded. The wireless system discussed in this study is the Raspberry pi board with Arm V6 processor which is programmed to transmit the data acquired by the sensor to the server using Wi-Fi adapter, the data is then hosted in the webpage. The data acquired from the wireless and wired SHM systems were compared and the design of the wireless system is verified.

Keywords: Condition assessment, damage detection, structural health monitoring, structural response, wireless sensor network.

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Authors: Fatemeh Amiri, Caro Lucas, Nasser Yazdani

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As the network based technologies become omnipresent, demands to secure networks/systems against threat increase. One of the effective ways to achieve higher security is through the use of intrusion detection systems (IDS), which are a software tool to detect anomalous in the computer or network. In this paper, an IDS has been developed using an improved machine learning based algorithm, Locally Linear Neuro Fuzzy Model (LLNF) for classification whereas this model is originally used for system identification. A key technical challenge in IDS and LLNF learning is the curse of high dimensionality. Therefore a feature selection phase is proposed which is applicable to any IDS. While investigating the use of three feature selection algorithms, in this model, it is shown that adding feature selection phase reduces computational complexity of our model. Feature selection algorithms require the use of a feature goodness measure. The use of both a linear and a non-linear measure - linear correlation coefficient and mutual information- is investigated respectively

Keywords: anomaly Detection, feature selection, Locally Linear Neuro Fuzzy (LLNF), Mutual Information (MI), liner correlation coefficient.

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Authors: Sherly Ferro Lensun

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Strategies influence the language abilities of both male and female learners in the learning process. Therefore, learning strategies are one of the critical factors for improving language learning and are essential as part of the initial learning effort. In general, language learning strategies differ between boys and girls. Therefore, this research aims to obtain a model that investigates the relationship between the selection of learning strategies, their frequency of use, and the learner's gender. In addition, we found differences in strategy use and their impact on language ability between males and females. 137 students participated and completed the questionnaire. There were 48 males (35%) and 90 females (65.7%). It was clear that most of the Japanese learners were women. Findings show that most Japanese learners in North Sulawesi used cognitive and social strategies and methods of involving others in learning Japanese.

Keywords: Learning strategies, Japanese Language, Gender by Japanese Learners, North sulawesi.

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Authors: Eyup Yurt, Ahmet Kurnaz, Ismail Sahin

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The purpose of the study is to investigate the education faculty students’ attitudes towards e-learning according to different variables. In current study, the data were collected from 393 students of an education faculty in Turkey. In this study, theattitude towards e‐learning scale and the demographic information form were used to collect data. The collected data were analyzed by t-test, ANOVA and Pearson correlation coefficient. It was found that there is a significant difference in students’ tendency towards e-learning and avoidance from e-learning based on gender. Male students have more positive attitudes towards e-learning than female students. Also, the students who used the internet lesshave higher levels of avoidance from e-learning. Additionally, it is found that there is a positive and significant relationship between the number of personal mobile learning devices and tendency towards e-learning. On the other hand, there is a negative and significant relationship between the number of personal mobile learning devices and avoidance from e-learning. Also, suggestions were presented according to findings.

Keywords: Education faculty students, attitude towards e-learning, gender, daily Internet usage time, m-learning.

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Authors: Khaing Win Mar, Thinn Thu Naing

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Nowadays, precipitation prediction is required for proper planning and management of water resources. Prediction with neural network models has received increasing interest in various research and application domains. However, it is difficult to determine the best neural network architecture for prediction since it is not immediately obvious how many input or hidden nodes are used in the model. In this paper, neural network model is used as a forecasting tool. The major aim is to evaluate a suitable neural network model for monthly precipitation mapping of Myanmar. Using 3-layerd neural network models, 100 cases are tested by changing the number of input and hidden nodes from 1 to 10 nodes, respectively, and only one outputnode used. The optimum model with the suitable number of nodes is selected in accordance with the minimum forecast error. In measuring network performance using Root Mean Square Error (RMSE), experimental results significantly show that 3 inputs-10 hiddens-1 output architecture model gives the best prediction result for monthly precipitation in Myanmar.

Keywords: Precipitation prediction, monthly precipitation, neural network models, Myanmar.

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Authors: Alireza M. goltabar, Issa shooshpasha , Reza Shamstabar kami , Mostafa Habibi

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Added stresses due to adjacent structure should be considered in foundation design and stress control in soil under the structure. This case is considered less than other cases in design and calculation whereas stresses in implementation are greater than analytical stress. Structure load are transmitted to earth by foundation and role of foundation is propagation of load on the continuous and half extreme soil. This act cause that, present stresses lessen to allowable strength of soil. Some researchers such as Boussinesq and westergaurd by using of some assumption studied on this issue, theorically. Target of this paper is study and evaluation of added stresses under structure due to adjacent structure. For this purpose, by using of assumption, theoric relation and numeral methods, effects of adjacent structure with 4 to 10 storeys on the main structure with 4 storeys are studied and effect of parameters and sensitivity of them are evaluated.

Keywords: stress, soil, adjacent structure, foundation, loading.

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Authors: R.Ramli

Abstract:

This paper discusses ways to foster cooperative learning through the integration of online communication technology. While the education experts believe constructivism produces a more positive learning experience, the educators are still facing problems in getting students to participate due to numerous reasons such as shy personality, language and cultural barriers. This paper will look into the factors that lead to lack of participations among students and how technology can be implemented to overcome these issues.

Keywords: cooperative learning, encouraging class participation, education, online discussion

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Authors: Chokri Slim

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The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy architecture based on Extended Kalman filter. To test the performance and applicability of the proposed neuro-fuzzy model, simulation study of nonlinear complex dynamic system is carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction of financial time series. A benchmark case studie is used to demonstrate that the proposed model is a superior neuro-fuzzy modeling technique.

Keywords: Neuro-fuzzy, Extended Kalman filter, nonlinear systems, financial time series.

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Authors: Muhammad Nawaz, Sandeep N. Kundu, Farha Sattar

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Augmented reality sandbox adds new dimensions to education and learning process. It can be a core component of geoscience teaching and learning to understand the geographic contexts and landform processes. Augmented reality sandbox is a useful tool not only to create an interactive learning environment through spatial visualization but also it can provide an active learning experience to students and enhances the cognition process of learning. Augmented reality sandbox can be used as an interactive learning tool to teach geomorphic and landform processes. This article explains the augmented reality sandbox and the constructivism approach for geoscience teaching and learning, and endeavours to explore the ways to teach the geographic processes using the three-dimensional digital environment for the deep learning of the geoscience concepts interactively.

Keywords: Augmented Reality Sandbox, constructivism, deep learning, geoscience.

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Authors: Matthew Montebello

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The way e-learning effectiveness has been notoriously measured within an academic setting is by comparing the e-learning medium to the traditional face-to-face teaching methodology. In this paper, a simple yet innovative comparison methodology is introduced, whereby the effectiveness of next generation e-learning systems are assessed in contrast not only to the face-to-face mode, but also to the classical e-learning modality. Ethical and logistical issues are also discussed, as this three-way approach to compare teaching methodologies was applied and documented in a real empirical study within a higher education institution.

Keywords: E-learning effectiveness, higher education, teaching modality comparison.

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