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

Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes حسب Muhammad Mohsin Khan (22150360)

Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation حسب Sherif Abdelfattah (17714523)

"…These studies, however, typically demand substantial communication and computational resources and may fail to conceal classification results and protect model intellectual property. …"

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

"…An accuracy result of 89.80 was reported for multi-class classification of 4 stages of Alzheimer disease (Cognitive Normal, Early Mild Cognitive Impairment, Late Mild Cognitive Impairment and Alzheimer Disease) for both Gray Matter (GM) and White Matter (WM). …"

Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques حسب Amith Khandakar (14151981)

"…The multilayer perceptron (MLP) classifier along with the features extracted from thermogram images showed an accuracy of 90.1% in multi-class classification, which outperformed the literature-reported performance metrics on this dataset.…"

Cyber-Resilient Detection of Power Quality Events with NSCT and PCA-SVM حسب Sayanti Chatterjee (1311438)

StEduCov: An Explored and Benchmarked Dataset on Stance Detection in Tweets towards Online Education during COVID-19 Pandemic حسب 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. Performances were affected by high vocabulary overlaps between classes and unreliable transfer learning using deep models pre-trained on general texts in relation to specific domains such as COVID-19 and distance education.…"

Dynamic model scaling based on segmented tumor size for breast cancer detection حسب Younes Akbari (16303286)

"…Scaling Convolutional Neural Networks (CNNs) can improve feature extraction, especially in multi-class problems that require varying levels of complexity to distinguish between different classes. …"

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

"…While, the Bayesian component is used to discriminate cyber-physical intrusions from the normal events in the binary and multi-class events. CNN layers are utilized to handle the high-dimensional feature space prior to the intrusions classification task. …"

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

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Defendroid: Real-time Android code vulnerability detection via blockchain federated neural network with XAI حسب Janaka Senanayake (21749174)

"…Trained on the LVDAndro dataset, the vanilla neural network model achieves a 96% accuracy and 0.96 F1-Score in binary classification for vulnerability detection. Additionally, in multi-class classification, the model accurately identifies Common Weakness Enumeration (CWE) categories with a 93% accuracy and 0.91 F1-Score. …"

The impact of cost function globality and locality in hybrid quantum neural networks on NISQ devices حسب Muhammad Kashif (3923483)

"…We consider two application scenarios namely, binary and multi-class classification, and show that for multiclass classification, the local cost function setting does not follow the claims of Cerezo et al; that is, the local cost function does not result in an extended quantum layer’s depth. …"

The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs حسب Muhammad Kashif (3923483)

"…We focus on the combined influence of data encoding, qubit entanglement, and ansatz expressibility in hybrid quantum neural networks (HQNNs) for multi-class classification tasks. Our framework aims to empirically analyze the joint impact of these factors on the training landscape of HQNNs. …"

Long Term HbA1c Prediction Using Multi-Stage CGM Data Analysis حسب Md Shafiqul Islam (7010348)

"…The CGM data have been rigorously investigated, and pertinent features were created along with a multi-stage multi-class (MSMC) classification model to predict futuristic HbA1c levels. …"

Real-time AI-based inference of people gender and age in highly crowded environments حسب Jasseur Abidi (17870426)

"…Our final Deep Learning architecture is an extended version of FairMOT, a multi-object tracking model, with two additional layers for multi-class gender classification and age regression. …"

Data mining approach to predict student's selection of program majors حسب SIDDARTHA, SHARMILA

"…The nature of academic data relating to student majors is multi class and imbalanced – there is always a niche major with few students enrolled. …"
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HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning حسب Umesh Kumar Lilhore (17727684)

"…To measure the performance of the proposed model, we conducted a multi-class classification analysis on various online industrial IDS datasets, i.e., ToN-IoT and UNW-NB15. …"