Norazak Senu

2.5k total citations
195 papers, 1.8k citations indexed

About

Norazak Senu is a scholar working on Numerical Analysis, Modeling and Simulation and Electrical and Electronic Engineering. According to data from OpenAlex, Norazak Senu has authored 195 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 127 papers in Numerical Analysis, 72 papers in Modeling and Simulation and 65 papers in Electrical and Electronic Engineering. Recurrent topics in Norazak Senu's work include Numerical methods for differential equations (112 papers), Differential Equations and Numerical Methods (82 papers) and Fractional Differential Equations Solutions (72 papers). Norazak Senu is often cited by papers focused on Numerical methods for differential equations (112 papers), Differential Equations and Numerical Methods (82 papers) and Fractional Differential Equations Solutions (72 papers). Norazak Senu collaborates with scholars based in Malaysia, Iraq and Türkiye. Norazak Senu's co-authors include Ali Ahmadian, Fudziah Ismail, Soheil Salahshour, Dumitru Bǎleanu, Sankar Prasad Mondal, Zanariah Abdul Majid, Nguyen Dinh Phu, Praveen Agarwal, Mohamed Suleiman and Zarina Bibi İbrahim and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Access.

In The Last Decade

Norazak Senu

179 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Norazak Senu Malaysia 22 636 579 282 252 218 195 1.8k
Mohammed Alghamdi Saudi Arabia 22 316 0.5× 295 0.5× 97 0.3× 172 0.7× 110 0.5× 196 1.9k
Majid Khan Pakistan 32 404 0.6× 564 1.0× 94 0.3× 264 1.0× 130 0.6× 147 3.2k
Mehdi Salimi Germany 18 324 0.5× 382 0.7× 41 0.1× 171 0.7× 176 0.8× 75 1.1k
Junaid Ali Khan Pakistan 21 205 0.3× 483 0.8× 194 0.7× 189 0.8× 189 0.9× 82 1.5k
Abd Allah A. Mousa Saudi Arabia 28 123 0.2× 200 0.3× 493 1.7× 548 2.2× 374 1.7× 120 2.4k
Shantanu Das India 29 391 0.6× 1.3k 2.2× 671 2.4× 202 0.8× 61 0.3× 127 3.7k
Ryan Loxton Australia 30 356 0.6× 92 0.2× 127 0.5× 107 0.4× 185 0.8× 90 2.0k
Guoyin Li Australia 31 1.3k 2.0× 85 0.1× 87 0.3× 95 0.4× 589 2.7× 123 2.8k
Wiyada Kumam Thailand 30 459 0.7× 237 0.4× 36 0.1× 1.2k 4.9× 932 4.3× 125 2.7k
Stefania Tomasiello Italy 19 180 0.3× 214 0.4× 115 0.4× 50 0.2× 60 0.3× 87 1.0k

Countries citing papers authored by Norazak Senu

Since Specialization
Citations

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

Fields of papers citing papers by Norazak Senu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Norazak Senu

This figure shows the co-authorship network connecting the top 25 collaborators of Norazak Senu. A scholar is included among the top collaborators of Norazak Senu 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 Norazak Senu. Norazak Senu 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.
Senu, Norazak, et al.. (2025). A high-performance neural network algorithm using a Legendre ensemble-based extreme learning machine for solving fractional partial differential equations. Journal of Computational and Applied Mathematics. 477. 117220–117220.
2.
Senu, Norazak, et al.. (2023). A new modern scheme for solving fractal–fractional differential equations based on deep feedforward neural network with multiple hidden layer. Mathematics and Computers in Simulation. 218. 311–333. 9 indexed citations
3.
Kılıçman, Adem, et al.. (2023). Approximate Solutions for Time-Fractional Fornberg–Whitham Equation with Variable Coefficients. Fractal and Fractional. 7(3). 260–260. 1 indexed citations
4.
Senu, Norazak, et al.. (2023). A Phase- and Amplification-Fitted 5(4) Diagonally Implicit Runge–Kutta–Nyström Pair for Oscillatory Systems. Bulletin of the Iranian Mathematical Society.. 49(3). 2 indexed citations
5.
6.
Senu, Norazak, et al.. (2023). Efficient Frequency-Dependent Coefficients of Explicit Improved Two-Derivative Runge-Kutta Type Methods for Solving Third-Order IVPs. Pertanika journal of science & technology. 31(2). 843–873. 4 indexed citations
7.
Senu, Norazak, et al.. (2023). Development of high-order adaptive multi-step Runge–Kutta–Nyström method for solving special second-order ODEs. Mathematics and Computers in Simulation. 216. 104–125. 1 indexed citations
8.
Chattopadhyay, Soham, Pawan Kumar Singh, Ali Ahmadian, et al.. (2021). MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features. Scientific Reports. 11(1). 24065–24065. 22 indexed citations
9.
Garain, Avishek, Biswarup Ray, Pawan Kumar Singh, et al.. (2021). GRA_Net: A Deep Learning Model for Classification of Age and Gender From Facial Images. IEEE Access. 9. 85672–85689. 25 indexed citations
10.
De, Debashis, et al.. (2020). DNA Sequences Compression by GP² R and Selective Encryption Using Modified RSA Technique. IEEE Access. 8. 76880–76895. 7 indexed citations
11.
12.
Long, Nik Mohd Asri Nik, et al.. (2019). Stress intensity factor for bonded dissimilar materials weakened by multiple cracks. Applied Mathematical Modelling. 77. 585–601. 7 indexed citations
13.
Long, Nik Mohd Asri Nik, et al.. (2019). Hypersingular integral equation for triple inclined cracks problems in half plane elasticity. Journal of Physics Conference Series. 1366(1). 12023–12023.
14.
Long, Nik Mohd Asri Nik, et al.. (2017). Stress intensity factor for multiple inclined or curved cracks problem in circular positions in plane elasticity. ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik. 97(11). 1482–1494. 12 indexed citations
15.
Ismail, Fudziah, et al.. (2016). A NEW OPTIMIZED RUNGE-KUTTA METHOD FOR SOLVING OSCILLATORY PROBLEMS. International Journal of Pure and Apllied Mathematics. 106(3). 4 indexed citations
16.
Hassan, Nasruddin, et al.. (2015). On modified interval repeated zoro symmetric single-step IRZSS1-5D procedure for bounding polynomial zeros simultaneously. AIP conference proceedings. 1682. 20011–20011. 1 indexed citations
17.
Shabanzadeh, Parvaneh, Norazak Senu, Kamyar Shameli, & Fudziah Ismail. (2013). APPLICATION OF ARTIFICIAL NEURAL NETWORK (ANN) FOR PREDICTION DIAMETER OF SILVER NANOPARTICLES BIOSYNTHESIZED IN CURCUMA LONGA EXTRACT. Digest Journal of Nanomaterials and Biostructures. 8(3). 1133–1144. 3 indexed citations
18.
Shabanzadeh, Parvaneh, et al.. (2013). Application of artificial neural network(ANN) for the prediction of size of silver nanoparticles prepared by green method. Digest Journal of Nanomaterials and Biostructures. 8(2). 541–549. 10 indexed citations
19.
Senu, Norazak, Mohamed Suleiman, Fudziah Ismail, & Mazliza Othman. (2010). A fourth-order diagonally implicit Runge-Kutta-Nyström method with dispersion of high order. International Conference on Applied Mathematics. 78–82. 3 indexed citations
20.
Senu, Norazak, et al.. (2010). A new diagonally implicit Runge-Kutta-Nyström method for periodic IVPs. Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia). 9(9). 679–688. 8 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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