A Critical Overview of Common Foodborne Toxicants and Methods for Their Reliable Detection by Betty Bowe Acquah, John‐Lewis Zinia Zaukuu

“…Consequently, regulatory bodies have set permissible limits for the ingestion of these toxicants. …”
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Pulmonary Nodules Detection Algorithm Combining Multi-view and Attention Mechanism by LIU Yu-bo, LIU Guo-zhu, SHI Cao, XU Can-hui

“…The final experimental results on LUNA16 data set show that the accuracy of the proposed model reaches 94.7%, which improves the accuracy and reduces the rate of misdiagnosis and missed diagnosis.…”
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Bandit Algorithms for Efficient Toxicity Detection in Competitive Online Video Games by Jacob Morrier, Rafal Kocielnik, R. Michael Alvarez

“…This algorithm balances exploration and exploitation to optimize long-term performance and is designed intentionally for easy deployment in production environments. Using data from the popular first-person action game Call of Duty®: Modern Warfare® III, we show that our algorithm consistently outperforms baseline algorithms that rely solely on individual players’ past behavior, achieving improvements in detection rate of up to 24.56 percentage points or 51.5%. …”
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Hybrid attention transformer integrated YOLOV8 for fruit ripeness detection by Jianyin Tang, Zhenglin Yu, ChangShun Shao

“…In addition, during the feature fusion stage, the Hybrid Attention Transformer (HAT) module is integrated into TopDownLayer2 to enhance the capture of long-term dependencies and the recovery of detailed information in the input data. To more accurately evaluate the similarity between the prediction box and the real bounding box, this paper uses the EIoU loss function instead of CIoU, thereby improving detection accuracy and accelerating model convergence. …”
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Infrared Tiny Structureless Object Detection Enhanced by Video Super-Resolution by Duixu Mao, Xiaxu Chen, Linhan Xu, Jun Ke

“…Subsequently, it employs restricted receptive field convolution and multi-receptive field feature fusion to enhance the detection accuracy of small targets. Experimental results on the SIATD data set show that our method performs better state-of-the-art approaches, with an F1 score of 0.957, precision of 99.2%, and recall of 92.4%. …”
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Multi-defect detection and classification for aluminum alloys with enhanced YOLOv8. by Ying Han, Xingkun Li, Gongxiang Cui, Jie Song, Fengyu Zhou, Yugang Wang

“…Defect features are extracted and analyzed more efficiently, and the network model better identifies defects such as jet, camouflage, etc. The average detection rate of all defects in the data set is improved. …”
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HTTD: A Hierarchical Transformer for Accurate Table Detection in Document Images by Mahmoud SalahEldin Kasem, Mohamed Mahmoud, Bilel Yagoub, Mostafa Farouk Senussi, Mahmoud Abdalla, Hyun-Soo Kang

“…HTTD addresses three key challenges: handling diverse document layouts, including historical and modern structures; improving computational efficiency and training convergence; and demonstrating adaptability to non-standard tasks like medical imaging and receipt key detection. Evaluated on benchmark datasets, HTTD achieves state-of-the-art results, with precision rates of 96.98% on ICDAR-2019 cTDaR, 96.43% on TNCR, and 93.14% on TabRecSet. …”
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DS-YOLOv7: Dense Small Object Detection Algorithm for UAV by Tao Sun, Haonan Chen, Haiying Liu, Lixia Deng, Lida Liu, Shuang Li

“…The experimental verification on the public data set VisDrone2019 shows that mAP50 and mAP50-95 have increased by 4.3% and 3.3% respectively, F1 scores have increased by 3.81%, model volume has decreased by 23.3MB, parameters have decreased by 13 million, and the improved algorithm is more conducive to UAV deployment and dense small object detection.…”
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Advancements in Landmine Detection: Deep Learning-Based Analysis With Thermal Drones by Daniel Heuschmid, Oliver Wacker, Yannick Zimmermann, Pascal Penava, Ricardo Buettner

“…Through meticulous data pre-processing and augmentation strategies, we enhance the model’s ability to generalize across different terrains and mine types, addressing one of the primary challenges in landmine detection. …”
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Perturb, Attend, Detect, and Localize (PADL): Robust Proactive Image Defense by Filippo Bartolucci, Iacopo Masi, Giuseppe Lisanti

“…Although being trained only on images of faces manipulated with STGAN, PADL generalizes to a range of unseen models with diverse architectural designs, such as StarGANv2, CycleGAN, BlendGAN, DiffAE, StableDiffusion, and StableDiffusionXL and also to unseen data domains. Finally, we propose a novel evaluation protocol that fairly assesses localization performance in relation to detection accuracy, providing a better reflection of real-world scenarios. …”
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Development of an accurate breast cancer detection classifier based on platelet RNA by Wenlong Xie, Jie Hu, Zehang Zhao, Huixin Lu, Yu Han, Boan Li, Zhong Ouyang

“…The model demonstrated high diagnostic performance for BC in an independent test set (n = 177) with an area under the curve of 0.957. …”
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Comprehensive Evaluation of Techniques for Intelligent Chatter Detection in Micro-Milling Processes by Guilherme Serpa Sestito, Wesley Angelino De Souza, Alessandro Roger Rodrigues, Maira Martins Da Silva

“…A smaller yet relevant set of features could aid these classifiers in coping with these requirements. …”
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The use of large language models in detecting Chinese ultrasound report errors by Yuqi Yan, Kai Wang, Bojian Feng, Jincao Yao, Tian Jiang, Zhiyan Jin, Yin Zheng, Yahan Zhou, Chen Chen, Lin Sui, Xiayi Chen, Yanhong Du, Jie Yang, Qianmeng Pan, Lingyan Zhou, Vicky Yang Wang, Ping Liang, Dong Xu

“…Six radiologists of varying experience levels performed error detection on a randomly selected test set. In zero-shot setting, Claude 3.5 Sonnet and GPT-4o achieved the highest error detection rates (52.3% and 41.2%, respectively). …”
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Zero-shot cross-lingual stance detection via adversarial language adaptation by Bharathi A., Arkaitz Zubiaga

“…Research in stance detection has often been limited to a single language and, where more than one language has been studied, research has focused on few-shot settings, overlooking the challenges of developing a zero-shot cross-lingual stance detection model. …”
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Fault detection and diagnosis method for heterogeneous wireless network based on GAN by Xiaorong ZHU, Peipei ZHANG

“…Aiming at the problem that in the process of network fault detection and diagnosis,how to train the precise fault diagnosis and detection model based on small data volume,a fault diagnosis and detection algorithm based on generative adversarial networks (GAN) for heterogeneous wireless networks was proposed.Firstly,the common network fault sources in heterogeneous wireless network environment was analyzed,and a large number of reliable data sets was obtained based on a small amount of network fault samples through GAN algorithm.Then,the extreme gradient boosting (XGBoost) algorithm was used to select the optimal feature combination of input parameters in the fault detection stage and completed fault diagnosis and detection based on these data.Simulation results show that the algorithm can achieve more accurate and efficient fault detection and diagnosis for heterogeneous wireless networks,with an accuracy of 98.18%.…”
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Study of Detection of Typical Pesticides in Paddy Water Based on Dielectric Properties by Shuanggen Huang, Mei Yang, Junshi Huang, Longwei Shang, Qi Chen, Fang Peng, Muhua Liu, Yan Wu, Jinhui Zhao

“…Frequency response data of 431 sets from nine types of paddy water samples were divided into training and prediction sets in a 3:1 ratio, and a content prediction model based on artificial neural networks (ANN) with multiple inputs and single output was established using amplitude and phase frequency response data after CARS feature extraction. …”
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Fault detection and diagnosis method for heterogeneous wireless network based on GAN by Xiaorong ZHU, Peipei ZHANG

“…Aiming at the problem that in the process of network fault detection and diagnosis,how to train the precise fault diagnosis and detection model based on small data volume,a fault diagnosis and detection algorithm based on generative adversarial networks (GAN) for heterogeneous wireless networks was proposed.Firstly,the common network fault sources in heterogeneous wireless network environment was analyzed,and a large number of reliable data sets was obtained based on a small amount of network fault samples through GAN algorithm.Then,the extreme gradient boosting (XGBoost) algorithm was used to select the optimal feature combination of input parameters in the fault detection stage and completed fault diagnosis and detection based on these data.Simulation results show that the algorithm can achieve more accurate and efficient fault detection and diagnosis for heterogeneous wireless networks,with an accuracy of 98.18%.…”
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Deep Learning Multi-Modal Melanoma Detection: Algorithm Development and Validation by Nithika Vivek, Karthik Ramesh

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Multimodal intelligent biosensors framework for fall disease detection and healthcare monitoring by Iqra Aijaz Abro, Shuaa S. Alharbi, Naif S. Alshammari, Asaad Algarni, Nouf Abdullah Almujally, Ahmad Jalal, Ahmad Jalal, Hui Liu, Hui Liu, Hui Liu

“…IntroductionIn the field of human action recognition, the fusion of multi-modal data from RGB and inertial modalities provides a valid technique for identifying activities of daily life and falls.MethodsOur approach uses two reference datasets: UR-Fall Detection and UMA_Fall Detection for ADL and Fall Events. …”
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Outlier Detection Method in Linear Regression Based on Sum of Arithmetic Progression by K. K. L. B. Adikaram, M. A. Hussein, M. Effenberger, T. Becker

“…With a reference element, which was not an outlier, the method detected all outliers from data sets with 6 to 1000 elements containing 50% outliers which deviated by a factor of ±1.0e-2 to ±1.0e+2 from the correct value.…”
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