Search Results - (( its application learning algorithm ) OR ( phone application need algorithm ))
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Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks
Published 2025thesis::doctoral thesis -
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Incremental learning for large-scale stream data and its application to cybersecurity
Published 2015“…As many human currently depend on technologies to assist with daily tasks, there are more and more applications which have been developed to be fit in one small gadget such as smart phone and tablet. …”
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Healthy Lifestyle Management System
Published 2011“…The intelligent algorithm that used in this application is Rule-Based algorithm. …”
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Final Year Project -
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Text messaging encryption system
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Undergraduates Project Papers -
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Development of portable automatic number plate recognition system on android mobile phone
Published 2013“…Thus, the significance efforts are to find suitable and adaptive algorithm for number plate recognition under mobile phone. …”
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Proceeding Paper -
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Development of portable automatic number plate recognition system on android mobile phone
Published 2013“…Significance efforts are being explored to find suitable and adaptive algorithm for implementation of ANPR on mobile phone. …”
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Tobi-RC mobile application: Development of remote control car program over bluetooth system / Ain Amirah Hussin
Published 2016“…For example, mobile control can control other device via mobile phone just by using the application such as television, air conditioner, lamp and fan. …”
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Effect of chaos noise on the learning ability of back propagation algorithm in feed forward neural network
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Working Paper -
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A modified generalized RBF model with EM-based learning algorithm for medical applications
Published 2006“…Radial Basis Function (RBF) has been widely used in different fields, due to its fast learning and interpretability of its solution. …”
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Proceeding Paper -
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SLIDING WINDOW TRAINING ALGORITHMS USING MLP-NETWORK FOR CORRELATED AND LOST PACKET DATA
Published 2012“…This thesis gives a systematic investigation of various MLP learning mainly Sliding Window (SW) learning mode which is treated as the adaptation of offline algorithms into online application Consequently this thesis reviews various offline algorithms including: batch backpropagation, nonlinear conjugate gradient, limited memory and full-memory Broyden, Fletcher, Goldfarb and Shanno algorithms and different forms of the latest proposed bimary ensemble learning. …”
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Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection
Published 2020“…Two main improvements were included into the original SSA algorithm to alleviate its drawbacks and adapt it for feature selection problems. …”
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Encrypted mobile messaging application using AES block cipher cryptography algorithm / Nur Rasyiqah Zulkifli
Published 2019“…The programming language that is used to write the program is Java Language that will run on android phones. As the result, this project was tested on the functionality of the mobile messaging application. …”
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Student Project -
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Ensemble dual recursive learning algorithms for identifying flow with leakage
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Conference or Workshop Item -
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Active force control with iterative learning control algorithm for a vehicle suspension
Published 2013“…The research focuses on the application of an active force control (AFC) strategy with iterative learning control (ILC) algorithms to compensate for the various introduced road profiles or 'disturbances' in a quarter car suspension system as an improvement to ride comfort performance. …”
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Thesis -
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Learning Algorithm effect on Multilayer Feed Forward Artificial Neural Network performance in image coding
Published 2007“…The essence of this study is to investigate the most efficient and effective training methods for use in image compression and its subsequent applications. The obtained results show that the Quasi-Newton based algorithm has better performance as compared to the other two algorithms.…”
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Machine learning: tasks, modern day applications and challenges
Published 2019“…Machine learning algorithms learned from available data. Further, this learning laid the foundation to develop AI for the various systems around us. …”
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