Automatic Roll-Profile Positioning Detection System Based on Contact Sensor by Jiali Zheng, Huagui Huang, Qiwei Hu

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Machine vision-based detection of key traits in shiitake mushroom caps by Jiuxiao Zhao, Jiuxiao Zhao, Wengang Zheng, Wengang Zheng, Yibo Wei, Yibo Wei, Qian Zhao, Qian Zhao, Jing Dong, Jing Dong, Xin Zhang, Xin Zhang, Mingfei Wang, Mingfei Wang

“…Firstly, an edge detection model was established. This model is called KL-Dexined. …”
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Improved Method of Detection Falsification Results the Digital Image in Conditions of Attacks by Kobozeva A.A., Grigorenko S.N.

“…The method is intended for clone detection areas and pre-image in terms of additional disturbing influences in the image after the cloning operation for "masking" of the results, which complicates the search process. …”
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Small parallel residual convolutional neural network and traffic congestion detection by Shan Jiang, Yuming Feng

“…Abstract In the development process of modern cities, traffic congestion has become an increasingly severe challenge. …”
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Detection of Attacks in Network Traffic with the Autoencoder-Based Unsupervised Learning Method by Yalçın Özkan

“…It is observed that supervised learning methods lead to difficulties and cost increases in the detection of cyber-attacks and the labeling process. …”
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Music Rhythm Detection Algorithm Based on Multipath Search and Cluster Analysis by Shuqing Ma

“…Music rhythm detection and tracking is an important part of the music comprehension system and visualization system. …”
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BN-SNN: Spiking neural networks with bistable neurons for object detection. by Siddiqui Muhammad Yasir, Hyun Kim

“…Additionally, the application of SNNs in object detection tasks remains largely under-explored. In this study, we propose a novel approach utilizing a bistable integrate-and-fire (BIF) neuron model integrated with a single-shot multibox detector (SSD) as the detection head. …”
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HTAD: a human-in-the-loop framework for supervised chromatin domain detection by Wei Shen, Ping Zhang, Yiwei Jiang, Hailin Tao, Zhike Zi, Li Li

“…HTAD begins with feature extraction for potential TAD border pairs, followed by an interactive labeling process through active learning. Performance assessments using public curation and synthetic datasets demonstrate HTAD’s superiority over other state-of-the-art methods and reveal highly hierarchical TAD structures, offering a human-in-the-loop solution for detecting complex genomic patterns.…”
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YOLOGX: an improved forest fire detection algorithm based on YOLOv8 by Caixiong Li, Yue Du, Xing Zhang, Peng Wu

“…Finally, the proposed Focal-SIoU loss function replaces the original loss function, effectively reducing directional errors by combining angle, distance, shape, and IoU losses, thus optimizing the model training process. YOLOGX was evaluated on the D-Fire dataset, achieving a mAP@0.5 of 80.92% and a detection speed of 115 FPS, surpassing most existing classical detection algorithms and specialized fire detection models. …”
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Lateral Change Detection of Ghezlozan River Channel from 1993 to 2013 by fariba Esfandiary Darabad, Masoud Rahimi, khodadad lotfy, ebadi elhameh

“…The channel duct was divided into 24 transects based on morphology and the process of change. The average migration rate of the Gezelozan River duct has been around 4.47 m / year over the past 20 years. …”
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Modeling of Preload Bolted Flange Connection Structure for Loosening Analysis and Detection by Weicheng Sun, Zhenqun Guan, Yan Chen, Jiacheng Pan, Yan Zeng

“…A fine hexahedral mesh model of the bolt is used to predict the dynamic response of the structure accurately. The tightening process, which is ignored in the traditional I-shaped simplified model of bolted flange connection structure, can be simulated well based on the proposed model. …”
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Target Detection Technology of Mechanical, Electrical, and Plumbing Components Based on CV by Guohua Wei, Ding Zhou, Xiaojun Yuan

“…The proposed architecture addresses the limitations of existing techniques in handling MEP complexities, and through an automatic comparison and verification process, it detects deviations promptly, ensuring adherence to design specifications. …”
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Research Status and Prospect of Torsional Vibration Detection Methods for Rotating Machinery by GUO Yan-ling, QIU Feng, LI Zhi-peng

“…In recent years, the number of large units accidents caused by torsional vibration is increasing. Therefore, how to detect torsional vibration has become a major focus in the machinery increasing.The process of torsional vibration of detection method can be divided into two steps: the measurement of torsional vibration signals and the extraction and analysis of torsional vibration signal. …”
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Deep learning for malignant lymph node segmentation and detection: a review by Wenxia Wu, Adrien Laville, Eric Deutsch, Roger Sun

“…This paper provides an in-depth review of the advancements in deep learning for malignant lymph node segmentation and detection. After a brief overview of deep learning methodologies, the review examines specific models and their outcomes for malignant lymph node segmentation and detection across five clinical sites: head and neck, upper extremity, chest, abdomen, and pelvis. …”
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Induction Machine Bearing Fault Detection Using Empirical Wavelet Transform by Ricardo Lopez-Gutierrez, Jose de Jesus Rangel-Magdaleno, Carlos Javier Morales-Perez, Arturo García-Perez

“…The detection of faults related to the optimal condition of induction motors is an important task to avoid the malfunction or loss of the motor, thus avoiding high repair or replacement costs and faults in the efficiency of the process to which they belong. …”
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The Case of a Primary Tuberculosis Complex in a Child With Late Detection by Irina Yu. Petrakova, Marina F. Gubkina, Yury S. Berezovsky, Mamed A. Bagirov, Bagirov V. Yukhimenko

“…We present a case of late detection and course of the primary tuberculosis complex in a child who was previously in an unknown contact with a tuberculosis patient at the first year of life. …”
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Detection of grains in aluminium metal matrix composites using image fusion by Tapasmini Sahoo, Sweta Rani Biswal, Kunal Kumar Das

“…This piece of work illustrates an image fusion approach using discrete wavelet transform (DWT) for the detection of grains present in the hybrid composite to study the metallographic characterization. …”
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Dual Feature-Based Intrusion Detection System for IoT Network Security by A. Biju, S. Wilfred Franklin

“…The proposed method utilizes the bald eagle search (BES) algorithm and butterfly optimization algorithm (BOA) to capture both flow and packet level features to enhance the accuracy of the intrusion detection process. Moreover, a multi-head attention-based bidirectional gated recurrent unit (MHA-BiGRU) is utilized to classify Attack and Non-Attack classes with high precision. …”
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Early Heart Attack Detection Using Hybrid Deep Learning Techniques by Niga Amanj Hussain, Aree Ali Mohammed

“…These algorithms can process large datasets, extracting valuable insights that help mitigate the risk of fatal outcomes. …”
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Approach of detecting low-rate DoS attack based on combined features by Zhi-jun WU, Jing-an ZHANG, Meng YUE, Cai-feng ZHANG

“…LDoS (low-rate denial of service) attack is a kind of RoQ (reduction of quality) attack which has the characteristics of low average rate and strong concealment.These characteristics pose great threats to the security of cloud computing platform and big data center.Based on network traffic analysis,three intrinsic characteristics of LDoS attack flow were extracted to be a set of input to BP neural network,which is a classifier for LDoS attack detection.Hence,an approach of detecting LDoS attacks was proposed based on novel combined feature value.The proposed approach can speedily and accurately model the LDoS attack flows by the efficient self-organizing learning process of BP neural network,in which a proper decision-making indicator is set to detect LDoS attack in accuracy at the end of output.The proposed detection approach was tested in NS2 platform and verified in test-bed network environment by using the Linux TCP-kernel source code,which is a widely accepted LDoS attack generation tool.The detection probability derived from hypothesis testing is 96.68%.Compared with available researches,analysis results show that the performance of combined features detection is better than that of single feature,and has high computational efficiency.…”
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