Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things by Ashok Kumar K (21441108)

“…Hence, Binary Black Widow Optimization Algorithm (BBWOA) is proposed in this manuscript to improve the BRBPNN classifier that detects intrusion precisely. …”

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging by D. Andrew Brown (6584837)

Performance of the proposed method for binary classification of lung cancer. by Md. Sabbir Hossain (9958939)

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Graphical comparison of binary class lung cancer classification with different approaches. by Md. Sabbir Hossain (9958939)

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EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit by Gonzalo Colmenarejo (650249)

Effectiveness of image augmentation operations. by Hailang Li (12557543)

(a) Accuracy and (b) loss curves of training and validation during binary class classification for 512 features extracted by the CNN. by Md. Sabbir Hossain (9958939)

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ROC curves of the proposed method for binary class classification: (A) GNB, (B) GBM, (C) KNN, (D) SVM, (E) RF, and (F) Ensemble learning. by Md. Sabbir Hossain (9958939)

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Application of ABC SMC to the fitting of the binary flip with cusp model. by Elena Camacho-Aguilar (10906327)

Image from video analysis. by Yunlin Li (6049214)

Bayesian sequential design for sensitivity experiments with hybrid responses by Yuxia Liu (1779592)

“…To deal with the problem of complex computation involved in searching for optimal designs, fast algorithms are presented using two strategies to approximate the optimal criterion, denoted as SI-optimal design and Bayesian D-optimal design, respectively. …”

Lung cancer CT images before and after pre-processing. by Md. Sabbir Hossain (9958939)

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Overview of the proposed simple CNN for extracting features from CT scan images. by Md. Sabbir Hossain (9958939)

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Cell classification. by Adam L. Tyson (10892822)

Melanoma Detection by Means of Multiple Instance Learning by Annabella Astorino (11644324)

“…We present an application to melanoma detection of a multiple instance learning (MIL) approach, whose objective, in the binary case, is to discriminate between positive and negative sets of items. …”

Association between crowding and oral habits. by Kengo Oka (1420585)

“…Questionnaire data and tooth alignment images of the children were collected. The dataset was created, and AI-based binary classification models for malocclusion were developed using an automated machine learning platform (DataRobot) to construct three algorithms for determining malocclusion (deep bite, maxillary protrusion, and crowding). …”

Association between deep bite and oral habits. by Kengo Oka (1420585)

“…Questionnaire data and tooth alignment images of the children were collected. The dataset was created, and AI-based binary classification models for malocclusion were developed using an automated machine learning platform (DataRobot) to construct three algorithms for determining malocclusion (deep bite, maxillary protrusion, and crowding). …”

Breakdown of participants by residential area. by Kengo Oka (1420585)

“…Questionnaire data and tooth alignment images of the children were collected. The dataset was created, and AI-based binary classification models for malocclusion were developed using an automated machine learning platform (DataRobot) to construct three algorithms for determining malocclusion (deep bite, maxillary protrusion, and crowding). …”

Each variable for the dataset. by Kengo Oka (1420585)

“…Questionnaire data and tooth alignment images of the children were collected. The dataset was created, and AI-based binary classification models for malocclusion were developed using an automated machine learning platform (DataRobot) to construct three algorithms for determining malocclusion (deep bite, maxillary protrusion, and crowding). …”

Performance comparison between the proposed method and various TL models with SVM. by Md. Sabbir Hossain (9958939)

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