Search Results - (( using implementation svm algorithm ) OR ( java optimization based algorithm ))

Optimization of blood vessel detection in retina images using multithreading and native code for portable devices by Tran, Duc Ngoc, Hussin, Fawnizu Azmadi, Yusoff, Mohd Zuki

“…The optimization of a computationally intensive algorithm such as this on a mobile platform is challenging due to the limited resources available. …”
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Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

“…Hence, the objective of this research is to propose suitable and optimize algorithm for ANPR system on Android mobile phone. …”
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Classification of basal stem rot disease in oil palm using dielectric spectroscopy by Al-Khaled, Al-Fadhl Yahya Khaled

“…Without implementing any data reduction algorithm, the highest classification accuracy was found in SVM classifier with 79.55%. …”
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Implementation of Space Vector Modulation for Voltage Source Inverter by Sanusi, Syamim, Ibrahim, Zulkifli, Jidin , Auzani, JOPRI, MOHD HATTA, Abdul Karim, Kasrul, Othman, Md Nazri

“…This paper presents a development of a voltage source inverter (VSI) for electrical drive applications based on Space Vector Modulation (SVM) technique and the SVM algorithm is implemented using digital signal processor (DSP). …”
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

“…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters by Teguh, Sutanto, Muhammad Rafli, Aditya, Haldi, Budiman, M.Rezqy, Noor Ridha, Usman, Syapotro, Noor, Azijah

“…The purpose of this study is to evaluate and compare the performance of these algorithms in terms of accuracy. The methodology used includes data collection, preprocessing, and algorithm implementation with evaluation using crossvalidation techniques. …”
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Implementation Of SVM For Cascaded H-Bridge Multilevel Inverters Utilizing FPGA by Al-Jewari, Maher Abd Ibrahim

“…However, the use of multilevel inverters associated with SVM by using Digital Signal Processor (DSP) increases the complexity of control algorithm or computational burden and hence produces larger value of sampling time. …”
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An Arabic hadith text classification model using convolutional neural network and support vector machine / Mohd Irwan Mazlin by Mazlin, Mohd Irwan

“…In this study, the CNN-SVM model predicts all correct when using unseen data. …”
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Support Vector Machines (SVM) in Test Extraction by Ghazali, Nadirah

“…In this project, Support Vector Machines (SVM) is studied and experimented by the implementation ofa textual extractor. …”
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Support Vector Machines (SVM) in Test Extraction by Ghazali, Nadirah

“…In this project, Support Vector Machines (SVM) is studied and experimented by the implementation ofa textual extractor. …”
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Thematic textual hadith classification: an experiment in rapidminer using support vector machine (SVM) and naïve bayes algorithm by Yusof, Norzihani, Rosidi, Siti Aishah Rosidi, Ibrahim, Nuzulha Khilwani Ibrahim, Ahmed Ali, Ahmed El-Mogtaba Bannga

“…The classification for this thematic Hadith dataset is implemented using Rapidminer, a machine learning tool using Naïve Bayes and Support Vector Machine (SVM) methods. …”
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Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

“…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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Lightning fault classification for transmission line using support vector machine by Asman, Saidatul Habsah, Ab Aziz, Nur Fadilah, Ab Kadir, Mohd Zainal Abidin, Ungku Amirulddin, Ungku Anisa, Roslan, Nurzanariah, Elsanabary, Ahmed

“…The classification performance of the developed algorithms was evaluated using confusion matrix. Overall, SVM algorithm performed better than k-NN in terms of classification accuracy, achieving a value of 97.10% compared to k-NN’s 70.60%. …”
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Prediction of COVID-19 outbreak using Support Vector Machine / Muhammad Qayyum Mohd Azman by Mohd Azman, Muhammad Qayyum

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A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island by Mohd Shafri, Helmi Zulhaidi, Ramle, F. S. H.

“…The study indicates that the classification accuracy of SVM algorithm was better than DT algorithm. The overall accuracy of the SVM using four kernel types was above 73% and the overall accuracy of the DT method was 69%. …”
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Oil palm mapping over Peninsular Malaysia using Google Earth Engine and machine learning algorithms by Shaharum, Nur Shafira Nisa, Mohd Shafri, Helmi Zulhaidi, Wan Ab. Karim Ghani, Wan Azlina, Samsatli, Sheila, Al-Habshi, Mohammed Mustafa, Yusuf, Badronnisa

“…However, RF extracted oil palm information better than the SVM. The algorithms were compared and the McNemar's test showed significant values for comparisons between SVM and CART and RF and CART. …”
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Gender identification using support vector machines by Nur Ayuni Binti Jalaluddin

“…This thesis is introduces SVM theory application and its algorithmic implementations.…”

Implementation of welch pre-processing in SVM algorithm for improved accuracy on EEG data by Hariyady Hariyady, Ag Asri Ag Ibrahim, Jason Teo, Muhammad Balya Firjaun Barlaman, Muhammad Aulanas Bitaqwa, Azhana Ahmad, Fouziah Md Yassin, Carolyn Salimun, Ng, Giap Weng

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Lightning Fault Classification for Transmission Line Using Support Vector Machine by Asman S.H., Aziz N.F.A., Kadir M.Z.A.A., Amirulddin U.A.U., Roslan N., Elsanabary A.

“…The classification performance of the developed algorithms was evaluated using confusion matrix. Overall, SVM algorithm performed better than k-NN in terms of classification accuracy, achieving a value of 97.10% compared to k-NN's 70.60%. …”

Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification by Myaser, Jwan

“…Nevertheless, AC is not required for LCM if the original multi-spectral image is used. The last phase involves developing a new fusion algorithm using SVM and Fuzzy K-Means Clustering (FKM) algorithms for Sentinel 2 data to enhance LCM accuracy. …”
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