King Ma

520 total citations · 1 hit paper
11 papers, 348 citations indexed

About

King Ma is a scholar working on Artificial Intelligence, Signal Processing and Management Science and Operations Research. According to data from OpenAlex, King Ma has authored 11 papers receiving a total of 348 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 4 papers in Signal Processing and 3 papers in Management Science and Operations Research. Recurrent topics in King Ma's work include Time Series Analysis and Forecasting (4 papers), Stock Market Forecasting Methods (3 papers) and Anomaly Detection Techniques and Applications (3 papers). King Ma is often cited by papers focused on Time Series Analysis and Forecasting (4 papers), Stock Market Forecasting Methods (3 papers) and Anomaly Detection Techniques and Applications (3 papers). King Ma collaborates with scholars based in Canada, Bangladesh and China. King Ma's co-authors include Henry Leung, Wei Wang, Zhongyang Han, Jun Zhao, Daniel Q. Huang and Henry Leung and has published in prestigious journals such as SHILAP Revista de lepidopterología, Chaos Solitons & Fractals and IEEE Sensors Journal.

In The Last Decade

King Ma

9 papers receiving 342 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A Review of Deep Learning Models for Time Series Prediction

2019 314 citations Zhongyang Han, Jun Zhao et al. IEEE Sensors Journal profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
King Ma Canada	4	109	71	69	58	40	11	348
Chuanyang Gong China	5	121 1.1×	77 1.1×	65 0.9×	84 1.4×	60 1.5×	9	455
Deqiang Kong China	5	106 1.0×	59 0.8×	107 1.6×	77 1.3×	65 1.6×	7	452
Gaurang Panchal India	5	88 0.8×	76 1.1×	67 1.0×	23 0.4×	50 1.3×	8	402
Yuteng Xiao China	8	91 0.8×	81 1.1×	38 0.6×	37 0.6×	28 0.7×	10	340
Wendong Zheng China	9	136 1.2×	97 1.4×	92 1.3×	78 1.3×	37 0.9×	13	364
Hannes Vietz Germany	5	107 1.0×	35 0.5×	83 1.2×	35 0.6×	35 0.9×	9	424
Maulana Azad Iran	6	110 1.0×	30 0.4×	76 1.1×	34 0.6×	57 1.4×	10	484
Youru Li China	9	183 1.7×	81 1.1×	127 1.8×	98 1.7×	75 1.9×	25	545
Minbo Ma China	7	113 1.0×	63 0.9×	56 0.8×	52 0.9×	38 0.9×	9	311

Countries citing papers authored by King Ma

Since Specialization

Citations

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

Fields of papers citing papers by King Ma

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of King Ma

This figure shows the co-authorship network connecting the top 25 collaborators of King Ma. A scholar is included among the top collaborators of King Ma 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 King Ma. King Ma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

1.

Ma, King, et al.. (2024). Hydra-Mask-RCNN: An Adaptive HydraNet Architecture for Autonomous Aerial Vehicle Object Detection. SHILAP Revista de lepidopterología. 4. 1–12.

2.

Ma, King, et al.. (2024). Federated Learning for Complex AI Models in multi-UAV Systems Assisted by Mobile Edge Servers. 394–399.

3.

Ma, King, et al.. (2023). A Deep Learning Approach for a QoS Prediction System in Cellular Networks. 1–6. 1 indexed citations

4.

Ma, King, et al.. (2023). Comparison of 4G LTE and 5G NR in UAV Networks: A Simu5G-Based Performance Evaluation. 1–6. 1 indexed citations

5.

Ma, King, et al.. (2023). Nonlinear modeling of sparkling drink bubbles using a physics informed long short term memory network. Chaos Solitons & Fractals. 175. 113928–113928. 2 indexed citations

6.

Han, Zhongyang, Jun Zhao, Henry Leung, King Ma, & Wei Wang. (2019). A Review of Deep Learning Models for Time Series Prediction. IEEE Sensors Journal. 21(6). 7833–7848. 314 indexed citations breakdown →

7.

Ma, King & Henry Leung. (2019). A Novel LSTM Approach for Asynchronous Multivariate Time Series Prediction. 1–7. 15 indexed citations

8.

Ma, King, et al.. (2019). Prediction of Stock Prices with Sentiment Fusion and SVM Granger Causality. 207–214. 4 indexed citations

9.

Leung, Henry, et al.. (2017). Information fusion of stock prices and sentiment in social media using Granger causality. 614–619. 5 indexed citations

10.

Ma, King, et al.. (2017). Fiber-Optic Acoustic-Based Disturbance Prediction in Pipelines Using Deep Learning. IEEE Sensors Letters. 1(6). 1–4. 3 indexed citations

11.

Ma, King, et al.. (2017). Deep learning on temporal-spectral data for anomaly detection. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10190. 101900D–101900D. 3 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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