Integration of convolutional neural network and extreme gradient boosting for breast cancer detection

Sugiharti, Endang; Arifudin, Riza; Wiyanti, Dian Tri; Susilo, Arief Broto

doi:10.11591/eei.v11i2.3562

Cited by 5 publications

(5 citation statements)

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“…In fact, the dimensionality reduction did not affect the performance of the model. The XGBoost model is widely used in many fields, including medicine [21]- [24], agriculture [25], the service sector [26], as well as industry sector [27].…”

Section: Introductionmentioning

confidence: 99%

Black spots identification on rural roads based on extreme learning machine

Mbarek

Jiber

Yahyaouy

et al. 2023

IJECE

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Accident black spots are usually defined as road locations with a high risk of fatal accidents. A thorough analysis of these areas is essential to determine the real causes of mortality due to these accidents and can thus help anticipate the necessary decisions to be made to mitigate their effects. In this context, this study aims to develop a model for the identification, classification and analysis of black spots on roads in Morocco. These areas are first identified using extreme learning machine (ELM) algorithm, and then the infrastructure factors are analyzed by ordinal regression. The XGBoost model is adopted for weighted severity index (WSI) generation, which in turn generates the severity scores to be assigned to individual road segments. The latter are then classified into four classes by using a categorization approach (high, medium, low and safe). Finally, the bagging extreme learning machine is used to classify the severity of road segments according to infrastructures and environmental factors. Simulation results show that the proposed framework accurately and efficiently identified the black spots and outperformed the reputable competing models, especially in terms of accuracy 98.6%. In conclusion, the ordinal analysis revealed that pavement width, road curve type, shoulder width and position were the significant factors contributing to accidents on rural roads.

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Section: Introductionmentioning

confidence: 99%

Black spots identification on rural roads based on extreme learning machine

Mbarek

Jiber

Yahyaouy

et al. 2023

IJECE

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“…However, in the cases of [42] and [43] facing glycose levels predictions, XGBoost was not considered the optimal solution and DNN and RF were the selected algorithms respectively. Furthermore, the XGBoost algorithm was selected for pregnancy risk monitoring with 96% accuracy [44] whereas an improvement of the proposed approach combining CNN and XGBoost methodology was proposed for renal stone diagnosis [45], breast cancer detection [46] and image classification [47] with accuracies of 99.5 %. Finally, feature selection combined with ensemble learning was proposed for epileptic seizure detection and classification from electroencephalogram signals with an effectiveness of 96% [48], [49].…”

Section: Resultsmentioning

confidence: 99%

Predictive maintenance framework for assessing health state of centrifugal pumps

Mallioris,

Diamantis,

Bialas

et al. 2024

IJ-AI

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Combined with advances in sensing technologies and big data analytics, critical information can be extracted from continuous production processes for predicting the health state of equipment and safeguarding upcoming failures. This research presents a methodology for applying predictive maintenance (PdM) solutions and showcases a PdM application for health state prediction and condition monitoring, increasing the safety and productivity of centrifugal pumps for a sustainable and resilient PdM ecosystem. Measurements depicting the healthy and maintenance-prone stages of two centrifugal pumps were collected on the university campus. The dataset consists of 5,118 records and includes both running and standstill values. Additionally, Spearman statistical analysis was conducted to measure the correlation of collected measurements with the predicted output of machine conditions and select the most appropriate features for model optimization. Several machine learning (ML) algorithms, namely random forest (RF), Naïve Bayes, support vector machines (SVM), and <a name="_Hlk137498016"></a>extreme gradient boosting (XGBoost) were analyzed and evaluated during the data mining process. The results indicated the effectiveness and efficiency of XGBoost for the health state prediction of centrifugal pumps. The contribution of this research is to propose an effective framework collectong multistage health data for PdM applications and showcase its effectiveness in a real-world use case.

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“…Hence, the correlation coefficient r is used to measure the strength of the relationship among various variables. This analytical technique is based on the premise that determining the significance of a pertinent attribute in the data can be conducted by analyzing the strength of the association between dependent and target variables [ 32 , 33 , 34 , 35 ].…”

Section: Methodsmentioning

confidence: 99%

Age Assessment through Root Lengths of Mandibular Second and Third Permanent Molars Using Machine Learning and Artificial Neural Networks

et al. 2023

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The present study explores the efficacy of Machine Learning and Artificial Neural Networks in age assessment using the root length of the second and third molar teeth. A dataset of 1000 panoramic radiographs with intact second and third molars ranging from 12 to 25 years was archived. The length of the mesial and distal roots was measured using ImageJ software. The dataset was classified in three ways based on the age distribution: 2–Class, 3–Class, and 5–Class. We used Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression models to train, test, and analyze the root length measurements. The mesial root of the third molar on the right side was a good predictor of age. The SVM showed the highest accuracy of 86.4% for 2–class, 66% for 3–class, and 42.8% for 5–Class. The RF showed the highest accuracy of 47.6% for 5–Class. Overall the present study demonstrated that the Deep Learning model (fully connected model) performed better than the Machine Learning models, and the mesial root length of the right third molar was a good predictor of age. Additionally, a combination of different root lengths could be informative while building a Machine Learning model.

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Integration of convolutional neural network and extreme gradient boosting for breast cancer detection

Cited by 5 publications

References 0 publications

Black spots identification on rural roads based on extreme learning machine

Black spots identification on rural roads based on extreme learning machine

Predictive maintenance framework for assessing health state of centrifugal pumps

Age Assessment through Root Lengths of Mandibular Second and Third Permanent Molars Using Machine Learning and Artificial Neural Networks

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