Ensemble methods for instance-based Arabic language authorship attribution

The Authorship Attribution (AA) is considered as a subfield of authorship analysis and it is an important problem as the range of anonymous information increased with fast growing of internet usage worldwide. In other languages such as English, Spanish and Chinese, such issue is quite well studied. However, in Arabic language, the AA problem has received less attention from the research community due to complexity and nature of Arabic sentences. The paper presented an intensive review on previous studies for Arabic language. Based on that, this study has employed the Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS) method to choose the base classifier of the ensemble methods. In terms of attribution features, hundreds of stylometric features and distinct words using several tools have been extracted. Then, Adaboost and Bagging ensemble methods have been applied on Arabic enquires (Fatwa) dataset. The findings showed an improvement of the effectiveness of the authorship attribution task in the Arabic language

Cryo-EM structure of nucleotide-bound Tel1ATM unravels the molecular basis of inhibition and structural rationale for disease-associated mutations

Yeast Tel1 and its highly conserved human ortholog ataxia-telangiectasia mutated (ATM) are large protein kinases central to the maintenance of genome integrity. Mutations in ATM are found in ataxia-telangiectasia (A-T) patients and ATM is one of the most frequently mutated genes in many cancers. Using cryoelectron microscopy, we present the structure of Tel1 in a nucleotide-bound state. Our structure reveals molecular details of key residues surrounding the nucleotide binding site and provides a structural and molecular basis for its intrinsically low basal activity. We show that the catalytic residues are in a productive conformation for catalysis, but the phosphatidylinositol 3-kinase-related kinase (PIKK) regulatory domain insert restricts peptide substrate access and the N-lobe is in an open conformation, thus explaining the requirement for Tel1 activation. Structural comparisons with other PIKKs suggest a conserved and common allosteric activation mechanism. Our work also provides a structural rationale for many mutations found in A-T and cancer

Modeling cancer genomic data in yeast reveals selection against ATM function during tumorigenesis

The DNA damage response (DDR) comprises multiple functions that collectively preserve genomic integrity and suppress tumorigenesis. The Mre11 complex and ATM govern a major axis of the DDR and several lines of evidence implicate that axis in tumor suppression. Components of the Mre11 complex are mutated in approximately five percent of human cancers. Inherited mutations of complex members cause severe chromosome instability syndromes, such as Nijmegen Breakage Syndrome, which is associated with strong predisposition to malignancy. And in mice, Mre11 complex mutations are markedly more susceptible to oncogene- induced carcinogenesis. The complex is integral to all modes of DNA double strand break (DSB) repair and is required for the activation of ATM to effect DNA damage signaling. To understand which functions of the Mre11 complex are important for tumor suppression, we undertook mining of cancer genomic data from the clinical sequencing program at Memorial Sloan Kettering Cancer Center, which includes the Mre11 complex among the 468 genes assessed. Twenty five mutations in MRE11 and RAD50 were modeled in S. cerevisiae and in vitro. The mutations were chosen based on recurrence and conservation between human and yeast. We found that a significant fraction of tumor-borne RAD50 and MRE11 mutations exhibited separation of function phenotypes wherein Tel1/ATM activation was severely impaired while DNA repair functions were mildly or not affected. At the molecular level, the gene products of RAD50 mutations exhibited defects in ATP binding and hydrolysis. The data reflect the importance of Rad50 ATPase activity for Tel1/ATM activation and suggest that inactivation of ATM signaling confers an advantage to burgeoning tumor cells

An optimized stacking ensemble model for phishing websites detection

Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learning methods, including random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM. The optimized classifiers were then ranked, and the best three models were chosen as base classifiers of a stacking ensemble method. The experiments were conducted on three phishing website datasets that consisted of both phishing websites and legitimate websites—the Phishing Websites Data Set from UCI (Dataset 1); Phishing Dataset for Machine Learning from Mendeley (Dataset 2, and Datasets for Phishing Websites Detection from Mendeley (Dataset 3). The experimental results showed an improvement using the optimized stacking ensemble method, where the detection accuracy reached 97.16%, 98.58%, and 97.39% for Dataset 1, Dataset 2, and Dataset 3, respectivel

Phishing websites detection by using optimized stacking ensemble model

Phishing attacks are security attacks that do not affect only individuals’ or organizations’ websites but may affect Internet of Things (IoT) devices and networks. IoT environment is an exposed environment for such attacks. Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users. Machine and deep learning and other methods were used to design detection methods for these attacks. However, there is still a need to enhance detection accuracy. Optimization of an ensemble classification method for phishing website (PW) detection is proposed in this study. A Genetic Algorithm (GA) was used for the proposed method optimization by tuning several ensemble Machine Learning (ML) methods parameters, including Random Forest (RF), AdaBoost (AB), XGBoost (XGB), Bagging (BA), GradientBoost (GB), and LightGBM (LGBM). These were accomplished by ranking the optimized classifiers to pick out the best classifiers as a base for the proposed method. A PW dataset that is made up of 4898 PWs and 6157 legitimate websites (LWs) was used for this study's experiments. As a result, detection accuracy was enhanced and reached 97.16 percent

Antenatal depression among pregnant mothers in Afghanistan: a cross-sectional study

Background: Approximately one in five pregnant women experience antenatal depression globally. The purpose of the present study was to estimate the prevalence of antenatal depression and explore its relationship between various demographic variables, recent sexual engagement, and recent adverse life events among pregnant Afghan women. Methods: A cross-sectional survey study was carried out between January, 2023 and April 2023 among 460 women aged 15–45 years who were recruited using convenience sampling from Herat province (Afghanistan). Logistic regression models were utilized to explore the relationship between antenatal depression and socio-demographic characteristics among the participants. Results: The prevalence of antenatal depression symptoms was 78.5%. Multiple regression analysis indicated that antenatal depression was significantly associated with (i) being aged 30–45 years (AOR: 4.216, 95% CI: 1.868–9.515, p = .001), (ii) being of low economic status (AOR:2.102, 95% CI: 1.051–4.202, p = .036), (iii) not being employed (AOR: 2.445, 95% CI:1.189–5.025, p = .015), (iv) not having had sex during the past seven days (AOR: 2.335, 95% CI: 1.427–3.822, p = .001), and (v) not experiencing a traumatic event during the past month (AOR:0.263, 95% CI: 0.139–0.495, p < .001). Conclusion: The present study provides insight into the factors associated with the high prevalence of antenatal depression among pregnant Afghan women (e.g., demographic variables, recent adverse life events, and recent sexual engagement). It highlights the urgency of addressing antenatal depression in Afghanistan and provides a foundation for future research and interventions aimed at improving the mental health and well-being of pregnant women in the Afghan context

The First Amendment: Broadening the Information Superhighway

[No abstract

Surface Photovoltage Spectroscopy of Cd0.97\text{}_{0.97}Fe0.03\text{}_{0.03}Se and Cd0.97\text{}_{0.97}Fe0.03\text{}_{0.03}Te Crystals

The surface electronic structure of the Cd$\text{}_{1-x}$Fe$\text{}_{x}$Se and Cd$\text{}_{1-x}$Fe$\text{}_{x}$Τe crystals with x = 0 and x = 0.03 has been studied by Surface Photovoltage Spectroscopy (SPS). The change of surface photovoltage was observed due to photo-excitation of the electrons from the deep donor state Fe 3d to conduction band edge. This gave possibility to determine energy position of the Fe 3d state at 0.64 and 0.15 eV over the top of the valence band for CdSe and CdTe, respectively