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

Genome sequencing has significantly improved the understanding of HIV and AIDS through accurate data on viral transmission, evolution and anti-therapeutic processes. Deep learning algorithms, like the Fined-Tuned Gradient Descent Fused Multi-Kernal Convolutional Neural Network (FGD-MCNN), can predict strain behaviour and evaluate complex patterns. Using genotypic-phenotypic data obtained from the Stanford University HIV Drug Resistance Database, the FGD-MCNN created three files covering various antiretroviral medications for HIV predictions and drug resistance. These files include PIs, NRTIs and NNRTIs. FGD-MCNNs classify genetic sequences as vulnerable or resistant to antiretroviral drugs by analyzing chromosomal information and id

Two nanocomposite corrosion inhibitors were synthesized from Aloe vera extract: one incorporating sodium thiosulfate and the other silver nitrate. Both nanocomposites were subjected to structural characterization using atomic force microscopy (AFM), which revealed distinct morphological features. The sodium thiosulfate-based nanocomposite exhibited uniform and well-dispersed nanoparticles with an average size of 47.51 nm, suggesting a stable and homogeneous distribution. In contrast, the silver nitrate-based nanocomposite displayed slightly larger particles with an average diameter of 58.34 nm, indicating a tendency toward moderate aggregation. The corrosion inhibition performance of these nanocomposites for carbon steel (CS1137) was invest

This article showcases the development and utilization of a side-polished fiber optic sensor that can identify altered refractive index levels within a glucose solution through the investigation of the surface Plasmon resonance (SPR) effect. The aim was to enhance efficiency by means of the placement of a 50 nm-thick layer of gold at the D-shape fiber sensing area. The detector was fabricated by utilizing a silica optical fiber (SOF), which underwent a cladding stripping process that resulted in three distinct lengths, followed by a polishing method to remove a portion of the fiber diameter and produce a cross-sectional D-shape. During experimentation with glucose solution, the side-polished fiber optic sensor revealed an adept detection

Two nanocomposite corrosion inhibitors were synthesized from Aloe vera extract: one incorporating sodium thiosulfate and the other silver nitrate. Both nanocomposites were subjected to structural characterization using atomic force microscopy (AFM), which revealed distinct morphological features. The sodium thiosulfate-based nanocomposite exhibited uniform and well-dispersed nanoparticles with an average size of 47.51 nm, suggesting a stable and homogeneous distribution. In contrast, the silver nitrate-based nanocomposite displayed slightly larger particles with an average diameter of 58.34 nm, indicating a tendency toward moderate aggregation. The corrosion inhibition performance of these nanocomposites for carbon steel (CS1137) was invest

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

    The widespread house sparrow Passer domesticus biblicus has a close association with humans and  inhabits almost all ecosystems near human settlements in Iraq. It is exposed to different kinds of parasites in its habitats. Examining of house sparrow for the cestode parasites revealed that 25 specimens of 56 were infected with Raillietina echinobothrida. Intensity among infected male and female hosts with this cestode and its description  is provided and discussed. The present finding constitutes the first record for this parasite in house sparrow in Iraq.

The ascaroid nematode Contracaecum rudolphii was recovered in large numbers from the
digestive tract of Phalacrocorax carbo collected in Baghdad area, Central Iraq. The infection
rates of the two sexes of the bird and some meristic and morphometric characters of the
parasite that allowed species determination of the nematode Contracaecum rudolphii were
discussed. This finding represents a new host record for this nematode in Iraq.