Search Results for author: Israat Haque

Found 6 papers, 0 papers with code

A Generalized Transformer-based Radio Link Failure Prediction Framework in 5G RANs

no code implementations6 Jul 2024 Kazi Hasan, Thomas Trappenberg, Israat Haque

Radio link failure (RLF) prediction system in Radio Access Networks (RANs) is critical for ensuring seamless communication and meeting the stringent requirements of high data rates, low latency, and improved reliability in 5G networks.

Graph Neural Network Prediction +1

Root Cause Analysis of Anomalies in 5G RAN Using Graph Neural Network and Transformer

no code implementations21 Jun 2024 Antor Hasan, Conrado Boeira, Khaleda Papry, Yue Ju, Zhongwen Zhu, Israat Haque

Secondly, current intelligent solutions are tailored to LTE networks and do not fully capture the spatio-temporal characteristics present in the data.

Anomaly Detection Graph Neural Network

Learn to Compress (LtC): Efficient Learning-based Streaming Video Analytics

no code implementations22 Jul 2023 Quazi Mishkatul Alam, Israat Haque, Nael Abu-Ghazaleh

In this paper, we introduce LtC, a collaborative framework between the video source and the analytics server, that efficiently learns to reduce the video streams within an analytics pipeline.

Video Compression

Preventing Discriminatory Decision-making in Evolving Data Streams

no code implementations16 Feb 2023 Zichong Wang, Nripsuta Saxena, Tongjia Yu, Sneha Karki, Tyler Zetty, Israat Haque, Shan Zhou, Dukka Kc, Ian Stockwell, Albert Bifet, Wenbin Zhang

However, most fair machine learning (fair-ML) work to address bias in decision-making systems has focused solely on the offline setting.

Decision Making Fairness

Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking

no code implementations16 Feb 2023 Zichong Wang, Yang Zhou, Israat Haque, David Lo, Wenbin Zhang

The increasing use of Machine Learning (ML) software can lead to unfair and unethical decisions, thus fairness bugs in software are becoming a growing concern.

Benchmarking counterfactual +1

Attention Mechanism based Cognition-level Scene Understanding

no code implementations17 Apr 2022 Xuejiao Tang, Tai Le Quy, Eirini Ntoutsi, Kea Turner, Vasile Palade, Israat Haque, Peng Xu, Chris Brown, Wenbin Zhang

Given a question-image input, the Visual Commonsense Reasoning (VCR) model can predict an answer with the corresponding rationale, which requires inference ability from the real world.

Question Answering Scene Understanding +2

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