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    • kongjianlei@btbu.edu.cn
    Academic Editor
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    Contributions by role
    Author 1
    Editor 2
    ORCID
    0000-0002-0074-3467
    Jianlei Kong
    School of Computer Science and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
    Summary
    Jianlei Kong received the B.E. degree in industrial automation, the master’s degree in control theory engineering, and the Ph.D. degree in forestry engineering from Beijing Forestry University, China, in 2011, 2013, and 2016. He is currently an Associate Professor of system science with Beijing Technology and Business University. He has published a number of invention patents, software copyrights, and academic papers, including eight ESI hot papers (Top 0.1%) and 16 ESI highly cited papers (Top 1%). His research interests include time-series prediction, pattern recognition, deep learning, and blockchain traceability.
    Edited Journals
    IECE Transactions on Intelligent Systematics
    IECE Transactions on Sensing, Communication, and Control
    IECE Contributions
    Articles
    1
    Edited
    2
    Free Access | Research Article | 21 October 2024
    RF Planning And Optimization Of 5G On The City Campus (MUST) of Mirpur, Pakistan
    by
    Adnan Khokher
    ,
    Bilal Mushtaq
    ,
    Muhammad Abdul Rehman
    ,
    Muhamamd Jamshed Abbas
    IECE Transactions on Sensing, Communication, and Control | Volume 1, Issue 1: 52-59, 2024 | DOI:10.62762/TSCC.2024.670663
    Abstract
    As we know, the world is rapidly moving towards 5G and B5G technology to achieve high data rates, massive communication capacity, connectivity, and low latency. 5G offers a latency of less than 1 ms and extremely high data volume compared to previous technologies. The main challenge is the complex nature of 5G network deployment, especially at high frequencies (3–300 GHz) on a university campus with varied building structures. In this paper, we will discuss a scenario for deploying 5G at the Mirpur University of Science and Technology (MUST) in Mirpur, Pakistan so that telecom operators and vendors who wish to deploy a 5G network on the campus in the future can draw on our research finding... More >
    Graphical Abstract
    RF Planning And Optimization Of 5G On The City Campus (MUST) of Mirpur, Pakistan
    Free Access | Research Article | 20 October 2024
    Comparison of Deep Learning Algorithms for Retail Sales Forecasting
    by
    Rana Shakeel Ahmed
    ,
    Muhammad Hasnain
    ,
    Muhammad Hamza Mahmood
    ,
    Muhammad Abid Mehmood
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 3: 101-115, 2024 | DOI:10.62762/TIS.2024.300700
    Abstract
    We investigate the use of deep learning models for retail sales predictions in this research. Having a proper sales forecasting can lead to optimization in inventory management, marketing strategies, and other core business operations. This research proposed to assess deep learning models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Multilayer Perceptron and hybrid CNN-LSTM model. The models are further improved by using some dense layers to embed daily sales data from the biggest pharmaceutical firm in the study. Models are then trained on 80% of the dataset and tested on remaining 20%. The accuracy of the proposed research is compared using evaluation metrics... More >
    Graphical Abstract
    Comparison of Deep Learning Algorithms for Retail Sales Forecasting
    Free Access | Research Article | 08 June 2024
    GPS Tracking Based on Stacked-Serial LSTM Network
    by
    Xuebo Jin
    ,
    Songzheng Liu
    ,
    Jianlei Kong
    ,
    Yuting Bai
    ,
    Tingli Su
    ,
    Huijun Ma
    Chinese Journal of Information Fusion | Volume 1, Issue 1: 50-62, 2024 | DOI:10.62762/CJIF.2024.361889
    Abstract
    Maneuvering target tracking is widely used in unmanned vehicles, missile navigation, underwater ships, etc. Due to the uncertainty of the moving characteristics of maneuvering targets and the low sensor measurement accuracy, trajectory tracking has always been an open research problem and challenging work. This paper proposes a trajectory estimation method based on LSTM neural network for uncertain motion characteristics. The network consists of two LSTM networks with stacked serial relationships, one of which is used to predict the movement dynamics, and the other is used to update the track's state. Compared with the classical Kalman filter based on the maneuver model, the method proposed... More >
    Graphical Abstract
    GPS Tracking Based on Stacked-Serial LSTM Network