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

“…When embedded with a classification model, our solution achieves an average of 5.5% improvement in classification accuracy, compared with the traditional pipeline of separate enhancement followed by classification. …”
Get full text
Get full text
Get full text

A Complete Classification of 2 x 2 Linear Iterative Systems by Habre, Samer

“…As in the case of linear systems of differential equations, the classification of the equilibrium solution (0; 0) leads to an analysis of the eigenvalues and eigenvectors of the system matrix. …”
Get full text
Get full text

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

Subjects:

Classification and segmentation of kidney MRI images for chronic kidney disease detection by Md. Sakib Bin Islam (20494559)

Subjects:

Machine learning approach for the classification of corn seed using hybrid features by Aqib Ali (19680145)

Subjects:

Feature fusion based on joint sparse representations and wavelets for multiview classification by Younes Akbari (14150781)

“…Generally, a dataset can be created in different views, features, or modalities. To improve the classification rate, local information is shared among different views by various fusion methods. …”

Lung cancer medical images classification using hybrid CNN-SVM by Abdulrazak Yahya Saleh (18520026)

Subjects:

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

“…Three types of classification models were built: person-specific models, generic classification models, and calibrated-generic classification models. …”

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. …”

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

Subjects:

Hybrid Neural Networks for Precise Hydronephrosis Classification Using Deep Learning by Abdus Salam (1918387)

“…The framework also supported hydronephrosis classification using the fluid-to-kidney area ratio, with a threshold of 0.213 derived from prior literature. …”

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

“…A Master of Science thesis in Computer Engineering by Murad Mohammad Qasaimeh entitled, "FPGA-based Parallel Hardware Architecture for Real-time Object Classification," submitted in June 2014. Thesis advisor is Dr. …”
Get full text

LungVision: X-ray Imagery Classification for On-Edge Diagnosis Applications by Aldamani, Raghad

Subjects: Get full text

Advancing crop classification in smallholder agriculture: A multifaceted approach combining frequency-domain image co-registration, transformer-based parcel segmentation, and Bi-LSTM for crop classification by Waleed Khan (9427344)

Subjects:

Exposure of Botnets in Cloud Environment by Expending Trust Model with CANFES Classification Approach by Nagendra Prabhu Selvaraj (17542041)

“…On this work, the port access verification in trust model is achieved by a Heuristic factorizing algorithm which verifies the port accessibility between client-end-user and client server. …”

Modelling of Asphalt’s Adhesive Behaviour Using Classification and Regression Tree (CART) Analysis by Md Arifuzzaman (11471123)

“…<p>The modification by polymers and nanomaterials can significantly improve different properties of asphalt. …”

CLASSIFICATION OF REFACTORING METHODS FOR ASPECT ORIENTED PROGRAMMING BASED ON SOFTWARE QUALITY ATTRIBUTES by Al-Jamimi, Hamdi Ali

Get full text

Kernel-Ridge-Regression-Based Randomized Network for Brain Age Classification and Estimation by Raveendra Pilli (21633287)

“…Features from MRI images are extracted using 3-D-CNN and fed into the wavelet KRR-RVFL network for brain age classification and prediction. The proposed algorithm achieved high classification accuracy, 97.22%, 99.31%, and 95.83% for GM, WM, and CSF regions, respectively. …”

Benchmark on a large cohort for sleep-wake classification with machine learning techniques by Joao Palotti (8479842)

Active Learning Based Federated Learning for Waste and Natural Disaster Image Classification by Lulwa Ahmed (16869936)

Subjects: