There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Data Mining - Concepts and Techniques.

How have Japanese companies become world leaders in the automotive and electronics industries, among others? What is the secret of their success? Two leading Japanese business experts, Ikujiro Nonaka and Hirotaka Takeuchi, are the first to tie the success of Japanese companies to their ability to create new knowledge and use it to produce successful products and technologies. In The Knowledge-Creating Company, Nonaka and Takeuchi provide an inside look at how Japanese companies go about creating this new knowledge organizationally. The authors point out that there are two types of knowledge: explicit knowledge, contained in manuals and procedures, and tacit knowledge, learned only by experience, and communicated only indirectly, through metaphor and analogy. U.S. managers focus on explicit knowledge. The Japanese, on the other hand, focus on tacit knowledge. And this, the authors argue, is the key to their success--the Japanese have learned how to transform tacit into explicit knowledge. To explain how this is done--and illuminate Japanese business practices as they do so--the authors range from Greek philosophy to Zen Buddhism, from classical economists to modern management gurus, illustrating the theory of organizational knowledge creation with case studies drawn from such firms as Honda, Canon, Matsushita, NEC, Nissan, 3M, GE, and even the U.S. Marines. For instance, using Matsushita's development of the Home Bakery (the world's first fully automated bread-baking machine for home use), they show how tacit knowledge can be converted to explicit knowledge: when the designers couldn't perfect the dough kneading mechanism, a software programmer apprenticed herself withthe master baker at Osaka International Hotel, gained a tacit understanding of kneading, and then conveyed this information to the engineers. In addition, the authors show that, to create knowledge, the best management style is neither top-down nor bottom-up, but rather what they call "middle-up-down," in which the middle managers form a bridge between the ideals of top management and the chaotic realities of the frontline. As we make the turn into the 21st century, a new society is emerging. Peter Drucker calls it the "knowledge society," one that is drastically different from the "industrial society," and one in which acquiring and applying knowledge will become key competitive factors. Nonaka and Takeuchi go a step further, arguing that creating knowledge will become the key to sustaining a competitive advantage in the future. Because the competitive environment and customer preferences changes constantly, knowledge perishes quickly. With The Knowledge-Creating Company, managers have at their fingertips years of insight from Japanese firms that reveal how to create knowledge continuously, and how to exploit it to make successful new products, services, and systems.

The knowledge-creating company

The Web holds valuable, vast, and unstructured information about public opinion. Here, the history, current use, and future of opinion mining and sentiment analysis are discussed, along with relevant techniques and tools.

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New Avenues in Opinion Mining and Sentiment Analysis

On the truck dispatching problem

Reduction the 10%KDD training dataset up to 99.8% by using modified K-means.New high quality training datasets are constructed for training SVM and ELM.Multi-level model is proposed to improve the performance of detection accuracy.Improve the detection rate of DoS, U2R and R2L attacks.Overall accuracy of 95.75% is achieved with whole Corrected KDD dataset. Intrusion detection has become essential to network security because of the increasing connectivity between computers. Several intrusion detection systems have been developed to protect networks using different statistical methods and machine learning techniques. This study aims to design a model that deals with real intrusion detection problems in data analysis and classify network data into normal and abnormal behaviors. This study proposes a multi-level hybrid intrusion detection model that uses support vector machine and extreme learning machine to improve the efficiency of detecting known and unknown attacks. A modified K-means algorithm is also proposed to build a high-quality training dataset that contributes significantly to improving the performance of classifiers. The modified K-means is used to build new small training datasets representing the entire original training dataset, significantly reduce the training time of classifiers, and improve the performance of intrusion detection system. The popular KDD Cup 1999 dataset is used to evaluate the proposed model. Compared with other methods based on the same dataset, the proposed model shows high efficiency in attack detection, and its accuracy (95.75%) is the best performance thus far.

Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system

Microarray technology can be used as an efficient diagnostic system to recognise diseases such as tumours or to discriminate between different types of cancers in normal tissues. This technology has received increasing attention from the bioinformatics community because of its potential in designing powerful decision-making tools for cancer diagnosis. However, the presence of thousands or tens of thousands of genes affects the predictive accuracy of this technology from the perspective of classification. Thus, a key issue in microarray data is identifying or selecting the smallest possible set of genes from the input data that can achieve good predictive accuracy for classification. In this work, we propose a two-stage selection algorithm for gene selection problems in microarray data-sets called the symmetrical uncertainty filter and harmony search algorithm wrapper SU-HSA. Experimental results show that the SU-HSA is better than HSA in isolation for all data-sets in terms of the accuracy and achieves a lower number of genes on 6 out of 10 instances. Furthermore, the comparison with state-of-the-art methods shows that our proposed approach is able to obtain 5 out of 10 new best results in terms of the number of selected genes and competitive results in terms of the classification accuracy.

Hybrid feature selection algorithm using symmetrical uncertainty and a harmony search algorithm

Meta-harmony search algorithm for the vehicle routing problem with time windows

Hybridising harmony search with a Markov blanket for gene selection problems

Gene expression data comprises a huge number of genes but have only few samples that can be used to address supervised classification problems. This paper is aimed at identifying a small set of genes, to efficiently distinguish various types of biological sample; hence we have proposed a three-stage of gene selection algorithm for genomic data. The proposed approach combines ReliefF, mRMR (Minimum Redundancy Maximum Relevance) and GA (Genetic Algorithm) coded as (R-m-GA). In the first stage, the candidate gene set is identified by applying the ReliefF. While, the second minimizes the redundancy with the help of mRMR method, which facilitates the selection of effectual gene subset from the candidate set. In the third stage, GA with classifier (used as a fitness function by the GA) is applied to choose the most discriminating genes. The proposed method is validated on the tumor datasets such as: CNS, DLBCL and Prostate cancer, using IB1 classifier. The comparative analysis of the R-m-GA against GA and ReliefF-GA has revealed that the proposed method is capable of finding the smallest gene subset that offers the highest classification accuracy.

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Mohd Zakree Ahmad Nazri

Papers

Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system

Hybrid feature selection algorithm using symmetrical uncertainty and a harmony search algorithm

Meta-harmony search algorithm for the vehicle routing problem with time windows

Hybridising harmony search with a Markov blanket for gene selection problems

Hybridizing relieff, mRMR filters and GA wrapper approaches for gene selection