Famous Algorithm Research Articles

SQL Model in Language Encapsulation and Compression Technique for Association Rules Mining

Discovery of association rules is an important for Data mining. One of the most famous association rule learning algorithms is Apriori rule. Apriori algorithm is one of algorithms for generation of association rules. The drawback of Apriori Rule algorithm is the number of time to read data in the database equal number of each candidate is generate. Many research papers have been published trying to reduce the amount of time needed to read data from the database. In this paper, we propose a new algorithm that will work rapidly and without frequency tree or temporary candidate itemsets in RAM or Hard disk. SQL Model in Language Encapsulation and Compression Technique for Association Rules Mining (SMILE-ARM). This algorithm will generate candidates are greater than minimum support on-the-fly by SQL. This algorithm is based on two major ideas. Firstly, compress data. Secondly, generation of candidate itemsets on-the-fly by SQL. Based on the experimental results, an increase in the number of transactions or the number of items did not affect the speed at which candidates were generated by this algorithm. The construction method of SQL Model in Language Encapsulation and Compression Technique for Association Rules Mining (SMILE-ARM) technique has twenty times higher mining efficiency in execution time than Apriori Rule.

Hybrid discrete particle swarm optimization for multi-objective flexible job-shop scheduling problem

Flexible job-shop problem has been widely addressed in literature. Due to its complexity, it is still under consideration for research. This paper addresses flexible job-shop scheduling problem (FJSP) with three objectives to be minimized simultaneously: makespan, maximal machine workload, and total workload. Due to the discrete nature of the FJSP problem, conventional particle swarm optimization (PSO) fails to address this problem and therefore, a variant of PSO for discrete problems is presented. A hybrid discrete particle swarm optimization (DPSO) and simulated annealing (SA) algorithm is proposed to identify an approximation of the Pareto front for FJSP. In the proposed hybrid algorithm, DPSO is significant for global search and SA is used for local search. Furthermore, Pareto ranking and crowding distance method are incorporated to identify the fitness of particles in the proposed algorithm. The displacement of particles is redefined and a new strategy is presented to retain all non-dominated solutions during iterations. In the presented algorithm, pbest of particles are used to store the fixed number of non-dominated solutions instead of using an external archive. Experiments are performed to identify the performance of the proposed algorithm compared to some famous algorithms in literature. Two benchmark sets are presented to study the efficiency of the proposed algorithm. Computational results indicate that the proposed algorithm is significant in terms of the number and quality of non-dominated solutions compared to other algorithms in the literature.

Hybrid BBO and GA algorithms based on elites operation

As a novel heuristic optimisation algorithm, biogeography-based optimisation (BBO) has a huge potential to be further developed. Genetic algorithm (GA) is a famous algorithm in optimisation as well. In this paper, two hybrid algorithms of BBO and GA are proposed based on elites operations. According to the property of the two algorithms, we optimised the elites' migration model in BBO by using GA. The one is named global migration hybrid strategy (GMHS), and the other is hierarchical migration hybrid strategy (HMHS). From the test results, it is obvious that the two strategies both perform better than BBO or GA alone. In addition, some comparisons among the new two hybrid strategies and other famous hybrid algorithms are shown in this paper. And an application of semiconductor manufacturing lines is implemented by the hybrid algorithm. According to the results, we know the hybrid strategies have a better capability to solve optimisation problems.

Survey of Shadow Volume Algorithms in Computer Graphics

This study will provide an overview of popular and famous algorithms and techniques in shadow generation based on shadow volumes. It characterizes well-known techniques, describing each of them in detail, and discusses the advantages and drawbacks of each. Basic ideas, improvements, and future works of the techniques are also comprehensively summarized and analyzed in depth. Often, programmers have difficulty selecting an appropriate shadow generation algorithm based on shadow volumes that is specific to their purpose. We have classified and systemized these techniques. The main goal of this paper is to provide researchers with background on a progress of shadow volume techniques so as to make it easier for researchers to choose the method best suited to their aims. It is also hoped that our analysis will help researchers find solutions to the shortcomings of each technique.

A Wavelet Relational FuzzyC-Means Algorithm for 2D Gel Image Segmentation

One of the most famous algorithms that appeared in the area of image segmentation is the Fuzzy C-Means (FCM) algorithm. This algorithm has been used in many applications such as data analysis, pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to improve its segmentation quality. The proposed segmentation algorithm in this paper is based on the Fuzzy C-Means algorithm adding the relational fuzzy notion and the wavelet transform to it so as to enhance its performance especially in the area of 2D gel images. Both proposed modifications aim to minimize the oversegmentation error incurred by previous algorithms. The experimental results of comparing both the Fuzzy C-Means (FCM) and the Wavelet Fuzzy C-Means (WFCM) to the proposed algorithm on real 2D gel images acquired from human leukemias, HL-60 cell lines, and fetal alcohol syndrome (FAS) demonstrate the improvement achieved by the proposed algorithm in overcoming the segmentation error. In addition, we investigate the effect of denoising on the three algorithms. This investigation proves that denoising the 2D gel image before segmentation can improve (in most of the cases) the quality of the segmentation.

Comparison for applicability of different trilinear decomposition algorithms to liquid chromatography-mass spectrometry data measured from multiple samples

The applicability of different trilinear decomposition algorithm to LC-MS data measured from multiple samples is discussed in this paper. An actual LC-MS data set contained a low abundance peptide was adopted to make a test for these algorithms. The bilinear method was not able to handle this type of low abundance situations, and made a mathematical separation as expected. It is found out that the famous trilinear decomposition algorithm could not be used in the LC-MS data directly. The most probable reason is the sparsity property of the pure MS spectra, which means they have positive response values at some m/z coordinates where the ions emerged and zero values elsewhere. A novel algorithm named NNATLD (non-negative alternating has been designed by the present authors to make an effective trilinear decomposition for the three-way data set constructed by LC-MS data. The new algorithm adapts the property of MS spectra, saves the computing resources, and converges fast.

A FUZZY DIFFERENCE BASED EDGE DETECTOR

In this paper, a new algorithm for edge detection based on fuzzy concept is suggested. The proposed approach defines dynamic membership functions for different groups of pixels in a 3 by 3 neighborhood of the central pixel. Then, fuzzy distance and �-cut theory are applied to detect the edge map by following a simple heuristic thresholding rule to produce a thin edge image. A large number of experiments are employed to confirm the robustness of the proposed algorithm. In the experiments different cases such as normal images, images corrupted by Gaussian noise, and uneven lightening images are involved. The results obtained are compared with some famous algorithms such as Canny and Sobel operators, a competitive fuzzy edge detector, and a statistical based edge detector. The visual and quantitative comparisons show the effectiveness of the proposed algorithm even for those images that were corrupted by strong noise.

Target detection using difference measured function based matched filter for hyperspectral imagery

Automatic target detection is a research focus in hyperspectral image processing field. Algorithms such as the matched filter (MF) and the adaptive coherence/cosine estimator (ACE) directly use the prior knowledge of target spectral signatures to detect targets. In this paper, we propose a difference measured function based matched filter (DFMF), which could include the famous algorithm MF as a special case. The DFMF uses a new measured function to build an objective function, and utilizes the gradient descent method to find an optimal projection vector. After finding the optimal projection vector, the interesting targets can be detected in the projection space. The experimental results demonstrate the proposed algorithm could detect interesting targets effectively and performs better than some other experimental algorithms.

복수객체의 윤곽추출을 위한 스네이크 분리 및 연결 알고리즘의 실험적 분석

복수객체의 윤곽추출을 위해 스네이크를 분리하고 연결하는 대표적인 방법이 스네이크 포인트의 거리를 이용한 최소거리방법이다. 이 방법은 객체 위상에 따라 스네이크를 분리하지 못하는 경우가 발생한다. 본 논문에서는 스네이크를 분리하지 못하는 경우를 실험적으로 증명하고 스네이크 세그먼트의 벡터를 이용한 새로운 방법이 스네이크를 성공적으로 분리 및 연결함을 실험적으로 보임으로써 최소거리방법의 문제점을 해결하였다. 하나의 영상 안에 3개 및 5개 객체가 있는 실험영상에 실험을 하여 제안한 방법이 우수하다는 것을 보여준다. The most famous algorithm of splitting and connecting Snake for extracting the boundary of multiple objects is the nearest method using the distance between snake points. It often can't split and connect Snake due to object topology. In this paper, its problem was discussed experimentally. The new algorithm using vector between Snake segment is proposed in order to split and connect Snake with complicated topology of objects. It is shown by experiment of two test images with 3 and 5 objects that the proposed one works better than the nearest one.

Economic lot sizing with constant capacities and concave inventory costs

AbstractThis article studies the classical single‐item economic lot‐sizing problem with constant capacities, fixed‐plus‐linear order costs, and concave inventory costs, where backlogging is allowed. We propose an O(T3) optimal algorithm for the problem, which improves upon the O(T4) running time of the famous algorithm developed by Florian and Klein (Manage Sci18 (1971) 12–20). Instead of using the standard dynamic programming approach by predetermining the minimal cost for every possible subplan, we develop a backward dynamic programming algorithm to obtain a more efficient implementation. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012

빔공간-영역 다채널 비음수 행렬 분해 알고리즘을 이용한 음원 분리 기법 Part I: 빔공간-영역 다채널 비음수 행렬 분해 시스템

본 논문에서는 다채널 음향 신호의 음원 분리를 수행하기 위하여, 빔공간-영역에서 다채널 비음수 행렬 분해 기법을 이용하는 음원 분리 시스템을 제안한다. 비음수 행렬 분해(NMF) 기법은 음원 분리에서 최근 널리 쓰이는 알고리즘이며, 특히 최근에는 다채널 비음수 행렬 분해(MC-NMF) 기법으로 발전하여 다채널 음향 신호에 대해서 적용되고 있다. 본 논문에서 제안하는 다채널 비음수 행렬 분해 기법은 빔공간-영역에서 수행되어, 기존의 다채널 비음수 행렬 분해 기법에 비해 좋은 성능을 가진다. 제안되는 비음수 행렬 분해 기법은 SiSEC 2010의 데이터셋을 이용하여 검증되었다. In this paper, we develop a multichannel blind source separation algorithm based on a beamspace transform and the multichannel non-negative matrix factorization (NMF) method. The NMF algorithm is a famous algorithm which is used to solve the source separation problems. In this paper, we consider a beamspace-time-frequency domain data model for multichannel NMF method, and enhance the conventional method using a beamspace transform. Our decomposition algorithm is applied to audio source separation, using a dataset from the international Signal Separation Evaluation Campaign 2010 (SiSEC 2010) for evaluation.

An Approach to Cryptosystem through a Proposed and Secured Protocol

The Diffie-Hellman key algorithm was the first proposed public key algorithm by which two parties can communicate with each other without having any prior knowledge of each other over an insecure communication channel proposed by Harn.et.al. Diffie-Hellman key exchange algorithm is the most famous algorithm to exchange keys over a network but it has some false and drawbacks. So in our work we have proposed a new agreement protocol based on key confirmation as well as Diffie-Hellman algorithm. This protocol also works on the elliptic curve cryptography in asymmetric encryption. General Terms Key Agreement Protocol, Asymmetric Encryption or Public Key Cryptography, Diffie-Hellman Key Exchange Algorithm.

A novel neural network method for shortest path tree computation

Shortest path tree (SPT) computation is a critical issue in many real world problems, such as routing in networks. It is also a constrained optimization problem, which has been studied by many authors in recent years. Typically, it is solved by heuristic algorithms, such as the famous Dijkstra's algorithm, which can quickly provide a good solution in most instances. However, with the scale of problem increasing, these methods are inefficient and may consume a considerable amount of CPU time. Neural networks, which are massively parallel models, can solve this question easily. This paper presents an efficient modified continued pulse coupled neural network (MCPCNN) model for SPT computation in a large scale instance. The proposed model is topologically organized with only local lateral connections among neurons. The start neuron fires first, and then the firing event spreads out through the lateral connections among the neurons, like the propagation of a wave. Each neuron records its parent, that is, the neighbor which caused it to fire. It proves that the generated wave in the network spreads outward with travel times proportional to the connection weight between neurons. Thus, the generated path is always the global optimal shortest path from the source to all destinations. The proposed model is also applied to generate SPTs for a real given graph step by step. The effectiveness and efficiency of the proposed approach is demonstrated through simulation and comparison studies.

On the decidability of semigroup freeness

This paper deals with the decidability of semigroup freeness. More precisely, the freeness problem over a semigroup S is defined as: given a finite subset X ⊆ S, decide whether each element of S has at most one factorization over X. To date, the decidabilities of two freeness problems have been closely examined. In 1953, Sardinas and Patterson proposed a now famous algorithm for the freeness problem over the free monoid. In 1991, Klarner, Birget and Satterfield proved the undecidability of the freeness problem over three-by-three integer matrices. Both results led to the publication of many subsequent papers. The aim of the present paper is three-fold: (i) to present general results concerning freeness problems, (ii) to study the decidability of freeness problems over various particular semigroups (special attention is devoted to multiplicative matrix semigroups), and (iii) to propose precise, challenging open questions in order to promote the study of the topic.

An efficient method for impulse noise reduction from images using fuzzy cellular automata

Impulse noise reduction from corrupted images plays an important role in image processing. This problem will also affect on image segmentation, object detection, edge detection, compression, etc. Generally, median filters or nonlinear filters have been used for noise reduction but these methods will destroy the natural texture and important information in the image like the edges. In this paper, to eliminate impulse noises from noisy images, we used a hybrid method based on cellular automata (CA) and fuzzy logic called Fuzzy Cellular Automata (FCA) in two steps. In the first step, based on statistical information, noisy pixels are detected by CA; then using this information, the noisy pixel will change by FCA. Regularly, CA is used for systems with simple components where the behavior of each component will be defined and updated based on its neighbors. The proposed hybrid method is characterized as simple, robust and parallel which keeps the important details of the image effectively. The proposed approach has been performed on well-known gray scale test images and compared with other conventional and famous algorithms, is more effective.

Clustering Network Topology Control Method Based on Responsibility Transmission

The topology control is an effective approach which can improve the quality of wireless sensor network at all sides. Through studying the mechanism of sensor network data transmission, the nature of data transmission in wireless sensor network is concluded as a kind of responsibility transmission. By redefining the responsibility and availability of nodes, the strategy for cluster head selection is studied, the responsibility and availability is determined by the combination of the residual energy, location and current flow of nodes. Based on the above, new clustering network topology control algorithm based on responsibility transmission CNTCABRT and hierarchical multi-hop CNTCABRT is presented in this paper, whose algorithm structure is along the famous LEACH algorithm. Experimental result demonstrates its promising performance over the famous LEACH algorithm in the cluster head selection, the size of cluster, the deployment of nodes and the lifetime of nodes, and several innovative conclusions are proposed finally.

An approach to the application of shift-and-add algorithms on engineering and industrial processes

Different kinds of algorithms can be chosen so as to compute elementary functions. Among all of them, it is worthwhile mentioning the shift-and-add algorithms due to the fact that they have been specifically designed to be very simple and to save computer resources. In fact, almost the only operations usually involved with these methods are additions and shifts, which can be easily and efficiently performed by a digital processor. Shift-and-add algorithms allow fairly good precision with low cost iterations. The most famous algorithm belonging to this type is CORDIC. CORDIC has the capability of approximating a wide variety of functions with only the help of a slight change in their iterations. In this paper, we will analyze the requirements of some engineering and industrial problems in terms of type of operands and functions to approximate. Then, we will propose the application of shift-and-add algorithms based on CORDIC to these problems. We will make a comparison between the different methods applied in terms of the precision of the results and the number of iterations required.

NK-SORTING ALGORITHM

Sorting has been a profound area for the algorithmic researchers and many resources are invested to suggest more works for sorting algorithms. For this purpose, many existing sorting algorithms were observed in terms of the efficiency of the algorithmic complexity.Many algorithms are very well known for sorting the unordered lists.In this proposed algorithm, we suggested a new algorithm for sorting integers number depending on dividing the input array to many sub-arrays (which represents a vector or array with one dimension), according to the number of digits in each integer number, the relation between sub-array elements is determined, and this relation used to determines the right location of each element in sub-arrays. Collision may happen, which is solved by moving elements in sub-array to next location. Finally, all ordered sub-arrays will be merged together to rebuild the origin array. The proposed algorithm compared with many famous algorithms gives promising results.Â

Modeling asphalt pavement overlay transverse cracks using the genetic operation tree and Levenberg–Marquardt Method

The Artificial Neural Network (ANN) and the nonlinear regression method are commonly used to build models from experimental data. However, the ANN has been criticized for incapable of providing clear relationships and physical meanings, and is usually regarded as a black box. The nonlinear regression method needs predefined and correct formula structures to process parameter search in terms of the minimal sum of square errors. Unfortunately, the formula structures of these models are often unclear and cannot be defined in advance. To overcome these challenges, this study proposes a novel approach, called “LMGOT,” that integrates two optimization techniques: the Levenberg–Marquardt (LM) Method and the genetic operation tree (GOT). The GOT borrows the concept from the genetic algorithm, a famous algorithm for solving discrete optimization problems, to generate operation trees (OTs), which represent the structures of the formulas. Meanwhile, the LM takes advantage of its merit for solving nonlinear continuous optimization problems, and determines the coefficients in the GOTs that best fit the experimental data. This paper uses the LMGOT to investigate the data sets of pavement cracks from a 15-year experiment conducted by the Texas Departments of Transportation. Results show a concise formula for predicting the length of pavement transverse cracking, and indicate that the LMGOT is an efficient approach to building an accurate crack model.

An Algorithm to Optimal Two-Stage Homogenous Strip Patterns of Rectangular Pieces

This paper presents an algorithm for two-stage homogenous strip patterns for rectangular pieces. The algorithm is appropriate for the shearing and punching process. It proposes the two-stage homogenous strip patterns that can be cut into homogenous strips in two stages, with another two stages being required to cut the strips into pieces. Firstly vertical cuts divide the stock sheet into segments, and then horizontal cuts divide the segments into homogenous strips. A homogenous strip contains pieces of the same type. The algorithm uses a dynamic programming recursion to determine the strip layout on each segment, solves knapsack problems to obtain the segment layout on the sheet. The algorithm is tested through benchmark problems, and compares with two famous algorithms. The pattern value and the computation speed of this paper’s algorithm are better than that of the classic two-stage algorithm. What’s more, this paper’s algorithm can give solutions very close to optimality.