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An Intrusion Detection System using Opposition based Particle Swarm Optimization Algorithm and PNN

An Intrusion Detection System using Opposition based Particl...

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2019 international conference on machine learning, Big data, Cloud and Parallel Computing, COMITCon 2019

作者： Kala, T. Sree Christy, A. Department of Computer Science Bharathiar University Coimbatore India Department of Computer Science Sathyabama University Chennai India

ISBN: (纸本)9781728102115

Network security became a viral topic nowadays, Anomaly-based Intrusion Detection Systems [1] (IDSs) plays an indispensable role in identifying the attacks from networks and the detection rate and accuracy are said to be high. The proposed work explore this topic and solve this issue by the IDS model developed using Artificial Neural Network (ANN). This model uses Feed - Forward Neural Net algorithms and Probabilistic Neural Network and oppositional based on Particle Swarm optimization Algorithm for lessen the computational overhead and boost the performance level. The whole computing overhead produced in its execution and training are get minimized by the various optimization techniques used in these developed ANN-based IDS system. The experimental study on the developed system tested using the standard NSL-KDD dataset performs well, while compare with other intrusion detection models, built using NN, RB and OPSO algorithms. © 2019 IEEE.

关键词： Network security

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Computer-Aided Diagnosis (CAD) to Detect Abnormality on CT Image of Liver 3

Computer-Aided Diagnosis (CAD) to Detect Abnormality on CT I...

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3rd Annual Scientific Meeting on Medical Physics and Biophysics, PIT-FMB in conjunction with the 17th South-East Asia Congress of Medical Physics, SEACOMP 2019

作者： Wahyuni, W.S. Prajitno, P. Soejoko, D.S. Department of Physics Faculty of Mathematics and Natural Science Universitas Indonesia Depok16424 Indonesia

Liver cancer on CT-scan image has different shapes, locations and textures in every image. The contrast difference between abnormal and healthy liver is often indistinguishable, making it difficult to evaluate. Liver abnormalities are such as swelling, fibrosis, and the presence of benign or malignant tumor. The difference of low contrast with wide size on the image is easily known as abnormality, but it is very hard to evaluate for small mass and low contrast. In this research, CAD was conducted to help the evaluation on liver abnormality, especially abnormality in small size. The research method used was active contour-based segmentation method. The research data were secondary data, the abdomen image was produced from the modality of Computed Tomography Scanner (CT-Scan) in Regional Public Hospital of Cibinong, Bogor. The data collection techniques were through observation on the image data of abnormal liver from either liver cancer patients, normal liver patients, as well as patients of other diseases as diagnosed by the doctor. Meanwhile, the data was processed through feature extraction process using the texture analysis of Gray-Level Co-occurrence Matrix (GLCM) with machine learning of Artificial Neural Network (ANN) to detect abnormality on image. The research stated that ANN can be used to categorize the images into normal and abnormal groups at 89% accuracy, 86% sensitivity, 92% specificity, 91% precision, and 10% overall error. © Published under licence by IOP Publishing Ltd.

关键词： Computer aided diagnosis

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BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees 19

BlinkML: Efficient Maximum Likelihood Estimation with Probab...

引用

ACM SIGMOD international conference on Management of data (SIGMOD)

作者： Park, Yongjoo Qing, Jingyi Shen, Xiaoyang Mozafari, Barzan Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781450356435

The rising volume of datasets has made training machine learning (ML) models a major computational cost in the enterprise. Given the iterative nature of model and parameter tuning, many analysts use a small sample of their entire data during their initial stage of analysis to make quick decisions (e.g., what features or hyperparameters to use) and use the entire dataset only in later stages (i.e., when they have converged to a specific model). This sampling, however, is performed in an ad-hoc fashion. Most practitioners cannot precisely capture the effect of sampling on the quality of their model, and eventually on their decision-making process during the tuning phase. Moreover, without systematic support for sampling operators, many optimizations and reuse opportunities are lost. In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML training. BlinkML allows users to make error-computation tradeoffs: instead of training a model on their full data (i.e., full model), BlinkML can quickly train an approximate model with quality guarantees using a sample. The quality guarantees ensure that, with high probability, the approximate model makes the same predictions as the full model. BlinkML currently supports any ML model that relies on maximum likelihood estimation (MLE), which includes Generalized Linear Models (e.g., linear regression, logistic regression, max entropy classifier, Poisson regression) as well as PPCA (Probabilistic Principal Component Analysis). Our experiments show that BlinkML can speed up the training of large-scale ML tasks by 6.26x-629x while guaranteeing the same predictions, with 95% probability, as the full model.

关键词： quality guarantees approximate machine learning maximum likelihood estimation

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Innovative machine learning Techniques for Biomedical Imaging

Innovative Machine Learning Techniques for Biomedical Imagin...

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IEEE international conference on Microwaves, Antennas, Communications and Electronic Systems (COMCAS)

作者： Salucci, Marco Marcantonio, Davide Li, Maokun Oliveri, Giacomo Rocca, Paolo Massa, Andrea Univ Trento ELEDIA Res Ctr ELEDIA UniTN I-38123 Trento Italy Tsinghua Univ ELEDIA Res Ctr ELEDIA TSINGHUA Beijing 100084 Peoples R China Xidian Univ ELEDIA Res Ctr ELEDIA XIDIAN Xian 710071 Peoples R China ELEDIA Res Ctr ELEDIA L2S UMR 8506 F-91192 Gif Sur Yvette France ELEDIA Res Ctr ELEDIA UESTC Sch Elect Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9781538695494

machine learning (ML) is a powerful paradigm to solve several inverse problems arising in biomedical imaging with very high computational efficiency. As a matter of fact, learning-by-examples (LBE) strategies can be successfully exploited to predict the status of the domain under investigation (DoI) starting from measured data with almost real-time performance. Some recent advances of ML as applied to brain stroke detection, classification, and localization, as well as to human chest monitoring are presented. An illustrative example concerned with a novel LBE strategy for the real-time prediction of the lungs dimensions from electrical impedance tomography (EIT) measurements is given, as well.

关键词： Biomedical imaging machine learning (ML) learning-by-examples (LBE) brain stroke imaging chest imaging microwave imaging (MI) electrical impedance tomography (EIT)

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Parallel latent dirichlet allocation using vector processors 21

Parallel latent dirichlet allocation using vector processors

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21st IEEE international conference on High Performance Computing and Communications, 17th IEEE international conference on Smart City and 5th IEEE international conference on data science and Systems, HPCC/SmartCity/DSS 2019

作者： Tian, Zhongyuan Yokoyama, Harumichi Araki, Takuya Hong Kong University of Science and Technology Hong Kong SAR Hong Kong Data Science Research Laboratories NEC Corporation Kawasaki Japan

ISBN: (纸本)9781728120584

Latent Dirichlet Allocation (LDA) is a widely used machine learning technique for topic modeling. The ever-growing size of training data and topic models has been attracting great attention to parallel LDA implementation. In this paper, we present VLDA, which accelerates LDA training by exploiting the data-level and the thread-level parallelism using vector processors. The priority-aware scheduling approach is proposed to address the high memory requirement and workload imbalance issues with existing works. Experimental results on various datasets demonstrate a 3.2x~12.9x speedup of VLDA over state-of-the-art parallel LDA methods based on an x86 architecture, which are PLDA and LightLDA. We also show that VLDA is capable of processing large-scale datasets with good scalability to multiple vector processors. © 2019 IEEE.

关键词： Vectors

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Long-Term Prediction of Small Time-Series data Using Generalized Distillation

Long-Term Prediction of Small Time-Series Data Using General...

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international Joint conference on Neural Networks (IJCNN)

作者： Hayashi, Shogo Tanimoto, Akira Kashima, Hisashi Kyoto Univ Grad Sch Informat Kyoto Japan NEC Corp Ltd Data Sci Res Labs Kawasaki Kanagawa Japan

ISBN: (纸本)9781728119854

The recent increase of "big data" in our society has led to major impacts of machine learning and data mining technologies in various fields ranging from marketing to science. On the other hand, there still exist areas where only small-sized data are available for various reasons, for example, high data acquisition costs or the rarity of targets events. machine learning tasks using such small data are usually difficult because of the lack of information available for training accurate prediction models. In particular, for long-term time-series prediction, the data size tends to be small because of the unavailability of the data between input and output times in training. Such limitations on the size of time-series data further make long-term prediction tasks quite difficult;in addition, the difficulty that the far future is more uncertain than the near future. In this paper, we propose a novel method for long-term prediction of small time-series data designed in the framework of generalized distillation. The key idea of the proposed method is to utilize the middle-time data between the input and output times as "privileged information," which is available only in the training phase and not in the test phase. We demonstrate the effectiveness of the proposed method on both synthetic data and real-world data. The experimental results show the proposed method performs well, particularly when the task is difficult and has high input dimensions.

关键词： time-series data small data generalized distillation privileged information

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Prediction of hourly solar radiation using fuzzy clustering and linguistic modifiers

Prediction of hourly solar radiation using fuzzy clustering ...

引用

2019 international conference of Computer science and Renewable Energies, ICCSRE 2019

作者： Bahani, Khalid Moujabbir, Mohammed Ramdani, Mohammed Department of Computer Science Faculty of Sciences and Technics of Mohammedia Hassan II University Mohammedia Morocco

ISBN: (纸本)9781728108278

Solar energy is subject to large temporal and spatial fluctuations. These fluctuations require predicting the share of solar energy and its requirements in energy supply systems for optimal use. These predictions form the basis for various solar management options such as storage and control. However, before predicting the production of solar systems, it is necessary to focus on predicting solar radiation. The global prediction of solar radiation is divided into two broad categories of prediction methods: cloud images associated with physical models and automated learning models. In this paper we present a new leaning MAMDANI fuzzy rules based system FRLC (Fuzzy Rule learning through Clustering) for solar radiation prediction with meteorological data. FRLC based on linguistic modifiers and fuzzy clustering is compared to the most accurate machine learning algorithms such as multilayer feed-forward neural network, radial basis function neural network, support vector regression, and adaptive neuro-fuzzy inference system. FRLC outperforms all algorithms at interpretability level by offering a linguistic knowledge base to the experts of the domain. © 2019 IEEE.

关键词： Solar energy

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WordPrep: Word-based Preposition Prediction Tool

WordPrep: Word-based Preposition Prediction Tool

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IEEE international conference on Big data (Big data)

作者： Bhagat, Pooja Varde, Aparna S. Feldman, Anna Wells Fargo Dept Data Mgmt & Insights Summit NJ 07901 USA Montclair State Univ Dept Comp Sci Montclair NJ USA Montclair State Univ Dept Linguist Montclair NJ USA

ISBN: (纸本)9781728108582

As big data heads towards big knowledge, data management and machine learning techniques work together to address several interesting problems. In this paper, we address a problem in natural language processing that involves learning by mining from large text databases. More specifically, we deal with the problem of preposition prediction, especially for ESL (English as a second language) learners. Prepositions are function words that typically show a relationship between a noun or a pronoun and other elements of a sentence. They play a key role in determining the meaning of a sentence. Accurate prediction of correct prepositions in a sentence is a challenging job since preposition usage is one of the most subtle aspects of the English grammar, making it difficult for non-native speakers. This paper proposes an approach for preposition prediction called WordPrep based on which we build a tool. WordPrep relies on mining based on the words themselves rather than on their lexical or syntactic connotations. This addresses the challenges of prepositions appearing in idiomatic phrases or in different semantic contexts, due to which the actual words are better than their grammatical positions. Our proposed solution entails a direct data-driven approach to predict the missing preposition in a sentence by learning from matching tokens consisting of n-grams with words before and after the preposition. Using various searches and pattern-matching methods against a large number of database records from big text corpora, this approach predicts the missing preposition(s). We describe our pilot approach, tool implementation and experiments in this paper. This work is particularly helpful for pedagogical applications.

关键词： Big data and Big Knowledge ESL Learners Intelligent Tutoring Systems machine learning Natural Language Processing Pedagogical Tools Text Mining Writing Aids

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Basic Daily Activity Recognition with a data Glove 10

Basic Daily Activity Recognition with a Data Glove

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10th international conference on Ambient Systems, Networks and Technologies (ANT) / 2nd international conference on Emerging data and Industry 4.0 (EDI40)

作者： Maitre, Julien Rendu, Clement Bouchard, Kevin Bouchard, Bruno Gaboury, Sebastien Univ Quebec Chicoutimi 555 Blvd Univ Chicoutimi PQ G7H 2B1 Canada

Many people in the world are affected by the Alzheimer disease leading to the dysfunctionality of the hand. In one side, this symptom is not the most important of this disease and not much attention is given to this one. In the other side, the literrature provides two main solutions such as computer vision and data glove allowing to recognize hand gestures for virtual reality or robotic applications. From this finding and need, we decided to developed our own data glove prototype allowing to monitor the evolution of the dysfunctionality of the hand by recognizing objects in basic daily activities. Our approach is simple, cheap (similar to 220$) and efficient (similar to 100% of correct predictions) considering that we are abstracting all the theory about the gesture recognition. Also, we can access directly and easily to the raw data. Finally, the proposed prototype is described in a way that researchers can reproduce it. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the conference Program Chairs.

关键词： data glove Object recognition Basic daily activities Signal processing machine learning

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learning Discrete and Continuous Factors of data via Alternating Disentanglement 36

Learning Discrete and Continuous Factors of Data via Alterna...

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36th international conference on machine learning (ICML)

作者： Jeong, Yeonwoo Song, Hyun Oh Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9781510886988

We address the problem of unsupervised disentanglement of discrete and continuous explanatory factors of data. We first show a simple procedure for minimizing the total correlation of the continuous latent variables without having to use a discriminator network or perform importance sampling, via cascading the information flow in the beta-vae framework. Furthermore, we propose a method which avoids offloading the entire burden of jointly modeling the continuous and discrete factors to the variational encoder by employing a separate discrete inference procedure. This leads to an interesting alternating minimization problem which switches between finding the most likely discrete configuration given the continuous factors and updating the variational encoder based on the computed discrete factors. Experiments show that the proposed method clearly disentangles discrete factors and significantly outperforms current disentanglement methods based on the disentanglement score and inference network classification score. The source code is available at https://***/snumllab/DisentanglementICML19.

关键词： Importance sampling

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