Deep learning models for detection of explosive ordnance using autonomous robotic systems: trade-off between accuracy and real-time processing speed by Vadym Mishchuk, Herman Fesenko, Vyacheslav Kharchenko

“…The objectives are as follows: 1) conduct a comparative analysis of YOLOv8 and RT-DETR image processing models for explosive ordnance (EO) detection, focusing on accuracy and real-time processing speed;2) to explore the impact of different input image resolutions on model performance for identifying the optimal resolution for EO detection tasks;3) to analyze how object size (small, medium, large) affects detection efficiency for enhancing EO recognition accuracy; 4) to develop recommendations for EO detection model configurations; 5) to propose methods for enhancing EO detection model performance in complex environments. …”
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Establishment and application of a rapid new detection method for antimicrobial susceptibility testing of Klebsiella pneumoniae based on MALDI-TOF MS by Yapei Zhang, Fanghua Fan, Xuan Wang, Jie Zhu, Shilei Dong

“…Current rapid AST techniques are hampered by factors such as high costs, technological complexities, and limited detection capabilities. We present a novel rapid method and applied to the determination of the susceptibility of K. pneumoniae to imipenem. …”
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The Influence and Compensation of Environmental Factors (pH, Temperature, and Conductivity) on the Detection of Chemical Oxygen Demand in Water by UV-Vis Spectroscopy by Jingwei Li, Yipei Ding, Yijing Lu, Jia Liu, Chenxuan Zhou, Zhiyu Shao

“…Considering the complexity of environmental factors, a data fusion method is proposed to compensate for the influence of three environmental factors simultaneously. …”
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RNA-BioLens: A Novel Raspberry Pi-Based Digital Microscope With Image Processing for Acute Lymphoblastic Leukemia Detection by Angeline Dwi Sanjaya, Rachmad Setiawan, Nada Fitrieyatul Hikmah

“…This system aims to enhance detection accuracy and speed while balancing complexity and procurement costs. …”
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Evaluating the Performance of Machine Learning Classifiers for Network Intrusion Detection: A Comparative Study Using the UNSW-NB15 Dataset by Iwan Handoyo Putro

“…It is because of their growing adoption and complexity of cyber-attacks. Therefore, protecting network infrastructures and identifying malicious behavior becomes a necessity. …”
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Enhancing UAV Object Detection in Low-Light Conditions with ELS-YOLO: A Lightweight Model Based on Improved YOLOv11 by Tianhang Weng, Xiaopeng Niu

“…ELS-YOLO features a re-parameterized backbone (ER-HGNetV2) with integrated Re-parameterized Convolution and Efficient Channel Attention mechanisms, a Lightweight Feature Selection Pyramid Network (LFSPN) for multi-scale object detection, and a Shared Convolution Separate Batch Normalization Head (SCSHead) to reduce computational complexity. …”
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Real-Time Detection and Instance Segmentation Models for the Growth Stages of <i>Pleurotus pulmonarius</i> for Environmental Control in Mushroom Houses by Can Wang, Xinhui Wu, Zhaoquan Wang, Han Shao, Dapeng Ye, Xiangzeng Kong

“…Challenges such as scene complexity and overlapping mushroom clusters can impact the accuracy of growth stage detection and target segmentation. …”
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Automatic Segmentation and Characterization of Structure Planes From Borehole Images Based on Deep Learning by Shuangyuan Chen, Zengqiang Han, Yi Cheng, Chao Wang

“…By providing a reliable and efficient tool for structure plane segmentation and parameter characterization, this study enhances the accuracy and efficiency of rock mass structure detection and analysis.…”
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Training a convolutional neural network for exoplanet classification with transit photometry data by Juliana Wang

“…As a result, efforts to enhance the efficiency of exoplanet research have led to the development of various detection methods, including transit photometry. …”
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Ensemble Deep Learning Models for Enhanced Brain Tumor Classification by Leveraging ResNet50 and EfficientNet-B7 on High-Resolution MRI Images by Retinderdeep Singh, Sheifali Gupta, Salil Bharany, Ahmad Almogren, Ayman Altameem, Ateeq Ur Rehman

“…Brain tumors are among the most significant and challenging medical conditions because of their sometimes-fatal outcomes and complexity of treatment options. Early, exact detection of brain tumors is essential for both the most effective treatment programs and much higher patient survival rates. …”
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RDW-YOLO: A Deep Learning Framework for Scalable Agricultural Pest Monitoring and Control by Jiaxin Song, Ke Cheng, Fei Chen, Xuecheng Hua

“…Due to target diversity, life-cycle variations, and complex backgrounds, traditional pest detection methods often struggle with accuracy and efficiency. …”
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Enhancing Wheat Spike Counting and Disease Detection Using a Probability Density Attention Mechanism in Deep Learning Models for Precision Agriculture by Ruiheng Li, Wenjie Hong, Ruiming Wu, Yan Wang, Xiaohan Wu, Zhongtian Shi, Yifei Xu, Zixu Han, Chunli Lv

“…Addressing the shortcomings of traditional detection methods, we propose an advanced feature extraction strategy and a model based on the probability density attention mechanism, designed to more effectively handle feature extraction in complex backgrounds and dense areas. …”
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FM‐YOLOv8:Lightweight gesture recognition algorithm by Fanghai Li, Xitai Na, Jinshuo Shi, Qingbin Sun

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Determination of Nitrofuran Metabolites in Complex Food Matrices Using a Rough, Cheap, Easy-Made Wooden-Tip-Based Solid-Phase Microextraction Probe and LC-MS/MS by Xiaoming Gong, Kai Li, Wenjuan Xu, Liming Jin, Mengxiao Liu, Mengmeng Hua, Guoning Tian

“…In addition, the main parameters affecting the SPME procedure efficiency were investigated in detail and the optimal conditions were found. …”
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A Comparative Study of Two-Stage Intrusion Detection Using Modern Machine Learning Approaches on the CSE-CIC-IDS2018 Dataset by Isuru Udayangani Hewapathirana

“…The findings emphasize the complementary nature of the two approaches, suggesting that a combined model could achieve a balance between accuracy and computational efficiency. This work provides valuable insights for designing scalable, high-performance intrusion detection systems in modern network environments.…”
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A Novel Training Strategy for Deep Learning Model Compression Applied to Automatic Modulation Classification by Mateus A. Goldbarg, Micael Balza, Sergio N. Silva, Lucileide M. D. Silva, Marcelo A. C. Fernandes

“…However, their computational demands pose a significant hurdle for real-time modulation detection. To tackle this issue, a novel training strategy is proposed in this study. …”
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Cross-attention swin-transformer for detailed segmentation of ancient architectural color patterns by Lv Yongyin, Yu Caixia

“…Traditional approaches, including convolutional neural networks (CNNs) and standard transformer-based models, have achieved significant success; however, they often face challenges in capturing fine-grained details and maintaining efficiency across diverse datasets. These methods struggle with balancing precision and computational efficiency, especially when dealing with complex patterns and high-resolution images.MethodsTo address these limitations, we propose a novel segmentation model that integrates a hierarchical vision transformer backbone with multi-scale self-attention, cascaded attention decoding, and diffusion-based robustness enhancement. …”
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A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization by Jianqiu Chen, Huan Xiong, Shixuan Zhou, Xiang Wang, Benxiao Lou, Longtang Ning, Qingwei Hu, Yang Tang, Guobin Gu

“…This method provides technical support for railroad construction safety monitoring and effectively addresses personnel detection tasks in complex construction environments.…”
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Effectiveness and optimization of bidirectional long short-term memory (BiLSTM) based fast detection of deep fake face videos for real-time applications by Haoxiang Wang

“…The aim is to overcome the limitations of current technologies in terms of efficiency and accuracy. An optimized BiLSTM architecture and training strategy are employed, enhancing recognition capabilities through data preprocessing and feature enhancement while also minimizing computational complexity and resource consumption during detection. …”
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DETECTION OF METALLIC AND NON-METALLIC ORIGIN FOREIGN BODIES IN BIOLOGICAL TISSUES OBTAINED AS A RESULT OF MILITARY TRAUMA: A LITERATURE REVIEW by Viktor A. Chernyak, Dmytro Ye. Dubenko, Kostiantyn K. Karpenko, Lidiia V. Butska, Vitalii S. Myrona, Nataliia Yu. Kondratiuk

“…Development of new imaging techniques: Development of non-radioactive fragments detection in gunshot wounds using noise emission technology, imaging and intraoperative navigation will increase the efficiency of detection and treatment of patients with FB.…”
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