Model of Predicting the Rating of Bridge Conditions in Indonesia with Regression and K-Fold Cross Validation

,; Winoto, Antonius Aldy; Roy, Andreas F.V.; ,

doi:10.30880/ijscet.2023.14.01.022

Cited by 2 publications

(2 citation statements)

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“…To implement the cross-validation method of our smart and lean Pick-and-Place solution, we used a simple method for accuracy estimation; Hold-out with random resampling [19] for a larger dataset, followed by a second method, K-Fold cross-validation, for a smaller dataset.…”

Section: Methodsmentioning

confidence: 99%

A Comparative Analysis of Cross-Validation Techniques for a Smart and Lean Pick-and-Place Solution with Deep Learning

et al. 2023

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As one of the core applications of computer vision, object detection has become more important in scenarios requiring high accuracy but with limited computational resources such as robotics and autonomous vehicles. Object detection using machine learning running on embedded device such as Raspberry Pi provides the high possibility to detect any custom objects without the recalibration of camera. In this work, we developed a smart and lean object detection model for shipping containers by using the state-of-the-art deep learning TensorFlow model and deployed it to a Raspberry Pi. Using EfficientDet-Lite2, we explored the different cross-validation strategies (Hold-out and K-Fold). The experimental results show that compared with the baseline EfficientDet-Lite2 algorithm, our model improved the mean average precision (mAP) by 44.73% for the Hold-out dataset and 6.26% for K-Fold cross-validation. We achieved Average Precision (AP) of more than 80% and best detection scores of more than 93% for the Hold-out dataset. For the 5-Fold lean dataset, the results show the Average Precision across the three lightweight models are generally high as the models achieved more than 50% average precision, with YOLOv4 Tiny performing better than EfficientDet-Lite2 and Single Shot Detector (SSD) MobileNet V2 Feature Pyramid Network (FPN) 320 as a lightweight model.

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Section: Methodsmentioning

confidence: 99%

A Comparative Analysis of Cross-Validation Techniques for a Smart and Lean Pick-and-Place Solution with Deep Learning

et al. 2023

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“…K-fold validation's primary purpose is to offer a more stable estimate of model performance as well as reliable information regarding training errors. The K-fold validation strategy with k = 5 was utilized in this experiment [73], [74]. This means the data will be folded five times and the validation procedure will be performed five times.…”

Section: Classification Using Svm+mobilenetv2 Algorithm (Scheme 3)mentioning

confidence: 99%

Breaking Boundaries in Diagnosis: Non-Invasive Anemia Detection Empowered by AI

Muljono,

Wulandari,

Azies

et al. 2024

IEEE Access

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This article evolved because several instances of anemia are still discovered too late, especially in communities with limited medical resources and access to laboratory tests. Invasive diagnostic technologies and expensive expenses are additional impediments to early diagnosis. To detect anemia, an effective, accurate, and non-invasive method is required. In this study, the conjunctival image of the eye is analyzed as a non-invasive method of detecting anemia. Various model approaches were tested in an endeavor to categorize anemic and healthy patients as accurately as possible. The Support Vector Machine (SVM) algorithm-integrated MobileNetV2 method was determined to be the most effective plan. With this combination, the accuracy of 93%, sensitivity of 91%, and specificity of 94%. These findings show that the model can successfully identify healthy patients while accurately identifying anemic patients. This method offers a non-invasive means of detecting anemia early on, making it promising for use in clinical settings. The SVM+MobileNetV2 technique relies on images of the conjunctiva of the eye and has the potential to improve healthcare by identifying people who may have had anemia earlier. This technique stands out as a solid option for the efficient and precise diagnosis of anemia when accuracy, sensitivity, and specificity are balanced.

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Model of Predicting the Rating of Bridge Conditions in Indonesia with Regression and K-Fold Cross Validation

Cited by 2 publications

References 5 publications

A Comparative Analysis of Cross-Validation Techniques for a Smart and Lean Pick-and-Place Solution with Deep Learning

A Comparative Analysis of Cross-Validation Techniques for a Smart and Lean Pick-and-Place Solution with Deep Learning

Breaking Boundaries in Diagnosis: Non-Invasive Anemia Detection Empowered by AI

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