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Proceedings of ieee/acs international conference on computer systems and applications, aiccsa

Proceedings of IEEE/ACS International Conference on Computer...

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15th ieee/acs international conference on computer systems and applications, aiccsa 2018

ISBN: (纸本)9781538691205

the proceedings contain 119 papers. the topics discussed include: number of texture unit as feature to breast's disease classification from thermal images;survey: deep learning concepts and techniques for electronic health record;a supervised learning approach using the combination of semantic and lexical features for Arabic community question answering;automatic classification of reciters of Quranic audio clips;speech processing for early Alzheimer disease diagnosis: machine learning based approach;social networks analysis in a business intelligence context;face recognition framework based on correlated images and back-propagation neural network;and rapid design of real-time image fusion FPGA using HLS and other techniques.

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computer Aided System for Users with Visual Impairments 15

Computer Aided System for Users with Visual Impairments

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15th ieee/acs international conference on computer systems and applications (aiccsa)

作者： Kirboyun, Sevgi Univ Arizona Coll Educ Dept Disabil & Psychoeduc Studies Tucson AZ 85721 USA

ISBN: (纸本)9781538691205

Screen readers enable people with visual impairment (VI) to have equal access to written information both on computers and smartphones. this paper presents an overview of screen readers that allow users with VI read the text materials and describe images on the computer screen by using a special software program to synthesize as speech. We study three aspects of the screen readers in terms of their accessibility. First, we invest on developing websites, apps, and software programs by following web content accessibility guidelines to ensure accessibility. We than conduct research on compatibility of the websites, apps, and software programs with screen readers. Last, we inform about descriptions of the images by creating alternative texts.

关键词： screen readers visual impairment assistive technologies mainstream devices component

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A Neural Network based Digital Forensics Classification 15

A Neural Network based Digital Forensics Classification

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15th ieee/acs international conference on computer systems and applications (aiccsa)

作者： Mohammad, Rami M. Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Comp Informat Syst Dept POB 1982 Dammam Saudi Arabia

ISBN: (纸本)9781538691205

Digital forensics (DF) turn out to be an urgent and a timely subject to investigate with the significant rise in computer crimes these days. Typically, DF analysis aims to maintain all collected evidence in its most original status by determining, collecting, and evaluating the digital data for re-constructing past incidents. Most evidence related to digital crime are kept within the computer system files. this article explores and evaluates the applicability of Neural Network techniques in DF analysis by analysing information related to computer's file system to determine whether they have been manipulated by a specific application program. A data set described as a vector of attributes related to file system activities thru a specific time is collected and utilized for creating a neural network classification model. the experimental results show good results with respect to different performance estimation measures.

关键词： Digital Forensic File System computer Crimes Machine Learning Log file

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Number of Texture Unit as Feature to Breast's Disease Classification from thermal Images 15

Number of Texture Unit as Feature to Breast's Disease Classi...

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15th ieee/acs international conference on computer systems and applications (aiccsa)

作者： Resmini, R. Araujo, A. Conci, A. Silva, L. Moran, M. Univ Fed Mato Grosso Sistemas Informacao Rondonopolis Brazil Univ Fed Fluminense Inst Comp Niteroi RJ Brazil

ISBN: (纸本)9781538691205

this paper presents the use of the Number of Texture Unit as a feature extractor for classification of breast images. the Number of Texture Unit served as the basis for the idealization of the Local Binary Pattern a technique that is widely used in facial recognition. We compared the proposed strategy with the Gray Level Co-occurrence Matrix which is the most used texture analysis technique in the literature. With this work we have been able to show that the combination of the two techniques of feature extraction improves the final result of classification. To perform the tests we used the Support Vectors Machine classifier and obtained a result of 96.15% Area Under the Curve (Receiver Operating Characteristic Curve).

关键词： Local Binary Pattern infrared images feature extraction computer aided diagnosis support vector machine machine learning

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Malicious HTML File Prediction: A Detection and Classification Perspective with Noisy Data 15

Malicious HTML File Prediction: A Detection and Classificati...

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15th ieee/acs international conference on computer systems and applications (aiccsa)

作者： Hess, Samuel Satam, Pratik Ditzler, Gregory Hariri, Salim NSF Ctr Cloud & Auton Comp Denton TX 76203 USA Univ Arizona Dept Elect & Comp Engn Tucson AZ 85721 USA

ISBN: (纸本)9781538691205

Cybersecurity plays a critical role in protecting sensitive information and the structural integrity of networked systems. As networked systems continue to expand in numbers as well as in complexity, so does the threat of malicious activity and the necessity for advanced cybersecurity solutions. Furthermore, both the quantity and quality of available data on malicious content as well as the fact that malicious activity continuously evolves makes automated protection systems for this type of environment particularly challenging. Not only is the data quality a concern, but the volume of the data can be quite small for some of the classes. this creates a class imbalance in the data used to train a classifier;however, many classifiers are not well equipped to deal with class imbalance. One such example is detecting malicious HMTL files from static features. Unfortunately, collecting malicious HMTL files is extremely difficult and can be quite noisy from HTML files being mislabeled. this paper evaluates a specific application that is afflicted by these modern cybersecurity challenges: detection of malicious HTML files. Previous work presented a general framework for malicious HTML file classification that we modify in this work to use a chi(2) feature selection technique and synthetic minority oversampling technique (SMOTE). We experiment with different classifiers (i.e., AdaBoost, GentleBoost, RobustBoost, RusBoost, and Random Forest) and a pure detection model (i.e., Isolation Forest). We benchmark the different classifiers using SMOTE on a real dataset that contains a limited number of malicious files (40) with respect to the normal files (7,263). It was found that the modified framework performed better than the previous framework's results. However, additional evidence was found to imply that algorithms which train on both the normal and malicious samples are likely overtraining to the malicious distribution. We demonstrate the likely overtraining by determining tha

关键词： Feature extraction computer security Hypertext systems Browsers Intrusion detection Machine learning Decision trees

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Improved Speaker Recognition over VoIP using Auditory Features 15

Improved Speaker Recognition over VoIP using Auditory Featur...

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15th ieee/acs international conference on computer systems and applications, aiccsa 2018

作者： Yasmine, Zergat Kawthar Ahmed, Selouani Sid Abderrahmane, Amrouche Mohamed, Debyeche Speech Com. Signal Proc. Lab.-LCPTS Faculty of Electronics and Computer Sciences USTHB Bab Ezzouar Algiers Algeria Department of Information Management University of Moncton Compus of Shippagan Shippagan Canada

ISBN: (纸本)9781538691205

this work presents a novel framework based on a discriminative auditory feature extractor for speaker recognition over VoIP network. the auditory model that simulates the mid-external and inner ear is incorporated into the conventional Mel Frequency Cepstral Coefficients (MFCC) scheme to produce new parameters that we named Ear Frequency Cepstral Coefficients (EFCCs). the experiments are conducted on the TIMIT corpus and the EFCC, used as input parameters tothe i-vector algorithm. Results showed interesting performances compared to the conventional MFCC parameters. In addition, this improvement was obtained by using fewer parameters (43 EFCCs vs 60 MFCCs) in our experiment. © 2018 ieee.

关键词： Voice/data communication systems

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A New Intelligent Hybrid Feature Selection Method 15

A New Intelligent Hybrid Feature Selection Method

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15th ieee/acs international conference on computer systems and applications, aiccsa 2018

作者： Alenizi, Majed A. Mansour, Hussein Y. Abu College of Computer and Information Security Naif Arab University for Security Sciences Riyadh Saudi Arabia

ISBN: (纸本)9781538691205

In recent years, the adoption of technologies has grown notably in many domains and resulted in dense amounts of data. processing such large big data became one of the main challenge in the technology society and nictitates intelligent tools to deal with. the feature extraction and selection are main steps in classification systems that aims to lower the complexity, reduce the dimensionality, generalizable models building and most importantly enhancing the performance as well as accuracy rates due to the quality and significance of the selected features. the aforementioned steps often increase the model performance and often enhance the classification accuracy rate. In this paper we introduced a new hybrid feature selection method and evaluate it against ten datasets form UCI repository, experimental results show that the classifier we've adopted to the experiment has achieved better classification accuracy when compared with the other version that used a single feature selection method. © 2018 ieee.

关键词： Classification (of information)

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Automated Detection of Benign and Malignant in Breast Histopathology Images 15

Automated Detection of Benign and Malignant in Breast Histop...

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15th ieee/acs international conference on computer systems and applications, aiccsa 2018

作者： Baker, Qanita Bani Zaitoun, Toqa Abu Banat, Sajda Eaydat, Eman Alsmirat, Mohammad Computer Sciene Department Jordan University of Science and Technology Irbid Jordan

ISBN: (纸本)9781538691205

Breast cancer detection and classification using histological images play a critical role in the breast cancer diagnosis process. this paper presents a framework for autodetection and classification of breast cancer from microscopic histological images. the images are classified into benign or malignant. the proposed framework involves several steps which include image enhancement, image segmentation, features extraction, and images classification. the proposed framework utilizes a novel combination of K-means clustering and watershed algorithms in the segmentation step. We used K-means clustering to produce an initial segmented image and then we applied the watershed segmentation algorithm. Classification results show that the proposed method effectively detect and classify breast cancer from histological image with accuracy of 70.7% using a proposed Rule-Based classifier and 86.5% using a Decision Tree classifier. © 2018 ieee.

关键词： Image enhancement

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Optimal U-net architecture for object recognition problems in multispectral satellite imagery 16

Optimal U-net architecture for object recognition problems i...

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16th ieee/acs international conference on computer systems and applications (aiccsa)

作者： Gudzius, Povilas Kurasova, Olga Filatovas, Ernestas Vilnius Univ Inst Data Sci Akad Str 4 LT-08412 Vilnius Lithuania

ISBN: (纸本)9781728150529

Geospatial data follows Moore's law. On the back of improvements of optical Earth observation satellite hardware [1] (weight, propulsion systems, signal transmission and resolution) as well as reduced costs of rocket launch carrying these satellites, number of nanosatellites deployed to Lower Earth Orbit (LEO) in 2018 was larger than in the previous 10 years combined [2]. this allowed an exponential growth in satellite imagery data production that is available for military and commercial use. Machine learning tools enable us to process high resolution, multi-spectral satellite imagery data to recognize objects at scale [3] and generate insights with practical industry applications. It allowa us to calculate global oil reserves, track tanker ships or estimate retail revenue based on the car count, all exceptionally valuable financial information. In this paper, we investigate various computer vision techniques to develop an optimal machine learning technique for object recognition problems at this unique type of the dataset: multi-spectral satellite imagery.

关键词： Convolutional Neural networks U-net computer Vision Object recognition Semantic Segmentation Geospatial data Satellite Imagery

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Using series of infrared data and SVM for breast normality evaluation 16

Using series of infrared data and SVM for breast normality e...

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16th ieee/acs international conference on computer systems and applications (aiccsa)

作者： Araujo, A. S. da Silva, T. A. E. Moran, M. B. H. Conci, A. Fed Fluminense Univ Comp Inst Niteroi RJ Brazil

ISBN: (纸本)9781728150529

Breast cancer is one of the cancer types most commonly diagnosed among women worldwide. Diagnostic techniques are constantly being developed. Dynamic thermography emerges as a tool to aid in this process. Images captured under dynamic protocol were used here to obtain breast behavior on achieving thermal equilibrium. the regions of interest (ROIs) are segmented from these images and used for analysis of the temperatures during the time of the exam. Features based on statistic and clustering are used in these analyses. Time series are formed with these features using combinations of intervals constructing subsets of different cardinalities for following their evolution over time. Groups of features are classified by Support Vector Machine, using the Leave-One-Out Cross-Validation method. Achieved results for classifications on healthy or abnormal breast from a sample of 64 breasts (half healthy and half with some abnormality) are presented.

关键词： thermography computer Aided Diagnosis Breast Cancer Pattern Recognition Support Vector Machine Time Series Feature Computation

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