Mahmudul Hasan Shauqi
Shauqi
PhD Candidate at Stony Brook University
Languages
Top Repositories
Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.
Code Repository for Semi-Supervised Contrastive VAE for Disentanglement of Digital Pathology Images
This research is based on analysis of retinal image using deep learning algorithms.
This Project is simple oracle sql database project on PhotoGallery
This software track the rss feed of several websites and show the title, description and link
Repositories
39Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.
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Code Repository for Semi-Supervised Contrastive VAE for Disentanglement of Digital Pathology Images
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Tips for Writing a Research Paper using LaTeX
This research is based on analysis of retinal image using deep learning algorithms.
This Project is simple oracle sql database project on PhotoGallery
Different target Natural Language Processing (NLP) tasks demand for pretrained semantic embedding of words also having syntactic considerations. In general, these tasks are Part of Speech (PoS) tagging, Opinion Mining, Named Entity Recognition, Textual Entailment, Semantic Role Tagging, Conference Resolution etc.
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How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study
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Dashboard which visualizes Neural Networks internal layers analysis.
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Detectron2 is FAIR's next-generation platform for object detection and segmentation.
ResNeSt: Split-Attention Networks
This software track the rss feed of several websites and show the title, description and link
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Notebooks for learning deep learning
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KUET CSE 13
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Website on photography
Machine learning datasets used in tutorials on MachineLearningMastery.com
The most cited deep learning papers
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
CS224n: Natural Language Processing with Deep Learning (Stanford University)