An example of 2-dimensional non-linearly separable and non-separable data for binary classification. by Yuji Oyamada (11714754)

ROC curve for binary classification. by Nicodemus Songose Awarayi (18414494)

“…The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. …”

Confusion matrix for binary classification. by Nicodemus Songose Awarayi (18414494)

“…The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. …”

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

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One advantage of our algorithm is that it is not binary in nature. by Yong Kuk Kim (13955214)

Dataset 1: Zip file containing the figures of the presented methods and results in jpeg files by Suchismita Behera (22027316)

“…<p dir="ltr">Figures represented here illustrates the <b>metaheuristic-based band selection framework</b> for hyperspectral image classification using <b>Binary Jaya Algorithm enhanced with a mutation operator</b> to improve population diversity and avoid premature convergence. …”

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

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Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

(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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Optimization of tracking parameters based on <i>P</i>_<i>swap</i>. by Benjamin Gallois (10131683)

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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Hair removal preprocessing using the DullRazor algorithm before image generation. by Mujung Kim (22367223)

Performance of the individual and proposed methods for multiclass classification of lung cancer. by Md. Sabbir Hossain (9958939)

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

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Data_Sheet_1_Improving Crowdsourcing-Based Image Classification Through Expanded Input Elicitation and Machine Learning.PDF by Romena Yasmin (12970919)

“…<p>This work investigates how different forms of input elicitation obtained from crowdsourcing can be utilized to improve the quality of inferred labels for image classification tasks, where an image must be labeled as either positive or negative depending on the presence/absence of a specified object. …”

(A) Accuracy and (B) loss of training and validation sets of multiclass classification using CNN. by Md. Sabbir Hossain (9958939)

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Structure of the proposed hybrid CNN-SVD-based ensemble model for lung cancer classification. by Md. Sabbir Hossain (9958939)

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Proposed Algorithm. by Hend Bayoumi (22693738)

Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm by Hussein Ali Bardan (21976208)

“…In this work, we propose a novel framework that integrates </p><p dir="ltr">Convolutional Neural Networks (CNNs) for image classification and a binary Grey Wolf Optimization (GWO) </p><p dir="ltr">algorithm for feature selection. …”

Comparisons between ADAM and NADAM optimizers. by Hend Bayoumi (22693738)