Comparison between ILSTM and machine learning-based algorithms in binary classification. by Asmaa Ahmed Awad (16726315)

Comparison between ILSTM and deep learning-based algorithms in binary classification. by Asmaa Ahmed Awad (16726315)

MSE for ILSTM algorithm in binary classification. by Asmaa Ahmed Awad (16726315)

“…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”

Several loop patterns found in binary code. by Alba Pedro-Zapater (8600382)

<i>Patient 1</i> GA-optimized model error. by Dmitrii Smirnov (8822324)

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<i>Patient 8</i> GA-optimized model error. by Dmitrii Smirnov (8822324)

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Optimal Drug-Target Docking Visualization. by Qijun Wo (12992781)

Algorithm verification. by Dmitrii Smirnov (8822324)

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Genetic Algorithm (GA) and CAGE-based personalization block diagrams. by Dmitrii Smirnov (8822324)

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Screening MMRG gene variables associated with UC based on the optimal machine learning model. by Li Li (14993)

<i>Patient 2</i> CAGE-based model error. by Dmitrii Smirnov (8822324)

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<i>Patient 9–11</i> RNA-seq based models error. by Dmitrii Smirnov (8822324)

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Comparison of presented algorithm precision to Bot et al. by Dmitrii Smirnov (8822324)

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Model convergence to steady state. by Dmitrii Smirnov (8822324)

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Key gene identification based on two machine learning algorithms. by Lijun Guo (605276)

Distributions of model parameters mirror differentially expressed ion channel genes. by Daniele Linaro (227790)

<i>Patient 8</i> GA output model. by Dmitrii Smirnov (8822324)

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The flow chart of harmony search-based OED using a double nested loops. by Wei Zhang (405)

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)

“…Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). …”

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)

“…Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). …”