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

The aim of the current study was to develop a nanostructured double-layer for hydrophobic molecules delivery system. The developed double-layer consisted of polyethylene glycol-based polymeric (PEG) followed by gelatin sub coating of the core hydrophobic molecules containing sodium citrate. The polymeric composition ratio of PEG and the amount of the sub coating gelatin were optimized using the two-level fractional method. The nanoparticles were characterized using AFM and FT-IR techniques. The size of these nano capsules was in the range of 39-76 nm depending on drug loading concentration. The drug was effectively loaded into PEG-Gelatin nanoparticles (≈47%). The hydrophobic molecules-release characteristics in terms of controlled-releas

Face recognition and identity verification are now critical components of current security and verification technology. The main objective of this review is to identify the most important deep learning techniques that have contributed to the improvement in the accuracy and reliability of facial recognition systems, as well as highlighting existing problems and potential future research areas. An extensive literature review was conducted with the assistance of leading scientific databases such as IEEE Xplore, ScienceDirect, and SpringerLink and covered studies from the period 2015 to 2024. The studies of interest were related to the application of deep neural networks, i.e., CNN, Siamese, and Transformer-based models, in face recogni

The permeability estimates for the uncored wells and a porosity function adopting a modified flow zone index-permeability crossplot are given in this work. The issues with implementing that approach were mostly crossplots, due to the influence of geological heterogeneity, did not show a clear connection (scatter data). Carbonate reservoir flow units may now be identified and characterized using a new approach, which has been formally confirmed. Due to the comparable distribution and flow of clastic and carbonate rock fluids, this zoning method is most effective for reservoirs with significant primary and secondary porosity. The equations and correlations here are more generalizable since they connect these variables by combining cor

This study designed to prepare ultrafine apixaban (APX) o/w nanoemulsion (NE) based gel with droplet size below 50 nm as a good method for transdermal APX delivery without using permeation enhancer, alternatively, the formulation components itself act as permeation enhancer. APX, a potent oral anticoagulant drug that selectively and directly inhibit coagulation factor Xa, was selected as a good candidate for transdermal delivery as it displays poor water solubility (0.028 mg/mL) and low bioavailability (50%). APX-NE gel was prepared using triacetin, triton-x-100 and carbitol as oil phase, surfactant and cosurfactant respectively, while Carbopol 940 used as a gelling agent. Ex vivo permeation of APX-NE gel through human stratum c

Malicious software (malware) performs a malicious function that compromising a computer system’s security. Many methods have been developed to improve the security of the computer system resources, among them the use of firewall, encryption, and Intrusion Detection System (IDS). IDS can detect newly unrecognized attack attempt and raising an early alarm to inform the system about this suspicious intrusion attempt. This paper proposed a hybrid IDS for detection intrusion, especially malware, with considering network packet and host features. The hybrid IDS designed using Data Mining (DM) classification methods that for its ability to detect new, previously unseen intrusions accurately and automatically. It uses both anomaly and misuse dete