A Two-Stage Bin Packing Algorithm for Minimizing Machines and Operators in Cyclic Production Systems by Yossi Hadad, Baruch Keren

“…This study presents a novel, two-stage algorithm that minimizes the number of machines and operators required to produce multiple product types repeatedly in cyclic scheduling. Our algorithm treats the problem of minimum machines as a bin packing problem (BPP), and the problem of determining the number of operators required is also modeled as the BPP, but with constraints. …”
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Adaptive Anomaly Detection in Network Flows With Low-Rank Tensor Decompositions and Deep Unrolling by Lukas Schynol, Marius Pesavento

“…We apply deep unrolling to derive a novel deep network architecture based on our proposed algorithm, treating the regularization parameters as learnable weights. …”
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Green vehicle routing optimization based on dynamic constraint selection co-evolutionary algorithm by Lu-jie Zhou, Hai-fei Zhang, Jun-hao Fu

“…Second, a constrained multi-objective evolutionary algorithm based on the co-evolutionary framework was proposed for model solving. The algorithm treats the complete problem model as a complex task and introduces a shift crowding distance calculation that considers both individual distribution and convergence information when solving this complex task, effectively balancing the convergence and diversity of solutions. …”
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ICSO: A Novel Hybrid Evolutionary Approach with Crisscross and Perturbation Mechanisms for Optimizing Generative Adversarial Network Latent Space by Zhihui Chen, Ting Lan, Zhanchuan Cai, Zonglin Liu, Renzhang Chen

“…This paper proposes a novel improved crisscross optimization (ICSO) algorithm, a hybrid evolutionary approach that integrates crisscross optimization and perturbation mechanisms to find the suitable latent vector. The ICSO algorithm treats the quality and diversity as separate objectives, balancing them through a normalization strategy, while a gradient regularization term (i.e., GP) is introduced into the discriminator’s objective function to stabilize training and mitigate gradient-related issues. …”
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Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning by Ilya Larin, Alexander Karabelsky

“…., the normalized cuts algorithm treated segmentation as a graph partitioning problem—but only recently have such ideas merged with deep learning in an unsupervised manner. …”
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Strategies to alleviate flickering: Bayesian and smoothing methods for deep learning classification in video by Noah Miller, Glen Ryan Drumm, Lance Champagne, Bruce Cox, Trevor Bihl

“…Design/methodology/approach – This “flickering” behavior often results from CV algorithms treating successive observations as independent, which ignores the dependence inherent in most videos. …”
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Comparative Analysis of Machine Learning Algorithms and Statistical Techniques for Data Analysis in Crop Growth Monitoring with NDVI by M. Arunachalam, S. Sekar, A. M. Erdmann, V. V. Sajith Variyar, R. Sivanpillai

“…Affinity Propagation (AP) identifies the number of clusters by considering all data points as potential exemplars and iteratively refine the set, while Gaussian Mixture Model (GMM) algorithm treats the data as a mixture of several Gaussian distributions, allowing for flexible cluster shapes. …”
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An Asynchronous Training-Free SSVEP-BCI Detection Algorithm for Non-Equal Prior Probability Scenarios by Junsong Wang, Yuntian Cui, Hongxin Zhang, Haolin Wu, Chen Yang

“…Most of the existing steady-state visual evoked potential (SSVEP) detection algorithms treat the prior probability of each alternative target being selected as equal. …”
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