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Artigos de revistas sobre o tema "Algorithm"

Autor: Grafiati

Publicado: 4 de junho de 2021

Última modificação: 25 de julho de 2025

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1

Gangavane, Ms H. N. "A Comparison of ABK-Means Algorithm with Traditional Algorithms." International Journal of Trend in Scientific Research and Development Volume-1, Issue-4 (2017): 614–21. http://dx.doi.org/10.31142/ijtsrd2197.

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Jay, Kishore Sahani, and Kumar Yadav Arvind. "The Bees Algorithms in Optimization: An Overview." MATHEMATICS EDUCATION LV, no. 3, September 2021 (2021): 20–28. https://doi.org/10.5281/zenodo.7275730.

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            Metaheuristic algorithms have become powerful tools for modeling and optimization. In this article, we provide an overview of Bee Algorithms and their applications. We will briefly introduce algorithms such as bee algorithms, virtual bee algorithm, artificial bee algorithm, bee mating algorithm, etc. We also briefly the main characteristics of these algorithms and outline some recent applications of these algorithms. 

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3

Toleushova, A. T., D. M. Uypalakova, and A. B. Imansakipova. "SIGNATURE RECOGNITION ALGORITHMS. BEZIER ALGORITHM." Bulletin of Shakarim University. Technical Sciences, no. 3(7) (February 10, 2023): 47–53. http://dx.doi.org/10.53360/2788-7995-2022-1(5)-7.

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This article focuses on improving the human and machine interface, which should ensure efficient processing of data and knowledge in simple, fast and accessible ways. One of the ways to organize it is the introduction of the manuscript (entering text, drawings, drawings, etc.). Handwritten signatures can be considered as handwritten words, but they are more suitable for drawings, because the signer tries to make his signature unique, using not only his first and last names, but also additional graphic elements. Creating a signature is quite simple, although it is impossible to reproduce the re

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4

Lian, Jian, Yan Zhang, and Cheng Jiang Li. "An Efficient K-Shortest Paths Based Routing Algorithm." Advanced Materials Research 532-533 (June 2012): 1775–79. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1775.

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We present an efficient K-shortest paths routing algorithm for computer networks. This Algorithm is based on enhancements to currently used link-state routing algorithms such as OSPF and IS-IS, which are only focusing on finding the shortest path route by adopting Dijkstra algorithm. Its desire effect to achieve is through the use of K-shortest paths algorighm, which has been implemented successfully in some fileds like traffic engineering. The correctness of this Algorithm is discussed at the same time as long as the comparison with OSPF.

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Wisam, Abdulelah Qasim. "A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOLF OPTIMIZATION ALGORITHM." International Journal of Artificial Intelligence and Applications (IJAIA) 11, January (2020): 31–44. https://doi.org/10.5281/zenodo.3690787.

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In this research, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents invasive weed optimization. This algorithm is a random numerical algorithm and the second algorithm representing the grey wolves optimization. This algorithm is one of the algorithms of swarm intelligence in intelligent optimization. The algorithm of invasive weed optimization is inspired by nature as the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006. Invasive weeds are a serious threat to cultivated plants because of their adaptability and are a t

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Ciric, Vladimir, Aleksandar Cvetkovic, Ivan Milentijevic, and Oliver Vojinovic. "All-Pairs Shortest Paths Algorithm for Regular 2D Mesh Topologies." JUCS - Journal of Universal Computer Science 22, no. (11) (2016): 1437–55. https://doi.org/10.3217/jucs-022-11-1437.

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Motivated by the large number of vertices that future technologies will put in the front of path-search algorithms, and inspired by highly regular 2D mesh structures that exist in the domain applications, in this paper we propose a new allpairs shortest paths algorithm, for any given regular 2D mesh topology, with complexity Ο(|V|2), where |V| is the number of vertices in the graph. The proposed algorithm can achieve better runtime than other known algorithms at the cost of narrowing the scope of the graphs that it can process to the graphs with regular 2D topology. The algorithm is developed

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Shaw, Dr Shaik Mohiddin, Dr Dharmaiah Gurram, Hari Krishna Gurram, and Ramakrishna Gurram. "Transitive Closure Algorithm using Binary OR Operation: Primes Algorithm, GHK Algorithm." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 03, no. 02 (2015): 01–05. http://dx.doi.org/10.9756/sijcsea/v3i2/03030100101.

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8

Huang, Yuan Jiang, and Jie Huang. "A New Feature Detection Algorithm Based on RANSAC." Advanced Materials Research 971-973 (June 2014): 1477–80. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1477.

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A improved RANSAC algorithm was introduced into the segmentation of LiDAR and r-radius point density was put forward to the estimation criterion,which aims to remove the discrete point outside the feature plane.an accurate registration is achieved by improving RANSAC algorithim after an analysis on the advantages and disadvantages of the algorithm for objects with many planar feature.The algorithm are implemented with VC++ and VTK platform,tested by real data collected on the test area,it verify the effectiveness and accuracy of the proposed algorithms.

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9

Deghbouch, Hicham, and Fatima Debbat. "Hybrid Bees Algorithm with Grasshopper Optimization Algorithm for Optimal Deployment of Wireless Sensor Networks." Inteligencia Artificial 24, no. 67 (2021): 18–35. http://dx.doi.org/10.4114/intartif.vol24iss67pp18-35.

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This work addresses the deployment problem in Wireless Sensor Networks (WSNs) by hybridizing two metaheuristics, namely the Bees Algorithm (BA) and the Grasshopper Optimization Algorithm (GOA). The BA is an optimization algorithm that demonstrated promising results in solving many engineering problems. However, the local search process of BA lacks efficient exploitation due to the random assignment of search agents inside the neighborhoods, which weakens the algorithmâ€™s accuracy and results in slow convergence especially when solving higher dimension problems. To alleviate this shortcoming,

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Sami N. Hussein and Nazar K. Hussein. "Improving Moth-Flame Optimization Algorithm by using Slime-Mould Algorithm." Tikrit Journal of Pure Science 27, no. 1 (2022): 99–109. http://dx.doi.org/10.25130/tjps.v27i1.86.

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The MFO algorithm is one of the modern optimization algorithms based on swarm intelligence, and the SMA algorithm is also one of the latest algorithms in the same field and has the advantages of fast convergence, high convergence accuracy, robust and robust. In this research paper, we introduce an optimized algorithm for MFO based on the SMA algorithm to get better performance using the features in the two algorithms, and two different algorithms are proposed in this field. The two predicted new algorithms were tested with standard test functions and the results were encouraging compared to th

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11

Okazaki, Hiroyuki, Yosiki Aoki, and Yasunari Shidama. "Extended Euclidean Algorithm and CRT Algorithm." Formalized Mathematics 20, no. 2 (2012): 175–79. http://dx.doi.org/10.2478/v10037-012-0020-2.

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Summary In this article we formalize some number theoretical algorithms, Euclidean Algorithm and Extended Euclidean Algorithm [9]. Besides the a gcd b, Extended Euclidean Algorithm can calculate a pair of two integers (x, y) that holds ax + by = a gcd b. In addition, we formalize an algorithm that can compute a solution of the Chinese remainder theorem by using Extended Euclidean Algorithm. Our aim is to support the implementation of number theoretic tools. Our formalization of those algorithms is based on the source code of the NZMATH, a number theory oriented calculation system developed by

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Chen, Zheyuan, Jiani Lu, Yuqi Shang, and Diwen Xu. "Path planning algorithms of sweeping robots." Applied and Computational Engineering 41, no. 1 (2024): 99–105. http://dx.doi.org/10.54254/2755-2721/41/20230716.

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Different categories of path planning algorithms for sweeping robot are introduced, including Dijkstra algorithm and A*Algorithm in Traditional path-planning Algorithm, PRM Algorithm and RRT Algorithm in sampling algorithm, and Ant Colony Optimization Algorithms and Genetic algorithms in Intelligent bionic algorithm. Each algorithm has its principles and features introduced. At the same time, several algorithms are compared, and summarized, each algorithm has its advantages and disadvantages, in the future development should be combined with their strengths to optimize the path planning algori

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13

Kulkarni, Anuj, Saish Padave, Satyam Shrivastava, and Mrs Vidya Kawtikwar. "Algorithm Visualizer." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 1818–23. http://dx.doi.org/10.22214/ijraset.2023.54837.

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Abstract: In recent years, computer science education has become increasingly important as technology continues to play a dominant role in our lives. The understanding of algorithms and their implementation is a crucial aspect of computer science education. Visualizing algorithms can be a powerful tool to help students understand and retain the concepts behind them. This paper presents a new algorithm visualizer that focuses on two main types of algorithms: sorting algorithms and graph pathfinding algorithms. The algorithm visualizer was created using React.js, a popular JavaScript library, an

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14

Beth, T., and D. Gollman. "Algorithm engineering for public key algorithms." IEEE Journal on Selected Areas in Communications 7, no. 4 (1989): 458–66. http://dx.doi.org/10.1109/49.17708.

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15

Yazdani, Maziar, and Fariborz Jolai. "Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm." Journal of Computational Design and Engineering 3, no. 1 (2015): 24–36. http://dx.doi.org/10.1016/j.jcde.2015.06.003.

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Abstract During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. In this paper, a new population based algorithm, the Lion Optimization Algorithm (LOA), is introduced. Special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm. Some benchmark problems are sele

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16

Ozkan, Ramazan, and Ruya Samli. "Flood algorithm: a novel metaheuristic algorithm for optimization problems." PeerJ Computer Science 10 (October 2, 2024): e2278. http://dx.doi.org/10.7717/peerj-cs.2278.

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Metaheuristic algorithms are an important area of research that provides significant advances in solving complex optimization problems within acceptable time periods. Since the performances of these algorithms vary for different types of problems, many studies have been and need to be done to propose different metaheuristic algorithms. In this article, a new metaheuristic algorithm called flood algorithm (FA) is proposed for optimization problems. It is inspired by the flow of flood water on the earth’s surface. The proposed algorithm is tested both on benchmark functions and on a real-world p

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17

Alaa, A. Alomoush, Rahman A. Alsewari Abdul, Z. Zamli Kamal, Alrosan Ayat, Alomoush Waleed, and Alissa Khalid. "Enhancing three variants of harmony search algorithm for continuous optimization problems." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (2021): 2343–49. https://doi.org/10.11591/ijece.v11i3.pp2343-2349.

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Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. Harmony search (HS) is a recognized meta-heuristic algorithm with an efficient exploration process. But the HS has a slow convergence rate, which causes the algorithm to have a weak exploitation process in finding the global optima. Different variants of HS introduced in the literature to enhance the algorithm and fix its problems, but in most cases, the algorithm still has a slow convergence rate. Meanwhile, opposition-based learning (OBL), is an effective technique used to improve the

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18

Erdemir, Erkan. "Hybrid algorithm proposal for optimizing benchmarking problems: Salp swarm algorithm enhanced by arithmetic optimization algorithm." International Journal of Industrial Engineering Computations 14, no. 2 (2023): 309–22. http://dx.doi.org/10.5267/j.ijiec.2023.1.002.

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Metaheuristic algorithms are easy, flexible and nature-inspired algorithms used to optimize functions. To make metaheuristic algorithms better, multiple algorithms are combined and hybridized. In this context, a hybrid algorithm (HSSAOA) was developed by adapting the exploration phase of the arithmetic optimization algorithm (AOA) to the position update part of the salp swarm algorithm (SSA) of the leader salps/salps. And also, there have also been a few new additions to the SSA. The proposed HSSAOA was tested in three different groups using 22 benchmark functions and compared with 7 well-know

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19

Andriansyah, Andriansyah, and Prima Denny Sentia. "PENENTUAN RUTE KENDARAAN PADA SISTEM DISTRIBUSI LOGISTIK PASCA BENCANA (STUDI KASUS)." Jurnal Manajemen Industri dan Logistik 2, no. 1 (2018): 79–89. http://dx.doi.org/10.30988/jmil.v2i1.28.

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The success indicators of disaster mitigation can be seen from the disaster logistics system. Effective and efficient distribution network can make a good disaster logistics system. The problem that related to the design of this network is the vehicle routing problem. The objective is determined optimal route of relief distribution from warehouse to victims with minimum time duration. The problem is solved by branch and bound, insertion heuristic, and local search algorithms. The results obtained by branch and bound and local search algorithm are optimal global. Time duration of vehicle using

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20

Peng, Qiang, Renjun Zhan, Husheng Wu, and Meimei Shi. "Comparative Study of Wolf Pack Algorithm and Artificial Bee Colony Algorithm." International Journal of Swarm Intelligence Research 15, no. 1 (2024): 1–24. http://dx.doi.org/10.4018/ijsir.352061.

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Swarm intelligence optimization algorithms have been widely used in the fields of machine learning, process control and engineering prediction, among which common algorithms include ant colony algorithm (ACO), artificial bee colony algorithm (ABC) and particle swarm optimization (PSO). Wolf pack algorithm (WPA) as a newer swarm intelligence optimization algorithm has many similarities with ABC. In this paper, the basic principles, algorithm implementation processes, and related improvement strategies of these two algorithms were described in detail; A comparative analysis of their performance

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21

Chen, Zhenpeng, Yuanjie Zheng, Xiaojie Li, et al. "Interactive Trimap Generation for Digital Matting Based on Single-Sample Learning." Electronics 9, no. 4 (2020): 659. http://dx.doi.org/10.3390/electronics9040659.

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Image matting refers to the task of estimating the foreground of images, which is an important problem in image processing. Recently, trimap generation has attracted considerable attention because designing a trimap for every image is labor-intensive. In this paper, a two-step algorithm is proposed to generate trimaps. To use the proposed algorithm, users must only provide some clicks (foreground clicks and background clicks), which are employed as the input to generate a binary mask. One-shot learning technique achieves remarkable progress on semantic segmentation, we extend this technique to

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22

Jiang, Dazhi, and Zhun Fan. "The Algorithm for Algorithms: An Evolutionary Algorithm Based on Automatic Designing of Genetic Operators." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/474805.

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At present there is a wide range of evolutionary algorithms available to researchers and practitioners. Despite the great diversity of these algorithms, virtually all of the algorithms share one feature: they have been manually designed. A fundamental question is “are there any algorithms that can design evolutionary algorithms automatically?” A more complete definition of the question is “can computer construct an algorithm which will generate algorithms according to the requirement of a problem?” In this paper, a novel evolutionary algorithm based on automatic designing of genetic operators

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23

Huang, Yuxuan, and Yiming Ren. "A new interpolation algorithm based on Hibbard-Laroche algorithm and its superiority." Applied and Computational Engineering 15, no. 1 (2023): 119–33. http://dx.doi.org/10.54254/2755-2721/15/20230822.

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In order to optimize the possible problems and improvements in the existing color image restoration interpolation algorithms, we conduct research based on the existing bilinear interpolation method, cok algorithm and Hibbard-Laroche algorithm. Our method is to use our own comparison method to compare different types of images through three algorithms to find the advantages and disadvantages and to some extent combine the advantages of bilinear interpolation and Hibbard-Laroche algorithm to try to innovate a new algorithm to compare with the existing three algorithms. The results show that the

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24

Ababneh, Jehad. "Greedy particle swarm and biogeography-based optimization algorithm." International Journal of Intelligent Computing and Cybernetics 8, no. 1 (2015): 28–49. http://dx.doi.org/10.1108/ijicc-01-2014-0003.

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Purpose – The purpose of this paper is to propose an algorithm that combines the particle swarm optimization (PSO) with the biogeography-based optimization (BBO) algorithm. Design/methodology/approach – The BBO and the PSO algorithms are jointly used in to order to combine the advantages of both algorithms. The efficiency of the proposed algorithm is tested using some selected standard benchmark functions. The performance of the proposed algorithm is compared with that of the differential evolutionary (DE), genetic algorithm (GA), PSO, BBO, blended BBO and hybrid BBO-DE algorithms. Findings –

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Nico, Nico, Novrido Charibaldi, and Yuli Fauziah. "Comparison of Memetic Algorithm and Genetic Algorithm on Nurse Picket Scheduling at Public Health Center." International Journal of Artificial Intelligence & Robotics (IJAIR) 4, no. 1 (2022): 9–23. http://dx.doi.org/10.25139/ijair.v4i1.4323.

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 One of the most significant aspects of the working world is the concept of a picket schedule. It is difficult for the scheduler to make an archive since there are frequently many issues with the picket schedule. These issues include schedule clashes, requests for leave, and trading schedules. Evolutionary algorithms have been successful in solving a wide variety of scheduling issues. Evolutionary algorithms are very susceptible to data convergence. But no one has discussed where to start from, where the data converges from making schedules using evolutionary algorithms. The best algorit

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26

Hiendro, Ayong. "Projectile-target search algorithm: a stochastic metaheuristic optimization technique." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 3772. http://dx.doi.org/10.11591/ijece.v9i5.pp3772-3778.

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This paper proposes a new stochastic metaheuristic optimization algorithm which is based on kinematics of projectile motion and called projectile-target search (PTS) algorithm. The PTS algorithm employs the envelope of projectile trajectory to find the target in the search space. It has 2 types of control parameters. The first type is set to give the possibility of the algorithm to accelerate convergence process, while the other type is set to enhance the possibility to generate new better projectiles for searching process. However, both are responsible to find better fitness values in the sea

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Ayong, Hiendro. "Projectile-target search algorithm: a stochastic metaheuristic optimization technique." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 3772–78. https://doi.org/10.11591/ijece.v9i5.pp3772-3778.

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This paper proposes a new stochastic metaheuristic optimization algorithm which is based on kinematics of projectile motion and called projectile-target search (PTS) algorithm. The PTS algorithm employs the envelope of projectile trajectory to find the target in the search space. It has 2 types of control parameters. The first type is set to give the possibility of the algorithm to accelerate convergence process, while the other type is set to enhance the possibility to generate new better projectiles for searching process. However, both are responsible to find better fitness values in the sea

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28

Triasih, Rina, Finny Fitry Yani, Diah Asri Wulandari, et al. "Treatment-Decision Algorithm of Child TB: Evaluation of WHO Algorithm and Development of Indonesia Algorithm." Tropical Medicine and Infectious Disease 10, no. 4 (2025): 106. https://doi.org/10.3390/tropicalmed10040106.

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Clinical algorithms for child tuberculosis (TB) are a valuable guide for healthcare workers to initiate treatment. We evaluated the agreement of pediatric TB diagnosis using the current Indonesia diagnostic algorithms with the 2022 WHO treatment decision algorithm and developed a new Indonesia algorithm for child TB based upon our findings and expert opinion. We conducted a retrospective study at 10 hospitals in Indonesia, involving children (0–10 years), who were evaluated for TB diagnosis in 2022. A panel of child TB experts used participants’ records to make a diagnosis using the 2022 WHO a

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Zhang, Zhaoyang. "Review on String-Matching Algorithm." SHS Web of Conferences 144 (2022): 03018. http://dx.doi.org/10.1051/shsconf/202214403018.

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String-matching algorithm is one of the most researched algorithms in computer science which has become an important factor in many technologies. This field aims at utilizing the least time and resources to find desired sequence of character in complex data content. The most classical and famous string-search algorithms are Knuth-Morris-Pratt (KMP) algorithm and Boyer-Moore (DM) algorithm. These two algorithms provide efficient heuristic jump rules by prefix or suffix. Bitap algorithm was the first to introduce bit-parallelism into string-matching field. Backward Non-Deterministic DAWG Matchin

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Li, Yifeng, and Ying Tan. "Hierarchical Collaborated Fireworks Algorithm." Electronics 11, no. 6 (2022): 948. http://dx.doi.org/10.3390/electronics11060948.

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The fireworks algorithm (FWA) has achieved significant global optimization ability by organizing multiple simultaneous local searches. By dynamically decomposing the target problem and handling each one with a sub-population, it has presented distinct property and applicability compared with traditional evolutionary algorithms. In this paper, we extend the theoretical model of fireworks algorithm based on search space partition to obtain a hierarchical collaboration model. It maintains both multiple local fireworks for local exploitation and one global firework for overall population distribut

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Zeng, Yi, Shiqun Yin, Jiangyue Liu, and Miao Zhang. "Research of Improved FP-Growth Algorithm in Association Rules Mining." Scientific Programming 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/910281.

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Association rules mining is an important technology in data mining. FP-Growth (frequent-pattern growth) algorithm is a classical algorithm in association rules mining. But the FP-Growth algorithm in mining needs two times to scan database, which reduces the efficiency of algorithm. Through the study of association rules mining and FP-Growth algorithm, we worked out improved algorithms of FP-Growth algorithm—Painting-Growth algorithm and N (not) Painting-Growth algorithm (removes the painting steps, and uses another way to achieve). We compared two kinds of improved algorithms with FP-Growth al

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32

Ratseev, S. M., and O. I. Cherevatenko. "ON DECODING ALGORITHMS FOR GENERALIZED REED — SOLOMON CODES WITH ERRORS AND ERASURES." Vestnik of Samara University. Natural Science Series 26, no. 3 (2020): 17–29. http://dx.doi.org/10.18287/2541-7525-2020-26-3-17-29.

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The article is devoted to the decoding algorithms for generalized Reed Solomon codes with errorsand erasures. These algorithms are based on Gao algorithm, Sugiyama algorithm, Berlekamp Massey algorithm (Peterson Gorenstein Zierler algorithm). The first of these algorithms belongs to syndrome-free decoding algorithms, the others to syndrome decoding algorithms. The relevance of these algorithms is that they are applicable for decoding Goppa codes, which are the basis of some promising post-quantum cryptosystems. These algorithms are applicable for Goppa codes over an arbitrary field, as opposed

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Mitsos, Alexander, Jaromił Najman, and Ioannis G. Kevrekidis. "Optimal deterministic algorithm generation." Journal of Global Optimization 71, no. 4 (2018): 891–913. http://dx.doi.org/10.1007/s10898-018-0611-8.

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Abstract A formulation for the automated generation of algorithms via mathematical programming (optimization) is proposed. The formulation is based on the concept of optimizing within a parameterized family of algorithms, or equivalently a family of functions describing the algorithmic steps. The optimization variables are the parameters—within this family of algorithms—that encode algorithm design: the computational steps of which the selected algorithms consist. The objective function of the optimization problem encodes the merit function of the algorithm, e.g., the computational cost (possi

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Liu, Zizhuo. "Investigation of progress and application related to Multi-Armed Bandit algorithms." Applied and Computational Engineering 37, no. 1 (2024): 155–59. http://dx.doi.org/10.54254/2755-2721/37/20230496.

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This paper discusses four Multi-armed Bandit algorithms: Explore-then-Commit (ETC), Epsilon-Greedy, Upper Confidence Bound (UCB), and Thompson Sampling algorithm. ETC algorithm aims to spend the majority of rounds on the best arm, but it can lead to a suboptimal outcome if the environment changes rapidly. The Epsilon-Greedy algorithm is designed to explore and exploit simultaneously, while it often tries sub-optimal arm even after the algorithm finds the best arm. Thus, the Epsilon-Greedy algorithm performs well when the environment continuously changes. UCB algorithm is one of the most used M

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Bai, Xiaotong, Yuefeng Zheng, Yang Lu, and Yongtao Shi. "Chain hybrid feature selection algorithm based on improved Grey Wolf Optimization algorithm." PLOS ONE 19, no. 10 (2024): e0311602. http://dx.doi.org/10.1371/journal.pone.0311602.

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Hybrid feature selection algorithm is a strategy that combines different feature selection methods aiming to overcome the limitations of a single feature selection method and improve the effectiveness and performance of feature selection. In this paper, we propose a new hybrid feature selection algorithm, to be named as Tandem Maximum Kendall Minimum Chi-Square and ReliefF Improved Grey Wolf Optimization algorithm (TMKMCRIGWO). The algorithm consists of two stages: First, the original features are filtered and ranked using the bivariate filter algorithm Maximum Kendall Minimum Chi-Square (MKMC

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Challenger, Moharram, Elif Haytaoglu, Gorkem Tokatli, Orhan Dagdeviren, and Kayhan Erciyes. "A Hybrid Distributed Mutual Exclusion Algorithm for Cluster-Based Systems." Mathematical Problems in Engineering 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/703414.

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Distributed mutual exclusion is a fundamental problem which arises in various systems such as grid computing, mobile ad hoc networks (MANETs), and distributed databases. Reducing key metrics like message count per any critical section (CS) and delay between two CS entrances, which is known as synchronization delay, is a great challenge for this problem. Various algorithms use either permission-based or token-based protocols. Token-based algorithms offer better communication costs and synchronization delay. Raymond's and Suzuki-Kasami's algorithms are well-known token-based ones. Raymond's algo

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Tilahun, Surafel Luleseged, and Hong Choon Ong. "Prey-Predator Algorithm: A New Metaheuristic Algorithm for Optimization Problems." International Journal of Information Technology & Decision Making 14, no. 06 (2015): 1331–52. http://dx.doi.org/10.1142/s021962201450031x.

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Nature-inspired optimization algorithms have become useful in solving difficult optimization problems in different disciplines. Since the introduction of evolutionary algorithms several studies have been conducted on the development of metaheuristic optimization algorithms. Most of these algorithms are inspired by biological phenomenon. In this paper, we introduce a new algorithm inspired by prey-predator interaction of animals. In the algorithm randomly generated solutions are assigned as a predator and preys depending on their performance on the objective function. Their performance can be e

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YE, DESHI, and QINMING HE. "WORST-CASE PERFORMANCE EVALUATION ON MULTIPROCESSOR TASK SCHEDULING WITH RESOURCE AUGMENTATION." International Journal of Foundations of Computer Science 22, no. 04 (2011): 971–82. http://dx.doi.org/10.1142/s0129054111008519.

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We study the worst-case performance of approximation algorithms for the problem of multiprocessor task scheduling on m identical processors with resource augmentation, whose objective is to minimize the makespan. In this case, the approximation algorithms are given k (k ≥ 0) extra processors than the optimal off-line algorithm. For on-line algorithms, the Greedy algorithm and shelf algorithms are studied. For off-line algorithm, we consider the LPT (longest processing time) algorithm. Particularly, we prove that the schedule produced by the LPT algorithm is no longer than the optimal off-line

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Han, Zhonghai, Haotian Sun, Junfu Huang, Jiejie Xu, Yu Tang, and Xintian Liu. "Path Planning Algorithms for Smart Parking: Review and Prospects." World Electric Vehicle Journal 15, no. 7 (2024): 322. http://dx.doi.org/10.3390/wevj15070322.

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Path planning algorithms are crucial components in the process of smart parking. At present, there are many path planning algorithms designed for smart parking. A well-designed path planning algorithm has a significant impact on the efficiency of smart parking. Firstly, this paper comprehensively describes the principles and steps of four types of path planning algorithms: the Dijkstra algorithm (including its optimized derivatives), the A* algorithm (including its optimized derivatives), the RRT (Rapidly exploring Random Trees) algorithm (including its optimized derivatives), and the BFS (Bre

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Brooks, Stephen P. "Algorithms AS 298: A Hybrid Optimization Algorithm." Applied Statistics 44, no. 4 (1995): 530. http://dx.doi.org/10.2307/2986143.

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Jobson, K. "International Psychopharmacology Algorithm Project: Algorithms in Psychopharmacology." International Journal of Psychiatry in Clinical Practice 1, sup1 (1997): S3. http://dx.doi.org/10.3109/13651509709024748.

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Chatain, Peter, Rocky Garg, and Lauren Tompkins. "Evolutionary Algorithms for Tracking Algorithm Parameter Optimization." EPJ Web of Conferences 251 (2021): 03071. http://dx.doi.org/10.1051/epjconf/202125103071.

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The reconstruction of charged particle trajectories, known as tracking, is one of the most complex and CPU consuming parts of event processing in high energy particle physics experiments. The most widely used and best performing tracking algorithms require significant geometry-specific tuning of the algorithm parameters to achieve best results. In this paper, we demonstrate the usage of machine learning techniques, particularly evolutionary algorithms, to find high performing configurations for the first step of tracking, called track seeding. We use a track seeding algorithm from the software

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S, Gajawada. "Lord Rama Devotees Algorithm: A New Human-Inspired Metaheuristic Optimization Algorithm." Advances in Robotic Technology 1, no. 1 (2023): 1–2. http://dx.doi.org/10.23880/art-16000105.

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Several Human-Inspired Metaheuristic Optimization Algorithms were proposed in literature. But the concept of DevoteesInspired Metaheuristic Optimization Algorithms is not yet explored. In this article, Lord Rama Devotees Algorithm (LRDA) is proposed which is a new Devotees-Inspired Metaheuristic Optimization Algorithm.

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Kaya, Ebubekir, Ceren Baştemur Kaya, Emre Bendeş, Sema Atasever, Başak Öztürk, and Bilgin Yazlık. "Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking." Biomimetics 8, no. 5 (2023): 402. http://dx.doi.org/10.3390/biomimetics8050402.

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One of the most used artificial intelligence techniques for maximum power point tracking is artificial neural networks. In order to achieve successful results in maximum power point tracking, the training process of artificial neural networks is important. Metaheuristic algorithms are used extensively in the literature for neural network training. An important group of metaheuristic algorithms is swarm-intelligent-based optimization algorithms. In this study, feed-forward neural network training is carried out for maximum power point tracking by using 13 swarm-intelligent-based optimization al

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Nursania Simbolon and Yahfizham Yahfizham. "Studi Literartur Algoritma Pemograman Pada Pembelajaran Matematika." Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK ) 1, no. 4 (2023): 147–57. http://dx.doi.org/10.59061/jentik.v1i4.510.

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An algorithm is an effective step or method used to solve a particular problem or task. Algorithms are designed to be executed in a methodical manner, structured As it is. logical in this way, thus enabling consistent and efficient problem solving. A few issues that could come up during the process of designing an algorithm include: 1. Inappropriate Structure, .2. Illogical Algorithm, .3. Difficulty in Solving Algorithms: Sometimes, solving algorithms can be difficult, especially if the problem at hand is complex. This can cause confusion and require extra time to design the right algorithm. P

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Wu, Hai Fei, and Tong Zhao. "A Study of MRife Algorithm and CSB Algorithm on Frequency Online Estimation." Applied Mechanics and Materials 128-129 (October 2011): 789–93. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.789.

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Online estimation of instantaneous frequencies is a method to treat information signals by following a set of certain algorithm for the purpose of extracting the sine wave signal frequency that is overwhelmed in the noise. Among several algorithms available nowadays, two are selected here-one is the modification of Rife (MRife) algorithm based on frequency-domain analysis; and the other is the correlation series based (CSB) algorithm. With simulation methods, the estimation error, stabilization time and computation load of these two algorithms are analyzed and compared respectively. Finally, t

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Yang, Huiwei. "Application of Hybrid Encryption Algorithm in Hardware Encryption Interface Card." Security and Communication Networks 2022 (May 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/7794209.

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In order to effectively solve the increasingly prominent network security problems, cryptographic algorithm is the key factor affecting the effectiveness of IPSec VPN encryption. Therefore, this paper mainly studies cryptographic algorithms and puts forward the following solutions: briefly analyze the concept and function of IPSec VPN, as well as the basic theoretical knowledge of IPSec Security Protocol and cryptography, and analyze the traditional cryptography, modern cryptography, symmetric cryptographic algorithms and asymmetric algorithms, and their security. At the same time, the executa

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Wheeler, Bradley J., and Hassan A. Karimi. "Enhancing Hyperspectral Anomaly Detection Algorithm Comparisons: Leveraging Dataset and Algorithm Characteristics." Remote Sensing 16, no. 20 (2024): 3879. http://dx.doi.org/10.3390/rs16203879.

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Validating the contributions of new algorithms is a critical step in hyperspectral anomaly detection (HAD) research. Typically, validation involves comparing the performance of a proposed algorithm against other algorithms using a series of benchmark datasets. Despite the longstanding use of this comparison process, little attention has been paid to the characteristics of datasets and algorithms that ensure each algorithm has an equal opportunity of performing well. Characteristics of datasets and algorithms that inadvertently favor one algorithm can skew results, leading to misleading conclus

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Bhagya Sri, Mukku, Rachita Bhavsar, and Preeti Narooka. "String Matching Algorithms." International Journal Of Engineering And Computer Science 7, no. 03 (2018): 23769–72. http://dx.doi.org/10.18535/ijecs/v7i3.19.

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To analyze the content of the documents, the various pattern matching algorithms are used to find all the occurrences of a limited set of patterns within an input text or input document. In order to perform this task, this research work used four existing string matching algorithms; they are Brute Force algorithm, Knuth-Morris-Pratt algorithm (KMP), Boyer Moore algorithm and Rabin Karp algorithm. This work also proposes three new string matching algorithms. They are Enhanced Boyer Moore algorithm, Enhanced Rabin Karp algorithm and Enhanced Knuth-Morris-Pratt algorithm. Findings: For expe

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Li, Gang. "Research on Smartphone Trojan Detection Based on the Wireless Sensor Network." Security and Communication Networks 2022 (July 9, 2022): 1–7. http://dx.doi.org/10.1155/2022/2455102.

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In order to reduce false positives of Trojan horse detection algorithms in smartphones, a voting algorithm based on multiple machine learning algorithms in wireless sensor networks was proposed. Through setting up the experiment, the preliminary preparations of the experiment, including sample set selection, feature set extraction method, and algorithm effect evaluation criteria, were described first. The K-nearest neighbors algorithm, random forests algorithm, support vector machine, and voting algorithm were compared. The experimental results showed that SVM and KNN algorithms took the short

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