Unrolled Optimization via Physics-Assisted Convolutional Neural Network for MR-Based Electrical Properties Tomography: A Numerical Investigation by Sabrina Zumbo, Stefano Mandija, Ettore F. Meliado, Peter Stijnman, Thierry G. Meerbothe, Cornelis A.T. van den Berg, Tommaso Isernia, Martina T. Bevacqua

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Golden eagle optimized CONV-LSTM and non-negativity-constrained autoencoder to support spatial and temporal features in cancer drug response prediction by Wesam Ibrahim Hajim, Suhaila Zainudin, Kauthar Mohd Daud, Khattab Alheeti

“…This article aims to apply the Non-Negativity-Constrained Auto Encoder (NNCAE) network to tackle these issues, enhance the adaptive search for the optimal size of sliding windows, and ensure that deep network architectures are adept at learning the vital hidden features. …”
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Memetic Salp Swarm Algorithm for economic load dispatch problems by Mohammed A. Awadallah, Mohammed Azmi Al-Betar, Malik Braik, Raed Abu Zitar, Khaled Assaleh, Mahmud Alkoffash, Qusai Yousef Shambour

“…The Adaptive $$\beta$$ -hill climbing optimizer (A $$\beta$$ HCO) ais hybridized with a newly developed local search method with SSA as a new operator. …”
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Escherichia coli Nissle 1917 efficiently expresses the RBD domain of SARS-CoV-2 spike protein without codon optimization by Shanmugaraja Meenakshi, R. Suvetha, S. Ramadevi

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Performance analysis of a filtering variational quantum algorithm by Gabriel Marin-Sanchez, David Amaro

“…The ATSP encoding employed reduces the number of qubits and avoids the need of constraints compared to the standard quadratic unconstrained binary optimization/Ising model. …”
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Optimized genomic editing of a common Duchenne muscular dystrophy mutation in patient-derived muscle cells and a new humanized mouse model by Mateusz Z. Durbacz, Yu Zhang, Hui Li, Takahiko Nishiyama, Efrain Sanchez- Ortiz, John R. McAnally, Damir Alzhanov, Ning Liu, Eric N. Olson

“…Duchenne muscular dystrophy (DMD) is a fatal X-linked, recessive disease caused by mutations in the DMD gene encoding dystrophin, a membrane-associated protein necessary for maintaining muscle structure and function. …”
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Research on dynamic prediction and optimization of high altitude photovoltaic power generation efficiency using GVSAO-CNN Model under 8-climate modes by Xiaoming Xiong, Heng Hu, Qiangfu Jia, Rongjian Zhang, Chongan Huang, Qingyuan Lu

“…The GVSAO algorithm is a sophisticated optimization technique that fine-tunes the hyperparameters of CNNs. …”
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Reinforcement Learning for Efficient Drone-Assisted Vehicle Routing by Aigerim Bogyrbayeva, Bissenbay Dauletbayev, Meraryslan Meraliyev

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Prediction of Lithium-Ion Battery State of Health Using a Deep Hybrid Kernel Extreme Learning Machine Optimized by the Improved Black-Winged Kite Algorithm by Juncheng Fu, Zhengxiang Song, Jinhao Meng, Chunling Wu

“…Next, to tackle the challenge of parameter selection for DHKELM, an optimal point set strategy, the Gompertz growth model, and a Levy flight strategy are employed to optimize the parameters of DHKELM using IBKA before model training. …”
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An Optimized Transformer–GAN–AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets by Ahmad Salehiyan, Pardis Sadatian Moghaddam, Masoud Kaveh

“…Extensive experiments are conducted to assess the model’s performance compared to several state-of-the-art techniques, including standard GAN, convolutional neural network (CNN), deep belief network (DBN), time-series transformer (TST), bidirectional encoder representations from transformers (BERT), and extreme gradient boosting (XGBoost). …”
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A deep learning model to predict dose distributions for breast cancer radiotherapy by Xiaorong Hou, Weishi Cheng, Jing Shen, Hui Guan, Yimeng Zhang, Lu Bai, Shaobin Wang, Zhikai Liu

“…The total dose error of our model was below 1.16 Gy, complying with clinical usage standards. When compared to other exceptional models, our model optimally predicted eight out of nine regions, and the prediction errors for the first planning target volume (PTV1) and PTV2 were merely 1.03 Gy and 0.74 Gy. …”
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Integrating autoencoder and decision tree models for enhanced energy consumption forecasting in microgrids: A meteorological data-driven approach in Djibouti by Fathi Farah Fadoul, Abdoulaziz Ahmed Hassan, Ramazan Çağlar

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Impact of Network Complexity on the Computational Performance of Ising Machines by Seokmin Hong

“…Among various network topologies, the success probability of finding optimal solutions using standard simulated annealing generally decreases with the addition of nodes, as expected, and several orders of magnitude more Markov Chain Monte Carlo (MCMC) samples are required to achieve comparable performance in larger networks. …”
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Panoramic video coding based on spherical distortion with spatio-temporal dependency by Xu YANG, Ce ZHU, Hongwei GUO, Lei LUO

“…The planar coding distortion, which affects objective quality, and spherical distortion, which affects subjective quality, as well as existing independent rate-distortion optimization techniques that fail to consider the temporal propagation of spherical distortion and its impact on coding performance, result in coding performance degradation.To address these issues, a spatio-temporal dependent spherical distortion model was proposed for optimizing panoramic video coding.Firstly, a spatial mapping model was proposed to map spherical distortions to coding distortions, aiming to align subjective and objective quality assessments.Secondly, a temporal propagation model for spherical distortions was introduced to enhance the overall coding performance of all coding units in the propagation chain.Finally, the coding parameters were adjusted by computing the weights for spatial mapping and temporal propagation of spherical distortions.Experimental results demonstrate that, under low-delay encoding configurations, compared to the VTM14.0, a state-of-the-art video coding benchmark, the proposed algorithm achieves an average bitrate savings of 7.4% (up to 22.1%) and reduction in coding time of 9%.…”
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Task Scheduling of Multiple Humanoid Robot Manipulators by Using Symbolic Control by Mete Özbaltan, Nihan Özbaltan, Hazal Su Bıçakcı Yeşilkaya, Murat Demir, Cihat Şeker, Merve Yıldırım

“…The control objectives encompass the fundamental behaviors of the system, strict rules, and mutual exclusions over shared resources, categorized as the safety property, whereas the optimization objectives are directed toward maximizing the throughput of robot-processed products with optimal efficiency. …”
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Deep Reinforcement Learning Algorithm Based on Graph Weight Multi-Pointer Network for Solving Multiobjective Traveling Salesman Problem by Xiaoyu Fu, Shenshen Gu

“…The multiobjective traveling salesman problem (MOTSP) is a class of classical multiobjective combinatorial optimization problems (MOCOPs), attracting significant interest across various disciplines. …”
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V2Coder: A Non-Autoregressive Vocoder Based on Hierarchical Variational Autoencoders by Takato Fujimoto, Kei Hashimoto, Yoshihiko Nankaku, Keiichi Tokuda

“…VAEs tend to suffer from a phenomenon known as posterior collapse, in which little data information is encoded in the latent variable. To address this problem, we introduce a carefully designed architecture and skip loss that encourage encoding to latent variables in deep layers. …”
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Priority-aware task offloading for LEO satellite edge computing network: a multi-agent deep reinforcement learning-based approach by Juan Chen, Jie Zhong, Zongling Wu, Di Tian, Yujie Chen

“…Furthermore, the agent design adopts an encoder-decoder architecture, combined with self-attention (SA) mechanisms and temporal convolutional network (TCN) technology to extract environmental features, achieving real-time optimization of task scheduling. …”
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Secure Cooperative Dual-RIS-Aided V2V Communication: An Evolutionary Transformer–GRU Framework for Secrecy Rate Maximization in Vehicular Networks by Elnaz Bashir, Francisco Hernando-Gallego, Diego Martín, Farzaneh Shoushtari

“…Extensive simulations demonstrate the superiority of the proposed framework compared to existing baselines, such as transformer, bidirectional encoder representations from transformers (BERT), deep reinforcement learning (DRL), long short-term memory (LSTM), and GRU models. …”
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Illumination-adaptative granularity progressive multimodal image fusion method by Chuanyun WANG, Dongdong SUN, Mingqi ZHOU, Tian WANG, Qian GAO, Zhaokui LI

“…This module leverages a pretrained image encoder to model the scene, generating scene vectors that are processed through various linear layers. …”
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