Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics by Zhichao Liu (191718)

“…To bridge this gap, we here developed “Trace”, a software framework that incorporates machine learning (ML) to automate feature selection and optimization for the extraction of trace-level signals from HRMS data. …”

Architecture of proposed system model. by Nanavath Kiran Singh Nayak (22184565)

“…Initially, Four-Q curve authentication is performed, followed by univariate ensemble feature selection to select optimal switches. Then, the data collected through the switches are classified as normal, assault, and suspect packets based on the Dual Discriminator Conditional Generative Adversarial Network (DDcGAN) approach. …”

Network simulation of the proposed method. by Nanavath Kiran Singh Nayak (22184565)

“…Initially, Four-Q curve authentication is performed, followed by univariate ensemble feature selection to select optimal switches. Then, the data collected through the switches are classified as normal, assault, and suspect packets based on the Dual Discriminator Conditional Generative Adversarial Network (DDcGAN) approach. …”

Simulation parameters of SDN network. by Nanavath Kiran Singh Nayak (22184565)

“…Initially, Four-Q curve authentication is performed, followed by univariate ensemble feature selection to select optimal switches. Then, the data collected through the switches are classified as normal, assault, and suspect packets based on the Dual Discriminator Conditional Generative Adversarial Network (DDcGAN) approach. …”

Generator loss of the proposed method. by Nanavath Kiran Singh Nayak (22184565)

“…Initially, Four-Q curve authentication is performed, followed by univariate ensemble feature selection to select optimal switches. Then, the data collected through the switches are classified as normal, assault, and suspect packets based on the Dual Discriminator Conditional Generative Adversarial Network (DDcGAN) approach. …”

Simulation parameters of DDcGAN. by Nanavath Kiran Singh Nayak (22184565)

“…Initially, Four-Q curve authentication is performed, followed by univariate ensemble feature selection to select optimal switches. Then, the data collected through the switches are classified as normal, assault, and suspect packets based on the Dual Discriminator Conditional Generative Adversarial Network (DDcGAN) approach. …”

Performance metrics. by Nanavath Kiran Singh Nayak (22184565)

“…Initially, Four-Q curve authentication is performed, followed by univariate ensemble feature selection to select optimal switches. Then, the data collected through the switches are classified as normal, assault, and suspect packets based on the Dual Discriminator Conditional Generative Adversarial Network (DDcGAN) approach. …”

Flow diagram for the proposed methodology. by Nanavath Kiran Singh Nayak (22184565)

“…Initially, Four-Q curve authentication is performed, followed by univariate ensemble feature selection to select optimal switches. Then, the data collected through the switches are classified as normal, assault, and suspect packets based on the Dual Discriminator Conditional Generative Adversarial Network (DDcGAN) approach. …”

Discriminator loss of suggested technique. by Nanavath Kiran Singh Nayak (22184565)

“…Initially, Four-Q curve authentication is performed, followed by univariate ensemble feature selection to select optimal switches. Then, the data collected through the switches are classified as normal, assault, and suspect packets based on the Dual Discriminator Conditional Generative Adversarial Network (DDcGAN) approach. …”

System model of the proposed method. by Nanavath Kiran Singh Nayak (22184565)

“…Initially, Four-Q curve authentication is performed, followed by univariate ensemble feature selection to select optimal switches. Then, the data collected through the switches are classified as normal, assault, and suspect packets based on the Dual Discriminator Conditional Generative Adversarial Network (DDcGAN) approach. …”

Label-Free Assessment of the Drug Resistance of Epithelial Ovarian Cancer Cells in a Microfluidic Holographic Flow Cytometer Boosted through Machine Learning by Lu Xin (728966)

“…Furthermore, it reflects strong potentialities to develop data-driven individualized chemotherapy treatments in the future.…”

Label-Free Assessment of the Drug Resistance of Epithelial Ovarian Cancer Cells in a Microfluidic Holographic Flow Cytometer Boosted through Machine Learning by Lu Xin (728966)

“…Furthermore, it reflects strong potentialities to develop data-driven individualized chemotherapy treatments in the future.…”

Performance metrics for BrC. by Afnan M. Alhassan (18349378)

“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”

Proposed CVAE model. by Afnan M. Alhassan (18349378)

“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”

Proposed methodology. by Afnan M. Alhassan (18349378)

“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”

Loss vs. Epoch. by Afnan M. Alhassan (18349378)

“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”

Sample images from the BreakHis dataset. by Afnan M. Alhassan (18349378)

“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”

Accuracy vs. Epoch. by Afnan M. Alhassan (18349378)

“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”

Segmentation results of the proposed model. by Afnan M. Alhassan (18349378)

“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”

S1 Dataset - by Afnan M. Alhassan (18349378)

“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”