Search Results - (( simulation organization learning algorithm ) OR ( java implementation graph algorithm ))

Implementation of locust inspired scheduling algorithm with huge number of servers for energy efficiency in a cloud datacenter by Azhar, Nur Huwaina

“…In the benchmark paper, the result of implementation of LACE algorithm in 400, 600, 800 and 1000 servers were plotted at different graphs. …”
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Octane number prediction for gasoline blends using convolution neural network / Zhu Yue by Zhu , Yue

“…At present, there are many prediction algorithms based on machine learning. According to the "80/20 rule" for building machine learning model, 80% of the time is spent of finding, cleaning, and organizing data, while the remaining 20% for training of the machine learning model. …”
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Priority and dynamic quantum time algorithms for central processing unit scheduling by Mohammed, Maysoon A.

“…The proposed algorithms (Priority Dynamic Quantum Time and Multi Priority Dynamic Quantum Time Algorithms) are implemented using JAVA programming language and validated using Key Performance Indicators equations. …”
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A New Machine Learning-based Hybrid Intrusion Detection System and Intelligent Routing Algorithm for MPLS Network by Ridwan M.A., Radzi N.A.M., Azmi K.H.M., Abdullah F., Ahmad W.S.H.M.W.

Optimizing the production of valuable metabolites using a hybrid of constraint-based model and machine learning algorithms : A review by Kauthar, Mohd Daud, Ananda, Ridho, Suhaila, Zainudin, Chan, Weng Howe, Moorthy, Kohbalan, Nurul Izrin, Md Saleh

“…This review paper summarizes research on the hybrid of constraint-based models and machine learning algorithms in optimizing valuable metabolites. …”
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Self-organizing network technique for resource allocation and mobility management in LTE femtocell network / Labeeb Mohsin Abdullah by Abdullah, Labeeb Mohsin

“…MATLAB and Vienna LTE simulators were used to conduct the experiments for the proposed algorithms, approaches and schemes and also used to verify the results. …”
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An ontology-based reasoning framework for reaction mechanisms simulation by Alicia Tang, Y., Zain, S., Abdul Rahman, N., Abdullah, R.

“…This work discusses a novel framework using Qualitative Reasoning (QR) to provide means for learning reaction mechanisms through simulation. The framework consists of a number of functional components. …”
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Driver Behaviour Monitoring And Alert System By Using Machine Learning by Nur Arina Hazwani Samsun Zaini, Fauzun Abdullah Asuhaimi, Liyana Ramli, Khairul Nabilah Zainul Ariffin

An evaluation on KNN-SVM algorithm for detection and prediction of DDoS attack by Yusof, Ahmad Riza’ain, Udzir, Nur Izura, Selamat, Ali

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Virtual power plant and microgrids controller for energy management based on optimization techniques by Abdolrasol M.G.M., Mohamed A., Hannan M.A.

Effective adsorption of metolachlor herbicide by MIL-53(Al) metal-organic framework: Optimization, validation and molecular docking simulation studies by Ahmad Isiyaka, H., Jumbri, K., Soraya Sambudi, N., Uba Zango, Z., Ain Fathihah Binti Abdullah, N., Saad, B.

“…Effective removal and optimization models of metolachlor (MET) adsorption was carried out using MIL-53(Al) metalâ��organic framework (MOF), response surface methodology (RSM), artificial neural network (ANN) and molecular docking simulation. …”
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Effective adsorption of metolachlor herbicide by MIL-53(Al) metal-organic framework: Optimization, validation and molecular docking simulation studies by Ahmad Isiyaka, H., Jumbri, K., Soraya Sambudi, N., Uba Zango, Z., Ain Fathihah Binti Abdullah, N., Saad, B.

“…Effective removal and optimization models of metolachlor (MET) adsorption was carried out using MIL-53(Al) metalâ��organic framework (MOF), response surface methodology (RSM), artificial neural network (ANN) and molecular docking simulation. …”
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Self-Organizing Network technique for resource allocation and mobility management in LTE femtocell network / Labeeb Mohsin Abdullah by Abdullah, Labeeb Mohsin

“…MATLAB and Vienna LTE simulators were used to conduct the experiments for the proposed algorithms, approaches and schemes and also used to verify the results……”
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On some methods of feature engineering useful for craniodental morphometrics of rats, shrews and kangaroos / Aneesha Pillay Balachandran Pillay by Aneesha Pillay , Balachandran Pillay

“…A comparative study based on machine learning algorithms was also conducted by using all features and the RFE-selected features to classify the R. rattus sample based on the age groups. …”
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Algorithms for moderating effect of emotional value from a cross-media data fusion perspective: a case study of Chinese dating reality shows by Zhang, Shasha, Dong, Qiming, Yasin, Megat Al Imran, Fang, Ng Chwee

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Optimizations and docking simulation study for metolachlor adsorption from water onto MIL-101(Cr) metal�organic framework by Isiyaka, H.A., Jumbri, K., Sambudi, N.S., Zango, Z.U., Abdullah, N.A.F.B., Saad, B.

“…The artificial neural network learning algorithm was employed to predict the adsorption of MET with high level of accuracy R2 0.999 and RMSE 0.047. …”
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Optimizations and docking simulation study for metolachlor adsorption from water onto MIL-101(Cr) metal�organic framework by Isiyaka, H.A., Jumbri, K., Sambudi, N.S., Zango, Z.U., Abdullah, N.A.F.B., Saad, B.

“…The artificial neural network learning algorithm was employed to predict the adsorption of MET with high level of accuracy R2 0.999 and RMSE 0.047. …”
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Optimizations and docking simulation study for metolachlor adsorption from water onto MIL-101(Cr) metal�organic framework by Isiyaka, H.A., Jumbri, K., Sambudi, N.S., Zango, Z.U., Abdullah, N.A.F.B., Saad, B.

“…The artificial neural network learning algorithm was employed to predict the adsorption of MET with high level of accuracy R2 0.999 and RMSE 0.047. …”
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Evolutionary cost-cognizant regression test case prioritization for object-oriented programs by Bello, AbdulKarim

“…Therefore, this study proposed a cost-cognizant TCP approach for object-oriented software that uses path-based integration testing to identify the possible execution path extracted from the Java System Dependence Graph (JSDG) model of the source code using forward slicing technique. …”
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