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Gotowe bibliografie tematyczne / MCS selection algorithm

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Gotowa bibliografia na temat „MCS selection algorithm”

Autor: Grafiati

Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "MCS selection algorithm"

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Li, Zhuo, Zecheng Li, and Wei Zhang. "Quality-Aware Task Allocation for Mobile Crowd Sensing Based on Edge Computing." Electronics 12, no. 4 (2023): 960. http://dx.doi.org/10.3390/electronics12040960.

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In the field of mobile crowd sensing (MCS), the traditional client–cloud architecture faces increasing challenges in communication and computation overhead. To address these issues, this paper introduces edge computing into the MCS system and proposes a two-stage task allocation optimization method under the constraint of limited computing resources. The method utilizes deep reinforcement learning for the selection of optimal edge servers for task deployment, followed by a greedy self-adaptive stochastic algorithm for the recruitment of sensing participants. In simulations, the proposed method

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Meng, Qing Min, Xiong Gu, Feng Tian, and Bao Yu Zheng. "k-NN Based MCS Selection in Distributed OFDM Wireless Networks." Advanced Materials Research 225-226 (April 2011): 974–77. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.974.

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Cognitive radio is seen as an intelligent wireless communication system that can learn and adapt the surrounding environment. Cognitive engine is the core component of implementation of cognitive radio. Information in knowledge base of cognitive engine can be obtained by using of machine learning. In this work, we consider wireless networks with clustered nodes and OFDM physical layer and present a combined sub-channel selection and modulation and coding rate selection based on k-Nearest Neighbor classification algorithm. Computer simulation results show that, in frequency selective fading cha

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Cuevas, Erik, and Adolfo Reyna-Orta. "A Cuckoo Search Algorithm for Multimodal Optimization." Scientific World Journal 2014 (2014): 1–20. http://dx.doi.org/10.1155/2014/497514.

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Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of (1) the incorporation of a memory mechanism to efficiently register

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Kalaiarasu, Dr M., and Dr J. Anitha. "Modified Cuckoo Search-Support Vector Machine (MCS-SVM) Gene Selection and Classification for Autism Spectrum Disorder (ASD) Gene Expression." NeuroQuantology 18, no. 11 (2020): 01–13. http://dx.doi.org/10.14704/nq.2020.18.11.nq20228.

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Autism Spectrum Disorder (ASD) is a neuro developmental disorder characterized by weakened social skills, impaired verbal and non-verbal interaction, and repeated behavior. ASD has increased in the past few years and the root cause of the symptom cannot yet be determined. In ASD with gene expression is analyzed by classification methods. For the selection of genes in ASD, statistical philtres and a wrapper-based Geometric Binary Particle Swarm Optimization-Support Vector Machine (GBPSO-SVM) algorithm have recently been implemented. However GBPSO has provides lesser accuracy, if the dataset sam

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Wang, Yanan, Guodong Sun, and Xingjian Ding. "Coverage-Balancing User Selection in Mobile Crowd Sensing with Budget Constraint." Sensors 19, no. 10 (2019): 2371. http://dx.doi.org/10.3390/s19102371.

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Mobile crowd sensing (MCS) is a new computing paradigm for the internet of things, and it is widely accepted as a powerful means to achieve urban-scale sensing and data collection. In the MCS campaign, the smart mobilephone users can detect their surrounding environments with their on-phone sensors and return the sensing data to the MCS organizer. In this paper, we focus on the coverage-balancing user selection (CBUS) problem with a budget constraint. Solving the CBUS problem aims to select a proper subset of users such that their sensing coverage is as large and balancing as possible, yet wit

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Pošík, Petr, Waltraud Huyer, and László Pál. "A Comparison of Global Search Algorithms for Continuous Black Box Optimization." Evolutionary Computation 20, no. 4 (2012): 509–41. http://dx.doi.org/10.1162/evco_a_00084.

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Four methods for global numerical black box optimization with origins in the mathematical programming community are described and experimentally compared with the state of the art evolutionary method, BIPOP-CMA-ES. The methods chosen for the comparison exhibit various features that are potentially interesting for the evolutionary computation community: systematic sampling of the search space (DIRECT, MCS) possibly combined with a local search method (MCS), or a multi-start approach (NEWUOA, GLOBAL) possibly equipped with a careful selection of points to run a local optimizer from (GLOBAL). The

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Gu, Zheng Gang, and Kun Hong Liu. "Microarray Data Classification Based on Evolutionary Multiple Classifier System." Applied Mechanics and Materials 130-134 (October 2011): 2077–80. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.2077.

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Designing an evolutionary multiple classifier system (MCS) is a relatively new research area. In this paper, we propose a genetic algorithm (GA) based MCS for microarray data classification. We construct a feature poll with different feature selection methods first, and then a multi-objective GA is applied to implement ensemble feature selection process so as to generate a set of classifiers. When this GA stops, a set of base classifiers are generated. Here we use all the nondominated individuals in last generation to build an ensemble system and test the proposed ensemble method and the metho

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Alizadeh Moghaddam, S. H., M. Mokhtarzade, and S. A. Alizadeh Moghaddam. "A NEW MULTIPLE CLASSIFIER SYSTEM BASED ON A PSO ALGORITHM FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 71–75. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-71-2019.

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Abstract. Multiple classifier systems (MCSs) have shown great performance for the classification of hyperspectral images. The requirements for a successful MCS are 1) diversity between ensembles and 2) good classification accuracy of each ensemble. In this paper, we develop a new MCS method based on a particle swarm optimization (PSO) algorithm. Firstly, in each ensemble of the proposed method, called PSO-MCS, PSO identifies a subset of the spectral bands with a high J2 value, which is a measure of class-separability. Then, an SVM classifier is used to classify the input image, applying the se

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Abououf, Menatalla, Shakti Singh, Hadi Otrok, Rabeb Mizouni, and Ernesto Damiani. "Machine Learning in Mobile Crowd Sourcing: A Behavior-Based Recruitment Model." ACM Transactions on Internet Technology 22, no. 1 (2022): 1–28. http://dx.doi.org/10.1145/3451163.

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With the advent of mobile crowd sourcing (MCS) systems and its applications, the selection of the right crowd is gaining utmost importance. The increasing variability in the context of MCS tasks makes the selection of not only the capable but also the willing workers crucial for a high task completion rate. Most of the existing MCS selection frameworks rely primarily on reputation-based feedback mechanisms to assess the level of commitment of potential workers. Such frameworks select workers having high reputation scores but without any contextual awareness of the workers, at the time of selec

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Krishnaveni P. and Balasundaram S. R. "Automatic Text Summarization by Providing Coverage, Non-Redundancy, and Novelty Using Sentence Graph." Journal of Information Technology Research 15, no. 1 (2022): 1–18. http://dx.doi.org/10.4018/jitr.2022010108.

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The day-to-day growth of online information necessitates intensive research in automatic text summarization (ATS). The ATS software produces summary text by extracting important information from the original text. With the help of summaries, users can easily read and understand the documents of interest. Most of the approaches for ATS used only local properties of text. Moreover, the numerous properties make the sentence selection difficult and complicated. So this article uses a graph based summarization to utilize structural and global properties of text. It introduces maximal clique based s

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Rozprawy doktorskie na temat "MCS selection algorithm"

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Lee, Unghee. "A Proactive Routing Protocol for Multi-Channel Wireless Ad-hoc Networks." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/28127.

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Wireless mobile ad-hoc networks consist of a collection of peer mobile nodes that form a network and are capable of communicating with each other without help from stationary infrastructure such as access points. The availability of low-cost, com-modity network interface cards (NICs) has made the IEEE 802.11 medium access control (MAC) protocol the de facto MAC protocol for wireless mobile ad-hoc net-works, even though it is not optimal. The IEEE 802.11 MAC protocol is designed to have stations share a single channel in a given network. However, many of the IEEE 802.11 physical (PHY) layer

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Wang, Ju-Chia, and 王汝嘉. "Joint Beam Training and MCS Mode Selection Algorithm under Multi-Path Fading Channel for WLANs." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/93043611866087509274.

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碩士<br>國立交通大學<br>資訊科學與工程研究所<br>105<br>As people’s need to internet needs increase, IEEE 802.11 is a standard for wireless local area network(WLAN) which had proposed an effective solution and is still continue developing. It uses 5GHz band that has higher bandwidth to replace the old 2.4GHz band, in order to provide a faster data transmit rate and a more stable signal. In 802.11ac standard, beamforming technology is included. The beamforming principle is to utilize multiple antennas in the space and to adjust the phase and amplitude for the sake of the signal can get the same phase, the same am

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Części książek na temat "MCS selection algorithm"

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Wang, Hao, Andrew Chi Sing Leung, and John Sum. "MCP Based Noise Resistant Algorithm for Training RBF Networks and Selecting Centers." In Neural Information Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04179-3_59.

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Phinyomark, Angkoon, Franck Quaine, and Yann Laurillau. "The Relationship Between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface." In Computer Vision. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5204-8.ch098.

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Muscle-computer interfaces (MCIs) based on surface electromyography (EMG) pattern recognition have been developed based on two consecutive components: feature extraction and classification algorithms. Many features and classifiers are proposed and evaluated, which yield the high classification accuracy and the high number of discriminated motions under a single-session experimental condition. However, there are many limitations to use MCIs in the real-world contexts, such as the robustness over time, noise, or low-level EMG activities. Although the selection of the suitable robust features can solve such problems, EMG pattern recognition has to design and train for a particular individual user to reach high accuracy. Due to different body compositions across users, a feasibility to use anthropometric variables to calibrate EMG recognition system automatically/semi-automatically is proposed. This chapter presents the relationships between robust features extracted from actions associated with surface EMG signals and twelve related anthropometric variables. The strong and significant associations presented in this chapter could benefit a further design of the MCIs based on EMG pattern recognition.

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Lucinska, Malgorzata, and Slawomir T. Wierzchon. "An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game." In Machine Learning. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-818-7.ch503.

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Multi-agent systems (MAS), consist of a number of autonomous agents, which interact with one-another. To make such interactions successful, they will require the ability to cooperate, coordinate, and negotiate with each other. From a theoretical point of view such systems require a hybrid approach involving game theory, artificial intelligence, and distributed programming. On the other hand, biology offers a number of inspirations showing how these interactions are effectively realized in real world situations. Swarm organizations, like ant colonies or bird flocks, provide a spectrum of metaphors offering interesting models of collective problem solving. Immune system, involving complex relationships among antigens and antibodies, is another example of a multi-agent and swarm system. In this chapter an application of so-called clonal selection algorithm, inspired by the real mechanism of immune response, is proposed to solve the problem of learning strategies in the pursuit-evasion problem.

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Streszczenia konferencji na temat "MCS selection algorithm"

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Satapathy, Shaswat, Shivani Singh, and Debani Prasad Mishra. "MCS: A Distributed Multi-User Channel Selection Algorithm for Cognitive Radio Networks." In 2019 International Conference on Information Technology (ICIT). IEEE, 2019. http://dx.doi.org/10.1109/icit48102.2019.00015.

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Bojnordi, Ehsan, Seyed Jalaleddin Mousavirad, Gerald Schaefer, and Iakov Korovin. "MCS-HMS: A Multi-Cluster Selection Strategy for the Human Mental Search Algorithm." In 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2021. http://dx.doi.org/10.1109/ssci50451.2021.9660143.

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Xiaofei Zhao, Xinyu Gu, Xiang Zhang, Yi Gong, Lin Zhang, and Wenyu Li. "Investigation of different MCS selection algorithm to reduce the impact of CSI-RS on LTE legacy UEs." In TENCON 2015 - 2015 IEEE Region 10 Conference. IEEE, 2015. http://dx.doi.org/10.1109/tencon.2015.7373083.

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Junbo, Liu, Ding Shuiting, and Li Guo. "Influence of Random Variable Dimension on the Fast Numerical Integration Method of Aero Engine Rotor Disk Failure Risk Analysis." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23513.

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Abstract In the risk assessment of turbine rotor disks, the probability of failure of a certain disk type (after N flight cycles) is a vital criterion for estimating whether the disk is safe to use. Monte Carlo simulation (MCS) is often used to calculate the failure probability but is costly because it requires a large sample size. The numerical integration (NI) algorithm has been proven more efficient than MCS in conditions entailing three random variables. However, the previous studies on the NI method have not dealt with the influence of random variable dimension on calculation efficiency.

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Chien, Ying-Ren, Sheng-Teng Wu, and Hen-Wai Tsao. "Correntropy-based Data-Selective MCC Algorithm." In 2022 IEEE International Conference on Consumer Electronics - Taiwan. IEEE, 2022. http://dx.doi.org/10.1109/icce-taiwan55306.2022.9869130.

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Li, Bohan, Xindi Zhang, Shaowei Cai, Jinkun Lin, Yiyuan Wang, and Christian Blum. "NuCDS: An Efficient Local Search Algorithm for Minimum Connected Dominating Set." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/209.

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The minimum connected dominating set (MCDS) problem is an important extension of the minimum dominating set problem, with wide applications, especially in wireless networks. Despite its practical importance, there are few works on solving MCDS for massive graphs, mainly due to the complexity of maintaining connectivity. In this paper, we propose two novel ideas, and develop a new local search algorithm for MCDS called NuCDS. First, a hybrid dynamic connectivity maintenance method is designed to switch alternately between a novel fast connectivity maintenance method based on spanning tree and i

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Mardini, Wail, Yaser Khamayseh, and Montaha Hani Khatatbeh. "Genetic algorithm for friendship selection in social IoT." In 2017 International Conference on Engineering & MIS (ICEMIS). IEEE, 2017. http://dx.doi.org/10.1109/icemis.2017.8273022.

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Abdullah, J., M. Y. Ismail, N. A. Cholan, and S. A. Hamzah. "GA-based QoS Route Selection Algorithm for Mobile Ad-Hoc Networks." In 2nd Malaysia Conferenced on Photonics (MCP). IEEE, 2008. http://dx.doi.org/10.1109/nctt.2008.4814299.

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Cho, Changgi, Hu Jin, Nah-Oak Song, and Dan Keun Sung. "MCS selection algorithms for a persistent allocation scheme to accommodate VoIP services in IEEE 802.16e OFDMA system." In 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC 2009). IEEE, 2009. http://dx.doi.org/10.1109/pimrc.2009.5449929.

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Ebian, Mohamed, Mohamed El-Sharkawy, and Salwa El-Ramly. "Adaptive error concealment algorithm for multiview coding based on lost MBs sizes and using dynamic selection of lower candidates MBs." In 2012 8th International Computer Engineering Conference (ICENCO). IEEE, 2012. http://dx.doi.org/10.1109/icenco.2012.6487085.

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