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. …”

Addressing Binary Classification over Class Imbalanced Clinical Datasets Using Computationally Intelligent Techniques by Vinod Kumar (48743)

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

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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. By restructuring a multiclass task into hierarchies of binary subproblems and linking each boundary to automatically selected statistical features, the developed method improves diagnostic accuracy and generalization. …”

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. …”

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. …”

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

“…To assess the performance of these versions, a benchmark of 24 datasets from various domains is used. The proposed methods are assessed concerning the fitness value, number of selected features, classification accuracy, specificity, sensitivity, and computational time. …”

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

“…In this paper, a new binary classification (called stressed and non-stressed) approach is proposed for a subject’s stress state in which the inter-beat intervals extracted from a photoplethysomogram (PPG) were transferred to spatial images and then to frequency domain images according to the number of consecutive. …”

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). …”

A Fusion-Based Approach for Skin Cancer Detection Combining Clinical Images, Dermoscopic Images, and Metadata by Siddiqui, Ansah Juned

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

“…Internet of Things (IoT) and Artificial Intelligence (AI) technologies are currently replacing the traditional methods of handling buildings, infrastructure, and facilities design, control, and maintenance due to their precision and ease of use. …”
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Fast fractal stack: fractal analysis of computed tomography scans of the lung by Costa, Alceu Ferraz

“…This paper proposes a new feature extraction method: the Fast Fractal Stack, or FFS. The extraction algorithm consists in decomposing the input grayscale image into a stack of binary images from which the fractal dimension values are computed, resulting in a compact and highly descriptive set of features. …”
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A novel methodology for offline English handwritten character recognition using ELBP-based sequential (CNN) by Muniba Humayun (21323750)

“…The key idea of this research paper is to recommend a deep learning-based ELBP-CNN method to help recognize English characters. This research paper proposes a deep learning CovNet with feature extraction and novel local binary pattern-based approaches, LBP (AND, OR), that is tested and compared with renowned pre-trained models using transfer learning. …”

Self-DSNet: A Novel Self-ONNs Based Deep Learning Framework for Multimodal Driving Distraction Detection by Mamun Or Rashid (21976373)

“…The model was evaluated using both single-modality and combined-modality data, focusing on binary classification to distinguish between distracted and non-distracted driving states. …”

Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks by Ahmed , Abdelrahman

“…The problem is formulated as a binary classification task using deep learning methods. …”
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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. …”

VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems by Hisham A. Kholidy (18891802)

“…VHDRA provides the following functionalities: (1) it vertically reduces the dataset features by selecting the most significant features and by reducing the NNGE’s hyperrectangles. (2) It horizontally reduces the size of data while preserving original key events and patterns within the datasets using an approach called STEM, State Tracking and Extraction Method. The experiments show that the overall performance of VHDRA using both the vertical and the horizontal reduction reduces the NNGE hyperrectangles by 29.06%, 37.34%, and 26.76% and improves the accuracy of the NNGE by 8.57%, 4.19%, and 3.78% using the Multi-, Binary, and Triple class datasets, respectively.…”

TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images by Tawsifur Rahman (14150523)

“…The proposed approach shows an accuracy of 93.32% for the classification of TB, non-TB, and healthy patients on the largest dataset while around 87.48% and 79.59% accuracy for binary classification (drug-resistant vs drug-sensitive TB), and three-class classification (multi-drug resistant (MDR), extreme drug-resistant (XDR), and sensitive TB), respectively, which is the best reported result compared to the literature. …”