Blockchain enabled deep learning model with modified coati optimization for sustainable healthcare disease detection and classification by Heba G. Mohamed, Fadwa Alrowais, Fahd N. Al-Wesabi, Mesfer Al Duhayyim, Anwer Mustafa Hilal, Abdelwahed Motwakel

“…The presented MCOBC-HDDC method provides an efficient and accurate disease diagnosis, utilizing a system that depends on DL techniques. …”
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Multiple Targets CFAR Detection Performance Based on an Intelligent Clustering Algorithm in K-Distribution Sea Clutter by Mansoor M. Al-dabaa, Eugen Laslo, Ahmed A. Emran, Ahmed Yahya, Ashraf Aboshosha

“…It is noteworthy that the proposed method achieves detection performance comparable to the more computationally intensive DBSCAN-CFAR while significantly reducing computational complexity. …”
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Leveraging explainable artificial intelligence for early detection and mitigation of cyber threat in large-scale network environments by G. Nalinipriya, S. Rama Sree, K. Radhika, E. Laxmi Lydia, Faten Khalid Karim, Mohamad Khairi Ishak, Samih M. Mostafa

“…Machine learning (ML) plays a crucial role in cybersecurity by making malware detection more scalable, efficient, and automated, reducing reliance on conventional human intervention methods. …”
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Research on Blueberry Maturity Detection Based on Receptive Field Attention Convolution and Adaptive Spatial Feature Fusion by Bingqiang Huang, Zongyi Xie, Hanno Homann, Zhengshun Fei, Xinjian Xiang, Yongping Zheng, Guolong Zhang, Siqi Sun

“…Detecting small objects in complex outdoor conditions remains challenging. …”
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YOLO-Tryppa: A Novel YOLO-Based Approach for Rapid and Accurate Detection of Small Trypanosoma Parasites by Davide Antonio Mura, Luca Zedda, Andrea Loddo, Cecilia Di Ruberto

“…Early detection of Trypanosoma parasites is critical for the prompt treatment of trypanosomiasis, a neglected tropical disease that poses severe health and socioeconomic challenges in affected regions. …”
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Research progress on the application of RPA-CRISPR/Cas12a in the rapid visual detection of pathogenic microorganisms by Tuo Ji, Tuo Ji, Tuo Ji, Xin Fang, Yuzhi Gao, Yuzhi Gao, Yuzhi Gao, Kun Yu, Kun Yu, Kun Yu, Xuzhu Gao, Xuzhu Gao, Xuzhu Gao

“…In an increasingly complex global public health landscape, the continuous emergence of novel pathogens and the growing problem of antibiotic resistance highlight the urgent need for rapid, efficient, and precise detection technologies for pathogenic microorganisms. …”
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CUNet-CLSTM: A Novel Fusion of CUNet and CLSTM for Superior Liver Cancer Detection in CT Scans by K. Vijayaprabakaran, Padmanaban Ramalingam, Rajakumar Ramalingam, A. Ilavendhan, R. Vedhapriyavadhana

“…Despite the triumph of convolutional neural networks in medical image analysis, challenges such as overfitting, limited labeled data, and complex tumor morphology hinder accurate detection of liver cancer. …”
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YOLO-Ssboat: Super-Small Ship Detection Network for Large-Scale Aerial and Remote Sensing Scenes by Yiliang Zeng, Xiuhong Wang, Jinlin Zou, Hongtao Wu

“…To tackle the challenge of wake waves from moving ships obscuring small targets, we introduce a gradient flow mechanism that improves detection efficiency under dynamic conditions. The Tail Wave Detection Method synergistically integrates gradient computation with target detection techniques. …”
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A Dual-Branches Multiscale Dynamic Partial Convolutional Attention Network for Remote Sensing Change Detection by Wenbin Tang, Shuli Cheng, Anyu Du

“…In addition, a bottom-up training strategy is applied to strengthen the completeness of change detection. Extensive experiments on three publicly available datasets demonstrate that, compared to other methods, our proposed approach achieves superior performance and detection results, while reducing parameters and computational complexity.…”
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Real-time driver drowsiness detection using transformer architectures: a novel deep learning approach by Osama F. Hassan, Ahmed F. Ibrahim, Ahmed Gomaa, M. A. Makhlouf, B. Hafiz

“…This represents a significant advancement over existing methods, demonstrating the effectiveness of transformer-based architectures in capturing complex spatial dependencies and extracting relevant features for drowsiness detection. …”
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Universal Method for Detecting Violations in the Integrity of a Digital Image Based on Analysis of Blocks of its Matrix by Bobok I., Kobozeva A.

“…Thus, the aim of the work is to increase the efficiency of identifying the fact of violation of image integrity by developing a universal expert method with low computational complexity. …”
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Improved cancer detection through feature selection using the binary Al Biruni Earth radius algorithm by El-Sayed M. El-Kenawy, Nima Khodadadi, Marwa M. Eid, Ehsaneh Khodadadi, Ehsan Khodadadi, Doaa Sami Khafaga, Amel Ali Alhussan, Abdelhameed Ibrahim, Mohamed Saber

“…Abstract With the advancement of medical technology, a large amount of complex data on cancers is produced for diagnosing and treating cancers. …”
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YOLOv10n-Based Defect Detection in Power Insulators: Attention Enhancement and Feature Fusion Optimization by Zhihao Wei, Yan Wei

“…In modern power systems, insulators, as key components of transmission lines, are crucial for defect detection for the safe operation of power grids. Aiming at the problems of low efficiency of traditional manual detection, the vulnerability of traditional image processing methods to environmental interference, and the insufficient ability of existing deep learning models to detect small target defects under complex backgrounds, this paper proposes an improved target detection model based on YOLOv10n, which is the first time to integrate the spatial channel attention mechanism (SEAttention) with the up-sampling expansion operation (Patch Expanding) in the Neck part of the model. …”
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A Review of Machine Learning and Transfer Learning Strategies for Intrusion Detection Systems in 5G and Beyond by Kinzah Noor, Agbotiname Lucky Imoize, Chun-Ta Li, Chi-Yao Weng

“…Naive Bayes (NB) stands out for its computational efficiency despite moderate performance in other areas. …”
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A Full-Scale Shadow Detection Network Based on Multiple Attention Mechanisms for Remote-Sensing Images by Lei Zhang, Qing Zhang, Yu Wu, Yanfeng Zhang, Shan Xiang, Donghai Xie, Zeyu Wang

“…Therefore, this model offers an efficient solution for shadow detection in aerial imagery.…”
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VMMCD: VMamba-Based Multi-Scale Feature Guiding Fusion Network for Remote Sensing Change Detection by Zhong Chen, Hanruo Chen, Junsong Leng, Xiaolei Zhang, Qi Gao, Weiyu Dong

“…Moreover, the former also leads to a reduction in speed. To guarantee the efficiency of change detection, encompassing both speed and accuracy, a VMamba-based Multi-scale Feature Guiding Fusion Network (VMMCD) is proposed. …”
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Sustainable phytoprotection: a smart monitoring and recommendation framework using Puma Optimization for potato pathogen detection by Amal H. Alharbi, Faris H. Rizk, Khaled Sh. Gaber, Marwa M. Eid, Marwa M. Eid, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy, Pushan Kumar Dutta, Doaa Sami Khafaga

“…In contrast, convergence analysis demonstrated PO-RBM’s computational efficiency relative to PSO, GWO, and GA alternatives. …”
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Enhanced Performance of Asymmetrically Clipped DC-Biased Optical OFDM Systems Using Adjacent Symbol Detection by Wei-Wen Hu

“…Compared to the direct current (DC) biased optical OFDM (DCO-OFDM) and asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) schemes, the asymmetrically clipped DC biased optical OFDM (ADO-OFDM) has demonstrated a better balance between optical power efficiency and spectrum efficiency. However, both the traditional ADO-OFDM and ADO-OFDM with iteration receiver suffer from an error floor issue when a lower DC bias is allocated to the DCO-OFDM branch. …”
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Inter-observational analysis of computed tomography parameters to predict nonobvious posterior ligament complex injury in neurologically intact patients with thoracolumbar trauma by Joana Araújo de Azevedo, Carolina Garcez Martins, Nuno Oliveira, Pedro Varanda, Bruno Direito-Santos

“…Introduction: Assessing the integrity of the posterior ligament complex (PLC), as a key element in the characterization of an unstable Thoracolumbar fracture (TLF), is challenging, but crucial in the choice of treatment. …”
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A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n by Lei Yang, Wenhao Cui, Jingqian Li, Guotao Han, Qi Zhou, Yubin Lan, Jing Zhao, Yongliang Qiao

“…To mitigate false negatives and false positives encountered by the original YOLOv11n model during detection, the EMA (efficient multi-scale attention) mechanism is introduced to enhance the weights of important features and suppress irrelevant regions, thereby improving the detection accuracy of the model. …”
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