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In construction industry, strength is a primary criterion in selecting a concrete for a particular application. Concrete used for construction gains strength over a long period of time after pouring. The characteristic strength of concrete that considered in structural design is defined as the compressive strength of a sample that has been aged for 28 days. So rapid and reliable prediction for the strength of concrete would be of great significance. Prediction of concrete strength, therefore, has been an active area of research and a considerable number of studies have been carried out. In this study, support vector machine model was proposed and developed for the prediction of concrete compressive strength at early age. The variables used in the prediction models were from the knowledge of the mix proportion elements and 7-day compressive strength. The models provide good estimation of compressive strength and yielded good correlations with the data used in this study relative to nonlinear multivariable regression. Moreover, the SVM model proved to be significant tool in prediction compressive strength of lightweight foamed concretes with minimal mean square errors and standard deviation.

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Support Vector Machine (SVM) is supervised machine learning technique which has become a popular technique for e-mail classifiers because its performance improves the accuracy of classification. The proposed method combines gain ratio (GR) which is feature selection method with one-class training SVM to increase the efficiency of the detection process and decrease the cost. The results show high accuracy up to 100% and less error rate with less number of feature to 5 features.

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In this paper, an Indian Number Handwriting Recognition Model (INHRM) is proposed. Mainly, the proposed model consists of four phases which are the image acquisition, image preprocessing, features extraction, and classification model. Initially, the captured images of the handwritten Indian numbers were enhanced and preprocessed to obtain the skeleton for the interested object. The extracted features of the handwritten Indian numbers were obtained by calculating four parameters for each captured number sample, these parameters are the number of starting points, the number of intersection points, the average zoning which consists of four values, and finally, the normalized chain vector of length of 10 elements. So, the resulted 16 values of the four parameters were arranged in a vectors of length of 16 elements. These features vectors were used in the training and testing processes of the proposed INHRM model. Multi-class SVM (MSVM) approach is suggested for the classification phase. An accumulation of 600 samples of various handwritten Indian numbers styles has been gathered from a group of 60 students. These samples were preprocessed, features extracted, then delivered to the classification phase by utilizing 500 samples of them for training while the remaining 100 samples were used for testing of the MSVM-classifier model. The results showed that the proposed INHRM achieved relatively high percentage of exactness of around 97%.

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Acute stress is the most common form of stress. It comes from demands and pressures of the recent past and anticipated demands and pressures of the near future. This research studied the stress on female students due to mathematical exercises in a noisy environment. Detection of this stress is important because it contributes to diverse pathophysiological changes including sudden death, ischemic diseases (myocardial infarction, angina), and wall motion abnormalities (the motion of a region of the heart muscle is abnormal), as well as to alterations in cardiac regulation as indexed by changes in sympathetic nervous system activity and hemostasis (process which causes bleeding to stop in order to keep blood within a damaged blood vessel unlike hemorrhage). Stress level is difficult to manage because it cannot be measured in a consistent and timely way. One current method to characterize an individual’s stress level is to conduct an interview or to administer a questionnaire during a visit with a physician or psychologist. HRV (Heart rate variability) can be analyzed using both time domain and frequency domain features. Selection of features which vary with the changes of the stress levels is significant and it is important to show relatively reliable behavior. Overall, heart rate variability spectra during baseline conditions related to Left ventricular hypertrophy and congestive heart failure are dominated by high frequency activity. Stress is accompanied by an increase in the Power Spectrum Density (PSD) of Low Frequency (LF) and decrease in PSD of High Frequency (HF). Data (ECG signal) was collected by AD (Data acquisition) Instrument from ten female subjects, in the age range of 20 to 24 years were of asked to perform three levels difficulties of arithmetic tasks. A total of ten statistical features were used in this research extracted through wavelet transform, including: Mean, Maximum, Minimum, Standard deviation, Variance, Mode, Median, power spectral density (PSD), energy, entropy and hybrid of them. The SVM (support vector machines) classifier give highest accuracy of 79.5 based on hybrid feature and ribo 3.7 wavelet through LF range.

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Abstract - This research proposes “Integrated Network Intrusion Detection System (INIDS)” which is NIDS for wire/wireless networks. INIDN consider features of the three layers; transport and Internet layers for wire and data link layer for wireless. The proposal is a Data Mining (DM)-based INIDS, which trained over a labeled wire and wireless datasets (each transaction labeled normal, intrusion name or unknown), INIDS is a hybrid IDS (anomaly and misuse). INIDS, train and construct two separated proposed models these are, Wire-NIDS and Wireless-NIDS then integrate the two models to build the final INIDS. Wire-NIDS use NSL-KDD dataset; use Principle Component Analysis (PCA) as a feature extraction, and use Support Vector Machine (SVM) with Artificial Neural Network (ANN) as classifiers. Wireless-NIDS use proposed Wdataset dataset, use Gain Ratio (GR) as feature selection, and use Naïve Bayesian (NB) as a classifier. The results obtained from executing the proposed INIDS model showing that Wire-NIDS and Wireless-NIDS classifier accuracy and detection rate is generally higher with the subset of features obtained by PCA (8 from 41) and GR (8 from 17) than with all sets of features. Proposed confusion matrix of INIDS gives less confusion in detection rates with reduced features.Keywords: IDS, SVM, ANN, NB, PCA, and GR.

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Due to the investigation of the images in several parts of the life and the arising of the fast technology make the management of these images an open research area. Basically, the color feature considered as informative information that can be extracted from the image and help in improve the application performance. Based on the literature, this research found that there are several datasets that content images considered as a colorful images but some of these images content poor color information. For that, it’s unfair to treat all the dataset images as colorful images and this may lead to unsuccessful classification due to unfair color features that extracted from these images. To overcome this problem, this paper has proposed a color detector that can be used as a pre-processing stage to separate the dataset images into two classes colorful and colorless. The experiments have been carried out by using Caltech 101 dataset and the proposed method shows high level of discriminative power.

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The huge explosion of information over World Wide Web forces us to use information security methods to keep it away fromintruders. One of these security methods is information hiding method. Advantage of this method over other security methods is hidingexistence of data using carrier to hold this data embedding inside it. Image-based information hiding represents one of widely usedhiding methods due to the image capability of holding large amount of data as well as its resistance to detectable distortion. In lastdecades, statistical methods (types of stego-analysis methods) are used to detect existing of hidden data. Therefore, areas that have colorvariation (edges area) are used to hide data instead of smooth areas. In this paper, Corners points are proposed to hide data instead ofedges, this to avoid statistical attacks that are used to expose hidden message. Additionally, this paper proposes clearing least significantbit (CLSB) method to retrieve data from stego-image without sending pixels' map; this will increase security of the proposed cornerbasedhidingmethod.Experimentalresultsshowthattheproposedmethodisrobustagainststatisticalattackscomparedwithedge-andsequential-basedhidingmethods.SVM classifier also confirms the outperformance of the proposed method over the previous methods by using Corel-1000 image dataset.

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Printed Arabic document image retrieval is a very important and needed system for many companies, governments and various users. In this paper, a printed Arabic document images retrieval system based on spotting the header words of official Arabic documents is proposed. The proposed system uses an efficient segmentation, preprocessing methods and an accurate proposed feature extraction method in order to prepare the document for classification process. Besides that, Support Vector Machine (SVM) is used for classification. The experiments show the system achieved best results of accuracy that is 96.8% by using polynomial kernel of SVM classifier.

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The Electrocardiogram (ECG) considered as one of the important issue in the medical field (hospitals and clinics), which is used to represent the health of a heart. Increasing patients of heart has supposed to design an automatic computerization technique to classify various abnormalities of the heart activities; to reduce the analysis time and detection mistakes. This research focusing on achieve high performance of classifying abnormal ECG by applying different methods. The first method is Discrete Wavelet Transform (DWT) with 4-level to transform the ECG signal and extract the feature extraction and Wavelet Energy (WE) during feature extraction as feature vector. In classification phase has used Support Vector Machine (SVM) to train datasets and classify the test samples, in matching phase, find closest vector of test to the training datasets method has used by applying Iterative Closest Point (ICP).

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The amount of the available data increases the ability to analyze and understand. The internet revolution has added billions of customer’s review data in its depots. This has given an interest in sentiment analysis and opinion mining in the recent years. People have to depend on machines to classify and process the data as there are terabytes of review data in stock of a single product. So that prediction customer sentiments is very important to analyze the reviews as it not only helps in increasing profits but also goes a long way in improving and bringing out better products. In this paper , we present a survey regarding the presently available techniques and applications that appear in the field of opinion mining , such as , economy , security , marketing , spam detection , decision making , and elections expectation.

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