Search Results - (( _ application drops algorithm ) OR ( some application learning algorithm ))*
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Cloud-based lightweight detection of hardhat compliance based on YOLOv5 in power construction site
Published 2025“…Therefore, this thesis explores and studies public hardhat datasets, deep learning algorithms, power Internet of Things (PIoT), and edge computing to address the above issues. …”
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Intelligent examination timetabling system using hybrid intelligent water drops algorithm
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Early detection of dengue disease using extreme learning machine
Published 2018“…The back propagation neural network is one of the popular machine learning technique that capable of learning some complex relationship and had been used in many applications. …”
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Multi-Backpropagation network
Published 2002“…The learning mechanism for Neural Network is its learning algorithm. …”
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Neighbour-based on-demand routing algorithms for mobile ad hoc networks
Published 2017“…In addition, with regard to the second algorithm SNBR, the results show that SNBR overcomes the NCPR algorithm terms of normalize routing overhead by 58.80% as its due to its dropping factor. …”
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A modified generalized RBF model with EM-based learning algorithm for medical applications
Published 2006“…An EM-based training algorithm is also introduced, which uses fewer parameters compared to some classical supervised learning methods. …”
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Three-term backpropagation algorithm for classification problem
Published 2006“…Standard Backpropagation Algorithm (BP) is a widely used algorithm in training Neural Network that is proven to be very successful in many diverse application. …”
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QoS evaluation of different TCPs congestion control algorithm using NS2
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Applying learning to filter text
Published 2005“…Text filtering has been a successful application especially in e-mail filtering. The use of probabilistic approaches such as naïve Bayes algorithm is the effective algorithms currently known for learning to filter or classify text document.Naïve Bayes algorithm is one of the algorithms in Machine Learning that manipulates probability estimation or reasoning about the observed data.The growing of bulk e-mail or known as spam e-mail becomes a threat to users’ privacy and network load and in the case of e -mail filtering,naïve Bayes classifier can be trained to automatically detect spam messages.Similar to the e-mail, forum application may be misused by the user to send bad messages and in some extent may offence other readers.Forum filtering may be less important compared to e-mail spam filtering; however there is a possibility of using naïve Bayes to learn the messages and automatically detect bad messages.Most of the forum application found in the web is applying keyword based text filtering which scan the words and change the detected words into certain representation.Instead of defining a set of keywords to filter the forum messages, this paper will explains the experiment in applying a learning to filter text especially in the educational and anonymous forum message, where there is no user registration required to submit messages.…”
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Particle swarm optimization for neural network learning enhancement
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Machine learning in botda fibre sensor for distributed temperature measurement
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On determination of input parameters of the mass transfer process by fuzzy approach.
Published 2005“…This is an extension of the work done on the mass transfer process of a single drop in single stage RDC column. The algorithm is based on fuzzy approach and the assumptions made in mass transfer process as adopted in previous work are also being used. …”
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