To build and train our neural network model, we used Tensor Flow (GPU version 1.15)41, a Python deep learning toolkit. All network weights were initialized using the initialization19, and the biases were initialized to zero. To optimize the…
Category: 7. Maths
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A flexible Weibull geometric distribution with characterizations and its parameter estimation
In this section, the performance of the \(\:NWG\) distribution to a real data on remission time is illustrated in months of random samples of 128 patients with bladder cancer. The real dataset used in this section corresponds to remission times…
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Scorecard for synthetic medical data evaluation
When a measure becomes a target, it ceases to be a good measure.
– Goodhart’s Law
In the context of SMD, this law underscores the risks of focusing exclusively on a single measure, such as…
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MDNCT: a multi-domain neurocognitive transformer architecture approach for early prediction of autism spectrum disorders
Chiurazzi, P. et al. Genetic analysis of intellectual disability and autism. Acta Bio Med. Atenei Parm. 91, e2020003 (2020).
Google Scholar
Posserud, M., Skretting Solberg, B., Engeland, A., Haavik,…
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An ensemble deep learning model for author identification through multiple features
Data
In this research, two different data sets are used to evaluate the performance of the proposed author identification model. The first dataset includes the literary works of four different authors. Each author in this dataset has at least 100…
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Multiclass classification of thalassemia types using complete blood count and HPLC data with machine learning
The classifier model trainings were conducted on a 2017 MacBook Pro, Core i5, and high-performance graphics. It undergoes numerous tests to assess the best classifiers for various conditions such as alpha thalassemia major and minor, and beta…
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Optimization and benefit evaluation model of a cloud computing-based platform for power enterprises
Generation-side benefits encompass the cost savings and efficiency improvements realized throughout the power production process by employing optimized and advanced technological methods. This includes direct reductions in operational costs,…
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Machine learning in subsurface physical properties and lithofacies prediction in a mining context
Machine learning (ML) techniques are revolutionizing mineral exploration and exploitation by addressing critical challenges of sustainability, efficiency, and resource estimation in geologically complex settings1,2,3,4,5,6,7. This transformation…
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Dung beetle optimizer based on mean fitness distance balance and multi-strategy fusion for solving practical engineering problems
This study evaluates the performance of the MMDBO using 13 comparative algorithms and tests the MMDBO on 41 benchmark functions. The MMDBO’s performance is evaluated across forty-one benchmark functions, sourced from the CEC2017 and CEC2022…
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Instagram fake profile detection using an ensemble learning method
In this section, the workflow included processes related to data-retrieval, merging, pre-processing, and then using algorithms, which were especially important given the use of Instagram datasets. Furthermore, the underlying model is founded on…
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