Non-exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks

Zhuang, Jun; Hasan, Mohammad Al

doi:10.1007/978-3-030-86520-7_1

Cited by 5 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Higher recall stands for less malware that the detection system will miss. [21][22] XGBoost and Random Forest models demonstrated similar recall rates, indicating their effectiveness in identifying true malware instances.…”

Section: Precision and Recall Analysismentioning

confidence: 99%

Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection

Li,

Zhu,

Liu

et al. 2024

IJCSIT

View full text Add to dashboard Cite

This study conducts a thorough examination of malware detection using machine learning techniques, focusing on the evaluation of various classification models using the Mal-API-2019 dataset. The aim is to advance cybersecurity capabilities by identifying and mitigating threats more effectively. Both ensemble and non-ensemble machine learning methods, such as Random Forest, XGBoost, K Nearest Neighbor (KNN), and Neural Networks, are explored. Special emphasis is placed on the importance of data pre-processing techniques, particularly TF-IDF representation and Principal Component Analysis, in improving model performance. Results indicate that ensemble methods, particularly Random Forest and XGBoost, exhibit superior accuracy, precision, and recall compared to others, highlighting their effectiveness in malware detection. The paper also discusses limitations and potential future directions, emphasizing the need for continuous adaptation to address the evolving nature of malware. This research contributes to ongoing discussions in cybersecurity and provides practical insights for developing more robust malware detection systems in the digital era.

show abstract

Section: Precision and Recall Analysismentioning

confidence: 99%

Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection

Li,

Zhu,

Liu

et al. 2024

IJCSIT

View full text Add to dashboard Cite

show abstract

“…During inference, it identifies instances which are far away from the distribution of the known classes and create a new class along with its probability distribution. Generally, Dirichlet process Gaussian Mixture Model is used for nonexhaustive classification (Görür and Edward Rasmussen, 2010;Zhuang and Al Hasan, 2021). The challenge in Bayesian nonexhaustive classification is that their performance becomes very poor if the assumed data distribution does not follow the actual data distribution.…”

Section: Leveraging Machine Learning For Agnostic Discoverymentioning

confidence: 99%

Tissue Cytometry With Machine Learning in Kidney: From Small Specimens to Big Data

et al. 2022

Self Cite

View full text Add to dashboard Cite

Advances in cellular and molecular interrogation of kidney tissue have ushered a new era of understanding the pathogenesis of kidney disease and potentially identifying molecular targets for therapeutic intervention. Classifying cells in situ and identifying subtypes and states induced by injury is a foundational task in this context. High resolution Imaging-based approaches such as large-scale fluorescence 3D imaging offer significant advantages because they allow preservation of tissue architecture and provide a definition of the spatial context of each cell. We recently described the Volumetric Tissue Exploration and Analysis cytometry tool which enables an interactive analysis, quantitation and semiautomated classification of labeled cells in 3D image volumes. We also established and demonstrated an imaging-based classification using deep learning of cells in intact tissue using 3D nuclear staining with 4′,6-diamidino-2-phenylindole (DAPI). In this mini-review, we will discuss recent advancements in analyzing 3D imaging of kidney tissue, and how combining machine learning with cytometry is a powerful approach to leverage the depth of content provided by high resolution imaging into a highly informative analytical output. Therefore, imaging a small tissue specimen will yield big scale data that will enable cell classification in a spatial context and provide novel insights on pathological changes induced by kidney disease.

show abstract

Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction

Zhang,

Alvandipour,

Xia

et al. 2024

J Sci Comput

View full text Add to dashboard Cite

Non-exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks

Cited by 5 publications

References 20 publications

Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection

Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection

Tissue Cytometry With Machine Learning in Kidney: From Small Specimens to Big Data

Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction

Contact Info

Product

Resources

About