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

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

CHROMIUM STATUS IN DIABETES MELLITUS

Fasting serum chromium, total cholesterol HDL-cholesterol, LDL-cholesterol, triacytglycerot and blood sugar were determined in fifty two diabetic patients with no other organic diseases anil compared with those obtained from a control group including fourty two healthy volunteers matched for age, sex ami body mass irutex (BMI). Fasting serum chromium and HDL-cholesterol were significantly lower in patients than in controls (p<0.0001 and p<0.001 respectively), but the mean triacytglycerot concentration was significantly higher in patients than in controls (p<002). Mean total cholesterol and LDL-cholesterol values were not significantly different in the two groups. Mean intake of energy, proteins, fats and chromium, estimated by the 24 hr dietary recall method were not significantly different in the two groups. We demonstrated that despite an adequate intake of chromium, the fasting serum chromium was lower in diabetic patients than in control subjects. Chromium deficiency in diabetic patients may act as a contributing factor in aggravating the disease's complications

Deep learning-based approach for emotion recognition using electroencephalography (EEG) signals using bi-directional Long Short-Term Memory (Bi-LSTM)

Emotions are an essential part of daily human communication. The emotional states and dynamics of the brain can be linked by electroencephalography (EEG) signals that can be used by the Brain–Computer Interface (BCI), to provide better human–machine interactions. Several studies have been conducted in the field of emotion recognition. However, one of the most important issues facing the emotion recognition process, using EEG signals, is the accuracy of recognition. This paper proposes a deep learning-based approach for emotion recognition through EEG signals, which includes data selection, feature extraction, feature selection and classification phases. This research serves the medical field, as the emotion recognition model helps diagnose psychological and behavioral disorders. The research contributes to improving the performance of the emotion recognition model to obtain more accurate results, which, in turn, aids in making the correct medical decisions. A standard pre-processed Database of Emotion Analysis using Physiological signaling (DEAP) was used in this work. The statistical features, wavelet features, and Hurst exponent were extracted from the dataset. The feature selection task was implemented through the Binary Gray Wolf Optimizer. At the classification stage, the stacked bi-directional Long Short-Term Memory (Bi-LSTM) Model was used to recognize human emotions. In this paper, emotions are classified into three main classes: arousal, valence and liking. The proposed approach achieved high accuracy compared to the methods used in past studies, with an average accuracy of 99.45%, 96.87% and 99.68% of valence, arousal, and liking, respectively, which is considered a high performance for the emotion recognition mod

Ensemble synthesized minority oversampling based generative adversarial networks and random forest algorithm for credit card fraud detection

The recent increase in credit card fraud is rapidly has caused huge monetary losses for individuals and financial institutions. Most credit card frauds are conducted online by illegally obtaining payment credentials through data breaches, phishing, or scamming. Many solutions have been suggested to address the credit card fraud problem for online transactions. However, the high class imbalance is the major challenge that faces the existing solutions to construct an effective detection model. Most of the existing techniques used for class imbalance overestimate the distribution of the minority class, resulting in highly overlapped or noisy and unrepresentative features, which cause either overfitting or imprecise learning. In this study, a credit card fraud detection model (CCFDM) is proposed based on ensemble learning and a generative adversarial network (GAN) assisted by Ensemble Synthesized Minority Oversampling techniques (ESMOTE-GAN). Multiple subsets were extracted using under-sampling and SMOTE was applied to generate less skewed sets to prevent the GAN from modeling the noise. These subsets were used to train diverse sets of GAN models to generate the synthesized subsets. A set of Random Forest classifiers was then trained based on the proposed ESMOTE-GAN technique. The probabilistic outputs of the trained classifiers were combined using a weighted voting scheme for decision-making. The results show that the proposed model achieved 1.9%, and 3.2% improvements in overall performance and the detection rate, respectively, with a 0% false alarm rate. Due to the massive number of transactions, even a tiny false positive rate can overwhelm the analysis team. Thus, the proposed model has improved the detection performance and reduced the cost needed for manual analys

RS_DCNN: a novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification

Developments in remote sensing technology have led to a continuous increase in the volume of remote-sensing data, which can be qualified as big remote sensing data. A wide range of potential applications is using these data including land cover classification, regional planning, catastrophe prediction and management, and climate-change estimation. Big remote sensing data are characterized by different types of resolutions (radiometric, spatial, spectral, and temporal), modes of imaging, and sensor types, and this range of options often makes the process of analyzing and interpreting such data more difficult. In this paper, which is the first study of its kind, we propose a novel distributed deep learning-based approach for the classification of big remote sensing images. Specifically, we propose Distributed Convolutional-Neural-Networks for handling RS image classification (RS-DCNN). The first step is to prepare the training dataset for RS-DCNN. Then, to ensure a data-parallel training on the top of the Apache Spark framework, a pixel-based convolutional-neural-network model across the big data cluster is performed using BigDL. Experiments are conducted on a real dataset covering many regions of Saudi Arabia and the results demonstrate high classification accuracy at a faster speed than other state-of-the-art classification methods

The flavones apigenin and luteolin induce FOXO1 translocation but inhibit gluconeogenic and lipogenic gene expression in human cells.

The flavones apigenin (4',5,7,-trihydroxyflavone) and luteolin (3',4',5,7,-tetrahydroxyflavone) are plant secondary metabolites with antioxidant, antiinflammatory, and anticancer activities. We evaluated their impact on cell signaling pathways related to insulin-resistance and type 2 diabetes. Apigenin and luteolin were identified in our U-2 OS (human osteosarcoma) cell screening assay for micronutrients triggering rapid intracellular translocation of the forkhead box transcription factor O1 (FOXO1), an important mediator of insulin signal transduction. Insulin reversed the translocation of FOXO1 as shown by live cell imaging. The impact on the expression of target genes was evaluated in HepG2 (human hepatoma) cells. The mRNA-expression of the gluconeogenic enzymes phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase (G6Pc), the lipogenic enzymes fatty-acid synthase (FASN) and acetyl-CoA-carboxylase (ACC) were down-regulated by both flavones with smaller effective dosages of apigenin than for luteolin. PKB/AKT-, PRAS40-, p70S6K-, and S6-phosphorylation was reduced by apigenin and luteolin but not that of the insulin-like growth factor receptor IGF-1R by apigenin indicating a direct inhibition of the PKB/AKT-signaling pathway distal to the IGF-1 receptor. N-acetyl-L-cysteine did not prevent FOXO1 nuclear translocation induced by apigenin and luteolin, suggesting that these flavones do not act via oxidative stress. The roles of FOXO1, FOXO3a, AKT, sirtuin1 (SIRT1), and nuclear factor (erythroid-derived2)-like2 (NRF2), investigated by siRNA knockdown, showed differential patterns of signal pathways involved and a role of NRF2 in the inhibition of gluconeogenic enzyme expression. We conclude that these flavones show an antidiabetic potential due to reduction of gluconeogenic and lipogenic capacity despite inhibition of the PKB/AKT pathway which justifies detailed investigation in vivo

Angular limb deformity associated with TSPAN18, NRG3 and NOVA2 in Rambouillet rams

Abstract Angular limb deformity (ALD) affects many species of livestock and companion animals. The mechanisms of ALD development are not well understood, but previous research suggests the involvement of genetic risk factors. A case-control genome-wide association study (GWAS) was conducted with 40 ALD-affected and 302 unaffected Rambouillet rams and 40,945 single nucleotide polymorphisms (SNPs). Forelimbs of 6 ALD-affected rams were examined and diagnosed with osteochondrosis. Genome-wide or chromosome-wide significant SNPs were positioned exonic, intronic or within the 3′UTR of genes TSPAN18, NRG3 and NOVA2, respectively. These genes have previously described roles related to angiogenesis and osteoblast, osteoclast and chondrocyte proliferation and differentiation, which suggests the possibility for their involvement in the pathogenesis of osteochondrosis. Functional consequences of SNPs were evaluated through transcription factor binding site analysis, which predicted binding sites for transcription factors of known importance to bone growth, including SOX6, SOX9 and RUNX2. The identification of genetic risk factors for ALD may help to improve animal welfare and production in Rambouillet, a breed known to be at risk for ALD development. This study proposes genes TSPAN18, NRG3 and NOVA2 as targets for further research towards understanding the etiology of ALD in Rambouillet sheep