The classification of skateboarding tricks <i>via</i> transfer learning pipelines

Abdullah, Muhammad Amirul; Ibrahim, M. Z.; Shapiee, Muhammad Nur Aiman; Zakaria, Muhammad Aizzat; Razman, Mohd Azraai Mohd; Musa, Rabiu Muazu; Osman, Noor Azuan Abu; Majeed, Anwar P. P. Abdul

doi:10.7717/peerj-cs.680

Cited by 18 publications

(3 citation statements)

References 18 publications

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“…2.1. The proposed architecture in the present investigation is a departure from a typical transfer learning pipeline, i.e., the fully connected layers are swapped with a conventional machine learning model [10,11]. The features from the VGG16 pre-trained CNN model is then fed to the SVM, kNN and RF classifiers with its hyperparameters set to default from the scikit-learn library.…”

Section: Methodsmentioning

confidence: 99%

A VGG16 Feature-Based Transfer Learning Evaluation for the Diagnosis of Oral Squamous Cell Carcinoma (OSCC)

Arzmi¹,

Majeed²,

Musa³

et al. 2023

Deep Learning in Cancer Diagnostics

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Oral Squamous Cell Carcinoma (OSCC) is the most prevalent type of oral cancer. Early detection of such cancer could increase a patient's survival rate by 83%. This chapter shall explore the use of a feature-based transfer learning model, i.e., VGG16 coupled with different types of conventional machine learning models, viz. Support Vector Machine (SVM), Random Forest as well as k-Nearest Neighbour (kNN) as a means to identify OSCC. A total of 990 evenly distributed normal and OSCC histopathological images are split into the 60:20:20 ratio for training, testing and validation, respectively. A testing accuracy of 93% was recorded via the VGG16-RF pipeline from the study. Consequently, the proposed architecture is suitable to be deployed as artificial intelligence-driven computer-aided diagnostics and, in turn, facilitate clinicians for the identification of OSCC. Keywords Computer-Aided Diagnosis• Transfer Learning • Oral Cancer • OSCC

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Section: Methodsmentioning

confidence: 99%

A VGG16 Feature-Based Transfer Learning Evaluation for the Diagnosis of Oral Squamous Cell Carcinoma (OSCC)

Arzmi¹,

Majeed²,

Musa³

et al. 2023

Deep Learning in Cancer Diagnostics

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“…Transfer learning provides a method to leverage pretrained CNNs on general image datasets and transfer the learned features to new tasks with limited data. As reported in the literature, the aforesaid technique has been successfully employed on different applications [4]- [9]. With regards to defect detection, Tabl et al [10] used a fine-tuned ResNet-50 CNN model to classify manufacturing defects as either normal or defective.…”

Section: Introductionmentioning

confidence: 99%

Surface Defect Detection: A feature-based transfer learning approach

Abdul Majeed,

Abdullah,

Ab. Nasir

et al. 2024

J. Phys.: Conf. Ser.

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Surface defect detection is critical for maintaining product quality in manufacturing. In this work, we apply a feature-based transfer learning approach for surface defect classification on the NEU surface defect database. The database contains defects across 6 categories captured under various conditions. We utilised two pretrained convolutional neural network (CNN) architectures - VGG16 and InceptionV3 - by removing the final classification layer and using the CNN as a fixed feature extractor. The output feature vectors were classified using a logistic regression (LR) model. The data was split into train, validation, and test sets with a 70:15:15 ratio. The VGG16-LR model achieved classification accuracy (CA) of 100%, 98%, and 99% for the train, validation, and test sets respectively. The InceptionV3-LR model attained CA of 100%, 91%, and 92% for train, validation, and test. The results demonstrate the effectiveness of transfer learning with CNN feature extraction for surface defect detection on challenging multi-category industrial datasets. Further work includes tuning hyperparameters and evaluating additional architectures.

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“…Dursun et al [31] Gitcoin[32], Kickflow[33] Cross-chain voting High development speed high participation, Safe Complex, costly MULTAV [34] Token-lock voting Safe, efficient Deflation Ping Pong [35], Decred…”

mentioning

confidence: 99%

The governance technology for blockchain systems: a survey

Zhu,

He,

et al. 2023

Front. Comput. Sci.

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After the Ethereum DAO attack in 2016, which resulted in significant economic losses, blockchain governance has become a prominent research area. However, there is a lack of comprehensive and systematic literature review on blockchain governance. To deeply understand the process of blockchain governance and provide guidance for the future design of the blockchain governance model, we provide an in-depth review of blockchain governance. In this paper, first we introduce the consensus algorithms currently used in blockchain and relate them to governance theory. Second, we present the main content of off-chain governance and investigate two well-known off-chain governance projects. Third, we investigate four common on-chain governance voting techniques, then summarize the seven attributes that the on-chain governance voting process should meet, and finally analyze four well-known on-chain governance blockchain projects based on the previous research. We hope this survey will provide an in-depth insight into the potential development direction of blockchain governance and device future research agenda.

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The classification of skateboarding tricks via transfer learning pipelines

Cited by 18 publications

References 18 publications

A VGG16 Feature-Based Transfer Learning Evaluation for the Diagnosis of Oral Squamous Cell Carcinoma (OSCC)

A VGG16 Feature-Based Transfer Learning Evaluation for the Diagnosis of Oral Squamous Cell Carcinoma (OSCC)

Surface Defect Detection: A feature-based transfer learning approach

The governance technology for blockchain systems: a survey

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