Information Technology

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.

Main motive of this paper describes to detect and watch any specific location precisely, which are arranged for highly secured components such as platinum, topaz and residential location etc. The main advantage of this paper is to intimate the image signals to our cell phone using SMS facilities, while showing the images/messages in the cell phone, sound indicator invokes in the cell phone. Through this new research tool, the computer will identify the secured location by using this Web Camera or security camera, with some image analysis software so that when a certain condition is triggered by the camera, this intelligent camera immediately activates and captures the images, analyzes it, and sends signals to the cell phone as SMS message to a particular number with a code in the message. The autonomous events that might trigger the taking of a photo (or still image from a video camera) could be appearance of a certain face available in a database of recognized faces.

This paper proposes an approach of adapting the vector space model with dependency parse relations to generate semantic summary from multiple documents. Traditional vector space models with tf-idf weighting was not able to completely capture the content similarity because it treats the words within a document are independent of each other. In the proposed system the dependency parse of the document has been used to modify the tf-idf weight of words by incorporating the dependency between each pair of words. To select relevant sentences, different combinations of features are applied through sentence ranking strategy. The experiment result shows that consistent improvement of proposed system over traditional approaches.