Hybrid CNN-Transformer Model for Accurate Impacted Tooth Detection in Panoramic Radiographs by Deniz Bora Küçük, Andaç Imak, Salih Taha Alperen Özçelik, Adalet Çelebi, Muammer Türkoğlu, Abdulkadir Sengur, Deepika Koundal

“…<b>Methods:</b> The proposed model combines YOLO (You Only Look Once) and RT-DETR (Real-Time Detection Transformer) models to leverage their strengths in real-time object detection and learning long-range dependencies, respectively. …”
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Unsupervised Anomaly Detection with Continuous-Time Model for Pig Farm Environmental Data by Heng Zhou, Seyeon Chung, Malik Muhammad Waqar, Muhammad Ibrahim Zain Ul Abideen, Arsalan Ahmad, Muhammad Ans Ilyas, Hyongsuk Kim, Sangcheol Kim

“…Environmental air anomaly detection is crucial for ensuring the healthy growth of livestock in smart pig farming systems. …”
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Integration of Convolutional Neural Network and Image Processing for Pulp Fibril Detection and Measurement by Tanachot Chirakitsakul, Pakaket Wattuya, Phichit Somboon, Panthira Jansakra, Chakrit Watcharopas

“…This study proposes a novel method that integrates deep learning with image processing techniques to automate fibril detection and fibrillation index computation. …”
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GCB‐YOLO: A Lightweight Algorithm for Wind Turbine Blade Defect Detection by Zhiming Zhang, Chaoyi Dong, Ze Wei, Xiaoyan Chen, Weidong Zan, Yao Xue

“…ABSTRACT For the current visual detection methods of wind turbine blade defects, their detection models are usually excessively large, making it difficult to achieve a balance between model accuracy and inference speed. …”
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Artificial intelligence in suicide prevention: Utilizing deep learning approach for early detection by Vikas Gaur, Gaurav Maggu, Khushboo Bairwa, Suprakash Chaudhury, Sana Dhamija, Tahoora Ali

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Development of a Paper-Based Microfluidic Chip for Point-of-Care Detection of PEDV by Renfeng Li, Xiangqin Tian, Wenyan Cao, Jiaxin Jiang, Jiakang Yuan, Linyue Li, Yonghe You, Yanlin Zhou, Ziliang Wang, Fangyu Wang

“…PEDV poses a significant threat to the global swine industry, necessitating rapid and accurate diagnostic methods for effective disease management. In this study, we developed a foldable, easy-to-use paper-based microfluidic analytical device (<i>μ</i>PAD) for on-site detection of PEDV. …”
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RGB-FIR Multimodal Pedestrian Detection with Cross-Modality Context Attentional Model by Han Wang, Lei Jin, Guangcheng Wang, Wenjie Liu, Quan Shi, Yingyan Hou, Jiali Liu

“…At the back-end of the RGB-FIR parallel network, a channel-space joint attention model (CBAM) and self-attention models are combined to obtain the final RGB-FIR fusion features at each scale for those RGB and FIR features optimized by CCAM. Compared with the current RGB-FIR multimodal YOLO model, comparative experiments on different performance evaluation indicators on multiple RGB-FIR public datasets indicate that this method can significantly enhance the accuracy and robustness of pedestrian detection.…”
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Nighttime traffic object detection via adaptively integrating event and frame domains by Yu Jiang, Yuehang Wang, Minghao Zhao, Yongji Zhang, Hong Qi

“…Motivated by this progress, we propose an adaptive selection and fusion detection method that leverages both event and RGB frame domains to optimize nighttime traffic object detection jointly. …”
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Research on Road Crack Detection Based on RGB-LPC-GPR Data Fusion by Z. Wang, D. Qiu, R. Wu, R. Wu, Y. Shi, W. Niu

“…Moreover, a trend prediction model integrating ConvLSTM and a spatiotemporal attention mechanism achieved an MAE of 8.7% in a six-month damage trend prediction experiment, reducing prediction error by 34% compared to existing methods, underscoring the model's effectiveness in forecasting damage progression.The experimental results demonstrate that the proposed framework exhibits strong adaptability and stability across diverse road damage detection tasks, particularly excelling in the joint detection of cracks and underground voids with high accuracy. …”
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Lateral flow immunochromatographic assay for rapid detection of dichlorvos residue in fruits and vegetables by Bao-Zhu Jia, Yan-Yan Liu, Feng-Yan Chen, Phannika Tongchai, Sumed Yadoung, Anurak Wongta, Zhen-Lin Xu, Lin Luo, Surat Hongsibsong

“…In this study, we firstly present a gold-nanoparticle-based lateral flow immunochromatographic assay (LFIA) employing a highly specific anti-DDVP monoclonal antibody for onsite DDVP detection within 20 min. The optimized LFIA exhibited exceptional analytical performance, with limits of detection spanning 16 μg/kg to 108 μg/kg across six fruit and vegetable matrices—all below the established maximum residue limits in foods. …”
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A Model for Fat Content Detection in Walnuts Based on Near-Infrared Spectroscopy by Langqin Luo, Honghua Zhang, Yu Wang, Jianliang Zhang, Rui Zhang, Shan Gao, Yuanyong Dian, Zijin Bai, Chunhui Feng, Ze Zhang

“…Near-infrared spectroscopy (NIR) is an efficient and accurate method for fat content detection in walnuts. ‘Wen 185’ walnut is grown in large quantities in southern Xinjiang, and its fat content is an important indicator for evaluating the intrinsic quality. …”
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Research on detection of wheat tillers in natural environment based on YOLOv8-MRF by Min Liang, Yuchen Zhang, Jian Zhou, Fengcheng Shi, Zhiqiang Wang, Yu Lin, Liang Zhang, Yaxi Liu

“…To bolster agricultural efficiency and precision, this study introduces the YOLOv8-MRF model (multi-path coordinate attention, receptive field attention convolution, and Focaler-CIoU-optimized YOLOv8), a groundbreaking advancement in automated detection of wheat tillers. …”
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Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with Leukemia by Mahwish Ilyas, Muhammad Bilal, Nadia Malik, Hikmat Ullah Khan, Muhammad Ramzan, Anam Naz

“…Medical diagnosis plays a critical role in the early detection and treatment of diseases by examining symptoms and supporting findings through advanced laboratory testing. …”
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Coordinated Jamming and Poisoning Attack Detection and Mitigation in Wireless Federated Learning Networks by Sofia Barkatsa, Maria Diamanti, Panagiotis Charatsaris, Stefanos Voikos, Eirini Eleni Tsiropoulou, Symeon Papavassiliou

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Retinal fundus imaging-based diabetic retinopathy classification using transfer learning and fennec fox optimization by Indresh Kumar Gupta, Shruti Patil, Supriya Mahadevkar, Ketan Kotecha, Awanish Kumar Mishra, Joel J.P.C. Rodrigues

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CIDNet: A Maritime Ship Detection Model Based on ISAR Remote Sensing by Fei Liu, Boyang Liu, Hang Zhou, Song Han, Kunlin Zou, Wenjie Lv, Chang Liu

“…Existing ship target detection techniques, especially target detection methods and detection models in complex settings, have problems such as long inference time and unstable robustness, meaning that they can easily miss the best time for detecting ship targets and cause intelligence loss. …”
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Risk factors for detection of Pseudomonas aeruginosa in clinical samples upon hospital admission by Romeo Reyle, Frank Schwab, Selin Saydan, Michael Behnke, Rasmus Leistner, Petra Gastmeier, Christine Geffers, Tobias Siegfried Kramer

“…Methods All patients 18 years of age and older with a detection of PAE and/or Enterobacterales in clinical samples taken within 48 h of admission to one of the hospitals of Charité Universitätsmedizin Berlin between 2015 and 2020 were included into this retrospective cohort study. …”
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Advanced deep transfer learning techniques for efficient detection of cotton plant diseases by Prashant Johri, SeongKi Kim, Kumud Dixit, Prakhar Sharma, Barkha Kakkar, Yogesh Kumar, Jana Shafi, Muhammad Fazal Ijaz

“…Image recognition plays an important role for the timely and accurate identification of diseases in cotton plants as it allows farmers to implement effective interventions and optimize resource allocation. Additionally, deep learning has begun as a powerful technique for to detect diseases in crops using images. …”
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Robust circulating microRNA signature for the diagnosis and early detection of pancreatobiliary cancer by Shuichi Mitsunaga, Masafumi Ikeda, Makoto Ueno, Satoshi Kobayashi, Masahiro Tsuda, Ikuya Miki, Takamichi Kuwahara, Kazuo Hara, Yukiko Takayama, Yutaro Matsunaga, Keiji Hanada, Akinori Shimizu, Hitoshi Yoshida, Tomohiro Nomoto, Kenji Takahashi, Hidetaka Iwamoto, Hideaki Iwama, Etsuro Hatano, Kohei Nakata, Masafumi Nakamura, Hiroko Sudo, Satoko Takizawa, Atsushi Ochiai

“…These findings can help improve clinical PBca detection by providing an optimized biomarker that overcomes the limitations of the current standard.…”
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Application of droplet digital PCR in detection of seed-transmitted pathogen Acidovorax citrulli by Yu LU, Hai-jun ZHANG, Zi-jing ZHAO, Chang-long WEN, Ping WU, Shun-hua SONG, Shuan-cang YU, Lai-xin LUO, Xiu-lan XU

“…In this study, we adapted a quantitative real-time PCR (qPCR) assay to droplet digital PCR (ddPCR) format for A. citrulli detection by optimizing reaction conditions. The performance of ddPCR in detecting A. citrulli pure culture, DNA, infested watermelon/melon seed and commercial seed samples were compared with multiplex PCR, qPCR, and dilution plating method. …”
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