M. Manohar

548 total citations
45 papers, 339 citations indexed

About

M. Manohar is a scholar working on Artificial Intelligence, Computer Networks and Communications and Information Systems. According to data from OpenAlex, M. Manohar has authored 45 papers receiving a total of 339 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 7 papers in Computer Networks and Communications and 7 papers in Information Systems. Recurrent topics in M. Manohar's work include Smart Agriculture and AI (6 papers), Network Security and Intrusion Detection (4 papers) and Leaf Properties and Growth Measurement (4 papers). M. Manohar is often cited by papers focused on Smart Agriculture and AI (6 papers), Network Security and Intrusion Detection (4 papers) and Leaf Properties and Growth Measurement (4 papers). M. Manohar collaborates with scholars based in India, United States and Iraq. M. Manohar's co-authors include Ramesh Vatambeti, K. V. D. Kiran, K. Hajela, James C. Tilton, Anila Dwivedi, Iram Fatima, Ruchi Saxena, B. Vishalakshi, Jeffrey A. Newcomer and William J. Tranquilli and has published in prestigious journals such as Circulation Research, Scientific Reports and Carbohydrate Polymers.

In The Last Decade

M. Manohar

39 papers receiving 321 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
M. Manohar India 10 58 54 49 39 32 45 339
Mohit Arora India 11 25 0.4× 39 0.7× 85 1.7× 45 1.2× 55 1.7× 24 392
Swaleha Zubair India 9 91 1.6× 23 0.4× 30 0.6× 132 3.4× 38 1.2× 24 603
Lulu Zhang China 12 67 1.2× 14 0.3× 28 0.6× 91 2.3× 52 1.6× 49 375
Wook-Dong Kim South Korea 13 152 2.6× 60 1.1× 28 0.6× 203 5.2× 9 0.3× 39 539
Xiaojun Guo China 10 28 0.5× 106 2.0× 10 0.2× 116 3.0× 13 0.4× 30 313
Ramandeep Kaur India 10 39 0.7× 23 0.4× 105 2.1× 90 2.3× 11 0.3× 28 361
Mohammad Ali Abdullah Almoyad Saudi Arabia 11 105 1.8× 32 0.6× 31 0.6× 115 2.9× 9 0.3× 37 552
Kabir Ahmed United States 10 61 1.1× 56 1.0× 22 0.4× 40 1.0× 19 0.6× 26 328
Shenyuan Xu China 12 28 0.5× 10 0.2× 17 0.3× 187 4.8× 29 0.9× 29 427
Shailendra Kumar Singh India 9 67 1.2× 11 0.2× 20 0.4× 45 1.2× 9 0.3× 29 286

Countries citing papers authored by M. Manohar

Since Specialization
Citations

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

Fields of papers citing papers by M. Manohar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Manohar

This figure shows the co-authorship network connecting the top 25 collaborators of M. Manohar. A scholar is included among the top collaborators of M. Manohar based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with M. Manohar. M. Manohar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Manohar, M., Srinivas Hebbar, Namdev Dhas, et al.. (2025). Emerging trends in chitosan based colloidal drug delivery systems: A translational journey from research to practice. Carbohydrate Polymers. 360. 123604–123604. 3 indexed citations
2.
Manohar, M., et al.. (2024). Evaluation of drug release efficiency and antibacterial property of a pH-responsive dextran-based silver nanocomposite hydrogel. International Journal of Biological Macromolecules. 268(Pt 2). 131783–131783. 16 indexed citations
3.
Manohar, M., et al.. (2024). Detection of Forest Fire Using Modified LSTM Based Feature Extraction with Waterwheel Plant Optimisation Algorithm Based VAE-GAN Model. International Journal of Safety and Security Engineering. 14(2). 329–340. 1 indexed citations
4.
5.
Manohar, M., et al.. (2023). An efficient load balancing in cloud computing using hybrid Harris hawks optimization and cuckoo search algorithm. International Journal of Advanced Technology and Engineering Exploration. 10(105). 1 indexed citations
6.
Pradhan, Nrusingha Charan, et al.. (2023). Classification of a New-Born Infant’s Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning. Revue d intelligence artificielle. 37(2). 257–265. 5 indexed citations
7.
Manohar, M., et al.. (2023). Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model. SN Computer Science. 4(6). 3 indexed citations
8.
Manohar, M., et al.. (2023). A Comprehensive Review on Crop Disease Prediction Based on Machine Learning and Deep Learning Techniques. Lecture notes in networks and systems. 481–503. 6 indexed citations
9.
Vatambeti, Ramesh, et al.. (2023). Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN. International Journal of Computer Network and Information Security. 15(6). 1–15. 2 indexed citations
10.
Vatambeti, Ramesh, et al.. (2023). Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet. Scientific Reports. 13(1). 15371–15371. 7 indexed citations
11.
Sivakami, R., et al.. (2022). Dirichlet Feature Embedding with Adaptive Long Short-Term Memory Model for Intrusion Detection System. Journal of System and Management Sciences. 1 indexed citations
12.
Manohar, M., et al.. (2022). Road Traffic Prediction and Optimal Alternate Path Selection Using HBI-LSTM and HV-ABC. Indian Journal of Science and Technology. 15(15). 689–699. 2 indexed citations
13.
Manohar, M., et al.. (2021). A Big Data Analysis Using Fuzzy Deep Convolution Network Based Model for Heart Disease Classification. International journal of intelligent engineering and systems. 14(2). 147–156. 5 indexed citations
14.
Manohar, M., et al.. (2020). A Prediction Technique for Heart Disease Based on Long Short Term Memory Recurrent Neural Network. International journal of intelligent engineering and systems. 13(2). 31–39. 15 indexed citations
15.
16.
Fatima, Iram, Vishal Chandra, Ruchi Saxena, et al.. (2011). 2,3-Diaryl-2H-1-benzopyran derivatives interfere with classical and non-classical estrogen receptor signaling pathways, inhibit Akt activation and induce apoptosis in human endometrial cancer cells. Molecular and Cellular Endocrinology. 348(1). 198–210. 15 indexed citations
17.
Kumar, Manoj, S. Nandi, & M. Manohar. (2010). Comparison of virus isolation and haemagglutination assay with polymerase chain reaction for diagnosis of canine parvovirus.. The Indian Veterinary Journal. 87(9). 849–852. 1 indexed citations
18.
Nandi, S., M. Manohar, & Manoj Kumar. (2010). Serosurveillance of infectious bovine rhinotracheitis in buffalo bulls.. The Indian Veterinary Journal. 87(6). 544–545. 3 indexed citations
19.
Nandi, S., M. Manohar, Awadh Bihari Pandey, & R.S. Chauhan. (2008). Sensitive detection of genomic DNA of BHV-1 in semen samples of bulls by polymerase chain reaction. 10(2). 132–136. 2 indexed citations
20.
Nandi, Shuvro P., Awadh Bihari Pandey, M. Manohar, & Rajinder Singh Chauhan. (2007). Sero-surveillance of infectious bovine rhinotracheitis (IBR) in cow bulls and buffalo bulls in India. Indian Journal of Comparative Microbiology Immunology and Infectious Diseases. 28. 1–3. 2 indexed citations

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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