An Exemplar Pyramid Feature Extraction Based Alzheimer Disease Classification Method by Heba Soliman Zaina (16904787)

“…In term of binary-class classification, we were able to achieve very good results using both GM and WM. …”

UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data by Behrouz Ahadzadeh (19757022)

“…RFE performs a local search in a very small subspace of the solutions obtained by UniBFS in different stages, and removes the redundant features which do not increase the classification accuracy. Moreover, the study proposes a hybrid algorithm that combines UniBFS with two filter-based FS methods, ReliefF and Fisher, to identify pertinent features during the global search phase. …”

Improved Machine Learning for Multiclass Fault Classification in Industrial Processes by Khaled Dhibi (16891524)

“…The proposed methodology aims to boost any base classifier via optimization, interval-based feature selection, and intelligent binary decomposition. …”

An enhanced binary Rat Swarm Optimizer based on local-best concepts of PSO and collaborative crossover operators for feature selection by Abu Zitar, Raed

“…Based on these enhancements, three versions of RSO are produced, referred to as Binary RSO (BRSO), Binary Enhanced RSO (BERSO), and Binary Enhanced RSO with Crossover operators (BERSOC). …”

Stress Classification Using Photoplethysmogram-Based Spatial and Frequency Domain Images by Sami Elzeiny (16891521)

“…By combining 20% of the samples collected from test subjects into the training data, the calibrated generic models’ accuracy was improved and outperformed the generic performance across both the spatial and frequency domain images. The average classification accuracy of 99.6%, 99.9%, and 88.1%, and 99.2%, 97.4%, and 87.6% were obtained for the training set, validation set, and test set, respectively, using the calibrated generic classification-based method for the series of inter-beat interval (IBI) spatial and frequency domain images. …”

Neuro-Fuzzy Random Vector Functional Link Neural Network for Classification and Regression Problems by M. Sajid (3070545)

“…Our research involves experiments on various UCI benchmark datasets, covering binary, multiclass classification, and regression tasks. …”

Integrating binary classification and clustering for multi-class dysarthria severity level classification: a two-stage approach by Afnan S. Al-Ali (22224889)

“…However, these classification-based approaches may not readily translate to real-world scenarios without predefined labels. …”

Binary Classification of 3D Small-Scale Medical Images Using Video Vision Transformers by Abi Younes, Simon

“…The VesselMNIST3d dataset binary classification experiment was implemented by treating the 3D image as video where the third dimension represents the number of frames. …”
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Addressing Binary Classification over Class Imbalanced Clinical Datasets Using Computationally Intelligent Techniques by Vinod Kumar (48743)

Type 2 Diabetes Mellitus Automated Risk Detection Based on UAE National Health Survey Data: A Framework for the Construction and Optimization of Binary Classification Machine Learning Models Based on Dimensionality Reduction by Mohamed, AlShuweihi

“…The first is a comprehensive ML framework for the construction of diagnostic binary classification high accuracy models to predict T2DM in the United Arab Emirates based on STEPS style National Health Survey. …”
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Learning-Based Automatic Modulation Classification by Abdelmutalab, Ameen Elsiddig

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A Bayesian Approach to Feature Selection in Classification Problems by Emarly, Maher

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A Bayesian Deep Learning Approach With Convolutional Feature Engineering to Discriminate Cyber-Physical Intrusions in Smart Grid Systems by Devinder Kaur (264278)

“…The proposed method is validated using real-time Industrial control systems (ICS) dataset against the standard deep learning-based classification methods such as recurrent neural networks (RNN) and long-short term memory (LSTM). …”

Efficient Detection of Hepatic Steatosis in Ultrasound Images Using Convolutional Neural Networks: A Comparative Study by Fahad M. Alshagathrh (19365478)

“…Problem Statement: This study aims to evaluate deep learning methods for binary classification of hepatic steatosis using ultrasound images. …”

A Multimodal Approach to Improve Performance Evaluation of Call Center Agent by Ahmed , Abdelrahman

“…The third technique combines the agent’s recorded call speech with the corresponding transcribed text for binary classification. The speech modeling and text modeling are based on combinations of the Convolutional Neural Networks (CNNs) and Bi-directional Long-Short Term Memory (BiLSTMs). …”
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Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine by Debendra Muduli (20748758)

“…For mass fundus image-based glaucoma classification, an improved automated computer-aided diagnosis (CAD) model performing binary classification (glaucoma or healthy), allowing ophthalmologists to detect glaucoma disease correctly in less computational time. …”

Molecular Classification of Breast Cancer Utilizing Long Non-Coding RNA (lncRNA) Transcriptomes Identifies Novel Diagnostic lncRNA Panel for Triple-Negative Breast Cancer by Hibah Shaath (5599658)

AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniques by Khalid, Naji

“…The VGG19 network yielded a health condition classification accuracy of 100% with an RMSE of 0.33% and a maximum classification error score of 0.87 %.…”
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StEduCov: An Explored and Benchmarked Dataset on Stance Detection in Tweets towards Online Education during COVID-19 Pandemic by Omama Hamad (21363476)

“…The average accuracy in the 10-fold cross-validation of these models ranged from 75% to 84.8% and from 52.6% to 68% for binary and multi-class stance classifications, respectively. …”

Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms by Arafat Rahman (8065562)

“…These results outperform the individual modalities with a significant margin (~5%). We also developed a binary template matching-based algorithm, which gives 93.64% accuracy 6X faster. …”