Search Results - (( java implementation mead algorithm ) OR ( using code tree algorithm ))

Using modification of Prim's algorithm and GNU Octave and to solve the multiperiods installation problem by ,, Wamiliana, Usman, Mustofa, ,, Warsono, Daoud, Jamal Ibrahim

“…, and that name is chosen because it is like Unix but differs from Unix because it is free and contains no Unix code. Those algorithms were implemented using 300 randomly generated problems. …”
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Partial Binary Tree Network (Pbtn): A New Dynamic Element Matching (Dem) Approach To Current Steering Digital Analog Converter (Dac) by Teh , Choon Yan

“…In this research, a new DEM algorithm is proposed on Current-Steering DACs with Partial Binary Tree Network (PBTN) algorithm to overcome glitches transitions with low complexity. …”
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Cloud Worm Detection and Response Technique By Integrating The Enhanced Genetic Algorithm An Threat Level by Hasan Mahmoud Sha'ban Kanaker

WA VELET-DCT BASED IMAGE CODER FOR VIDEO CODING APPLICATIONS by GANI, REGINA

“…Some recommended areas to be delved for future work on video compression are distributed video coding, ridgelet transform and dual-tree complex wavelet transform.…”
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Optimal generator matrix G by Harun, Harlisya, Dimyati, Kaharudin, Ungku Chulan, Ungku Azmi Iskandar

“…Maximising the minimum determinant is equivalent to obtaining optimal generator matrix G. Instead of using state diagrams, optimal generator matrix G discussed in this paper is obtained using an improved algorithm which is based on Lisya tree structure. …”
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CUDA implementation of fractal image compression by Al Sideiri A., Alzeidi N., Al Hammoshi M., Chauhan M.S., AlFarsi G.

Current Steering Digital Analog Converter (DAC) Using Partial Binary Tree Network (PBTN) by Mohammad Alias, Mohd Azim

“…Previous research is able to increase the boundaries of Partial Binary Tree Network (PBTN) to 8-Bits. In this research, DEM algorithm is used, known as Partial Binary Tree Network (PBTN) that been proposed from previous research that aims to push the boundaries of past research from 8-bits to 10-bits. …”
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Algorithm optimization and low cost bit-serial architecture design for integer-pixel and sub-pixel motion estimation in H.264/AVC / Mohammad Reza Hosseiny Fatemi by Hosseiny Fatemi, Mohammad Reza

“…Regarding the IME of H.264/AVC, we introduce two low cost bit-serial architectures, which are based on full search (FS) algorithm due to its regularity and coding performance. …”
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Analysis & design low power multiplier using TSMC 0.18µm CMOS technology by Norsaifulrudin Mat Zuki

“…Low power multipliers are developed through minimizing switching activities of partial product using the radix 4 booth algorithm. Before computation for two input data, the one with a smaller effective dynamic range is processed to generate booth codes, thereby increasing the probability that the partial products become zero. …”
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Wearable based-sensor fall detection system using machine learning algorithm by Ishak, Anis Nadia, Habaebi, Mohamed Hadi, Yusoff, Siti Hajar, Islam, Md. Rafiqul

“…Then, a Machine Learning Algorithm (MLA) is used to train and test the data before a classifier is used to classify the new incoming dataset. …”
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Prediction of atmospheric carbon monoxide concentration utilizing different machine learning algorithms: A case study in Kuala Lumpur, Malaysia by Latif S.D., Almalayih M., Yafouz A., Ahmed A.N., Zaini N., Irwan D., AlDahoul N., Sherif M., El-Shafie A.

Do CEO and chairman characteristics affect green innovation? evidence from a comparative analysis of machine learning models by Xue, Ruixiang, Ong, Tze San, Demir, Ezgi

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Machine learning-based enhanced deep packet inspection for IP packet priority classification with differentiated services code point for advance network management by Khan, Fazeel Ahmed, Abubakar, Adamu

“…This study presents an approach to enhance intelligent packet forwarding priority classification on Differentiated Services Code Point (DSCP), leveraging classifiers from machine learning algorithms for Deep Packet Inspection (DPI). …”
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Morphology, molecular characterization and habitat preference of harmful Benthic dinoflagellate gambierdiscus (Dinophyceae) in Malaysia / Nurin Izzati Mustapa by Nurin Izzati , Mustapa

“…Morphological character states were coded and mapped onto the molecular phylogenetic tree (MP tree) to investigate the character state evolution of Gambierdiscus species. …”
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Development of a modified adaptive protection scheme using machine learning technique for fault classification in renewable energy penetrated transmission line by Olufemi, Osaji Emmanuel

“…The Random Tree standalone ML-AP relay model presented the best performing models from the ML-APS relay model with the best average performance for the correctly classified fault types of 97.61 % at 5 % significance level above other ML algorithms. …”
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Analysis of banana plant health using machine learning techniques by Thiagarajan, Joshva Devadas, Kulkarni, Siddharaj Vitthal, Jadhav, Shreyas Anil, Waghe, Ayush Ashish, Raja, S.P., Rajagopal, Sivakumar, Poddar, Harshit, Subramaniam, Shamala

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Discovering optimal features using static analysis and a genetic search based method for Android malware detection by Firdaus, Ahmad, Anuar, Nor Badrul, Karim, Ahmad, Razak, Mohd Faizal Ab

“…To evaluate the best features determined by GS, we used five machine learning classifiers, namely, Naïve Bayes (NB), functional trees (FT), J48, random forest (RF), and multilayer perceptron (MLP). …”
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XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection by Bouke, Mohamed Aly, Alramli, Omar Imhemed, Abdullah, Azizol

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Leveraging mechanomyography signal for quantitative muscle spasticity assessment of upper limb in neurological disorders using machine learning by Daud, Muhamad Aliff Imran, Ahmad Puzi, Asmarani, Sidek, Shahrul Na'im, Zainuddin, Ahmad Anwar, Mohd Khairuddin, Ismail, Abd Mutalib, Mohd Azri

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Discovering optimal features using static analysis and a genetic search based method for Android malware detection by Ahmad Firdaus, Zainal Abidin, Nor Badrul, Anuar, Ahmad, Karim, Mohd Faizal, Ab Razak

“…To evaluate the best features determined by GS, we used five machine learning classifiers, namely, Naïve Bayes (NB), Functional Trees (FT), J48, Random Forest (RF), and Multilayer Perceptron (MLP). …”
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