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

“…<p dir="ltr">Multiclass fault classification in complex processes is challenging due to many classes, nonlinear dynamics, overlapping fault signatures, and expanding fault taxonomies. …”

Multiclass feature selection with metaheuristic optimization algorithms: a review by Abu Zitar, Raed

“…Selecting relevant feature subsets is vital in machine learning, and multiclass feature selection is harder to perform since most classifications are binary. …”
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AI-Based Multiclass Grading of Hepatic Steatosis From B-Mode Ultrasound: Generalization Across Modalities and Clinical Comparison With Radiologists by Fahad Muflih Alshagathrh (18427950)

“…We present the Deep Domain Adaptation Neural Network (DDANN), a deep learning system for multiclass steatosis classification (Normal, Mild, Moderate, Severe) from ultrasound that emphasizes cross-device generalizability. …”

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

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Classification of Cognitive Workload Levels under Vague Visual Stimulation by Mahmoud, Rwan Adil Osman

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Novel Classification System for Classifying Cognitive Workload Levels under Vague Visual Stimulation by Mahmoud, Rwan Adil Osman

“…This is followed by variable selection using stepwise regression and multiclass linear classification. …”
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Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice by Turker Tuncer (16677966)

“…The main contribution of this paper consists of proposing a multiclass-pathologic voice classification using a novel multileveled textural feature extraction with iterative feature selector. …”

Deep learning-based cross-device standardization of surface-enhanced Raman spectroscopy for enhanced bacterial recognition by Sakib Mahmud (22652357)

“…We propose a deep learning framework comprising: (1) SERS-D2DNet, a one-dimensional sequence-to-sequence neural network that transforms spectra from portable devices into high-fidelity laboratory-grade equivalents, and (2) SuperRaman, a lightweight super-operational neural network (Super-ONN) for efficient multiclass bacterial classification. Primary and ablation studies confirm the complementary role of domain transformation and classification, demonstrating improved feature separability and reduced misclassification rates. …”

Classifying online corporate reputation with machine learning: a study in the banking domain by Anette Rantanen (18060340)

“…Moreover, the authors demonstrate that using a limited amount of training data can yield a satisfactory multiclass classifier when using CNN.…”

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

“…The approach includes a methodology to manage data mining projects, sampling techniques to handle imbalanced data and multiclass data, a set of classification algorithms to predict and measures to evaluate performance of models. …”
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Diagnostic performance of artificial intelligence in detecting and subtyping pediatric medulloblastoma from histopathological images: A systematic review by Hiba Alzoubi (18001609)

“…Techniques (e.g., model ensembling and multimodal data integration) are needed for better multiclass classification. Further reviews are needed to assess AI's role in other pediatric brain tumors.…”

Machine Learning-Based Management of Electric Vehicles Charging: Towards Highly-Dispersed Fast Chargers by Mostafa Shibl (18810412)

“…These approaches are chosen as they are classifiers known to have the leading results for multiclass classification problems. The results found shed insight on the importance of the techniques used and their high potential in providing a reliable solution for the coordinated charging of EVs, thus improving the performance of the power grid, and reducing power losses and voltage fluctuations. …”