Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network by Kamorudeen Amuda, Almustapha Wakili, Tomilade Amoo, Lukman Agbetu, Qianlong Wang, Jinjuan Feng

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Traffic Classification in Software-Defined Networking Using Genetic Programming Tools by Spiridoula V. Margariti, Ioannis G. Tsoulos, Evangelia Kiousi, Eleftherios Stergiou

“…This is most evident in the feature construction method where at each generation of the genetic algorithm, a set of learning models is required to be trained to evaluate the generated artificial features.…”
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Isfahan Artificial Intelligence Event 2023: Macular Pathology Detection Competition by Farnaz Sedighin, Maryam Monemian, Zahra Zojaji, Ahmadreza Montazerolghaem, Mohammad Amin Asadinia, Seyed Mojtaba Mirghaderi, Seyed Amin Naji Esfahani, Mohammad Kazemi, Reza Mokhtari, Maryam Mohammadi, Mohadese Ramezani, Mahnoosh Tajmirriahi, Hossein Rabbani

“…Researchers tested their algorithms and competed for the best classification results. …”
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Ensemble machine learning algorithm for anti-VEGF treatment efficacy prediction in diabetic macular edema by Yu Fang, Jianwei Lin, Peiwen Xie, Huishan Zhu, Tsz Kin Ng, Guihua Zhang

“…Conclusion This study established an ensemble ML algorithm based on 3D-OCT images and clinical features for automatic detection of treatment responses to anti-VEGF treatment in DME patients to predict the efficacy of anti-VEGF treatment in DME patients and assist clinicians in optimal treatment decisions.…”
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Advanced Color Edge Detection Using Clifford Algebra in Satellite Images by Uzair Aslam Bhatti, Zhou Ming-Quan, Qingsong Huo, Sajid Ali, Aamir Hussain, Yan Yuhuan, Zhaoyuan Yu, Linwang Yuan, Saqib Ali Nawaz

“…Satellite images are rich in information about objects with different color intensity and have a large amount of noise, so it is difficult to achieve recognition, classification, and feature extraction of small objects through traditional edge detection algorithms. …”
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Multiradar Collaborative Task Scheduling Algorithm Based on Graph Neural Networks with Model Knowledge Embedding by Haoqing LI, Dian YU, Changchun PAN, Wenxian YU, Dongying LI

“…During the feature extraction stage, the algorithm employs a random unitary matrix transformation. …”
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Contextual information based anomaly detection for multi-scene aerial videos by Girisha S, Ujjwal Verma, Manohara M M. Pai, Radhika M. Pai

“…Further, the lack of standard UAV-based anomaly detection datasets has restricted the development of novel algorithms. …”
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A hybrid super learner ensemble for phishing detection on mobile devices by Routhu Srinivasa Rao, Cheemaladinne Kondaiah, Alwyn Roshan Pais, Bumshik Lee

“…To address these limitations, this paper proposes a novel hybrid super learner ensemble model named Phish-Jam, a mobile application specifically designed for phishing detection on mobile devices. Phish-Jam utilizes a super learner ensemble that combines predictions from diverse Machine Learning (ML) algorithms to classify legitimate and phishing websites. …”
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Detection and location of EEG events using deep learning visual inspection. by Mohammad Amin Fraiwan

“…The model then produces bounding boxes that accurately delineate the location of these events within the image. Several object detection algorithms (i.e., Faster R-CNN, YOLOv4, and YOLOX) and multiple backbone CNN architectures were evaluated under a wide range of conditions, revealing their true representative performance. …”
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Enhanced object detection in low-visibility haze conditions with YOLOv9s. by Yang Zhang, Bin Zhou, Xue Zhao, Xiaomeng Song

“…Low-visibility haze environments, marked by their inherent low contrast and high brightness, present a formidable challenge to the precision and robustness of conventional object detection algorithms. This paper introduces an enhanced object detection framework for YOLOv9s tailored for low-visibility haze conditions, capitalizing on the merits of contrastive learning for optimizing local feature details, as well as the benefits of multiscale attention mechanisms and dynamic focusing mechanisms for achieving real-time global quality optimization. …”
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Lightweight and robust ship detection method driven by self-attention mechanism by Feng MA, Zihui SHI, Jie SUN, Chen CHEN, Xianbin MAO, Xinping YAN

“…To address this issue, a novel ship detection method called ShipDet is proposed which significantly improves performance through the design of a dedicated backbone network, improved feature extraction process, and constrained microscopic detection heads. …”
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Curb Detection and Mapping via Robust Iterative Gaussian Process Regression by Di Wang, Si Chen, Zhenni Ma, Jiajia Shi, Fuchun Zhang

“…In this paper, an efficient curb detection and mapping algorithm is proposed to achieve an accurate representation of curb shape. …”
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A tree crown edge-aware clipping algorithm for airborne LiDAR point clouds by Shangshu Cai, Yong Pang

“…Then, these two features are linearly weighted and integrated for cutting line detection. …”
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Enhancing surface detection: A comprehensive analysis of various YOLO models by G. Deepti Raj, B. Prabadevi

“…However, implementing accurate and automated object detection algorithms mitigates these challenges, allowing errors or defects to be detected before processing. …”
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Detecting Obfuscated Malware Infections on Windows Using Ensemble Learning Techniques by Yadigar Imamverdiyev, Elshan Baghirov, John Chukwu Ikechukwu

“…The findings contribute to cybersecurity efforts by comprehensively assessing machine learning algorithms for obfuscated malware detection through memory analysis. …”
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Fast and Accurate Detection of Forty Types of Fruits and Vegetables: Dataset and Method by Xiaosheng Bu, Yongfeng Wu, Hongtai Lv, Youling Yu

“…Additionally, we propose a novel framework for fruit and vegetable detection—FVRT-DETR. Based on the Transformer architecture, this framework features an end-to-end real-time detection algorithm. …”
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Scene Text Detection and Recognition Using Maximally Stable Extremal Region by Golda Jeyasheeli P, Athinarayanan B, Manish T, Mohamad Umar M

“…Finally, to recognize text, CRNN is used to recognize the text present in the detected regions. Our CRNN architecture consists of convolutional and recurrent layers, which enable us to capture both spatial and temporal features of the text. …”
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Research on a Burn Severity Detection Method Based on Hyperspectral Imaging by Sijia Wang, Minghui Gu, Mingle Zhang, Xin Tan

“…The accurate detection of burn wounds is a key research direction in the field of burn medicine, as diagnostic results directly influence the risk of wound infection and the formation of hypertrophic scars. …”
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Small Target Detection for Power Line Components in Complex UAV Imagery by Zhiyuan Sun, Xu Wang

“…First, to enhance the model’s ability to detect small and low-resolution targets, we incorporate SPDConv into the backbone of the feature extraction network, significantly improving the extraction of small object features. …”
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Iterative PolInSAR Target Decomposition for Scattering Characterization and Building Detection by Di Zhuang, Lamei Zhang, Bin Zou

“…In densely rotated built-up areas, restricted by interactive and complex scatterings, most polarimetric synthetic aperture radar scattering analyses and unsupervised building detection algorithms have failed, especially under conditions of large radar look angles. …”
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