Статьи журнала - International Journal of Intelligent Systems and Applications

Все статьи: 761

0/1 Knapsack Problem using Diversity based Dual Population Genetic Algorithm

0/1 Knapsack Problem using Diversity based Dual Population Genetic Algorithm

A. J. Umbarkar, M. S. Joshi

Статья научная

The 0/1 Knapsack Problem is an optimization problem solved using various soft computing methods. The solution to the 0/1 Knapsack Problem (KP) can be viewed as the result of a sequence of decisions. Simple Genetic Algorithm (SGA) effectively solves knapsack problem for large data set. But it has problems like premature convergence and population diversity. Dual Population Genetic Algorithm (DPGA) is an improved version of Genetic Algorithm (GA) with the solution to above problems. This paper proposes Dual Population GA for solving 0/1 knapsack Problem. Experimental results of knapsack on SGA and DPGA are compared on standard as well as random data sets. The experimental result shows DPGA performs better than knapsack on SGA.

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A Bag Theoretic Approach towards the Count of an Intuitionistic Fuzzy Set

A Bag Theoretic Approach towards the Count of an Intuitionistic Fuzzy Set

B.K.Tripathy, S.Khandelwal, M.K.Satapathy

Статья научная

The cardinality of fuzzy sets was introduced by DeLuca and termini, Zadeh and Tripathy et al, where the first one is a basic one, the second one is based on fuzzy numbers and the final one introduces a bag theoretic approach. The only approach to find the cardinality of an intuitionistic fuzzy set is due to Tripathy et al. In this paper, we introduce a bag theoretic approach to find the cardinality of intuitionistic fuzzy set, which extends the corresponding definition of fuzzy sets introduced by Tripathy et al. In fact three types of intuitionistic fuzzy counts are introduced and we also establish several properties of these count functions.

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A Centroid Model for the Depth Assessment of Images using Rough Fuzzy Set Techniques

A Centroid Model for the Depth Assessment of Images using Rough Fuzzy Set Techniques

P. Swarnalatha, B.K. Tripathy

Статья научная

Detection of affected areas in images is a crucial step in assessing the depth of the affected area for municipal operators. These affected areas in the underground images, which are line images are indicative of the condition of buried infrastructures like sewers and water mains. These images identify affected areas and extract their properties like structures from the images, whose contrast has been enhanced... A Centroid Model for the Depth Assessment of Images using Rough Fuzzy Set Techniques presents a three step method which is a simple, robust and efficient one to detect affected areas in the underground concrete images. The proposed methodology is to use segmentation and feature extraction using structural elements. The main objective for using this model is to find the dimensions of the affected areas such as the length, width, depth and the type of the defects/affected areas. Although human eye is extremely effective at recognition and classification, it is not suitable for assessing defects in images, which might have spread over thousands of miles of image lines. The reasons are mainly fatigue, subjectivity and cost. Our objective is to reduce the effort and the labour of a person in detecting the affected areas in underground images. A proposal to apply rough fuzzy set theory to compute the lower and upper approximations of the affected area of the image is made in this paper. In this connection we propose to use some concepts and technology developed by Pal and Maji.

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A Comparative Analysis of Multigranular Approaches and on Topoligical Properties of Incomplete Pessimistic Multigranular Rough Fuzzy Sets

A Comparative Analysis of Multigranular Approaches and on Topoligical Properties of Incomplete Pessimistic Multigranular Rough Fuzzy Sets

B.K.Tripathy, M. Nagaraju

Статья научная

Rough sets, introduced by Pawlak as a model to capture impreciseness in data have been a very useful tool in several applications. These basic rough sets are defined by taking equivalence relations over a universe. In order to enhance the modeling powers of rough sets, several extensions to the basic definition has been introduced over the past few years. Extending the single granular structure of research in classical rough set theory two notions of Multigranular approaches; Optimistic Multigranulation and Pessimistic Multigranulation have been introduced so far. Topological properties of rough sets along with accuracy measures are two important features of rough sets from the application point of view. Topological properties of Optimistic Multigranular rough sets Optimistic Multigranular rough fuzzy sets and Pessimistic Multigranular rough sets have been studied. Incomplete information systems take care of missing values for items in data tables. Optimistic and pessimistic MGRS have also been extended to such type of incomplete information systems. In this paper we provide a comparative study of the two types of Multigranular approaches along with other related notions. Also, we extend the study to topological properties of incomplete pessimistic MGRFS. These results hold both for complete and incomplete information systems.

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A Comparison of BA, GA, PSO, BP and LM for Training Feed forward Neural Networks in e-Learning Context

A Comparison of BA, GA, PSO, BP and LM for Training Feed forward Neural Networks in e-Learning Context

Koffka Khan, Ashok Sahai

Статья научная

Training neural networks is a complex task of great importance in the supervised learning field of research. We intend to show the superiority (time performance and quality of solution) of the new metaheuristic bat algorithm (BA) over other more “standard” algorithms in neural network training. In this work we tackle this problem with five algorithms, and try to over a set of results that could hopefully foster future comparisons by using a standard dataset (Proben1: selected benchmark composed of problems arising in the field of Medicine) and presentation of the results. We have selected two gradient descent algorithms: Back propagation and Levenberg-Marquardt, and three population based heuristic: Bat Algorithm, Genetic Algorithm, and Particle Swarm Optimization. Our conclusions clearly establish the advantages of the new metaheuristic bat algorithm over the other algorithms in the context of eLearning.

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A Comparison of Crowding Differential Evolution Algorithms for Multimodal Optimization Problems

A Comparison of Crowding Differential Evolution Algorithms for Multimodal Optimization Problems

O. Tolga Altinoz

Статья научная

Multimodal problems are related to locating multiple, redundant global optima, as opposed to single solution. In practice, generally in engineering problems it is desired to obtain many redundant solutions instead of single global optima since the available resources cannot be enough or not possible to implement the solution in real-life. Hence, as a toolbox for finding multimodal solutions, modified single objective algorithms can able to use. As one of the fundamental modification, from one of the niching schemes, crowding method was applied to Differential Evolution (DE) algorithm to solve multimodal problems and frequently preferred to compared with developed methods. Therefore, in this study, eight different DE are considered/evaluated on ten benchmark problems to provide best possible DE algorithm for crowding operation. In conclusion, the results show that the time varying scale mutation DE algorithm outperforms against other DE algorithms on benchmark problems.

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A Dynamic Self-Adjusted Buffering Mechanism for Peer-to-Peer Real-Time Streaming

A Dynamic Self-Adjusted Buffering Mechanism for Peer-to-Peer Real-Time Streaming

Jun-Li Kuo, Chen-Hua Shih, Yaw-Chung Chen

Статья научная

Multimedia live stream multicasting and on-line real-time applications are popular recently. Real-time multicast system can use peer-to-peer technology to keep stability and scalability without any additional support from the underneath network or a server. Our proposed scheme focuses on the mesh architecture of peer-to-peer live streaming system and experiments with the buffering mechanisms. We design the dynamic buffer to substitute the traditional fixed buffer. According to the existing measurements and our simulation results, using the traditional static buffer in a dynamic peer-to-peer environment has a limit of improving quality of service. In our proposed method, the buffering mechanism can adjust buffer to avoid the frozen or reboot of streaming based on the input data rate. A self-adjusted buffer control can be suitable for the violently dynamic peer-to-peer environment. Without any support of infrastructure and modification of peer-to-peer protocols, our proposed scheme can be workable in any chunk-based peer-to-peer streaming delivery. Hence, our proposed dynamic buffering mechanism varies the existing peer-to-peer live streaming system less to improve quality of experience more.

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A Few Applications of Imprecise Matrices

A Few Applications of Imprecise Matrices

Sahalad Borgoyary

Статья научная

This article introduces generalized form of extension definition of the Fuzzy set and its complement in the sense of reference function namely in imprecise set and its complement. Discuss Partial presence of element, Membership value of an imprecise number in the normal and subnormal imprecise numbers. Further on the basis of reference function define usual matrix into imprecise form with new notation. And with the help of maximum and minimum operators, obtain some new matrices like reducing imprecise matrices, complement of reducing imprecise matrix etc. Along with discuss some of the classical matrix properties which are hold good in the imprecise matrix also. Further bring out examples of application of the addition of imprecise matrices, subtraction of imprecise matrices etc. in the field of transportation problems.

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A Framework for Detecting Cloning Attacks in OSN Based on a Novel Social Graph Topology

A Framework for Detecting Cloning Attacks in OSN Based on a Novel Social Graph Topology

Ali M. Meligy, Hani M. Ibrahim, Mohamed F. Torky

Статья научная

Online Social Networks (OSN) are considering one of the most popular internet applications which attract millions of users around the world to build several social relationships. Emerging the Web 2.0 technology allowed OSN users to create, share, or exchange types of contents in a popular fashion. The other hand, OSN are considering one of the most popular platforms for the intruders to spread several types of OSN attacks. Creating fake profiles for launching cloning attacks is one of the most risky attacks which target Users' profiles in Online Social Networks, the attacker seek to impersonate user's identity through duplicating user's online presence in the same or across several social networks, therefore, he can deceive OSN users into forming trusting social relations with his created fake profiles. These malicious profiles aim to harvest sensitive user's information or misuse the reputation of the legitimate profile's owner, as well as it may be used as a spy profiles for other criminal parties. Detecting these fake profiles still represent a major problem from OSN Security and Privacy point of view. In this paper we introduced a theoretical framework which depends on a novel topology of a social graph called Trusted Social Graph (TSG) which used to visualize trusted instances of social communications between OSN users. Another contribution is a proposed detection model that based on TSG topology as well as two techniques; Deterministic Finite Automaton (DFA) and Regular Expression. Our proposed detection model used to recognize the stranger instances of communications and social actions that performed using fake profiles in OSN.

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A Framework for Mining Coherent Patterns Using Particle Swarm Optimization based Biclustering

A Framework for Mining Coherent Patterns Using Particle Swarm Optimization based Biclustering

Suvendu Kanungo, Somya Jaiswal

Статья научная

High-throughput microarray technologies have enabled development of robust biclustering algorithms which are capable of discovering relevant local patterns in gene expression datasets wherein subset of genes shows coherent expression patterns under subset of experimental conditions. In this work, we have proposed an algorithm that combines biclustering technique with Particle Swarm Optimization (PSO) structure in order to extract significant biological relevant patterns from such dataset. This algorithm comprises of two phases for extracting biclusters, one is the seed finding phase and another is the seed growing phase. In the seed finding phase, gene clustering and condition clustering is done separately on the gene expression data matrix and the result obtained from both the clustering is combined to form small tightly bound submatrices and those submatrices are used as seeds for the algorithm, which are having the Mean Squared Residue (MSR) value less than the defined threshold value. In the seed growing phase, the number of genes and the number of conditions are added in these seeds to enlarge it by using the PSO structure. It is observed that by using our technique in Yeast Saccharomyces Cerevisiae cell cycle expression dataset, significant biclusters are obtained which are having large volume and less MSR value in comparison to other biclustering algorithms.

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A General Framework for Multi-Objective Optimization Immune Algorithms

A General Framework for Multi-Objective Optimization Immune Algorithms

Chen Yunfang

Статья научная

Artificial Immune System (AIS) is a hotspot in the area of Computational Intelligence. While the Multi-Objective Optimization (MOP) problem is one of the most widely applied NP-Complete problems. During the past decade more than ten kinds of Multi-Objective optimization algorithms based on AIS were proposed and showed outstanding abilities in solving this kind of problem. The paper presents a general framework of Multi-Objective Immune Algorithms, which summarizes a uniform outline of this kind of algorithms and gives a description of its principles, mainly used operators and processing methods. Then we implement the proposed framework and build four typical immune algorithms on it: CLONALG, MISA, NNIA and CMOIA. The experiment results showed the framework is very suitable to develop the various multi-objective optimization immune algorithms.

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A Genetic Algorithm for Allocating Project Supervisors to Students

A Genetic Algorithm for Allocating Project Supervisors to Students

Hamza O. Salami, Esther Y. Mamman

Статья научная

Research projects are graduation requirements for many university students. If students are arbitrarily assigned project supervisors without factoring in the students' preferences, they may be allocated supervisors whose research interests differ from theirs or whom they just do not enjoy working with. In this paper we present a genetic algorithm (GA) for assigning project supervisors to students taking into account the students' preferences for lecturers as well as lecturers' capacities. Our work differs from several existing ones which tackle the student project allocation (SPA) problem. SPA is concerned with assigning research projects to students (and sometimes lecturers), while our work focuses on assigning supervisors to students. The advantage of the latter over the former is that it does not require projects to be available at the time of assignment, thus allowing the students to discuss their own project ideas/topics with supervisors after the allocation. Experimental results show that our approach outperforms GAs that utilize standard selection and crossover operations. Our GA also compares favorably to an optimal integer programming approach and has the added advantage of producing multiple good allocations, which can be discussed in order to adopt a final allocation.

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A Glowworm Optimization Method for the Design of Web Services

A Glowworm Optimization Method for the Design of Web Services

Koffka Khan, Ashok Sahai

Статья научная

A method for adaptive usability evaluation of B2C eCommerce web services is proposed. For measuring eCommerce usability a checklist integrating eCommerce quality and usability is developed. By a Glowworm swarm optimization (GSO) neural networks-based model the usability dimensions and their checklist items are adaptively selected. A case study for usability evaluation of an eCommerce anthurium retail website is carried out. The experimental results show that GSO with neural networks supports the allocation of usability problems and the defining of relevant improvement measures. The main advantage of the approach is the adaptive selection of most significant checklist dimensions and items and thus significant reduction of the time for usability evaluation and design.

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A Growing Evolutionary Algorithm and Its Application for Data Mining

A Growing Evolutionary Algorithm and Its Application for Data Mining

Ning Hou, Zhanmin Wang

Статья научная

An unsuitable representation will make the task of mining classification rules very hard for a traditional evolutionary algorithm (EA). But for a given dataset, it is difficult to decide which one is the best representation used in the mining progress. In this paper, we analyses the effects of different representations for a traditional EA and proposed a growing evolutionary algorithm which was robust for mining classification rules in different datasets. Experiments showed that the proposed algorithm is effective in dealing with problems of deception, linkage, epistasis and multimodality in the mining task.

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A Heterogeneous Access Remote Integrating Surveillance Heuristic Model for a Moving Train in Tunnel

A Heterogeneous Access Remote Integrating Surveillance Heuristic Model for a Moving Train in Tunnel

Tanuja.P.Patgar, Shankaraiah

Статья научная

Many number of real time applications are available for train monitoring using satellite based navigation system with high level of speed and precision. But these systems have faced lot of issues such as multipath loss and line of sight which results in lesser accuracy measurements. When the train is moving in low satellite visible areas such as tunnels, mountains, forest etc, then no position information is available. The service failure in tunnel made big challenge to demonstrate a self supporting innovative platform for navigation of train. This paper is focused on designing a novel approach by integrating Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID) system for continuous monitoring of train moving in tunnel. The wireless tracking controller based on quadratic optimal control theory is considering for analysis. Overall performance of the control design is based on Liapunov approach, where quadratic performance index is directly related to Liapunov functions. By minimizing and maximizing the performance index value corresponding to control inputs will trace the tracking error inaccuracies. As maximizing the performance index, the tracking error produces 0.04% inaccuracy. The data loss is 0.06% when minimizing the performance value. Simulation is carried out using Mat lab.

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A High Performance Image Authentication Algorithm on GPU with CUDA

A High Performance Image Authentication Algorithm on GPU with CUDA

Caiwei Lin, Lei Zhao, Jiwen Yang

Статья научная

There has been large amounts of research on image authentication method. Many of the schemes perform well in verification results; however, most of them are time-consuming in traditional serial manners. And improving the efficiency of authentication process has become one of the challenges in image authentication field today. In the future, it’s a trend that authentication system with the properties of high performance, real-time, flexible and ease for development. In this paper, we present a CUDA-based implementation of an image authentication algorithm with NVIDIA’s Tesla C1060 GPU devices. Comparing with the original implementation on CPU, our CUDA-based implementation works 20x-50x faster with single GPU device. And experiment shows that, by using two GPUs, the performance gains can be further improved around 1.2 times in contras to single GPU.

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A Hybrid Algorithm for Privacy Preserving in Data Mining

A Hybrid Algorithm for Privacy Preserving in Data Mining

Sridhar Mandapati, Raveendra Babu Bhogapathi, Ratna Babu Chekka

Статья научная

With the proliferation of information available in the internet and databases, the privacy-preserving data mining is extensively used to maintain the privacy of the underlying data. Various methods of the state art are available in the literature for privacy-preserving. Evolutionary Algorithms (EAs) provide effective solutions for various real-world optimization problems. Evolutionary Algorithms are efficiently employed in business practice. In privacy-preserving domain, the existing EA solutions are restricted to specific problems such as cost function evaluation. In this work, it is proposed to implement a Hybrid Evolutionary Algorithm using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Both GA and PSO in the proposed system work with the same population. In the proposed framework, k-anonymity is accomplished by generalization of the original dataset. The hybrid optimization is used to search for optimal generalized feature set.

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A Hybrid Artificial Bee Colony and Harmony Search Algorithm to Generate Covering Arrays for Pair-wise Testing

A Hybrid Artificial Bee Colony and Harmony Search Algorithm to Generate Covering Arrays for Pair-wise Testing

Priti Bansal, Sangeeta Sabharwal, Nitish Mittal

Статья научная

Combinatorial Interaction Testing (CIT) is a cost effective testing technique that aims to detect interaction faults generated as a result of interaction between components or parameters in a software system. CIT requires the generation of effective test sets that cover all possible t-way (t denotes the strength of testing) interactions between parameters. Covering array (CA) and mixed covering array (MCA) are often used to represent test sets. This paper presents a hybrid algorithm that integrates artificial bee colony algorithm (ABC) and harmony search algorithm (HS) to construct CAs for testing all 2-way interactions (pair-wise testing) in software systems. The performance of the proposed hybrid algorithm ABCHS-CAG is compared and analyzed by performing experiments on a set of benchmark problems on pair-wise testing. The results show that ABCHS-CAG generates smaller CAs than its greedy counterparts whereas its performance is comparable to the existing state-of-the-art meta-heuristic algorithms.

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A Hybrid RBF-SVM Ensemble Approach for Data Mining Applications

A Hybrid RBF-SVM Ensemble Approach for Data Mining Applications

M.Govindarajan

Статья научная

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. This paper addresses using an ensemble of classification methods for data mining applications like intrusion detection, direct marketing, and signature verification. In this research work, new hybrid classification method is proposed for heterogeneous ensemble classifiers using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using a Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. Here, modified training sets are formed by resampling from original training set; classifiers constructed using these training sets and then combined by voting. The proposed RBF-SVM hybrid system is superior to individual approach for intrusion detection, direct marketing, and signature verification in terms of classification accuracy.

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A Hybrid Wavelet-ANN-Based Protection Scheme for FACTS Compensated Transmission Lines

A Hybrid Wavelet-ANN-Based Protection Scheme for FACTS Compensated Transmission Lines

A.Y. Abdelaziz, Amr M. Ibrahim

Статья научная

This paper proposes an approach for the protection of transmission lines with FACTS based on Artificial Neural Networks (ANN) using Wavelet Transform (WT). The required features for the proposed algorithm are extracted from the measured transient current and voltage waveforms using discrete wavelet transform (DWT). Those features are employed for fault detection and faulted phase selection using ANN. The type of FACTS compensated transmission lines is the Thyristor-Controlled Series Capacitor (TCSC). System simulation and test results indicate the feasibility of using neural networks using wavelet transforms in the fault detection, classification and faulted phase selection of FACTS compensated transmission lines.

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