Sensitivity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Specificity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Fitness values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Number of features selected for IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Classification accuracy values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

F-score values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Optimal ALARM BN discovered by CausNet. by Nand Sharma (21519845)

Subjects:

Proposed Algorithm. by 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. …”

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”

Optimal ovarian cancer BNs learned by CausNet and CausNet-partial. by Nand Sharma (21519845)

Subjects:

Comparisons between ADAM and NADAM optimizers. by 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. …”

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”

Algorithm 2: Maintain inhibitory densities coherence. by Dimitri Rodarie (10148838)

Algorithm 1: Cap inhibitory densities to number of neurons. by Dimitri Rodarie (10148838)

List of ageing-associated genes (STRING identifier and corresponding gene symbol) within imputed genes based on the JenAge Ageing Factor Database for each tissue (brain and liver)... by Konstantina Charmpi (9901053)

Network propagation identifies ageing-associated genes as well as genes distinguishing prostate tumors of different grades. by Konstantina Charmpi (9901053)

Table 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf by Valentina Di Salvatore (6296114)

“…<p>Recent concerns about off-target immune activation following non-targeted mRNA vaccine delivery have prompted the need for rational design strategies that optimize nanoparticle formulations. Building upon our previous in silico work using the Universal Immune System Simulator to characterize immune responses to mRNA vaccines, we present a computational framework that integrates synthetic transcriptomics with artificial intelligence-driven optimization to guide the development of safer and more targeted lipid nanoparticles. …”

Presentation 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf by Valentina Di Salvatore (6296114)

“…<p>Recent concerns about off-target immune activation following non-targeted mRNA vaccine delivery have prompted the need for rational design strategies that optimize nanoparticle formulations. Building upon our previous in silico work using the Universal Immune System Simulator to characterize immune responses to mRNA vaccines, we present a computational framework that integrates synthetic transcriptomics with artificial intelligence-driven optimization to guide the development of safer and more targeted lipid nanoparticles. …”

Table 2_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf by Valentina Di Salvatore (6296114)

“…<p>Recent concerns about off-target immune activation following non-targeted mRNA vaccine delivery have prompted the need for rational design strategies that optimize nanoparticle formulations. Building upon our previous in silico work using the Universal Immune System Simulator to characterize immune responses to mRNA vaccines, we present a computational framework that integrates synthetic transcriptomics with artificial intelligence-driven optimization to guide the development of safer and more targeted lipid nanoparticles. …”