no code implementations • 31 Dec 2023 • Ali Hanif, Sajid Ahmed, Tareq Y. Al-Naffouri, Mohamed-Slim Alouin
This review paper examines the concept and advancements in the evolving landscape of Dual-functional Radar Communication (DFRC) systems.
no code implementations • 25 May 2023 • Cheng Guo, Sajid Ahmed, Mohamed-Slim Alouini
Simulation results show that the performance of our proposed methodology is better than the state-of-the-art techniques for the same database.
no code implementations • 28 Nov 2021 • Muhammad Zubair, Sajid Ahmed, Mohamed-Slim Alouini
In dual-function radar communication (DFRC), different waveforms are transmitted after each PRI.
no code implementations • 5 May 2020 • Maryam Khalid, Osama Amin, Sajid Ahmed, Basem Shihada, Mohamed-Slim Alouini
In this paper, we propose studying the disease spread mechanism in the atmosphere as an engineering problem.
no code implementations • 21 Dec 2019 • Ruhul Amin, Chowdhury Rafeed Rahman, Md. Habibur Rahman Sifat, Md Nazmul Khan Liton, Md. Moshiur Rahman, Swakkhar Shatabda, Sajid Ahmed
We present iPromoter-BnCNN for identification and accurate classification of six types of promoters - sigma24, sigma28, sigma32, sigma38, sigma54, sigma70.
no code implementations • 19 Jun 2018 • Farshid Rayhan, Sajid Ahmed, Zaynab Mousavian, Dewan Md. Farid, Swakkhar Shatabda
In this paper, we present FRnet-DTI, an auto encoder and a convolutional classifier for feature manipulation and drug target interaction prediction.
no code implementations • 18 Dec 2017 • Farshid Rayhan, Sajid Ahmed, Asif Mahbub, Md. Rafsan Jani, Swakkhar Shatabda, Dewan Md. Farid, Chowdhury Mofizur Rahman
The performance of MEBoost has been evaluated on 12 benchmark imbalanced datasets with state of the art ensemble methods like SMOTEBoost, RUSBoost, Easy Ensemble, EUSBoost, DataBoost.
1 code implementation • 12 Dec 2017 • Farshid Rayhan, Sajid Ahmed, Asif Mahbub, Md. Rafsan Jani, Swakkhar Shatabda, Dewan Md. Farid
We evaluated the performance of CUSBoost algorithm with the state-of-the-art methods based on ensemble learning like AdaBoost, RUSBoost, SMOTEBoost on 13 imbalance binary and multi-class datasets with various imbalance ratios.
no code implementations • 15 Nov 2017 • Sajid Ahmed, Farshid Rayhan, Asif Mahbub, Md. Rafsan Jani, Swakkhar Shatabda, Dewan Md. Farid, Chowdhury Mofizur Rahman
The problem of class imbalance along with class-overlapping has become a major issue in the domain of supervised learning.