For the generality of fuzzy ideals in TM-algebra, a cubic ideal in this algebra has been studied, such as cubic ideals and cubic T-ideals. Some properties of these ideals are investigated. Also, we show that the cubic T-ideal is a cubic ideal, but the converse is not generally valid. In addition, a cubic sub-algebra is defined, and new relations between the level subset and a cubic sub-algebra are discussed. After that, cubic ideals and cubic T-ideals under homomorphism are studied, and the image (pre-image) of cubic T-ideals is discussed. Finally, the Cartesian product of cubic ideals in Cartesian product TM-algebras is given. We proved that the product of two cubic ideals of the Cartesian product of two TM-algebras is also a cubic ideal.

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

In this paper, third order non-polynomial spline function is used to solve 2nd kind Volterra integral equations. Numerical examples are presented to illustrate the applications of this method, and to compare the computed results with other known methods.

This study investigates asset returns within the Iraq Stock Exchange by employing both the Fama-MacBeth regression model and the Fama-French three-factor model. The research involves the estimation of cross-sectional regressions wherein model parameters are subject to temporal variation, and the independent variables function as proxies. The dataset comprises information from the first quarter of 2010 to the first quarter of 2024, encompassing 22 publicly listed companies across six industrial sectors. The study explores methodological advancements through the application of the Single Index Model (SIM) and Kernel Weighted Regression (KWR) in both time series and cross-sectional analyses. The SIM outperformed the K

This contribution investigates the effect of the addition of the Hubbard U parameter on the electronic structural and mechanical properties of cubic (C-type) lanthanide sesquioxides (Ln2O3). Calculated Bader's charges confirm the ionic character of Lnsingle bondO bonds in the C-type Ln2O3. Estimated structural parameters (i.e., lattice constants) coincide with analogous experimental values. The calculated band gaps energies at the Ueff of 5 eV for these compounds exhibit a non-metallic character and Ueff of 6.5 eV reproduces the analogous experimental band gap of cerium sesquioxide Ce2O3. We have thoroughly investigated the effect of the O/Ce ratios and the effect of hafnium (Hf) and zirconium (Zr) dopants on the reduction energies of C