Medical image classification using an efficient data mining technique

Data mining refers to extracting or "mining" knowledge from large amounts of data. It is an increasingly popular field that uses statistical, visualization, machine learning, and other data manipulation and knowledge extraction techniques aimed at gaining an insight into the relationships and patterns hidden in the data. Availability of digital data within picture archiving and communication systems raises a possibility of health care and research enhancement associated with manipulation, processing and handling of data by computers.That is the basis for computer-assisted radiology development. Further development of computer-assisted radiology is associated with the use of new intelligent capabilities such as multimedia support and data mining in order to discover the relevant knowledge for diagnosis. It is very useful if results of data mining can be communicated to humans in an understandable way. In this paper, we present our work on data mining in medical image archiving systems. We investigate the use of a very efficient data mining technique, a decision tree, in order to learn the knowledge for computer-assisted image analysis. We apply our method to the classification of x-ray images for lung cancer diagnosis. The proposed technique is based on an inductive decision tree learning algorithm that has low complexity with high transparency and accuracy. The results show that the proposed algorithm is robust, accurate, fast, and it produces a comprehensible structure, summarizing the knowledge it induces.

The inadequacy of Western media theory in third world internal conflicts : the case of military censorship in Sri Lanka

Research on media production and on media effects under military censorship has centred primarily on cross-border conflicts being waged by Western regimes. There is a noticeable void in understanding internal conflicts, particularly so within the Asian region. To help address this lacuna, we content-analyse Sri Lankan newspaper samples of two critical military campaigns within the Sri Lankan separatist conflict to gain insight into an Asian internal conflict within the context of military censorship and culture. We discover that supposedly stringent censorship regulations and the rules of the official communication systems have almost no effect on Sri Lankan newspapers, which demonstrate a unique talent to circumvent these strictures. During these bloody internal conflicts, the Sri Lankan press enjoyed freedom of expression, flaunted social responsibility and their watchdog instincts, and, most interestingly for us, demonstrated the failure of the Western propaganda model and other theoretical perspectives. The importance of understanding cultural dimensions before theorizing media behaviour in order to obtain a more rational understanding of the sociology of media is posited by this study. We conclude with discussion of a new model of media censorship which has more relevance in Asian internal conflicts.

Power saving in sensor networks via mobile base station repositioning

This research investigated the effect of dynamically repositioning the geographic location of a mobile base station within a sensor network in order to reduce energy consumption and increase network lifetime. Through simulation and experimental methodology, the proposed approach outperformed existing methods by extending network lifetime whilst reducing energy consumption.

Image indexing for learning content in a mobile learning environment

The outcome of this research is a hybrid system called Image Indexing for Mobile Phone (ID4MP). It was developed based on a new hybrid algorithm called Ferial's Hybrids Algorithm (FHSA). The system has a good feature of efficiency, accuracy and performance in retrieving and delivering learning content to students' mobile phones.

A hermeneutic phenomenological investigation of business and IT alignment

The maximum potential potential alignment between business and IT is constrained by the mutually interacting factors of communications, shared understanding, trust, use of an appropriately scaled project and risk management methodology, distributed business ownership of all its processes and a pervasive IT ownership of every technology-enables business process.

Series feature aggregation for content-based image retrieval

Feature aggregation is a critical technique in content-based image retrieval systems that employ multiple visual features to characterize image content. One problem in feature aggregation is that image similarity in different feature spaces can not be directly comparable with each other. To address this problem, a new feature aggregation approach, series feature aggregation (SFA), is proposed in this paper. In contrast to merging incomparable feature distances in different feature spaces to get aggregated image similarity in the conventional feature aggregation approach, the series feature aggregation directly deal with images in each feature space to avoid comparing different feature distances. SFA is effectively filtering out irrelevant images using individual features in each stage and the remaining images are images that collectively described by all features. Experiments, conducted with IAPR TC-12 benchmark image collection (ImageCLEF2006) that contains over 20,000 photographic images and defined queries, have shown that SFA can outperform the parallel feature aggregation and linear distance combination schemes. Furthermore, SFA is able to retrieve more relevant images in top ranked outputs that brings better user experience in finding more relevant images quickly.

Channel estimation of dual-hop MIMO relay system via parallel factor analysis

The optimal source precoding matrix and relay amplifying matrix have been developed in recent works on multiple-input multiple-output (MIMO) relay communication systems assuming that the instantaneous channel state information (CSI) is available. However, in practical relay communication systems, the instantaneous CSI is unknown, and therefore, has to be estimated at the destination node. In this paper, we develop a novel channel estimation algorithm for two-hop MIMO relay systems using the parallel factor (PARAFAC) analysis. The proposed algorithm provides the destination node with full knowledge of all channel matrices involved in the communication. Compared with existing approaches, the proposed algorithm requires less number of training data blocks, yields smaller channel estimation error, and is applicable for both one-way and two-way MIMO relay systems with single or multiple relay nodes. Numerical examples demonstrate the effectiveness of the PARAFAC-based channel estimation algorithm.

Medication errors in hospitalised children

This study aims to explore the characteristics of reported medication errors occurring among children in an Australian children's hospital, and to examine the types, causes and contributing factors of medication errors.

Separating spatially correlated signals using first-order precoders

This paper deals with blind separation of spatially correlated signals mixed by an instantaneous system. Taking advantage of the fact that the source signals are accessible in some man-made systems such as wireless communication systems, we preprocess the source signals in transmitters by a set of properly designed first-order precoders and then the coded signals are transmitted. At the receiving side, information about the precoders are utilized to perform signal separation. Compared with the existing precoder-based methods, the new method only employs the simplest first-order precoders, which reduces the delay in data transmission and is easier to implement in practical applications.

Image encryption based on compressed sensing and blind source separation

A novel image encryption scheme based on compressed sensing and blind source separation is proposed in this work, where there is no statistical requirement to plaintexts. In the proposed method, for encryption, the plaintexts and keys are mixed with each other using a underdetermined matrix first, and then compressed under a project matrix. As a result, it forms a difficult underdetermined blind source separation (UBSS) problem without statistical features of sources. Regarding the decryption, given the keys, a new model will be constructed, which is solvable under compressed sensing (CS) frame. Due to the usage of CS technology, the plaintexts are compressed into the data with smaller size when they are encrypted. Meanwhile, they can be decrypted from parts of the received data packets and thus allows to lose some packets. This is beneficial for the proposed encryption method to suit practical communication systems. Simulations are given to illustrate the availability and the superiority of our method.

A multilayer non-repudiation system: a Suite-B approach

 Security provisioning is an essential part in the design of any communication systems, which becomes more critical for wireless systems. The consideration and comparisons of security algorithms in various Open Systems Interconnection layers is a difficult task, because there are many performance metrics involved. The aim of this novel research article is to present research results for the design of a wireless system revolving around the practical and low-cost implementation of Suite-B algorithms in different layers. Suite-B, promulgated by the National Security Agency, is a set of cryptographic algorithms, including non-repudiation. The end results include the deployment of Suite-B algorithms at the application, transport, and network layers and the protocol flow at each layer.