Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques by Antonietta Eliana Barrasso, Claudio Perone, Roberto Romaniello

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A Dual-Stream Dental Panoramic X-Ray Image Segmentation Method Based on Transformer Heterogeneous Feature Complementation by Tian Ma, Jiahui Li, Zhenrui Dang, Yawen Li, Yuancheng Li

“…To address the widespread challenges of significant multi-category dental morphological variations and interference from overlapping anatomical structures in panoramic dental X-ray images, this paper proposes a dual-stream dental segmentation model based on Transformer heterogeneous feature complementarity. Firstly, we construct a parallel architecture comprising a Transformer semantic parsing branch and a Convolutional Neural Network (CNN) detail capturing pathway, achieving collaborative optimization of global context modeling and local feature extraction. …”
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An enhanced BERT model with improved local feature extraction and long-range dependency capture in promoter prediction for hearing loss by Jing Sun, Yangfan Huang, Jiale Fu, Li Teng, Xiao Liu, Xiaohua Luo

“…The CNN module is able to capture local regulatory features, while the BiLSTM module can effectively model long-distance dependencies, enabling efficient integration of global and local features of promoter sequences. …”
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DuSAFNet: A Multi-Path Feature Fusion and Spectral–Temporal Attention-Based Model for Bird Audio Classification by Zhengyang Lu, Huan Li, Min Liu, Yibin Lin, Yao Qin, Xuanyu Wu, Nanbo Xu, Haibo Pu

“…This research presents DuSAFNet, a lightweight deep neural network for fine-grained bird audio classification. …”
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Advances in Federated Learning: Combining Local Preprocessing With Adaptive Uncertainty Symmetry to Reduce Irrelevant Features and Address Imbalanced Data by Zahraa Khduair Taha, Johnny Koh Siaw Paw, Yaw Chong Tak, Tiong Sieh Kiong, Kumaran Kadirgama, Foo Benedict, Tan Jian Ding, Kharudin Ali, Azher M. Abed

“…However, challenges such as feature selection and client imbalanced data persist. …”
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Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information by Ruibin Zhang, Yingshi Guo, Yunze Long, Yang Zhou, Chunyan Jiang

“…Time sequence high-level abstract combination features in the multiview scene are then extracted by an improved VGG19 network model and are fused with the potential spatiotemporal interaction of the multitarget operation state data extraction features detected by the laser radar by using a one-dimensional convolution neural network. …”
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A Multi‐Scale Time Adaptive Fusion Network for Transformer Fault Diagnosis by XuMing Liu, XiaoKun He, YongLin Li

“…The adaptive high‐order hybrid network (AHOHN) module then fuses multi‐scale temporal data with transformer features using a hybrid attention mechanism, extracting temporal variation patterns. …”
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Mal-Detect: An intelligent visualization approach for malware detection by Olorunjube James Falana, Adesina Simon Sodiya, Saidat Adebukola Onashoga, Biodun Surajudeen Badmus

“…New malware images are then generated using a deep generative adversarial neural network from original malware samples. The generated malware images with original malware and benign files images are pre-processed and trained with Deep Convolutional Neural Networks to extract important features from the dataset. …”
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UAV ad hoc network link prediction based on deep graph embedding by Jian SHU, Qining WANG, Linlan LIU

“…Aiming at the characteristics of the UAV ad hoc network (UAANET), such as topological temporal-varying, node mobility and intermittent connection, a temporal graph embedding model was proposed to present the preprocessed UAANET.To improve the sampling efficiency, the sampling interval was calculated based on linear probability.The network structure features were mapped to the relationship between nodes, and the contextual semantic features of nodes were extracted by adversarial training.With the help of long and short-term memory network, the temporal characteristics of the UAANET were extracted to predict the connection at the next moment.AUC, MAP, and Error Rate were employed as evaluation indexes.The simulation experiments based on NS-3 show that compared with Node2vec, DDNE and E-LSTM-D, the proposed method has a better accuracy.…”
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Video-Based Sign Language Recognition via ResNet and LSTM Network by Jiayu Huang, Varin Chouvatut

“…ResNet extracts the sign language features. Then, the learned feature space is used as the input of the LSTM network to obtain long sequence features. …”
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A power grid partitioning method based on complex network coupling matrices by XIONG Jun, TANG Fan, CHEN Zeming, WANG Yimiao, MEI Tao

“…Finally， considering the grid’s operational characteristics and topological features， the local expansion method of complex network theory is adaptively improved to achieve non-overlapping optimal grid partitioning. …”
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Self-Supervised Multiscale Contrastive and Attention-Guided Gradient Projection Network for Pansharpening by Qingping Li, Xiaomin Yang, Bingru Li, Jin Wang

“…Then, the multiscale high-frequency features of PAN and MS images are extracted using discrete wavelet transform (DWT). …”
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Resolution-Aware Deep Learning with Feature Space Optimization for Reliable Identity Verification in Electronic Know Your Customer Processes by Mahasak Ketcham, Pongsarun Boonyopakorn, Thittaporn Ganokratanaa

“…To address these challenges, this study proposes a novel and robust identity verification framework that integrates super-resolution preprocessing, a convolutional neural network (CNN), and Monte Carlo dropout-based Bayesian uncertainty estimation for enhanced facial recognition in electronic know your customer (e-KYC) processes. …”
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Transformer Fault Diagnosis Based on Knowledge Distillation and Residual Convolutional Neural Networks by Haikun Shang, Yanlei Wei, Shen Zhang

“…The approach begins by extracting features from DGA data using the ratio method and Multiscale sample entropy (MSE), then reconstructs the state space of the feature data using recursive plots to generate interpretable two-dimensional image features. …”
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MCRNet: Underwater image enhancement using multi-color space residual network by Ningwei Qin, Junjun Wu, Xilin Liu, Zeqin Lin, Zhifeng Wang

“…To address these issues, we propose a Multi-Color space Residual Network (MCRNet) for underwater image enhancement. …”
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Ultrawideband Non-Line-of-Sight Classification Using Transformer-Convolutional Neural Networks by Hae-Ji Hwang, Seon-Geun Jeong, Won-Joo Hwang

“…The Transformer captures global temporal dependencies through self-attention, while convolutional neural networks efficiently extract local spatial features. …”
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A Novel PROMSIS Vertical Handoff Decision Algorithm for Heterogeneous Wireless Networks by Shengmei Liu, Zhongjiu Zheng, Su Pan

“…A novel preference ranking organization method by similarity to ideal solution (PROMSIS) vertical handoff algorithm is proposed for heterogeneous wireless networks, and its essential idea includes the preference structure of the PROMETHEE and the concept of Euclid distance of the TOPSIS. …”
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Biological Image Processing Algorithm Based on Attention Mechanism and Convolutional Neural Network by Lisong Ou

“…Secondly, attention modules are used to capture remote dependencies in one spatial direction, while accurate position information is retained in another spatial direction, so that information in both vertical and horizontal directions can be retained; after a series of transformations, the attention vector is obtained and multiplied back to the original feature vector as a weight factor. The experimental results show that the proposed algorithm can effectively improve the image quality, improve the image clarity, avoid color distortion, and achieve good results in both synthetic and real low-illumination images, and the subjective and objective evaluation indicators are better than the contrast algorithm.…”
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TSRF: A Trust-Aware Secure Routing Framework in Wireless Sensor Networks by Junqi Duan, Dong Yang, Haoqing Zhu, Sidong Zhang, Jing Zhao

“…In recent years, trust-aware routing protocol plays a vital role in security of wireless sensor networks (WSNs), which is one of the most popular network technologies for smart city. …”
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Overall Layout Method of Frame Structure Plane Based on Generative Adversarial Network by ZHONG Yan, LEI Xin, LONG Danbing, FANG Changjian, KANG Yongjun

“…The core of this method involves constructing a model for the overall layout of the structural plan, which includes three main stages: constructing datasets, training and evaluating the model, and applying the model.MethodsIn the dataset construction stage, due to the limited number of data samples, and to reduce model training parameters, refine sample features, and improve training outcomes, this paper proposes three information representation methods for architecture, beams, and columns. …”
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