Robust Miner Detection in Challenging Underground Environments: An Improved YOLOv11 Approach by Yadong Li, Hui Yan, Dan Li, Hongdong Wang

“…The Efficient Channel Attention (ECA) mechanism was integrated into the YOLOv11 model to enhance the model’s ability to focus on key features, thereby significantly improving detection accuracy. …”
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Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm by Amit Sagu, Nasib Singh Gill, Preeti Gulia, Noha Alduaiji, Piyush Kumar Shukla, Mohd Asif Shah

“…The SUCMO algorithm fine-tunes the deep learning model’s hyperparameters to improve classification accuracy. …”
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Multi-target personnel tracking algorithm for coal mine based on improved YOLOv7 and ByteTrack by Pengcheng QU, Jingzhao LI, Zechao LIU

“…In order to solve the problems of low accuracy and poor real-time performance of existing target tracking algorithms in the complex environment of coal mines, a YOLO-FasterNet+ByteTrack coal mine personnel tracking algorithm was proposed based on the Tracking by Detection (TBD) paradigm. …”
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Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms by Mehmet Tahir Huyut, Andrei Velichko, Maksim Belyaev, Yuriy Izotov, Şebnem Karaoğlanoğlu, Bünyamin Sertoğullarından, Sıddık Keskin, Dmitry Korzun

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Infrared Ship Detection in Complex Nearshore Scenes Based on Improved YOLOv5s by Xiuwen Liu, Mingchen Liu, Yong Yin

“…Experimental results demonstrate that CGSE-YOLOv5s achieves a mean average precision (mAP@0.5) of 94.8%, outperforming YOLOv5s by 1.3% and surpassing other detection algorithms.…”
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Detection of DDoS Attacks in SDN Switches with Deep Learning and Swarm Intelligence Approach by Mohsen Eghbali, Mohammadreza Mollkhalili Maybodi

“…Experimental results obtained in MATLAB, using the NSL-KDD dataset, demonstrate the proposed method’s effectiveness, achieving an accuracy of 99.34%, a sensitivity of 99.16%, and a precision of 98.93% in attack detection. The proposed method outperforms feature selection methods based on WOA, HHO, and AO algorithms, and deep learning methods like LSTM, RNN, and CNN, particularly in detecting DDoS attacks.…”
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Ensemble-based eye disease detection system utilizing fundus and vascular structures by Hongjie Yu, Xingbo Dong

“…By using vascular features and mitigating the risk of overfitting, this framework demonstrates superior performance in terms of multiple metrics. …”
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Recent advances in machine learning for defects detection and prediction in laser cladding process by X.C. Ji, R.S. Chen, C.X. Lu, J. Zhou, M.Q. Zhang, T. Zhang, H.L. Yu, Y.L. Yin, P.J. Shi, W. Zhang

“…Furthermore, it encapsulates prevalent machine learning models and algorithms employed for defect detection. The findings highlight the efficacy of machine learning algorithms in detecting defects within laser cladding coatings, while concurrently establishing correlations between feature signals, coating defects, and cladding processes. …”
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Lightweight anomaly detection model for UAV networks based on memory-enhanced autoencoders by HU Tianzhu, SHEN Yulong, REN Baoquan, HE Ji, LIU Chengliang, LI Hongjun

“…The hierarchical clustering algorithm was used to divide the composite statistical features, and the separated features were input to multiple small memory-enhanced autoencoders in the integrated architecture for independent training, which reduced the computational complexity and solved the problem of false negatives caused by the overfitting of the reconstruction effect of the traditional autoencoder. …”
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BCSM-YOLO: An Improved Product Package Recognition Algorithm for Automated Retail Stores Based on YOLOv11 by Pingqing Hou, Shaoze Huang

“…To address YOLOv11’s limitations in supermarket scenarios, such as missed small targets and low positioning accuracy, this paper proposes BCSM-YOLO, an improved algorithm based on YOLOv11. Firstly, introducing the Space-to-Depth Convolution (SPD-Conv) can maximize the preservation of detailed information such as commodity texture and shape in the downsampling stage, which provides a rich information base for the subsequent feature extraction. …”
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Enhanced Detection of Leishmania Parasites in Microscopic Images Using Machine Learning Models by Michael Contreras-Ramírez, Jhonathan Sora-Cardenas, Claudia Colorado-Salamanca, Clemencia Ovalle-Bracho, Daniel R. Suárez

“…This study addresses the development of a system based on machine learning algorithms to detect <i>Leishmania</i> spp. parasite in direct smear microscopy images, contributing to the diagnosis of cutaneous leishmaniasis. …”
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A Survey of the Multi-Sensor Fusion Object Detection Task in Autonomous Driving by Hai Wang, Junhao Liu, Haoran Dong, Zheng Shao

“…In the field of autonomous driving, multi-sensor fusion object detection has become a hot research topic. To further explore the future development trends of multi-sensor fusion object detection, we introduce the mainstream framework Transformer model of the multi-sensor fusion object detection algorithm, and we also provide a comprehensive summary of the feature fusion algorithms used in multi-sensor fusion object detection, specifically focusing on the fusion of camera and LiDAR data. …”
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Graph-Based COVID-19 Detection Using Conditional Generative Adversarial Network by Imran Ihsan, Azhar Imran, Tahir Sher, Mahmood Basil A. Al-Rawi, Mohammed A. Elmeligy, Muhammad Salman Pathan

“…The proposed methodology encompasses four distinct phases: initial segmentation of raw chest radiographs employing Conditional Generative Adversarial Networks (CGAN), followed by feature extraction through a tailored pipeline integrating both manual computer vision algorithms and pre-trained Deep Neural Network (DNN) models. …”
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NID-DETR: A novel model for accurate target detection in dark environments by Qingyuan Pan, Qiang Liu, Wei Huang

“…Current mainstream algorithms face challenges in extracting meaningful features under low-light conditions, which significantly limits their effectiveness. …”
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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

“…The effectiveness and superiority of this approach are validated through comparisons with other widely used segmentation detection algorithms.…”
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SCoralDet: Efficient real-time underwater soft coral detection with YOLO by Zhaoxuan Lu, Lyuchao Liao, Xingang Xie, Hui Yuan

“…In recent years, climate change and marine pollution have significantly degraded coral reefs, highlighting the urgent need for automated coral detection to monitor marine ecosystems. However, underwater coral detection presents unique challenges, including low image contrast, complex coral structures, and dense coral growth, which limit the effectiveness of general object detection algorithms. …”
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Classification Model for Bot-IoT Attack Detection Using Correlation and Analysis of Variance by Firgiawan Faira, Dandy Pramana Hostiadi

“…This research focuses on detecting Bot-IoT activity using the Bot-IoT UNSW Canberra 2018 dataset. …”
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SD-YOLO: A Robust and Efficient Object Detector for Aerial Image Detection by Shuaihui Qi, Yi Sun, Xiaofeng Song, Jiting Li, Tongfei Shang, Li Yu

“…Particularly, when deploying detection algorithms on edge computing platforms like uncrewed aerial vehicles (UAVs), it is essential to find out a lightweight network with good trade-off on efficiency and accuracy. …”
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Detection of AI-Generated Texts: A Bi-LSTM and Attention-Based Approach by John Blake, Abu Saleh Musa Miah, Krzysztof Kredens, Jungpil Shin

“…This paper presents a novel algorithm that leverages cutting-edge machine-learning techniques to accurately and efficiently detect AI-generated texts. …”
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An optimized multi-task contrastive learning framework for HIFU lesion detection and segmentation by Matineh Zavar, Hamid Reza Ghaffari, Hamid Tabatabaee

“…To address these challenges, this paper introduces an innovative framework called the Optimized Multi-Task Contrastive Learning Framework (OMCLF), which leverages self-supervised learning (SSL) and genetic algorithms (GA) to enhance HIFU lesion detection and segmentation. …”
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