MEDIC: a multi-task learning dataset for disaster image classification by Firoj Alam (14158866)

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Robust Training of Social Media Image Classification Models by Firoj Alam (14158866)

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Multi-Label Image Classification by Feature Attention Network by Zheng Yan (194231)

“…<p dir="ltr">Learning the correlation among labels is a standing-problem in the multi-label image recognition task. The label correlation is the key to solve the multi-label classification but it is too abstract to model. …”

Drone/Bird Classification Based on Features of Tracks Trajectories by Kengeskanov, Maksat

“…This paper presents the outcome of several machine learning techniques used for the task of bird/drone classification based on their tracks. …”
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Both decreasing interstructural space size and increasing total cover increase shrimp abundance on artificial structures deployed in a UAE seagrass bed by Bartholomew, Aaron

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Low-Light Image Enhancement for Object Classification using Deep Learning by Al Sobbahi, Rayan

“…We then address a common problem of these models depicted by their design as standalone solutions without focusing on the impact of enhancement on high-level computer vision tasks like object classification. Our review and empirical evaluations show that enhancing LLI visual quality does not necessarily correlate with improved object detection and classification performance, and may even deteriorate it, especially in cases where enhanced images include extreme artifacts. …”
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L1-CAM knock-down radiosensitizes neuroblastoma IMR-32 cells by simultaneously decreasing MycN, but increasing PTEN protein expression by Rached, Johnny

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Low-Light Homomorphic Filtering Network for integrating image enhancement and classification by Al Sobbahi, Rayan

“…However, most existing techniques are developed as standalone solutions and do not take into account the impact of LLI enhancement on high-level computer vision tasks like object classification. In this paper, we propose a new LLI enhancement model titled LLHFNet (Low-light Homomorphic Filtering Network) which performs image-to-frequency filter learning and is designed for seamless integration into classification models. …”
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Lung cancer medical images classification using hybrid CNN-SVM by Abdulrazak Yahya Saleh (18520026)

“…This paper presents an image classification method based on the hybrid Convolutional Neural Network (CNN) algorithm and Support Vector Machine (SVM). …”

Serum Vitamin D Concentration ≥75 nmol/L Is Related to Decreased Cardiometabolic and Inflammatory Biomarkers, Metabolic Syndrome, and Diabetes; and Increased Cardiorespiratory Fitn... by Vijay Ganji (8710491)

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

“…The applications of the pure Transformer model on sequences of image patches achieved promising results, comparable to those of the Convolutional Neural Networks (CNNs), the leading models of computer vision tasks. …”
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Sliding Window Neural Generated Tracking Based on Measurement Model by Ejjawi, Haya

“…This paper presents the outcome of several machine learning techniques used for the task of bird/drone classification based on their tracks. …”
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EOG-Based Reading Detection in the Wild Using Spectrograms and Nested Classification Approach by Sriman Bidhan Baray (15302398)

FPGA-based Parallel Hardware Architecture for Real-time Object Classification by Qasaimeh, Murad Mohammad

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The Anti-Tumor Agent Sodium Selenate Decreases Methylated PP2A, Increases GSK3βY216 Phosphorylation, Including Tau Disease Epitopes and Reduces Neuronal Excitability in SHSY-5Y Neu... by Wesal Habbab (17346961)

“…Somewhat surprisingly, the catalytically active form, methylated PP2A (mePP2A) was significantly decreased. In close correlation to these data, the phosphorylation state of two substrate proteins, sensitive to PP2A activity, GSK3β and Tau were found to be increased. …”

Dual-stage segmentation and classification framework for skin lesion analysis using deep neural network by Khadija Manzoor (22565369)

“…</p><h3>Conclusion</h3><p dir="ltr">The proposed dual-stage deep learning framework demonstrates high accuracy and robustness across segmentation and classification tasks on diverse datasets. Its adaptability to large-scale, non-dermoscopic data such as SLICE-3D confirms its potential for deployment in real-world skin cancer triage and teledermatology applications.…”

Advancing paleontology: a survey on deep learning methodologies in fossil image analysis by Mohammed Yaqoob (344668)

“…Despite these developments, key fossil image processing and analysis tasks, such as segmentation and classification, still require significant user intervention, which can be labor-intensive and subject to human bias. …”

Comparing deep learning models for low-light natural scene image enhancement and their impact on object detection and classification: Overview, empirical evaluation, and challenges by Al Sobbahi, Rayan

“…Fifth, we perform a feature analysis of DL feature maps extracted from normal, low-light, and enhanced images, and perform the occlusion experiment to better understand the effect of LLI enhancement on the object detection and classification task. …”
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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection by Faizul Rakib Sayem (21399890)

“…In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. …”

Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach by Zubair Saeed (19325647)

“…Unlike traditional machine learning methods requiring manual feature extraction, DL models are adept at handling complex data like MRIs and excel in classification tasks, making them well-suited for medical image analysis applications. …”