Dias Issa

704 citations
5 papers · 483 · 1 hit paper · h-index 4

Impact in

Papers in

Dias Issa

5 papers receiving 456 citations

Dias Issa's Hit Papers

Speech emotion recognition with deep convolutional neural networks 2020 · 388 citations
3880+2+4Years since publication100200300

Peers

Dias Issa
Comparison fields: 5 of 55
  • Signal Processing 298
  • Experimental and Cognitive Psychology 319
  • Artificial Intelligence 159
  • Pharmacy 21
  • Computer Vision and Pattern Recognition 73
Replace Seyedmahdad Mirsamadi with:
Seyedmahdad Mirsamadi United States
Zengwei Yao China
Duc Le United States
Panikos Heracleous Japan
Tadahisa Kondo Japan
Yaacob Sazali Malaysia
Michalis Papakostas United States
Youssef Serrestou France
Valentin Enescu Belgium
Ryuichi Nisimura Japan
Dias Issa relative to Seyedmahdad Mirsamadi United States Seyedmahdad Mirsamadi's profile →
Citations per field
00.5×3.5×
Seyedmahdad Mirsamadi · 1×
Citations per year

Countries citing papers authored by Dias Issa

Since Specialization
Citations

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

Fields of papers citing papers by Dias Issa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 12 scholars most cited alongside Dias Issa, 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 Dias Issa Line = papers co-authored together Dias Issa links everyone, so they are left out of the graph.

All Works

5 of 5 papers shown
#Work
1
Speech emotion recognition with deep convolutional neural networks
Hit paper breakdown →
2020388
2 202257
3 202127
4 202110
5 20231

About Dias Issa

Dias Issa is a scholar working on Signal Processing, Artificial Intelligence, Experimental and Cognitive Psychology, Computer Vision and Pattern Recognition and Mechanics of Materials, having authored 5 papers that have together received 483 indexed citations. Recurring topics across this work include Speech and Audio Processing (3 papers), Music and Audio Processing (3 papers), Speech Recognition and Synthesis (2 papers), Advanced Image and Video Retrieval Techniques (1 paper), Phonetics and Phonology Research (1 paper), Emotion and Mood Recognition (1 paper), Robotics and Sensor-Based Localization (1 paper) and Advanced Vision and Imaging (1 paper). The work is most often cited by research in Signal Processing (298 citations), Experimental and Cognitive Psychology (319 citations), Artificial Intelligence (159 citations), Pharmacy (21 citations) and Computer Vision and Pattern Recognition (73 citations). Dias Issa has collaborated with scholars based in South Korea, United States and Kazakhstan. Frequent co-authors include M. Fatih Demirci, Adnan Yazıcı, Mark Hasegawa‐Johnson, Chang D. Yoo, Young-Hoon Jung, Gwangsu Kim, Keon Jae Lee, Trung X. Pham, Bo‐Yeon Lee and Leda Sarı. Their work appears in journals such as Soft Computing, Nano Energy, Biomedical Signal Processing and Control and Speech Communication.

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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