Corn-associated weed detection model based on WAAP-YOLO by Zhiyong MENG, Yawei JIA, Xiuqing ZHANG, Yongjing NI, Ming ZHANG, Qi WU, Chenxi WU

“…Experimental results show that the WAAP-YOLO detection algorithm achieves 97.2% mAP@0.5, 85.8% mAP@0.5∶0.95, 94.0% F1 score, and a parameter count of 2.1×106, outperforming common object detection models such as YOLOv5s, YOLOv8n, and YOLOv10n. …”
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Indoor fire and smoke detection based on optimized YOLOv5. by Md Shafak Shahriar Sozol, M Rubaiyat Hossain Mondal, Achmad Husni Thamrin

“…This study aimed to address these challenges in fire and smoke detection in indoor settings. It presents a hyperparameter-optimized YOLOv5 (HPO-YOLOv5) model optimized by a genetic algorithm. …”
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Improved YOLOv5: Efficient Object Detection for Fire Images by Dongxing Yu, Shuchao Li, Zhongze Zhang, Xin Liu, Wei Ding, Xinyi Zhao

“…This network utilizes the YOLOv5 algorithm. The default versions of the You Only Look Once (YOLO) technique demonstrate limited accuracy when applied to fire detection instances, even after undergoing training and testing. …”
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Enhancing placental pathology detection with GAMatrix-YOLOv8 model by Weirui Wu, Zhifa Jiang, Jingwen Liu, Jiahui Ji, Xiaoyan Wei, Xiangyun Ye, Zhen Zhang

“…Based on the Gamatrix-YOLOV8 model, we developed an intuitive and easy-to-use graphical user interface, which enables users to easily upload pictures, view the prediction results of the model in real time, analyze and verify the detection results. Conclusions: GAMatrix-YOLOv8 achieves unprecedented prediction accuracy in detecting delayed villous maturation in placental pathological tissues through algorithmic innovation. …”
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Aerial Biological Target Classification Based on Time–Frequency Multi-Scale Feature Fusion Network by Lianjun Wang, Rui Wang, Weidong Li, Jiangtao Wang, Yujia Yan, Cheng Hu

“…The analysis revealed that radar cross section (RCS) features are insufficient to support insect and bird classification tasks, as aerial biological targets may be detected in radar sidelobes, leading to uncertainty in RCS values. …”
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Detecting cognitive motor dissociation by functional near-infrared spectroscopy by Yan Wang, Yan Wang, Yan Wang, Wentao Zeng, Wentao Zeng, Wentao Zeng, Leyao Zou, Leyao Zou, Leyao Zou, Qijun Wang, Bingkai Ren, Bingkai Ren, Bingkai Ren, Qi Xiong, Yang Bai, Yang Bai, Yang Bai, Zhen Feng, Zhen Feng, Zhen Feng

“…Seven features of hemodynamic responses were extracted during the task and the rest conditions. …”
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A Multimodal Image Registration Method for UAV Visual Navigation Based on Feature Fusion and Transformers by Ruofei He, Shuangxing Long, Wei Sun, Hongjuan Liu

“…This paper proposes a CNN-Transformer hybrid network model for feature detection and feature matching. ResNet50 is used as the backbone network for feature extraction. …”
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A hybrid critical channels and optimal feature subset selection framework for EEG fatigue recognition by Hanying Guo, Siying Chen, Yongjiang Zhou, Ting Xu, Yuhao Zhang, Hongliang Ding

“…To minimize redundant information, we propose an improved Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm for selecting the optimal feature subset, ensuring both the efficiency and accuracy of fatigue recognition. …”
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Multi-Scale Spatiotemporal Feature Enhancement and Recursive Motion Compensation for Satellite Video Geographic Registration by Yu Geng, Jingguo Lv, Shuwei Huang, Boyu Wang

“…Based on the SuperGlue matching algorithm, the method achieves automatic matching of inter-frame image points by introducing the multi-scale dilated attention (MSDA) to enhance the feature extraction and adopting a joint multi-frame optimization strategy (MFMO), designing a recursive motion compensation model (RMCM) to eliminate the cumulative effect of the orbit error and improve the accuracy of the inter-frame image point matching, and using a rational function model to establish the geometrical mapping between the video and the ground points to realize the georeferencing of satellite video. …”
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Breast Cancer MRI Classification Based on Fractional Entropy Image Enhancement and Deep Feature Extraction by Ali M. Hasan, Asaad F. Qasim, Hamid A. Jalab, Rabha W. Ibrahim

“…In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. …”
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Performance of predictive algorithms in estimating the risk of being a zero-dose child in India, Mali and Nigeria by Sebastian Bauhoff, John Tucker, Arpita Biswas

“…Furthermore, the algorithmic approach was able to detect ZD children across 176 more areas as compared with the geographical rule, for the same number of children targeted.Interpretation Predictive algorithms applied to existing data can effectively identify ZD children and could be deployed at low cost to target interventions to reduce ZD prevalence and inequities in vaccination coverage.…”
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Pengenalan Barang Pada Kereta Belanja Menggunakan Metode Scale Invariant Feature Transform (SIFT) by Ronny Makhfuddin Akbar, Nani Sunarmi

“…The algorithm proposed to detect and identify several items with image matching using Scale Invariant Feature Transform (SIFT). …”
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Towards Effective Parkinson’s Monitoring: Movement Disorder Detection and Symptom Identification Using Wearable Inertial Sensors by Umar Khan, Qaiser Riaz, Mehdi Hussain, Muhammad Zeeshan, Björn Krüger

“…The proposed pipeline employs and evaluates manual feature crafting for classical machine learning algorithms, as well as an RNN-CNN-inspired deep learning model that does not require manual feature crafting. …”
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The use of autofluorescence stomatoscopy in the algorithm of diagnosing of the oral mucosal abnormalities of patients with tuberculosis infection by T. P. Goryacheva, D. A. Zaplutanova, I. P. Goryacheva, O. A. Aleshina

“…The clinical picture of the diagnosed pathological conditions of the oral mucosa, autofluorescence images and their pathomorphological features were compared. The reports of the examined persons regarding the autofluorescence stomatoscopy were evaluated.RESULTS. …”
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Mopidip: a modular real-time pipeline for machinery diagnosis and prognosis based on deep learning algorithms by Mattia Pujatti, Davide Calzà, Andrea Gobbi, Piergiorgio Svaizer, Marco Cristoforetti

“…The tool integrates the data acquisition and processing steps with a configurable feature extraction phase. Moreover, the system also integrates deep learning algorithms for diagnosis and prognosis, including a domain adaptation stage to permit transfer learning and increase generalizability. …”
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Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural features by Rajkumar Govindarajan, K. Thirunadanasikamani, Komal Kumar Napa, S. Sathya, J. Senthil Murugan, K. G. Chandi Priya

“…This method offers a practical tool for clinicians and researchers to support early diagnosis and personalized risk assessment of AD, thus aiding in timely and informed clinical decision-making.Accurate Prediction: Gradient Boosting model achieved 93.9 % accuracy for early Alzheimer’s detection.Explainability: SHAP values provided interpretable insights into key clinical features.Clinical Tool: A Streamlit-based web app enabled real-time, explainable predictions for users.…”
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Bio inspired optimization techniques for disease detection in deep learning systems by A. Ashwini, Vanajaroselin Chirchi, S. Balasubramaniam, Mohd Asif Shah

“…Abstract Numerous contemporary computer-aided disease detection methodologies predominantly depend on feature engineering techniques; yet, they possess several drawbacks, including the presence of redundant features and excessive time consumption. …”
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Severity Classification of Freezing of Gait Using Machine-Learning Algorithms: A Hidden State Model Approach by Aditi Site, Elena Simona Lohan, Jari Nurmi

“…E-health sensors and wearables play an important role in the detection and classification of many chronic diseases. …”
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DETECTION OF KERATOCONUS DISEASE DEPENDING ON CORNEAL TOPOGRAPHY USING DEEP LEARNING by Aseel Abdulhasan Hashim, Mahdi Mazinani

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Low-dimensional multiscale fast SAR image registration method by Jiamu Li, Wenbo Yu, Zijian Wang, Jiaxin Xie, Xiaojie Zhou, Yabo Liu, Zhongjun Yu, Meng Li, Yi Wang

“…Many existing SAR image registration methods major in describing detected features in a unique, identifiable, but maybe complex way. …”
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