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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
Thu Jun 30 2022
Journal Name
Ipri Journal
Hybrid regimes: An Overview
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According to different types of democracy Indexes, hybrid regimes or those in the gray zone, make up the majority of regime transformations in the third wave of democracy. However, after nearly three decades, conceptual confusion about hybrid regimes persists and grows, while obstructing the accumulation of knowledge about the nature of hybrid regimes. This leads to significant political repercussions for democratization. This Paper attempts to provide a clearer view of different and overlapping concepts and classifications in this complex field, and sustain development in literature on democratic transformation. To achieve this, we followed an approach based on the classification of concepts and terms in three distinct categories, b

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Periodontal & Implant Science
Bio-hybrid dental implants prepared using stem cells with β-TCP-coated titanium and zirconia
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Purpose This study investigated periodontal ligament (PDL) restoration in osseointegrated implants using stem cells. Methods Commercial pure titanium and zirconium oxide (zirconia) were coated with beta-tricalcium phosphate (β-TCP) using a long-pulse Nd:YAG laser (1,064 nm). Isolated bone marrow mesenchymal cells (BMMSCs) from rabbit tibia and femur, isolated PDL stem cells (PDLSCs) from the lower right incisor, and co-cultured BMMSCs and PDLSCs were tested for periostin markers using an immunofluorescent assay. Implants with 3D-engineered tissue were implanted into the lower right central incisors after extraction from rabbits. Forty implants (Ti or zirconia) were subdivided according to the duration of implantation (healing period: 45 o

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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
Multichannel Optimization With Hybrid Spectral- Entropy Markers for Gender Identification Enhancement of Emotional-Based EEGs
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Publication Date
Sun Aug 07 2022
Journal Name
Nanomaterials
Efficient Heat Transfer Augmentation in Channels with Semicircle Ribs and Hybrid Al2O3-Cu/Water Nanofluids
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Global technological advancements drive daily energy consumption, generating additional carbon-induced climate challenges. Modifying process parameters, optimizing design, and employing high-performance working fluids are among the techniques offered by researchers for improving the thermal efficiency of heating and cooling systems. This study investigates the heat transfer enhancement of hybrid “Al2O3-Cu/water” nanofluids flowing in a two-dimensional channel with semicircle ribs. The novelty of this research is in employing semicircle ribs combined with hybrid nanofluids in turbulent flow regimes. A computer modeling approach using a finite volume approach with k-ω shear stress transport turbulence model was used in these simu

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Publication Date
Fri Jan 01 2021
Journal Name
Journal Of Engineering
A Proposed Adaptive Bitrate Scheme Based on Bandwidth Prediction Algorithm for Smoothly Video Streaming
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A robust video-bitrate adaptive scheme at client-aspect plays a significant role in keeping a good quality of video streaming technology experience. Video quality affects the amount of time the video has turned off playing due to the unfilled buffer state. Therefore to maintain a video streaming continuously with smooth bandwidth fluctuation, a video buffer structure based on adapting the video bitrate is considered in this work. Initially, the video buffer structure is formulated as an optimal control-theoretic problem that combines both video bitrate and video buffer feedback signals. While protecting the video buffer occupancy from exceeding the limited operating level can provide continuous video str

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Publication Date
Tue Oct 01 2019
Journal Name
2019 12th International Conference On Developments In Esystems Engineering (dese)
Roadway Deterioration Prediction Using Markov Chain Modeling (Wasit Governorate/ Iraq as a Case Study)
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Publication Date
Sun Mar 17 2019
Journal Name
Baghdad Science Journal
A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures
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Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate relationship among users over Movie Lens data set rating matrix. The advantages and disadvantages of each measure are spotted. From the study, a n

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Publication Date
Tue Dec 27 2022
Journal Name
2022 3rd Information Technology To Enhance E-learning And Other Application (it-ela)
Diabetes Prediction Using Machine Learning
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Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

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Publication Date
Mon Dec 30 2024
Journal Name
Iraqi Journal Of Science
The Impact of Fear and Anti-Predator Behavior on the Dynamics of Stage-Structure Prey–Predator Model With a Harvesting
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In this paper, an ecological model with stage-structure in prey population, fear, anti-predator and harvesting are suggested. Lotka-Volterra and Holling type II functional responses have been assumed to describe the feeding processes . The local and global stability of steady points of this model are established. Finally, the global dynamics are studied numerically to investigate the influence of the parameters on the solutions of the system, especially the effect of fear and anti-predation.

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Publication Date
Tue Dec 01 2015
Journal Name
Journal Of Economics And Administrative Sciences
A Comparison Between Some Estimator Methods of Linear Regression Model With Auto-Correlated Errors With Application Data for the Wheat in Iraq
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This research a study model of linear regression problem of autocorrelation of random error is spread when a normal distribution as used in linear regression analysis for relationship between variables and through this relationship can predict the value of a variable with the values of other variables, and was comparing methods (method of least squares, method of the average un-weighted, Thiel method and Laplace method) using the mean square error (MSE) boxes and simulation and the study included fore sizes of samples (15, 30, 60, 100). The results showed that the least-squares method is best, applying the fore methods of buckwheat production data and the cultivated area of the provinces of Iraq for years (2010), (2011), (2012),

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