The flowchart of the proposed algorithm. by Muhammad Ayyaz Sheikh (18610943)

“…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …”

Parameter Settings for the Proposed Hybrid Model. The Particle Swarm Optimization (PSO) parameters (swarm size, iterations, inertia weight, cognitive and social coefficient) were e... by Medhat A. Tawfeek (22522087)

Robustness of the optimization process in a real dataset. by Neta Ravid Tannenbaum (2940351)

Summary of literature review. by Muhammad Ayyaz Sheikh (18610943)

“…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …”

Topic description. by Muhammad Ayyaz Sheikh (18610943)

“…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …”

Notations along with their descriptions. by Muhammad Ayyaz Sheikh (18610943)

“…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …”

Detail of the topics extracted from DUC2002. by Muhammad Ayyaz Sheikh (18610943)

“…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …”

The proposed framework model–HMLFSM. by Murad Al-Rajab (10661360)

“…Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. …”

Phase 2 performance measures for Dataset 2. by Murad Al-Rajab (10661360)

“…Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. …”

Phase 2 performance measures for Dataset 1. by Murad Al-Rajab (10661360)

“…Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. …”

S1 Dataset - by Murad Al-Rajab (10661360)

“…Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. …”

Phase 1 performance measures for Dataset 3. by Murad Al-Rajab (10661360)

“…Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. …”

Phase 1 performance measures for Dataset 1. by Murad Al-Rajab (10661360)

“…Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. …”

Method of journal papers’ selection. by Murad Al-Rajab (10661360)

“…Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. …”

Phase 2 performance measures for Dataset 3. by Murad Al-Rajab (10661360)

“…Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. …”

Phase 1 performance measures for Dataset 2. by Murad Al-Rajab (10661360)

“…Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. …”

Comparative performance metrics of ML models. by Rajeev Bopche (20208606)

“…Olavs Hospital in Trondheim, Norway, encompassing 35,591 patients, the framework integrates demographic, laboratory, and comprehensive medical history data to classify patients into high-risk and low-risk BSI groups. …”

China’s annual forest age dataset at a 30-m spatial resolution from 1986 to 2022 by Rong Shang (17306089)

Data_Sheet_1_Selecting and Scaling of Energy-Compatible Ground Motion Records.DOCX by George Papazafeiropoulos (7120757)

Table_1_An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.pdf by Muhammad Awais (263096)

“…Next, a hybrid feature extraction approach is presented leveraging transfer learning from selected deep neural network models, InceptionV3 and DenseNet201, to extract comprehensive feature sets. To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. …”