D. Binu

30 papers receiving 1.0k citations

Hit Papers

RideNN: A New Rider Optimization Algorithm-Based Neural Network for Fault Diagnosis in Analog Circuits 2018 · 260 citations
260201820262020202350100150200250

Peers

D. Binu
Comparison fields: 5 of 118
  • Hardware and Architecture 105
  • Health Information Management 60
  • Artificial Intelligence 411
  • Computer Vision and Pattern Recognition 185
  • Industrial and Manufacturing Engineering 82
Replace Amit Chhabra with:
Amit Chhabra India
Arpit Jain India
Samih M. Mostafa Egypt
Ayman El‐Sayed Egypt
Muhammad Zakarya Pakistan
Vitaly Levashenko Slovakia
Tao Ye China
Imed Ben Dhaou Saudi Arabia
Ghulam Abbas Pakistan
Raj Rajkumar India
D. Binu relative to Amit Chhabra India Amit Chhabra's profile →
Citations per field
00.5×3.2×
Amit Chhabra · 1×
Citations per year

Countries citing papers authored by D. Binu

Since Specialization
Citations

This map shows the geographic impact of D. Binu'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 D. Binu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites D. Binu more than expected).

Fields of papers citing papers by D. Binu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by D. Binu. 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 D. Binu. The network helps show where D. Binu may publish in the future.

Co-authorship network

The 25 scholars most cited alongside D. Binu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with D. Binu Line = papers co-authored together D. Binu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20240
3 20233
4 20225
5 20221
6 202211
7 202210
8 202293
9
Artificial Intelligence in Data Mining: Theories and Applications
20217
10 202067
11 20202
12 20203
13 2019213
14 20183
15 2018136
16 20161
17 201510
18 201476
19 201316
20 20126

About D. Binu

D. Binu is a scholar working on Hardware and Architecture, Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence and Information Systems, having authored 33 papers that have together received 1.1k indexed citations. Recurring topics across this work include Integrated Circuits and Semiconductor Failure Analysis (6 papers), Advancements in Semiconductor Devices and Circuit Design (5 papers), VLSI and Analog Circuit Testing (5 papers), Advanced Clustering Algorithms Research (4 papers), Metaheuristic Optimization Algorithms Research (4 papers), Data Mining Algorithms and Applications (3 papers), Face and Expression Recognition (3 papers) and Sentiment Analysis and Opinion Mining (2 papers). The work is most often cited by research in Hardware and Architecture (105 citations), Health Information Management (60 citations), Artificial Intelligence (411 citations), Computer Vision and Pattern Recognition (185 citations) and Industrial and Manufacturing Engineering (82 citations). D. Binu has collaborated with scholars based in India, Nepal and Oman. Frequent co-authors include B S Kariyappa, B. R. Rajakumar, S. Muthukrishnan, S. Praveena, Aloysius George, K. C. Ramya, B. Sangeetha, R. Meenal, Rajasekaran Ekambaram and K. Vinoth Kumar. Their work appears in journals such as Kybernetes, IEEE Transactions on Industrial Electronics, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, Indian Journal of Science and Technology and Expert Systems with Applications.

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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2026