Tai-Hoon Kim

A New Multi-layer Perceptrons Trainer Based on Ant Lion Optimization Algorithm

In this paper, Ant Lion Optimizer (ALO) was presented to train Multi-Layer Perceptron (MLP). ALO was used to find the weights and biases of the MLP to achieve a minimum error and a high classification rate. Four standard classification datasets were used to benchmark the performance of the proposed method. In addition, the performance of the proposed method were compared with three well-known optimization algorithms, namely, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). The experimental results showed that the ALO algorithm with the MLP was very competitive as it solved the local optima problem and achieved a high accuracy rate.

Adaptive routing protocol for mobile ad hoc networks

Artificial immune systems (AIS) are used for solving complex optimization problems and can be applied to the detection of misbehaviors, such as a fault tolerant. We present novel techniques for the routing optimization from the perspective of the artificial immunology theory. We discussed the bioinspired protocol AntOR and analyze its new enhancements. This ACO protocol based on swarm intelligence takes into account the behavior of the ants at the time of obtaining the food. In the simulation results we compare it with the reactive protocol AODV observing how our proposal improves it according to Jitter, the delivered data packet ratio, throughput and overhead in number of packets metrics.

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluations.

An improved brain MR image binarization method as a preprocessing for abnormality detection and features extraction

This paper propose a computerized method of magnetic resonance imaging (MRI) of brain binarization for the uses of preprocessing of features extraction and brain abnormality identification. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to extensive black background or large variation in contrast between background and foreground of MRI. We have proposed a binarization that uses mean, variance, standard deviation and entropy to determine a threshold value followed by a non-gamut enhancement which can overcome the binarization problem of brain component. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error. A comparison is carried out among the obtained outcome with this innovative method with respect to other well-known methods.

An effective method for computerized prediction and segmentation of multiple sclerosis lesions in brain MRI

BACKGROUND AND OBJECTIVES Multiple sclerosis is one of the major diseases and the progressive MS lesion formation often leads to cognitive decline and physical disability. A quick and perfect method for estimating the number and size of MS lesions in the brain is very important in estimating the progress of the disease and effectiveness of treatments. But, the accurate identification, characterization and quantification of MS lesions in brain magnetic resonance imaging (MRI) is extremely difficult due to the frequent change in location, size, morphology variation, intensity similarity with normal brain tissues, and inter-subject anatomical variation of brain images. METHODS This paper presents a method where adaptive background generation and binarization using global threshold are the key steps for MS lesions detection and segmentation. After performing three phase level set, we add third phase segmented region with contour of brain to connect the normal tissues near the boundary. Then remove all lesions except maximum connected area and corpus callosum of the brain to generate adaptive background. The binarization method is used to select threshold based on entropy and standard deviation preceded by non-gamut image enhancement. The background image is then subtracted from binarized image to find out segmented MS lesions. RESULTS The step of subtraction of background from binarized image does not generate spurious lesions. Binarization steps correctly identify the MS lesions and reduce over or under segmentation. The average Kappa index is 94.88%, Jacard index is 90.43%, correct detection ration is 92.60284%, false detection ratio is 2.55% and relative area error is 5.97% for proposed method. Existing recent methods does not have such accuracy and low value of error rate both mathematically as well as visually due to many spurious lesions generation and over segmentation problems. CONCLUSIONS Proposed method accurately identifies the size and number of lesions as well as location of lesions detection as a radiologist performs. The adaptability of the proposed method creates a number of potential opportunities for use in clinical practice for the detection of MS lesions in MRI. Proposed method gives an improved accuracy and low error compare to existing recent methods.

Virtual learning communities: unsolved troubles

Virtual learning communities are defined as groups where multiple organizations share learning resources and make collaborative learning activities. The Learning Management Systems (LMS) are technological platforms that allow the realization of these spaces in a cooperative cloud of e-learning. In the present paper it is highlighted that despite the fact that organizations agree to cooperate with each other, to develop virtual learning communities, the generation of these spaces are not common, for this reason the paper raises the Ho hypothesis: The LMS present inability to cooperating to generate virtual learning communities that are developed among multiple organizations. The main objective in the present work is to corroborate the hypothesis raised, for which the problem is described and also to review the state of the art is made, noticing that it appears recurrently. Besides, the degree of fitness in current LMS is valued, verifying if they possess functionalities in their architectures to enable communities in a cooperative cloud of e-learning. This paper is useful for organizations that want to develop cooperative learning tasks for their members (students, teachers, directors) given that it identifies functional requirements that LMS must attend in order to make such communities possible. In this paper the problem is divided in variables that facilitate its comprehension and allow validating the hypothesis raised.

Feature Selection Approach Based on Social Spider Algorithm: Case Study on Abdominal CT Liver Tumor

This paper addresses a new subset feature selection performed by new Social Spider Optimization algorithm (SSOA) to find optimal regions of the complex search space through the interaction of individuals in the population. SSOA is a new evolutionary computation technique which mimics the behavior of cooperative social-spiders based on the biological laws of the cooperative colony. The performance of SSOA associated with two reasons: (a) operators allow to increasing find the global optima in the search space, and (b) division of the population into male and female, provides the use of different rates between exploration and exploitation during the evolution process. A theoretical analysis on abdominal CT liver tumor dataset that models the number of correctly classified data is proposed using Confusion Matrix, Precision, Recall, and accuracy. The results show that the mechanism of SSOA provides very good exploration, local minima avoidance, and exploitation simultaneously.

GTrust: Group Extension for Trust Models in Distributed Systems

This paper proposes and describes a trust model for distributed systems based on groups of peers. A group is defined as a collection of entities with particular affinities and capabilities. All entities may have a trust and a reputation value of each other in the system. In many cases it may be necessary to trust the whole system instead of one particular entity. In such cases group trust represents the trust of their particular members. To achieve this, this paper presents a group trust calculation model. We implemented the proposed model in a P2P simulation tool and presented main results for group trust calculation.

A Zone-Based Media Independent Information Service for IEEE 802.21 Networks

Next generation networks integrate different wireless technologies, including Wi-Fi, Wi-Max, and 3GPP (UMTS, HSPA, and/or LTE), in which the mobile node (MN) has the opportunity to switch from one network to another, under an always best connected scheme. In such heterogeneous environment, discovering which types of network connectivity and services are available is a critical challenge. The IEEE 802.21 standard specifies a network information server entity providing network information within a geographical area by which the MN can discover a service or a network. In this paper, we propose a zone-based media independent information service using the IEEE 802.21 standard to accelerate the neighbor discovery procedure. In the proposed scheme, the access networks are associated and grouped in mobility zones, through an efficient set of rules, to minimize the amount of control messages flowing in the core network. Through a NS-2 based simulation, the results demonstrate that the proposed scheme reduces the neighbor discovery delay as well as the signaling overhead if compared with the standard MIIS deployment.

An Iterative Implementation of Level Set for Precise Segmentation of Brain Tissues and Abnormality Detection from MR Images

ABSTRACT In this paper, an iterative implement of level set methodology has been proposed for the precise segmentation of normal and abnormal tissues in magnetic resonance imaging (MRI) brain images. In this segmentation, the normal tissues such as WM (white matter), GM (grey matter), and CSF (cerebrospinal fluid) with other regions of human head such as skull, marrow, and muscles skin are segmented and abnormal tissues such as haemorrhage, oedema, and tumour can be segmented if any. The segmentation is done by using iterative three region level set method based on the condition sharp peak greater than three. The iterative segmented component generates a hierarchical structure for correct segmentation. The performance of the segmentation method is estimated by different metrics such as accuracy, similarity index, and relative error. The performance of segmentation method is examined using a defined set of MRI brain.