Search Results - parallel solution ((clustering algorithm) OR (learning algorithm))*

Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm by Al-Jumaili A.H.A., Muniyandi R.C., Hasan M.K., Singh M.J., Paw J.K.S., Al-Jumaily A.

“…Parallel power loads anomalies are processed by a fast-density peak clustering technique that capitalizes on the hybrid strengths of Canopy and K-means algorithms all within Apache Mahout's distributed machine-learning environment. …”

Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things by Hadi, Ahmed Adnan

“…Therefore, we propose a prominent approach that integrates each of the NN, a meta-heuristic based on an evolutionary genetic algorithm (GA), and a core online-offline clustering (Core). …”
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Solving traveling salesman problem on cluster compute nodes by I.A., Aziz, Haron, N., Mehat, M., Jung, L.T., Mustapa, A.N., Akir, E.A.P.

“…The sequential algorithm is then converted into a parallel algorithm by integrating it with the Message Passing Interface (MPI) libraries so that it can be executed on a cluster computer. …”
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A spark-based parallel fuzzy C median algorithm for web log big data by Mallik, Moksud Alam, Zulkurnain, Nurul Fariza, Nizamuddin, Mohammed Khaja, Sarkar, Rashal, Chalil, Aboosalih Kakkat

“…This paper proposes an efficient parallel Fuzzy C median solution based on Spark for large-scale web log data. …”
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Parallel Processing of Forward-backward Time-stepping Method for Time Domain Inverse Scattering by Kismet, Hong Ping, Moriyama, Toshifumi, Yamaguchi, Yuji, Tanaka, Toshiyuki, Takenaka, Takashi

“…A cluster of 8 PCs is constructed and parallel processing is realized using MPI. …”
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PMT : opposition based learning technique for enhancing metaheuristic algorithms performance by Hammoudeh, S. Alamri

“…Addressing these OBL limitations, this research proposes a new general OBL technique inspired by a natural phenomenon of parallel mirrors systems called the Parallel Mirrors Technique (PMT). …”
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Application of parallel ensemble Monte Carlo technique in charge dynamics simulation by Umar, Roslan, -, Othman, -, A.P., -, Gopir

“…The use of Parallel Virtual Machine (PVM) standards when running the parallel algorithm of the ensemble MC simulation been proposed Some results of the development are also presented in this paper.…”
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Application of parallel ensemble Monte Carlo technique in charge dynamics simulation by A. P. Othman, R. Umar, G. Gopir

“…We have proposed the use of Parallel Virtual Machine (PVM) standards when running the parallel algorithm of the ensemble MC simulation. …”
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Improving parallel self-organizing map using heterogeneous uniform memory access / Muhammad Firdaus Mustapha by Mustapha, Muhammad Firdaus

“…Self-organizing Map (SOM) is a very popular algorithm that has been used as clustering algorithm and data exploration. …”
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Quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach by Othman, Khairulnizam

“…In addition, a new image clustering algorithm anticipates the need for largescale serial and parallel processing. …”
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Single and Multiple variables control using Tree Physiology Optimization by Halim, A.H., Ismail, I.

“…TPO is a metaheuristic optimization algorithm that has a clustered diversification search strategy inspired from plant shoots growth. …”
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PMT: opposition-based learning technique for enhancing meta-heuristic performance by Alamri, Hammoudeh S., Kamal Z., Zamli

“…Addressing these issues, this research proposes a new general opposition-based learning (OBL) technique inspired by a natural phenomenon of parallel mirrors systems called the parallel mirrors technique (PMT). …”
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Parallel CFD Simulations of Multiphase Systems: Jet into a Cylindrical Bath and Rotary Drum on a Rectangular Bath. by Hasan, Nurul

“…For commercial CFD packages, in many cases the solution algorithms are black boxes, even though parallel computing helps in many cases to overcome the limitations, as shown here. …”
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A parallel ensemble learning model for fault detection and diagnosis of industrial machinery by Shing, Chiang Ta, Mohammed Al-Andoli, Mohammed Nasser, Kok, Swee Sim, Seera, Manjeevan, Chee, Peng Lim

“…Accordingly, this paper proposes a new parallel ensemble model comprising hybrid machine and deep learning for undertaking FDD tasks. …”
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Grid portal technology for web based education of parallel computing courses, applications and researches by Alias, Norma, Islam, Md. Rajibul, Mydin, Suhaimi, Hamzah, Norhafiza, Safiza Abd. Ghaffar, Zarith, Satam, Noriza, Darwis, Roziha

“…This paper proposes the web service education technology for postgraduate parallel computing course, e-learning students, real-time solutions and for supervising projects related to the application of parallel computing, that focuses on the fundamental principles to parallel computer architecture, multimedia, communication cost, master-worker model, parallel algorithm, web services and performance evaluations. …”
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Measuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problem by Sulaiman M.A.H.B., Suliman A., Ahmad A.R.

K Nearest Neighbor Joins And Mapreduce Process Enforcement For The Cluster Of Data Sets In Bigdata by Md Shah, Wahidah, Othman, Mohd Fairuz Iskandar, Hussian Hassan, Ali Abdul, Talib, Mohammed Saad, Mohammed, Ali Abdul Jabbar

“…K Nearest Neighbor Joins (KNN join) are regarded as highly primitive and expensive operations in the data mining.The efficient use of KNN join has proven good results in finding the objects from two data sets prevailed in the huge databases.This has been achieved with the combination of K-Nearest Neighbor query and join operation to find the distinct objects from different data sets.MapReduce is a newly introduced program with the combination of Map Procedure method and Reduce Method widely used in BigData.MapReduce is enriched with parallel distributed algorithm to find the results on a cluster of data sets in BigData.In this paper,the combination of KNN join and MapReduce methods are utilized on the cluster of data sets in BigData for knowledge discovery.Exploring the pinpoint data from huge data sets stored in Big Data demands the distributed large scale data processing.The present research paper is focusing on generic steps for KNN joins exploration operations on MapReduce.The operations of KNN Join are targeted to perform the data partitioning and data pre-processing and necessary calculations.By utilizing the combination of KNN joins with MapReduce methods on BigData data sets will demonstrate a solution for complex computational analysis. …”
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A review on security and privacy issues in E-learning and the MapReduce aproach by Noor Akma, Abu Bakar, Mazlina, Abdul Majid, Khalid, Adam, Kirahman, Ab Razak, Noorhuzaimi@Karimah, Mohd Noor

“…Then, we proposed e-Learning using MapReduce algorithm in protecting the security and privacy of eLearning. …”
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Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks by Ukasyah bin Mahamod

Adaptive genetic algorithm to improve negotiation process by agents e-commerce by Ebadi, Sahar

“…The proposed negotiation algorithm employs Bayesian learning and similarity functions in order to predict opponent agent’s type and preferences. …”
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