Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for
Modern statistical techniques offer a range of methodologies for modelling time series data, with conditional and unconditional approaches providing complementary insights that enhance overall model accuracy. This article introduced a modified ARIMA model employing conditional and unconditional parameter estimates. The methodology for the new model based on novel methods is provided. The prediction process, one and two steps ahead, is covered in detail, and a novel algorithm is presented. The best model is picked based on various measurement criteria, such as coefficient of determination (R2), root mean squared error (RMSE), and mean absolute scaled error (MASE). The suggested model is applied to a monthly petrol sales dataset (Jan
... Show MoreThis work is devoted to define new generalized gamma and beta functions involving the recently suggested seven-parameter Mittag-Leffler function, followed by a review of all related special cases. In addition, necessary investigations are affirmed for the new generalized beta function, including, Mellin transform, differential formulas, integral representations, and essential summation relations. Furthermore, crucial statistical application has been realized for the new generalized beta function.
In this paper, chip and powder copper are used as reinforcing phase in polyester matrix to form composites. Mechanical properties such as flexural strength and impact test of polymer reinforcement copper (powder and chip) were done, the maximum flexural strength for the polymer reinforcement with copper (powder and chip) are (85.13 Mpa) and (50.08 Mpa) respectively was obtained, while the maximum observation energy of the impact test for the polymer reinforcement with copper (powder and chip) are (0.85 J) and (0.4 J) respectively
The Mishrif Formation is one of the most important geological formations in Iraq consisting of limestone, marl, and shale layers since it is one of the main oil producing reservoirs in the country, which contain a significant portion of Iraq's oil reserves. The formation has been extensively explored and developed by the Iraqi government and international oil companies, with many oil fields being developed within it. The accurate evaluation of the Mishrif formation is key to the successful exploitation of this field. However, its geological complexity poses significant challenges for oil production, requiring advanced techniques to accurately evaluate its petrophysical properties.
This study used advanced well-logging analysi
... Show MoreThis paper was aimed to evaluate the polyurethane (PU) and polyurethane/polyvinyl chloride (90 wt. % / 10 wt. %) as organic coating of carbon steel substrate against marine environment (3.5 wt.% NaCl aqueous solution) as a severe corrosion environment . The electrochemical impedance spectroscopy (EIS) and fitting impedance data by ZsimpWin 3.22 software were used to estimate the physical barrier of the samples for different exposure times. Different equivalent electrical circuits were proposed for the physical barrier at different immersion times to get appropriate fitting .Both PU and PU/PVC coatings showed excellent corrosion protection ability for steel .The PU/PVC coating showed better protection and stability than PU coating against
... Show MoreThis article includes the preparation of luminescence materials from rare earth (Eu ) ion doping Yttrium Oxide (Y2O3) 70% and SiO2 25% and study the characteristics of phosphors for ultraviolet to visible conversion. The phosphor materials have been synthesized by two steps: Preparing the powder by solid state method using Y2O3, SiO2 and Eu2O3 with doping materials concentration (70%, 25% and 5%) respectively and different calcination temperature (1000, 1200 and 1400 oC).
The second step is to prepare the colloid solution by dispersing the produced powder in a polyvinyl alcohol solution (4%) .
Powde
... Show MoreIn this work gold nanoparticles (AuNPs), were prepared. Chemical method (Seed-Growth) was used to prepare it, then doping AuNPs with porous silicon (PS), used silicon wafer p-type to produce (PS) the processes doping achieved by electrochemical etching, the solution etching consist of HF, ethanol and AuNPs suspension, the result UV-visible absorption for AuNPs suspension showed the single peak located at ~(530 – 521) nm that related to SPR, the single peak is confirmed that the NPs present in the suspension is spherical shape and non-aggregated. X-ray diffraction analysis indicated growth AuNPs with PS. compare the PS layer without AuNPs and with AuNPs doped for electrical properties and sensitivity properties we found AuNPs:PS is more
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