Stud Pose Detection Based on Photometric Stereo and Lightweight YOLOv4

Zhang, Xuan; Wang, Guo Hui

doi:10.37965/jait.2021.12005

Cited by 18 publications

(18 citation statements)

References 19 publications

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“…In addition, various researchers have worked on different ML techniques to classify breast cancer types. It is found that the existing models suffer from the gradient vanishing [ 22 – 24 ], overfitting [ 25 , 26 ], and data leakage [ 27 , 28 ] kind of problems. Even development of generalized model [34,35] is still defined as an ill-posed problem.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Feature Importance Score‐Based Functional Link Artificial Neural Networks for Breast Cancer Classification

Singh

Jangir²,

Kumar

et al. 2022

BioMed Research International

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Growth of malignant tumors in the breast results in breast cancer. It is a cause of death of many women across the world. As a part of treatment, a woman might have to go through painful surgery and chemotherapy that may further lead to severe side effects. However, it is possible to cure it if it is diagnosed in the initial stage. Recently, many researchers have leveraged machine learning (ML) techniques to classify breast cancer. However, these methods are computationally expensive and prone to the overfitting problem. A simple single-layer neural network, i.e., functional link artificial neural network (FLANN), is proposed to overcome this problem. Further, the F-score is used to reduce the issue of overfitting by selecting features having a higher significance level. In this paper, FLANN is proposed to classify breast cancer using Wisconsin Breast Cancer Dataset (WBCD) (with 699 samples) and Wisconsin Diagnostic Breast Cancer (WDBC) (with 569 samples) datasets. Experimental results reveal that the proposed models can diagnose breast cancer with higher performance. The proposed model can be used in the early breast cancer diagnosis with 99.41% accuracy.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Feature Importance Score‐Based Functional Link Artificial Neural Networks for Breast Cancer Classification

Singh

Jangir²,

Kumar

et al. 2022

BioMed Research International

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“…An improved lightweight YOLOv4 neural network is applied to detect stud position to address the shortcomings of traditional testing methods. The analytic and experimental results show that the stud pose detection system proposed in the paper achieves rapid detection and high accuracy positioning of the stud [5].…”

Section: Deployment Of Ai In Application Domainsmentioning

confidence: 94%

Foundations of Artificial Intelligence and Applications

Mian

2022

JAIT

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The latest generation of artificial intelligence (AI) is based on today's cutting-edge technologies, including big data processing, cloud computing, machine learning, robotics, service-oriented computing, quantum computing, and Internet of Things (IoT). On one side, AI is supported and improved by these cutting-edge technologies. On the other side, AI is applied in many domains to augment the performance and capacities of many applications. Both the usefulness of AI in these domains and the inherent improvements that have been made in the pursuit of these large-scale technologies has caused an exponential increase in modern AI capabilities.In the following sections, we will present an overview of the papers in this issue that support the development of AI and apply AI to extend and improve performance and capacities in several application domains. These papers include both academic research papers and industrial application papers.

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“…From the existing literature, it is found that the majority of the existing models suffer from hyper-parameters tuning, (i.e., optimization of initial control parameters), over-fitting and data insensitivity problems [23] , [24] . Hence, there is a need to develop an efficient model for severity classification for Chikungunya disease.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Based Cyber-Physical System for Severity Classification of Chikungunya Disease

Singh

Kaur

Kumar

et al. 2022

IEEE J. Transl. Eng. Health Med.

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Background: Artificial intelligence techniques are widely used in solving medical problems. Recently, researchers have used various deep learning techniques for the severity classification of Chikungunya disease. But these techniques suffer from overfitting and hyper-parameters tuning problems. Methods: In this paper, an artificial intelligence-based cyber-physical system (CPS) is proposed for the severity classification of Chikungunya disease. In CPS system, the physical components are integrated with computational algorithms to provide better results. Random forest (RF) is used to design the severity classification model for Chikungunya disease. However, RF suffers from overfitting and poor computational speed problems due to complex architectures and large amounts of connection weights. Therefore, an evolving RF model is proposed using the adaptive crossover-based genetic algorithm (ACGA). Results: ACGA can efficiently optimize the architecture of RF to achieve better results with better computational speed. Extensive experiments are performed by utilizing the Chikungunya disease dataset. Conclusion: Performance analysis demonstrates that ACGA-RF achieves higher performance as compared to the competitive models in terms of F-measure, accuracy, sensitivity, and specificity. The proposed CPS system can prevent users from visiting hospitals and can render services to patients living far away from hospitals.

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Stud Pose Detection Based on Photometric Stereo and Lightweight YOLOv4

Cited by 18 publications

References 19 publications

[Retracted] Feature Importance Score‐Based Functional Link Artificial Neural Networks for Breast Cancer Classification

[Retracted] Feature Importance Score‐Based Functional Link Artificial Neural Networks for Breast Cancer Classification

Foundations of Artificial Intelligence and Applications

Artificial Intelligence-Based Cyber-Physical System for Severity Classification of Chikungunya Disease

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