Enhancing surface detection: A comprehensive analysis of various YOLO models by G. Deepti Raj, B. Prabadevi

“…Version 5 of You Only Look Once (YOLO), a precisely optimized learning model, has undergone extensive testing on steel strip datasets, providing effective solutions for recognition and detection in industry environments. This study presents an improved YOLOv5 detection model, exploiting the efficient channel attention (ECA) and coordinated attention (CoordAtt) mechanisms. …”
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Explainable Anomaly Detection Based on Operational Sequences in Industrial Control Systems by Ka-Kyung Kim, Joon-Seok Kim, Ieck-Chae Euom

“…Many anomaly detection algorithms based on deep learning models have good performance but often involve complex neural network structures, creating a black-box issue where users cannot interpret the decisions made by the models. …”
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High‐Sensitivity Dopamine Detection with Copper Terephthalate‐Modified Carbon Electrode by Aleksander Ejsmont, Klaudia Bucoń, Teresa Łuczak, Joanna Goscianska

“…This work positions Cu‐MOFs as scalable, efficient, and competitive alternatives to complex, multicomponent sensors for neurotransmitter detection.…”
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Improved YOLOv8 Algorithm was Used to Segment Cucumber Seedlings Under Complex Artificial Light Conditions by Duokuo Zhang, Na Li, Mingfu Zhao, Kun Xu

“…Simultaneously, the Efficient Multi-Scale Attention (EMA) mechanism is introduced after MSFM, enabling the model to flexibly adjust the attention allocated to different regions within the image, thus enhancing its robustness to complex lighting variations. …”
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Authenticity in bakery products: Detection of pistachio fraud using NGS metabarcoding by Sina Rammouz, Anne Reichstein, Jochen Riehle, Ansgar Ferner, Markus Fischer, Christian Schäfers

“…In this study we introduce a Next-generation sequencing (NGS)-based DNA metabarcoding approach as a sensitive, reliable, and semi-quantitative method that enables a successfully detection of such adulterations, even in complex and highly processed food matrices. …”
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Research on Vertical Shaft Detection System Based on CMOS-Camera and Lidar by Chi Ma, Samsil Arefin Mozumder, Md Mahbub Hossen, Manman Shao, Yuqiang Jiang

“…Deformations and fractures in the shaft wall can cause significant risks to mine operations, which require regular safety inspection to prevent hazards. Existing shaft detection systems cannot provide any prior notifications before damage occurs, with complex systems and low intelligence. …”
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Lightweight Detection of Train Underframe Bolts Based on SFCA-YOLOv8s by Zixiao Li, Jinjin Li, Chuanlong Zhang, Huajun Dong

“…To achieve efficient detection, a lightweight detection method based on SFCA-YOLOv8s is proposed. …”
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Multimodal-Based Non-Contact High Intraocular Pressure Detection Method by Zibo Lan, Ying Hu, Shuang Yang, Jiayun Ren, He Zhang

“…To address these limitations, we present a multi-modal framework incorporating CycleGAN for data augmentation, Swin Transformer for visual feature extraction, and the Kolmogorov–Arnold Network (KAN) for efficient fusion of heterogeneous data. KAN approximates complex nonlinear relationships with fewer parameters, making it effective in small-sample scenarios with intricate variable dependencies. …”
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An Improved 2D Pose Estimation Algorithm for Extracting Phenotypic Parameters of Tomato Plants in Complex Backgrounds by Yawen Cheng, Ni Ren, Anqi Hu, Lingli Zhou, Chao Qi, Shuo Zhang, Qian Wu

“…We enhanced a novel heatmap-free method, YOLOv8s-pose, by integrating the Convolutional Block Attention Module (CBAM) and Content-Aware ReAssembly of FEatures (CARAFE), to develop an improved YOLOv8s-pose (IYOLOv8s-pose) model, which efficiently focuses on salient image features with minimal parameter overhead while achieving a superior recognition performance in complex backgrounds. …”
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PS-YOLO: A Lighter and Faster Network for UAV Object Detection by Han Zhong, Yan Zhang, Zhiguang Shi, Yu Zhang, Liang Zhao

“…The operational environment of UAVs poses unique challenges for object detection compared to conventional methods. When UAVs capture remote sensing images from elevated altitudes, objects often appear minuscule and can be easily obscured by complex backgrounds. …”
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Cardiovascular Disease Detection through Innovative Imbalanced Learning and AUC Optimization by Karthikeyan Palanisamy, Krishnaveni Krishnasamy, Praba Venkadasamy

“…Furthermore, we have incorporated a tailored Differential Evolution (DE) algorithm designed to navigate the complex hyperparameter space with finesse. The performance of this model was rigorously evaluated using comprehensive data from a medical survey conducted in 2012, which included an extensive cohort of 26,002 athletes. …”
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Optimized driver fatigue detection method using multimodal neural networks by Shengli Cao, Peihua Feng, Wei Kang, Zeyi Chen, Bo Wang

“…These results highlight the advantages of the multimodal feature-coupled model in addressing the challenges of driver fatigue detection, making it a valuable tool for enhancing road safety through advanced, efficient monitoring systems in vehicles.…”
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Detecting microcephaly and macrocephaly from ultrasound images using artificial intelligence by Abraham Keffale Mengistu, Bayou Tilahun Assaye, Addisu Baye Flatie, Zewdie Mossie

“…Objective This study aims to develop a fetal head abnormality detection model from ultrasound images via deep learning. …”
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Damage Detection of Steel Roof Systems Using Planet Optimization Algorithm by Thanh Sang-To, Van The Huy-Nguyen, Samir Khatir, The Vi-Huynh, Hoang Le-Minh, Thanh Cuong-Le

“…Also, the results proved that this technique provides an efficient solution to the complex problem with many constraints in unknown search space.…”
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Cloud-edge collaborative data anomaly detection in industrial sensor networks. by Tao Yang, Xuefeng Jiang, Wei Li, Peiyu Liu, Jinming Wang, Weijie Hao, Qiang Yang

“…Industrial sensor networks exhibit heterogeneous, federated, large-scale, and intelligent characteristics due to the increasing number of Internet of Things (IoT) devices and different types of sensors. Efficient and accurate anomaly detection of sensor data is essential for guaranteeing the system's operational reliability and security. …”
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Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer’s Disease Detection by Opeyemi Taiwo Adeniran, Blessing Ojeme, Temitope Ezekiel Ajibola, Ojonugwa Oluwafemi Ejiga Peter, Abiola Olayinka Ajala, Md Mahmudur Rahman, Fahmi Khalifa

“…With the advancements in deep learning methods, AI systems now perform at the same or higher level than human intelligence in many complex real-world problems. The data and algorithmic opacity of deep learning models, however, make the task of comprehending the input data information, the model, and model’s decisions quite challenging. …”
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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

“…The pervasive threat of landmines across conflict-affected regions necessitates advancements in detection technologies to enhance safety and efficiency in demining efforts. …”
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SR-DETR: Target Detection in Maritime Rescue from UAV Imagery by Yuling Liu, Yan Wei

“…Specifically, the conventional multi-head self-attention (MHSA) mechanism is replaced with Efficient Additive Attention (EAA), which facilitates more efficient feature interaction while substantially reducing computational complexity. …”
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Deep Learning in Defect Detection of Wind Turbine Blades: A Review by Katleho Masita, Ali N. Hasan, Thokozani Shongwe, Hasan Abu Hilal

“…Additionally, transfer learning and attention mechanisms have been instrumental in enhancing the precision and speed of defect detection, enabling real-time applications. Notable approaches like YOLO (You Only Look Once) and its variants have shown exceptional performance in detecting defects with varying scales and complexities, leveraging innovations such as feature pyramid networks and efficient loss functions. …”
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Room Temperature InGaAs/AlGaAsSb Single Photon Avalanche Diode by J. Taylor-Mew, L. Li, T. Blain, C. H. Tan, J. S. Ng

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