Dino Isa

2.8k citations
81 papers · 2.2k indexed · h-index 23

Impact in

Papers in

Dino Isa

78 papers receiving 2.1k citations

Peers

Dino Isa
Comparison fields: 5 of 136
  • Energy Engineering and Power Technology 196
  • Automotive Engineering 422
  • Control and Systems Engineering 446
  • Electronic, Optical and Magnetic Materials 351
  • Renewable Energy, Sustainability and the Environment 262
Replace Haris M. Khalid with:
Haris M. Khalid United Arab Emirates
João Martins Portugal
Jean‐Philippe Martin France
Yitao Liu China
Ran Li China
Mohamed M. F. Darwish Egypt
Sungshin Kim South Korea
Muhammad Jawad Pakistan
Hasmat Malik India
Zheng Li China
Dino Isa relative to Haris M. Khalid United Arab Emirates Haris M. Khalid's profile →
Citations per field
00.5×7.2×
Haris M. Khalid · 1×
Citations per year

Countries citing papers authored by Dino Isa

Since Specialization
Citations

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

Fields of papers citing papers by Dino Isa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Dino Isa, 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 Dino Isa Line = papers co-authored together Dino Isa links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1
Palm Oil Fresh Fruit Bunch Ripeness Grading Recognition Using Convolutional Neural Network
201850
2 201813
3 201824
4 20173
5 201643
6 2016148
7 201432
8 201420
9 20145
10 201420
11 201412
12 20121
13 20123
14 201250
15 20093
16
Column Vectorizing Algorithms for Support Vector Machines
20081
17 200872
18 200849
19 20050
20 19913

About Dino Isa

Dino Isa is a scholar working on Energy Engineering and Power Technology, Automotive Engineering, Electronic, Optical and Magnetic Materials, Artificial Intelligence and Information Systems, having authored 81 papers that have together received 2.2k indexed citations. Recurring topics across this work include Advanced Battery Technologies Research (16 papers), Text and Document Classification Technologies (14 papers), Supercapacitor Materials and Fabrication (12 papers), Face and Expression Recognition (9 papers), Spam and Phishing Detection (8 papers), Electric and Hybrid Vehicle Technologies (8 papers), Electric Vehicles and Infrastructure (7 papers) and Microgrid Control and Optimization (7 papers). The work is most often cited by research in Energy Engineering and Power Technology (196 citations), Automotive Engineering (422 citations), Control and Systems Engineering (446 citations), Electronic, Optical and Magnetic Materials (351 citations) and Renewable Energy, Sustainability and the Environment (262 citations). Dino Isa has collaborated with scholars based in Malaysia, United Kingdom and United States. Frequent co-authors include Lam Hong Lee, Rajprasad Kumar Rajkumar, Yee Wan Wong, Lee Wai Chong, Vish Kallimani, Poi Sim Khiew, Wee Siong Chiu, Niusha Shafiabady, Raj Rajkumar and T.K. Tan. Their work appears in journals such as Expert Systems with Applications, Journal of Power Sources, Chemical Engineering Journal, Journal of Applied Sciences and Applied Intelligence.

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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