Machine learning detection of heteroresistance in Escherichia coliResearch in context by Andrei Guliaev, Karin Hjort, Michele Rossi, Sofia Jonsson, Hervé Nicoloff, Lionel Guy, Dan I. Andersson

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Machine Learning Detection of Melting Layers From Radar Observations by Yan Xie, Fraser King, Claire Pettersen, Mark Flanner

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Deep learning detects retropharyngeal edema on MRI in patients with acute neck infections by Oona Rainio, Heidi Huhtanen, Jari-Pekka Vierula, Janne Nurminen, Jaakko Heikkinen, Mikko Nyman, Riku Klén, Jussi Hirvonen

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Machine learning detection of Gaussian steering in continuous-variable systems under data imbalance by Jie Guo, Taotao Yan, Jinchuan Hou, Xiaofei Qi, Kan He

“…The speed advantage of machine learning detection will be more obvious in the case of higher modes. …”
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Extended VGG16 Deep-Learning Detects COVID-19 from Chest CT Images by abolfazl karimiyan abdar, seyyed mostafa sadjadi, ali bashirgonbadi, mehran naghibi, Hamid Soltanian-Zadeh

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Unraveling deep-learning detection of atmospheric Kármán vortex streets: dataset, baseline benchmarking, and optimization by Yihan Zhang, Chaoyue Wu, Qiao Su, Yuqi Zhang, Daoyi Chen

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Deep learning detects entire multiple-size lunar craters driven by elevation data and topographic knowledge by Liyang Xiong, Yanxiang Wang, Haoyu Cao, Yingchao Ren, Sijin Li, Yang Chen, Guoan Tang

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An early warning system based on machine learning detects huge forest loss in Ukraine during the war by Roberto Cazzolla Gatti, Rocìo Beatriz Cortès Lobos, Michele Torresani, Duccio Rocchini

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Fluid and White Matter Suppression contrasts MRI improves Deep Learning detection of Multiple Sclerosis Cortical Lesions by Pedro M. Gordaliza, Jannis Müller, Alessandro Cagol, Nataliia Molchanova, Francesco La Rosa, Charidimos Tsagkas, Cristina Granziera, Meritxell Bach Cuadra

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Improving machine learning detection of Alzheimer disease using enhanced manta ray gene selection of Alzheimer gene expression datasets by Zahraa Ahmed, Mesut Çevik

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Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned by Marketa Ciharova, Khadicha Amarti, Ward van Breda, Ward van Breda, Martin J. Gevonden, Sina Ghassemi, Annet Kleiboer, Christiaan H. Vinkers, Christiaan H. Vinkers, Christiaan H. Vinkers, Christiaan H. Vinkers, Milou S. C. Sep, Milou S. C. Sep, Milou S. C. Sep, Milou S. C. Sep, Sophia Trofimova, Alexander C. Cooper, Xianhua Peng, Xianhua Peng, Mieke Schulte, Mieke Schulte, Eirini Karyotaki, Eirini Karyotaki, Eirini Karyotaki, Pim Cuijpers, Pim Cuijpers, Pim Cuijpers, Heleen Riper, Heleen Riper

“…., visual, acoustic, verbal, and physiological), or a combination of visual, acoustic and verbal features, performance ranged from acceptable to good, but only for the presentation task (accuracy up to.71, F1-score up to.73).ConclusionsThe complexity of input features needed for machine-learning detection of stress severity based on multimodal data requires large sample sizes with wide variability of stress reactions and inputs among participants. …”
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Real-Time Pedestrian Tracking and Counting with TLD by Jiawei Shi, Xianmei Wang, Huer Xiao

“…Then each pedestrian patch is handled by real-time TLD (Tracking-Learning-Detection) to get a new predication position according to similarity measure. …”
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Passive forensic based on spatio-temporal localization of video object removal tampering by Linqiang CHEN, Quanxin YANG, Lifeng YUAN, Ye YAO, Zhen ZHANG, Guohua WU

“…To address the problem of identification of authenticity and integrity of video content and the location of video tampering area,a deep learning detection algorithm based on video noise flow was proposed.Firstly,based on SRM (spatial rich model) and C3D (3D convolution) neural network,a feature extractor,a frame discriminator and a RPN (region proposal network) based spatial locator were constructed.Secondly,the feature extractor was combined with the frame discriminator and the spatial locator respectively,and then two neural networks were built.Finally,two kinds of deep learning models were trained by the enhanced data,which were used to locate the tampered area in temporal domain and spatial domain respectively.The test results show that the accuracy of temporal-domain location is increased to 98.5%,and the average intersection over union of spatial localization and tamper area labeling is 49%,which can effectively locate the tamper area in temporal domain and spatial domain.…”
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An Indian annotated weed dataset for computer vision tasks in precision farmingMendeley Data by Sayali Shinde, Vahida Attar

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SURABHI: Self-Training Using Rectified Annotations-Based Hard Instances for Eidetic Cattle Recognition by Manu Ramesh, Amy R. Reibman

“…We show that the final cow ID prediction accuracy on previously unseen cows also improves significantly after applying SURABHI to a deep-learning detection model with high capacity, especially when available training data are minimal. …”
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Deepfake Video Traceability and Authentication via Source Attribution by Canghai Shi, Minglei Qiao, Zhuang Li, Zahid Akhtar, Bin Wang, Meng Han, Tong Qiao

“…Currently, most of the mainstream detectors are based on deep neural networks. Such deep learning detection frameworks often face several problems that need to be addressed, for example, dependence on large-annotated datasets, lack of interpretability, and limited attention to source traceability. …”
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Precision Weeding in Agriculture: A Comprehensive Review of Intelligent Laser Robots Leveraging Deep Learning Techniques by Chengming Wang, Caixia Song, Tong Xu, Runze Jiang

“…Furthermore, we analyze the challenges and open problems associated with deep learning detection systems and explore future trends in this research field. …”
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Odour generalisation and detection dog training by Lyn Caldicott, Thomas W. Pike, Helen E. Zulch, Daniel S. Mills, Fiona J. Williams, Kevin R. Elliker, Bethany Hutchings, Anna Wilkinson

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Image Target Detection and Recognition Method Using Deep Learning by Hongyan Sun

“…Based on the analysis of the existing theories of deep learning detection and recognition, this paper summarized the composition and working principle of the traditional image target detection and recognition system and compared the basic models of target detection and recognition, such as R-CNN network, Fast-RCNN network, and Faster-RCNN network. …”
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Deep learning-based approach to third molar impaction analysis with clinical classifications by Yunus Balel, Kaan Sağtaş

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