Semi-supervised machine learning can be used for obtaining subsets of unlabeled or partially labeled dataset based on the applicable metrics of dissimilarity. At later stage, the data is completely ...assigned the labels as per the observed differentiation. This paper provides a clustering based approach to distinguish the data representing flows of network traffic which include both normal and Distributed Denial of Service (DDoS) traffic. The features are taken for victim-end identification of attacks and the work is demonstrated with three features which can be monitored at the target machine. The clustering methods include agglomerative and K-means with feature extraction under Principal Component Analysis (PCA). A voting method is also proposed to label the data and obtain classes to distinguish attacks from normal traffic. After labeling, supervised machine learning algorithms of k-Nearest Neighbors (kNN), Support Vector Machine (SVM) and Random Forest (RF) are applied to obtain the trained models for future classification. The kNN, SVM and RF models in experimental results provide 95%, 92% and 96.66% accuracy scores respectively under optimized parameter tuning within given sets of values. In the end, the scheme is also validated using a subset of benchmark dataset with new vectors of attack.
Uncertainty and isolation have been linked to mental health problems. Uncertainty surrounding the COVID-19 pandemic has the potential to trigger mental health problems, which include anxiety, stress, ...and depression. This paper evaluates the prevalence, psychological responses, and associated correlates of depression, anxiety, and stress in a global population during the Coronavirus Disease (COVID-19) pandemic. A cross-sectional study design was adopted. 678 completed forms were collected during the COVID-19 quarantine/lockdown. An online questionnaire was designed and DASS-21 was used as the screening tool. A non-probability sampling technique strategy was applied. 50.9% of participants showed traits of anxiety, 57.4% showed signs of stress, and 58.6% exhibited depression. Stress, anxiety, and depression are overwhelmingly prevalent across the globe during this COVID-19 pandemic, and multiple factors can influence the rates of these mental health conditions. Our factorial analysis showed notable associations and manifestations of stress, anxiety, and depressive symptoms. People aged 18–24, females, and people in non-marital relationships experienced stress, anxiety, and depression. Separated individuals experienced stress and anxiety. Married people experienced anxiety. Single and divorced people experienced depression. Unemployed individuals experienced stress and depression. Students experienced anxiety and depression. Canada, the UK, and Pakistan are all countries that are experiencing stress and depression as a whole. An extended number of days in quarantine was associated with increased stress, anxiety, and depression. Family presence yielded lower levels of stress, anxiety, and depression. Lastly, lack of exercise was associated with increased stress, anxiety, and depression.
This paper applies an organized flow of feature engineering and machine learning to detect distributed denial-of-service (DDoS) attacks. Feature engineering has a focus to obtain the datasets of ...different dimensions with significant features, using feature selection methods of backward elimination, chi2, and information gain scores. Different supervised machine learning models are applied on the feature-engineered datasets to demonstrate the adaptability of datasets for machine learning under optimal tuning of parameters within given sets of values. The results show that substantial feature reduction is possible to make DDoS detection faster and optimized with minimal performance hit. The paper proposes a strategic-level framework which incorporates the necessary elements of feature engineering and machine learning with a defined flow of experimentation. The models are also validated with cross-validation and evaluated for area-under-curve analyses. It provides comprehensive solutions which can be trusted to avoid the overfitting and collinearity problems of data while detecting DDoS attacks. In the case study of DDoS datasets, K-nearest neighbors algorithm overall exhibits the best performance followed by support vector machine, whereas low-dimensional datasets of discrete feature types perform better under the Random Forest model as compared to high dimensions with numerical features. The accuracy scores of dataset with the lowest number of features remain competitive with other datasets under all machine learning models, leading to a substantially reduced processing overhead. The experiments show that approximately 68% reduction in the feature space is possible with an impact of only about 0.03% on accuracy.
Background Diabetes mellitus (DM) is well known for related micro and macrovascular complications. Uncontrolled hyperglycemia in diabetes mellitus leads to endothelial dysfunction, inflammation, ...microvascular impairment, myocardial dysfunction, and skeletal muscle changes which affect multiple organ systems. This study was designed to take an extensive view of cardiorespiratory dynamics in patients with type 2 DM. Methods One hundred healthy controls (HC) and 100 DM patients were enrolled. We measured and compared the breathing patterns (spirometry), VO.sub.2 max levels (heart rate ratio method) and self-reported fitness level (international fitness scale) of individuals with and without diabetes. Data was analyzed in SPSS v.22 and GraphPad Prism v8.0. Results We observed restrictive spirometry patterns (FVC <80%) in 22% of DM as compared to 2% in HC (p = 0.021). There was low mean VO.sub.2 max in DM as compared to HC(32.03 ± 5.36 vs 41.91 ± 7.98 ml/kg/min; p value <0.001). When evaluating physical fitness on self-reported IFiS scale, 90% of the HC report average, good, or very good fitness levels. In contrast, only 45% of the DM shared this pattern, with a 53% proportion perceiving their fitness as poor or very poor (p = <0.05). Restrictive respiratory pattern, low VO.sub.2 max and fitness level were significantly associated with HbA1c and long-standing DM. Conclusion This study shows decreased pulmonary functions, decreased cardiorespiratory fitness (VO.sub.2 max) and IFiS scale variables in diabetic population as compared to healthy controls which are also associated with glycemic levels and long-standing DM. Screening for pulmonary functions can aid optimum management in this population.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In this article, I present a critique of Robert Geraci's Apocalyptic artificial intelligence (AI) discourse, drawing attention to certain shortcomings which become apparent when the analytical lens ...shifts from religion to the race–religion nexus. Building on earlier work, I explore the phenomenon of existential risk associated with Apocalyptic AI in relation to “White Crisis,” a modern racial phenomenon with premodern religious origins. Adopting a critical race theoretical and decolonial perspective, I argue that all three phenomena are entangled and they should be understood as a strategy, albeit perhaps merely rhetorical, for maintaining white hegemony under nonwhite contestation. I further suggest that this claim can be shown to be supported by the disclosure of continuity through change in the long‐durée entanglement of race and religion associated with the establishment, maintenance, expansion, and refinement of the modern/colonial world system if and when such phenomena are understood as iterative shifts in a programmatic trajectory of domination which might usefully be framed as “algorithmic racism.”
In this research, Graphene oxide (GO), prepared by modified hammer method, is characterized using X-ray Diffraction (XRD), Fourier Transform Infrared (FT-IR) Spectrometry and Raman spectra. The ...dispersion efficiency of GO in aqueous solution is examined by Ultraviolet–visible spectroscopy and it is found that GO sheets are well dispersed. Thereafter, rheological properties, flow diameter, hardened density, compressive strength and electrical properties of GO based cement composite are investigated by incorporating 0.03% GO in cement matrix. The reasons for improvement in strength are also discussed. Rheological results confirm that GO influenced the flow behavior and enhanced the viscosity of the cement based system. From XRD and Thermogravimetric Analysis (TGA) results, it is found that more hydration occurred when GO was incorporated in cement based composite. The GO based cement composite improves the compressive strength and density of mortar by 27% and 1.43%, respectively. Electrical properties results showed that GO–cement based composite possesses self-sensing characteristics. Hence, GO is a potential nano-reinforcement candidate and can be used as self-sensing sustainable construction material.
Aluminium (Al) is the third most abundant element in the earth's crust and its compounds are used in the form of house hold utensils, medicines and in antiperspirant etc. Increasing number of ...evidences suggest the involvement of Al+3 ions in a variety of neurodegenerative disorders including Alzheimer's disease. Here, we have attempted to investigate the role of Al in endoplasmic reticulum stress and the regulation of p53 during neuronal apoptosis using neuroblastoma cell line. We observed that Al caused oxidative stress by increasing ROS production and intracellular calcium levels together with depletion of intracellular GSH levels. We also studied modulation of key pro- and anti-apoptotic proteins and found significant alterations in the levels of Nrf2, NQO1, pAKT, p21, Bax, Bcl2, Aβ1-40 and Cyt c together with increase in endoplasmic reticulum (ER) stress related proteins like CHOP and caspase 12. However, with respect to the role of p53, we observed downregulation of its transcript as well as protein levels while analysis of its ubiquitination status revealed no significant changes. Not only did Al increase the activities of caspase 9, caspase 12 and caspase 3, but, by the use of peptide inhibitors of specific and pan-caspases, we observed significant protection against neuronal cell death upon inhibition of caspase 12, demonstrating the prominent role of endoplasmic reticulum stress generated responses in Al toxicity. Overall our findings suggest that Al induces ER stress and ROS generation which compromises the antioxidant defenses of neuronal cells thereby promoting neuronal apoptosis in p53 independent pathway.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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