15 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…
Ghaith Manita, Ouajdi Korbaa
DNA Microarray technology is an emergent field, which offers the possibility of obtaining simultaneous estimates of the expression levels of several thousand genes in an organism in a single experiment. One of the most significant challenges in this research field is to select high relevant genes from gene expression…
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, 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…
Jian Zhu, Jianhua Liu, Yuxiang Chen, Xingsi Xue + 4 more
'Heming Jia' 'Laith Abualigah' 'Xuewen Xia'] Restructuring Particle Swarm Optimization (RPSO) algorithm has been developed as an intelligent approach based on the linear system theory of particle swarm optimization (PSO). It streamlines the flow of the PSO algorithm, specifically targeting continuous optimization…
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…
Sha-sha Guo, Jie-sheng Wang, Meng-wei Guo
Particle swarm optimization (PSO) algorithm is a swarm intelligent searching algorithm based on population that simulates the social behavior of birds, bees, or fish groups. The discrete binary particle swarm optimization (BPSO) algorithm maps the continuous search space to a binary space through a new transfer…
Olaide N. Oyelade, Jeffrey O. Agushaka, Absalom E. Ezugwu, Aytaç Altan
'Aytaç Altan'] Feature selection problem represents the field of study that requires approximate algorithms to identify discriminative and optimally combined features. The evaluation and suitability of these selected features are often analyzed using classifiers. These features are locked with data increasingly being…
Reham Kamal, Eman Amin, Diaa Salama AbdElminaam, Rasha Ismail
Feature selection is a key step in machine learning-based decision systems, especially in medical and biomedical applications, where datasets often contain a large number of features that can negatively affect both accuracy and interpretability. In this study, we introduce the binary secretary bird optimization…
José Lemus-Romani, Broderick Crawford, Felipe Cisternas-Caneo, Ricardo Soto + 2 more
'Ricardo Soto' 'Marcelo Becerra-Rozas' 'Ameer Hamza Khan'] In this work, an approach is proposed to solve binary combinatorial problems using continuous metaheuristics. It focuses on the importance of binarization in the optimization process, as it can have a significant impact on the performance of the algorithm.…
Łukasz Grodzki, Mateusz Slysz, Grzegorz Waligóra, Zhian Jia + 1 more
Quantum computing offers new possibilities for solving combinatorial optimization problems with rapidly growing search spaces. Among emerging hardware platforms, photonic quantum computers based on boson sampling provide a promising approach for sampling-based optimization methods. In this work, we investigate the…
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…
Kentaro Inoue, Kazuhiro Maeda, Yuki Kato, Shinpei Tonami + 2 more
'Shogo Takagi' 'Hiroyuki Kurata'] Computer simulation has been an important technique to capture the dynamics of biochemical networks. In most networks, however, few kinetic parameters have been measured in vivo because of experimental complexity. We develop a kinetic parameter estimation system, named the CADLIVE…
Shuvendu K. Lahiri, Chao Wang, Elvira Albert, Pablo Gordillo + 2 more
With the advent of smart contracts that execute on the blockchain ecosystem, a new mode of reasoning is required for developers that must pay meticulous attention to the gas spent by their smart contracts, as well as for optimization tools that must be capable of effectively reducing the gas required by the smart…