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Bitcoin Prediction with a hybrid model
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In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction accuracy criterion and matching curve-fitting in this work demonstrated that if the residuals of the revised model are white noise, the forecasts are unbiased. Future work investigating robust hybrid model forecasting using fuzzy neural networks would be very interesting.

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Publication Date
Tue Mar 30 2021
Journal Name
Baghdad Science Journal
MyBotS Prototype on Social Media Discord with NLP
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The continuous growth in technology and technological devices has led to the development of machines to help ease various human-related activities. For instance, irrespective of the importance of information on the Steam platform, buyers or players still get little information related to the application. This is not encouraging despite the importance of information in this current globalization era. Therefore, it is necessary to develop an attractive and interactive application that allows users to ask questions and get answers, such as a chatbot, which can be implemented on Discord social media. Artificial Intelligence is a technique that allows machines to think and be able to make their own decisions. This research showed that the dis

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Publication Date
Fri Jan 01 2021
Journal Name
International Journal Of Agricultural And Statistical Sciences
STATISTICAL COMPUTATION AND APPLICATION WITH GENERALIZED POISSON DISTRIBUTION
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Scopus
Publication Date
Tue Jan 29 2019
Journal Name
Journal Of The College Of Education For Women
Personality Type and its Relationship with Withdrawal Observer
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The current research was aimed at the following:
1. Measurement of Personality Type Observer of the University students.
2. Identify the differences in Personality Type Observer among the University students according to variable of Sex (male / female). And according to variable of Specialization (scientific / literary)
3. Measurement of Withdrawal of the University students.
4. Identify the differences in Withdrawal among the University students according to variable of Sex (male / female). And according to variable of Specialization (scientific / literary).
5. Identify the relationship between Personality Type Observer and Withdrawal.
To achieve this aims of the research, the researchers set up the instrument is scale

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Publication Date
Tue Jan 09 2024
Journal Name
Nigerian Journal Of Parasitology
Toxoplasmosis and Metabolic Disorders among Children with Autism.
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Publication Date
Sat Apr 01 2023
Journal Name
Baghdad Science Journal
Coincidence of Fixed Points with Mixed Monotone Property
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The purpose of this paper is to introduce and prove some coupled coincidence fixed point theorems for self mappings satisfying -contractive condition with rational expressions on complete partially ordered metric spaces involving altering distance functions with mixed monotone property of the mapping. Our results improve and unify a multitude of coupled fixed point theorems and generalize some recent results in partially ordered metric space. An example is given to show the validity of our main result. 

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Publication Date
Thu Jun 16 2022
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Optimization algorithms for transportation problems with stochastic demand
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The purpose of this paper is to solve the stochastic demand for the unbalanced transport problem using heuristic algorithms to obtain the optimum solution, by minimizing the costs of transporting the gasoline product for the Oil Products Distribution Company of the Iraqi Ministry of Oil. The most important conclusions that were reached are the results prove the possibility of solving the random transportation problem when the demand is uncertain by the stochastic programming model. The most obvious finding to emerge from this work is that the genetic algorithm was able to address the problems of unbalanced transport, And the possibility of applying the model approved by the oil products distribution company in the Iraqi Ministry of Oil to m

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Scopus (11)
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Publication Date
Fri Oct 02 2015
Journal Name
American Journal Of Applied Sciences
Advances in Document Clustering with Evolutionary-Based Algorithms
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Document clustering is the process of organizing a particular electronic corpus of documents into subgroups of similar text features. Formerly, a number of conventional algorithms had been applied to perform document clustering. There are current endeavors to enhance clustering performance by employing evolutionary algorithms. Thus, such endeavors became an emerging topic gaining more attention in recent years. The aim of this paper is to present an up-to-date and self-contained review fully devoted to document clustering via evolutionary algorithms. It firstly provides a comprehensive inspection to the document clustering model revealing its various components with its related concepts. Then it shows and analyzes the principle research wor

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Publication Date
Fri Mar 18 2022
Journal Name
Aro-the Scientific Journal Of Koya University
Detecting Deepfakes with Deep Learning and Gabor Filters
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The proliferation of many editing programs based on artificial intelligence techniques has contributed to the emergence of deepfake technology. Deepfakes are committed to fabricating and falsifying facts by making a person do actions or say words that he never did or said. So that developing an algorithm for deepfakes detection is very important to discriminate real from fake media. Convolutional neural networks (CNNs) are among the most complex classifiers, but choosing the nature of the data fed to these networks is extremely important. For this reason, we capture fine texture details of input data frames using 16 Gabor filters indifferent directions and then feed them to a binary CNN classifier instead of using the red-green-blue

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Scopus (8)
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Publication Date
Fri Aug 05 2016
Journal Name
International Journal Of Advances In Scientific Research And Engineering
Image Encryption Using Modified AES with Bio-Chaotic
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Due to the vast using of digital images and the fast evolution in computer science and especially the using of images in the social network.This lead to focus on securing these images and protect it against attackers, many techniques are proposed to achieve this goal. In this paper we proposed a new chaotic method to enhance AES (Advanced Encryption Standards) by eliminating Mix-Columns transformation to reduce time consuming and using palmprint biometric and Lorenz chaotic system to enhance authentication and security of the image, by using chaotic system that adds more sensitivity to the encryption system and authentication for the system.

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Publication Date
Sun Mar 19 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Swelling Behavior of Cross-link PVA with Glutaraldehyde
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             The polymeric hydrogels composed of poly vinyl alcohol (m.wt 72000) and glutaraldehyde(5%,8% and 10%) , have been thermally prepared for the purpose of studying their swelling and drug  release behavior .      The swelling ratio was measured for all the hydrogel samples at 37°C, in three different media pH (1.2, 4.7 and 6.8) as a function of time. The results show  that the maximum swelling ratios were arranged as follows :pH =6.8 >  pH =4.7 > pH =1.2 hydrogels cross linked PVA showed a typical pH  responsive behavior such as high pH has maximum swelling while low  pH  shows minimum swelling.

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