Detecting faults in electronic combustion engine control systems by acoustic parameters by V. D. Gabidulin, V. N. Dobromirov

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Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer’s Disease Detection by Opeyemi Taiwo Adeniran, Blessing Ojeme, Temitope Ezekiel Ajibola, Ojonugwa Oluwafemi Ejiga Peter, Abiola Olayinka Ajala, Md Mahmudur Rahman, Fahmi Khalifa

“…With the advancements in deep learning methods, AI systems now perform at the same or higher level than human intelligence in many complex real-world problems. The data and algorithmic opacity of deep learning models, however, make the task of comprehending the input data information, the model, and model’s decisions quite challenging. …”
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Image-based maturity detection for selective cauliflower harvesting in field condition by Ajay Kushwah, P.K. Sharma, H.L. Kushwaha, Brij Bihari Sharma, Naseeb Singh, Ramineni Harsha Nag, Manojit Chowdhury, Nrusingh Charan Pradhan, Gopal Carpenter, Silpa Mandal, Ali Salem

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Complexation reaction with Sm(III): a facile spectrophotometric quantification of daunorubicin in pharmaceutical preparation and biological fluids by Basima A. A. Saleem

“…The proposed method demonstrated a significant linear relationship between absorbance and daunorubicin concentration in the range of 1–48 µg/mL, with a correlation coefficient of 0.9974. Detection limits were established at 0.0025 and 0.0250 µg/mL. …”
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Apnea detection using wrist actigraphy in patients with heterogeneous sleep disorders by Xiaoman Xing, Sizhi Ai, Jihui Zhang, Rui Huang, Yaping Liu, Dongming Quan, Jiacheng Ma, Guoli Wu, Jiangen Xu, Yuan Zhang, Hongliang Feng, Wen-fei Dong

“…This study introduces an enhanced single-sensor-based OSA screening method, leveraging novel signal processing and machine learning to ensure accurate detection across diverse populations. Wrist actigraphy is a widely-used and energy-efficient tool for respiratory rate estimation. …”
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Lightweight obstacle detection for unmanned mining trucks in open-pit mines by Guangwei Liu, Jian Lei, Zhiqing Guo, Senlin Chai, Chonghui Ren

“…In addition, this paper also adopts the amplitude-based layer adaptive sparse pruning algorithm (LAMP) to further compress the model size while maintaining efficient detection performance. Through this pruning strategy, the model further reduces its dependence on computing resources while maintaining key performance. …”
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Detection of cucumber downy mildew spores based on improved YOLOv5s by Chen Qiao, Kaiyu Li, Xinyi Zhu, Jiaping Jing, Wei Gao, Lingxian Zhang

“…Consequently, developing a rapid, accurate, and efficient method for detecting cucumber downy mildew spores is critical for advancing disease control. …”
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A triple protein-based ELISA for differential detection of ASFV antibodies by Shuai Zhang, Yuzhu Zuo, Wenyuan Gu, Yunhuan Zhao, Ying Liu, Jinghui Fan, Jinghui Fan

“…As no vaccines or effective drugs are available to aid prevention and control, early detection is crucial. The emergence of the low-virulence ASFV strain not expressing CD2v/MGFs (ASFVΔCD2v/ΔMGFs) has been identified domestically and internationally and has even become an epidemic in China, resulting in a complex epidemic. …”
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DRCO: Dense-Label Refinement and Cross Optimization for Semi-Supervised Object Detection by Yunlong Qin, Yanjun Li, Feifan Ji, Yan Liu, Yu Wang, Ji Xiang

“…In semi-supervised object detection (SSOD), the methods based on dense pseudo-labeling bypass complex post-processing while maintaining competitive performance compared to the methods based on sparse pseudo-labeling. …”
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RE-YOLOv5: Enhancing Occluded Road Object Detection via Visual Receptive Field Improvements by Tianyu Li, Xuanrui Xiong, Yuan Zhang, Xiaolin Fan, Yushu Zhang, Haihong Huang, Dan Hu, Mengting He, Zhanjun Liu

“…The complexity and variability of real-world road environments make the detection of densely occluded objects more challenging in autonomous driving scenarios. …”
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Efficient extraction of experimental data from line charts using advanced machine learning techniques by Wenjin Yang, Jie He, Xiaotong Zhang

“…Lastly, we present a method for automatically converting image data into structured JSON data, significantly enhancing the efficiency and accuracy of data extraction. Experimental results demonstrate that this method exhibits high efficiency and accuracy in handling complex charts, achieving an average extraction accuracy of 93% on public datasets, significantly surpassing the current state-of-the-art methods. …”
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Tear replacement by cationic emulsion in complex therapy of superficial punctate keratitis in hypotensively medicated glaucoma patients by S. V. Trufanov, S. A. Malozhen, D. A. Krakhmaleva, E. A. Pivin

“…The testing procedures and efficiency monitoring included biomicroscopy, Schirmer test, Norn test and evaluation of tear film osmolarity. …”
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An Efficient Large-Scale Sensor Deployment Using a Parallel Genetic Algorithm Based on CUDA by Jae-Hyun Seo, Yourim Yoon, Yong-Hyuk Kim

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PLL-VO： An Efficient and Robust Visual Odometry Integrating Point-Line Features and Neural Networks by L. Zhao, Y. Yang, D. Ma, X. Lin, W. Wang

“…To overcome the impact of complex lighting conditions, a self-supervised learning method for interest point detection and a line detection algorithm that combines line optical flow tracking with cross-constraints is presented. …”
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Insulator Defect Detection Algorithm Based on Improved YOLOv11n by Junmei Zhao, Shangxiao Miao, Rui Kang, Longkun Cao, Liping Zhang, Yifeng Ren

“…Ensuring the reliability and safety of electrical power systems requires the efficient detection of defects in high-voltage transmission line insulators, which play a critical role in electrical isolation and mechanical support. …”
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RMFKAN: Network Spammers Detection Method Based on Improved Graph Mamba by WANG Yuzhe, YAN Jinghua, BU Fanliang, WANG Yifan, LI Jia, HAN Zhuxuan

“…Specifically, subgraphs are tokenized by the random walk strategy, message passing neural networks are input to independently handle different types of edges, and features are enhanced by KAN improved with Fourier coefficients. The feature matrix is input into Bi-Mamba to improve the ability of capturing long-distance dependencies and effectively reduce training complexity. …”
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Intelligent Detection of Tomato Ripening in Natural Environments Using YOLO-DGS by Mengyuan Zhao, Beibei Cui, Yuehao Yu, Xiaoyi Zhang, Jiaxin Xu, Fengzheng Shi, Liang Zhao

“…To achieve accurate detection of tomato fruit maturity and enable automated harvesting in natural environments, this paper presents a more lightweight and efficient maturity detection algorithm, YOLO-DGS, addressing the challenges of subtle maturity differences between regular and cherry tomatoes, as well as fruit occlusion. …”
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Edge-Optimized Lightweight YOLO for Real-Time SAR Object Detection by Caiguang Zhang, Ruofeng Yu, Shuwen Wang, Fatong Zhang, Shaojia Ge, Shuangshuang Li, Xuezhou Zhao

“…Experimental results demonstrate that the proposed method achieves comparable detection performance to state-of-the-art YOLO variants while drastically reducing computational complexity (4.4 GFLOP) and parameter count (1.9 M). …”
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Contrastive Learning Algorithm for Low-Resource Cryptographic Attack Event Detection by Peng Luo, Rangjia Cai, Yuanbo Guo

“…Additionally, assigning appropriate weights to keywords of different event types and employing clustering methods for effective classification reduce reliance on manually labeled data, thereby facilitating the training of an efficient cryptographic attack event detection model. …”
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A Novel Approach to Speed Up Hampel Filter for Outlier Detection by Mario Roos-Hoefgeest Toribio, Alejandro Garnung Menéndez, Sara Roos-Hoefgeest Toribio, Ignacio Álvarez García

“…This paper proposes a novel Hampel filter variant that replaces the MAD with an original estimator (mMAD) that retains statistical robustness but is computationally more efficient. This reduces the filter’s computational complexity from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><mi>N</mi><mo>·</mo><mi>w</mi><mo form="prefix">log</mo><mi>w</mi><mo>)</mo></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><mi>N</mi><mo>·</mo><mi>w</mi><mo>)</mo></mrow></semantics></math></inline-formula>, where <i>N</i> is the data length and <i>w</i> the window size. …”
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