Research and Optimization of White Blood Cell Classification Methods Based on Deep Learning and Fourier Ptychographic Microscopy by Mingjing Li, Junshuai Wang, Shu Fang, Le Yang, Xinyang Liu, Haijiao Yun, Xiaoli Wang, Qingyu Du, Ziqing Han

“…The proposed method introduces four key innovations to enhance detection accuracy and model efficiency: (1) A novel Conv2Former (Convolutional Transformer) backbone was designed to combine the local pattern extraction capability of convolutional neural networks (CNNs) with the global contextual reasoning of transformers, thereby improving the expressiveness of feature representation. (2) The CARAFE (Content-Aware ReAssembly of Features) upsampling operator was adopted to replace conventional interpolation methods, thereby enhancing the spatial resolution and semantic richness of feature maps. (3) An Efficient Multi-scale Attention (EMA) module was introduced to refine multi-scale feature fusion, enabling the model to better focus on spatially relevant features critical for WBC classification. (4) Soft-NMS (Soft Non-Maximum Suppression) was used instead of traditional NMS to better preserve true positives in densely packed or overlapping cell scenarios, thereby reducing false positives and false negatives. …”
Get full text

Path planning of coal mine rescue robot based on blocked grid map by Xiaoqiang SHAO, Mingqian LIU, Bo MA, Hao LI, Zhiyue LYU, Zehui HAN

“…Finally, each type of subgraph is matched with the initial grid map through a two-dimensional convolution operation to block nodes that do not require expansion. …”
Get full text

Research Advances in Underground Bamboo Shoot Detection Methods by Wen Li, Qiong Shao, Fan Guo, Fangyuan Bian, Huimin Yang

“…Future research should prioritize edge-computing solutions, cost-effective sensor networks, and cross-disciplinary collaborations to bridge technical and practical gaps. …”
Get full text

Combining OBIA, CNN, and UAV imagery for automated detection and mapping of individual olive trees by Oumaima Ameslek, Hafida Zahir, Hanane Latifi, El Mostafa Bachaoui

“…With the development of technology, this process can be made more automated by using intelligent algorithms such as CNN.This work presents an OBIA-CNN (Object Based Image Analysis-Convolution Neural Network) approach that combines CNNs with OBIA to automatically detect and count olive trees from Phantom4 advanced drone imagery. …”
Get full text

Early Breast Cancer Prediction Using Thermal Images and Hybrid Feature Extraction-Based System by Doaa Youssef, Hanan Atef, Shaimaa Gamal, Jala El-Azab, Tawfik Ismail

“…Infrared thermal imaging has great potential for early prediction of breast cancer, offering non-invasive, cost-effective, non-radiation exposure and applicability to be used regularly for both young and old women. …”
Get full text

Evaluation of Deep Learning Models for Polymetallic Nodule Detection and Segmentation in Seafloor Imagery by Gabriel Loureiro, André Dias, José Almeida, Alfredo Martins, Eduardo Silva

“…Traditional deep learning methods are based on the use of convolutional layers to extract features, while recent architectures, such as transformer-based architectures, use self-attention mechanisms to obtain global context. …”
Get full text

Hyper Spectral Camera ANalyzer (HyperSCAN) by Wen-Qian Chang, Hsun-Ya Hou, Pei-Yuan Li, Michael W. Shen, Cheng-Ling Kuo, Tang-Huang Lin, Loren C. Chang, Chi-Kuang Chao, Jann-Yenq Liu

“…The advantages of HyperSCAN are that it is designed for modular design, is compact and lightweight, and low-cost using commercial off-the-shelf (COTS) optical components. …”
Get full text

Real-time detection and identification of fish skin health in the underwater environment based on improved YOLOv10 model by Duanrui Wang, Meng Wu, Xingyue Zhu, Qiwei Qin, Shaowen Wang, Haibin Ye, Kaiyuan Guo, Chi Wu, Yi Shi

“…The C2f-D-LKA layer is employed in place of the C2f convolutional layer, improving the model’s capacity to capture irregularly shaped and sized objects while effectively reducing computational overhead and parameter load. …”
Get full text

DMCF-Net: Dilated Multiscale Context Fusion Network for SAR Flood Detection by Zhimin Wang, Lingli Zhao, Nan Jiang, Weidong Sun, Jie Yang, Lei Shi, Hongtao Shi, Pingxiang Li

“…DFR module uses convolutions with varying kernel sizes to refine the deepest features, improving the accuracy of flood detection. …”
Get full text

Medium density EMG armband for gesture recognition by Eisa Aghchehli, Eisa Aghchehli, Milad Jabbari, Chenfei Ma, Matthew Dyson, Kianoush Nazarpour

“…To enhance decoding accuracy, we introduced a novel spatio-temporal convolutional neural network that integrates spatial information from additional EMG sensors with temporal dynamics. …”
Get full text

A Spaceborne Passive Localization Algorithm Based on MSD-HOUGH for Multiple Signal Sources by Liting Zhang, Hao Huan, Tao Ran, Shangyu Zhang, Yushu Zhang, Hao Ding

“…Firstly, an improved convolutional neural network based on the adaptive histogram equalization method (AHE-CNN) is proposed to achieve source number estimation. …”
Get full text

A Diagnosis Method for Noise and Intermittent Faults in Analog Circuits Based on the Fusion of Multiscale Fuzzy Entropy Features and Amplitude Features by Junyou Shi, Yilei Hou, Zili Wang, Zhilin Yang, Zhenyang Lv

“…Finally, the two features are fed into a convolutional neural network for diagnosis. The method is applied to two typical analog circuits. …”
Get full text

Automated Detection of the Kyphosis Angle Using a Deep Learning Approach: A Cross-Sectional Study on Young Adults by Onur Kocak, Cansel Ficici, Ilknur Ezgi Dogan, Ziya Telatar, Nihan Ozunlu Pekyavas

“…In regard to clinical diagnosis and evaluation methods, high-cost radiological measurements and a variety of non-radiological clinical methods are employed. …”
Get full text

Construction and Recording Method of a Three-Dimensional Model to Automatically Manage Thermal Abnormalities in Building Exteriors by Jonghyeon Yoon, Sangjun Hwang, Kyonghoon Kim, Sanghyo Lee

“…This study proposes an automated three-dimensional (3D)-modeling method that combines convolutional neural networks (CNNs) with unmanned aerial vehicle (UAV) technology for the efficient management of thermal anomalies in building exteriors. …”
Get full text

Research on damage detection technology for wind turbine blade acoustic signals by fusion of sparse representation, compressive sensing and deep learning by Liang Wang, Chun Yang, Chao Yuan, Yanan Liu, Yanqing Chen

“…Abstract In view of the problem that the high noise and data redundancy in the voiceprint signal of the wind turbine blade lead to insufficient diagnostic accuracy and real-time performance and increase the acquisition cost, this paper combines sparse representation, compressed sensing, and deep learning technology to apply a new wind turbine blade damage detection method, aiming to enhance the accuracy and real-time performance of wind turbine blade damage diagnosis. …”
Get full text

An AI-Based Horticultural Plant Fruit Visual Detection Algorithm for Apple Fruits by Bin Yan, Xiameng Li, Rongshan Yan

“…Therefore, this method improves the original YOLOv5s network architecture by replacing the embedded Depthwise Separable Convolution in its Backbone network, which reduces the size and parameter count of the model while ensuring detection accuracy. …”
Get full text

Advancing Ton-Bag Detection in Seaport Logistics with an Enhanced YOLOv8 Algorithm by Xiulin Qiu, Haozhi Zhang, Chang Yuan, Qinghua Liu, Hongzhi Yao

“…Then, with reference to spatial and channel reconstruction convolution and deformable convolution, the C2f-SCTT block is designed for the backbone network, which reduces the spatial and channel redundancy between features in the network. …”
Get full text

Scenario-adaptive wireless fall detection system based on few-shot learning by Yuting ZENG, Suzhi BI, Lili ZHENG, Xiaohui LIN, Hui WANG

“…A scenario robust fall detection system based on few-shot learning (FDFL) in wireless environment was designed.The performance of existing fall detection methods based on Wi-Fi channel state information (CSI) degrades significantly across scenarios, which requires collecting and marking a large number of CSI samples in each application scenario, resulting in high cost for large-scale deployment.Therefore, the method of few-shot learning was introduced, which can maintain the performance of fall detection with high accuracy when the number of annotated samples in unfa-miliar scenes is insufficient.The proposed FDFL was mainly divided into two stages, source domain meta-training and target domain meta-learning.The meta training stage of the source domain consists of two parts: data preprocessing and classification training.In the data preprocessing stage, the collected original CSI amplitude and phase data were denoised and segmented.In the classification training stage, a large number of processed source domain data samples were used to train a CSI feature extractor based on convolutional neural network.In the meta-learning stage of the target domain, the limited labeled data sampled in the target domain was effectively extracted based on the feature extractor trained in the meta-training module, and then a lightweight machine learning classifier was trained to detect the fall behavior under the cross-scene.Through several experiments in different scenarios, FDFL can achieve an average accuracy of 95.52% for the four classification tasks of falling, sitting, walking and sit down with only a small number of samples in the target domain, and maintain robust detection accuracy for changes in test environment, personnel target and equipment location.…”
Get full text

Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions by Asmaa A. Abdelwahab, Mustafa A. Elattar, Sahar Ali Fawzi

“…Traditional approaches, while foundational, often face challenges related to cost, scalability, and translatability. This review provides a comprehensive exploration of how graph-based computational techniques, including Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), have emerged as powerful tools for modeling complex CYP enzyme interactions and predicting ADMET properties with improved precision. …”
Get full text

Navigating the Challenges and Opportunities of Tiny Deep Learning and Tiny Machine Learning in Lung Cancer Identification by Yasir Salam Abdulghafoor, Auns Qusai Al-Neami, Ahmed Faeq Hussein

“…The combination of lightweight Convolutional Neural Networks and limited resources could produce a portable model with low computational cost that has the ability to substitute the skill and experience of doctors needed in urgent cases. …”
Get full text