In modern technology, the ownership of electronic data is the key to securing their privacy and identity from any trace or interference. Therefore, a new identity management system called Digital Identity Management, implemented throughout recent years, acts as a holder of the identity data to maintain the holder’s privacy and prevent identity theft. Therefore, an overwhelming number of users have two major problems, users who own data and third-party applications will handle it, and users who have no ownership of their data. Maintaining these identities will be a challenge these days. This paper proposes a system that solves the problem using blockchain technology for Digital Identity Management systems. Blockchain is a powerful technique to build a digital identity in chain matters that enables a secure environment. The idea of Blockchain is to distribute the data across multiple devices in a cryptographic way, which will reduce the ability to an impossible level. Therefore, in this paper a proposed Digital Identity based on Blockchain (ERC 725, and ERC 735) with MD6 as a hashing algorithm will be implemented in a Secure smart contract can prevent function calls from being carried out until the sender has received confirmation from a reliable issuer; for example, we might include a feature that restricts smart contract interactions to legitimate users only. Many additional use cases are possible with ERC-725, including multi-sig execution approvals and contract call verification in place of key validation.
Due to the difficulties that Iraqi students face when writing in the English language, this preliminary study aimed to improve students' writing skills by using online platforms remotely. Sixty first-year students from Al-Furat Al–Awsat Technical University participated in this study. Through these platforms, the researchers relied on stimuli, such as images, icons, and short titles to allow for deeper and more accurate participations. Data were collected through corrections, observations, and feedback from the researchers and peers. In addition, two pre and post-tests were conducted. The quantitative data were analysed by SPSS statistical Editor, whereas the qualitative data were analyzed using the Piot table, an Excel sheet. The resu
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In this paper we investigate the automatic recognition of emotion in text. We propose a new method for emotion recognition based on the PPM (PPM is short for Prediction by Partial Matching) character-based text compression scheme in order to recognize Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method is very effective when compared with traditional word-based text classification methods. We have also found that our method works best if the sizes of text in all classes used for training are similar, and that performance significantly improves with increased data.
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A LiF (TLD-700) PTFED disc has adiameter of (13mm) and thickness of (0.4mm) for study the response and sensetivity of this material for gamma and beta rays by using (TOLEDO) system from pitman company. In order to calibrate the system and studying the calibration factor. Discs were irradiated for Gamma and Beta rays and comparing with the theoretical doses. The exposure range is between 15×10-2 mGy to 1000×10-2 mGy. These doses are within the range of normal radiation field for workers.
In this paper, an algorithm for reconstruction of a completely lost blocks using Modified
Hybrid Transform. The algorithms examined in this paper do not require a DC estimation
method or interpolation. The reconstruction achieved using matrix manipulation based on
Modified Hybrid transform. Also adopted in this paper smart matrix (Detection Matrix) to detect
the missing blocks for the purpose of rebuilding it. We further asses the performance of the
Modified Hybrid Transform in lost block reconstruction application. Also this paper discusses
the effect of using multiwavelet and 3D Radon in lost block reconstruction.
Machine learning-based techniques are used widely for the classification of images into various categories. The advancement of Convolutional Neural Network (CNN) affects the field of computer vision on a large scale. It has been applied to classify and localize objects in images. Among the fields of applications of CNN, it has been applied to understand huge unstructured astronomical data being collected every second. Galaxies have diverse and complex shapes and their morphology carries fundamental information about the whole universe. Studying these galaxies has been a tremendous task for the researchers around the world. Researchers have already applied some basic CNN models to predict the morphological classes
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This paper presents a study of wavelet self-organizing maps (WSOM) for face recognition. The WSOM is a feed forward network that estimates optimized wavelet based for the discrete wavelet transform (DWT) on the basis of the distribution of the input data, where wavelet basis transforms are used as activation function.