بدائل البحث:
model optimization » codon optimization (توسيع البحث), global optimization (توسيع البحث), based optimization (توسيع البحث)
swarm » warm (توسيع البحث)
model optimization » codon optimization (توسيع البحث), global optimization (توسيع البحث), based optimization (توسيع البحث)
swarm » warm (توسيع البحث)
-
21
The architecture of ILSTM.
منشور في 2023"…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. …"
-
22
Parameter setting for LSTM.
منشور في 2023"…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. …"
-
23
LITNET-2020 data splitting approach.
منشور في 2023"…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. …"
-
24
Transformation of symbolic features in NSL-KDD.
منشور في 2023"…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. …"
-
25
-
26
-
27
-
28
Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data
منشور في 2022"…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. …"
-
29
-
30
ROC curve for binary classification.
منشور في 2024"…The model further showed superior results on binary classification compared with existing methods. …"
-
31
Confusion matrix for binary classification.
منشور في 2024"…The model further showed superior results on binary classification compared with existing methods. …"
-
32
-
33
The comparison of the accuracy score of the benchmark and the proposed models.
منشور في 2025الموضوعات: -
34
-
35
-
36
<i>hi</i>PRS algorithm process flow.
منشور في 2023"…<b>(B)</b> Focusing on the positive class only, the algorithm exploits FIM (<i>apriori</i> algorithm) to build a list of candidate interactions of any desired order, retaining those that have an empirical frequency above a given threshold <i>δ</i>. …"
-
37
Comparison of baseline and hybrid machine learning models in predicting IVF outcomes (%).
منشور في 2025الموضوعات: -
38
-
39
Calibration curve of the ABC–LR–RF hybrid model for IVF outcome prediction.
منشور في 2025الموضوعات: -
40
ROC and PR–AUC curves of the ABC–LR–RF hybrid model for IVF outcome prediction.
منشور في 2025الموضوعات: