GhostConv+CA-YOLOv8n: a lightweight network for rice pest detection based on the aggregation of low-level features in real-world complex backgrounds by Fei Li, Yang Lu, Qiang Ma, Shuxin Yin, Rui Zhao

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Research on Iterative Learning Method for Lower Limb Exoskeleton Rehabilitation Robot Based on RBF Neural Network by Jing Li, Huimin Jiang, Moyao Gao, Shuang Li, Zhanli Wang, Zaixiang Pang, Yang Zhang, Yang Jiao

“…We propose a novel control strategy integrating iterative learning with RBF neural network-based sliding mode control, featuring a single hidden-layer pre-feedback architecture. …”
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Radiomics machine learning based on asymmetrically prominent cortical and deep medullary veins combined with clinical features to predict prognosis in acute ischemic stroke: a retr... by Hongyi Li, Cancan Chang, Bo Zhou, Yu Lan, Peizhuo Zang, Shannan Chen, Shouliang Qi, Ronghui Ju, Yang Duan

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Short-term photovoltaic power forecasting based on a new hybrid deep learning model incorporating transfer learning strategy by Tiandong Ma, Feng Li, Renlong Gao, Siyu Hu, Wenwen Ma

“…Subsequently, the temporal characteristics between the features are extracted in the BiLSTM layer. Finally, an attention mechanism is used to strengthen the key features by assigning weights to efficiently construct the relationship between the features and output variables. …”
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Graph-enhanced implicit aspect-level sentiment analysis based on multi-prompt fusion by Xu Li, Xinlong Wang, Chunlong Yao, Yang Li

“…Additionally, they often use short texts with few available features, making the analysis more challenging. To this end, this paper proposes a generative model for graph-enhanced implicit aspect-level sentiment analysis based on multi-prompt fusion. …”
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Cyber security entity recognition method based on residual dilation convolution neural network by Bo XIE, Guowei SHEN, Chun GUO, Yan ZHOU, Miao YU

“…In recent years,cybersecurity threats have increased,and data-driven security intelligence analysis has become a hot research topic in the field of cybersecurity.In particular,the artificial intelligence technology represented by the knowledge graph can provide support for complex cyberattack detection and unknown cyberattack detection in multi-source heterogeneous threat intelligence data.Cybersecurity entity recognition is the basis for the construction of threat intelligence knowledge graphs.The composition of security entities in open network text data is very complex,which makes traditional deep learning methods difficult to identify accurately.Based on the pre-training language model of BERT (pre-training of deep bidirectional transformers),a cybersecurity entity recognition model BERT-RDCNN-CRF based on residual dilation convolutional neural network and conditional random field was proposed.The BERT model was used to train the character-level feature vector representation.Combining the residual convolution and the dilation neural network model to effectively extract the important features of the security entity,and finally obtain the BIO annotation of each character through CRF.Experiments on the large-scale cybersecurity entity annotation dataset constructed show that the proposed method achieves better results than the LSTM-CRF model,the BiLSTM-CRF model and the traditional entity recognition model.…”
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Application of optical flow method in metro train speed measurement by LYU Hongqiang, HUANG Tao, NIE Xingjia, CHEN Keyu, CAI Zhengkai, SHAN Qi

“…Subway speed is an important parameter of train control system. The traditional subway speed measurement methods have some defects. …”
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Adversarial training driven malicious code detection enhancement method by Yanhua LIU, Jiaqi LI, Zhengui OU, Xiaoling GAO, Ximeng LIU, Weizhi MENG, Baoxu LIU

“…To solve the deficiency of the malicious code detector’s ability to detect adversarial input, an adversarial training driven malicious code detection enhancement method was proposed.Firstly, the applications were preprocessed by a decompiler tool to extract API call features and map them into binary feature vectors.Secondly, the Wasserstein generative adversarial network was introduced to build a benign sample library to provide a richer combination of perturbations for malicious sample evasion detectors.Then, a perturbation reduction algorithm based on logarithmic backtracking was proposed.The benign samples were added to the malicious code in the form of perturbations, and the added benign perturbations were culled dichotomously to reduce the number of perturbations with fewer queries.Finally, the adversarial malicious code samples were marked as malicious and the detector was retrained to improve its accuracy and robustness of the detector.The experimental results show that the generated malicious code adversarial samples can evade the detector well.Additionally, the adversarial training increases the target detector’s accuracy and robustness.…”
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Colour guided ground-to-UAV fire segmentation by Rui Zhou, Tardi Tjahjadi

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Load identification method based on one class classification combined with fuzzy broad learning by Wang Yi, Wang Xiaoyang, Li Songnong, Chen Tao, Hou Xingzhe, Fu Xiuyuan

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An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules by Xianzhi Huang, Fangyi Xu, Wenchao Zhu, Lin Yao, Jiahuan He, Junhao Su, Wending Zhao, Hongjie Hu

“…The CT images was randomly divided into training and testing cohorts (80:20), with radiomic features extracted from the training cohort. …”
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Rectified Adam Optimizer and LSTM with Attention Mechanism for ECG-Based Multi-class Classification of Cardiac Arrhythmia by T. Sivaranjani, B. Sasikumar, G. Sugitha

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A Study on Canopy Volume Measurement Model for Fruit Tree Application Based on LiDAR Point Cloud by Na Guo, Ning Xu, Jianming Kang, Guohai Zhang, Qingshan Meng, Mengmeng Niu, Wenxuan Wu, Xingguo Zhang

“…Additionally, improvements to the mesh integral volume method incorporate the effects of canopy gaps in height difference calculations, significantly enhancing the accuracy of canopy volume estimation. For feature selection, a random forest-based recursive feature elimination method with cross-validation was employed to filter 10 features. …”
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Audio recognition method of belt conveyor roller fault based on convolutional neural network and linear regression by Xiangyuan CHEN, Wei QIN, Yanchi LIU, Minghua LUO

“…Finally, based on two weak classifiers, using the spectrogram and sound quality features as data sources, fusion of multimodal faulty features and enrich data dimensions, based on the spectrogram and sound quality features, residual convolutional neural network computing image features, fast fitting of audio basic features using multiple linear regression, a roller fault voiceprint representation model combining convolutional neural network and linear regression is generated for joint training. …”
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A Spatiotemporal Sequence Prediction Framework Based on Mask Reconstruction: Application to Short-Duration Precipitation Radar Echoes by Zhi Yang, Changzheng Liu, Ping Mei, Lei Wang

“…During pre-training, the model learns global structural features of meteorological systems from sparse contexts by randomly masking local spatiotemporal regions of radar images. …”
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P-TAME: Explain Any Image Classifier With Trained Perturbations by Mariano V. Ntrougkas, Vasileios Mezaris, Ioannis Patras

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Sentinel-2 Masking CNNs Trained on Physics-Supervised Labels by Efrain Padilla-Zepeda, Kevin Alonso, Raquel De Los Reyes, Deni Torres-Roman, Avi Putri Pertiwi, Tobias Storch

“…Rather than relying on manually labeled data, the proposed method selects high-quality training samples from Python-based atmospheric correction software (PACO), using pixel selection strategies to remove ambiguous or inconsistent labels. …”
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Methodology for the organisation of professional training of senior citizens: general concept by T. M. Rezer

“…The conceptual model for the organisation of professional training of senior citizens is developed from the point of system-based approach and includes the following elements: 1) clarification of conceptual apparatus of senior adult education; 2) determination of anatomical and physiological features of senior citizens; 3) argumentation of the need of senior citizens for professional training; 4) determination of the peculiarities of professional training for senior citizens; 5) development of prerequisites list for successful learning of senior citizens; 6) formulation of learning objectives and tasks for its organisation; 7) choice of approaches for building the model of training and its principles as well as methodology for determining the quality and results of training.Practical significance. …”
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Machine Training of the System of Functional Diagnostics of the Shaft Lifting Machine by Dovbysh A. S., Zimovets V. I., Zuban Y. A., Prikhodchenko A. S.

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Preoperative MR - based model for predicting prognosis in patients with intracranial extraventricular ependymoma by Liyan Li, Xueying Wang, Zeming Tan, Yipu Mao, Deyou Huang, Xiaoping Yi, Muliang Jiang, Bihong T. Chen

“…Objectives: To develop and validate a prediction model based on brain MRI features to predict disease-free survival (DFS) and overall survival (OS) for patients with intracranial extraventricular ependymoma (IEE). …”
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