Loading...
Search for:
support-vector-machine--svm
0.016 seconds
Total 59 records
AIDSLK: an anomaly based intrusion detection system in linux kernel
, Article Communications in Computer and Information Science ; Volume 31 , 2009 , Pages 232-243 ; 18650929 (ISSN); 9783642004049 (ISBN) ; Azmi, R ; Berenji, S ; Sharif University of Technology
2009
Abstract
The growth of intelligent attacks has prompted the designers to envision the intrusion detection as a built-in process in operating systems. This paper investigates a novel anomaly-based intrusion detection mechanism which utilizes the manner of interactions between users and kernel processes. An adequate feature list has been prepared for distinction between normal and anomalous behavior. The method used is introducing a new component to Linux kernel as a wrapper module with necessary hook function to log initial data for preparing desired features list. SVM neural network was applied to classify and recognize input vectors. The sequence of delayed input vectors of features was appended to...
EEG based Analysis and Classification of Children with Learning Disability Compared to Normal Children
, M.Sc. Thesis Sharif University of Technology ; Jahed, Mehran (Supervisor)
Abstract
Learning disability (LD) is a neurological condition that interferes with an individual’s ability to store, process, or produce information. There are different types of learning disabilities affecting reading, writing, speaking, spelling, etc. Based on a study conducted by National Center for Learning Disabilities, 2.4 million American public school students are diagnosed with learning disability. They attend school in order to learn and be successful while they do not know their learning process is different from their peers. LD diagnosis in children is especially important as such cases must be identified early enough in order to provide them with proper education.This project targets LD...
Intelligent Anomaly-Based Intrusion Detection in Linux Kernel
, M.Sc. Thesis Sharif University of Technology ; Azmi, Reza (Supervisor)
Abstract
The growth of intelligent attacks has prompted the designers to envision the intrusion detection as a built-in process in operating systems. This thesis investigates a novel anomaly-based intrusion detection mechanism which utilizes the manner of interactions between users and kernel processes to bring functionality to this notion. In fact, this mechanism is inspired by homeostatic behavior of an organism. Homeostatic is the property of an open system or a closed system, particularly a living organism, which regulates its internal environment to maintain a stable, constant condition. Such a developed mechanism can provide the computer system with a high level of protection from artificial...
Parental control based on speaker class verification
, Article IEEE Transactions on Consumer Electronics ; Volume 54, Issue 3 , 2008 , Pages 1244-1251 ; 00983063 (ISSN) ; Sameti, H ; Shirali Shahreza, M ; Sharif University of Technology
2008
Abstract
Restricting children access to materials unsuitable for them such as violence scenes is very important for parents. So there is a feature named Parental Control in devices such as televisions and computers to define the contents children can access. The parental control setting must be protected from children and is usually done by a password. In this paper, we propose a new method for distinguishing between adult users and child users based on human speech. In our proposed method, the user must say a word and the adult users are identified by processing the speech. Our current implementation has 92.5% accuracy for distinguishing adult users from children. © 2008 IEEE
An Intrusion Detection System for the Grid Environment
, M.Sc. Thesis Sharif University of Technology ; Jalili, Rasool (Supervisor)
Abstract
Existing Intrusion Detection Systems (IDSs) are not designed to deal with all categories of processing environments. This thesis focuses on IDSs for the Grid computing environment, and concentrates on feature selection and performance. An existing framework, Globus, is used as the basis for the consideration and development of the research issue in Grid computing. The system is based on two engine designs: (a) Signature and (b) Support Vector Machine; SVM has been selected for pattern discovery in traffic analysis. We found that the performance of the system greatly depends on the efficiency of the underlying framework and the number of Intrusion Detection System instances. We demonstrate...
Face Detection in Color Images
, M.Sc. Thesis Sharif University of Technology ; Kasaei, Shohreh (Supervisor)
Abstract
Human face detection is an important research area with several applications such as human computer interface (HCI), face recognition, surveillance systems, security systems, and content-based image retrieval (CBIR). Face detection problem can be stated as “determining whether there are human faces in the image” and if there are “returning the location of each human face in the image” regardless of its position, size, scale, orientation, and lighting condition. In this thesis, we have proposed a new face detection method which combines the AdaBoost algorithm with skin color information and support vector machine (SVM). First, a cascade classifier based on AdaBoost is used to detect faces in...
Real Time Car Model and Color Recognition Using SVM
, M.Sc. Thesis Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
Recently many works has been focused on Automatic Security Systems. Car type and color recognition systems are needed in vehicle based access control systems in buildings, outdoor sites and even housing estates. Another usage of car type and color recognition is when a certain model of car is being investigated in highways and streets. In this thesis two independent systems are designed; one for car type recognition and another for car color recognition. They both require to know the license plate location, so we proposed a license plate detection system. In the license plate detection system, first the plate candidates are extracted from image by gradient and morphological operations, then...
Genome-Wide Association Study via Machine Learning Techniques
, M.Sc. Thesis Sharif University of Technology ; Fatemizadeh, Emad (Supervisor) ; Motahari, Abolfazl (Co-Advisor)
Abstract
Development of DNA sequencing technologies in the recent years magnifies the need for computational tools in genomic data processing, and thus has attracted inten- sive research interest to this area. Among them, Genome-Wide Association Study (GWAS) refers to discovering of causal relationships among genetic sequences of living organisms and the macroscopic phenotypes present in their physiological structure. Chosen phenotypes for genomic association studies are mostly vulnerability or im- munity to common genetic diseases. Conventional methods in GWAS consists of statistical hypothesis testing algorithms in case/control approaches; Most of which are based upon single-locus analysis and...
Hybrid Multiscale Modeling of Cancer Cell Behavior
, Ph.D. Dissertation Sharif University of Technology ; Habibi, Jafar (Supervisor)
Abstract
Cancer is a class of diseases characterized by out-of-control cell growth. Cancer is among the leading causes of death worldwide.Cancer modeling is increasingly being recognized as a powerful tool to refine hypotheses, focus experiments, and enable predictions that are more accurate.We investigate a three-dimensional multiscale model of vascular tumour growth, which couples blood flow, angiogenesis, vascular remodelling, nutrient/growth factor transport, movement of, and interactions between, normal and tumour cells. We constructed a hybrid multi- scale agent-based model that combines continuous and discrete methods.Each cell is represented as an agent. The agents have rules that they must...
Steganalysis of Digital Images Based on Optimization in Feature Space
, M.Sc. Thesis Sharif University of Technology ; Ghaemmaghami, Shahrokh (Supervisor)
Abstract
Nowadays, steganography is one of the secure communication methods. Exploitation of modern technologies has increased transmission bandwidth to a considerable scale. As a result of this, the multimedia signals such as Audio and Image have been used in communications widely. This application has led to the prevalent use of these signals as cover signals for carrying hidden messages. Given the proliferation of digital images, especially on the Internet, and given the large amount of redundant bits present in the digital representation of an image, images are the most popular cover objects for steganography. Simultaneously, steganalysis tries to defeat the very purpose of steganography by...
Vibration-Based Fault-Diagnosis of Rotating Machinery Relying on Frequency Transforms Time -
, M.Sc. Thesis Sharif University of Technology ; Manzuri, Mohammad Taghi (Supervisor)
Abstract
This thesis presents several methods time-frequency based approach for classifying the vibration signals of rotatery machine. It uses the features extracted from the time-frequency distribution (TFD) of the vibration signal segments. Results of applying the method to a database of real signals reveal that, for the given classification task, the selected features consistently exhibit a high degree of discrimination between the vibration signals collected from healthy and fault machine. A comparison between the performances of the features extracted from several TFDs shows that the STFT slightly outperforms other reduced interference TFDs.
Monotonic Change Point Estimation in Multistage Profiles
, M.Sc. Thesis Sharif University of Technology ; Khedmati, Majid (Supervisor)
Abstract
In this thesis, a hybrid method is proposed to estimate the change point in the parameters of simple linear profiles in multistage processes under monotonic changes. In monotonic changes, the type of change is not known a priori, and the only assumption is the changes are of non-decreasing (isotonic) or non-increasing (monotonic) type. In the proposed method, at first, the stages and the parameters experiencing the change are identified and then, the changes occurred in these stages and parameters are identified and examined based on the moving window approach and support vector machine (SVM) algorithm. Finally, the maximum likelihood estimator of the change point is proposed. The...
Machine Learning in 2D Compressed Sensing Datasets
, M.Sc. Thesis Sharif University of Technology ; Babaiezadeh, Massoud (Supervisor)
Abstract
Compressed Sensing (CS) technique refers to the digitalization process that efficiently reduces the number of measurements below the Nyquist rate while preserving signal structure. This technique was originally developed for the analysis of vector datasets. An x ∈R^n vector is transformed into an y ∈R^m vector so that n≪m. For a sufficient number of measurements, this transformation has been shown to preserve the signal structure. Therefore, the technique has been applied to machine learning applications.2D-CS was further developed for matrices (image datasets) so that they could be directly applied to matrices without flattening. X ∈R^(n×n) is transformed into Y ∈R^(m×m) via 2D-CD such...
BSS: Boosted steganography scheme with cover image preprocessing
, Article Expert Systems with Applications ; Volume 37, Issue 12 , December , 2010 , Pages 7703-7710 ; 09574174 (ISSN) ; Jamzad, M ; Sharif University of Technology
2010
Abstract
The existing powerful steganalyzers can find out the presence of secret information in images with high accuracy. Increasing the embedding capacity of cover images reduces the detection risk of stego images. In this respect, we introduce boosted steganography scheme (BSS) that has a preprocessing stage before applying steganography methods. The goal of BSS is increasing the undetectability of stego images. Due to the dependence of embedding capacity of images to their content, we proposed an ensemble steganalyzer to estimate the embedding capacity of each cover image. Since the content of cover images has less significance in steganography, therefore to have more security, the steganographer...
Identification of Conductive Particles in Transformer Oil Model using Partial Discharge Signal
, M.Sc. Thesis Sharif University of Technology ; Vakilian, Mehdi (Supervisor)
Abstract
Transformer are one of the most important equipment in transmission and distribution network. Transformer unplanned outage have severe impacts on the continuity of power system operation and is also an irreparable economic harm to power network operators. To improve the reliability of transformers and to achieve an optimum operation cost, online condition monitoring is inevitbale. Information about the quality of the transformer insulation system is known as the best parameter to be monitored in transformer. Since partiale discharge (PD) signals are initiated long before the beginning of a severe damage, monitoring and its evaluation can be employed to warn the operator. Data mining on the...
Intelligent-Based Anti-Islanding Methods in Distributed Generation
, M.Sc. Thesis Sharif University of Technology ; Ranjbar, Ali Mohammad (Supervisor)
Abstract
Islanding detection methods are divided into three main groups as remote, active and passive. Although passive schemes have larger Non Detection Zones (NDZ) relative to other schemes, they are more used in utilities due to their low costs and less PQ problems than in other schemes. Passive Schemes are based on the measurements of passive system parameters. These parameters are measured at the point of common coupling (PCC). A new approach in passive techniques is the use of data-mining to classify the system parameters which affect the islanding detection. Therefore, by finding an efficient and robust data-mining algorithm, the inherent problem of passive schemes, which is their relatively...
Automatic Headline Generation for Persian News Texts
, M.Sc. Thesis Sharif University of Technology ; Ghassem-Sani, Gholamreza (Supervisor)
Abstract
The news headlines should represent the main and the most important topics of their stories. The task of selecting an appropriate headline for news stories is mainly done by journalists. The goal of this project has been the design and implementation of a system to automate this task, that is generating headlines for news. This task has been done for Persian news stories. There are various methods for automatic headline generation in English and some other languages, but no work has been done for Persian, yet. Thus, we have adopted some of the ideas from those methods, and do the remaining by our initiation. Our proposed method consists of three main parts: keyword extraction, most important...
Utilizing Latent Topic Models for Persian Document Classification and Providing Appropriate Solutions to Improve It
, M.Sc. Thesis Sharif University of Technology ; Bahrani, Mohammad (Supervisor) ; Vazirnezhad, Bahram (Co-Advisor)
Abstract
Text classification accompanied by high precision has become a challenging issue in computational linguistics and natural language processing science. Proper data set accessibility, utilizing the best method and prominent linguistics features has been always regarded as the basic concern of this process. The following study relying on Bijan Khan Corpus is tried to represent keywords vectors of different documents using tf_idf. These vectors are regarded as an input for latent topic models algorithms including probabilistic latent semantic analysis. The output of this algorithm will be the documents feature vectors which will be later used in order to train different classifiers like K...
Application of Data Mining in Healtcare
, M.Sc. Thesis Sharif University of Technology ; Salmasi, Nasser (Supervisor)
Abstract
Data mining is the one of top ten developing knowledge in the world. This study followed three fold objectives; Firstly, An efficient model based on data mining algorithms is proposed to predict the duration of hospitalization time for patients of digestive system disease that need short term care. Duration of hospitalization is an important criterion to be used for predicting the hospital resources. In order to, a combined model based on CHAID and C.5 decision trees and a neural network is suggested. The suggested model predict the duration of hospitalization with 82% accuracy. The second object of this study is to propose an algorithm based on likelihood ratio. The suggested algorithm...
Application of Image Processing in Weed Management
, M.Sc. Thesis Sharif University of Technology ; Haj Sadeghi, Khosrow (Supervisor)
Abstract
Weed management is the important issue in agriculture. Using herbicides is one of the strategies to control weed. But using huge amount of herbicides are destructive for environment. Smart spraying system is an impressive solution for this problem. This system detects weeds and sprays just them instead of spraying overall field. In this thesis a new method for plant detection is presented by using Lab color space. We determine the type of plants (broadleaf/grass) to spray specific herbicides onto specific type of plant. One feature of grass plants (the parallel edges of leaf) is used to detect grass plants. A convolutional neural network with four layers and fuzzy logic are used to separate...