The research aims to shed light on the role of artificial intelligence in achieving Ambidexterity performance, as banks work to take advantage of modern technologies, artificial intelligence is an innovation that is expected to have a long-term impact, as well as banks can improve the quality of their services and analyze data to ensure that customers' future needs are understood. . The Bank of Baghdad and the Middle East Bank were chosen as a community for the study because they had a role in the economic development of the country as well as their active role in the banking market. A sample of department managers was highlighted in collecting data and extracting results based on the checklist, which is the main tool for the study. An assumption was built that artificial intelligence contributes to building Ambidexterity performance ... as the study reached a set of results, namely the existence of different gaps for the dimensions of artificial intelligence, as the largest size was the space for the dimension (21%) of the Bank of Baghdad for the dimension of training and development. (21%), also that there is a high gap size for the Middle East bank due to the availability of expertise (17%). Also, after exploring opportunities, there was a high gap size for the Middle East bank.
Cover crops (CC) improve soil quality, including soil microbial enzymatic activities and soil chemical parameters. Scientific studies conducted in research centers have shown positive effects of CC on soil enzymatic activities; however, studies conducted in farmer fields are lacking in the literature. The objective of this study was to quantify CC effects on soil microbial enzymatic activities (β-glucosidase, β-glucosaminidase, fluorescein diacetate hydrolase, and dehydrogenase) under a corn (Zea mays L.)–soybean (Glycine max (L.) Merr.) rotation. The study was conducted in 2016 and 2018 in Chariton County, Missouri, where CC were first established in 2012. All tested soil enzyme levels were significantly different between 2016 and 2018
... Show MoreSemi-parametric models analysis is one of the most interesting subjects in recent studies due to give an efficient model estimation. The problem when the response variable has one of two values either 0 ( no response) or one – with response which is called the logistic regression model.
We compare two methods Bayesian and . Then the results were compared using MSe criteria.
A simulation had been used to study the empirical behavior for the Logistic model , with different sample sizes and variances. The results using represent that the Bayesian method is better than the at small samples sizes.
... Show MoreDifferent formula of bioagents (Rhizobium cicceri cp-93, Azospirillum sp.,
Pseudomonas fluorescence, Trichoderma harzianum ) used in this study as a
biofertilizer on wheat crop with two level of chemical fertilizer (0 and 12.5
kg/donm Dap) compared to 50kg/donm Dap (standard amount).the study carried out
in Iraq/Diyala –Alkhales during November 2014,results showed significant increase
in no. of spikes, no. of spikelet’s, length of spike ,Weight of 1000 seed and yield of
one m2 when adding (Rhizobium cicceri cp-93,Azospirillumsp+ Trichoderma
harzianum +12.5 kg/donm Dap) in comparison with the 50kg/donm Dap. Other
formulas recorded same results with the treatment 50kg/Donm Dap with not
significant differences
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
Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall
... Show MoreThe purpose of this paper is to introduce and study the concepts of fuzzy generalized open sets, fuzzy generalized closed sets, generalized continuous fuzzy proper functions and prove results about these concepts.
Abstract
This research presents a on-line cognitive tuning control algorithm for the nonlinear controller of path-tracking for dynamic wheeled mobile robot to stabilize and follow a continuous reference path with minimum tracking pose error. The goal of the proposed structure of a hybrid (Bees-PSO) algorithm is to find and tune the values of the control gains of the nonlinear (neural and back-stepping method) controllers as a simple on-line with fast tuning techniques in order to obtain the best torques actions of the wheels for the cart mobile robot from the proposed two controllers. Simulation results (Matlab Package 2012a) show that the nonlinear neural controller with hybrid Bees-PSO cognitive algorithm is m
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