World's Best Scientists 2026 revealed!
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Shahab S. Band
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Computer Science
Taiwan
2026

D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Computer Science 71 1809 1751 9 9 261 14444

Shahab S. Band publications per year

The chart shows the history of publications by Shahab S. Band between 2018 and 2026, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Shahab S. Band published across 9 years, from 2018 to 2026, averaging 32.4 papers a year. Output peaked at 75 publications in 2022. 15 of the 292 publications appeared in the last two years.

No. of publications
25 50 75
Bar chart. Horizontal axis: year, 2018 to 2026. Vertical axis: number of publications, 0 to 75. Peak 75 publications in 2022. 2018: 10 publications 2019: 34 publications 2020: 48 publications 2021: 57 publications 2022: 75 publications 2023: 39 publications 2024: 14 publications 2025: 11 publications 2026: 4 publications
2018 2026

292 publications in total across all disciplines

View publications per year as a table
Shahab S. Band: publications per year, 2018 to 2026
Year Publications
2018 10
2019 34
2020 48
2021 57
2022 75
2023 39
2024 14
2025 11
2026 4
Total 292
Download as CSV

Shahab S. Band publications per year - data summary

  • Shahab S. Band, a Computer Science scholar from National Yunlin University of Science and Technology, has 292 publications recorded across 9 years, from 2018 to 2026.
  • The oldest publication on record dates to 2018 and the most recent to 2026.
  • The most productive year is 2022, with 75 publications.
  • The least productive year with any output is 2026, with 4 publications.
  • The rate of publication averages 32.4 papers per year over the whole span, or 32.4 per year counting only the 9 years with at least one publication.
  • The last 5 years on the chart (2022-2026) hold 143 publications, 49% of the career total.
  • Split into equal eras - 2018-2020: 92 publications (30.7 per year); 2021-2023: 171 publications (57.0 per year); 2024-2026: 29 publications (9.7 per year).
  • Comparing the opening and closing eras, the overall trend of publication is declining.

Shahab S. Band publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Shahab S. Band sits on this spectrum.

No. of scientists
200 400 600
Bar chart with 97 bars. Horizontal axis: publications, 32–41 to 991+. Vertical axis: number of scientists, 0 to 609. Most scientists, 609, have 142–151 publications. The last bar groups every scientist with 991 publications or more. The highlighted bar, 252–261 publications, is where this scientist sits. 32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32–41 publications 991+

This scientist: 261 publications — 65th percentile

65% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

View publications distribution as a table
Number of Computer Science scientists by publication count, Research.com 2026 ranking edition. Based on 14,188 ranked scientists.
Publications Scientists This scientist
32–41 7
42–51 22
52–61 82
62–71 134
72–81 249
82–91 324
92–101 421
102–111 420
112–121 497
122–131 544
132–141 555
142–151 609
152–161 559
162–171 534
172–181 556
182–191 583
192–201 519
202–211 508
212–221 490
222–231 437
232–241 423
242–251 408
252–261 377 261
262–271 301
272–281 335
282–291 320
292–301 293
302–311 250
312–321 238
322–331 206
332–341 209
342–351 208
352–361 162
362–371 176
372–381 127
382–391 158
392–401 128
402–411 104
412–421 94
422–431 99
432–441 83
442–451 108
452–461 73
462–471 77
472–481 69
482–491 84
492–501 62
502–511 54
512–521 57
522–531 51
532–541 51
542–551 32
552–561 38
562–571 28
572–581 43
582–591 33
592–601 41
602–611 32
612–621 28
622–631 25
632–641 27
642–651 17
652–661 20
662–671 17
672–681 15
682–691 14
692–701 21
702–711 13
712–721 12
722–731 19
732–741 14
742–751 12
752–761 10
762–771 10
772–781 11
782–791 10
792–801 11
802–811 8
812–821 8
822–831 7
832–841 11
842–851 10
852–861 5
862–871 9
872–881 4
882–891 6
892–901 3
902–911 6
912–921 3
922–931 2
932–941 2
942–951 2
952–961 3
962–971 3
972–981 3
982–990 5
991+ 100
Download as CSV

Shahab S. Band publication distribution in Computer Science in 2026 - data summary

  • The chart plots the publication count of all 14,188 Computer Science scientists ranked by Research.com in 2026, grouped into 97 ranges running from 32–41 to 991+ publications.
  • Shahab S. Band, a Computer Science scholar from National Yunlin University of Science and Technology, records 261 publications - the 65th percentile of the discipline.
  • 65% of ranked Computer Science scientists score the same or lower than Shahab S. Band, and about 35% score higher.
  • The median of the discipline falls in the 202–211 publications range, and Shahab S. Band ranks above the median.
  • The most crowded range is 142–151 publications, holding 609 scientists (4% of the field).
  • 70% of the field sits in the lowest quarter of the value range (up to 272–281 publications), so the distribution is heavily right-skewed and high scores are rare.
  • The final bar has no upper bound: it groups every scientist with 991 publications or more, 100 scientists in all (<1% of the field).

Shahab S. Band D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Shahab S. Band sits on this spectrum.

No. of scientists
200 400 600 800
Bar chart with 52 bars. Horizontal axis: D-Index, 30–31 to 131+. Vertical axis: number of scientists, 0 to 990. Most scientists, 990, have 36–37 D-Index. The last bar groups every scientist with 131 D-Index or more. The highlighted bar, 70–71 D-Index, is where this scientist sits. 30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30–31 D-Index 131+

This scientist: 71 D-Index — 88th percentile

88% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

View D-Index distribution as a table
Number of Computer Science scientists by D-index, Research.com 2026 ranking edition. Based on 14,188 ranked scientists.
D-Index Scientists This scientist
30–31 879
32–33 983
34–35 918
36–37 990
38–39 968
40–41 907
42–43 821
44–45 763
46–47 689
48–49 543
50–51 543
52–53 518
54–55 500
56–57 458
58–59 400
60–61 337
62–63 308
64–65 292
66–67 249
68–69 213
70–71 192 71
72–73 189
74–75 165
76–77 139
78–79 119
80–81 121
82–83 113
84–85 88
86–87 87
88–89 75
90–91 69
92–93 57
94–95 46
96–97 38
98–99 34
100–101 36
102–103 27
104–105 37
106–107 18
108–109 31
110–111 19
112–113 16
114–115 12
116–117 20
118–119 15
120–121 5
122–123 20
124–125 8
126–127 5
128–129 7
130 3
131+ 98
Download as CSV

Shahab S. Band D-index placement in Computer Science in 2026 - data summary

  • The chart plots the discipline H-index (D-index) of all 14,188 Computer Science scientists ranked by Research.com in 2026, grouped into 52 ranges running from 30–31 to 131+ D-Index.
  • Shahab S. Band, a Computer Science scholar from National Yunlin University of Science and Technology, records 71 D-Index - the 88th percentile of the discipline.
  • 88% of ranked Computer Science scientists score the same or lower than Shahab S. Band, and about 12% score higher.
  • The median of the discipline falls in the 44–45 D-Index range, and Shahab S. Band ranks above the median.
  • The most crowded range is 36–37 D-Index, holding 990 scientists (7% of the field).
  • 71% of the field sits in the lowest quarter of the value range (up to 54–55 D-Index), so the distribution is heavily right-skewed and high scores are rare.
  • The final bar has no upper bound: it groups every scientist with 131 D-Index or more, 98 scientists in all (<1% of the field).

Research.com Recognitions

  • 2026 - Research.com Computer Science in Taiwan Leader Award
  • 2025 - Research.com Computer Science in Taiwan Leader Award
  • 2023 - Research.com Computer Science in Taiwan Leader Award
  • 2022 - Research.com Computer Science in Taiwan Leader Award

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

His primary scientific interests are in Support vector machine, Artificial neural network, Mean squared error, Soft computing and Adaptive neuro fuzzy inference system. His study in Support vector machine is interdisciplinary in nature, drawing from both Firefly algorithm, Data mining and Radial basis function. The study incorporates disciplines such as Genetic programming, Wavelet transform, Time horizon and Pan evaporation in addition to Artificial neural network.

His work deals with themes such as Coefficient of determination, Correlation coefficient and Meteorology, which intersect with Mean squared error. His Soft computing research incorporates elements of Intrusion detection system, Intrusion prevention system, Mechanical engineering, Wireless sensor network and Cloud computing. He combines subjects such as Computational fluid dynamics and Neuro-fuzzy with his study of Adaptive neuro fuzzy inference system.

His most cited work include:

  • A systematic literature review on agile requirements engineering practices and challenges (225 citations)
  • Coupling a firefly algorithm with support vector regression to predict evaporation in Northern Iran (189 citations)
  • Survey of computational intelligence as basis to big flood management: challenges, research directions and future work (189 citations)

What are the main themes of his work throughout his whole career to date?

Shahaboddin Shamshirband mainly investigates Adaptive neuro fuzzy inference system, Support vector machine, Artificial neural network, Soft computing and Artificial intelligence. His Adaptive neuro fuzzy inference system study incorporates themes from Wind power, Wind speed and Neuro-fuzzy. His Support vector machine research includes elements of Firefly algorithm, Data mining, Radial basis function and Mean squared error, Statistics.

His research in Artificial neural network intersects with topics in Algorithm and Genetic programming. His Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition. Shahaboddin Shamshirband usually deals with Fuzzy logic and limits it to topics linked to Intrusion detection system and Wireless sensor network.

He most often published in these fields:

  • Adaptive neuro fuzzy inference system (31.28%)
  • Support vector machine (21.81%)
  • Artificial neural network (17.84%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (15.86%)
  • Machine learning (9.91%)
  • Random forest (1.98%)

In recent papers he was focusing on the following fields of study:

His primary areas of investigation include Artificial intelligence, Machine learning, Random forest, Support vector machine and Deep learning. Artificial neural network is the focus of his Artificial intelligence research. His biological study spans a wide range of topics, including Data mining, Expression, Streamflow, Inflow and Autoregressive model.

The study of Machine learning is intertwined with the study of Fuzzy logic in a number of ways. He has included themes like Correlation coefficient, Decision tree, Significant wave height, Wave height and Algorithm in his Support vector machine study. His Mean squared error course of study focuses on Adaptive neuro fuzzy inference system and Shear velocity.

Between 2019 and 2021, his most popular works were:

  • Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method. (51 citations)
  • Spatial hazard assessment of the PM10 using machine learning models in Barcelona, Spain. (35 citations)
  • Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. (33 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

The scientist’s investigation covers issues in Artificial intelligence, Support vector machine, Machine learning, Random forest and Mean squared error. The concepts of his Support vector machine study are interwoven with issues in Radial basis function, Decision tree, Neuro-fuzzy, Algorithm and Wavelet transform. His research integrates issues of Groundwater model and Resampling in his study of Machine learning.

His Mean squared error study is concerned with the larger field of Statistics. His research integrates issues of Wind speed, Adaptive neuro fuzzy inference system, Pan evaporation and k-nearest neighbors algorithm in his study of Statistics. His Correlation coefficient research incorporates themes from Artificial neural network, Recurrent neural network, Data mining, Deep learning and Hydrogeology.

Best Publications

  • State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

    Amir Mosavi;Amir Mosavi;Amir Mosavi;Mohsen Salimi;Sina Faizollahzadeh Ardabili;Timon Rabczuk

  • Sustainable Business Models: A Review

    Saeed Nosratabadi;Amir Mosavi;Shahaboddin Shamshirband;Edmundas Kazimieras Zavadskas

  • Accurate brain tumor detection using deep convolutional neural network

    Unknown

  • A Deep Learning Ensemble Approach for Diabetic Retinopathy Detection

    Sehrish Qummar;Fiaz Gul Khan;Sajid Shah;Ahmad Khan

  • A Survey of Deep Learning Techniques: Application in Wind and Solar Energy Resources

    Shahab Shamshirband;Timon Rabczuk;Kwok Wing Chau

  • Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method.

    Farzaneh Sajedi Hosseini;Bahram Choubin;Amir Mosavi;Narjes Nabipour

  • A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues.

    Shahab Shamshirband;Shahab Shamshirband;Mahdis Fathi;Abdollah Dehzangi;Anthony Theodore Chronopoulos

  • IS A PROMINENT STERNITE RELATED TO SEX RATIOS ANDABUNDANCE IN CENTROBOLUS COOK, 1897?

    Unknown

  • DOES (PREDICTED) MASS CORRELATE WITH MATING FREQUENCIES IN CENTROBOLUS COOK, 1897?

    Unknown

  • IS SIZE OR SSD RELATED TO ABUNDANCE IN CENTROBOLUS COOK, 1897?

    Unknown

  • Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions

    Seyedeh Narjes Fallah;Ravinesh Chand Deo;Mohammad Shojafar;Mauro Conti

  • Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

    Unknown

  • Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms

    Shahab S. Band;Saeid Janizadeh;Subodh Chandra Pal;Asish Saha

  • Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm

    Saeed Samadianfard;Sajjad Hashemi;Katayoun Kargar;Mojtaba Izadyar

  • AI-empowered, blockchain and SDN integrated security architecture for IoT network of cyber physical systems

    Sohaib A. Latif;Fang B. Xian Wen;Celestine Iwendi;Li-li F. Wang

  • Forecast of rainfall distribution based on fixed sliding window long short-term memory

    Unknown

  • Meta-heuristic algorithm-tuned neural network for breast cancer diagnosis using ultrasound images

    Unknown

  • Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

    Sevda Shabani;Saeed Samadianfard;Mohammad Taghi Sattari;Amir Mosavi

  • Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT

    Unknown

  • Evaluation of electrical efficiency of photovoltaic thermal solar collector

    Mohammad Hossein Ahmadi;Alireza Baghban;Milad Sadeghzadeh;Mohammad Zamen

  • Prediction of significant wave height; comparison between nested grid numerical model, and machine learning models of artificial neural networks, extreme learning and support�…

    Unknown

  • Predicting solubility of CO2 in brine by advanced machine learning systems: Application to carbon capture and sequestration

    Nait Amar Menad;Abdolhossein Hemmati-Sarapardeh;Amir Varamesh;Shahaboddin Shamshirband

  • A New Online Learned Interval Type-3 Fuzzy Control System for Solar Energy Management Systems

    Zhi Liu;Ardashir Mohammadzadeh;Hamza Turabieh;Majdi Mafarja

  • Principal Component Analysis to Study the Relations between the Spread Rates of COVID-19 in High Risks Countries

    Mohammad Reza Mahmoudi;Mohammad Hossein Heydari;Sultan Noman Qasem;Sultan Noman Qasem;Amirhosein Mosavi

  • Snow avalanche hazard prediction using machine learning methods

    Bahram Choubin;Moslem Borji;Amir Mosavi;Amir Mosavi;Farzaneh Sajedi-Hosseini

  • Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

    Shahab S. Band;Shahab S. Band;Saeid Janizadeh;Subodh Chandra Pal;Asish Saha

  • SmartBlock-SDN: An Optimized Blockchain-SDN Framework for Resource Management in IoT

    Anichur Rahman;Md. Jahidul Islam;Antonio Montieri;Mostofa Kamal Nasir

  • A Hybrid clustering and classification technique for forecasting short‐term energy consumption

    Mehrnoosh Torabi;Sattar Hashemi;Mahmoud Reza Saybani;Shahaboddin Shamshirband

  • Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning

    Amir Mosavi;Amir Mosavi;Shahaboddin Shamshirband;Ely Salwana;Kwok wing Chau

Frequent Co-Authors

Dalibor Petković
Dalibor Petković University of Nis
Amir Mosavi
Amir Mosavi Óbuda University
Kasra Mohammadi
Kasra Mohammadi University of Utah
Nor Badrul Anuar
Nor Badrul Anuar University of Malaya
Miss Laiha Mat Kiah
Miss Laiha Mat Kiah University of Malaya
Kwok-wing Chau
Kwok-wing Chau Hong Kong Polytechnic University
Abdullah Gani
Abdullah Gani University of Malaya
Hossein Bonakdari
Hossein Bonakdari University of Ottawa
Ali Mostafaeipour
Ali Mostafaeipour California State University, Fullerton
Timon Rabczuk
Timon Rabczuk Bauhaus University, Weimar

External Links

Personal website of Shahab S. Band

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Ozgur Kisi

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Kwok-wing Chau

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Isa Ebtehaj

Isa Ebtehaj

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Publications: 31

Nor Badrul Anuar

Nor Badrul Anuar

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Publications: 28

Arash Karimipour

Arash Karimipour

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Publications: 27

Abdullah Gani

Abdullah Gani

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Publications: 27

Vahid Nourani

Vahid Nourani

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Publications: 24

Masoud Afrand

Masoud Afrand

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Publications: 23

Ali Mostafaeipour

Ali Mostafaeipour

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Publications: 23

Bahram Gharabaghi

Bahram Gharabaghi

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Publications: 20

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2027 Which Schools Offer Flexible Start-Anytime Enrollment for an Artificial Intelligence Program?
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