Maqsood Mahmud

Stock market forecasting by using a hybrid model of exponential fuzzy time series

The initial aim of this study is to propose a hybrid method based on exponential fuzzy time series and learning automata based optimization for stock market forecasting. For doing so, a two-phase approach is introduced. In the first phase, the optimal lengths of intervals are obtained by applying a conventional fuzzy time series together with learning automata swarm intelligence algorithm to tune the length of intervals properly. Subsequently, the obtained optimal lengths are applied to generate a new fuzzy time series, proposed in this study, named exponential fuzzy time series. In this final phase, due to the nature of exponential fuzzy time series, another round of optimization is required to estimate certain method parameters. Finally, this model is used for future forecasts. In order to validate the proposed hybrid method, forty-six case studies from five stock index databases are employed and the findings are compared with well-known fuzzy time series models and classic methods for time series. The proposed model has outperformed its counterparts in terms of accuracy. In this study a two phase approach is proposed based on exponential fuzzy time series and learning automata.In the first phase, the optimal lengths of intervals are estimated by applying LA based EAs in training set.Second phase aim is to estimate certain adjusting parameters for minimizing errors in training set.The conventional FTS in the first phase is applied and in the second phase EFTS is employed.Forty six case studies from five stock index databases are employed in extensive experiments.

Combining ARFIMA models and fuzzy time series for the forecast of long memory time series

Long memory time series are stationary processes in which there is a statistical long range dependency between the current value and values in different times of the series. Therefore, in this class of series, there is a slow decay of the autocorrelation function as the time difference increases. Many practical forecasting problems fall in this class, for instance, in financial time series, hydrology and earth sciences applications. This research introduces a hybrid method combining Auto Regressive Fractional Integrated Moving Average (ARFIMA) models and Fuzzy Time Series (FTS) for the forecast of long memory (long-range) time series. The proposed method is developed as one algorithm consisting of two phases. The first phase is related to the autoregressive part of the model, while the second phase is related to the Moving Average part. Based on these ideas, the combined ARFIMA and FTS model is introduced. For the parameter estimation of the model, Particle Swarm Optimization (PSO) method is selected, based on its performance on similar optimization problems. In order to illustrate the benefit and potential of the proposed ARFIMA-FTS method, it has been applied to the two stock index databases, namely Taiwan Capitalization Weighted Stock Index (TAIEX)and Dow Jones Industrial Average (DJIA), together with exchange rate data of nine main currencies versus USD. Based on the reported results, it is possible to conclude the superiority of the proposed hybrid method, compared with classical ARFIMA models and other methods in the literature.

Bio-chaotic Stream Cipher-Based Iris Image Encryption

Conventional cryptography uses encryption key, which are long bit strings and are very hard to memorize such a long random numbers. Also it can be easily attacked by using the brute force search or technique. Instead of traditional cryptography, biometric e.g. fingerprint, iris, face, voice etc uniquely identifies a person and a secure method for stream cipher, because Biometric characteristics are ever living and unstable in nature (with respect to recognition). In this paper we used the idea of bio-chaotic stream cipher which encrypts the images over the electronic media by using a biometric key and a bio-chaotic function. It enhances the security of the images and it should not be compromised. The idea also gives birth to a new kind of stream cipher named bio-chaotic stream cipher. The paper also describes how to generate a key from a biometric string and how to encrypt and decrypt the desired data by using the bio-chaotic function.

Security Analysis of Firewall Rule Sets in Computer Networks

Firewalls are the screening gates for the internet/intranet traffic in computer networks. However, deploying a firewall is simply not enough since it needs to be configured by the system administrator according to the needs of the organization. There are many reasons due to which it is hard for the administrator to configure the firewall properly. Specifying firewall rule set is complicated and error prone. Once the firewall rules are defined, then firewall should be tested, whether it actually implements firewall policy. In this paper, one of the approaches of the firewall rule set analysis, i.e., the problems with the structure of the firewall rule set is being addressed. The structure of a sample firewall rule set is analyzed to detect and resolve conflicts using two structural analysis methodologies, i.e., Policy Tree and Relational Algebra. Then the results obtained from the test by using an automated tool PolicyVisor, based on the policy tree methodology, are analyzed. It is found from the analysis that even a set of only six rules has number of anomalies. Moreover, it is hard for the human to find such anomalies manually in a larger rule set and failure to find such anomalies leads to change the firewall policy.Design and implementation of wireless systems, comprising mobile and fixed radios, requires the knowledge of the propagation characteristics of the channel. The random nature of the radio channel parameters and the complexity of the propagation phenomena suggest that characterization of the channel can be achieved based on statistical analysis of field measurements. This approach leads to statistical and empirical models of the propagation channel. Alternatively, deterministic modelling, based on approximate solutions of the wave equation, can be used in the prediction of propagation effects in some situations. This paper presents an overview of the characteristics of the radio channel and the models to predict the main effects of the propagation medium on terrestrial links.

MR imaging enhancement and segmentation of tumor using fuzzy curvelet

Medical image segmentation is a very important issue in medical imaging. An automatic brain MR image segmentation method has been proposed for tumor detection in this paper. Discretize local window and wrapping based Curvelet transform has been used to remove rician noise. A modified fuzzy C-mean algorithm is used for segmentation of brain MR image. Proposed system has been tested on different datasets of MR Images. Proposed system performs well on all types of MR images including T1, T2 and PD brain MR images. Key words:

Most known vulnerabilities in Saudi Arabian web servers

Vulnerabilities are the loopholes that arise due to poor programming or mis-configuration. Web Applications are considered to be very vulnerable to attack as compared to desktop programs on sole computers. Keeping this thing in our minds we decided to find out the all possible vulnerabilities in Saudi Arabian organizationpsilas Web servers. To assess these vulnerabilities we selected number of open source tools and tested about 169 most popular Web servers of government, Financial, Academic, organizations and commercial organizations. This problem seemed to us interesting because of two reasons, first security is a burning issue of the world and it can be minimized by finding out the vulnerabilities. Secondly it is in the interest of Saudi Arabian national goals. This problem was not addressed before for Saudi Arabian Organizations Web servers so thatpsilas why it carries high importance.

Biotransformation of Finasteride by Ocimum sanctum L., and tyrosinase inhibitory activity of transformed metabolites: Experimental and computational insights

Transformation of Finasteride (I) by cell suspension cultures of Ocimum sanctum L. was investigated. Fermentation of compound (I) with O. sanctum afforded three oxidized derivatives, 16β-hydroxyfinasteride (II), 11α-hydroxyfinasteride (III) and 15β-hydroxyfinasteride (IV). Among these metabolites, compound (II) was a new metabolite. Compound (I) and its derivatives were studied for their tyrosinase inhibition assay. All test compounds exhibited significant activity compared to standard drug kojic acid, with compound IV being the most potent member with an IC50 of 1.87μM. Molecular docking revealed significant molecular interactions behind the potent tyrosinase inhibitory activity of the tested compounds.

Biometric-Gaussian-Stream (BGS) Cipher with New Aspect of Image Encryption (Data Hiding)

Generally, a stream cipher is a symmetric key cipher where plaintext bits are combined with a pseudorandom Keystream bits to achieve desired cipher text. In our paper, we are proposing a cryptosystem named Biometric-Gaussian-Stream(BGS) cryptosystem. It is basically the combination of Gaussian noise and stream ciphers. In this system first, we pass an image through Gaussian noise function to add complexity to our system. This function is applied with specific parameters of mean and variance which works as a parallel key. To implement stream cipher with help of Biometric images, we extract Initial condition for LFSR from iriscode. We extracted iriscode using CASIA Iris database with help of Gabor Wavelet equation. A NASA image of “Saturn”, with entropy of 3.97, was chosen for encryption and decryption purpose.

A Lightweight Secure Data Aggregation Technique for Wireless Sensor Network

Wireless sensor network (WSN) consists of resource constraint sensor nodes where nodes (sensors) send data to the base station/sink node and communicate with each other by either forming a cluster or without forming a cluster. Data aggregation in WSN takes place at the responsible nodes (aggregators) in a cluster before sending data to the base station, based on the query that is received from the base station. Thus data aggregation reduces energy consumption of the nodes due to minimized communication. As a result, the life time of the individual sensors prolong in the case of aggregation compared to the data transmission that occurs without performing aggregation. One of the major security challenges for data aggregation in WSN is that the aggregators expose clear data at the aggregation level. Therefore, this aggregation level is vulnerable to attacks by intruders. Existing research has addressed this problem and proposed solutions by considering static node topology of WSN. However, in WSN the nodes can either be static or dynamic. Therefore, the existing approaches do not tackle the security issues that arise in dynamic node WSN. The proposed research aims to explore this problem and propose solutions based on a cryptographic approach.

Efficient Securing of Iris Image Template by Using Grayscale Level Incremented/Decremented Method

The proposed techniques in the paper is based on grayscale level incremented/decremented method. In this paper we used CASIA dataset for experimentation. Efficient results were achieved with respect to image hiding validated by entropy. Graphical results are also shown at the end to visualize our results.