EHA-YOLOv5: An Efficient and Highly Accurate Improved YOLOv5 Model for Workshop Bearing Rail Defect Detection Application by Jiyong Hu, Hongfei Yang, Jiatang He, Dongxu Bai, Hongda Chen

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PdYOLO: A Lightweight Algorithm for Detecting Peach Fruits Against a Peach Tree Background by Jiajun Zhang, Jialin Zhang, Kazuki Kobayashi

“…Finally, a new head layer P2-SLD was designed, which replaced the original detection head with a shared lightweight detection head SLD, introduced the P2 layer and removed the P5 layer, which improved the lightweight level of the model, improved the processing efficiency and reduced the energy consumption while ensuring the detection accuracy. …”
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FGS-YOLOv8s-seg: A Lightweight and Efficient Instance Segmentation Model for Detecting Tomato Maturity Levels in Greenhouse Environments by Dongfang Song, Ping Liu, Yanjun Zhu, Tianyuan Li, Kun Zhang

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SAM-Based Efficient Feature Integration Network for Remote Sensing Change Detection: A Case Study on Macao Sea Reclamation by Junqing Huang, Junqi Bao, Min Xia, Xiaochen Yuan

“…To utilize the visual recognition capabilities of SAM for improving RSCD, we propose an SAM-based efficient feature integration network (EFI-SAM). Random fourier features adaptor (RFFA) is utilized to enhance Mobile SAM’s capability for extracting general features from complex RSIs, resulting in adaptive Mobile SAM, which serves as the feature extractor of EFI-SAM. …”
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Assessment of Doppler Wind Lidar Detection Efficiency and Influencing Factors at Plateau Airport: A Case Study of Lhasa Gonggar Airport by Junjie Wu, Hongyu Du, Chunjiong Xia, Xiaoyuan Huang

“…Doppler wind lidar (DWL) demonstrates significant advantages in wind field detection under clear weather conditions and has been widely applied in airports with complex wind environments. …”
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RDRM-YOLO: A High-Accuracy and Lightweight Rice Disease Detection Model for Complex Field Environments Based on Improved YOLOv5 by Pan Li, Jitao Zhou, Huihui Sun, Jian Zeng

“…Rice leaf diseases critically threaten global rice production by reducing crop yield and quality. Efficient disease detection in complex field environments remains a persistent challenge for sustainable agriculture. …”
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Efficient Switched-Beam Detection for Dynamic Spectrum Sharing in 6G Wireless Networks With Full Duplex Technology at the THz Band by Andrea Tani, Dania Marabissi

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Para-YOLO: An Efficient High-Parameter Low-Computation Algorithm Based on YOLO11n for Remote Sensing Object Detection by Hang Chen, Qi Cao, Yongqiang Wang, Shang Wang, Haisheng Fu, Zhenjiao Chen, Feng Liang

“…Moreover, the increasing demand for real-time onboard image processing imposes stringent requirements on both algorithmic complexity and detection accuracy. To overcome these challenges, we propose Para-YOLO, an efficient and cost-effective algorithm consisting of a novel feature fusion network and three innovative modules. …”
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Optimizing Security in IoT Ecosystems Using Hybrid Artificial Intelligence and Blockchain Models: A Scalable and Efficient Approach for Threat Detection by William Villegas-Ch, Jaime Govea, Rommel Gutierrez, Aracely Mera-Navarrete

“…Traditional security solutions, based on centralized architectures, are neither scalable nor efficient enough to handle the increasing complexity and number of IoT devices, leading to high latencies, increased energy consumption, and inadequate intrusion detection. …”
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HGCS-Det: A Deep Learning-Based Solution for Localizing and Recognizing Household Garbage in Complex Scenarios by Houkui Zhou, Chang Chen, Zhongyi Xia, Qifeng Ding, Qinqin Liao, Qun Wang, Huimin Yu, Haoji Hu, Guangqun Zhang, Junguo Hu, Tao He

“…However, due to the interference of complex environments, coupled with the influence of the irregular features of garbage, garbage detection in complex scenarios still faces significant challenges. …”
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Using U-Net models in deep learning for brain tumor detection from MRI scans by Minh Khiem Nguyen, Phuoc Huy Tran, Tan Tai Phan

“… Tumor diseases in the nervous system are both dangerous and complex. Magnetic Resonance Imaging (MRI) is crucial for detecting brain disease; however, identifying the presence of tumors from these is time-consuming and requires a professional doctor. …”
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Using U-Net models in deep learning for brain tumor detection from MRI scans by Minh Khiem Nguyen, Phuoc Huy Tran, Tan Tai Phan

“… Tumor diseases in the nervous system are both dangerous and complex. Magnetic Resonance Imaging (MRI) is crucial for detecting brain disease; however, identifying the presence of tumors from these is time-consuming and requires a professional doctor. …”
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THE EFFICIENCY OF TECHNIQUE OF DOUBLE ARCHES BY THE TREATMENT OF PATIENTS WITH ANOMALIES OF OCCLUSION by Sergei Vladimirovich Dmitrienko, Dmitry Anatolyevich Domenyuk, Anzhelika Vladimirovna Kokareva, Ernessa Grigorievna Vedeshina

“…As a result of examination and treatment of patients of the first period of mature age with various forms of occlusion anomalies it was detected an efficiency of preformed arches treatment and technique of double arches taking into account individual parameters of the dental arches on the morphometric parameters of the craniofacial complex in conjunction with the main parameters of dentoalveolar arches and individual tooth sizes. …”
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YOLO-EFM: Efficient traffic flow monitoring algorithm with enhanced multi-level information fusion by Shizhou Xu, Kaidi Cui

“…Modern drones are capable of autonomously and efficiently collecting traffic images in complex environments to implement downstream detection tasks. …”
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Simple Single-Person Fall Detection Model Using 3D Pose Estimation Mechanisms by Jinmo Yang, R. Young Chul Kim

“…Therefore, we propose the simple fall detection model (SFDM) that is computationally efficient with moderate accuracy. …”
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Signal Detection for Enhanced Spatial Modulation-Based Communication: A Block Deep Neural Network Approach by Shaopeng Jin, Yuyang Peng, Fawaz AL-Hazemi, Mohammad Meraj Mirza

“…In ESM, traditional signal detection methods such as maximum likelihood (ML) have the drawback of high complexity. …”
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A Low Complexity Near-Optimal Detector Based on Teaching-Learning Algorithm for Massive MIMO by Hamid Amiriara, Mohammadreza Zahabi

“…In this paper, a low-complexity receiver is proposed using a Teaching-Learning based optimization (TLBO) heuristic algorithm for a large-scale system. …”
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SIC based RL for massive MIMO NOMA signal detection for different modulation schemes under diverse channel conditions by Arun Kumar, Aziz Nanthaamornphong, Mohammed H. Alsharif, Mehedi Masud

“…Abstract Massive-multiple input and Multiple Outputs Non orthogonal multiple access (M-MIMO–NOMA) systems require efficient signal detection techniques to mitigate interference and enhance spectral efficiency, especially under diverse channel conditions and varying modulation schemes. …”
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Fresh Tea Leaf-Grading Detection: An Improved YOLOv8 Neural Network Model Utilizing Deep Learning by Zejun Wang, Yuxin Xia, Houqiao Wang, Xiaohui Liu, Raoqiong Che, Xiaoxue Guo, Hongxu Li, Shihao Zhang, Baijuan Wang

“…The incorporation of an Efficient Multi-Scale Attention Module with Cross-Spatial Learning serves to attenuate the influence of irrelevant features in complex backgrounds, which in turn, elevates the model’s detection Precision. …”
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Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas by Safiah Almarri, Hur Al Safwan, Shahd Al Qisoom, Soufien Gdaim, Abdelkrim Zitouni

“…Wildfires are complex natural disasters that significantly impact ecosystems and human communities. …”
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