Nanocomposite films of PVP/PEO containing MoO 3 nanoplates were prepared using a casting procedure. XRD results showed that the addition of MoO 3 in the virgin PVP/PEO blend increases the amorphous domains of polymer nanocomposite (PNC) films. The DFT/FT-IR results have established the miscibility and the interaction between PVP and PEO polymers via hydrogen bonds. Also, FTIR studies showed a coordination interaction between MoO 3 nanoplates and the C=O group of PVP and/or the C-O-C group of PEO of the PVP/PEO blend. The optical band gap values of PNC films of PVP/PEO/MoO 3 decreased with increasing MoO 3 content. The relaxation processes and dielectric dispersion of the prepared PNC films were examined and discussed. The electrical conductivity was enhanced via the addition of MoO 3 nanoplates because of an increase in the number density of the charge carriers. The dominated conduction mechanism of PNC films reveals the correlated barrier hopping model, while the type of relaxation process follows the non-Debye one.These observations illustrate the applicability and potential uses of these PNC films as a nanodielectric material in ceramic capacitors and electrochemical applications such as devices of energy storage and separators in batteries.
A new approach for feature extraction using neural response has been developed in this paper through combining the hierarchical architectures with the sparse coding technique. As far as proposed layered model, at each layer of hierarchy, it concerned two components that were used are sparse coding and pooling operation. While the sparse coding was used to solve increasingly complex sparse feature representations, the pooling operation by comparing sparse outputs was used to measure the match between a stored prototype and the input sub-image. It is recommended that value of the best matching should be kept and discarding the others. The proposed model is implemented and tested taking into account two ranges of recognition tasks i.e. image recognition and speech recognition (on isolated word vocabulary). Experimental results with various parameters demonstrate that proposed scheme leads to extract more efficient features than other methods.
Due to the advancement and easy accessibility to computer and internet technology, network security has become vulnerable to hacker threats. Ransomware is a frequently used malware in cyber-attacks to trick the victim users to expose sensitive and private information to the attackers. Consequently, victims may not be able to access their data any longer until they pay a ransom for stolen files or data. Different methods have been introduced to overcome these issues. It is evident through an extensive literature review that some lexical features are not always sufficient to detect categories of malicious URLs. This paper proposes a model to detect Ransomware using machine and deep learning approaches. This model was introduced as a novel feature for classification using the idea that starts with "https://www." This feature was not considered in the earlier papers on malicious URLs identification. In addition, this paper introduced a novel dataset that consists of 405,836 records. Two main experiments were carried out utilizing malicious URL features to defend Ransomware using the proposed dataset. Moreover, to enhance and optimize the experimental accuracy, various hyper-parameters were tested on the same dataset to define the optimal factors of every method. According to the comparative and experimental results of the applied classification techniques, the proposed model achieved the best performance at 99.8% accuracy rate for detecting malicious URLs using machine and deep learning.
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