Neural Network World is a bimonthly journal providing the latest developments in the field of informatics.
Czech Technical University in Prague, Faculty of Transportation Sciences
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TRAFFIC DETECTION OF TRANSMISSION OF BOTNET THREAT USING BP NEURAL NETWORK
With the gradual improvement of the telecommunication infrastructure in China, the Internet and other new technologies have been frequently used. The Internet technology also brings many network security threats while bringing convenience. The most serious threat to network security faced by the Internet is a variety of malicious network attacks on the carrier of botnets. Back propagation (BP) neural network is proposed to detect botnet threat transmission. In this study, a botnet detection model was established using BP neural network system. BP neural network classifier could identify the botnet traffic and normal traffic. Moreover a test was carried out to detect botnet traffic using BP neural network; the performance of the BP neural network classifier was evaluated by the detection rate and false alarm rate. The results showed that it had high detection rate and low false alarm rate, which indicated that the BP neural network had a good performance in detecting the traffic of botnet threat transmission.
Improving Word Meaning Representations using Wikipedia Categories
In this paper we extend Skip-Gram and Continuous Bag-of-Words models via global context information. We use Wikipedia corpus where articles are organized in a hierarchy of categories. These categories provide useful topical information about each article. We present several approaches how to enrich word meaning representation with such information. We experiment with English Wikipedia and evaluate our models on standard word similarity and word analogy datasets. Proposed models significantly outperform other word representation methods when similar size training data are used and provide similar performance compared with methods trained on much larger datasets.
The paper presents a theory of information systems based on advanced analogies with both classical and quantum physical models. In the first step the information analogies with magneto-electric circuits are introduced and the information parameters are defined under this inspiration. Well-known potential and flow values (e.g. potential and kinetic energy, voltage and electrical current, etc.) are transformed into information values, “information content” and “information flow”. In the second step the quantum information models are introduced together with values “ware information flow” and “wave information content”. By using these variables, the complex information models are described in more detail together with illustrative examples.
A Hybrid Neural Network based Prediction of Rainfall
A novel rainfall prediction method has been proposed. In the present work rainfall prediction in Southern part of West Bengal (India) has been conducted. A two-step method has been employed. Greedy forward selection algorithm is used to reduce the feature set and to find the most promising features for rainfall prediction. First, in the training phase the data is clustered by applying k-means algorithm, then for each cluster a separate Neural Network (NN) is trained. The proposed two step prediction model (Hybrid Neural Network or HNN) has been compared with MLP-FFN (multilayer perceptron feed-forward network trained with scaled conjugate gradient descent algorithm) in terms of Accuracy, Precision, Recall and F-Measure. The data for experimental purpose is collected by Dumdum meteorological station (West Bengal, India) over the period from 1989 to 1995. The experimental results have suggested a reasonable improvement over traditional methods in predicting rainfall. The proposed HNN model outperformed the compared models by achieving 84.26% accuracy without feature selection and 89.54% accuracy with feature selection.
An Approach to Automatic Diabetic Retinopathy Detection using Gaussian filtering combined with Single Layer Perceptron Classification
This paper presents an algorithm for the design of a Computer Aided Diagnosis system to detect, quantify and classify the lesions of Non-Proliferative Diabetic Retinopathy as well as dry Age related Macular Degeneration from the fundus retina images. Symptoms of Non Proliferative Diabetic Retinopathy in images consist of bright lesions like Hard Exudates, Cotton Wool Spots and dark lesions like Microaneurysms, Hemorrhages. Dry Age related Macular Degeneration is manifested as a bright lesion called drusen. The proposed system consists of two parts, namely, an image processing procedure where preprocessed gray scale images are segmented using a combination of Gaussian filtering and Multi-level thresholding. This is followed by classification of the different lesions in Non Proliferative Diabetic Retinopathy and Age related Macular Degeneration using the Single Layer Perceptron. The lesions obtained from abnormal retina fundus images of Diabetic Retinopathy and Age related Macular Degeneration cases, are primarily separated into bright and dark lesions. Single Layer Perceptron is a neural network algorithm which achieves high computational speed and is used for the classification. Symptoms appearing in retinal abnormalities considered have well defined features which make them amenable to simple mapping to the perceptron nodes. Hence, Single Layer Perceptron is thus well suited to the problem domain. From the comparative performance analysis of the classification with Naïve Bayes classifier and Support Vector Machine, Single Layer Perceptron results in higher performance score of 94\% sensitivity, 96.2\% specificity and 95.4984\% accuracy for bright lesion classification and 94.65\% sensitivity, 89.3\% specificity and 94.8936\% accuracy for dark lesion classification.
In this work, a Nadaraya-Watson kerneled learning system which owns general regression neural network topology is adapted to Q learning method to evaluate a quick and ecient action selection policy for reinforcement learning problems. By means of the proposed method Q value function is generalized and learning speed of Q agent is accelerated. The training data of the developed neural network are obtained by a standard Q learning agent on closed loop simulation system. The eciency of the proposed method is tested on populer reinforcement learning benchmarks and its performance is compared with other popular regression methods and Q-learning utilized methods.
This article provides a simple and practical tutorial on how to use floppy logic. The floppy logic is a method suitable for systems control and description. It preserves the simplicity of the fuzzy logic and the accuracy of the probability theory. The floppy logic allows to work consistently and simultaneously with data in the form of exact numbers, probability distributions and fuzzy sets.
Optimal cluster based key management system using signcryption algorithm for wireless sensor networks
Key management system maintains the confident of secret information from unauthorized users and verifying the integrity of exchanged messages and authenticity. But recent advances in electronics and computer technologies create the complexity of key management in wireless sensor networks (WSN). Additionally, the traditional key management systems are not up to the mark due to limited resources like memory, and energy constraints. In this paper, we propose an optimal cluster based key management system (OC-KMS) for WSNs. The proposed system consist of two contributions, in first, we perform the energy efficient clustering using modified animal Diaspora (MAD) optimization algorithm and cluster head (CH) selection using JAYA trust model. In second contribution, we propose the certificate less signcryption algorithm, which generates and distributes the public and private keys for each node in sensor networks. The proposed system resists various network layer attacks without affecting the network performance. The simulation result describes that the proposed system perform very efficient than existing in terms of both performance and security wise.
PATIENT-ADAPTED AND INTER-PATIENT ECG CLASSIFICATION USING NEURAL NETWORK AND GRADIENT BOOSTING
Heart disease diagnosis is important non-invasive technique. Therefore, there exists effort to increase accuracy of arrhythmia classification based on ECG signals. In this work, we present a novel approach of heart arrhythmia detection. The model consists of two parts. First part extracts important features from raw ECG signal using Auto-Encoder Neural Network. Extracted features obtained by Auto-Encoder represent an input for the second part of the model, the Gradient Boosting and Feedforward Neural Network classifiers. For comparison purpose, we evaulated our approach with using MIT-BIH ECG database and also we fellow recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for ECG class labeling. We divided our experiment into two scenarios. First scenario represents classification task for patient-adapted paradigm and second one was dedicated to inter-patient paradigm. The measured results we compared with state-of-the-art methods and it shows that our method outperform state-of-the art methods in the Ventricular Ectopic (VEB) class for both paradigms and Supraventricular Ectopic (SVEB) class in inter-patient paradigm.
Information model of resonance phenomena in brain neural networks
The paper presents an information model for representation of brain linear and nonlinear resonance phenomena based on information nullors. In the brain functions the rhythms and quasi periodicity of processes in neural networks play the outstanding role. It is why adaptive resonance theory (ART) including resonant effects is for long time studied by many authors. The periodicity in the transfers of signals between the long-term memory (LTM) and short-term memory (STM) creates possibility of resonance system structure. LTM with information content representing expectations and STM covering sensory information in resonance process offer an effective learning. Nonlinear adaptive resonance creates condition for a new knowledge, or inventory observation. In the paper this feature is newly modelled by information gyrator that best suits for these linear and non-linear phenomena.
Automatic Classification of Agricultural Grains: Comparison of Neural Networks
In this study, applications of well-known neural networks such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) for wheat grain classification into three species are comparatively presented. The species of wheat grains which are Kama (#70), Rosa (#70) and Canadian (#70) are designated as outputs of neural network models. The classification is carried out through data of wheat grains (#210) acquired using X-ray technique. The data set includes seven grain’s geometric parameters: Area, perimeter, compactness, length, width, asymmetry coefficient and groove length. The neural networks input with the geometric parameters are trained through 189 wheat grain data and their accuracies are tested via 21 data. The performance of neural network models is compared to each other with regard to their accuracy, efficiency and convenience. The ANN, ANFIS and SVM models numerically calculate the outputs with mean absolute error (MAE) of 0.014, 0.018 and 0.135, and classify the grains with accuracy of 100%, 100% and 95.23%, respectively. Furthermore, data of 210 grains is synthetically increased to 3210 in order to investigate the proposed models under big data. It is seen that the models are more successful if the size of data is increased. These results point out that the neural networks can be successfully applied to classification of agricultural grains whether they are properly modelled and trained.
Fuzzy Multi-Objective Optimization Algorithms for Solving Multi-Mode Automated Guided Vehicles by Considering Machine Break Time and Artificial Neural Network
In this paper, a novel model is presented for machines and automated guided vehicles’ simultaneous scheduling, which addresses an extension of the blocking job shop scheduling problem. An artificial neural network approach is used to estimate machine’s breakdown indexes. Since the model is strictly NP-hard and because objectives contradict each other, two developed meta-heuristic algorithms called “fuzzy multi-objective invasive weeds optimization algorithm” and “fuzzy multi-objective cuckoo search algorithm” with a new chromosome structure which guarantees the feasibility of solutions are developed to solve the proposed problem. Since there is no benchmark available on literature, three other metaheuristic algorithms are developed with a similar solution structure to validate performance of the proposed algorithms. Computational results showed that developed fuzzy multi-objective invasive weeds optimization algorithm had the best performance in terms of solving problems compared to four other algorithms.
Artificial bee colony algorithm optimized error minimized extreme learning machine and its application in short-term wind speed prediction
Accurate short-term wind speed prediction has important theoretical significance and practical application for wind power industry. In order to improve the prediction accuracy of short-term wind speed, a short-term wind speed prediction model based on artificial bee colony algorithm optimized error minimized extreme learning machine model is proposed. The extreme learning machine have the advantages of fast learning speed and strong generalization ability. As a kind of improved extreme learning machine, incremental extreme learning machine has been applied in many applications because it does not have the optimal hidden layer nodes and the over fitting problem. But many useless neurons of incremental extreme learning machine has little influences on the final output, at the same time, reduce the efficiency of the algorithm. The optimal parameters of the hidden layer nodes will make network output error of incremental extreme learning machine decrease with fast speed. If the optimal parameters are obtained, this means that it can greatly improve the learning speed of algorithm. It is very necessary to optimize the parameters of the hidden layer nodes. As an intelligent optimization algorithm, artificial bee colony algorithm has highly competitive, which is very suitable for the optimization of the parameters of the hidden layer nodes. Based on the error minimized extreme learning machine, artificial bee colony algorithm is introduced to optimize the parameters of the hidden layer nodes, decrease the number of useless neurons, reduce training and prediction error, achieve the goal of reducing the network complexity and improve the efficiency of the algorithm. The error minimized extreme learning machine prediction model is constructed with the obtained optimal parameters. The stability and convergence property of artificial bee colony algorithm optimized error minimized extreme learning machine model are proved. The practical short-term wind speed time series is used as the research object and verify the validity of the prediction model. Multi step prediction simulation of short-term wind speed is carried out in simulations. Compared with other prediction models, simulation results show that the prediction model proposed in this paper reduce the training time of the prediction model, decrease the number of hidden layer nodes. The prediction model has higher prediction accuracy and reliability performance, meanwhile improve the performance indicators.
Optimized Fuzzy min-max neural network: an efficient approach for supervised outlier detection
Fuzzy min-max neural network (FMN) proposed by Simpson [18] is a well-known supervised fuzzy-neural classifier that has been successfully used by many researchers for pattern recognition. However, the FMN represents the learned knowledge with exhaustive details in ‘fine-grained’ manner that reduce its performance for pattern recognition in terms of recall time per pattern. In this paper, we adapt the basic architecture of FMN to represent the learned knowledge in a compact way, i.e., in a ‘coarse-grained’ manner which is closed to human thinking. Working of the proposed method, that is fuzzy min-max neural network with knowledge compaction (FMN_KC), is described using Fisher Iris dataset. The potential of using FMN_KC for supervised outlier detection is demonstrated using a time-series disk defect dataset published by NASA and KDD cup99 dataset available in UCI repository. The proposed method has achieved around 50% gain in the recall time compared to the original FMN and the recognition rate is also comparable. We strongly recommend using the proposed architecture called FMN_KC for supervised outlier detection in real time applications where recall time per pattern is one of the key parameters.
Comparative Evaluation of Fuzzy Inference System, Support Vector Machine and Multilayer Feed-Forward Neural Network in Making Lane Changing Decisions
This paper compares Fuzzy Inference System (FIS), Support Vector Machine (SVM) and MultiLayer Feed-forward neural network (MLF) in modeling a driver’s decision when making discretionary lane changing move on freeways. The FIS model has been developed and published in an earlier work by the authors, whereas the SVM and MLF models are newly developed in this research. The FIS, SVM and MLF models use the same four inputs: the gap between the subject vehicle and the leading vehicle in the original lane, the gap between the subject vehicle and the leading vehicle in the destination lane, the gap between the subject vehicle and the trailing vehicle in the destination lane, and the distance between the preceding and trailing vehicles in the destination lane. The models give a binary decision of “no, stay in the same lane” or “yes, move to the destination lane now”. These models were trained and then tested with the Next Generation SIMulation (NGSIM) vehicle trajectory data. The results have shown that the FIS has the highest accuracies in making correct lane changing decisions. It recommends “yes, move to the destination lane now” with 82.2% accuracy, and “no, stay in the same lane” with 99.5% accuracy. The SVM model also outperformed the traditional gap acceptance model which was used as the benchmark. However, the MLF model was not as accurate as the gap acceptance model.
Improved artificial neural network based on hidden layer nodes number adaptive selection and genetic algorithm optimizing parameters
The neural network model based on back-propagation (BP) algorithm is a widely used prediction model. However, the nodes number of the first hidden layer, the learning rate and momentum factor are usually determined manually, which affect the network forecast accuracy. Therefore, in this paper, to improve the forecast accuracy, firstly, the nodes number of the first hidden layer is selected adaptively based on minimizing mean square error (MSE). Secondly, improved genetic algorithm (GA) is proposed to train the learning rate and momentum factor dynamically. Thirdly, we construct a new neural network model based on the adaptively selected nodes number of the first hidden layer, the dynamically selected learning rate and momentum factor, which is called HN-GA-BP neural network model. Finally, the proposed neural network model is used to forecast the carbon dioxide levels in China for fifty years. Experimental results demonstrate the effectiveness of the proposed HN-GA-BP neural network model.
ARTIFICIAL INTELLIGENCE IN PROBLEMS OF ENERGY PERFORMANCE OPTIMIZATION OF SMART CITIES
The future of energy lies in energy self-sufficiency, renewable electricity generation, and digital technologies. The main focus is on optimizing the generation and consumption of electric power. Decentralized energy, optimization and regulation of generation and consumption of electricity to the level of the smallest producers and consumers - this is a power industry of the future. This is closely related to the solution of optimization of organisation of the electric power sources mainly from renewable energy sources (RES) in the system of their composition in the so-called RES microgrid as a decentralized power industry. A computer program will be designed to address the planning of local RES source organisation and their supply with electric power from the RES microgrid of a fictitious smart urban area. The purpose of the solution is stable energy balance in order to minimize the total cost of electricity generation, which is, in this case, determined by the prediction of its consumption in a specified period with sampling every hour. A program and a breakdown of organisation of electric power sources and their outputs covering the predicted consumption will be created. A prerequisite for the optimization or more precisely multicriterial optimization is development of special-purpose (cost, critical) function with the simultaneous definition of an optimality criterion such as penalty and formulation of restrictive conditions. Through the experiment, the conclusion of an efficient solution to the problem of optimization of electric power source organisation will be proven. The proposed optimization problem should be solved by a stochastic algorithm – simulated annealing, which is the most efficient algorithm for solution of our problem optimization. At the end, the procedures of the optimization problem solution for the special-purpose function of the RES microgrid energy system when supplying smart cities with electric power will be evaluated. The recorded annual history of electric power consumption can be sorted by day and by hour, and, then, we can see it as multidimensional data. Identification of so-called typical daily consumption patterns can be compared to a cluster analysis of the multidimensional data, where the type day charts are formed by prototypes of identified clusters correlated with a certain season. The modelled yearly history of energy consumption and the use of cluster analysis, including the use of so-called self-organizing neural network and, through the Kohenen map, the required type day charts of the relevant period have been obtained. In our experiment published hereto, we will come out of the historical electric power consumption data for working day – Wednesday. We will not address the issue of type day charts in this article; this is another very important independent scientific contribution.
CSRNCVA: a Model of Cross-media Semantic Retrieval based on Neural Computing of Visual and Auditory Sensations
Cross-media semantic retrieval (CSR) and cross-modal semantic mapping are key problems of the multimedia search engine. The cognitive function and neural structure for visual and auditory information process are an important reference for the study of brain-inspired CSR. In this paper, we analyze the hierarchy, the functionality and the structure of visual and auditory in the brain. Considering an idea from deep belief network and hierarchical temporal memory, we presented a brain-inspired intelligent model, called cross-media semantic retrieval based on neural computing of visual and auditory sensation (CSRNCVA). Algorithms based on CSRNCVA were developed. It employs belief propagation algorithms of probabilistic graphical model and hierarchical learning. The experiments show that our model and algorithms can be effectively applied to the CSR. This work provides an important significance for brain-inspired cross-media intelligence framework.
A Method of Fine-grained Text Sentiment Analysis Based on Machine Learning
Text sentiment analysis is an important part of social network information mining. It is also the theoretical foundation and basis of personalized recommendation, circle of interest classification and public opinion analysis. In view of the existing algorithms for feature extraction and weight calculation, we find that they fail to fully take into account the influence of emotion words. Therefore, this paper proposed a fine-grained short text sentiment analysis method based on Naive Bayes. To improve the calculation method of feature selection and weighting and proposed a more suitable sentiment analysis algorithm for features extraction named N-CHI and weight calculation named W-TF-IDF, increasing the proportion and weight of sentiment words in the feature words Through experimental analysis and comparison, the classification accuracy of this method is obviously improved compared with other methods.
HOMEOSTATIC LEARNING RULE FOR ARTIFICIAL NEURAL NETWORKS
This article presents an improvement of learning algorithm for articial neural network that makes the learning more similar to biological neuron, but still simple enough to be easily programmed. The idea is based on autonomous articial neurons that are working together and at same time competing for resources; every neuron is trying to be better then the others, but also needs the feed back from other neurons. The proposed articial neuron has similar forward signal processing as the standard articial perceptron, the main dierence is the learning phase. The learning is based on observing the weights of other neurons, but only in biologically plausible way, no back propagation of error or 'teacher' is allowed. The neuron is sending the signal in forward direction into the higher layer, the information about its function is being propagated in the opposite direction. This information does not have the form of energy, it is the observation of how is the neuron's output accepted by others. The neurons are trying to nd such setting of their internal parameters that are optimal for the whole network. For this algorithm, it is necessary that the neurons are organized in layers. The tests proved the viability of this concept, the learning is slower; but has other advantages, such as resistance against catastrophic interference or higher generalization.
Outlier detection in self-organizing maps and their quality estimation
In the paper, an algorithm that allows to detect and reject outliers in a self-organizing map (SOM) has been proposed. SOM is used for data clustering as well as dimensionality reduction and the results obtained are presented in a special graphical form. To detect outliers in SOM, a genetic algorithm-based Travelling Salesman approach has been applied. After outliers are detected and removed, the SOM quality has to be estimated. A measure has been proposed to evaluate the coincidence of data classes and clusters obtained in SOM. A larger value of the measure means that the distance between centers of different classes in SOM is longer and the clusters corresponding to the data classes separate better. With a view to illustrate the proposed algorithm, two datasets (numerical and textual) are used in this investigation.
FPGA Implementation of ANN Training using Levenberg and Marquardt Algorithm
ANN training using gradient based LM algorithm has been implemented on FPGA for the solution of dynamic system identification problems within the scope of study. In the implementation, IEEE 754 floating-point number format has been used because of dynamism and sensitivity that it has provided. Mathematical approaches have been preferred in order to implement the activation function, which is the most critical phase of the study. ANN is tested by using input-output sample sets, which are shown or not shown to the network in training phase, and success rates are given for every sample set. The results obtained show that it is possible to iplementation of ANN training as FPGA based by using LM algorithm and that the ANN make a good generalization.
LARGE EARTHQUAKE MAGNITUDE PREDICTION IN TAIWAN BASED ON DEEP LEARNING NEURAL NETWORK
In this paper, a deep learning-based method for earthquake prediction is proposed. Large-magnitude earthquakes and tsunamis triggered by earthquakes can kill thousands of people and cause millions of dollars worth of economic losses. The accurate prediction of large-magnitude earthquakes is a worldwide problem. In recent years, deep learning technology that can automatically extract features from mass data has been applied in image recognition, natural language process- ing, object recognition, etc., with great success. We attempt to apply deep learning technology to earthquake prediction. We propose a deep learning method for continuous earthquake prediction using historical seismic events. First, we project the historical seismic events onto a topographic map. Taking Taiwan as an example, we generate the images of the dataset for deep learning and mark a label "1" or "0" depending on whether in the upcoming 30 days a greater than M6 earthquake will occur. Second, we train our deep leaning network model, using the images of the dataset. Finally, we make earthquake predictions using the trained network model. The result shows that we can get the best result when we predict earthquakes in the upcoming 30 days using data from the past 120 days. The best R score is 0.289. Although the R score is not high enough, using the past 120 days' historic seismic event to predict the upcoming 30 days' biggest earthquake magnitude can be regarded as the pattern of Taiwan earthquake because the R score is rather good compared to other datasets. The proposed method performs well without manually designing feature vectors, as in the traditional neural network method. This method can be applied to earthquake prediction in other seismic zones.
Genetic programming with either stochastic or deterministic constant evaluation
Constant evaluation is a key problem for symbolic regression, one solved by means of genetic programming. For constant evaluation, other evolutionary methods are often used. Typical examples are some variants of genetic programming or evolutionary systems, all of which are stochastic. The article compares these methods with a deterministic approach using exponentiated gradient descent. All the methods were tested on single sample function to maintain the same conditions and results are presented in graphs. Finally, three different tasks (ten times each) are compared to check the reliability of the methods tested in the article.
Sparse Representation Learning of Data by Autoencoders with L_1/2 Regularization
Autoencoder networks have been demonstrated to be efficient for unsupervised learning of representation of images, documents and time series. Sparse representation can improve the interpretability of the input data and the generalization of a model by eliminating redundant features and extracting the latent structure of data. In this paper, we use L_1/2 regularization method to enforce sparsity on the hidden representation of an autoencoder for achieving sparse representation of data. The performance of our approach in terms of unsupervised feature learning and supervised classification is assessed on the MNIST digit data set, the ORL face database and the Reuters-21578 text corpus. The results demonstrate that the proposed autoencoder can produce sparser representation and better reconstruction performance than the Sparse Autoencoder and the L_1 regularization Autoencoder. The new representation is also illustrated to be useful for a deep network to improve the classification performance.
Wind Energy Potential Assessment Based on Wind Direction Modelling and Machine Learning
Precise wind energy potential assessment is vital for wind energy generation and planning and development of new wind power plants. This work proposes and evaluates a novel two-stage method for location-specific wind energy potential assessment. It combines accurate statistical modelling of annual wind direction distribution in a given location with supervised machine learning of efficient estimators that can approximate energy efficiency coefficients from the parameters of optimized statistical wind direction models. The statistical models are optimized using differential evolution and energy efficiency is approximated by evolutionary fuzzy rules.
PRINCIPLES OF MOTOR RECOVERY IN POST-STROKE PATIENTS USING HAND EXOSKELETON CONTROLLED BY THE BRAIN-COMPUTER INTERFACE BASED ON MOTOR IMAGERY
Motor recovery in post{stroke and post{traumatic patients using exoskeleton controlled by the brain-computer interface (BCI) is a new and promising rehabilitation procedure. Its development is a multidisciplinary research, which requires the teamwork of experts in neurology, neurophys- iology, physics, mathematics, biomechanics and robotics. Some aspects of all these elds of study concerning the development of this rehabilita-tion procedure are described in the paper. The description includes the principles and physiological prerequisites of BCI based on motor imagery, biologically adequate principles of exoskeleton design and control and the results of clinical application.
Using the LISp-Miner System for Credit Risk Assessment
Credit risk assessment, credit scoring or loan applications approval are one of the typical tasks that can be solved using machine learning or data mining techniques. From this perspective, loan applications evaluation is a classification task, in which the final decision can be either a crisp yes/no decision about the loan or a numeric score expressing the financial standing of the applicant. The knowledge to be used is inferred from data about past decisions. These data usually consists off socio-demographic characteristics, economic characteristics (e.g. income, deposit), the characteristics of the loan, and, the loan approval decision. A number of machine learning algorithms can be used for this purpose. In this paper we show how this task can be solved using the LISp-Miner system, a tool that is under development at the University of Economics, Prague. LISp-Miner is primary oriented on mining for various types of association rules, but unlike "classical" association rules proposed by Agrawal, LISp-Miner introduces a greater variety of different types of relations between left-hand and right-hand side of a rule. Beside this, two other procedures that can be used for classification task are implemented in LISp-Miner as well. We describe the 4FT-Miner and KEX procedures and show how they can be used to analyze data about loan applications. We also compare the results obtained using the presented algorithms with results from standard rule learning methods.
CHANGEOVER FROM DECISION TREE APPROACH TO FUZZY LOGIC APPROACH WITHIN HIGHWAY MANAGEMENT
This paper deals with the changeover from decision tree (bivalent logic) approach to fuzzy logic approach to highway traffic control, particularly to variable speed limits display. The usage of existing knowledge from decision tree control is one of the most suitable methods for identication of new fuzzy model. However, such method introduces several difficulties. These difficulties are described and possible measures are proposed. Several fuzzy logic algorithms were developed and tested by a microsimulation model. The results are presented and the finest algorithm is recommended for testing on the Prague City Ring Road in real conditions. This paper provides a guidance for researchers and practitioners dealing with similar problem formulation.
DG SOLVER FOR THE SIMULATION OF SIMPLIFIED ELASTIC WAVES IN TWO-DIMENSIONAL PIECEWISE HOMOGENEOUS MEDIA
The theory of elasticity is a very important discipline which has a lot of applications in science and engineering. In this paper we are interested in elastic materials with different properties between interfaces implicated the discontinu- ous coefficients in the governing elasticity equations. The main aim is to develop a practical numerical scheme for modeling the behaviour of a simplified piecewise homogeneous medium subjected to an external action in 2D domains. Therefore, the discontinuous Galerkin method is used for the simulation of elastic waves in such elastic materials. The special attention is also paid to treatment of boundary and interface conditions. For the treatment of the time dependency the implicit Euler method is employed. Moreover, the limiting procedure is incorporated in the resulting numerical scheme in order to overcome nonphysical spurious overshoots and undershoots in the vicinity of discontinuities in discrete solutions. Finally, we present computational results for two-component material, representing a planar elastic body subjected to a mechanical hit or mechanical loading.