Binary form of Table 1. حسب Muhammad Waqar (500788)

MSE for ILSTM algorithm in binary classification. حسب Asmaa Ahmed Awad (16726315)

"…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. …"

Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm حسب Hussein Ali Bardan (21976208)

"…In this work, we propose a novel framework that integrates </p><p dir="ltr">Convolutional Neural Networks (CNNs) for image classification and a binary Grey Wolf Optimization (GWO) </p><p dir="ltr">algorithm for feature selection. …"

Proposed Algorithm. حسب 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. …"

Analysis and design of algorithms for the manufacturing process of integrated circuits حسب Sonia Fleytas (16856403)

"…The (approximate) solution proposals of state-of-the-art methods include rule-based approaches, genetic algorithms, and reinforcement learning. There is a binary integer programming model for this problem in the literature, from which its authors proposed a genetic algorithm to obtain approximate solutions. …"

The steps of proposed method of RWMA detection using echocardiography images for RWMA detection, consisting with cardiac structure segmentation using a trained U-Net on both A4C an... حسب Sazzli Kasim (11218558)

Raw Data for "Development of Decision Support Systems Based on Fuzzy and Binary Logic for the FOREX Foreign Exchange Market" حسب Serhii Hetsko (22413559)

"…The above indicates the need for a comprehensive study that would combine the advantages of various logical approaches, machine learning and multi-timeframe analysis within a single hybrid DSS. …"

Fig 1 - حسب Yuqing Lei (11600190)

Fig 3 - حسب Yuqing Lei (11600190)

Fig 4 - حسب Yuqing Lei (11600190)

Fig 7 - حسب Yuqing Lei (11600190)

Fig 5 - حسب Yuqing Lei (11600190)

Fig 6 - حسب Yuqing Lei (11600190)

Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification حسب Vaibhavi Patel (19843282)

A* Path-Finding Algorithm to Determine Cell Connections حسب Max Weng (22327159)

"…To address this, the research integrates a modified A* pathfinding algorithm with a U-Net convolutional neural network, a custom statistical binary classification method, and a personalized Min-Max connectivity threshold to automate the detection of astrocyte connectivity.…"

An Example of a WPT-MEC Network. حسب 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. …"

Table_1_Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model.docx حسب Chenglin Wang (430151)

Schematic representation of semi-automated algorithm for organoids analyzing. حسب Anna Demchenko (18449439)

AttentiveSkin: To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods حسب Zejun Huang (45753)

"…Then, a series of binary classifiers were developed with five machine learning (ML) algorithms and six molecular representations. …"

Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx حسب Veera Narayana Balabathina (22518524)

"…</p>Methods<p>Thirteen supervised classification algorithms were comparatively evaluated, encompassing traditional spectral/statistical classifiers—Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Parallelepiped, Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Binary Encoding—and machine learning algorithms including Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). …"