Data Mining - Concepts and Techniques.

In numerous industrial applications where safety, efficiency, and reliability are among primary concerns, condition-based maintenance (CBM) is often the most effective and reliable maintenance policy. Prognostics, as one of the key enablers of CBM, involves the core task of estimating the remaining useful life (RUL) of the system. Neural networks-based approaches have produced promising results on RUL estimation, although their performances are influenced by handcrafted features and manually specified parameters. In this paper, we propose a multiobjective deep belief networks ensemble (MODBNE) method. MODBNE employs a multiobjective evolutionary algorithm integrated with the traditional DBN training technique to evolve multiple DBNs simultaneously subject to accuracy and diversity as two conflicting objectives. The eventually evolved DBNs are combined to establish an ensemble model used for RUL estimation, where combination weights are optimized via a single-objective differential evolution algorithm using a task-oriented objective function. We evaluate the proposed method on several prognostic benchmarking data sets and also compare it with some existing approaches. Experimental results demonstrate the superiority of our proposed method.

Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics

Service Management: Operations, Strategy, Information Technology

The query efficiency of a data structure that stores a set of objects, can normally be assessed by analysing the number of objects, pointers etc. looked at when answering a query. However, if the data structure is too big to fit in main memory, data may need to be fetched from disk. In that case, the query efficiency is easily dominated by moving the disk head to the correct locations, rather than by reading the data itself.

To reduce the number of disk accesses, once can group the data into blocks, and strive to bound the number of different blocks accessed rather than the number of individual data objects read. An R-tree is a general-purpose data structur that stores a hierarchical grouping of geometric objects into blocks. Many heuristics have been designed to determine which objects should be grouped together, but none of these heuristics could give a guarantee on the resulting worst-case query time.

We present the Priority R-tree, or PR-tree, which is the first R-tree variant that always answers a window query by accessing $O((N/B)^{1-1/d} + T/B)$ blocks, where $N$ is the number of $d$-dimensional objects stored, $B$ is the number of objects per block, and $T$ is the number of objects whose bounding boxes intersect the query window. This is provably asymptotically optimal. Experiments show that the PR-tree performs similar to the best known heuristics on real-life and relatively nicely distributed data, but outperforms them significantly on more extreme data.

The Priority R-Tree: A Practically Efficient and Worst-Case-Optimal R-Tree

Over the ages, nature has constantly been a rich source of inspiration for science, with much still to discover about and learn from. Swarm Intelligence (SI), a major branch of artificial intelligence, was rendered to model the collective behavior of social swarms in nature. Ultimately, Particle Swarm Optimization algorithm (PSO) is arguably one of the most popular SI paradigms. Over the past two decades, PSO has been applied successfully, with good return as well, in a wide variety of fields of science and technology with a wider range of complex optimization problems, thereby occupying a prominent position in the optimization field. However, through in-depth studies, a number of problems with the algorithm have been detected and identified; e.g., issues regarding convergence, diversity, and stability. Consequently, since its birth in the mid-1990s, PSO has witnessed a myriad of enhancements, extensions, and variants in various aspects of the algorithm, specifically after the twentieth century, and the related research has therefore now reached an impressive state. In this paper, a rigorous yet systematic review is presented to organize and summarize the information on the PSO algorithm and the developments and trends of its most basic as well as of some of the very notable implementations that have been introduced recently, bearing in mind the coverage of paradigm, theory, hybridization, parallelization, complex optimization, and the diverse applications of the algorithm, making it more accessible. Ease for researchers to determine which PSO variant is currently best suited or to be invented for a given optimization problem or application. This up-to-date review also highlights the current pressing issues and intriguing open challenges haunting PSO, prompting scholars and researchers to conduct further research both on the theory and application of the algorithm in the forthcoming years.

Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application

The traditional method for detecting the tumor diseases in the human MRI brain images is done manually by physicians. Automatic classification of tumors of MRI images requires high accuracy, since the non-accurate diagnosis and postponing delivery of the precise diagnosis would lead to increase the prevalence of more serious diseases. To avoid that, an automatic classification system is proposed for tumor classification of MRI images. This work shows the effect of neural network (NN) and K-Nearest Neighbor (K-NN) algorithms on tumor classification. We used a benchmark dataset MRI brain images. The experimental results show that our approach achieves 100% classification accuracy using K-NN and 98.92% using NN.

A Classifier to Detect Tumor Disease in MRI Brain Images

Many researchers have developed population-based techniques to solve numerical optimization problems. Almost none of these techniques demonstrate consistent performance over a wide range of problems as these problems differ substantially in their characteristics. In the state-of-the-art cultural algorithms (CAs), problem solving is facilitated by the exchange of knowledge between a network of active knowledge sources in the belief space and networks of individuals in the population space. To enhance the performance of CAs, we restructure the social fabric interconnections to facilitate flexible communication among problem solvers in the population space. Several social network reconfiguration mechanisms and types of communications are examined. This extended CA is compared with other variants of CAs and other well-known state-of-the-art algorithms on a set of challenging real-world problems. The numerical results show that the injection of neighborhoods with flexible subnetworks enhances performance on a diverse landscape of numerical optimization problems.

Leveraged Neighborhood Restructuring in Cultural Algorithms for Solving Real-World Numerical Optimization Problems

Association Rule Mining (ARM) is one of the data mining techniques used to extract hidden knowledge from datasets, that can be used by an organization's decision makers to improve overall profit. However, performing ARM requires repeated passes over the entire database. Obviously, for large database, the role of input/output overhead in scanning the database is very significant. A popular solution to improve the speed of ARM is to apply the mining algorithm on a sample instead of the entire database. In this paper, a parameterized sampling algorithm for ARM is presented. This algorithm extracts sample datasets based on three parameters: transaction frequency, transaction length and transaction frequency-length. To evaluate its performance and accuracy, a comparison against a two-phase sampling-based algorithm is performed using real and synthetic datasets. The experimental results show that the proposed sampling algorithm in some cases outperforms two-phase sampling algorithm, and achieves up to 98% accuracy.

A new sampling technique for association rule mining

Arabic has a complex structure, which makes it difficult to apply natural language processing (NLP) Much research on Arabic NLP (ANLP) does exist; however, it is not as mature as that of other languages Finding Arabic roots is an important step toward conducting effective research on most of ANLP applications The authors have studied and compared six root-finding algorithms with success rates of over 90p All algorithms of this study did not use the same testing corpus and-or benchmarking measures They unified the testing process by implementing their own algorithm descriptions and building a corpus out of 3823 triliteral roots, applying 73 triliteral patterns, and with 18 affixes, producing around 276 million words They tested the algorithms with the generated corpus and have obtained interesting results; they offer to share the corpus freely for benchmarking and ANLP research © 2010 Wiley Periodicals, Inc

A comparison study of some Arabic root finding algorithms

This paper investigates the impact of using different indexing approaches full-word, stem, and root when classifying Arabic text. In this study, the naive Bayes classifier is used to construct the multinomial classification models and is evaluated using stratified k-fold cross-validation k ranges from 2 to 10. It is also uses a corpus that consists of 1000 normalized Arabic documents. The results of one experiment in this study show that significant accuracy improvements have occurred when the full-word form is used in most k-folds. Further experiments show that the classifier has achieved the highest accuracy in the eight-fold by using 7/8-1/8 train-test ratio, despite the indexing approach being used. The overall results of this study show that the classifier has achieved the maximum micro-average accuracy 99.36%, either by using the full-word form or the stem form. This proves that the stem is a better choice to use when classifying Arabic text, because it makes the corpus dataset smaller and this will enhance both the processing time and storage utilization, and achieve the highest level of accuracy.

Amer Al-Badarneh

Papers

A Classifier to Detect Tumor Disease in MRI Brain Images

Leveraged Neighborhood Restructuring in Cultural Algorithms for Solving Real-World Numerical Optimization Problems

A new sampling technique for association rule mining

A comparison study of some Arabic root finding algorithms

The impact of indexing approaches on Arabic text classification