In this study, we tackle the understudied area of Artificial Intelligence (AI) and its role in examining how modern revolutions may affect political systems across the Middle Eastern region. despite hundreds of studies documenting Middle Eastern uprisings over the past three decades, there has been little effort to harness AI to better understand or predict these multifaceted events. This study seeks to address this gap by assessing the performance of AI-intelligence in analyzing (broadly) revolutionary processes and their effects on regional political systems. The research uses a mixedmethod methodology that involves a systematic literature review of contemporary scholarly articles, and an analytics study using AI tools. Our results show that AIdriven sentiment analysis can accurately track shifts in public opinion over the course of an entire revolution with a 40% rise in level of positive sentiment during peak protest periods, then a 25% decline post-revolution. Topic modeling found a 20% increase in discourse about political representation and a 15% decrease in topics related to security post-revolution. Statistical significance was achieved (R2 = 0.85) in predictively modeling political stability and was able to outperform traditional statistical approaches by a factor of 30%. Such results also highlight the considerable promise of AI over traditionally human-based means for improving political analysis within the regi on.
The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample
... Show MoreAn abstract is a brief summary of a research article, thesis, Schiff base ligand (L) was prepared by the reaction of 4-aminantipyrine with o-phenylenediamine, the prepared ligand characterized by Micro elemental Analysis, FT. IR, UV-Vis, and 1H,13C-NMR spectroscopy.complexes of Mn(II), Co(II), Ni(II), Cu(II) and Hg(II) with Schiff base and 1,10-phenanthroline (Phen) have been investigated in aqueous ethanol with (1:1:1) (M:L:Phen). The prepared complexes were characterized using flame atomic absorption, (C. H. N) Analysis, FT. IR and UV-Vis spectroscopic methods as well as magnetic susceptibility and conductivity measurements. From the obtained data the octahedral structure was suggested for all complexes. The biological screening effects o
... Show MoreThe new 4-[(7-chloro-2,1,3-benzoxadiazole)azo]-4,5-diphenyl imidazole (L) have been synthesized and characterized by micro elemental and thermal analyses as well as 1H.NMR, FT-IR, and UV-Vis spectroscopic techniques. (L) acts as a ligand coordinating with some metal ionsV(IV), Fe(III), Co(II), Ni(II), Cu(II), and Zn(II). Structures of the new compounds were characterized by elemental and thermal analyses as well as FT-IR and UV-Vis Spectra. The magnetic properties and electrical conductivities of metal complexes were also determined. Study of the nature of the complexes formed in ethanol following the mole ratio method.. The work also include a theoretical treatment of the formed complexes in the gas phase, this was done using the (hyperch
... Show MoreNew mixed ligand complexes of New Schiff base 4,4'- ((naphthalen-1-ylimino) methylene) dibenzene-1,3-diol and 8-hydroxy quinoline: Synthesis, Spectral Characterization, Thermal studies and Biological Activities
Green biosynthesized selenium nanoparticles from
The Co (II), Ni (II) ,Cu(II), Zn(II) ,Cd(II) and Hg(II) complexes of mixed of amino acid (L-Alanine ) and Trimethoprim antibiotic were synthesized. The complexes were characterized using melting point, conductivity measurement and determination the percentage of the metal in the complexes by flame (AAS). Magnetic susceptibility, Spectroscopic Method [FTIR and UV-Vis]. The general formula have been given for the prepared mixed ligand complexes [M(Ala)2(TMP)(H2O)] where L- alanine (abbreviated as (Ala ) = (C5H9NO2) deprotonated primary ligand, L- Alanine ion .= (C5H8NO2 -) Trimethoprim (abbreviated as (TMP ) = C10H11N3O3S M(II) = Co (II),Ni(II) ,Cu(II), Zn(II) ,Cd(II) and Hg(II). The results showed that the deprotonated L- Alanine by KOH (Ala
... Show MoreEnticed by the present scenario of infectious diseases, four new Co(II), Ni(II), Cu(II), and Cd(II) complexes of Schiff base ligand were synthesized from 6,6′-((1E-1′E)(phenazine-2,3-dielbis(azanylidene)-bis-(methanylidene)-bis-(3-(diethylamino)phenol)) (
This research explores the use of solid polymer electrolytes (SPEs) as a conductive medium for sodium ions in sodium‐ion batteries, presenting a possible alternative to traditional lithium‐ion battery technology. The researchers prepare SPEs with varying molecular weight ratios of polyacrylonitrile (PAN) and sodium tetrafluoroborate (NaBF4) using a solution casting method with dimethyl formamide as the solvent. Through optical absorbance measurements, we identified the PAN:NaBF4 (80:20) SPE composition as having the lowest energy band gap value (4.48 eV). This composition also exhibits high thermal stability based on thermogravimetric analysis results.