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161
Animal migration optimization algorithm: novel optimizer, analysis, and applications
Published 2024“…Optimization algorithms are applied to find the optimal solutions in many domains and fields such as image processing, machine learning, and others. …”
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162
CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children
Published 2023“…Soft voting and hard voting strategies are employed to ensemble these CNN models. The pre-trained DenseNet201 network is used for CNN feature extraction from each task-specific handwritten image data. …”
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163
CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children
Published 2023“…Soft voting and hard voting strategies are employed to ensemble these CNN models. The pre-trained DenseNet201 network is used for CNN feature extraction from each task-specific handwritten image data. …”
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164
An efficient approach for textual data classification using deep learning
Published 2022“…<p dir="ltr">Text categorization is an effective activity that can be accomplished using a variety of classification algorithms. In machine learning, the classifier is built by learning the features of categories from a set of preset training data. …”
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165
Data mining approach to predict student's selection of program majors
Published 2019“…The approach includes a methodology to manage data mining projects, sampling techniques to handle imbalanced data and multiclass data, a set of classification algorithms to predict and measures to evaluate performance of models. …”
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166
Use of Data Mining Techniques to Detect Fraud in Procurement Sector
Published 2022“…The method used in this research is a classification of models and algorithms used in data mining. All techniques also will be studied; they include clustering, tracking patterns, classifications and outlier detection. …”
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167
Blue collar laborers’ travel pattern recognition: Machine learning classifier approach
Published 2021“…Raw data preprocessing and outliers detection and filtering algorithms were applied at the first stage of the analysis, and consequently, an activity-based travel matrix was developed for each household. …”
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168
Type 2 Diabetes Mellitus Automated Risk Detection Based on UAE National Health Survey Data: A Framework for the Construction and Optimization of Binary Classification Machine Learn...
Published 2020“…LR with the reduced feature set using the intersection between CS and RFE proved to be the best model among the tested algorithms. This model can be used in a clinical setting as a decision support system or for public health awareness as an informal risk prediction system. …”
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169
A method for optimizing test bus assignment and sizing for system-on-a-chip
Published 2017“…Test access mechanism (TAM) is an important element of test access architectures for embedded cores and is responsible for on-chip test patterns transport from the source to the core under test to the sink. …”
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conferenceObject -
170
Assessment of static pile design methods and non-linear analysis of pile driving
Published 2006“…The pile/soil interaction system is described by a mass/spring/dashpot system where the properties of each component are derived from rigorous analytical solutions or finite element analysis. The outcome of this research is an algorithm that can be used to predict pile displacement and driving stresses. …”
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masterThesis -
171
Evolutionary algorithms for state justification in sequential automatic test pattern generation
Published 2005“…A common search operation in sequential Automatic Test Pattern Generation is to justify a desired state assignment on the sequential elements. State justification using deterministic algorithms is a difficult problem and is prone to many backtracks, which can lead to high execution times. …”
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172
Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier
Published 2023“…Earlier diagnosis of ASD from brain image is necessary for reducing the effect of disorder. …”
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173
Wild Blueberry Harvesting Losses Predicted with Selective Machine Learning Algorithms
Published 2022“…The LR model showed the foremost predictions of ground loss as compared to all the other models analyzed. …”
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174
A Novel Steganography Technique for Digital Images Using the Least Significant Bit Substitution Method
Published 2022“…The LSB substitution method can minimize the error rate in embedding process and can achieve greater reliability in criteria, using novel algorithm based on value difference. In this paper, we proposed a novel technique in steganography within the digital images such is RGB, Gray Scale, Texture, Aerial images to achieve higher security, imperceptibility, capacity, and robustness as compared with existing methods. …”
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175
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176
Decision-level fusion for single-view gait recognition with various carrying and clothing conditions
Published 2017“…Gait samples are fed into the MPCA and MPCALDA algorithms using a novel tensor-based form of the gait images. …”
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177
Microwave brain imaging system to detect brain tumor using metamaterial loaded stacked antenna array
Published 2022“…Later, a nine-antenna array-based microwave brain imaging (MBI) system is implemented and investigated by using phantom model. …”
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178
Customs Trade Facilitation and Compliance for Ecommerce using Blockchain and Data Mining
Published 2021“…Additionally, the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology is employed for modelling the two proposed clustering algorithms to identify transactional risks. …”
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179
Digital twin in energy industry: Proposed robust digital twin for power plant and other complex capital-intensive large engineering systems
Published 2022“…Furthermore, this paper demonstrates the advantages of the developed ADL algorithm approach and DSM prediction of the DT using vector autoregressive model for anomaly detection in utility gas turbines with data from an operational power plant.…”
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180
Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images
Published 2023“…Moreover, it is faster and requires fewer parameters to train than other CNN based models, making it a good choice for large-scale deployment in clinical settings and a promising tool for automated lung cancer diagnosis from CT scan images.…”