A secured and fault-tolerant algorithm for electing an efficient coordinator in distributed networks by Shital S. Supase, Jayshree R. Pansare

“…The findings show that RSCEA effectively selects a coordinator with linear message complexity proportional to the number of nodal features, denoted as O(n.m) where n is number of nodes and m indicates the number of attributes. …”
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Hybrid metaheuristic optimization for detecting and diagnosing noncommunicable diseases by Saleem Malik, S. Gopal Krishna Patro, Chandrakanta Mahanty, Saravanapriya Kumar, Ayodele Lasisi, Quadri Noorulhasan Naveed, Anjanabhargavi Kulkarni, Abdulrajak Buradi, Addisu Frinjo Emma, Naoufel Kraiem

“…CPSO-RST-NFS combines meta-heuristic optimization with feature selection, resulting in improved efficiency and accuracy. …”
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Efficiency of anthelmintic treatment and its effect on microparasite dynamics in wild Mastomys natalensis by Marre van de Ven, Bram Vanden Broecke, Alexis Ribas, Herwig Leirs, Christopher Sabuni, Joachim Mariën

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Link stability - based optimal routing path for efficient data communication in MANET by Renisha Pulinchuvallil Salim, Rajesh Ramachandran

“…The paper delves into the complexities of Mobile Ad hoc Networks (MANETs), which consist of a diverse array of wireless nodes. …”
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Preparation and Characterization of Efficient and Safe Rotenone Solid Nanodispersion by Self-Emulsifying Technique by Yunfei Zhang, Xuesheng Lin, Yunlong Qian, Mingda Qin, Shujing Zhang, Lanying Wang, Yanping Luo

“…However, it is still unknown whether this technology can be employed to cope with the drawbacks of botanical insecticides including poor water solubility, rapid photodegradation and limited targeting efficiency. In this study, rotenone (Rot) was selected as a model of botanical insecticides, and its solid nanodispersion (Rot–SND) was prepared by a self-emulsifying method combined with parameter optimization. …”
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EcoDetect-YOLOv2: A High-Performance Model for Multi-Scale Waste Detection in Complex Surveillance Environments by Jing Su, Ruihan Chen, Mingzhi Li, Shenlin Liu, Guobao Xu, Zanhong Zheng

“…Even with surveillance imagery, challenges such as cluttered backgrounds, scale variation, and small object sizes often lead to missed detections and reduced robustness. To address these challenges, this study introduces EcoDetect-YOLOv2, a lightweight and high-efficiency object detection model developed using the Intricate Environment Waste Exposure Detection (IEWED) dataset. …”
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Can deep learning technology really recognize Mpox? A positive response from the EfficientNet model by Xiaoqian Zhao, Xiaoqian Zhao, Long Lyu, Li Zhang, Li Zhang

“…Additionally, ablation experiments demonstrate that as the version of EfficientNet’s expanded network increases, the model complexity rises, with performance showing a trend of initially increasing before decreasing. …”
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Graph-contrast ransomware detection (GCRD) with advanced feature selection and deep learning by Suneeta Satpathy, Pratik Kumar Swain

“…To overcome the limitations of conventional detection strategies, this study proposes the Graph-Contrast Ransomware Detection (GCRD) model comprising Graph-Based Feature Selection (GFS), Contrastive Learning (CLR), and Transformer-Based Classification (FT-Transformer + MLP). …”
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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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EDB-Net: Efficient Dual-Branch Convolutional Transformer Network for Hyperspectral Image Classification by Hufeng Guo, Wenyi Liu

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Enhanced YOLOv8 for Robust Pig Detection and Counting in Complex Agricultural Environments by Jian Li, Wenkai Ma, Yanan Wei, Tan Wang

“…Previous approaches often struggle with complex agricultural environments where lighting conditions, pig postures, and crowding levels create challenging detection scenarios. …”
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Flexi-YOLO: A lightweight method for road crack detection in complex environments. by Jiexiang Yang, Renjie Tian, Zexing Zhou, Xingyue Tan, Pingyang He

“…Road crack detection is critical to global infrastructure maintenance and public safety, and complex background environments and nonlinear damage crack patterns challenge the need for real-time, efficient, and accurate detection.This paper proposes a lightweight yet robust Flexi-YOLO model based on the YOLOv8 algorithm. …”
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GLS-YOLO: A Lightweight Tea Bud Detection Model in Complex Scenarios by Shanshan Li, Zhe Zhang, Shijun Li

“…The efficiency of tea bud harvesting has been greatly enhanced, and human labor intensity significantly reduced, through the mechanization and intelligent management of tea plantations. …”
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Infrared Ship Detection in Complex Nearshore Scenes Based on Improved YOLOv5s by Xiuwen Liu, Mingchen Liu, Yong Yin

“…Accurate ship identification and classification are central to this objective, underscoring the critical importance of ship detection technology. However, compared to open-sea surface, dense vessel distributions and complex backgrounds in nearshore areas substantially limit detection efficacy. …”
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Tea Disease Detection Method Based on Improved YOLOv8 in Complex Background by Junchen Ai, Yadong Li, Shengxiang Gao, Rongsheng Hu, Wengang Che

“…Experimental results show that the proposed YOLO-SSM algorithm has obvious advantages in accuracy and model complexity and can provide reliable theoretical support for efficient and accurate detection and identification of tea leaf diseases in natural scenes.…”
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A review: Multi-Objective Algorithm for Community Detection in Complex Social Networks by Mariwan Wahid Ahmed, Kamaran Faraj

“…Recently, research on multi-objective optimization algorithms for community detection in complex networks has grown considerably. …”
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Investigation towards nanomechanical sensor array for real-time detection of complex gases by Md. Abdul Momin, Masaya Toda, Zhuqing Wang, Mai Yamazaki, Krzysztof Moorthi, Yasuaki Kawaguchi, Takahito Ono

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YOLO-RDM: Innovative Detection Methods for Eggplants and Stems in Complex Natural Environment by Qin Liu, Zhibin Zhou, Lu Xiong, Meilian Lu, Jingwen Ouyang

“…To achieve rapid identification of eggplants and stems in complex environments and enhance overall accuracy, this study establishes a database for eggplants and stems and proposes an efficient detection model based on the YOLOv8n network. …”
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An enhanced lightweight model for apple leaf disease detection in complex orchard environments by Ge Wang, Wenjie Sang, Fangqian Xu, Yuteng Gao, Yue Han, Qiang Liu

“…However, in complex natural environments, factors such as light variations, shading from branches and leaves, and overlapping disease spots often result in reduced accuracy in detecting apple diseases. …”
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Application of GPR Underground Pipeline Detection Technology in Urban Complex Geological Environments by Xiaoqiang Liang, Da Hu, Yongsuo Li, Yunyi Zhang, Xian Yang

“…This research applies bottom detection radar to urban underground pipeline detection technology under complex conditions for the first time, innovatively uses the action mechanism of bottom detection radar, integrates its high precision and high efficiency into underground pipeline detection technology, and ensures the effectiveness of the detection work.…”
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