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Thematische Bibliographien / Algorithm performance / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Algorithm performance“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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1

Mary, A. Viji Amutha, and Dr T. Jebarajan Dr. T. Jebarajan. "Performance Metrics of Clustering Algorithm." Indian Journal of Applied Research 4, no. 8 (2011): 165–67. http://dx.doi.org/10.15373/2249555x/august2014/47.

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Han, Xiaofeng, Xuejia Qi, Jincheng Zhang, Songlin Yang, and Shuling Chen. "The Analysis Method of Performance Comprehensive Performance Optimization Design for Multi-body Unmanned Boat." Journal of Physics: Conference Series 2219, no. 1 (2022): 012048. http://dx.doi.org/10.1088/1742-6596/2219/1/012048.

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Abstract In order to compare and analyze the comprehensive performance of multi-hull unmanned boats under specific conditions, this paper conducts in-depth research on the multi-objective decision-making theory and intelligent optimization algorithms of boat type design. First of all, this paper establishes the corresponding ship type and resistance database using the response surface fitting method through the drag resistance test of the ship model. Secondly, comprehensively considering the impact of the three major performances of multi-hull unmanned craft on the design of the boat’s speed,

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Qiu, Jian Lin, Li Chen, Jian Ping Chen, Xiang Gu, and Yan Yun Chen. "Grid-Based Task Scheduling PMTS Algorithm." Applied Mechanics and Materials 121-126 (October 2011): 4491–97. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4491.

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This paper analyses the Min-min algorithm and its improved algorithms through the performances of load balance, time span, quality of service and economic principle. Based on the analysis of the merits of these algorithms, we propose an improved algorithm as PMTS (Priority-based maximum time-span algorithm) by integrating. In the instance of the application, we analyse and compare the performances of these algorithms, and experimental results show that, PMTS algorithm is better than other algorithms in the comprehensive performance of load-balance, time-span, quality of service and other aspec

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Civicioglu, P., U. H. Atasever, C. Ozkan, E. Besdok, A. E. Karkinli, and A. Kesikoglu. "Performance Comparison Of Evolutionary Algorithms For Image Clustering." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 71–74. http://dx.doi.org/10.5194/isprsarchives-xl-7-71-2014.

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Evolutionary computation tools are able to process real valued numerical sets in order to extract suboptimal solution of designed problem. Data clustering algorithms have been intensively used for image segmentation in remote sensing applications. Despite of wide usage of evolutionary algorithms on data clustering, their clustering performances have been scarcely studied by using clustering validation indexes. In this paper, the recently proposed evolutionary algorithms (i.e., Artificial Bee Colony Algorithm (ABC), Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Adaptive Di

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Kaur, Baljit. "Performance Comparison between LMS and NLMS Algorithm." Indian Journal of Applied Research 1, no. 6 (2011): 63–65. http://dx.doi.org/10.15373/2249555x/mar2012/20.

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Saini, Deepali, and Prof Anand Rajavat. "Performance Evaluation System for Decision Tree Algorithms." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 8 (2013): 2879–86. http://dx.doi.org/10.24297/ijct.v11i8.3006.

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In the machine learning process, classification can be described by supervise learning algorithm. Classification techniques have properties that enable the representation of structures that reflect knowledge of the domain being classified. Industries, education, business and many other domains required knowledge for the growth.Â Some of the common classification algorithms used in data mining and decision support systems is: Neural networks, Logistic regression, Decision trees etc. The decision regarding most suitable data mining algorithm cannot be made spontaneously. Selection of appropriate

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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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Zhang, Qingyang, Tianji Peng, Guangchun Zhang, et al. "An Efficient Scheme for Coupling OpenMC and FLUENT with Adaptive Load Balancing." Science and Technology of Nuclear Installations 2021 (September 24, 2021): 1–16. http://dx.doi.org/10.1155/2021/5549602.

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This paper develops a multi-physics interface code MC-FLUENT to couple the Monte Carlo code OpenMC with the commercial computational fluid dynamics code ANSYS FLUENT. The implementations and parallel performances of block Gauss–Seidel-type and block Jacobi-type Picard iterative algorithms have been investigated. In addition, this paper introduces two adaptive load-balancing algorithms into the neutronics and thermal-hydraulics coupled simulation to reduce the time cost of computation. Considering that the different scalability of OpenMC and FLUENT limits the performance of block Gauss–Seidel a

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Qiu, Dong Wei, and Shan Shan Wan. "Research on Algorithms Performance about JSP Scheduling." Advanced Materials Research 457-458 (January 2012): 20–25. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.20.

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Three typical intelligent evolutionary algorithms are applied on Job Shop scheduling problem which are Quantum algorithm, Genetic Algorithm and Population Based Incremental Learning algorithm. They three algorithms have some common features in computation, encoding strategy and probability application, but with the different problems and different scale sizes of the same problem they show different performance. In this paper we take JSP as example to test their performance difference and analyze their applicability. Two benchmark Job Shop problems are used to fulfill the comparison. The result

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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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Wei, Xian Min. "Routing Lookup Algorithm Performance Analysis and Research." Advanced Materials Research 181-182 (January 2011): 109–12. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.109.

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This paper describes several current routing lookup algorithms, to study and analysize the complexity and operating practical performance of these routing lookup algorithms. The results show that although the binary search algorithm is not greatly improved in the searching performance, but in IPv6 environment, because searching performance of multi-branch Trie tree will decrease greatly, thus the superiority of binary search algorithm will be reflected better.

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Yang, Yeling, Feng Yi, Chuancheng Deng, and Guang Sun. "Performance Analysis of the CHAID Algorithm for Accuracy." Mathematics 11, no. 11 (2023): 2558. http://dx.doi.org/10.3390/math11112558.

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The chi-squared automatic interaction detector (CHAID) algorithm is considered to be one of the most used supervised learning methods as it is adaptable to solving any kind of problem at hand. We are keenly aware of the non-linear relationships among CHAID maps, and they can empower predictive models with stability. However, we do not precisely know how high its accuracy. To determine the perfect scope the CHAID algorithm fits into, this paper presented an analysis of the accuracy of the CHAID algorithm. We introduced the causes, applicable conditions, and application scope of the CHAID algori

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Arslan, Sibel, Yıldız Zoralioğlu, and Muhammed Furkan Gul. "A Comparative Analysis Of African Vultures Optimization Algorithm With Current Metaheuristics." Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 8, no. 1 (2025): 325–52. https://doi.org/10.47495/okufbed.1480875.

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With the increasing complexity of optimization problems, new metaheuristic algorithms are being developed. These algorithms show their success by exhibiting superior performances on different problems. In this paper, the performance of 4 recently proposed metaheuristic algorithms, namely Artificial Hummingbird Algorithm (AHA), African Vultures Optimization Algorithm (AVOA), Crayfish Optimization Algorithm (COA) and Marine Predators Optimization Algorithm (MPA) on 26 test functions are compared. As a result of the comparisons, it was observed that the algorithms outperformed each other with ver

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Barbulescu, L., A. E. Howe, L. D. Whitley, and M. Roberts. "Understanding Algorithm Performance on an Oversubscribed Scheduling Application." Journal of Artificial Intelligence Research 27 (December 28, 2006): 577–615. http://dx.doi.org/10.1613/jair.2038.

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The best performing algorithms for a particular oversubscribed scheduling application, Air Force Satellite Control Network (AFSCN) scheduling, appear to have little in common. Yet, through careful experimentation and modeling of performance in real problem instances, we can relate characteristics of the best algorithms to characteristics of the application. In particular, we find that plateaus dominate the search spaces (thus favoring algorithms that make larger changes to solutions) and that some randomization in exploration is critical to good performance (due to the lack of gradient informa

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Lehong, Charles, Bassey Isong, Francis Lugayizi, and Adnan Abu-Mahfouz. "A Spreading Factor Congestion Status-Aware Adaptive Data Rate Algorithm." Journal of Sensor and Actuator Networks 10, no. 4 (2021): 70. http://dx.doi.org/10.3390/jsan10040070.

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LoRaWAN has established itself as one of the leading MAC layer protocols in the field of LPWAN. Although the technology itself is quite mature, its resource allocation mechanism, the Adaptive Data Rate (ADR) algorithm is still quite new, unspecified and its functionalities still limited. Various studies have shown that the performance of the ADR algorithm gradually suffers in dense networks. Recent studies and proposals have been made as attempts to improve the algorithm. In this paper, the authors proposed a spreading factor congestion status aware ADR version and compared its performance aga

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Sumertajaya, I. Made Sumertajaya, Wiwik Andriyani Lestari Ningsih, Asep Saefuddin, and Embay Rohaeti. "Biclustering Performance Evaluation of Cheng and Church Algorithm and Iterative Signature Algorithm." JTAM (Jurnal Teori dan Aplikasi Matematika) 7, no. 3 (2023): 643. http://dx.doi.org/10.31764/jtam.v7i3.14778.

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Biclustering has been widely applied in recent years. Various algorithms have been developed to perform biclustering applied to various cases. However, only a few studies have evaluated the performance of bicluster algorithms. Therefore, this study evaluates the performance of biclustering algorithms, namely the Cheng and Church algorithm (CC algorithm) and the Iterative Signature Algorithm (ISA). Evaluation of the performance of the biclustering algorithm is carried out in the form of a comparative study of biclustering results in terms of membership, characteristics, distribution of bicluste

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Yang, Ying, Xingchuang Xiong, Zilong Liu, Shangzhong Jin, and Juan Wang. "High-Performance Encryption Algorithms for Dynamic Images Transmission." Electronics 13, no. 1 (2023): 131. http://dx.doi.org/10.3390/electronics13010131.

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With the proliferation of the internet, the issue of image tampering has escalated, necessitating robust image encryption schemes. Despite the multitude of proposed image encryption algorithms, challenges such as slow computational speed, weak security, and suboptimal visibility persist. This study addresses these challenges by introducing a high-performance encryption algorithm tailored for dynamic images—QEDI (Quick Encryption Algorithm for Dynamic Images). QEDI leverages elliptic curve-based asymmetric encryption algorithms and hash algorithms. The process involves using a hash algorithm to

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Li, Jonathan, Rohan Potru, and Farhad Shahrokhi. "A Performance Study of Some Approximation Algorithms for Computing a Small Dominating Set in a Graph." Algorithms 13, no. 12 (2020): 339. http://dx.doi.org/10.3390/a13120339.

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We implement and test the performances of several approximation algorithms for computing the minimum dominating set of a graph. These algorithms are the standard greedy algorithm, the recent Linear programming (LP) rounding algorithms and a hybrid algorithm that we design by combining the greedy and LP rounding algorithms. Over the range of test data, all algorithms perform better than anticipated in theory, and have small performance ratios, measured as the size of output divided by the LP objective lower bound. However, each have advantages over the others. For instance, LP rounding algorith

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Kim, Bong-Seok, Youngseok Jin, Jonghun Lee, and Sangdong Kim. "FMCW Radar Estimation Algorithm with High Resolution and Low Complexity Based on Reduced Search Area." Sensors 22, no. 3 (2022): 1202. http://dx.doi.org/10.3390/s22031202.

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We propose a frequency-modulated continuous wave (FMCW) radar estimation algorithm with high resolution and low complexity. The fast Fourier transform (FFT)-based algorithms and multiple signal classification (MUSIC) algorithms are used as algorithms for estimating target parameters in the FMCW radar systems. FFT-based and MUSIC algorithms have tradeoff characteristics between resolution performance and complexity. While FFT-based algorithms have the advantage of very low complexity, they have the disadvantage of a low-resolution performance; that is, estimating multiple targets with similar p

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Grinshpoun, T., and A. Meisels. "Completeness and Performance Of The APO Algorithm." Journal of Artificial Intelligence Research 33 (October 23, 2008): 223–58. http://dx.doi.org/10.1613/jair.2611.

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Asynchronous Partial Overlay (APO) is a search algorithm that uses cooperative mediation to solve Distributed Constraint Satisfaction Problems (DisCSPs). The algorithm partitions the search into different subproblems of the DisCSP. The original proof of completeness of the APO algorithm is based on the growth of the size of the subproblems. The present paper demonstrates that this expected growth of subproblems does not occur in some situations, leading to a termination problem of the algorithm. The problematic parts in the APO algorithm that interfere with its completeness are identified and

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Alsammak, Ihab L. Hussein, Ali Hussein Mohammed, and ntedhar Shakir Nasir. "E-learning and COVID-19: Predicting Student Academic Performance Using Data Mining Algorithms." Webology 19, no. 1 (2022): 3419–32. http://dx.doi.org/10.14704/web/v19i1/web19225.

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The satisfaction of E-learners has the main effect on the success of the E-learning process and leads to improvements in the E-learning system's quality and several factors affect this satisfaction. Based on the dimensions of e-learning, the main objective of this study was to evaluate the factors that contributed to students' satisfaction with e-learning during pandemic the Covid-19 and to give a thorough understanding and knowledge of different data mining techniques that have been used to predict student performance and development, as well as how these techniques help in the identification

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Zheng, Junlong, Chaiyan Jettanasen, and Pathomthat Chiradeja. "Performance and Application Analysis of a New Optimization Algorithm." Computation 12, no. 1 (2023): 1. http://dx.doi.org/10.3390/computation12010001.

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Our research focused on an optimization algorithm. Our work makes three contributions. First, a new optimization algorithm, the Maritime Search and Rescue Algorithm (MSRA), is creatively proposed. The algorithm not only has better optimization performance, but also has the ability to plan the path to the best site. For other existing intelligent optimization algorithms, it has never been found that they have both of these performances. Second, the mathematical model of the MSRA was established, and the computer program pseudo-code was created. Third, the MSRA was verified by experiments.

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Lian, Naiyu, Hengzhe Xu, and Feiyang Zhang. "The Differentiation of Residents’ Cultural Consumption Tendency and Consumption Recommendation System Based on Network Inference Algorithm." Foundations of Computing and Decision Sciences 49, no. 2 (2024): 121–38. http://dx.doi.org/10.2478/fcds-2024-0008.

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Abstract To address the issue of insufficient accuracy in consumer recommendation systems, a new biased network inference algorithm is proposed based on traditional network inference algorithms. This new network inference algorithm can significantly improve the resource allocation ability of the original one, thereby improving recommendation performance. Then, the performance of this algorithm is verified through comparative experiments with network-based inference algorithms, network inference algorithms with initial resource optimization, and heterogeneous network inference algorithms. The r

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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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Fang, Jie. "Performance Tuning of Coordinated Active Traffic Control Algorithm: Simultaneously Improving Corridor Safety and Mobility Performances." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/130804.

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Proactive traffic control based on macroscopic traffic flow model is an innovative approach to active traffic management. An online, model predictive control (MPC) based active traffic control algorithm, DynaTAM, is proposed to implement integrated control through ramp metering (RM) and variable speed limit (VSL). DynaTAM predicts traffic states to anticipate incoming traffic congestion and to provide control plan recommendations for optimizing the network traffic conditions. However, as with other sophisticated prediction-based control algorithms, a system fine-tuning procedure is required fo

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Seçkiner, Serap Ulusam, and Şeyma Yilkici Yüzügüldü. "A new health-based metaheuristic algorithm: cholesterol algorithm." International Journal of Industrial Optimization 4, no. 2 (2023): 115–30. http://dx.doi.org/10.12928/ijio.v4i2.7651.

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This paper seeks to explore the effectiveness of a new health-based metaheuristic algorithm inspired by the cholesterol metabolism of the human body. In the study, the main idea is the focus on the performance of the cholesterol algorithm on unconstrained continuous optimization problems. The performances of the proposed cholesterol algorithm are evaluated based on 23 comparison tests and results were compared with Particle Swarm Optimization, Genetic Algorithm, Grey Wolf Optimization, Whale Optimization Algorithm, Harris Hawks Optimization, Differential Evolution, FireFly Algorithm, Cuckoo Se

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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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Soroushnia, Shima, Masoud Daneshtalab, Juha Plosila, Tapio Pahikkala, and Pasi Liljeberg. "High Performance Pattern Matching on Heterogeneous Platform." Journal of Integrative Bioinformatics 11, no. 3 (2014): 88–98. http://dx.doi.org/10.1515/jib-2014-253.

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Summary Pattern discovery is one of the fundamental tasks in bioinformatics and pattern recognition is a powerful technique for searching sequence patterns in the biological sequence databases. Fast and high performance algorithms are highly demanded in many applications in bioinformatics and computational molecular biology since the significant increase in the number of DNA and protein sequences expand the need for raising the performance of pattern matching algorithms. For this purpose, heterogeneous architectures can be a good choice due to their potential for high performance and energy ef

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Hidayat, Trifenaus Prabu, and Andre Sugioko. "Performance Analyzes of Bee Colony Split-Plot Algorithm." International Journal of Information and Education Technology 5, no. 7 (2015): 549–52. http://dx.doi.org/10.7763/ijiet.2015.v5.566.

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Priya, C. Geetha, and S. Thilagavathi. "Performance of SISID Algorithm for LTE Channel Models." Journal of Advances in Computer Networks 4, no. 1 (2016): 1–5. http://dx.doi.org/10.18178/jacn.2016.4.1.194.

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Siva Brindha, G., and M. Gobi. "Improving the MapReduce Performance using Symmetric Key Algorithm." International Journal of Science and Research (IJSR) 10, no. 4 (2021): 690–95. https://doi.org/10.21275/sr21204101422.

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Emima, A., and D. I. George Amalarethinam. "Integrative Ensemble Learning Algorithm for Predicting Students’ Performance." Indian Journal Of Science And Technology 18, no. 1 (2025): 72–84. https://doi.org/10.17485/ijst/v18i1.3718.

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Objectives: To create a stable student performance prediction model utilizing ensemble learning methods. Methods: The study uses boosting techniques such as CatBoost, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) as simple classifiers, which are then combined into a composite classifier to improve predictive accuracy. During the training phase, a 5-level hyperparameter optimization for the basic classifiers is performed using ETLBO Optimization IELA's distinguishing feature is its Stacking ensemble method, which functions as an ensemble technique, combinin

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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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Hasibuan, Eka Hayana, Surya Hendraputra, GS Achmad Daengs, and Liharman Saragih. "Comparison Fletcher-Reeves and Polak-Ribiere ANN Algorithm for Forecasting Analysis." Journal of Physics: Conference Series 2394, no. 1 (2022): 012008. http://dx.doi.org/10.1088/1742-6596/2394/1/012008.

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Abstract Each method and algorithm ANN has different performances depending on the algorithm used and the parameters given. The purpose of this research is to obtain the best algorithm information from the two algorithms that will be compared based on the performance value or the smallest / lowest MSE value so that it can be used as a reference and information for solving forecasting problems. The ANN algorithms compared were Conjugate Gradient Fletcher-Reeves and Conjugate Gradient Polak-Ribiere. The conjugate gradient algorithm can solve unlimited optimization problems and is much more effic

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Lei, Tao, Deng Ping He, and Fang Tang Chen. "Study of BLAST Signal Detection Algorithm in LTE System." Advanced Materials Research 756-759 (September 2013): 3183–88. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3183.

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BLAST can achieve high speed data communication. Its signal detection directly affects performance of BLAST receiver. This paper introduced several signal detection algorithmsZF algorithm, MMSE algorithm, ZF-SIC algorithm and MMSE-SIC algorithm. The simulation results show that the traditional ZF algorithm has the worst performance, the traditional MMSE algorithm and the ZF-SIC algorithm is similar, but with the increase of the SNR, the performance of ZF-SIC algorithm is better than MMSE algorithm. MMSE-SIC algorithm has the best detection performance in these detection algorithms.

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Adane, Michael Donkor, Joshua Kwabla Deku, and Emmanuel Kwaku Asare. "Performance Analysis of Machine Learning Algorithms in Prediction of Student Academic Performance." Journal of Advances in Mathematics and Computer Science 38, no. 5 (2023): 74–86. http://dx.doi.org/10.9734/jamcs/2023/v38i51762.

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The advancement in technology has contributed largely to the application of data mining in education in recent times. However, selecting appropriate algorithm(s) to “mine” knowledge about educational data presents a difficult challenge to researchers and analyst. This paper contributes to the use of classification algorithms in academic performance prediction. The predictive ability of four popular algorithms; C4.5 Decision tree (CDT), Multilayer Perceptron (MLP), Naïve Bayes (NB) and Random Forest (RF) algorithms were compared. The models were built using student dataset from selected private

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Mahmud, Hasan, A. K. M. Najmul Islam, Xin (Robert) Luo, and Patrick Mikalef. "Decoding algorithm appreciation: Unveiling the impact of familiarity with algorithms, tasks, and algorithm performance." Decision Support Systems 179 (April 2024): 114168. http://dx.doi.org/10.1016/j.dss.2024.114168.

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Zheng, Rong, Heming Jia, Laith Abualigah, Qingxin Liu, and Shuang Wang. "Deep Ensemble of Slime Mold Algorithm and Arithmetic Optimization Algorithm for Global Optimization." Processes 9, no. 10 (2021): 1774. http://dx.doi.org/10.3390/pr9101774.

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In this paper, a new hybrid algorithm based on two meta-heuristic algorithms is presented to improve the optimization capability of original algorithms. This hybrid algorithm is realized by the deep ensemble of two new proposed meta-heuristic methods, i.e., slime mold algorithm (SMA) and arithmetic optimization algorithm (AOA), called DESMAOA. To be specific, a preliminary hybrid method was applied to obtain the improved SMA, called SMAOA. Then, two strategies that were extracted from the SMA and AOA, respectively, were embedded into SMAOA to boost the optimizing speed and accuracy of the solu

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Ahmed, Mohiuddin, Raihan Seraj, and Syed Mohammed Shamsul Islam. "The k-means Algorithm: A Comprehensive Survey and Performance Evaluation." Electronics 9, no. 8 (2020): 1295. http://dx.doi.org/10.3390/electronics9081295.

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The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provi

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Bijwe, Abhijit, and C. G. Dethe. "Performance Analysis of Vertical Handoff Metrics Using Variance Based Algorithms." Journal of Applied Computer Science Methods 7, no. 1 (2015): 33–42. http://dx.doi.org/10.1515/jacsm-2015-0008.

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Abstract Vertical handover is the evolving concept in 4G for seamless communication between heterogeneous networks. In this paper, our main objective is to analyze handover between two WLAN, two Wimax, two UMTS networks. The vertical handover decision is taken based on the variance based algorithm, which calculates the variance of parameters such as delay, jitter, bandwidth and packet loss for various above mentioned networks and the network with most of the parameters with minimum variance is selected. This algorithm is also compared with other algorithms such as MEW (Multiplicative experimen

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Manaseer, Saher, and Ahmad K. Al Hwaitat. "Measuring Parallel Performance of Sorting Algorithms." Modern Applied Science 12, no. 10 (2018): 23. http://dx.doi.org/10.5539/mas.v12n10p23.

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The performance evaluation of sorting algorithm play a major role in understanding the behavior which has great benefit in most of the field of sciences, knowing the difference between parallel and sequential performance will help the researchers to choose the best algorithm bucket and bubble sort to use and implement. In this research we study the performance of two sorting algorithm and evaluate the difference in performance in aspect of speed up and efficiency, the two algorithms has been tested on IMAN1 super computer with different evaluate input size and different number of processors. T

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Ordys, Andrzej W., Masayoshi Tomizuka, and Michael J. Grimble. "State-Space Dynamic Performance Preview-Predictive Controller." Journal of Dynamic Systems, Measurement, and Control 129, no. 2 (2006): 144–53. http://dx.doi.org/10.1115/1.2431810.

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The paper discusses state-space generalized predictive control and the preview control algorithms. The optimization procedure used in the derivation of predictive control algorithms is considered. The performance index associated with the generalized predictive controller (GPC) is examined and compared with the linear quadratic (LQ) optimal control formulation used in preview control. A new performance index and consequently a new algorithm is proposed dynamic performance predictive controller (DPPC) that combines the features of both GPC and preview controller. This algorithm minimizes the pe

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Xiao, Shi Song, Ao Lin Wang, and Hui Feng. "An Improved Algorithm Based on AC-BM Algorithm." Applied Mechanics and Materials 380-384 (August 2013): 1576–79. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1576.

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The pattern matching algorithm is the mainstream technology in the instruction detection system, and therefore as a pattern-matching methods core string matching algorithm directly affect an intrusion detection system performance and efficiency. So based on the discussions of the most fashionable pattern matching algorithms at present, an improved algorithm of AC-BM is presented. From the experiments in the Snort ,it is concluded that the improved algorithm of the performance and efficiency is higher than AC-BM algorithm.

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Ghimire, Roshani, and Ram Kumar Basnet. "Shortest Path Routing Performance Evaluation over SDN Environment." December 2023 5, no. 4 (2023): 405–22. http://dx.doi.org/10.36548/jei.2023.4.004.

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Static routing has a manual configuration setup system, and the scope of static routing in an SDN network is just for small networks. The solution to this problem rises up with the new technology deﬁned as software-deﬁned networking (SDN) based on shortest path ﬁrst dynamic routing. SDN has the facility of a centralized controller that smooth the controls and routes computation over a data packet. The performance analysis of SDN networks that have SDN switches connected to the network based on the shortest path ﬁrst protocol are simulated on Mininet. The POX controller with Mininet programming

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Lee, Dongkyu, Jae-Weon Jeong, and Guebin Choi. "Short Term Prediction of PV Power Output Generation Using Hierarchical Probabilistic Model." Energies 14, no. 10 (2021): 2822. http://dx.doi.org/10.3390/en14102822.

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Photovoltaics are methods used to generate electricity by using solar cells, which convert natural energy from the sun. This generation makes use of unlimited natural energy. However, this generation is irregular because they depend on weather occurrences. For this reason, there is a need to improve their economic efficiency through accurate predictions and reducing their uncertainty. Most researches were conducted to predict photovoltaic generation with various machine learning and deep learning methods that have complicated structures and over-fitted performances. As improving the performanc

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Abdelwahab, Amira, and Nesma Youssef. "Performance Evaluation of Sequential Rule Mining Algorithms." Applied Sciences 12, no. 10 (2022): 5230. http://dx.doi.org/10.3390/app12105230.

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Data mining techniques are useful in discovering hidden knowledge from large databases. One of its common techniques is sequential rule mining. A sequential rule (SR) helps in finding all sequential rules that achieved support and confidence threshold for help in prediction. It is an alternative to sequential pattern mining in that it takes the probability of the following patterns into account. In this paper, we address the preferable utilization of sequential rule mining algorithms by applying them to databases with different features for improving the efficiency in different fields of appli

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Shi, Ruifeng, Ning Zhang, Runhai Jiao, Zhenyu Zhou, and Li Zhang. "Study on Evolutionary Algorithm Online Performance Evaluation Visualization Based on Python Programming Language." Journal of Systems Science and Information 2, no. 1 (2014): 86–96. http://dx.doi.org/10.1515/jssi-2014-0086.

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Abstract Evolutionary computations are kinds of random searching algorithms derived from natural selection and biological genetic evolution behavior. Evaluating the performance of an algorithm is a fundamental task to track and find the way to improve the algorithm, while visualization technique may play an important act during the process. Based on current existing algorithm performance evaluation criteria and methods, a Python-based programming tracking strategy, which employs 2-D graphical library of python matplotlib for online algorithm performance evaluation, is proposed in this paper. T

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Krimpenis, Agathoklis A., and Loukas Athanasakos. "“Optimizing the Optimization”: A Hybrid Evolutionary-Based AI Scheme for Optimal Performance." Computers 14, no. 3 (2025): 97. https://doi.org/10.3390/computers14030097.

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Optimization algorithms for solving technological and scientific problems often face long convergence times and high computational costs due to numerous input/output parameters and complex calculations. This study focuses on proposing a method for minimizing response times for such algorithms across various scientific fields, including the design and manufacturing of high-performance, high-quality components. It introduces an innovative mixed-scheme optimization algorithm aimed at effective optimization with minimal objective function evaluations. Indicative key optimization algorithms—namely,

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Ertas, Gokhan. "Fitting Intravoxel Incoherent Motion Model to Diffusion MR Signals of the Human Breast Tissue using Particle Swarm Optimization." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 9, no. 2 (2019): 105–12. http://dx.doi.org/10.11121/ijocta.01.2019.00642.

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Intravoxel incoherent motion (IVIM) modeling offers the parameters f, D and D* as biomarkers for different lesion types and cancer stages from diffusion MR signals. Challenges with the available optimization algorithms in fitting the model to the signals motive new studies for improved parameter estimations. In this study, one thousand value sets of f, D, D* for human breast are assembled and used to generate five thousand diffusion MR signals considering noise-free and noisy situations exhibiting signal-to-noise ratios (SNR) of 20, 40, 60 and 80. The estimates of f, D, D* are obtained using L

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Pramudita, Rully, Saludin Muis, Nadya Safitri, and Fitri Shafirawati. "Optimasi Algoritma Machine Learning Menggunakan Teknik Bagging Pada Klasifikasi Diagnosis Kanker Payudara." TEMATIK 11, no. 1 (2024): 128–34. http://dx.doi.org/10.38204/tematik.v11i1.1928.

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Classification algorithms have a very important role in Machine Learning, but not all algorithms have the same performance in every case. Algorithm performance can be affected by the type of data used, differences in problem characteristics, and the parameters used. Additionally, ensemble learning techniques such as Bagging can affect algorithm performance. Therefore, the problem arises of how to choose the most suitable algorithm for a particular classification task and how to optimize the performance of the algorithm. This research aims to carry out a comparative analysis and optimization of

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