Muhammad Sajjad

7.2k citations
116 papers · 5.0k indexed · 4 hit papers · h-index 36

Muhammad Sajjad

110 papers receiving 4.8k citations

Hit Papers

A Novel CNN-GRU-Based Hybrid Approach for Short-Term Resi...3722017202620202023200400600

Peers

Muhammad Sajjad
Comparison fields: 5 of 171
  • Computer Vision and Pattern Recognition 2.8k
  • Neurology 612
  • Artificial Intelligence 1.5k
  • Signal Processing 477
  • Human-Computer Interaction 182
Replace Amin Ullah with:
Amin Ullah South Korea
Li Zhang China
Usman Tariq Saudi Arabia
Weibo Liu China
D. Jude Hemanth India
João Paulo Papa Brazil
Imran Razzak Australia
Clinton Fookes Australia
Munish Kumar India
Guiguang Ding China
Muhammad Sajjad relative to Amin Ullah South Korea Amin Ullah's profile →
Citations per field
00.5×8.4×
Amin Ullah · 1×
Citations per year

Countries citing papers authored by Muhammad Sajjad

Since Specialization
Citations

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

Fields of papers citing papers by Muhammad Sajjad

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
2 20252
3 20251
4 20251
5 20241
6 202418
7 20247
8 20235
9 20237
10 202212
11 20225
12 202257
13 202156
14 201951
15 20185
16
Improving yield and mineral profile of tomato through changing crop micro-environment.
20171
17
Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Featuresbreakdown →
2017541
18 201683
19
Participation of women in agriculture activities in District Peshawar.
201225
20
Farmers field schools and rice productivity: an empirical analysis of District Malakand.
20123

About Muhammad Sajjad

Muhammad Sajjad is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction and Signal Processing, having authored 116 papers that have together received 5.0k indexed citations. Recurring topics across this work include Video Analysis and Summarization (15 papers), Advanced Image and Video Retrieval Techniques (15 papers), Video Surveillance and Tracking Methods (11 papers), Anomaly Detection Techniques and Applications (11 papers), Image Retrieval and Classification Techniques (10 papers), AI in cancer detection (9 papers), Advanced Image Processing Techniques (9 papers) and COVID-19 diagnosis using AI (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.8k citations), Neurology (612 citations) and Artificial Intelligence (1.5k citations). Muhammad Sajjad has collaborated with scholars based in South Korea, Pakistan and Norway. Frequent co-authors include Sung Wook Baik, Khan Muhammad, Amin Ullah, Jamil Ahmad, Irfan Mehmood, Salman Khan, Mustaqeem Mustaqeem, Ali Shariq Imran, Tanveer Hussain and Wanqing Wu. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and IEEE Transactions on Image Processing.

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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