Dewan Md. Farid

2.5k total citations · 1 hit paper
99 papers, 1.6k citations indexed

About

Dewan Md. Farid is a scholar working on Artificial Intelligence, Information Systems and Computer Networks and Communications. According to data from OpenAlex, Dewan Md. Farid has authored 99 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Artificial Intelligence, 18 papers in Information Systems and 16 papers in Computer Networks and Communications. Recurrent topics in Dewan Md. Farid's work include Imbalanced Data Classification Techniques (17 papers), Data Stream Mining Techniques (16 papers) and Machine Learning and Data Classification (16 papers). Dewan Md. Farid is often cited by papers focused on Imbalanced Data Classification Techniques (17 papers), Data Stream Mining Techniques (16 papers) and Machine Learning and Data Classification (16 papers). Dewan Md. Farid collaborates with scholars based in Bangladesh, United States and United Kingdom. Dewan Md. Farid's co-authors include Chowdhury Mofizur Rahman, Swakkhar Shatabda, Mohammad Alamgir Hossain, Rebecca Strachan, Mohammad Zahidur Rahman, Li Zhang, Li Zhang, Sajid Ahmed, Abdollah Dehzangi and Ming Jiang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and Brain Research.

In The Last Decade

Dewan Md. Farid

88 papers receiving 1.5k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks

2013 287 citations Dewan Md. Farid et al. profile →

Peers

Dewan Md. Farid

AI

CNC

MB

SP

IS

Mohd Zakree Ahmad Nazri Malaysia

Nawal El‐Fishawy Egypt

Qinbao Song China

Mohammad Saniee Abadeh Iran

Saeed Jalili Iran

Rui Zhou China

Ruichu Cai China

Ahmad Sharieh Jordan

Shaoning Pang New Zealand

Ralf Klinkenberg Germany

Mohd Zakree Ahmad Nazri Malaysia

Dewan Md. Farid

700 ×0.9

AI

427 ×0.9

CNC

108 ×0.4

MB

187 ×0.8

SP

199 ×1.2

IS

Citations per year, relative to Dewan Md. Farid Dewan Md. Farid (= 1×) peers Mohd Zakree Ahmad Nazri

Countries citing papers authored by Dewan Md. Farid

Since Specialization

Citations

This map shows the geographic impact of Dewan Md. Farid's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dewan Md. Farid with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dewan Md. Farid more than expected).

Fields of papers citing papers by Dewan Md. Farid

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dewan Md. Farid. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dewan Md. Farid. The network helps show where Dewan Md. Farid may publish in the future.

Co-authorship network of co-authors of Dewan Md. Farid

This figure shows the co-authorship network connecting the top 25 collaborators of Dewan Md. Farid. A scholar is included among the top collaborators of Dewan Md. Farid based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dewan Md. Farid. Dewan Md. Farid is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Faruqui, Nuruzzaman, et al.. (2025). A Robust Algorithm for Identifying Malicious IPs Enhancing Cybersecurity Defense. 486–492.

2.

Mahmud, Ilias, et al.. (2024). Predicting Crop Prices using Machine Learning Algorithms for Sustainable Agriculture. University of Southern Denmark Research Portal (University of Southern Denmark). 1–6. 2 indexed citations

3.

Farid, Dewan Md., et al.. (2023). Real-Time Face Recognition with Mask using Deep Convolutional Neural Network. 457–461. 1 indexed citations

4.

Farid, Dewan Md., et al.. (2023). Deep Learning for Traffic Data Mining. 1–6. 2 indexed citations

5.

Farid, Dewan Md., et al.. (2023). Boosting Oversampling Methods for Imbalanced Data Classification. 6. 1–6.

6.

Ghosh, Tapotosh, et al.. (2023). A Comprehensive Review on Precision Agriculture and Machine Learning Approach in Bangladesh. 8. 1–6. 2 indexed citations

7.

Rahman, Mohammad Masudur & Dewan Md. Farid. (2023). Exploring Federated Learning with Naïve Bayes using AVC Information. 1. 1–6. 1 indexed citations

8.

Shatabda, Swakkhar, et al.. (2023). Recyclable Waste Image Classification using Convolutional Neural Networks. 7. 1–6. 1 indexed citations

9.

Rahman, Sadia, et al.. (2023). Bangladeshi Vehicle Classification using Transfer Learning with YOLOv7. 1–6. 1 indexed citations

10.

Farid, Dewan Md., et al.. (2023). Detecting Lung Cancer with Federated and Transfer Learning. 1–6. 1 indexed citations

11.

Ahmed, Sajid, et al.. (2020). FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction. Heliyon. 6(3). e03444–e03444. 43 indexed citations

12.

Ahmed, Sajid, et al.. (2018). CFSBoost: Cumulative feature subspace boosting for drug-target interaction prediction. Journal of Theoretical Biology. 464. 1–8. 20 indexed citations

13.

Islam, Md Mofijul, et al.. (2018). iProtGly‐SS: Identifying protein glycation sites using sequence and structure based features. Proteins Structure Function and Bioinformatics. 86(7). 777–789. 30 indexed citations

14.

Farid, Dewan Md., et al.. (2018). Effective DNA binding protein prediction by using key features via Chou’s general PseAAC. Journal of Theoretical Biology. 460. 64–78. 47 indexed citations

15.

Ahmed, Sajid, et al.. (2018). FRnet-DTI: Convolutional Neural Networks for Drug-Target Interaction.. arXiv (Cornell University). 1 indexed citations

16.

Sharma, Alok, et al.. (2018). EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features. Journal of Theoretical Biology. 443. 138–146. 28 indexed citations

17.

Muhammod, Rafsanjani, et al.. (2018). iRecSpot-EF: Effective sequence based features for recombination hotspot prediction. Computers in Biology and Medicine. 103. 17–23. 17 indexed citations

18.

Satu, Md. Shahriare, et al.. (2017). Mining traffic accident data of N5 national highway in bangladesh employing decision trees. 722–725. 18 indexed citations

19.

Al‐Mamun, Mohammad A., et al.. (2015). An in silico model to demonstrate the effects of Maspin on cancer cell dynamics. Journal of Theoretical Biology. 388. 37–49. 9 indexed citations

20.

Farid, Dewan Md., et al.. (2014). Efficient and scalable multi-class classification using naïve Bayes tree. 1–4. 8 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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