Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things by Ashok Kumar K (21441108)

“…Hence, Binary Black Widow Optimization Algorithm (BBWOA) is proposed in this manuscript to improve the BRBPNN classifier that detects intrusion precisely. …”

Comparison results of scMSI and MSIsensor for different proportions of primary clones. by Yuqian Liu (1413763)

Models’ performance without optimization. by Muhammad Usman Tariq (11022141)

“…To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. …”

RNN performance comparison with/out optimization. by Muhammad Usman Tariq (11022141)

“…To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. …”

Bayesian sequential design for sensitivity experiments with hybrid responses by Yuxia Liu (1779592)

“…To deal with the problem of complex computation involved in searching for optimal designs, fast algorithms are presented using two strategies to approximate the optimal criterion, denoted as SI-optimal design and Bayesian D-optimal design, respectively. …”

Data_Sheet_1_A Bayesian Model to Analyze the Association of Rheumatoid Arthritis With Risk Factors and Their Interactions.PDF by Leon Lufkin (11276856)

XNet: A Bayesian Approach to Extracted Ion Chromatogram Clustering for Precursor Mass Spectrometry Data by Mathew Gutierrez (5844431)

“…Many methods are particularly dependent on user parameters, and because they lack a means to optimize parameters, tend to perform poorly. To this end we present XNet, a parameter-less Bayesian machine learning approach to isotopic envelope extraction through the clustering of extracted ion chromatograms. …”

Proposed method approach. by Muhammad Usman Tariq (11022141)

“…To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. …”

LSTM model performance. by Muhammad Usman Tariq (11022141)

“…To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. …”

Descriptive statistics. by Muhammad Usman Tariq (11022141)

“…To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. …”

CNN-LSTM Model performance. by Muhammad Usman Tariq (11022141)

“…To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. …”

MLP Model performance. by Muhammad Usman Tariq (11022141)

“…To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. …”

RNN Model performance. by Muhammad Usman Tariq (11022141)

“…To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. …”

CNN Model performance. by Muhammad Usman Tariq (11022141)

“…To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. …”

Bi-directional LSTM Model performance. by Muhammad Usman Tariq (11022141)

“…To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. …”

The baseline case–the model in the absence of sentinel plants. by Francesca A. Lovell-Read (14378253)

Supplementary information for Efficient distributed edge computing for dependent delay-sensitive tasks in multi-operator multi-access networks by Alia Asheralieva (17562462)

“…We prove that the game has a perfect Bayesian equilibrium (PBE) yielding unique optimal values, and formulate new Bayesian reinforcement learning and Bayesian deep reinforcement learning algorithms enabling each PN to reach the PBE autonomously (without communicating with other PNs).…”

<b>Spatial modeling of gully density on the Qinghai-Tibet Plateau: Application of hyperparameter optimization in interpretable machine learning</b> by Zhoujiang Liu (22741966)

“…Various machine learning models were used, and different hyperparameter optimization algorithms were selected to train the models to obtain the best model. …”

Data_Sheet_1_A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis.docx by Qin-Yu Zhao (10014626)

Data_Sheet_2_A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis.xlsx by Qin-Yu Zhao (10014626)