11 papers · ranked by Valyu relevance
Olaide N. Oyelade, Eric Aghiomesi Irunokhai, Hui Wang
There is a wide application of deep learning technique to unimodal medical image analysis with significant classification accuracy performance observed. However, real-world diagnosis of some chronic diseases such as breast cancer often require multimodal data streams with different modalities of visual and textual…
Dante Leiva, Benjamín Ramos-Tapia, Broderick Crawford, Ricardo Soto + 2 more
'Felipe Cisternas-Caneo' 'Ameer Hamza Khan'] The set-covering problem aims to find the smallest possible set of subsets that cover all the elements of a larger set. The difficulty of solving the set-covering problem increases as the number of elements and sets grows, making it a complex problem for which traditional…
Ayşe Beşkirli, Changsheng Zhang, Haitong Zhao
In this study, the pied kingfisher optimizer (PKO) algorithm is adapted to the uncapacitated facility location problem (UFLP), and its performance is evaluated. The PKO algorithm is binarized with fourteen different transfer functions (TF), and each variant is tested on a total of fifteen different Cap problems. In…
Broderick Crawford, Benjamín López Cortés, Felipe Cisternas-Caneo, José Manuel Gómez-Pulido + 7 more
Binarizing continuous metaheuristics to solve challenging NP-hard binary optimization problems is a fundamental step in adapting continuous algorithms for discrete domains. Binary optimization problems, such as the Set Covering Problem and the 0-1 Knapsack Problem, demand tailored approaches to efficiently explore and…
Broderick Crawford, Álex Paz, Ricardo Soto, Álvaro Peña Fritz + 7 more
Metaheuristics are a fundament pillar of Industry 4.0, as they allow for complex optimization problems to be solved by finding good solutions in a reasonable amount of computational time. One category of important problems in modern industry is that of binary problems, where decision variables can take values of zero…
Xu Yang, Hu He
The VLIW architecture can be exploited to greatly enhance instruction level parallelism, thus it can provide computation power and energy efficiency advantages, which satisfies the requirements of future sensor-based systems. However, as VLIW codes are mainly compiled statically, the performance of a VLIW processor is…
Nathan LeRoy, Caleigh Roleck
Codon optimization is a commonly used tool in many fields of scientific research to optimize the expression profile of recombinant proteins in a target organism. It seeks to swap synonymous codons in a recombinant gene to reflect the specific codon usage bias of the expression system. While many tools for codon…
Charlotte Merzbacher, Oisin Mac Aodha, Diego A. Oyarzún
Recent advances in synthetic biology have enabled the construction of molecular circuits that operate across multiple scales of cellular organization, such as gene regulation, signalling pathways and cellular metabolism. Computational optimization can effectively aid the design process, but current methods are…
Kazunori D Yamada
In the deep learning era, a gradient descent method is the most common method to optimize parameters of neural networks. Among various mathematical optimization methods, a gradient descent method is the most naive method. Although controlling a learning rate of the method is necessary for quick convergence, the…
Fabian Fröhlich, Peter K. Sorger
Ordinary differential equation (ODE) models are widely used to describe biochemical processes, since they effectively represent mass action kinetics. Optimization-based calibration of ODE models on experimental data can be challenging, even for low-dimensional problems. However, reliable model calibration is a…
Noam Teyssier, Alexander Dobin
Modern genomics produces billions of sequencing records per run, which are typically stored as gzip-compressed FASTQ files. While this format is widely used, it is not optimal for high-throughput processing due to its reliance on single-threaded decompression and sequential parsing of irregularly sized records. This…