A Network Intrusion Detection System Using Hybrid Multilayer Deep Learning Model.

An intrusion detection system (IDS) is designed to detect and analyze network traffic for suspicious activity. Several methods have been introduced in the literature for IDSs; however, due to a large amount of data, these models have failed to achieve high accuracy. A statistical approach is proposed in this research due to the unsatisfactory results of traditional intrusion detection methods. The features are extracted and selected using a multilayer convolutional neural network, and a softmax classifier is employed to classify the network intrusions. To perform further analysis, a multilayer deep neural network is also applied to classify network intrusions. Furthermore, the experiments are performed using two commonly used benchmark intrusion detection datasets: NSL-KDD and KDDCUP'99. The performance of the proposed model is evaluated using four performance metrics: accuracy, recall, F1-score, and precision. The experimental results show that the proposed approach achieved better accuracy (99%) compared with other IDSs.

Classification of COVID-19 and Influenza Patients Using Deep Learning.

Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 emerged in China and quickly spread around the globe, resulting in the coronavirus epidemic of 2019-22. As this virus is very similar to influenza in its early stages, its accurate detection is challenging. Several techniques for detecting the virus in its early stages are being developed. Deep learning techniques are a handy tool for detecting various diseases. For the classification of COVID-19 and influenza, we proposed tailored deep learning models. A publicly available dataset of X-ray images was used to develop proposed models. According to test results, deep learning models can accurately diagnose normal, influenza, and COVID-19 cases. Our proposed long short-term memory (LSTM) technique outperformed the CNN model in the evaluation phase on chest X-ray images, achieving 98% accuracy.

A Novel Image Retrieval Based on Visual Words Integration of SIFT and SURF.

With the recent evolution of technology, the number of image archives has increased exponentially. In Content-Based Image Retrieval (CBIR), high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level image concepts is an open research problem. In this paper, we present a novel visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). The two local features representations are selected for image retrieval because SIFT is more robust to the change in scale and rotation, while SURF is robust to changes in illumination. The visual words integration of SIFT and SURF adds the robustness of both features to image retrieval. The qualitative and quantitative comparisons conducted on Corel-1000, Corel-1500, Corel-2000, Oliva and Torralba and Ground Truth image benchmarks demonstrate the effectiveness of the proposed visual words integration.

(2S,3S)-2,6-dimethylheptane-1,3-diol, the oxygenated side chain of 22(S)-hydroxycholestrol, and its synthetic precursor (R)-4-benzyl-3-[(2R,3S)-3-hydroxy-2,6-dimethylheptanoyl]-1,3-oxazolidin-2-one.

(2S,3S)-2,6-dimethylheptane-1,3-diol, C9H20O2, (I), was synthesized from the ketone (R)-4-benzyl-3-[(2R,3S)-3-hydroxy-2,6-dimethylheptanoyl]-1,3-oxazolidin-2-one, C19H27NO4, (II), containing C atoms of known chirality. In both structures, strong hydrogen bonds between the hydroxy groups form tape motifs. The contribution from weaker C-H···O hydrogen bonds is much more evident in the structure of (II), which furthermore contains an example of a direct short Osp(3)···Csp(2) contact that represents a usually unrecognized type of intermolecular interaction.

Investigation of sickle-cell haemoglobin polymerisation under electrochemical control.

We describe an electrochemistry-based technique to control and monitor the polymerisation of sickle-cell haemoglobin (HbS). The polymerisation was monitored as a change in turbidity during the depletion of oxygen in a small volume custom-built thin-layer electrochemical cell. The cell allowed the investigation of HbS polymerisation as a function of HbS concentration, temperature and solution pH. We confirm that the oxygen was efficiently depleted using finite-element modelling to accurately recreate the electrochemical thin-layer cell. Understanding the nucleation and growth of HbS polymerisation will provide a better understanding of the pathophysiology of sickle-cell disease in vivo, and thus help improve therapeutic strategies for this common and frequently disabling disorder.

Electrochemical modulation of sickle cell haemoglobin polymerisation.

Sickle cell haemoglobin (HbS) differs from normal haemoglobin by a single amino acid in its beta chain. This amino acid replacement, from glutamic acid to valine, causes polymerisation of proteins into defined long insoluble fibres with a typical diameter of 21.5 nm. The polymerisation is triggered by the formation of deoxyhaemoglobin (deoxyHb) from oxyhaemoglobin (oxyHb) in low oxygen partial pressures, which results in a conformational change in the secondary structure of the protein. We describe an electrochemical method to modulate the oxygen concentration in an optically transparent thin layer cell to produce deoxyhaemoglobin whilst monitoring the extent of polymerisation using turbidity measurements. The oxygen is depleted in the vicinity of the electrode and triggers the polymerisation. The kinetics of polymerisation were investigated using a model for fibrillogenesis describing a two-step process of nucleation followed by elongation. Rate constants describing the nucleation and growth at monomer concentration of 300 mg cm(-3) (4.65 x 10(-3) M) were determined to be 9.45 (+/-0.08) x 10(-6) s(-1) and 1.22 (+/-0.03) x 10(-3) s(-1) respectively, showing that nucleation was far slower than the growth. A similar difference between the rate constants for the nucleation (2.99 (+/-0.4) x 10(-8) s(-1)) and growth (1.08 (+/-0.2) x 10(-3) s(-1)) was seen at monomer concentration of 50 mg cm(-3) (7.75 x 10(-4) M). These results show that nucleation was monomer concentration dependent; however growth was largely independent of monomer concentration. In this study we present a methodology that may be used as a screening method for substances that effect the fibre nucleation and or growth that could be valuable to the pharmaceutical industry for treating sickle cell disease.