Proteins Classification Using An Improve Darknet-53 Deep Learning Model

Nisar, Dur-e-Maknoon; Mahum, Rabbia; Azim, Tabinda; Shah, Noor-ul-huda

doi:10.1109/majicc56935.2022.9994209

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A Fusion Framework of Pre-trained Deep Learning Models for Oral Squamous Cell Carcinoma Classification

Khan,

Mir,

Ullah

et al. 2024

Lecture Notes in Networks and Systems

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A Fusion Framework of Pre-trained Deep Learning Models for Oral Squamous Cell Carcinoma Classification

Khan,

Mir,

Ullah

et al. 2024

Lecture Notes in Networks and Systems

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A Comprehensive Survey of Machine Learning Techniques and Models for Object Detection

Trigka,

Dritsas

2025

Sensors

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Object detection is a pivotal research domain within computer vision, with applications spanning from autonomous vehicles to medical diagnostics. This comprehensive survey presents an in-depth analysis of the evolution and significant advancements in object detection, emphasizing the critical role of machine learning (ML) and deep learning (DL) techniques. We explore a wide spectrum of methodologies, ranging from traditional approaches to the latest DL models, thoroughly evaluating their performance, strengths, and limitations. Additionally, the survey delves into various metrics for assessing model effectiveness, including precision, recall, and intersection over union (IoU), while addressing ongoing challenges in the field, such as managing occlusions, varying object scales, and improving real-time processing capabilities. Furthermore, we critically examine recent breakthroughs, including advanced architectures like Transformers, and discuss challenges and future research directions aimed at overcoming existing barriers. By synthesizing current advancements, this survey provides valuable insights for enhancing the robustness, accuracy, and efficiency of object detection systems across diverse and challenging applications.

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Proteins Classification Using An Improve Darknet-53 Deep Learning Model

Cited by 2 publications

References 24 publications

A Fusion Framework of Pre-trained Deep Learning Models for Oral Squamous Cell Carcinoma Classification

A Fusion Framework of Pre-trained Deep Learning Models for Oral Squamous Cell Carcinoma Classification

A Comprehensive Survey of Machine Learning Techniques and Models for Object Detection

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