Mahmudul Hasan Shauqi

Attack-and-Anomaly-Detection-in-IoT-Sensors-in-IoT-Sites-Using-Machine-Learning-Approaches. 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.

SS-cVAE. Code Repository for Semi-Supervised Contrastive VAE for Disentanglement of Digital Pathology Images

Comparisonal-study-on-Deep-Learning-approaches-on-Retinal-OCT-Image-Analysis. This research is based on analysis of retinal image using deep learning algorithms.

PhotoGalleryDatabase. This Project is simple oracle sql database project on PhotoGallery

InfoTracker. This software track the rss feed of several websites and show the title, description and link

Shauqi.github.io. HTML

quip_cancer_segmentation. Python

NER_With_LLMs.

Histopathology-Dashboard. Dashboard which visualizes Neural Networks internal layers analysis.

Opinion_Mining. Opinion_Mining_on_Bengali_Dataset

Pix2Pix_IHC. Python

A-Determination-of-Effective-Word-Embedding-of-Bengali-Language-using-text-classification. 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.