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Biometrics refers to the automatic authentication of a living person based on physiological or behavioral characteristics. Hand recognition involves an analysis and measures of the features of the hand. In this work, an authentication system based on hand geometry (i.e., fingers' features) using artificial neural network are proposed and implemented. The features of the fingers are extracted from the hand images which are producing after passing through a sequence of preprocessing stages. The authentication process consists of two phases, enrollment phase: image capture, smoothing, binarization, gaps removal, edge detection, thinning and chain coding were implemented. In the verification phase, feature vector to the unknown person is extracted from its hand image and determine the most discriminating features. The proposed system suggested the BPNN for training and testing data. In final step, we encode and decode features to produce hash value based on base64 method. In this research the used data is the CASIA dataset, which consists of several image snapshots taken by scanners working at different wavelengths, in this study all the snapshots belong to different wavelengths are taken. The experimental results showed that the extracted feature vector has good discrimination capabilities that led to a recognition rate over 99.50%. [ABSTRACT FROM AUTHOR]

Electroencephalogram signals show how the brain's electricity is working. An EEG signal is a measurement of voltage changes caused by the flow of ions through the brain's neurons. They have been studied in medical research to help find out what's wrong with people with Alzheimer's and epilepsy. They have also been used in brain-computer interface (BCI) applications. This paper discusses how well the electroencephalogram (EEG) can tell the difference between the truth and a lie based on brain waves (EEG). In the past, the feature sets were introduced as part of an EEGbased identification system. In this paper, they are tested as parts of a detection system. The statistical moments serve as the foundation for the examined feature set. For classification tasks, publicly available EEG datasets like the Dryad dataset, obtained from 15 participants, are fed into a feedforward neural network classifier. The 12 channels were trained separately, where each channel was divided into a different number of blocks, and the results indicated that some channels were bad. Some were very encouraging, reaching 100% in block number 16. Comparison with other recently published efforts shows promising identification results, with a single channel and a few features achieving the best results, up to 100% accuracy. [ABSTRACT FROM AUTHOR]

Remote sensing is an advanced technique that can be used to detect hydrocarbons on land, and its advantages include rapid and low-cost detection. The detection of hydrocarbons on the surface is essential for environmental monitoring and the purpose of exploration. For oil and gas companies, the ability to locate hydrocarbon spill sites is important information for the success of future exploration wells. This study aimed to determine the quantities of hydrocarbons mixed with the soil as an indicator of its accumulation in the subsurface. In this paper, temperature data obtained from the landsat-8 satellite were used to determine the extent of the oil spill in the area using spatial interpolation and gradient techniques. Kriging interpolation is based on statistical models (i.e. including auto-correlation) geostatistical techniques can produce a forecast surface and also provide accurate forecasts. The results showed that there are 60 sites of thermal anomalies and temperature values are lowest and highest 23.2 oC to 91.11 oC, in the study area. Three different sites were chosen in terms of area and effect. They are located in the northern and southern Rumaila and Zubair fields. The location of the oil spills was determined with high accuracy after identifying the anomalies. [ABSTRACT FROM AUTHOR]

With the spread use of internet, especially the web of social media, an unusual quantity of information is found that includes a number of study fields such as psychology, entertainment, sociology, business, news, politics, and other cultural fields of nations. Data mining methodologies that deal with social media allows producing enjoyable scene on the human behaviour and interaction. This paper demonstrates the application and precision of sentiment analysis using traditional feedforward and two of recurrent neural networks (gated recurrent unit (GRU) and long short term memory (LSTM)) to find the differences between them. In order to test the system’s performance, a set of tests is applied on two public datasets. The first dataset is collected data from IMDB that contains movie reviews expressed through long sentences of English, whereas the second dataset is a collection of keyword search results of tweets using the Twitter Search API; these tweets are written in English words with short sentences. In this work, a certain pre-processing operation is added to the system and a set of tests is conducted to evaluate the performance enhancement on the whole system due to the addition of these operations. The results of the usage of the traditional feedforward neural networks are poor and do not perform the desired purpose in analysis, because of their inability to save information at a long term and, therefore, their loss of efficiency. While the results of using GRU and LSTM are relatively good and do perform the desired purpose in analysis. A recurrent neural network has been built so that any type of text-related data can be pushed to get the polarity of sentiment by multi deep operations that are dependent on the extracted information. [ABSTRACT FROM AUTHOR]

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In this study, the approach of combined features from two simultaneous Electroencephalogram (EEG) channels when a user is performing a certain mental task is discussed to increase the discrimination degree among subject classes, hence the visibility of using sets of features extracted from a single channel was investigated in previously published articles. The feature sets considered in previous studies is utilized to establish a combined set of features extracted from two channels. The first feature set is the energy density of power spectra of Discrete Fourier Transform (DFT) or Discrete Cosine Transform; the second one is the set of statistical moments of Discrete Wavelet Transform (DWT). Euclidean distance metric is used to accomplish feature set matching task. The combinations of features from two EEG channels showed high accuracy for the identification system, and competitive results for the verification system. The best achieved identification accuracy is (100%) for all proposed feature sets. For verification mode the best achieved Half Total Error Rate (HTER) is (0.88) with accuracy (99.12%) on Colorado State University (CSU) dataset, and (0.26) with accuracy (99.97%) on Motor Movement/Imagery (MMI) dataset. [ABSTRACT FROM AUTHOR]

The increased use of computer and internet had been related to the wide use of multimedia information. The requirement for protecting this information has risen dramatically. To prevent the confidential information from being tampered with, one needs to apply some cryptographic techniques. Most of cryptographic strategies have one similar weak point that is the information is centralized. To overcome this drawback the secret sharing was introduced. It's a technique to distribute a secret among a group of members, such that every member owns a share of the secret; but only a particular combination of shares could reveal the secret. Individual shares reveal nothing about the secret. The major challenge faces image secret sharing is the shadow size; that's the complete size of the lowest needed of shares for revealing is greater than the original secret file. So the core of this work is to use different transform coding strategies in order to get as much as possible the smallest share size. In this paper Compressive Sharing System for Images Using Transform Coding and Blackely Method based on transform coding illustration are introduced. The introduced compressive secret sharing scheme using an appropriate transform (Discrete cosine transform and Wavelet) are applied to de-correlate the image samples, then feeding the output (i.e., compressed image data) to the diffusion scheme which is applied to remove any statistical redundancy or bits of important attribute that will exist within the compressed stream and in the last the (k, n) threshold secret sharing scheme, where n is the number of generated shares and k is the minimum needed shares for revealing. For making a certain high security level, each produced share is passed through stream ciphering depends on an individual encryption key belongs to the shareholder. [ABSTRACT FROM AUTHOR]

In this paper a simple and fast image compression scheme is proposed, it is based on using wavelet transform to decompose the image signal and then using polynomial approximation to prune the smoothing component of the image band. The architect of proposed coding scheme is high synthetic where the error produced due to polynomial approximation in addition to the detail sub-band data are coded using both quantization and Quadtree spatial coding. As a last stage of the encoding process shift encoding is used as a simple and efficient entropy encoder to compress the outcomes of the previous stage. The test results indicate that the proposed system can produce a promising compression performance while preserving the image quality level. [ABSTRACT FROM AUTHOR]

The article discusses a steganographic method based on discrete cosine transform (DCT) for gray scale images. The embedding approach was designed to reach efficient tradeoff to maximize the amount of hidden message, minimize distortion between cover image and stego-image, and maximize validity of embedding. It says that the method is likely to create safe embedding area in the middle and high frequency area of the DCT domain via magnitude modulation technique.