Information Technology

Software reliability quantification plays a very significant role for software consistency and excellence. However, the conventional software quantification method mostly focuses on evaluation by use of failure data which is gained only after testing or usage in the late phase of the software life cycle. Therefore, to obtain and quantify the software reliability with the help of architecture style may be introduced. Ontology allows developers and users to better understand software architecture and reliability terminologies, assess software reliability, and communicate effectively with the software reliability engineers. Therefore, an Ontological Reliability Quantification Method (ORQM) is instigated in this paper, which focuses on various project categories correlative with architecture style and concerned project parameters. Finally, some case studies are presented to demonstrate the viability of this method.

Image cryptography and Steganography has attracted extensive research on the security of message that is to be transmitted in the open insecure medium. This is due to the fact that huge amounts of data can be hidden without perceptible impact to the carriers and possibly because of the popularity of electronic images and medical images that have become widely available. The chaotic based secret writing has its own advantage and it is mainly based on the initial condition which is the secret key for the secret writing. The chaotic based encryption serves as the robust mechanism against all sorts of attacks. In this paper, a novel image encryption and decryption scheme is proposed. Due to sensitivity to initial conditions, chaotic maps have a good potential for designing dynamic permutation map. Here a chaotic Henon map is used to generate permutation signal. Simulation results illustrate that the scheme is highly key sensitive and shows a good resistance against brute-force and statistical attacks.

Image information mining (IIM) approaches produce enormous amounts of features that are computationally expensive and in- efficient to process before the actual information discovery takes place[1]. Also, it is complicated because the combination of the features has little relevance to the hypothesis space. Hence, selecting a relevant subset of features is necessary to overcome these problems and to provide an efficient representation of the target class. In this paper, we propose survey onfeature selection and feature transformations.

The need for efficient Content Based Image Retrieval (CBIR) system has increased hugely. Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. Content Based Image Retrieval is a promising approach because of its automatic indexing retrieval based on their semantic features and visual appearance. In CBIR, image is described by several low level image features, such as colour, texture, shape or the combination of these features. With appealing time-frequency localization and multi-scale properties, wavelet transform proved to be effective in feature extraction and representation. This paper presents multilevel wavelet decomposition and adaptive thresholding technique to extract shape and texture feature of the query image and to retrieve the similar images from the database. Edge detection is done using Daubechies (db2) wavelet. Zernike moments (ZM) are used to represent the shape. Efficiency of retrieval method is tested using precision and recall on Wang’s dataset.

Information assurance (IA) projects serve as critical elements of the information technology industry, yet enjoy limited success since these pursuits are often plagued by classical project management failures stemming improperly managing budgets, cost overruns, and missing projected timelines, commonly attributed to performance of the project teams. The purpose of this phenomenological study was to explore the leadership and other strategies necessary to enhance IA project performance achievement and success. The Lewin (1939) situational leadership theory underpinned the study and served as a theoretical reference source for deeper interpretations of the study data, against these propositions. Interviews were conducted with 20 IA professionals located in the Washington, DC Metropolitan area of the United States. The data were transcribed, coded, and analyzed using a process of thematic analysis using the Moustakas’ modified van Kaam analysis method. The major themes from the analysis of the interviews of IA professionals denoted the importance of leveraging the technical knowledge of these resources, with a balanced mix of technical and subject matter experts in make-up of project teams. Training in increasing the success of these teams indicated that this must commence at the leadership level. The study results may contribute to existing knowledge in improving project success and in the development and growth of the IA industry.

This paper summarizes the introduction and importance of hidden markov model (HMM) as a classifier, learning and classification. A Markov process is a particular case of stochastic process, where the state at every time belongs to a finite set, the evolution occurs in a discrete time and the probability distribution of a state at a given time is explicitly dependent only on the last states and not on all the others. In this survey we present details of hmm, its mathematical foundations, advantages and applications in the field recognition.

A packet is a unit of data that is transmitted across a packet-switched network. A packet-switched network is an interconnected set of networks that are joined by routers or switching routers. Packet switching contrasts with another principal networking paradigm, circuit switching, a method which sets up a limited number of dedicated connections of constant bit rate and constant delay between nodes for exclusive use during the communication session. An overview of packet switching and packet technologies that use wired and wireless media Local Area Networks: Packets Switching.

Denoising an image is a crucial issue as images are widely used in various fields. Denoising deals with noise estimation and removing noise from it. While removing the noise it should preserve the sharpness and clarity of an image. This paper provides the image enhancement techniques with the estimation techniques. Various filtering approaches are suggested to remove the Gaussian additive as well as Gaussian multiplicative noise.

This paper dwells on combinatorial optimization with a view to unveiling various techniques for solving problems therein. We are interested in the combination of exact techniques (ET) and metaheuristics (MH) to provide optimal solutions or mainly to generate better heuristic solutions. In doing this, we were able to give a kind of categorization of the possible combinations, their usefulness and areas of applications.

Association rules mining algorithms can be used to discover all item associations (or rules) in a dataset. Majority voting is adopted as classification technique and on the basis of voting pattern, the consequent is chosen. The moving averaging is applied on the obtained consequents to identify the emerging pattern. Four moving averages on the basis of Fibonacci sequence are applied. It has been observed that number of trades is more in lower range of moving averages as compared to higher range and a longer days averaging has not been yielding good returns. It has been observed that the accuracy level is higher in case of smaller duration whereas the error rate is on the higher side in case of longer period averages.