Search results for: © Learning Network
4340 A Computer Model of Language Acquisition – Syllable Learning – Based on Hebbian Cell Assemblies and Reinforcement Learning
Authors: Sepideh Fazeli, Fariba Bahrami
Abstract:
Investigating language acquisition is one of the most challenging problems in the area of studying language. Syllable learning as a level of language acquisition has a considerable significance since it plays an important role in language acquisition. Because of impossibility of studying language acquisition directly with children, especially in its developmental phases, computer models will be useful in examining language acquisition. In this paper a computer model of early language learning for syllable learning is proposed. It is guided by a conceptual model of syllable learning which is named Directions Into Velocities of Articulators model (DIVA). The computer model uses simple associational and reinforcement learning rules within neural network architecture which are inspired by neuroscience. Our simulation results verify the ability of the proposed computer model in producing phonemes during babbling and early speech. Also, it provides a framework for examining the neural basis of language learning and communication disorders.Keywords: Brain modeling, computer models, language acquisition, reinforcement learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15424339 A GA-Based Role Assignment Approach for Web-based Cooperative Learning Environments
Authors: Yi-Chun Chang, Jian-Wei Li
Abstract:
Web-based cooperative learning focuses on (1) the interaction and the collaboration of community members, and (2) the sharing and the distribution of knowledge and expertise by network technology to enhance learning performance. Numerous research literatures related to web-based cooperative learning have demonstrated that cooperative scripts have a positive impact to specify, sequence, and assign cooperative learning activities. Besides, literatures have indicated that role-play in web-based cooperative learning environments enhances two or more students to work together toward the completion of a common goal. Since students generally do not know each other and they lack the face-to-face contact that is necessary for the negotiation of assigning group roles in web-based cooperative learning environments, this paper intends to further extend the application of genetic algorithm (GA) and propose a GA-based algorithm to tackle the problem of role assignment in web-based cooperative learning environments, which not only saves communication costs but also reduces conflict between group members in negotiating role assignments.
Keywords: genetic algorithm (GA), role assignment, role-play; web-based cooperative learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14084338 Prediction on Housing Price Based on Deep Learning
Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang
Abstract:
In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.
Keywords: Deep learning, convolutional neural network, LSTM, housing prediction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 48874337 Approximate Bounded Knowledge Extraction Using Type-I Fuzzy Logic
Authors: Syed Muhammad Aqil Burney, Tahseen Ahmed Jilani, C. Ardil
Abstract:
Using neural network we try to model the unknown function f for given input-output data pairs. The connection strength of each neuron is updated through learning. Repeated simulations of crisp neural network produce different values of weight factors that are directly affected by the change of different parameters. We propose the idea that for each neuron in the network, we can obtain quasi-fuzzy weight sets (QFWS) using repeated simulation of the crisp neural network. Such type of fuzzy weight functions may be applied where we have multivariate crisp input that needs to be adjusted after iterative learning, like claim amount distribution analysis. As real data is subjected to noise and uncertainty, therefore, QFWS may be helpful in the simplification of such complex problems. Secondly, these QFWS provide good initial solution for training of fuzzy neural networks with reduced computational complexity.
Keywords: Crisp neural networks, fuzzy systems, extraction of logical rules, quasi-fuzzy numbers.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16994336 Applications of Prediction and Identification Using Adaptive DCMAC Neural Networks
Authors: Yu-Lin Liao, Ya-Fu Peng
Abstract:
An adaptive dynamic cerebellar model articulation controller (DCMAC) neural network used for solving the prediction and identification problem is proposed in this paper. The proposed DCMAC has superior capability to the conventional cerebellar model articulation controller (CMAC) neural network in efficient learning mechanism, guaranteed system stability and dynamic response. The recurrent network is embedded in the DCMAC by adding feedback connections in the association memory space so that the DCMAC captures the dynamic response, where the feedback units act as memory elements. The dynamic gradient descent method is adopted to adjust DCMAC parameters on-line. Moreover, the analytical method based on a Lyapunov function is proposed to determine the learning-rates of DCMAC so that the variable optimal learning-rates are derived to achieve most rapid convergence of identifying error. Finally, the adaptive DCMAC is applied in two computer simulations. Simulation results show that accurate identifying response and superior dynamic performance can be obtained because of the powerful on-line learning capability of the proposed DCMAC.Keywords: adaptive, cerebellar model articulation controller, CMAC, prediction, identification
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13664335 Mapping Semantic Networks to Undirected Networks
Authors: Marko A. Rodriguez
Abstract:
There exists an injective, information-preserving function that maps a semantic network (i.e a directed labeled network) to a directed network (i.e. a directed unlabeled network). The edge label in the semantic network is represented as a topological feature of the directed network. Also, there exists an injective function that maps a directed network to an undirected network (i.e. an undirected unlabeled network). The edge directionality in the directed network is represented as a topological feature of the undirected network. Through function composition, there exists an injective function that maps a semantic network to an undirected network. Thus, aside from space constraints, the semantic network construct does not have any modeling functionality that is not possible with either a directed or undirected network representation. Two proofs of this idea will be presented. The first is a proof of the aforementioned function composition concept. The second is a simpler proof involving an undirected binary encoding of a semantic network.Keywords: general-modeling, multi-relational networks, semantic networks
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13874334 Personal Information Classification Based on Deep Learning in Automatic Form Filling System
Authors: Shunzuo Wu, Xudong Luo, Yuanxiu Liao
Abstract:
Recently, the rapid development of deep learning makes artificial intelligence (AI) penetrate into many fields, replacing manual work there. In particular, AI systems also become a research focus in the field of automatic office. To meet real needs in automatic officiating, in this paper we develop an automatic form filling system. Specifically, it uses two classical neural network models and several word embedding models to classify various relevant information elicited from the Internet. When training the neural network models, we use less noisy and balanced data for training. We conduct a series of experiments to test my systems and the results show that our system can achieve better classification results.Keywords: Personal information, deep learning, auto fill, NLP, document analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7804333 Automatic Classification of Periodic Heart Sounds Using Convolutional Neural Network
Authors: Jia Xin Low, Keng Wah Choo
Abstract:
This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.Keywords: Convolutional neural network, discrete wavelet transform, deep learning, heart sound classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11014332 An efficient Activity Network Reduction Algorithm based on the Label Correcting Tracing Algorithm
Authors: Weng Ming Chu
Abstract:
When faced with stochastic networks with an uncertain duration for their activities, the securing of network completion time becomes problematical, not only because of the non-identical pdf of duration for each node, but also because of the interdependence of network paths. As evidenced by Adlakha & Kulkarni [1], many methods and algorithms have been put forward in attempt to resolve this issue, but most have encountered this same large-size network problem. Therefore, in this research, we focus on network reduction through a Series/Parallel combined mechanism. Our suggested algorithm, named the Activity Network Reduction Algorithm (ANRA), can efficiently transfer a large-size network into an S/P Irreducible Network (SPIN). SPIN can enhance stochastic network analysis, as well as serve as the judgment of symmetry for the Graph Theory.Keywords: Series/Parallel network, Stochastic network, Network reduction, Interdictive Graph, Complexity Index.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13354331 Use of Semantic Networks as Learning Material and Evaluation of the Approach by Students
Authors: Philippe A. Martin
Abstract:
This article first summarizes reasons why current approaches supporting Open Learning and Distance Education need to be complemented by tools permitting lecturers, researchers and students to cooperatively organize the semantic content of Learning related materials (courses, discussions, etc.) into a fine-grained shared semantic network. This first part of the article also quickly describes the approach adopted to permit such a collaborative work. Then, examples of such semantic networks are presented. Finally, an evaluation of the approach by students is provided and analyzed.
Keywords: knowledge sharing, knowledge evaluation, e-learning
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14624330 Security of Mobile Agent in Ad hoc Network using Threshold Cryptography
Authors: S.M. Sarwarul Islam Rizvi, Zinat Sultana, Bo Sun, Md. Washiqul Islam
Abstract:
In a very simple form a Mobile Agent is an independent piece of code that has mobility and autonomy behavior. One of the main advantages of using Mobile Agent in a network is - it reduces network traffic load. In an, ad hoc network Mobile Agent can be used to protect the network by using agent based IDS or IPS. Besides, to deploy dynamic software in the network or to retrieve information from network nodes Mobile Agent can be useful. But in an ad hoc network the Mobile Agent itself needs some security. Security services should be guaranteed both for Mobile Agent and for Agent Server. In this paper to protect the Mobile Agent and Agent Server in an ad hoc network we have proposed a solution which is based on Threshold Cryptography, a new vibe in the cryptographic world where trust is distributed among multiple nodes in the network.
Keywords: Ad hoc network, Mobile Agent, Security, Threats, Threshold Cryptography.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19204329 Improving the Performance of Back-Propagation Training Algorithm by Using ANN
Authors: Vishnu Pratap Singh Kirar
Abstract:
Artificial Neural Network (ANN) can be trained using back propagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a twoterm algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm.
Keywords: Neural Network, Backpropagation, Local Minima, Fast Convergence Rate.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 34984328 Forecasting e-Learning Efficiency by Using Artificial Neural Networks and a Balanced Score Card
Authors: Petar Halachev
Abstract:
Forecasting the values of the indicators, which characterize the effectiveness of performance of organizations is of great importance for their successful development. Such forecasting is necessary in order to assess the current state and to foresee future developments, so that measures to improve the organization-s activity could be undertaken in time. The article presents an overview of the applied mathematical and statistical methods for developing forecasts. Special attention is paid to artificial neural networks as a forecasting tool. Their strengths and weaknesses are analyzed and a synopsis is made of the application of artificial neural networks in the field of forecasting of the values of different education efficiency indicators. A method of evaluation of the activity of universities using the Balanced Scorecard is proposed and Key Performance Indicators for assessment of e-learning are selected. Resulting indicators for the evaluation of efficiency of the activity are proposed. An artificial neural network is constructed and applied in the forecasting of the values of indicators for e-learning efficiency on the basis of the KPI values.Keywords: artificial neural network, balanced scorecard, e-learning
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14934327 Network Effects and QoS as Determining Factors in Selection of Mobile Operator: A Case Study from Higher Learning Institution in Dodoma Municipality in Tanzania
Authors: Justinian Anatory, Ekael Stephen Manase
Abstract:
The use of mobile phones is growing tremendously all over the world. In Tanzania there are a number of operators licensed by Tanzania Communications Regulatory Authority (TCRA) aiming at attracting customers into their networks. So far telecommunications market competition has been very stiff. Various measures are being taken by mobile operators to survive in the market. Such measure include introducing of different air time bundles on daily, weekly and monthly at lower tariffs. Other measures include the introduction of normal tariff, tourist package and one network. Despite of all these strategies, there is a dynamic competition in the market which needs to be explored. Some influences which attract customers to choose a certain mobile operator are of particular interest. This paper is investigating if the network effects and Quality of Services (QoS) influence mobile customers in selection of their mobile network operators. Seventy seven students from high learning institutions in Dodoma Municipality in Tanzania participated in responding to prepared questionnaires. The data was analyzed using Statistical Package for Social Science (SPSS) Software. The results indicate that, network coverage does influence customers in selection of mobile operators. In addition, this paper proposes further research in some areas especially where the study came up with different findings from what the theory has in place.
Keywords: Network effects, Quality of services, Consumer Buying, mobile operators.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22844326 Proffering a Brand New Methodology to Resource Discovery in Grid based on Economic Criteria Using Learning Automata
Authors: Ali Sarhadi, Mohammad Reza Meybodi, Ali Yousefi
Abstract:
Resource discovery is one of the chief services of a grid. A new approach to discover the provenances in grid through learning automata has been propounded in this article. The objective of the aforementioned resource-discovery service is to select the resource based upon the user-s applications and the mercantile yardsticks that is to say opting for an originator which can accomplish the user-s tasks in the most economic manner. This novel service is submitted in two phases. We proffered an applicationbased categorization by means of an intelligent nerve-prone plexus. The user in question sets his or her application as the input vector of the nerve-prone nexus. The output vector of the aforesaid network limns the appropriateness of any one of the resource for the presented executive procedure. The most scrimping option out of those put forward in the previous stage which can be coped with to fulfill the task in question is picked out. Te resource choice is carried out by means of the presented algorithm based upon the learning automata.
Keywords: Resource discovery, learning automata, neural network, economic policy
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14084325 Adaptive Network Intrusion Detection Learning: Attribute Selection and Classification
Authors: Dewan Md. Farid, Jerome Darmont, Nouria Harbi, Nguyen Huu Hoa, Mohammad Zahidur Rahman
Abstract:
In this paper, a new learning approach for network intrusion detection using naïve Bayesian classifier and ID3 algorithm is presented, which identifies effective attributes from the training dataset, calculates the conditional probabilities for the best attribute values, and then correctly classifies all the examples of training and testing dataset. Most of the current intrusion detection datasets are dynamic, complex and contain large number of attributes. Some of the attributes may be redundant or contribute little for detection making. It has been successfully tested that significant attribute selection is important to design a real world intrusion detection systems (IDS). The purpose of this study is to identify effective attributes from the training dataset to build a classifier for network intrusion detection using data mining algorithms. The experimental results on KDD99 benchmark intrusion detection dataset demonstrate that this new approach achieves high classification rates and reduce false positives using limited computational resources.Keywords: Attributes selection, Conditional probabilities, information gain, network intrusion detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 26454324 Word Recognition and Learning based on Associative Memories and Hidden Markov Models
Authors: Zöhre Kara Kayikci, Günther Palm
Abstract:
A word recognition architecture based on a network of neural associative memories and hidden Markov models has been developed. The input stream, composed of subword-units like wordinternal triphones consisting of diphones and triphones, is provided to the network of neural associative memories by hidden Markov models. The word recognition network derives words from this input stream. The architecture has the ability to handle ambiguities on subword-unit level and is also able to add new words to the vocabulary during performance. The architecture is implemented to perform the word recognition task in a language processing system for understanding simple command sentences like “bot show apple".Keywords: Hebbian learning, hidden Markov models, neuralassociative memories, word recognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14674323 Heuristic Optimization Techniques for Network Reconfiguration in Distribution System
Authors: A. Charlangsut, N. Rugthaicharoencheep, S. Auchariyamet
Abstract:
Network reconfiguration is an operation to modify the network topology. The implementation of network reconfiguration has many advantages such as loss minimization, increasing system security and others. In this paper, two topics about the network reconfiguration in distribution system are briefly described. The first topic summarizes its impacts while the second explains some heuristic optimization techniques for solving the network reconfiguration problem.Keywords: Network Reconfiguration, Optimization Techniques, Distribution System
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 27084322 Attacks Classification in Adaptive Intrusion Detection using Decision Tree
Authors: Dewan Md. Farid, Nouria Harbi, Emna Bahri, Mohammad Zahidur Rahman, Chowdhury Mofizur Rahman
Abstract:
Recently, information security has become a key issue in information technology as the number of computer security breaches are exposed to an increasing number of security threats. A variety of intrusion detection systems (IDS) have been employed for protecting computers and networks from malicious network-based or host-based attacks by using traditional statistical methods to new data mining approaches in last decades. However, today's commercially available intrusion detection systems are signature-based that are not capable of detecting unknown attacks. In this paper, we present a new learning algorithm for anomaly based network intrusion detection system using decision tree algorithm that distinguishes attacks from normal behaviors and identifies different types of intrusions. Experimental results on the KDD99 benchmark network intrusion detection dataset demonstrate that the proposed learning algorithm achieved 98% detection rate (DR) in comparison with other existing methods.Keywords: Detection rate, decision tree, intrusion detectionsystem, network security.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 35594321 New Curriculum Approach in Teaching Network Security Subjects for ICT Courses in Malaysia
Authors: Mohd Fairuz Iskandar Othman, Nazrulazhar Bahaman, Zulkiflee Muslim, Faizal Abdollah
Abstract:
This paper discusses a curriculum approach that will give emphasis on practical portions of teaching network security subjects in information and communication technology courses. As we are well aware, the need to use a practice and application oriented approach in education is paramount. Research on active learning and cooperative groups have shown that students grasps more and have more tendency towards obtaining and realizing soft skills like leadership, communication and team work as opposed to the more traditional theory and exam based teaching and learning. While this teaching and learning paradigm is relatively new in Malaysia, it has been practiced widely in the West. This paper examines a certain approach whereby students learning wireless security are divided into and work in small and manageable groups where there will be 2 teams which consist of black hat and white hat teams. The former will try to find and expose vulnerabilities in a wireless network while the latter will try their best to prevent such attacks on their wireless networks using hardware, software, design and enforcement of security policy and etc. This paper will try to show that the approach taken plus the use of relevant and up to date software and hardware and with suitable environment setting will hopefully expose students to a more fruitful outcome in terms of understanding of concepts, theories and their motivation to learn.Keywords: Curriculum approach, wireless networks, wirelesssecurity.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16534320 Performance Analysis of Traffic Classification with Machine Learning
Authors: Htay Htay Yi, Zin May Aye
Abstract:
Network security is role of the ICT environment because malicious users are continually growing that realm of education, business, and then related with ICT. The network security contravention is typically described and examined centrally based on a security event management system. The firewalls, Intrusion Detection System (IDS), and Intrusion Prevention System are becoming essential to monitor or prevent of potential violations, incidents attack, and imminent threats. In this system, the firewall rules are set only for where the system policies are needed. Dataset deployed in this system are derived from the testbed environment. The traffic as in DoS and PortScan traffics are applied in the testbed with firewall and IDS implementation. The network traffics are classified as normal or attacks in the existing testbed environment based on six machine learning classification methods applied in the system. It is required to be tested to get datasets and applied for DoS and PortScan. The dataset is based on CICIDS2017 and some features have been added. This system tested 26 features from the applied dataset. The system is to reduce false positive rates and to improve accuracy in the implemented testbed design. The system also proves good performance by selecting important features and comparing existing a dataset by machine learning classifiers.Keywords: False negative rate, intrusion detection system, machine learning methods, performance.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 10124319 Towards Growing Self-Organizing Neural Networks with Fixed Dimensionality
Authors: Guojian Cheng, Tianshi Liu, Jiaxin Han, Zheng Wang
Abstract:
The competitive learning is an adaptive process in which the neurons in a neural network gradually become sensitive to different input pattern clusters. The basic idea behind the Kohonen-s Self-Organizing Feature Maps (SOFM) is competitive learning. SOFM can generate mappings from high-dimensional signal spaces to lower dimensional topological structures. The main features of this kind of mappings are topology preserving, feature mappings and probability distribution approximation of input patterns. To overcome some limitations of SOFM, e.g., a fixed number of neural units and a topology of fixed dimensionality, Growing Self-Organizing Neural Network (GSONN) can be used. GSONN can change its topological structure during learning. It grows by learning and shrinks by forgetting. To speed up the training and convergence, a new variant of GSONN, twin growing cell structures (TGCS) is presented here. This paper first gives an introduction to competitive learning, SOFM and its variants. Then, we discuss some GSONN with fixed dimensionality, which include growing cell structures, its variants and the author-s model: TGCS. It is ended with some testing results comparison and conclusions.Keywords: Artificial neural networks, Competitive learning, Growing cell structures, Self-organizing feature maps.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15024318 Hybrid Structure Learning Approach for Assessing the Phosphate Laundries Impact
Authors: Emna Benmohamed, Hela Ltifi, Mounir Ben Ayed
Abstract:
Bayesian Network (BN) is one of the most efficient classification methods. It is widely used in several fields (i.e., medical diagnostics, risk analysis, bioinformatics research). The BN is defined as a probabilistic graphical model that represents a formalism for reasoning under uncertainty. This classification method has a high-performance rate in the extraction of new knowledge from data. The construction of this model consists of two phases for structure learning and parameter learning. For solving this problem, the K2 algorithm is one of the representative data-driven algorithms, which is based on score and search approach. In addition, the integration of the expert's knowledge in the structure learning process allows the obtainment of the highest accuracy. In this paper, we propose a hybrid approach combining the improvement of the K2 algorithm called K2 algorithm for Parents and Children search (K2PC) and the expert-driven method for learning the structure of BN. The evaluation of the experimental results, using the well-known benchmarks, proves that our K2PC algorithm has better performance in terms of correct structure detection. The real application of our model shows its efficiency in the analysis of the phosphate laundry effluents' impact on the watershed in the Gafsa area (southwestern Tunisia).
Keywords: Classification, Bayesian network; structure learning, K2 algorithm, expert knowledge, surface water analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4434317 Mining Network Data for Intrusion Detection through Naïve Bayesian with Clustering
Authors: Dewan Md. Farid, Nouria Harbi, Suman Ahmmed, Md. Zahidur Rahman, Chowdhury Mofizur Rahman
Abstract:
Network security attacks are the violation of information security policy that received much attention to the computational intelligence society in the last decades. Data mining has become a very useful technique for detecting network intrusions by extracting useful knowledge from large number of network data or logs. Naïve Bayesian classifier is one of the most popular data mining algorithm for classification, which provides an optimal way to predict the class of an unknown example. It has been tested that one set of probability derived from data is not good enough to have good classification rate. In this paper, we proposed a new learning algorithm for mining network logs to detect network intrusions through naïve Bayesian classifier, which first clusters the network logs into several groups based on similarity of logs, and then calculates the prior and conditional probabilities for each group of logs. For classifying a new log, the algorithm checks in which cluster the log belongs and then use that cluster-s probability set to classify the new log. We tested the performance of our proposed algorithm by employing KDD99 benchmark network intrusion detection dataset, and the experimental results proved that it improves detection rates as well as reduces false positives for different types of network intrusions.Keywords: Clustering, detection rate, false positive, naïveBayesian classifier, network intrusion detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 54924316 Information Security in E-Learning through Identification of Humans
Authors: Hassan Haleh, Zohreh Nasiri, Parisa Farahpour
Abstract:
During recent years, the traditional learning approaches have undergone fundamental changes due to the emergence of new technologies such as multimedia, hypermedia and telecommunication. E-learning is a modern world phenomenon that has come into existence in the information age and in a knowledgebased society. E-learning has developed significantly within a short period of time. Thus it is of a great significant to secure information, allow a confident access and prevent unauthorized accesses. Making use of individuals- physiologic or behavioral (biometric) properties is a confident method to make the information secure. Among the biometrics, fingerprint is more acceptable and most countries use it as an efficient methods of identification. This article provides a new method to compare the fingerprint comparison by pattern recognition and image processing techniques. To verify fingerprint, the shortest distance method is used together with perceptronic multilayer neural network functioning based on minutiae. This method is highly accurate in the extraction of minutiae and it accelerates comparisons due to elimination of false minutiae and is more reliable compared with methods that merely use directional images.Keywords: Fingerprint, minutiae, extraction of properties, multilayer neural network
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16024315 Distributed Relay Selection and Channel Choice in Cognitive Radio Network
Authors: Hao He, Shaoqian Li
Abstract:
In this paper, we study the cooperative communications where multiple cognitive radio (CR) transmit-receive pairs competitive maximize their own throughputs. In CR networks, the influences of primary users and the spectrum availability are usually different among CR users. Due to the existence of multiple relay nodes and the different spectrum availability, each CR transmit-receive pair should not only select the relay node but also choose the appropriate channel. For this distributed problem, we propose a game theoretic framework to formulate this problem and we apply a regret-matching learning algorithm which is leading to correlated equilibrium. We further formulate a modified regret-matching learning algorithm which is fully distributed and only use the local information of each CR transmit-receive pair. This modified algorithm is more practical and suitable for the cooperative communications in CR network. Simulation results show the algorithm convergence and the modified learning algorithm can achieve comparable performance to the original regretmatching learning algorithm.
Keywords: cognitive radio, cooperative communication, relay selection, channel choice, regret-matching learning, correlated equilibrium.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16274314 Explanatory of Relationship between Learning Motivation and Learning Performance
Authors: Chih Chin Yang
Abstract:
In this paper, the relationship between learning motivation and learning performance is explored by using exchange theory. The relationship is concluded that external performance can raise learning motivation and then increase learning performance. The internal performance should be not completely neglected and the external performance should be not attached important excessively. The parents need self-study and must be also reeducated. The existing education must be improved in raise of internal performance. The incorrect learning thinking will mislead the students, parents, and educators of next generation, when the students obtain good learning performance in the learning environment with excess stimulants. Over operation of external performance will result abnormal learning thinking and violating learning goal. Learning is not only to obtain performance. Learning quality and learning performance will be limited as without learning motivation. The best learning motivation is, the best learning performance is. The learning for reward is not good for learning performance. Strategies of promoting life-long learning are including the encouraging for learner, establishment of good interaction learning environment, and the advertisement of the merit and the importance of life-long learning, which can let the learner with the correct learning motivation.Keywords: exchange theory, learning motivation, learning performance, learning quality
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15694313 Hopfield Network as Associative Memory with Multiple Reference Points
Authors: Domingo López-Rodríguez, Enrique Mérida-Casermeiro, Juan M. Ortiz-de-Lazcano-Lobato
Abstract:
Hopfield model of associative memory is studied in this work. In particular, two main problems that it possesses: the apparition of spurious patterns in the learning phase, implying the well-known effect of storing the opposite pattern, and the problem of its reduced capacity, meaning that it is not possible to store a great amount of patterns without increasing the error probability in the retrieving phase. In this paper, a method to avoid spurious patterns is presented and studied, and an explanation of the previously mentioned effect is given. Another technique to increase the capacity of a network is proposed here, based on the idea of using several reference points when storing patterns. It is studied in depth, and an explicit formula for the capacity of the network with this technique is provided.
Keywords: Associative memory, Hopfield network, network capacity, spurious patterns.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 10644312 High Impedance Fault Detection using LVQ Neural Networks
Authors: Abhishek Bansal, G. N. Pillai
Abstract:
This paper presents a new method to detect high impedance faults in radial distribution systems. Magnitudes of third and fifth harmonic components of voltages and currents are used as a feature vector for fault discrimination. The proposed methodology uses a learning vector quantization (LVQ) neural network as a classifier for identifying high impedance arc-type faults. The network learns from the data obtained from simulation of a simple radial system under different fault and system conditions. Compared to a feed-forward neural network, a properly tuned LVQ network gives quicker response.Keywords: Fault identification, distribution networks, high impedance arc-faults, feature vector, LVQ networks.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21534311 Nonlinear Adaptive PID Control for a Semi-Batch Reactor Based On an RBF Network
Authors: Magdi M. Nabi, Ding-Li Yu
Abstract:
Control of a semi-batch polymerization reactor using an adaptive radial basis function (RBF) neural network method is investigated in this paper. A neural network inverse model is used to estimate the valve position of the reactor; this method can identify the controlled system with the RBF neural network identifier. The weights of the adaptive PID controller are timely adjusted based on the identification of the plant and self-learning capability of RBFNN. A PID controller is used in the feedback control to regulate the actual temperature by compensating the neural network inverse model output. Simulation results show that the proposed control has strong adaptability, robustness and satisfactory control performance and the nonlinear system is achieved.
Keywords: Chylla-Haase polymerization reactor, RBF neural networks, feed-forward and feedback control.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2624