Academic Editor
Author
Contributions by role
Author 8
Reviewer 1
Editor 8
Xue-Bo JIN
Beijing Technology and Business University
Summary
Edited Journals
IECE Contributions

Free Access | Research Article | 19 May 2025
Optimizing Cloud Security with a Hybrid BiLSTM-BiGRU Model for Efficient Intrusion Detection
IECE Transactions on Sensing, Communication, and Control | Volume 2, Issue 2: 106-121, 2025 | DOI: 10.62762/TSCC.2024.433246
Abstract
To address evolving security challenges in cloud computing, this study proposes a hybrid deep learning architecture integrating Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) for cloud intrusion detection. The BiLSTM-BiGRU model synergizes BiLSTM's long-term dependency modeling with BiGRU's efficient gating mechanisms, achieving a detection accuracy of 96.7% on the CIC-IDS 2018 dataset. It outperforms CNN-LSTM baselines by 2.2% accuracy, 3.3% precision, 3.6% recall, and 3.6% F1-score while maintaining 0.03% false positive rate. The architecture demonstrates operational efficiency through 20% reduced computational latency and 15% lower memory foo... More >

Graphical Abstract
Optimizing Cloud Security with a Hybrid BiLSTM-BiGRU Model for Efficient Intrusion Detection

Open Access | Research Article | 17 May 2025
Design and Practice of New Engineering Innovation Education for Automation Majors
IECE Transactions on Education and Learning Technologies | Volume 1, Issue 1: 1-13, 2025 | DOI: 10.62762/TELT.2025.700195
Abstract
This paper focuses on the needs of automation professional talent cultivation in the context of the construction of new engineering disciplines, and takes the course “Freshman Engineering Experience” as the research object, and carries out a systematic exploration of teaching reform in response to the problems of insufficient professional cognition and disconnection between theory and practice that exist in the current engineering education. By restructuring the curriculum system, innovating teaching methods and optimizing the practice platform, a progressive cultivation mode of “Cognition-Practice-Innovation” has been constructed. In curriculum design, the combination of professiona... More >

Graphical Abstract
Design and Practice of New Engineering Innovation Education for Automation Majors

Free Access | Research Article | 25 March 2025
Comparative Analysis of Automated Knee Osteoarthritis Severity Classification from X-Ray Images Using CNNs and VGG16 Architecture
IECE Transactions on Sensing, Communication, and Control | Volume 2, Issue 1: 36-47, 2025 | DOI: 10.62762/TSCC.2025.378503
Abstract
Osteoarthritis (OA) is a degenerative joint disease that primarily affects the knee, causing cartilage deterioration and discomfort. Early diagnosis is crucial for effective management, as it can slow disease progression and improve the quality of life. This study proposes a deep learning approach to automatically classify knee OA severity from X-ray images using Convolutional Neural Networks (CNNs) and the VGG16 model. The models were trained on a dataset of knee X-ray images, and performance was evaluated using accuracy, precision, recall, and F1-score. The proposed CNNs model achieved 99% training accuracy and 80% testing accuracy after 50 epochs, while the VGG16 model, after fine-tuning... More >

Graphical Abstract
Comparative Analysis of Automated Knee Osteoarthritis Severity Classification from X-Ray Images Using CNNs and VGG16 Architecture

Open Access | Research Article | 22 March 2025
A Deep-Learning Detector via Optimized YOLOv7-bw Architecture for Dense Small Remote-Sensing Targets in Harsh Food Supply Applications
Chinese Journal of Information Fusion | Volume 2, Issue 1: 38-58, 2025 | DOI: 10.62762/CJIF.2025.919344
Abstract
With the progressive advancement of remote sensing image technology, its application in the agricultural domain is becoming increasingly prevalent. Both cultivation and transportation processes can greatly benefit from utilizing remote sensing images to ensure adequate food supply. However, such images often exist in harsh environments with many gaps and dense distribution, which poses major challenges to traditional target detection methods. The frequent missed detections and inaccurate bounding boxes severely constrain the further analysis and application of remote sensing images within the agricultural sector. This study presents an enhanced version of the YOLO algorithm, specifically tai... More >

Graphical Abstract
A Deep-Learning Detector via Optimized YOLOv7-bw Architecture for Dense Small Remote-Sensing Targets in Harsh Food Supply Applications

Free Access | Research Article | 20 March 2025
Visual Intelligence in Neuro-Oncology: Effective Brain Tumor Detection through Optimized Convolutional Neural Networks
IECE Transactions on Sensing, Communication, and Control | Volume 2, Issue 1: 25-35, 2025 | DOI: 10.62762/TSCC.2024.964451
Abstract
Brain tumor detection (BTD) is a crucial task, as early detection can save lives. Medical professionals require visual intelligence assistance to efficiently and accurately identify brain tumors. Conventional methods often result in misrecognition, highlighting a critical research gap. To address this, a novel BTD system is proposed to predict the presence of a tumor in a given MRI image. The system leverages a convolutional neural network (CNN) architecture, combined with a multi-layer perceptron (MLP) for feature extraction and understanding complex pixel patterns. An extensive ablation study was conducted to empirically analyze and identify the optimal model for the task. The findings dem... More >

Graphical Abstract
Visual Intelligence in Neuro-Oncology: Effective Brain Tumor Detection through Optimized Convolutional Neural Networks
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