Search Results - (( evolution utilization modified algorithm ) OR ( java applications tree algorithm ))
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Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation
Published 2019“…In order to train Ridgelet probabilistic neural network, a modified differential evolution algorithm with new mutation phase, crossover process, and selection mechanism is introduced. …”
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Fair scheduling algorithm in LTE-advanced networks
Published 2015“…The results demonstrate that the effectiveness of enhanced Modified Largest Weighted Delay First (M-LWDF) algorithm in improving throughput.…”
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An FPGA implementation of exp-bet scheduling algorithm in LTE networks / Yusmardiah Yusuf
Published 2017“…It is performed by the scheduling algorithm implemented in the Long Term Evolution (LTE) base station, Evolved Node B. …”
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Self-configured link adaptation using channel quality indicator-modulation and coding scheme mapping with partial feedback for green long-term evolution cellular systems
Published 2015“…In this thesis, an analytical model that represents the tradeoff between energy efficiency (EE), spectral efficiency (SE), and transmission delay in both green and conventional Long-Term Evolution (LTE) eNodeB is formulated. Consequently, a modified downlink scheduler based on a Packet Prediction Mechanism (PPM) is conducted at the eNodeB. …”
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization
Published 2018“…M-MOPSO is compared with four other algorithms namely, MOPSO, Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Evolutionary Algorithm based on Decompositions (MOEA/D) and Multi-Objective Differential Evolution (MODE). …”
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A novel scheduling algorithm based on game theory and multicriteria decision making in LTE network
Published 2015“…However, 3GPP is not a standardized scheduling algorithm to utilize LTE properties in smart grid applications. …”
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Design And Development Of Modified-Proportional Fair Scheduler For LTE/LTE-Advanced
Published 2016“…The main contribution of this paper is the design of a new scheduling scheme and its performance is compared with the Proportional Fair (PF) and Round Robin (RR) downlink schedulers for LTE by utilizing LTE Downlink System Level Simulator. The proposed new scheduling algorithm, namely the Modified-PF scheduler divides a single subframe into multiple time slots and allocates the resource block (RB) to the targeted User Equipment (UE) in all time slots for each subframe based on the instantaneous Channel Quality Indicator (CQI) feedback received from UEs. …”
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Mining Sequential Patterns using I-PrefixSpan
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde
Published 2022“…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
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Enhancing the QoS performance for mobile station over LTE and WiMAX networks / Mhd Nour Hindia
Published 2015“…The results obtained show that in terms of handover issues, a modified multi criteria with two threshold algorithm increases the efficiency of target network selection. …”
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Mining Sequential Patterns Using I-PrefixSpan
Published 2007“…In this paper, we propose an improvement of pattern growth-based PrefixSpan algorithm, called I-PrefixSpan. The general idea of I-PrefixSpan is to use the efficient data structure for general tree-like framework and separator database to reduce the execution time and memory usage. …”
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Islanding detection method using ridgelet probabilistic neural network in distributed generation
Published 2019“…A modified differential evolution (MDF) algorithm with a new mutation phase, crossover process, and selection mechanism is proposed to train the RPNN. …”
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Optimization of chemotherapy using metaheuristic optimization algorithms / Prakas Gopal Samy
Published 2024“…The HM emerges as a dominant strategy, driven by the Multi-Objective Differential Evolution (MODE) algorithm under literature-based control parameter settings for the mathematical model. …”
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AI powered asthma prediction towards treatment formulation: an android app approach
Published 2022“…We utilized eight robust machine learning algorithms to analyze this dataset. We found that the Decision tree classifier had the best performance, out of the eight algorithms, with an accuracy of 87%. …”
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Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals
Published 2020“…The second method is called the Modified Binary Tree Growth Algorithm (MBTGA) that applies swap, crossover, and mutation operators. …”
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Design And Analysis Of Modified-Proportional Fair Scheduler For LTELTE-Advanced
Published 2016“…The proposed new scheduling algorithm, namely the Modified-PF scheduler, divides a single sub-frame into multiple time slots and allocates the resource block (RB) to the targeted UE in all time slots for each sub-frame based on the instantaneous Channel Quality Indicator (CQI) feedback received from UEs. …”
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AI powered asthma prediction towards treatment formulation : An android app approach
Published 2022“…We utilized eight robust machine learning algorithms to analyze this dataset. We found that the Decision tree classifier had the best performance, out of the eight algorithms, with an accuracy of 87%. …”
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Numerical simulation and experimental verification on distortions induced by wire-arc additive manufacturing components and costing analysis / Keval Priapratama Prajadhiana
Published 2024“…Once the substrate has been removed, the component distortion comparison is examined by means of modified substrate removal algorithm where the error percentage of the models ranges from 2.96% until 8.05%. …”
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