Science Explorer Interactive view Map
Topic · Computer Networks and Communications

Smart Systems and Machine Learning

Smart Systems and Machine Learning is a research topic within Computer Networks and Communications. Science Explorer counts 3.9k research works in it since 1960. 15.5% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on cognitive radio networks, machine learning, and smart technologies for applications such as spectrum handovers, smart farming, urban sustainability, IoT, and wireless sensor networks. It also addresses security techniques, energy efficiency, and social awareness in the context of cognitive radio networks.

  • Cognitive Radio Networks
  • Machine Learning
  • Spectrum Handovers
  • Smart Farming
  • Urban Sustainability
  • IoT
  • Security Techniques
  • Wireless Sensor Networks
  • Energy Efficiency
  • Social Awareness
Research works
3.9k
fractional, since 1960
In the world top 10%
604
per year above
Top-10% rate
15.5%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+596%
the tick is no change

Which countries lead Smart Systems and Machine Learning research?

By volume, India and Indonesia publish the most (1.2k and 134 works in 2022–2025).

By volume, 2022–2025

  1. 1 India 1.2k works
  2. 2 Indonesia 134 works
  3. 3 China 112 works
  4. 4 United States 104 works
  5. 5 ?? 65 works
  6. 6 Iraq 61 works
  7. 7 Malaysia 59 works
  8. 8 Nigeria 53 works
  9. 9 Saudi Arabia 49 works
  10. 10 Pakistan 36 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

India: 64.3%Indonesia: 7.1%China: 6.0%United States: 5.5%6 others listed: 17.2%64%largest
India1,212 · 64.3%Indonesia134 · 7.1%China112 · 6.0%United States104 · 5.5%6 others listed323 · 17.2%

Shares of the rows listed above, not of the whole node.

Which institutions lead Smart Systems and Machine Learning research?

By volume in 2022–2025, Saveetha University publishes the most Smart Systems and Machine Learning research, followed by SRM Institute of Science and Technology and Chitkara University.

By volume, 2022–2025

  1. 1 Saveetha UniversityIndia 122 works
  2. 2 SRM Institute of Science and TechnologyIndia 42 works
  3. 3 Chitkara UniversityIndia 31 works
  4. 4 Lovely Professional UniversityIndia 28 works
  5. 5 Graphic Era UniversityIndia 27 works
  6. 6 Vellore Institute of Technology UniversityIndia 27 works
  7. 7 Uttaranchal UniversityIndia 24 works
  8. 8 Chandigarh UniversityIndia 22 works
  9. 9 Mangalore UniversityIndia 19 works
  10. 10 Koneru Lakshmaiah Education FoundationIndia 17 works

Where is Smart Systems and Machine Learning research done?

The largest centres of Smart Systems and Machine Learning research in 2022–2025 are Chennai (India), Bengaluru (India), Dehra Dūn (India) and Coimbatore (India). Among places with at least 20 works in it, it is an unusually large share of all research in Dehra Dūn, Phagwāra and Chennai.

Largest cities, 2022–2025

  1. 1 Chennai India 254 works
  2. 2 Bengaluru India 63 works
  3. 3 Dehra Dūn India 59 works
  4. 4 Coimbatore India 54 works
  5. 5 Pune India 46 works
  6. 6 Chandigarh India 38 works
  7. 7 Hyderabad India 35 works
  8. 8 Greater Noida India 29 works
  9. 9 Vellore India 28 works
  10. 10 Phagwāra India 28 works

Where it is the local speciality

  1. Dehra DūnIN · 59.3 works26×
  2. PhagwāraIN · 27.8 works25×
  3. ChennaiIN · 253.7 works23×
  4. VijayawadaIN · 23.3 works22×
  5. Greater NoidaIN · 29.1 works21×
  6. MangaluruIN · 24.1 works19×
  7. MohaliIN · 24.2 works16×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Smart Systems and Machine Learning than the world average.

See Smart Systems and Machine Learning on the map

Where is the best place to study Smart Systems and Machine Learning?

Among universities, judged by research, Mangalore University, Lovely Professional University and Saveetha University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%60%mean 23.85%fractional works in this node (log) →share in the world top 10% →Mangalore University: 19, 51.2%Lovely Professional University: 28, 27.1%Saveetha University: 122, 7.4%Chitkara University: 31, 23.8%Graphic Era University: 27, 23.8%SRM Institute of Science and Technology: 42, 21.3%Amrita Vishwa Vidyapeetham: 14, 29.2%Vellore Institute of Technology University: 27, 19.6%Koneru Lakshmaiah Education Foundation: 17, 22.5%Institute of Engineering: 13, 12.6%Mangalore UniversityLovely Professional …Chitkara UniversitySaveetha University
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Mangalore UniversityIndia 68.651.2%67.4×19 —
2 Lovely Professional UniversityIndia 59.427.1%25.0×28 —
3 Saveetha UniversityIndia 58.97.4%56.1×122 —
4 Chitkara UniversityIndia 57.423.8%35.5×31 —
5 Graphic Era UniversityIndia 57.023.8%34.8×27 —
6 SRM Institute of Science and TechnologyIndia 56.921.3%21.9×42 —
7 Amrita Vishwa VidyapeethamIndia 53.429.2%11.5×14 —
8 Vellore Institute of Technology UniversityIndia 52.619.6%12.1×27 —
9 Koneru Lakshmaiah Education FoundationIndia 52.422.5%27.2×17 —
10 Institute of EngineeringNepal 51.212.6%29.4×13 —

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Smart Systems and Machine Learning research growing?

Output in 2018–2022 was 596% higher than in 2013–2017, peaking in 2024. The fastest-growing topics are Smart Systems and Machine Learning.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.