Search Results - (( data optimization modified algorithm ) OR ( data detection using algorithm ))
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1
Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi
Published 2019“…Based on the above components and circumstances, many studies have been performed on data clustering problems. Despite attempts to solve the data clustering issues, there are also many variants of modified algorithms in traditional information clustering that attempt to solve issues such as clustering algorithms based on condensation. …”
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2
Failure detection analysis of grid-connected photovoltaic systems in tropical climate region / Nurmalessa Muhammad @ Atan
Published 2020“…A modified threshold-based technique was used to build the FD algorithm based on data from PV system installed at GERC UiTM, Malaysia. …”
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3
An enhanced sequential exception technique for semantic-based text anomaly detection
Published 2019“…ESET performs text anomaly detection by employing optimized Cosine Similarity, hybridizing LSA with modified SET, and integrating it with Word Sense Disambiguation algorithms specifically Lesk and Selectional Preference. …”
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4
An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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5
Secured route optimization in mobile IPv6 wireless networks in terms of data integrity
Published 2008“…This algorithm is able to detect and prevent the attacker from modifying the data with using an encryption algorithm at a cost of a small increase in delay. …”
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6
Route optimization security in mobile IPv6 wireless networks: a test-bed experience
Published 2008“…This algorithm is able to detect and prevent the attacker from modifying the data with using an encryption algorithm by cost of little bit increase but tolerable delay. …”
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7
Security enhancement of route optimization in mobile IPv6 networks
Published 2008“…This algorithm is able to detect and prevent the attacker from modifying the data with using an encryption algorithm by cost of little bit increase but tolerable delay. …”
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8
Identifying diseases and diagnosis using machine learning
Published 2023“…For curing any disease it is very important to identify and detect that disease. For classify the disease classification algorithms are used. …”
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Enhanced Image Classification for Defect Detection on Solar Photovoltaic Modules
Published 2023“…The second algorithm uses K Nearest Neighbour using a ratio of training data and testing data of 95:05 resulting in an accuracy value of 62%. …”
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10
Multiple and solid data background scheme for testing static single cell faults on SRAM memories
Published 2013“…Therefore, the purpose of this thesis is to introduce a Data Background (DB) scheme to generate an optimal March Test Algorithm (MTA) for detecting faults of memory that are undetectable using existing algorithms. …”
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11
Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation
Published 2019“…Furthermore, to evaluate the efficiency of the proposed modified differential evolution for the training of ridgelet probabilistic neural network, four statistical search techniques, namely, particle swarm optimization, genetic algorithm, simulated angling, and classical differential evolution are used and their results are compared. …”
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12
Mental Stress Classification Among Higher Education Students In Malaysia From Electroencephalogram (Eeg) Using Convolutional Neural Network With Modified Stochastic Gradient D...
Published 2024“…The chosen algorithm, 1D-CNN, was modified using a tailored SGD optimizer that incorporates momentum and learning rate decay to improve convergence and address challenges like vanishing gradients. …”
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13
Parameter estimation of multicomponent transient signals using deconvolution and ARMA modelling techniques
Published 2003“…This approach also provides a graphical procedure for detecting and validating the number of exponential signals present in the data. …”
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14
Power System State Estimation In Large-Scale Networks
Published 2010“…Also the WLS algorithm is modified to include Unified Power Flow Controller (UPFC) parameters. …”
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15
A Subspace Approach for Extracting Signals Highly Corrupted by Colored Noise
Published 2010“…The simulation results produced by the post-modified SSA2 algorithm, show a higher degree of consistencies in detecting the VEP's P100, P200, and P300 peaks, in comparisons to the pre-modified SSA1 method. …”
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16
Secured dynamic request scheduling and optimal CSP selection for analyzing cloud service performance using intelligent approaches
Published 2023“…Five phases—Data User (DS) authentication, sensitivity-aware request scheduling, policy verification, trust management, and optimal CSP selection—are proposed. …”
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Video colorization using canny optimization technique / Irwin Kevin Mueson
Published 2016“…This is a hybrid technique from Colorization using Optimization, which was modified and improved the algorithms by adding Canny Edge Detection to improve the colorization process on the grayscale image or frames. …”
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18
Robust techniques for linear regression with multicollinearity and outliers
Published 2016“…The proposed method is formulated by incorporating robust MM-estimator and the modified generalized M-estimator (MGM) in the LRR algorithm. …”
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19
Modified Seird model: a novel system dynamics approach in modelling the spread of Covid-19 in Malaysia during the pre-vaccination period
Published 2023“…This study implemented the preliminary stage of forecasting the COVID-19 data using the proposed SEIRD model and highlighted the importance of parameter optimization. …”
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20
Optimized clustering with modified K-means algorithm
Published 2021“…Generally, the proposed modified k-means algorithm is able to determine the optimum number of clusters for huge data.…”
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