Russians among the Peoples of the Baltic States: the Origins of Mutual Perceptions and Relations by R. H. Simonyan, T. M. Kochegarova

“…For this cooperation to be productive, you must have an adequate view of the neighbors, about the processes that take place there. …”
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SMC-YOLO: A High-Precision Maize Insect Pest-Detection Method by Qinghao Wang, Yongkang Liu, Qi Zheng, Rui Tao, Yong Liu

“…Thus, in this study, we propose to use a pest detector called SMC-YOLO, which is proposed using You Only Look Once (YOLO) v8 as a reference model. …”
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The History of Miss Jane Pittman by Christopher Mulvey

“…‘What’s wrong with them books you already got?’ Mary said. ‘Miss Jane is not in them,’ I said” (Gaines v). …”
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Dental bur detection system based on asymmetric double convolution and adaptive feature fusion by HongLing Hou, Ao Yang, Xiangyao Li, Kangkai Zhu, Yandi Zhao, Zhiqiang Wu

“…The present study introduces You Only Look Once-Dental bur (YOLO-DB), an innovative deep learning-driven methodology for the accurate detection and counting of dental burs. …”
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Frontier machine learning techniques for melanoma skin cancer identification and categorization: An in-Depth review by Viomesh Singh, Kavita A. Sultanpure, Harshwardhan Patil

“…I took a deep dive into the pool of relevant studies, offering you an insider's look into the performance of the friendly neighborhood the k-nearest neighbor approach, the robust SVM, and the sophisticated CNN. …”
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STATE POLICY TO COUNTER CRIME IN BORDER REGIONS IN A DIGITAL SOCIETY by Maya G. Dieva, Dureja Z. Zijadova

“…During the period of digitalization of society, the problem of criminal law risks with the use of information technology is becoming more dangerous, large-scale and intractable. As you know, the era of the dominance of digital technologies has not only positive consequences, opening up new opportunities for the development of society and the state, but also creates additional criminal threats to national security. …”
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Fast and High-Precision Human Fall Detection Using Improved YOLOv8 Model by Ahlam R. Khekan, Hadi S. Aghdasi, Pedram Salehpour

“…Recently, Deep Learning based systems have shown promising results for detecting changes in postures and promptly responding to them in real-time. The You Only Look Once (YOLO) models have been widely used previously for designing and implementing human fall detection systems. …”
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The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition Performance by Mudasser Seraj, Andres Rosales-Castellanos, Amr Shalkamy, Karim El-Basyouny, Tony Z. Qiu

“…Hence, this study leveraged an industry-grade object detection and classification algorithm (You-Only-Look-Once, YOLO) to develop an automatic traffic sign recognition system that can identify widely used regulatory and warning signs in diverse driving conditions. …”
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Classification of Projects in the Oil and Gas Industry by A. V. Vlasov

“…Therefore, to select the optimal approach to the management of the specific project you first need to identify the distinctive features of this type and type of project. …”
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Midtarsal joint sprain - a quick review by Filip Kwiatkowski, Dariusz Popiela, Karina Urbańska, Łukasz Stojak, Mateusz Baczewski, Mateusz Grego, Katarzyna Grego, Witold Czyż

“…This review focuses on a brief overview of MJS to help you better understand when to consider the possibility of this injury, how to investigate it, and how to treat it. …”
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IoT-Based Pest Detection in Agriculture Using Raspberry Pi and YOLOv10m for Precision Farming by Zarboubi Mohamed, Bellout Abdelaaziz, Chabaa Samira, Dliou Azzedine, Zeroual Abdelouhab

“…The system seamlessly integrates a Raspberry Pi-based trap, the YOLOv10m (You Only Look Once) deep learning model, and the Ubidots IoT platform. …”
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Automated Fillet Weld Inspection Based on Deep Learning from 2D Images by Ignacio Diaz-Cano, Arturo Morgado-Estevez, José María Rodríguez Corral, Pablo Medina-Coello, Blas Salvador-Dominguez, Miguel Alvarez-Alcon

“…Following an extensive review of available solutions, algorithms, and networks based on this convolutional strategy, it was determined that the You Only Look Once algorithm in its version 8 (YOLOv8) would be the most suitable for object detection due to its performance and features. …”
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VETOZIM MULTI - AN INNOVATIVE SOLUTION FOR THE POULTRY INDUSTRY by O. Khodakivskyi, Ye. Baranovskyi

“…Calculation of economic indicators shows that the drug Vetozym Multi not only provides balanced nutrition for poultry, but also allows you to reduce the cost of feed due to more efficient use of less expensive components.…”
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Application of SIMETAW simulation model for prediction of climate parameters in different regions of Iran by Hooshmand Ataei, Mahsa Ravarian, Seyed Alireza Tashakori Hashemi

“…In addition, using this model, you can simulate daily meteorological data from meteorological data. …”
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MODUS OF SOCIAL INTEGRITY: PRACTICE OF CONSTRUCTING PRODUCTIVE CO-BEING by I. I. Brodetskaya

“…Existential knowledge focuses on the identity of the co-existence and serves only the prospect of overcoming the limitations of the totality. …”
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Grape Target Detection Method in Orchard Environment Based on Improved YOLOv7 by Fuchun Sun, Qiurong Lv, Yuechao Bian, Renwei He, Dong Lv, Leina Gao, Haorong Wu, Xiaoxiao Li

“…In response to the poor detection performance of grapes in orchards caused by issues such as leaf occlusion and fruit overlap, this study proposes an improved grape detection method named YOLOv7-MCSF based on the You Only Look Once v7 (YOLOv7) framework. Firstly, the original backbone network is replaced with MobileOne to achieve a lightweight improvement of the model, thereby reducing the number of parameters. …”
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Bringing Intelligence to SAR Missions: A Comprehensive Dataset and Evaluation of YOLO for Human Detection in TIR Images by Mostafa Rizk, Israa Bayad

“…The primary focus of this work is on YOLOv8, the latest version of the You Only Look Once (YOLO) object detection method. …”
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Lane and Traffic Sign Detection for Autonomous Vehicles: Addressing Challenges on Indian Road Conditions by H. S. Gowri Yaamini, Swathi K J, Manohar N, Ajay Kumar G

“…There are several state-of-art You Only Live Once (YOLO) models trained on benchmark datasets which fails to cater the challenges of Indian roads. …”
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Adversarial patch defense algorithm based on PatchTracker by Zhenjie XIAO, Shiyu HUANG, Feng YE, Liqing HUANG, Tianqiang HUANG

“…The application of deep neural networks in target detection has been widely adopted in various fields.However, the introduction of adversarial patch attacks, which add local perturbations to images to mislead deep neural networks, poses a significant threat to target detection systems based on vision techniques.To tackle this issue, an adversarial patch defense algorithm based on PatchTracker was proposed, leveraging the semantic differences between adversarial patches and image backgrounds.This algorithm comprised an upstream patch detector and a downstream data enhancement module.The upstream patch detector employed a YOLOV5 (you only look once-v5) model with attention mechanism to determine the locations of adversarial patches, thereby improving the detection accuracy of small-scale adversarial patches.Subsequently, the detected regions were covered with appropriate pixel values to remove the adversarial patches.This module effectively reduced the impact of adversarial examples without relying on extensive training data.The downstream data enhancement module enhanced the robustness of the target detector by modifying the model training paradigm.Finally, the image with removed patches was input into the downstream YOLOV5 target detection model, which had been enhanced through data augmentation.Cross-validation was performed on the public TT100K traffic sign dataset.Experimental results demonstrated that the proposed algorithm effectively defended against various types of generic adversarial patch attacks when compared to situations without defense measures.The algorithm improves the mean average precision (mAP) by approximately 65% when detecting adversarial patch images, effectively reducing the false negative rate of small-scale adversarial patches.Moreover, compared to existing algorithms, this approach significantly enhances the accuracy of neural networks in detecting adversarial samples.Additionally, the method exhibited excellent compatibility as it does not require modification of the downstream model structure.…”
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