Search Results - (( java application customization algorithm ) OR ( network implementation re algorithm ))
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Removal of heavy metals from water by functionalized carbon nanotubes with deep eutectic solvents: An artificial neural network approach / Seef Saadi Fiyadh
Published 2019“…The best result achieved for Pb2+ removal using ANFIS algorithm is with RE 7.078%. For As3+ removal using different adsorbents, two algorithms were applied for the modelling, the feed-forward back-propagation maximum RE achieved is 5.97% while, the NARX algorithm achieved better accuracy with maximum RE of 5.79%. …”
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Thesis -
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TDMA scheduling analysis of energy consumption for iot wireless sensor network
Published 2019“…Finally, represent the difference between experimental results and the old results of E-T-DRAND will determine if the algorithm has been efficiently re-implemented.…”
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Traffic management algorithms for LEO satellite networks
Published 2016“…In order to evaluate the proposed algorithm, a series of experiments to implement Discrete Event Simulation (DES) of a LEO satellite network by using ns-2 and C/C++ are conducted. …”
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An ensemble of neural network and modified grey wolf optimizer for stock prediction
Published 2019“…The research implements stock prediction analysis as a case study for training the neural network by adopting MGWO algorithm. …”
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An Enhanced Simulated Annealing Routing Algorithm for Semi-Diagonal Torus Network
Published 2017“…This network is both symmetric and regular; thus, make it very beneficial in the implementation process. …”
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Intelligent motion planning of a mobile robot by using convolutional neural network / Siti Asmah Abdullah
Published 2019“…The intelligence of the mobile robot is imitating CNN where the network consists of Convolutional Layer, Normalization Layer, ReLU layer, Fullyconnected Layer, Softmax Layer and the last layer is Classification Layer. …”
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Hybrid metaheuristic method for clustering in wireless sensor networks / Bryan Raj Peter Jabaraj
Published 2023“…Moreover, two improvised clustering techniques are introduced to reduce the energy overhead cost from the re-clustering process. The performance of aHSSOGA is evaluated based on average residual energy, network lifetime, total re-clustering occurrence, total data delivery, network throughput and end-to-end delay metrics. …”
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Evaluation of MAC's distributed queuing algorithm for Internet of things applications
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Outdoor marker-less tracking and pose calculating system for mobile augmented reality
Published 2014“…This research investigates and re-designs the “speed up robust feature” (SURF) algorithm for Smartphones to enable it to recognize the real world objects. …”
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Adaptive artificial neural network for power system security assessment and control action
Published 2012“…The objective of this work is to introduce new algorithms that can enhance power system security inclusive of the remedial action (generation re-dispatch/load shedding) for any scale of power system operation as well as improving the existing ANN to solve the problem faced by power system security assessment. …”
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Enhancing obfuscation technique for protecting source code against software reverse engineering
Published 2019“…The proposed technique can be enhanced in the future to protect games applications and mobile applications that are developed by java; it can improve the software development industry. …”
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Simulation of quantum growing network / Ong Boon Teong
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Design & Development of a Robotic System Using LEGO Mindstorm
Published 2006“…Since the model is built using LEGO bricks, the model is fully customized, in term of its applications, to perform any relevant tasks. …”
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Conference or Workshop Item -
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Query proof structure caching for incremental evaluation of tabled prolog programs / Taher Muhammad Ali
Published 2013“…The thesis presents the design, necessary data structures, algorithms and implementation details of JLOG. The results of comparing the performance of JLOG to regular PROLOG and Tabled PROLOG systems are also presented.…”
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State-of-charge estimation for lithium-ion batteries with optimized self-supervised transformer deep learning model
Published 2023“…In the second stage, the model is fine-tuned or re-trained using labeled data with supervised learning. …”
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