Journal Of AL-Turath University College

Women in the contemporary age have been labeled as revolutionary and anti-traditional depictions of their existence. Jean Rhys (24 August 1890 – 14 May 1979) was a Caribbean novelist, short story writer, and essayist whose writings vehemently advocated women’s modernist innovations and Creole civil rights. She is recognized for her post-colonial works which are primarily distinguished by their analysis of the quest for third-space identity, Creole Hybridity, and the impact of colonialism on them. Most of her writings are autobiographical and are regarded to be her life’s reflection. Rhys through her novel Wide Sargasso Sea (1966), henceforth “WSS” in quotations, portrays Antoinette Cosway, a white Creole woman and descendant of the colonizers, who is silenced and pushed mad in an attic. She is conflicted between her white Creole identity and her loyalty to postcolonial Caribbean black inhabitants. Rhys’ Antoinette is considered as the marginalized standpoint of Charlotte Brontë’s Bertha Mason, a Caribbean bride who was portrayed as a sinister madwoman in her novel Jane Eyre (1847).
Rhys examines women question through Wide Sargasso Sea, as she depicts key feminist ideals that are still relevant to date. In that novel, Rhys explores fundamental concerns of women’s liberty and autonomy, manifesting their discrimination and marginalization by patriarchy and their silence and suffering, further, attempting to deconstruct the imposed notion that women are the weak representation of society. Antoinette in Wide Sargasso Sea serves as both a patriarchal inscription and a challenge to male-dominated discourse and in many occasions of the novel, is linked to Rhys herself.
Wide Sargasso Sea by replies to the marginalization and exploitation of a Caribbean woman, ironically portrayed as the insane woman in the attic in Charlotte Brontë’s Jane Eyre. Further, in Wide Sargasso Sea, she associates madness with the quest for identity, isolation, and suffering, as well as a fascination with oppressive behavior by colonists from England's patriarchal society.

A novel ligand N-(2-hydroxyl phenyl)-(4-pentloxy-benzate-salicyIidene) (H2L) prepared by 1:1 molar ratio of 4-pentyloxy (4-formyl-3-hydroxy)-benzoate and 2-aminophenol, synthesis of [M(H2L)H2O] and [Cu2L2] complexes were described by IR spectroscopy, C.H.N.O, 1H, 13CNMR, GCmass. TGA/DSC to study Thermal properties, while liquid-crystalline properties was studied by using the (OPM). All the samples show (sematicA).

The distribution of the expanded exponentiated power function EEPF with four parameters, was presented by the exponentiated expanded method using the expanded distribution of the power function, This method is characterized by obtaining a new distribution belonging to the exponential family, as we obtained the survival rate and failure rate function for this distribution, Some mathematical properties were found, then we used the developed least squares method to estimate the parameters using the genetic algorithm, and a Monte Carlo simulation study was conducted to evaluate the performance of estimations of possibility using the Genetic algorithm GA.

Cheating reportedly affects online exams and might easily violate the educational process by providing an unfair competitive advantage to one student over others. Accordingly, several efforts have been made in the past years to find reliable and scalable approaches to solve this problem. Unfortunately, cheating behaviors are rather difficult to detect and existing approaches generally require human supervision. This paper proposes a model to automatically detect cheating in the final exam. The proposed model consists of two main phases. The first phase concern with prediction of students score using multiple linear regression. The second phase concern with applying three different classifier algorithms: Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbors (KNN) to classify students as a cheater or not. The experimental results show the good performance of the proposed method, especially for SVM when the accuracy reached 96%.

In this study, we made a comparison between the LASSO and SCAD methods, which are among the distinguished methods of dealing with models in partial quantile regression. The golden ratio method was used to estimate the smoothing parameter. (Nadarya & Watson Kernel) was used to estimate the nonparametric portion, in addition to using the generalized cross-validation method to estimate a penalty parameter. The above methods proved their efficiency in estimating the regression parameters, but the LASSO method according to criterion the mean squared error (MSE) was the best after estimating the missing data by the nearest neighbor method.

One of the common methods of feature extraction is SIFT (Scale Invariant Feature Transform) in computer vision that use for image's control points, or local features. These invariant control points may be utilized for image matching, object identification, scene detection, …, etc. Due to the local similarity of images, the SIFT matching of control points results from some false matches. The aim of the study is to improve the performance of the SIFT algorithm for extracting and selecting the strongest control points from palm print images using shark small optimization (SSO) and genetic algorithm (GA). The proposed system consists of seven steps, namely, input palm print images; preprocessing by converting color to gray scalar image; extraction of control points using SIFT algorithm; selection of control points using SSO; weighting using GA; creation of a hand palm print dataset; and saving control points into the dataset. The proposed method uses the THUPALMLAB publicly available database which contains 1,280 palm print images from eighty subjects (two palms per person and eight impressions per palm) captured using a commercial palm print scanner of Hisign. The results of error rate metrics of the proposed method show an excellent palm print matching as it offered a higher CVR of 99.80%, and lowered FAR and FRR of 0.00 and 0.0020, respectively. This proves that the enhancement method is more effective and accurate.

The purpose of this study is to investigate the journalistic postures of Western media against Muslim women. Because Western media has continuously provided inaccurate pictures of Muslim women, they are merely regarded as lustful and object of desire, alongside images of the illiterate, enslaved, and downtrodden. The information gleans from the magazines Time and Newsweek. The appraisal system, a theory from systemic functional linguistics, is used in this investigation. All data published in news publications about Muslim women studies within the dimensions of the attitude categories (appreciation, judgment, and affect) to provide readers with a new perspective on those articles. This strategy analyzes and evaluates western media attitudes regarding Muslim women. The study's findings indicated that progressive Muslim women are positively regarded, but oppressed, Muslim women are adversely characterized. Furthermore, some Western media outlets portray Muslim women favorably.

With the vast development of network and communication protecting digital data like images and videos became a must to protect the sensitive information from exploiters and vandals. Data encryption and its security is playing a prim role for individuals and industries in securing data transformation and transportation. Therefore, true random numbers (TRN) are used to provide unpredictability and robustness to the encrypted data which is proved to be better and stronger than Pseudorandom numbers (PRN). Chaotic features provide high randomness, sensitivity to initial conditions and periodicity. this paper intends to provide a review firstly, on image protection copyrights by watermarking using the (TRN)s generated by different chaotic techniques. Secondly, on video encryption using the latest chaotic techniques which proved to provide a higher protection in the field of video encryption. Finally, we derived our own conclusion of using true random generation in encryption with chaos.

In this paper, we compare different fuzzy estimators for hazard rate function of two parameters Weibull distribution. The two parameters (λ,θ) are estimated by maximum likelihood, moments, and percentile methods, then we apply simulation procedure through different values of (n), and fuzzy variable (k ̃) of (α-cut intervals) and different sets of (n=20,40,60,80). The comparison has been done using statistical measure (MSE), all results shown in tables.

Edgar Allan Poe (1809-1849), considered one of the most important symbols of romance in America, was assigned by many literary historians to the honor of having been the so-called "father" of the short story. He was also hailed as the founder of the detective novels that came after him.
Most of Poe’s stories are disturbing and without any clear moral purposes, for he followed the principle of “art for art’s sake” and he warned against “the heresy of didactic”. His vision of the human psyche is marvelous; he mostly dealt with a person’s hidden emotions that are contradictory to surface reality.
The main theme of Poe’s The Cask of Amontillado is the depiction of a psychological predicament of an individual who lives the feelings of revenge and hatred against someone else. In this psychological framework, Poe succeeds in addressing the issue of the shadow. According to Carl Gustav Jung, the shadow ‘’represents all the personal traits we have ignored, denied, or cut off from ourselves.’’(Jung,1980:P.21) Most of Poe’s protagonists undergo interior spiritual journeys to face the darkest thoughts hidden in their unconscious mind.
This study deals with the shadow archetype in The Cask of Amontillado within the psychological context of Carl Gustav Jung’s theory of the archetypes and individuation process.

Graph coloring is one of the NP-Complete problems. One of the main uses of this technique is coloring maps. This article proposed a new algorithm based on a learning automaton to color the graph. The learning process starts with several random automatons. Each automaton alone represents a random coloring and by repeating the learning process, the coloring process improves. Compared to genetic algorithms, An algorithm has been proposed that converges faster with the optimal solution, the reason is that genetic algorithms only look for the optimal chromosome in the population and do not care about the position of the genes in the chromosomes. On the contrary, object migrating automata (OMA) determine the optimal location of genes in the proposed algorithm.
Also, the proposed algorithm converges with the ideal solution in the presence of fewer educational iterations after comparing it with the number of generations of the genetic algorithm. The proposed algorithm that have the same results have been compared with the genetic algorithm results on the highlighted graphs.

This research aims to compare the some classical methods with the artificial intelligence methods in estimating the missing values in the univariate time series data to determine the most accurate methods that treat the missing values in the univariate time series data set, where different sample sizes (300,100,60) were chosen with the deletion of four Loss percentages (5%, 10%, 15%, 20%) in a way that meets the MAR random missing conditions, and this is done by using the simulation method, and in order to obtain the accuracy of the performance of the methods, the accuracy standard of the mean squared error (MSE) was used. The results indicated that the most accurate method in estimating the missing values is the (RBF) method because it produces the lowest value of mean squared error compared to other methods.