다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース)

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.

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A Survey on Evolutionary Computation Approaches to Feature Selection

There are various noisy non-linear mathematical optimization problems that can be effectively solved by Metaheuristic Algorithms. These are iterative search processes that efficiently perform the exploration and exploitation in the solution space, aiming to efficiently find near optimal solutions. Considering the solution space in a specified region, some models contain global optimum and multiple local optima. In this context, two types of meta-heuristics called Particle Swarm Optimization (PSO) and Firefly algorithms were devised to find optimal solutions of noisy non-linear continuous mathematical models. Firefly Algorithm is one of the recent evolutionary computing models which is inspired by fireflies behavior in nature. PSO is population based optimization technique inspired by social behavior of bird flocking or fish schooling. A series of computational experiments using each algorithm were conducted. The results of this experiment were analyzed and compared to the best solutions found so far on the basis of mean of execution time to converge to the optimum. The Firefly algorithm seems to perform better for higher levels of noise.

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Comparative Study of Firefly Algorithm and Particle Swarm Optimization for Noisy Non- Linear Optimization Problems

From the last few decades, people do various transaction activities like air ticket reservation, online banking, distance learning, group discussion and so on using the internet. Due to explosive growth of information exchange and electronic commerce in the recent decade, there is a need to implement some security mechanisms in order to protect sensitive information. Detection of any intrusive behavior is one of the most important activity for protecting our data and assets. Various intrusion detection systems are incorporated in the network for detecting intrusive behavior. In this paper, an analytical study of support vector machine (SVM)-based intrusion detection techniques is presented. Here, the methodology involves four major steps, namely, data collection, preprocessing, SVM technique for training and testing and decision. The simulated results have been analyzed based on overall detection accuracy, Receiver Operating Characteristic and (ROC) Confusion Matrix. NSL-KDD dataset is used to analyze the performance of SVM techniques. NSL-KDD dataset is a benchmark for intrusion detection technique and contains huge amount of network records. The analyzed results show that Linear SVM, Quadratic SVM, Fine Gaussian SVM and Medium Gaussian SVM give 96.1%, 98.6%, 98.7% and 98.5% overall detection accuracy, respectively.

Analysis of Support Vector Machine-based Intrusion Detection Techniques

Wireless sensor networks (WSNs) have gained global attention of both, the research community and various application users. Localisation in WSN plays a crucial role in implementing myriad of applications such as healthcare management, disaster management, environment management, and agriculture management. Localization algorithms have become an essential requirement to enhance the effectiveness of WSNs demonstrating relative estimation of sensor node position of anchor nodes with their absolute coordinates. We have done a comprehensive performance evaluation of some feedforward artificial neural networks (FFANNs) training algorithms for developing efficient localization framework in WSNs. The proposed work utilizes the received signal strength observed by anchor nodes by means of some multi-path propagation effects. This paper aims for best training algorithm output while comparing results of different training algorithms. The FFANNs is designed with 3-dimensional inputs and one hidden layer with variable neurons and two outputs. For hidden layer tansigmoid transfer function while for output layer linear transfer function is used. The best training algorithm of FFANNs based model can provide better position accuracy and precision for the future applications. We have analysed and proposed the usage of training algorithms that improves the accuracy and precision of localization algorithms. The simulation results demonstrate the effectiveness and huge potential in optimizing hardware for localization module in sensor nodes.

Analysis of Some Feedforward Artificial Neural Network Training Algorithms for Developing Localization Framework in Wireless Sensor Networks

Intelligent sensing, communication, and computing is leading the development of Wireless Sensor Networks (WSNs) and its applications. Artificial Neural Network (ANNs) is being extensively used to develop localization framework in WSN. In this paper, we present a comparative analysis of some conjugate gradient based Feedforward Artificial Neural Network (FFANNs) for developing localization framework in wireless sensor networks (WSNs). Localization is the process by which the sensor nodes in the network can identify their own location in the overall network. Localisation in WSN plays a crucial role in implementing myriad of applications such as healthcare management, disaster management, environment management, and agriculture management. Artificial neural network based localization framework are gaining importance due to faster speed of convergence and low cost of computation. We present analysis of conjugate gradient backpropagation with Powell-Beale restarts, conjugate gradient backpropagation with Fletcher-Reeves update, conjugate gradient backpropagation with Polak-Ribiere updates, one-step secant, and scaled conjugate gradient backpropagation training algorithms in this paper. Comprehensive evaluation of these training algorithms is done in perfect simulation scenario. The proposed method effectively demonstrates that conjugate gradient FFANNs based sensor motes can be designed for developing cost-effective localization framework.

Artificial Neural Networks for developing localization framework in Wireless Sensor Networks

Wireless sensor networks (WSNs) have many applications in the field of disaster management, military, healthcare and environmental monitoring. Capability of WSNs is further enhanced by the efficient localization algorithms. Localization is the process by which a sensor node determines its own location after deployment. Neural approaches are gaining popularity in evolving new localization algorithms that are capable of optimizing various parameters of WSNs. In this paper, we analyse two backpropagation algorithms based on multi-layer Perceptron (MLP) neural network. The network is trained using static sensor nodes placed in a grid with their coordinates known. The input values are distances from each anchor nodes to a particular sensor node. The output is the actual coordinates of the sensor nodes. After training, the network will be able to predict the coordinates of unknown sensor nodes. This MLP model is analyzed for Bayesian regularization and Levenberg-Marquardt training algorithm. Both algorithms are tested for the robustness and cross-validation. The simulation results demonstrate the effectiveness of the proposed model on localization error.

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Chandra Shekhar Rai

Papers

Comparative Study of Firefly Algorithm and Particle Swarm Optimization for Noisy Non- Linear Optimization Problems

Analysis of Support Vector Machine-based Intrusion Detection Techniques

Analysis of Some Feedforward Artificial Neural Network Training Algorithms for Developing Localization Framework in Wireless Sensor Networks

Artificial Neural Networks for developing localization framework in Wireless Sensor Networks

Comparative analysis of Bayesian regularization and Levenberg-Marquardt training algorithm for localization in wireless sensor network