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检索条件"任意字段=16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017"

共 780 条记录，以下是61-70 订阅

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Reverse Engineering Regulatory Networks in Cells Using a Dynamic Bayesian Network and Mutual Information Scoring Function 16

Reverse Engineering Regulatory Networks in Cells Using a Dyn...

引用

16th ieee international conference on machine learning and applications (icmla)

作者： Jiang, Haodi Turki, Turki Wang, Jason T. L. King Abdulaziz Univ Jeddah Saudi Arabia New Jersey Inst Technol Newark NJ 07102 USA

ISBN: (纸本)9781538614174

In systems biology, two important regulatory networks are gene regulatory networks (GRNs) and regulatory networks of microRNAs (RNMs). A GRN is modeled as a directed graph in which a node represents a gene or transcription factor (TF), and an edge from a TF to a gene indicates that the TF regulates the expression of the gene. An RNM is modeled as a bipartite directed graph with two disjoint sets of nodes: a set of nodes that represent microRNAs (miRNAs) and a set of nodes that represent genes or TFs. Directed edges between these two sets of nodes represent miRNA-target interactions or TF-miRNA regulatory relations. In this paper, we present an approach to reverse engineering GRNs and RNMs using a dynamic Bayesian network and mutual information scoring function. Our approach is able to automatically infer both GRNs and RNMs from time series of expression data. Experimental results on different datasets show that our approach is more accurate than other time-series based network inference methods.

关键词： data mining machine learning Bayesian networks mutual information systems biology

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Semi-Automated Segmentation of Glioblastomas in Brain MRI Using machine learning Techniques 16

Semi-Automated Segmentation of Glioblastomas in Brain MRI Us...

引用

16th ieee international conference on machine learning and applications (icmla)

作者： Joseph, Naomi Sanghani, Parita Ren, Hongliang Natl Univ Singapore Biomed Engn Singapore Singapore Univ Pittsburgh Pittsburgh PA USA NUSRI Suzhou Peoples R China

ISBN: (纸本)9781538614174

Glioblastomas (GBMs) are cancerous brain tumors that require careful and intricate analysis for surgical planning. Physicians employ Magnetic Resonance Imaging (MRI) in order to diagnose glioblastomas. the segmentation of the tumor is a crucial step in surgical planning. Clinicians manually segment the tumor voxel-by-voxel;however, this is very time consuming. Hence, extensive research has been conducted to semi-automate and fully-automate this segmentation process. this project explores manual segmentation and utilizes k-means clustering technique for semi-automated segmentation. the accuracy of the k-means clustering segmentation was measured using the Dice Coefficient (DC). the results show that k-means clustering provides high accuracy for the segmentation of the enhanced region of tumor (which appears bright in the T1 post contrast MR image) and hence, it can be efficiently used to speed up manual segmentation.

关键词： segmentation k-means clustering glioblastomas MRI

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Modeling a Classifier for Solving Safety-Critical Binary Classification Tasks 16

Modeling a Classifier for Solving Safety-Critical Binary Cla...

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16th ieee international conference on machine learning and applications (icmla)

作者： Alagoez, Ibrahim Hoiss, thomas German, Reinhard FAU Erlangen Nuremberg Dept Comp Sci 7 Erlangen Germany Automot Safety Technol GmbH Gaimersheim Germany

ISBN: (纸本)9781538614174

this paper introduces a novel machine learning approach for performing binary decision-making tasks under uncertainty. Reducing the regression test effort of safety-critical black box systems is a safety-critical task as system failures would remain undetected if corresponding failing test cases are not executed. the uncertainty of potentially undetected system failures persists due to the lack of implementation knowledge of black-box systems. We refer to executing test cases as a costly labeling process due to required special test equipment and testing time. However, we model a binary classifier for selecting test cases. Accordingly, only fault revealing test cases should be selected and thus executed in order to reduce the overall cost of the regression test effort. On the one side, the classifier's specificity has to be maximized. On the other side, the classifier's sensitivity has to meet a specific quality-level since the number of undetected system failures should be limited. We will show in an industrial case study the benefits of our classifier where we reduce the regression test effort of safety-critical systems. the experimental results indicate that our implemented classifier outperforms other machine learning approaches in terms of sensitivity.

关键词： Sensitivity learning systems Safety Testing Optimization Reliability

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DDoS Attack Modeling and Detection Using SMO 16

DDoS Attack Modeling and Detection Using SMO

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16th ieee international conference on machine learning and applications (icmla)

作者： Daneshgadeh, Salva Baykal, Nazife Ertekin, Seyda Middle East Tech Univ Dept Informat Syst Ankara Turkey Middle East Tech Univ Dept Comp Engn Ankara Turkey

ISBN: (纸本)9781538614174

Over the last decade, Distributed Denial of Service (DDoS) attacks have been employed to cause huge financial and prestige loss to different kinds of e-business. Attackers also target governmental websites using DDoS attack's as a new weapon in the world of cyber war. the importance of the issue has inspired many researchers,from academia and the industry to provide solutions to this type of challenging attack. In this study, we simulated DDoS attacks in a virtual lab and then collected firewall logs from the Security Information and Event Management (STEM) platform of a company in the field of security management solutions. We extracted 14 research features from firewall logs and applied a SMO algorithm to train our data using 10 fold cross-validation. the SMO with PolyKernel was able to create a prediction model without any false alarm. We also tested our model with two different datasets. this research is an ongoing multistep study. Future research will concentrate on online DDoS detection.

关键词： DDoS attack detection machine learning feature extraction

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Model Guided Deep learning Approach Towards Prediction of Physical System Behavior 16

Model Guided Deep Learning Approach Towards Prediction of Ph...

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16th ieee international conference on machine learning and applications (icmla)

作者： Das, Subhasish Agrawal, Anurag Banerjee, Ayan Gupta, Sandeep K. S. ASU CIDSE iMPACT Lab Tempe AZ 85281 USA

ISBN: (纸本)9781538614174

Cyber-physical control systems involve a discrete computational algorithm to control continuous physical systems. Often the control algorithm uses predictive models of the physical system in its decision making process. However, physical system models suffer from several inaccuracies when employed in practice. Mitigating such inaccuracies is often difficult and have to be repeated for different instances of the physical system. In this paper, we propose a model guided deep learning method for extraction of accurate prediction models of physical systems, in presence of artifacts observed in real life deployments. Given an initial potentially suboptimal mathematical prediction model, our model guided deep learning method iteratively improves the model through a data driven training approach. We apply the proposed approach on the closed loop blood glucose control system. Using this proposed approach, we achieve an improvement over predictive Bergman Minimal Model by a factor of around 100.

关键词： Artificial neural networks deep learning physical systems prediction

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Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis 16

Anomaly Detection in Multivariate Non-stationary Time Series...

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16th ieee international conference on machine learning and applications (icmla)

作者： Lee, Doyup Pohang Univ Sci & Technol Dept Creat IT Engn 77 Cheongam Ro Pohang Gyeongbuk South Korea

ISBN: (纸本)9781538614174

Anomaly detection in database management systems (DBMSs) is difficult because of increasing number of statistics (stat) and event metrics in big data system. In this paper, I propose an automatic DBMS diagnosis system that detects anomaly periods with abnormal DB stat metrics and finds causal events in the periods. Reconstruction error from deep autoencoder and statistical process control approach are applied to detect time period with anomalies. Related events are found using time series similarity measures between events and abnormal stat metrics. After training deep autoencoder with DBMS metric data, efficacy of anomaly detection is investigated from other DBMSs containing anomalies. Experiment results show effectiveness of proposed model, especially, batch temporal normalization layer. Proposed model is used for publishing automatic DBMS diagnosis reports in order to determine DBMS configuration and SQL tuning.

关键词： anomaly detection non-stationary time series automatic diagnosis reconstruction error statistical process control transfer learning

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Deep Multi-Camera People Detection 16

Deep Multi-Camera People Detection

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16th ieee international conference on machine learning and applications (icmla)

作者： Chavdarova, Tatjana Fleuret, Francois Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9781538614174

this paper addresses the problem of multi-view people occupancy map estimation. Existing solutions either operate per-view, or rely on a background subtraction preprocessing. Both approaches lessen the detection performance as scenes become more crowded. the former does not exploit joint information, whereas the latter deals with ambiguous input due to the foreground blobs becoming more and more interconnected as the number of targets increases. Although deep learning algorithms have proven to excel on remarkably numerous computer vision tasks, such a method has not been applied yet to this problem. In large part this is due to the lack of large-scale multi-camera data-set. the core of our method is an architecture which makes use of monocular pedestrian data-set, available at larger scale than the multi-view ones, applies parallel processing to the multiple video streams, and jointly utilises it. Our end-to-end deep learning method outperforms existing methods by large margins on the commonly used PETS 2009 data-set. Furthermore, we make publicly available a new three-camera HD data-set.

关键词： Cameras Estimation Detectors Training machine learning Computer architecture Object detection

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the Effect of Communication on Noncooperative Multiplayer Multi-Armed Bandit Problems 16

The Effect of Communication on Noncooperative Multiplayer Mu...

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16th ieee international conference on machine learning and applications (icmla)

作者： Evirgen, Noyan Kose, Alper Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Zurich Switzerland MIT Elect Res Lab Cambridge MA 02139 USA Ecole Polytech Fed Lausanne Dept Elect Engn Lausanne Switzerland

ISBN: (纸本)9781538614174

We consider decentralized stochastic multi-armed bandit problem with multiple players in the case of different communication probabilities between players. Each player makes a decision of pulling an arm without cooperation while aiming to maximize his or her reward but informs his or her neighbors in the end of every turn about the arm he or she pulled and the reward he or she got. Neighbors of players are determined according to an Erdos-Renyi graph with connectivity alpha which is reproduced in the beginning of every turn. We consider i.i.d. rewards generated by a Bernoulli distribution and assume that players are unaware about the arms' probability distributions and their mean values. In case of a collision, we assume that only one of the players who is randomly chosen gets the reward where the others get zero reward. We study the effects of alpha, the degree of communication between players, on the cumulative regret using well-known algorithms UCB1, epsilon-Greedy and thompson Sampling.

关键词： Multi-armed bandit online learning game theory reinforcement learning

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Schemes for Labeling Semantic Code Clones using machine learning 16

Schemes for Labeling Semantic Code Clones using Machine Lear...

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16th ieee international conference on machine learning and applications (icmla)

作者： Sheneamer, Abdullah Hazazi, Hanan Roy, Swarup Kalita, Jugal Jazan Univ Fac Comp Sci & Informat Syst Jazan 45142 Saudi Arabia Univ Colorado Coll Engn & Appl Sci Colorado Springs CO 80918 USA Regis Univ Coll Comp & Informat Sci Denver CO 80221 USA North Eastern Hill Univ Dept Informat Technol Shillong 793022 Meghalaya India

ISBN: (纸本)9781538614174

machine learning approaches built to identify code clones fail to perform well due to insufficient training samples and have been restricted only up to Type-III clones. A majority of the publicly available code clone corpora are incomplete in nature and lack labeled samples for semantic or Type-IV clones. We present here two schemes for labeling all types of clones including Type-IV clones. We restrict our study to Java code only. First, we use an unsupervised approach to label Type-IV clones and validate them using expert Java programmers. Next, we present a supervised scheme for labeling (or classifying) unknown samples based on labeled samples derived from our first scheme. We evaluate the performance of our schemes using six well-known Java code clone corpora and report on the quality of produced clones in terms of kappa agreement, mean error and accuracy scores. Results show that both schemes produce high quality code clones facilitating future use of machine learning in detecting clones of Type-IV.

关键词： machine learning Code Clones Semantic Clones AST PDG Features Classification

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CLASSIFICATION OF ECG ARRHYthMIA WIth machine learning TECHNIQUES 16

CLASSIFICATION OF ECG ARRHYTHMIA WITH MACHINE LEARNING TECHN...

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16th ieee international conference on machine learning and applications (icmla)

作者： Bulbul, Halil Ibrahim Usta, Nese Yildiz, Musa Gazi Univ Gazi Egitim Fac Dept Comp & Instruct Technol Ankara Turkey Gazi Univ Inst Sci Dept Comp Engn Ankara Turkey Ahmet Yesevi Univ Turkistan Kazakhstan

ISBN: (纸本)9781538614174

the ECG uses some methods to diagnose these cardiac arrhythmias and tries to correct the diagnosis. ECG signals are characterized by a collection of waves such as P, Q, R, S, T. these five waves are preformed, wave transformed, and classified. In the current literature, the P, Q, R, S, T waves in ECG signals are classified using some machine learning techniques. In the work to be done, MLP (Multi Layer Perceptron) and SVM (Support Vector machine) classification techniques which are not compared with each other using these signals will be compared. Is study, BP (Back Propagation) algorithm with MLP classifier and KA (Kernel-Adatron) algorithm with SVM classifier were used. In addition, the use of these methods is new in the field of ECG classification. It will try to find a more effective method with new uses in the study and the literature will contribute to this area. In addition, wave transformation techniques such as DWT, DCT, and CWT will be used to increase the success of the classification used in the study. this will lead to the most effective classification method in the existing data set. In the work to be done, it is aimed to bring improvements to the classification methods used in existing studies. It is aimed to develop a method to improve the calculation time and standard classification performance of MLP and SVM, and it is aimed to contribute to the informed consciousness of this work.

关键词： machine learning ECG SVM MLP DWT DCT CWT

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