The parameters of model. by Jiefei Zhang (8787494)

Subjects:

The riding time of metro/bus (min). by Jiefei Zhang (8787494)

Subjects:

The affected passengers’ OD demand. by Jiefei Zhang (8787494)

Subjects:

Resilience curves for different evacuation demands. by Jiefei Zhang (8787494)

Subjects:

Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach by Xiting Wang (9327613)

“…Then, a novel molecular property prediction model, called a multiembedding-based synthetic network (MESN), was generated by applying a deep learning algorithm based on the synthesis of multiple types of molecular embeddings. …”

Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach by Xiting Wang (9327613)

“…Then, a novel molecular property prediction model, called a multiembedding-based synthetic network (MESN), was generated by applying a deep learning algorithm based on the synthesis of multiple types of molecular embeddings. …”

Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach by Xiting Wang (9327613)

“…Then, a novel molecular property prediction model, called a multiembedding-based synthetic network (MESN), was generated by applying a deep learning algorithm based on the synthesis of multiple types of molecular embeddings. …”

Experiment 4: Accuracy of modularity-based consensus clustering methods on ring-of-cliques networks of varying sizes. by Yasamin Tabatabaee (18095491)

Optimization framework. by Nisha Ann Viswan (20371285)

The Pareto-optimal fronts on the random network with 10 added edges and its corresponding solutions. by Ruochen Zhang (3434996)

The Pareto-optimal fronts on the regular network with 10 added edges and its corresponding solutions. by Ruochen Zhang (3434996)

Line graph showing trend of solar irradiance data. by Muhammad Sadiq (9449915)

Comparative discussion based on routing. by Shanthi Amgothu (17692146)

“…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”

Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data by Changhun Kim (682542)

“…However, ToxCast assays differ in the amount of data and degree of class imbalance (CI). Therefore, the resampling algorithm employed should vary depending on the data distribution to achieve optimal classification performance. …”

Swarm intelligence algorithms for multi-objective IMP by Francisco Muñoz (9455441)

Proposed Algorithm. by Hend Bayoumi (22693738)

“…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. …”

Architecture of CDL Network. by Afnan M. Alhassan (18349378)

“…Furthermore, we have established an innovative selection mechanism by the hybrid of Jellyfish Search Optimizer (JSO) and Walrus Optimization Algorithm (WOA) to maximize the momentum of the model. …”

Parameter settings of the comparison algorithms. by Ying Li (38224)

“…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”

The stability factor of the optimized PA. by Mohammad Soruri (15419087)

“…The widths and lengths of the microstrip lines in the input and output matching networks are defined as the parameters that the Hidden Markov Model should optimize. …”

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. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”