Search results for: Classification and regression tree (CART)
1880 The Knapsack Sharing Problem: A Tree Search Exact Algorithm
Authors: Mhand Hifi, Hedi Mhalla
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In this paper, we study the knapsack sharing problem, a variant of the well-known NP-Hard single knapsack problem. We investigate the use of a tree search for optimally solving the problem. The used method combines two complementary phases: a reduction interval search phase and a branch and bound procedure one. First, the reduction phase applies a polynomial reduction strategy; that is used for decomposing the problem into a series of knapsack problems. Second, the tree search procedure is applied in order to attain a set of optimal capacities characterizing the knapsack problems. Finally, the performance of the proposed optimal algorithm is evaluated on a set of instances of the literature and its runtime is compared to the best exact algorithm of the literature.
Keywords: Branch and bound, combinatorial optimization, knap¬sack, knapsack sharing, heuristics, interval reduction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15491879 A Cognitive Model for Frequency Signal Classification
Authors: Rui Antunes, Fernando V. Coito
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This article presents the development of a neural network cognitive model for the classification and detection of different frequency signals. The basic structure of the implemented neural network was inspired on the perception process that humans generally make in order to visually distinguish between high and low frequency signals. It is based on the dynamic neural network concept, with delays. A special two-layer feedforward neural net structure was successfully implemented, trained and validated, to achieve minimum target error. Training confirmed that this neural net structure descents and converges to a human perception classification solution, even when far away from the target.Keywords: Neural Networks, Signal Classification, Adaptative Filters, Cognitive Neuroscience
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16551878 The Relative Efficiency of Parameter Estimation in Linear Weighted Regression
Authors: Baoguang Tian, Nan Chen
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A new relative efficiency in linear model in reference is instructed into the linear weighted regression, and its upper and lower bound are proposed. In the linear weighted regression model, for the best linear unbiased estimation of mean matrix respect to the least-squares estimation, two new relative efficiencies are given, and their upper and lower bounds are also studied.
Keywords: Linear weighted regression, Relative efficiency, Mean matrix, Trace.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24581877 Statistical Wavelet Features, PCA, and SVM Based Approach for EEG Signals Classification
Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh
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The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the supportvectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.
Keywords: Discrete Wavelet Transform, Electroencephalogram, Pattern Recognition, Principal Component Analysis, Support Vector Machine.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 31021876 Applications of Genetic Programming in Data Mining
Authors: Saleh Mesbah Elkaffas, Ahmed A. Toony
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This paper details the application of a genetic programming framework for induction of useful classification rules from a database of income statements, balance sheets, and cash flow statements for North American public companies. Potentially interesting classification rules are discovered. Anomalies in the discovery process merit further investigation of the application of genetic programming to the dataset for the problem domain.Keywords: Genetic programming, data mining classification rule.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15341875 Fusion of Colour and Depth Information to Enhance Wound Tissue Classification
Authors: Darren Thompson, Philip Morrow, Bryan Scotney, John Winder
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Patients with diabetes are susceptible to chronic foot wounds which may be difficult to manage and slow to heal. Diagnosis and treatment currently rely on the subjective judgement of experienced professionals. An objective method of tissue assessment is required. In this paper, a data fusion approach was taken to wound tissue classification. The supervised Maximum Likelihood and unsupervised Multi-Modal Expectation Maximisation algorithms were used to classify tissues within simulated wound models by weighting the contributions of both colour and 3D depth information. It was found that, at low weightings, depth information could show significant improvements in classification accuracy when compared to classification by colour alone, particularly when using the maximum likelihood method. However, larger weightings were found to have an entirely negative effect on accuracy.Keywords: Classification, data fusion, diabetic foot, stereophotogrammetry, tissue colour.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17011874 Ranking Genes from DNA Microarray Data of Cervical Cancer by a local Tree Comparison
Authors: Frank Emmert-Streib, Matthias Dehmer, Jing Liu, Max Muhlhauser
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The major objective of this paper is to introduce a new method to select genes from DNA microarray data. As criterion to select genes we suggest to measure the local changes in the correlation graph of each gene and to select those genes whose local changes are largest. More precisely, we calculate the correlation networks from DNA microarray data of cervical cancer whereas each network represents a tissue of a certain tumor stage and each node in the network represents a gene. From these networks we extract one tree for each gene by a local decomposition of the correlation network. The interpretation of a tree is that it represents the n-nearest neighbor genes on the n-th level of a tree, measured by the Dijkstra distance, and, hence, gives the local embedding of a gene within the correlation network. For the obtained trees we measure the pairwise similarity between trees rooted by the same gene from normal to cancerous tissues. This evaluates the modification of the tree topology due to tumor progression. Finally, we rank the obtained similarity values from all tissue comparisons and select the top ranked genes. For these genes the local neighborhood in the correlation networks changes most between normal and cancerous tissues. As a result we find that the top ranked genes are candidates suspected to be involved in tumor growth. This indicates that our method captures essential information from the underlying DNA microarray data of cervical cancer.
Keywords: Graph similarity, generalized trees, graph alignment, DNA microarray data, cervical cancer.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17461873 Effect of Personality Traits on Classification of Political Orientation
Authors: Vesile Evrim, Aliyu Awwal
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Today, there is a large number of political transcripts available on the Web to be mined and used for statistical analysis, and product recommendations. As the online political resources are used for various purposes, automatically determining the political orientation on these transcripts becomes crucial. The methodologies used by machine learning algorithms to do an automatic classification are based on different features that are classified under categories such as Linguistic, Personality etc. Considering the ideological differences between Liberals and Conservatives, in this paper, the effect of Personality traits on political orientation classification is studied. The experiments in this study were based on the correlation between LIWC features and the BIG Five Personality traits. Several experiments were conducted using Convote U.S. Congressional- Speech dataset with seven benchmark classification algorithms. The different methodologies were applied on several LIWC feature sets that constituted by 8 to 64 varying number of features that are correlated to five personality traits. As results of experiments, Neuroticism trait was obtained to be the most differentiating personality trait for classification of political orientation. At the same time, it was observed that the personality trait based classification methodology gives better and comparable results with the related work.Keywords: Politics, personality traits, LIWC, machine learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21501872 Processing and Economic Analysis of Rain Tree (Samanea saman) Pods for Village Level Hydrous Bioethanol Production
Authors: Dharell B. Siano, Wendy C. Mateo, Victorino T. Taylan, Francisco D. Cuaresma
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Biofuel is one of the renewable energy sources adapted by the Philippine government in order to lessen the dependency on foreign fuel and to reduce carbon dioxide emissions. Rain tree pods were seen to be a promising source of bioethanol since it contains significant amount of fermentable sugars. The study was conducted to establish the complete procedure in processing rain tree pods for village level hydrous bioethanol production. Production processes were done for village level hydrous bioethanol production from collection, drying, storage, shredding, dilution, extraction, fermentation, and distillation. The feedstock was sundried, and moisture content was determined at a range of 20% to 26% prior to storage. Dilution ratio was 1:1.25 (1 kg of pods = 1.25 L of water) and after extraction process yielded a sugar concentration of 22 0Bx to 24 0Bx. The dilution period was three hours. After three hours of diluting the samples, the juice was extracted using extractor with a capacity of 64.10 L/hour. 150 L of rain tree pods juice was extracted and subjected to fermentation process using a village level anaerobic bioreactor. Fermentation with yeast (Saccharomyces cerevisiae) can fasten up the process, thus producing more ethanol at a shorter period of time; however, without yeast fermentation, it also produces ethanol at lower volume with slower fermentation process. Distillation of 150 L of fermented broth was done for six hours at 85 °C to 95 °C temperature (feedstock) and 74 °C to 95 °C temperature of the column head (vapor state of ethanol). The highest volume of ethanol recovered was established at with yeast fermentation at five-day duration with a value of 14.89 L and lowest actual ethanol content was found at without yeast fermentation at three-day duration having a value of 11.63 L. In general, the results suggested that rain tree pods had a very good potential as feedstock for bioethanol production. Fermentation of rain tree pods juice can be done with yeast and without yeast.
Keywords: Fermentation, hydrous bioethanol, rain tree pods, village level.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15491871 Lipschitz Classifiers Ensembles: Usage for Classification of Target Events in C-OTDR Monitoring Systems
Authors: Andrey V. Timofeev
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This paper introduces an original method for guaranteed estimation of the accuracy for an ensemble of Lipschitz classifiers. The solution was obtained as a finite closed set of alternative hypotheses, which contains an object of classification with probability of not less than the specified value. Thus, the classification is represented by a set of hypothetical classes. In this case, the smaller the cardinality of the discrete set of hypothetical classes is, the higher is the classification accuracy. Experiments have shown that if cardinality of the classifiers ensemble is increased then the cardinality of this set of hypothetical classes is reduced. The problem of the guaranteed estimation of the accuracy for an ensemble of Lipschitz classifiers is relevant in multichannel classification of target events in C-OTDR monitoring systems. Results of suggested approach practical usage to accuracy control in C-OTDR monitoring systems are present.
Keywords: Lipschitz classifiers, confidence set, C-OTDR monitoring, classifiers accuracy, classifiers ensemble.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19391870 Automatic Classification of Periodic Heart Sounds Using Convolutional Neural Network
Authors: Jia Xin Low, Keng Wah Choo
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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 11351869 Exons and Introns Classification in Human and Other Organisms
Authors: Benjamin Y. M. Kwan, Jennifer Y. Y. Kwan, Hon Keung Kwan
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In the paper, the relative performances on spectral classification of short exon and intron sequences of the human and eleven model organisms is studied. In the simulations, all combinations of sixteen one-sequence numerical representations, four threshold values, and four window lengths are considered. Sequences of 150-base length are chosen and for each organism, a total of 16,000 sequences are used for training and testing. Results indicate that an appropriate combination of one-sequence numerical representation, threshold value, and window length is essential for arriving at top spectral classification results. For fixed-length sequences, the precisions on exon and intron classification obtained for different organisms are not the same because of their genomic differences. In general, precision increases as sequence length increases.Keywords: Exons and introns classification, Human genome, Model organism genome, Spectral analysis
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20461868 Musical Instrument Classification Using Embedded Hidden Markov Models
Authors: Ehsan Amid, Sina Rezaei Aghdam
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In this paper, a novel method for recognition of musical instruments in a polyphonic music is presented by using an embedded hidden Markov model (EHMM). EHMM is a doubly embedded HMM structure where each state of the external HMM is an independent HMM. The classification is accomplished for two different internal HMM structures where GMMs are used as likelihood estimators for the internal HMMs. The results are compared to those achieved by an artificial neural network with two hidden layers. Appropriate classification accuracies were achieved both for solo instrument performance and instrument combinations which demonstrates that the new approach outperforms the similar classification methods by means of the dynamic of the signal.Keywords: hidden Markov model (HMM), embedded hidden Markov models (EHMM), MFCC, musical instrument.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18771867 A Numerical Model for Simulation of Blood Flow in Vascular Networks
Authors: Houman Tamaddon, Mehrdad Behnia, Masud Behnia
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An accurate study of blood flow is associated with an accurate vascular pattern and geometrical properties of the organ of interest. Due to the complexity of vascular networks and poor accessibility in vivo, it is challenging to reconstruct the entire vasculature of any organ experimentally. The objective of this study is to introduce an innovative approach for the reconstruction of a full vascular tree from available morphometric data. Our method consists of implementing morphometric data on those parts of the vascular tree that are smaller than the resolution of medical imaging methods. This technique reconstructs the entire arterial tree down to the capillaries. Vessels greater than 2 mm are obtained from direct volume and surface analysis using contrast enhanced computed tomography (CT). Vessels smaller than 2mm are reconstructed from available morphometric and distensibility data and rearranged by applying Murray’s Laws. Implementation of morphometric data to reconstruct the branching pattern and applying Murray’s Laws to every vessel bifurcation simultaneously, lead to an accurate vascular tree reconstruction. The reconstruction algorithm generates full arterial tree topography down to the first capillary bifurcation. Geometry of each order of the vascular tree is generated separately to minimize the construction and simulation time. The node-to-node connectivity along with the diameter and length of every vessel segment is established and order numbers, according to the diameter-defined Strahler system, are assigned. During the simulation, we used the averaged flow rate for each order to predict the pressure drop and once the pressure drop is predicted, the flow rate is corrected to match the computed pressure drop for each vessel. The final results for 3 cardiac cycles is presented and compared to the clinical data.
Keywords: Blood flow, Morphometric data, Vascular tree, Strahler ordering system.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20891866 Hybrid Reliability-Similarity-Based Approach for Supervised Machine Learning
Authors: Walid Cherif
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Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.
Keywords: Data mining, knowledge discovery, machine learning, similarity measurement, supervised classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15181865 An SVM based Classification Method for Cancer Data using Minimum Microarray Gene Expressions
Authors: R. Mallika, V. Saravanan
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This paper gives a novel method for improving classification performance for cancer classification with very few microarray Gene expression data. The method employs classification with individual gene ranking and gene subset ranking. For selection and classification, the proposed method uses the same classifier. The method is applied to three publicly available cancer gene expression datasets from Lymphoma, Liver and Leukaemia datasets. Three different classifiers namely Support vector machines-one against all (SVM-OAA), K nearest neighbour (KNN) and Linear Discriminant analysis (LDA) were tested and the results indicate the improvement in performance of SVM-OAA classifier with satisfactory results on all the three datasets when compared with the other two classifiers.Keywords: Support vector machines-one against all, cancerclassification, Linear Discriminant analysis, K nearest neighbour, microarray gene expression, gene pair ranking.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 25541864 Optimizing Mobile Agents Migration Based on Decision Tree Learning
Authors: Yasser k. Ali, Hesham N. Elmahdy, Sanaa El Olla Hanfy Ahmed
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Mobile agents are a powerful approach to develop distributed systems since they migrate to hosts on which they have the resources to execute individual tasks. In a dynamic environment like a peer-to-peer network, Agents have to be generated frequently and dispatched to the network. Thus they will certainly consume a certain amount of bandwidth of each link in the network if there are too many agents migration through one or several links at the same time, they will introduce too much transferring overhead to the links eventually, these links will be busy and indirectly block the network traffic, therefore, there is a need of developing routing algorithms that consider about traffic load. In this paper we seek to create cooperation between a probabilistic manner according to the quality measure of the network traffic situation and the agent's migration decision making to the next hop based on decision tree learning algorithms.
Keywords: Agent Migration, Decision Tree learning, ID3 algorithm, Naive Bayes Classifier
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19831863 Internet Purchases in European Union Countries: Multiple Linear Regression Approach
Authors: Ksenija Dumičić, Anita Čeh Časni, Irena Palić
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This paper examines economic and Information and Communication Technology (ICT) development influence on recently increasing Internet purchases by individuals for European Union member states. After a growing trend for Internet purchases in EU27 was noticed, all possible regression analysis was applied using nine independent variables in 2011. Finally, two linear regression models were studied in detail. Conducted simple linear regression analysis confirmed the research hypothesis that the Internet purchases in analyzed EU countries is positively correlated with statistically significant variable Gross Domestic Product per capita (GDPpc). Also, analyzed multiple linear regression model with four regressors, showing ICT development level, indicates that ICT development is crucial for explaining the Internet purchases by individuals, confirming the research hypothesis.
Keywords: European Union, Internet purchases, multiple linear regression model, outlier
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 29451862 A Novel Approach to Fault Classification and Fault Location for Medium Voltage Cables Based on Artificial Neural Network
Authors: H. Khorashadi-Zadeh, M. R. Aghaebrahimi
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A novel application of neural network approach to fault classification and fault location of Medium voltage cables is demonstrated in this paper. Different faults on a protected cable should be classified and located correctly. This paper presents the use of neural networks as a pattern classifier algorithm to perform these tasks. The proposed scheme is insensitive to variation of different parameters such as fault type, fault resistance, and fault inception angle. Studies show that the proposed technique is able to offer high accuracy in both of the fault classification and fault location tasks.Keywords: Artificial neural networks, cable, fault location andfault classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18371861 A Green Method for Selective Spectrophotometric Determination of Hafnium(IV) with Aqueous Extract of Ficus carica Tree Leaves
Authors: A. Boveiri Monji, H. Yousefnia, M. Haji Hosseini, S. Zolghadri
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A clean spectrophotometric method for the determination of hafnium by using a green reagent, acidic extract of Ficus carica tree leaves is developed. In 6-M hydrochloric acid, hafnium reacts with this reagent to form a yellow product. The formed product shows maximum absorbance at 421 nm with a molar absorptivity value of 0.28 × 104 l mol⁻¹ cm⁻¹, and the method was linear in the 2-11 µg ml⁻¹ concentration range. The detection limit value was found to be 0.312 µg ml⁻¹. Except zirconium and iron, the selectivity was good, and most of the ions did not show any significant spectral interference at concentrations up to several hundred times. The proposed method was green, simple, low cost, and selective.
Keywords: Spectrophotometric determination, Ficus carica tree leaves, synthetic reagents, hafnium.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7271860 A 25-year Monitoring of the Air Pollution Depicted by Plane Tree Species in Tehran
Authors: S. A. A. Korori, H. Valipour K., S. Shabestani, A. shirvany, M. Matinizadeh
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Tehran, one of the heavily-populated capitals, is severely suffering from increasing air pollution. To show a documented trend of such pollutants during last years, plane tree species (Platanus orientalis) were suited to be studied as indicators, for the species have been planted throughout the city many years ago. Two areas (Saadatabad and Narmak districts) allotting different contents of crowed and highly-traffic routs but the same ecological characteristics were selected. Twelve sample individuals were cored twice perpendicularly in each area. Tree-rings of each core were measured by a binocular microscope and separated annually for the last 25 years. Two heavy metals including Cd and Pb accompanied by a mineral element (Ca) were analyzed using Hatch method. Treerings analysis of the two areas showed different groups in term of physiologically ability as the growths were plunged during the last 10 years in Saadatabad district and showed a slight decrease in the same period for another studying area. In direct contrast to decreasing growth trend in Saadatabad, all three mentioned elements increased sharply during last 25 years in the same area. When it came to Narmak district, the trend was completely different with Saadatabad. There were some fluctuations in absorbing trace elements like tree-rings widths were, yet calcium showed an upward trend all the last 25 years. The results of the study proved the possibility of using tree species of each region to monitor its air pollution trends of the past, hence to depict a pollution assessment of a populated city for last years and then to make appropriate decisions for the future as it is well-known what the trend is. On the other hand, risen values of calcium (as the stress-indicator element) accompanied by increased trace elements suggests non-sustainable state of the trees.Keywords: Air pollution, Platanus orientalis, Tehran, Traceelements, Tree rings.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16711859 An Educational Data Mining System for Advising Higher Education Students
Authors: Heba Mohammed Nagy, Walid Mohamed Aly, Osama Fathy Hegazy
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Educational data mining is a specific data mining field applied to data originating from educational environments, it relies on different approaches to discover hidden knowledge from the available data. Among these approaches are machine learning techniques which are used to build a system that acquires learning from previous data. Machine learning can be applied to solve different regression, classification, clustering and optimization problems.
In our research, we propose a “Student Advisory Framework” that utilizes classification and clustering to build an intelligent system. This system can be used to provide pieces of consultations to a first year university student to pursue a certain education track where he/she will likely succeed in, aiming to decrease the high rate of academic failure among these students. A real case study in Cairo Higher Institute for Engineering, Computer Science and Management is presented using real dataset collected from 2000−2012.The dataset has two main components: pre-higher education dataset and first year courses results dataset. Results have proved the efficiency of the suggested framework.
Keywords: Classification, Clustering, Educational Data Mining (EDM), Machine Learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 52001858 Analysis of Classifications of Unsolicited Bulk Emails
Authors: Jatinderkumar R. Saini, Apurva A. Desai
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In recent times, the problem of Unsolicited Bulk Email (UBE) or commonly known as Spam Email, has increased at a tremendous growth rate. We present an analysis of survey based on classifications of UBE in various research works. There are many research instances for classification between spam and non-spam emails but very few research instances are available for classification of spam emails, per se. This paper does not intend to assert some UBE classification to be better than the others nor does it propose any new classification but it bemoans the lack of harmony on number and definition of categories proposed by different researchers. The paper also elaborates on factors like intent of spammer, content of UBE and ambiguity in different categories as proposed in related research works of classifications of UBE.Keywords: E-mail, Scams, Spam Email, Unsolicited Bulk Email(UBE)
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17121857 Dynamic Routing to Multiple Destinations in IP Networks using Hybrid Genetic Algorithm (DRHGA)
Authors: K. Vijayalakshmi, S. Radhakrishnan
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In this paper we have proposed a novel dynamic least cost multicast routing protocol using hybrid genetic algorithm for IP networks. Our protocol finds the multicast tree with minimum cost subject to delay, degree, and bandwidth constraints. The proposed protocol has the following features: i. Heuristic local search function has been devised and embedded with normal genetic operation to increase the speed and to get the optimized tree, ii. It is efficient to handle the dynamic situation arises due to either change in the multicast group membership or node / link failure, iii. Two different crossover and mutation probabilities have been used for maintaining the diversity of solution and quick convergence. The simulation results have shown that our proposed protocol generates dynamic multicast tree with lower cost. Results have also shown that the proposed algorithm has better convergence rate, better dynamic request success rate and less execution time than other existing algorithms. Effects of degree and delay constraints have also been analyzed for the multicast tree interns of search success rate.
Keywords: Dynamic Group membership change, Hybrid Genetic Algorithm, Link / node failure, QoS Parameters.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14371856 Predicting Protein-Protein Interactions from Protein Sequences Using Phylogenetic Profiles
Authors: Omer Nebil Yaveroglu, Tolga Can
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In this study, a high accuracy protein-protein interaction prediction method is developed. The importance of the proposed method is that it only uses sequence information of proteins while predicting interaction. The method extracts phylogenetic profiles of proteins by using their sequence information. Combining the phylogenetic profiles of two proteins by checking existence of homologs in different species and fitting this combined profile into a statistical model, it is possible to make predictions about the interaction status of two proteins. For this purpose, we apply a collection of pattern recognition techniques on the dataset of combined phylogenetic profiles of protein pairs. Support Vector Machines, Feature Extraction using ReliefF, Naive Bayes Classification, K-Nearest Neighborhood Classification, Decision Trees, and Random Forest Classification are the methods we applied for finding the classification method that best predicts the interaction status of protein pairs. Random Forest Classification outperformed all other methods with a prediction accuracy of 76.93%Keywords: Protein Interaction Prediction, Phylogenetic Profile, SVM , ReliefF, Decision Trees, Random Forest Classification
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16021855 A New Method for Image Classification Based on Multi-level Neural Networks
Authors: Samy Sadek, Ayoub Al-Hamadi, Bernd Michaelis, Usama Sayed
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In this paper, we propose a supervised method for color image classification based on a multilevel sigmoidal neural network (MSNN) model. In this method, images are classified into five categories, i.e., “Car", “Building", “Mountain", “Farm" and “Coast". This classification is performed without any segmentation processes. To verify the learning capabilities of the proposed method, we compare our MSNN model with the traditional Sigmoidal Neural Network (SNN) model. Results of comparison have shown that the MSNN model performs better than the traditional SNN model in the context of training run time and classification rate. Both color moments and multi-level wavelets decomposition technique are used to extract features from images. The proposed method has been tested on a variety of real and synthetic images.Keywords: Image classification, multi-level neural networks, feature extraction, wavelets decomposition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16361854 Extended Least Squares LS–SVM
Authors: József Valyon, Gábor Horváth
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Among neural models the Support Vector Machine (SVM) solutions are attracting increasing attention, mostly because they eliminate certain crucial questions involved by neural network construction. The main drawback of standard SVM is its high computational complexity, therefore recently a new technique, the Least Squares SVM (LS–SVM) has been introduced. In this paper we present an extended view of the Least Squares Support Vector Regression (LS–SVR), which enables us to develop new formulations and algorithms to this regression technique. Based on manipulating the linear equation set -which embodies all information about the regression in the learning process- some new methods are introduced to simplify the formulations, speed up the calculations and/or provide better results.Keywords: Function estimation, Least–Squares Support VectorMachines, Regression, System Modeling
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19961853 Optimization of Slider Crank Mechanism Using Design of Experiments and Multi-Linear Regression
Authors: Galal Elkobrosy, Amr M. Abdelrazek, Bassuny M. Elsouhily, Mohamed E. Khidr
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Crank shaft length, connecting rod length, crank angle, engine rpm, cylinder bore, mass of piston and compression ratio are the inputs that can control the performance of the slider crank mechanism and then its efficiency. Several combinations of these seven inputs are used and compared. The throughput engine torque predicted by the simulation is analyzed through two different regression models, with and without interaction terms, developed according to multi-linear regression using LU decomposition to solve system of algebraic equations. These models are validated. A regression model in seven inputs including their interaction terms lowered the polynomial degree from 3rd degree to 1st degree and suggested valid predictions and stable explanations.
Keywords: Design of experiments, regression analysis, SI Engine, statistical modeling.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12431852 Churn Prediction: Does Technology Matter?
Authors: John Hadden, Ashutosh Tiwari, Rajkumar Roy, Dymitr Ruta
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The aim of this paper is to identify the most suitable model for churn prediction based on three different techniques. The paper identifies the variables that affect churn in reverence of customer complaints data and provides a comparative analysis of neural networks, regression trees and regression in their capabilities of predicting customer churn.Keywords: Churn, Decision Trees, Neural Networks, Regression.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 32841851 Wood Species Recognition System
Authors: Bremananth R, Nithya B, Saipriya R
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The proposed system identifies the species of the wood using the textural features present in its barks. Each species of a wood has its own unique patterns in its bark, which enabled the proposed system to identify it accurately. Automatic wood recognition system has not yet been well established mainly due to lack of research in this area and the difficulty in obtaining the wood database. In our work, a wood recognition system has been designed based on pre-processing techniques, feature extraction and by correlating the features of those wood species for their classification. Texture classification is a problem that has been studied and tested using different methods due to its valuable usage in various pattern recognition problems, such as wood recognition, rock classification. The most popular technique used for the textural classification is Gray-level Co-occurrence Matrices (GLCM). The features from the enhanced images are thus extracted using the GLCM is correlated, which determines the classification between the various wood species. The result thus obtained shows a high rate of recognition accuracy proving that the techniques used in suitable to be implemented for commercial purposes.Keywords: Correlation, Grey Level Co-Occurrence Matrix, ProbabilityDensity Function, Wood Recognition.
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