Simulation parameters. حسب Hend Bayoumi (22693738)

"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …"

Training losses for N = 10. حسب Hend Bayoumi (22693738)

"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …"

Normalized computation rate for N = 10. حسب Hend Bayoumi (22693738)

"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …"

Summary of Notations Used in this paper. حسب Hend Bayoumi (22693738)

"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …"

Microsoft excel file containing exhaustive list of Abbreviated-Injury Scale codes used to generate binary variables encoding the presence or absence of injury to various body regio... حسب Michael D. Cobler-Lichter (22492930)

The pseudocode related to the process of initial harmony generation, with considering SNP locus data. حسب Fariba Esmaeili (532865)

Secure MANET routing with blockchain-enhanced latent encoder coupled GANs and BEPO optimization حسب Sandeep Jagonda Patil (22048337)

DataSheet1_Optimal Sparsity Selection Based on an Information Criterion for Accurate Gene Regulatory Network Inference.PDF حسب Deniz Seçilmiş (13028616)

DataSheet1_Optimal Sparsity Selection Based on an Information Criterion for Accurate Gene Regulatory Network Inference.PDF حسب Deniz Seçilmiş (13028616)

A* Path-Finding Algorithm to Determine Cell Connections حسب Max Weng (22327159)

Fig 7 - حسب Olaide N. Oyelade (14047002)

DataSheet1_SS-RNN: A Strengthened Skip Algorithm for Data Classification Based on Recurrent Neural Networks.pdf حسب Wenjie Cao (780652)

Image_2_A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms.TIF حسب Qingqing Li (1505614)

"…First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. …"

Image_1_A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms.TIF حسب Qingqing Li (1505614)

"…First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. …"

Image_3_A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms.TIF حسب Qingqing Li (1505614)

"…First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. …"

Sampled population parameters and corresponding node-based metrics, node removal statistics and BRIDES results. حسب Cindy Bouchard (6844979)

الموضوعات:

Population graph of 19 wood turtle populations based on conditional genetic distance (cGD). حسب Cindy Bouchard (6844979)

الموضوعات:

MultiCRISPR-EGA: Optimizing Guide RNA Array Design for Multiplexed CRISPR Using the Elitist Genetic Algorithm حسب Yangyu Zhang (4609117)

"…Computational experiments on Escherichia coli gene targets demonstrate that the EGA can rapidly optimize multiplexed gRNA arrays, outperforming other intelligent optimization algorithms in CRISPR interference (CRISPRi) applications, while the GUI provides real-time design progress control and compatibility with various CRISPR-Cas systems. …"

Event-driven data flow processing. حسب Yixian Wen (12201388)

"…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …"

Different population subgraphs based on distinct node selection parameters using a proactive or reactive conservation approach. حسب Cindy Bouchard (6844979)

الموضوعات: