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Cervix cancer is the most common gynecological malignancy and second most common cancer among female in Malaysia after breast cancer. The objective of this study is to extract the size of nucleus and cytoplasm, as well as gray level values of cervical cells from ThinPrep images so that accurate value of those parameters can easily be obtained. An alternative approach of extracting features for Pap...
Association rule mining makes interesting associations and/or correlations among large sets of data. Those associations can be refined as decision rules to be used and stored in a knowledge base system. In this paper, an approach based on association rule and knowledge base is proposed and implemented in the fault diagnosis of a transformer system. According to the features of association rule, the...
In this paper, we propose an associative watermarking scheme which is conducted by the concept of Association Mining Rules (AMRs) and the ideas of Vector Quantization (VQ) and Soble operator. Performing associative watermarking rules to the images will reduct the amount of the embedded data, and using VQ indexing scheme can easily recall the embedded watermark for the purpose of image authentication,...
Internet contains a tremendous amount of valuable web applications that can be used in many systems. To use this kind of applications with other systems, the interaction needs to be in a standard structured format such as XML web service. In this paper, we present a method to convert the current web applications into standard XML web services. The system design and implementation are presented. We...
This paper presents a method of applying text mining techniques and data mining tools for pharmaceutical spam detection from Twitter data. A simple method based on a manually selected list of 65 pharmaceutical discriminating words is used for labeling spam training tweets. Preliminary experimental results show that J48 decision tree classifier has better performance over Naïve Bayesian algorithm.
Many approaches for preserving association rule privacy, such as association rule mining outsourcing, association rule hiding, and anonymity, have been proposed. In particular, association rule hiding on single transaction table has been well studied. However, hiding multi-relational association rule in data warehouses is not yet investigated. This work presents a novel algorithm to hide predictive...
In this paper we propose a new approach based on Symbolic Aggregate approximation (SAX), called improved iSAX to recognize efficient and accurate discovery of the important patterns, essential for time series data. The original SAX approach allows a very high-quality dimensionality reduction and distance measures to be defined on the symbolic approach and it is based on PAA (Piecewise Aggregate Approximation)...
This paper presents an efficient real-time knowledge base architecture for multi-agent based patient diagnostic system for chronic disease management, basically, the early detection of Inflammation of urinary bladder and Nephritis of renal pelvis origin diseases. The model integrates information stored heterogeneous and geographically distributed healthcare centers. The paper presents two main contributions...
Business intelligence is a new methodology to maximize the benefits for healthcare organization Business intelligence provides an integrated view of data that can be used to monitor, key performance indicators, identify hidden patterns in diagnosis and identify variations in cost factors. Intelligent techniques provide an effective computational methods and robust environment for business intelligence...
The classification of imbalanced data is a well-studied topic in data mining. However, there is still a lack of understanding of the factors that make the problem difficult. In this work, we study the two main reasons that make the classification of imbalanced datasets complex: overlapping and data fracture. We present a Genetic Programming-based feature extraction method driven by Rough Set Theory...
Inspite of the huge amounts of image data on the web, mining image data from the web is paid less attention than mining text data, since treating the semantics of images is much more difficult. This paper introduces a new system to mine visual knowledge on the web that aims to build a Domain Oriented Image Directory by using the Earth Mover's Distance and Color signatures. Instead of using a flat...
Association rules are adopted to discover the interesting relationship and knowledge in a large dataset. Knowledge may appear in terms of a frequent pattern discovered in a large number of production data. This knowledge can improve or solve production problems to achieve low cost production. To obtain knowledge and quality information, data mining can be applied to the manufacturing industry. In...
Mobile web browsing signifies accessing the content on web pages using a mobile device. It is common for Internet search engines to use keyword searching in which rank is assigned to each page based on several features. But it is an arduous task for a user to inscribe a keyword in such a delicate small mobile screen. A challenging research goal is the development of advanced web-based applications...
In this research, we introduce a stratified random sampling technique that guides the selection mechanism to select the events (exams) for the integrated two-stage multi-neighbourhood tabu search (ITMTS) in solving examination timetabling problem. This technique is used during the timetable improvement phase especially when dealing with the exhaustive search mechanism in order to reduce the possibilities...
In this paper we propose a rough classification modeling algorithm based on Ant Colony Optimization (ACO) reduction. We used ACO to compute the rough set reduct and later a modified rules generation method is employed to generate the classification rules. The rules generation algorithm used is the simplification of the Default Rules Generation Framework (DRGF) in order to fit with the ACO reduct....
Having an accurate Signature Detection Classification (SDC) Model has become highly demanding for Intrusion Detection Systems (IDS) to secure networks, especially when dealing with large and complex security audit data set. Selecting appropriate network features is one of the factors that influence the accuracy of SDC model. Past research has shown that the Hidden Marcov Chain, Genetic Algorithm,...
Outlier is strange data values that stand out from datasets. In some applications, finding outliers are more interesting than finding inliers in datasets, such as fraud detection, network system, financial and others. In this research, an algorithm is proposed to find minimum non-Reduct based on Rough set using Particle Swarm Optimization (PSO) for outlier detection. Like Genetic Algorithm (GA), PSO...
This paper proposes a new feature-selection strategy by integrating the Rough Set Theory (RST) and Particle Swarm Optimisation (PSO) algorithms to generate a set of discriminatory features for the classification problem. The proposed method is seen as a marriage between filter and wrapper approaches in which the RST is used to pre-reduce the feature set before optimisation by PSO, a meta-heuristic...
Individual protection, physically or mentally, is very important for someone living in a risk environment. Insurance is one of the individual protections due to accident, blaze, critical diseases or death. Insurance company plays a critical role in providing competitive product insurance that covers flexible features depend on customer requirements. In order to compete with other competitors and fulfill...
Mining techniques are needed to extract important information from huge high dimensional gene expression sets. Targeting unique expression behavior as over/under-expression is specific to gene expression data and is needed to explore another direction in the relation of genes to tumor conditions. This research proposes criteria for filtering over-expression genes, identifying over-expression related...
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