Chemical Grafting of Crosslinked Polypyrrole and Poly(vinyl Chloride) onto Modified Graphite: Fabrication,Characterization, and Material Properties

ABSTRACT

Conjugated polymer/graphite nanocomposites have been known as high performance materials owing to improve the physicochemical properties relative to conventional once. Multilayered polymer nanocomposites based on polypyrrole (PPy), polyvinylchloride (PVC) as matrices and p-phenylene diamine (PDA) as linker were prepared via chemical in situ polymerization process and subsequently investigated the physical characteristics of fabricated nanocomposites at various loadings. The structural characterization and morphology of prepared nanocomposites were inspected by Fourier transform infrared spectroscopy (FTIR), X-ray photon spectroscopy (XPS), energy dispersive X-ray spectroscope (EDX), field emission scanning electron microscope (FESEM), respectively. The composite III showed higher thermal stability at 10 wt% loading of PPy. According to differential scanning calorimetry (DSC), the glass transition temperature (T_g), melting temperature T_m, and crystallization temperature (T_c) of nanocomposites increases with PPy loading (2–10 wt%) owing to crosslinking and chain rigidity. Moreover, higher surface area was displayed by the multilayered PPy/PVC/PDA@FG nanocomposites. Remarkably, electrical conductivity of ultimate nanocomposites was also found to be a function of PPy loading.     

An image encryption algorithm based on DNA sequence operations and cellular neural network

This paper presents a new way of image encryption based on biologic DNA sequence operations and Cellular Neural Network (CNN), which consists of three processes; bit-substitution, key stream generation process, and diffusion process. Firstly, a plain-image is equally divided into four sub-images and a DNA sequence matrix of each sub-image is obtained. Then we employed the hamming distance (between DNA sequences) and DNA sequence operation to encrypt each DNA sub-image. The second process is a pseudo-random key stream generator based on Cellular Neural Network. The parameters and initial conditions of the CNN system are derived using a 256 bit-long external secret key by applying some algebraic transformations to the key. The original key stream is related to the plain-image which increases the level of security and key sensitivity of the proposed algorithm. In the final process, we use the chaotic sequences generated by CNN to modify the pixel gray level values and crack the strong correlations between adjacent pixels of an image simultaneously. This feature will significantly increase plaintext sensitivity. Moreover, in order to reach higher security and higher complexity, the proposed method employs the image size in key stream generation process. The experimental results reveal that the new image encryption algorithm has the advantages of large key space (2²⁵⁶), high security, high sensitivity (Number of Pixels Change Rate: NPCR >99.6201 %, Unified Average Changing Intensity: UACI >33.5065 %), and high entropy (> 7.9975). Also, the distribution of gray level values of the encrypted image has a semi-random behavior.

     

Adapted One-versus-All Decision Trees for Data Stream Classification

One versus all (OVA) decision trees learn k individual binary classifiers, each one to distinguish the instances of a single class from the instances of all other classes. Thus OVA is different from existing data stream classification schemes whose majority use multiclass classifiers, each one to discriminate among all the classes. This paper advocates some outstanding advantages of OVA for data stream classification. First, there is low error correlation and hence high diversity among OVA's component classifiers, which leads to high classification accuracy. Second, OVA is adept at accommodating new class labels that often appear in data streams. However, there also remain many challenges to deploy traditional OVA for classifying data streams. First, as every instance is fed to all component classifiers, OVA is known as an inefficient model. Second, OVA's classification accuracy is adversely affected by the imbalanced class distribution in data streams. This paper addresses those key challenges and consequently proposes a new OVA scheme that is adapted for data stream classification. Theoretical analysis and empirical evidence reveal that the adapted OVA can offer faster training, faster updating and higher classification accuracy than many existing popular data stream classification algorithms.     

Detecting intrusion transactions in databases using data item dependencies and anomaly analysis

Abstract: The purpose of the intrusion detection system (IDS) database is to detect transactions that access data without permission. This paper proposes a novel approach to identifying malicious transactions. The approach concentrates on two aspects of database transactions: (1) dependencies among data items and (2) variations of each individual data item which can be considered as time‐series data. The advantages are threefold. First, dependency rules among data items are extended to detect transactions that read or write data without permission. Second, a novel behaviour similarity criterion is introduced to reduce the false positive rate of the detection. Third, time‐series anomaly analysis is conducted to pinpoint intrusion transactions that update data items with unexpected pattern. As a result, the proposed approach is able to track normal transactions and detect malicious ones more effectively than existing approaches.     

Creating sentiment lexicon for sentiment analysis in Urdu: The case of a resource‐poor language

The sentiment analysis (SA) applications are becoming popular among the individuals and organizations for gathering and analysing user's sentiments about products, services, policies, and current affairs. Due to the availability of a wide range of English lexical resources, such as part‐of‐speech taggers, parsers, and polarity lexicons, development of sophisticated SA applications for the English language has attracted many researchers. Although there have been efforts for creating polarity lexicons in non‐English languages such as Urdu, they suffer from many deficiencies, such as lack of publically available sentiment lexicons with a proper scoring mechanism of opinion words and modifiers. In this work, we present a word‐level translation scheme for creating a first comprehensive Urdu polarity resource: “Urdu Lexicon” using a merger of existing resources: list of English opinion words, SentiWordNet, English–Urdu bilingual dictionary, and a collection of Urdu modifiers. We assign two polarity scores, positive and negative, to each Urdu opinion word. Moreover, modifiers are collected, classified, and tagged with proper polarity scores. We also perform an extrinsic evaluation in terms of subjectivity detection and sentiment classification, and the evaluation results show that the polarity scores assigned by this technique are more accurate than the baseline methods.     

Image compression approach with ridgelet transformation using modified neuro modeling for biomedical images

Image compression is applied to many fields such as television dissemination, remote sensing, image storage. Digitized images are compressed by a method which exploits the redundancy of the images so that the number of bits required to represent the image can be reduced with acceptable degradation of the decoded image. The humiliation of the image quality is limited with respect to the application used. There are various biomedical applications where accuracy is of major concern. To attain the objective of performance improvement with respect to decoded picture quality and compression ratios, in contrast to existing image compression techniques, an effective image coding technique which involves transforming the image into another domain with ridgelet function and then quantizing the coefficients with hybrid neural networks combining two different learning networks called auto-associative multilayer perceptron and self-organizing feature map is proposed. Ridge functions are effective in representing functions that have discontinuities along straight lines. Normal wavelet transforms not succeed to represent such functions effectively. The results obtained from the combination of finite ridgelet transform with hybrid neural networks found much better than that obtained from the JPEG2000 image compression system.     

A security-aware virtual machine placement in the cloud using hesitant fuzzy decision-making processes

The introduction of cloud computing systems brought with itself a solution for the dynamic scaling of computing resources leveraging various approaches for providing computing power, networking, and storage. On the other hand, it helped decrease the human resource cost by delegating the maintenance cost of infrastructures and platforms to the cloud providers. Nevertheless, the security risks of utilizing shared resources are recognized as one of the major concerns in using cloud computing environments. To be more specific, an intruder can attack a virtual machine and consequently extend his/her attack to other virtual machines that are co-located on the same physical machine. The worst situation is when the hypervisor is compromised in which all the virtual machines assigned to the physical node will be under security risk. To address these issues, we have proposed a security-aware virtual machine placement scheme to reduce the risk of co-location for vulnerable virtual machines. Four attributes are introduced to reduce the aforementioned risk including the vulnerability level of a virtual machine, the importance level of a virtual machine in the given context, the cumulative vulnerability level of a physical machine, and the capacity of a physical machine for the allocation of new virtual machines. Nevertheless, the evaluation of security risks, due to the various vulnerabilities’ nature as well as the different properties of deployment environments is not quite accurate. To manage the precision of security evaluations, it is vital to consider hesitancy factors regarding security evaluations. To consider hesitancy in the proposed method, hesitant fuzzy sets are used. In the proposed method, the priorities of the cloud provider for the allocation of virtual machines are also considered. This will allow the model to assign more weights to attributes that have higher importance for the cloud provider. Eventually, the simulation results for the devised scenarios demonstrate that the proposed method can reduce the overall security risk of the given cloud data center. The results show that the proposed approach can reduce the risk of attacks caused by the co-location of virtual machines up to 41% compared to the existing approaches.

     

Recent developments and progress of aerogel assisted environmental remediation: a review

Journal of Porous Materials - Aerogel is a special type of porous material with many outstanding physical and chemical properties, such as low density, high porosity, high surface area, and...