MSSA: multi-stage semantic-aware neural network for binary code similarity detection by Bangrui Wan, Jianjun Zhou, Ying Wang, Feng Chen, Ying Qian

“…Binary code similarity detection (BCSD) aims to identify whether a pair of binary code snippets is similar, which is widely used for tasks such as malware analysis, patch analysis, and clone detection. …”
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Network security traffic detection and legal supervision based on adaptive metric learning algorithm by Liu Zhen

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App-DDoS detection method using partial binary tree based SVM algorithm by Bin ZHANG, Zihao LIU, Shuqin DONG, Lixun LI

“…As it ignored the detection of ramp-up and pulsing type of application layer DDoS (App-DDoS) attacks in existing flow-based App-DDoS detection methods,an effective detection method for multi-type App-DDoS was proposed.Firstly,in order to fast count the number of HTTP GET for users and further support the calculation of feature parameters applied in detection method,the indexes of source IP address in multiple time windows were constructed by the approach of Hash function.Then the feature parameters by combining SVM classifiers with the structure of partial binary tree were trained hierarchically,and the App-DDoS detection method was proposed with the idea of traversing binary tree and feedback learning to distinguish non-burst normal flow,burst normal flow and multi-type App-DDoS flows.The experimental results show that compared with the conventional SVM-based and naïve-Bayes-based detection methods,the proposed method has more excellent detection performance and can distinguish specific App-DDoS types through subdividing attack types and training detection model layer by layer.…”
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Employing SAE-GRU deep learning for scalable botnet detection in smart city infrastructure by Usman Tariq, Tariq Ahamed Ahanger

“…These findings enhance the understanding of IoT security by offering a scalable and resource-efficient solution for botnet detection. The functional investigation establishes a foundation for future research into adaptive security mechanisms that address emerging threats and highlights the practical potential of advanced deep learning techniques in safeguarding next-generation smart city ecosystems.…”
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A Lightweight Network for UAV Multi-Scale Feature Fusion-Based Object Detection by Sheng Deng, Yaping Wan

“…To tackle the issues of small target sizes, missed detections, and false alarms in aerial drone imagery, alongside the constraints posed by limited hardware resources during model deployment, a streamlined object detection approach is proposed to enhance the performance of YOLOv8s. …”
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A Contrast-Enhanced Approach for Aerial Moving Target Detection Based on Distributed Satellites by Yu Li, Hansheng Su, Jinming Chen, Weiwei Wang, Yingbin Wang, Chongdi Duan, Anhong Chen

“…This method compensates for the range difference rather than the target range. In the detection period, we develop two weighting functions, i.e., the Doppler frequency rate (DFR) variance function and smooth spatial filtering function, to extract prominent areas and make efficient detection, respectively. …”
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Design and investigation of charge plasma-based TMD heterojunction TFET biosensor for ultrasensitive detection by Monika Kumari, Niraj Kumar Singh, Vinay Kantipudi, Manodipan Sahoo

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Quantifying the spatial impact of an invasive Acacia on ecosystem functioning using remote sensing by André Große-Stoltenberg, Christiane Werner, Christine Hellmann, Jens Oldeland, Jan Thiele

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GESC-YOLO: Improved Lightweight Printed Circuit Board Defect Detection Based Algorithm by Xiangqiang Kong, Guangmin Liu, Yanchen Gao

“…Printed circuit boards (PCBs) are an indispensable part of electronic products, and their quality is crucial to the operational integrity and functional reliability of these products. Currently, existing PCB defect detection models are beset with issues such as excessive model size and parameter complexity, rendering them ill-equipped to meet the requirements for lightweight deployment on mobile devices. …”
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Self-Powered Microsystem for Ultra-Fast Crash Detection via Prestressed Triboelectric Sensing by Yiqun Wang, Yuhan Wang, Xinzhi Liu, Xiaofeng Wang, Keren Dai, Zheng You

“…We further developed a self-powered, compact (<4.5 cm3) microsystem that integrates the shock sensor, a signal processing module, airbag triggering circuitry, and a high-g-resistant supercapacitor as a backup power source. The microsystem achieves ultra-fast shock detection and airbag activation with a delay of less than 0.2 ms. …”
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RSWD-YOLO: A Walnut Detection Method Based on UAV Remote Sensing Images by Yansong Wang, Xuanxi Yang, Haoyu Wang, Huihua Wang, Zaiqing Chen, Lijun Yun

“…Furthermore, to optimize the detection performance under hardware resource constraints, we apply knowledge distillation to RSWD-YOLO, thereby further improving the detection accuracy. …”
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Automatic detection of human gait events: a simple but versatile 3D algorithm by Théo Vancanneyt, Camille Le Moal, Maxence Blard, Juliette Lenoir, Nicolas Roche, Céline Bonnyaud, Fabien Dubois

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A lightweight UAV target detection algorithm based on improved YOLOv8s model by Fubao Ma, Ran Zhang, Bowen Zhu, Xirui Yang

“…Furthermore, the original loss function is replaced with SIoU to enhance detection accuracy. …”
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Soil Moisture Time Series Using Gamma‐Ray Spectrometry Detection Representing a Scale of Tens‐of‐Meters by Mie Andreasen, Steven Van derVeeke, Han Limburg, Ronald Koomans, Majken Caroline Looms

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YOLO-SRMX: A Lightweight Model for Real-Time Object Detection on Unmanned Aerial Vehicles by Shimin Weng, Han Wang, Jiashu Wang, Changming Xu, Ende Zhang

“…Unmanned Aerial Vehicles (UAVs) face a significant challenge in balancing high accuracy and high efficiency when performing real-time object detection tasks, especially amidst intricate backgrounds, diverse target scales, and stringent onboard computational resource constraints. …”
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Research on underwater disease target detection method of inland waterway based on deep learning by Tao Yu, Yu Xie, Jinsong Luo, Wei Zhu, Jie Liu

“…Abstract Aiming at the problems of low detection accuracy and poor generalization ability of underwater disease targets in inland waterways, an underwater disease target detection algorithm for inland waterways based on improved YOLOv5 is designed, which is denoted as YOLOv5-GBCE. …”
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Comparison of genotyping assays for detection of targeted CRISPR/Cas mutagenesis in highly polyploid sugarcane by Eleanor J. Brant, Eleanor J. Brant, David May, David May, Ayman Eid, Ayman Eid, Fredy Altpeter, Fredy Altpeter

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Enhancing Drone Detection via Transformer Neural Network and Positive–Negative Momentum Optimizers by Pavel Lyakhov, Denis Butusov, Vadim Pismennyy, Ruslan Abdulkadirov, Nikolay Nagornov, Valerii Ostrovskii, Diana Kalita

“…The developed algorithms for training NN architectures improved the accuracy of drone detection by achieving the global extremum of the loss function in fewer epochs using positive–negative pulse-based optimization algorithms. …”
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YOLOv9-GDV: A Power Pylon Detection Model for Remote Sensing Images by Ke Zhang, Ningxuan Zhang, Chaojun Shi, Qiaochu Lu, Xian Zheng, Yujie Cao, Xiaoyun Zhang, Jiyuan Yang

“…Finally, the Variable Minimum Point Distance Intersection over Union (VMPDIoU) loss is proposed to optimize the model’s loss function. This method employs variable input parameters to directly calculate key point distances between predicted and ground-truth boxes, more accurately reflecting positional differences between detection results and reference targets, thus effectively improving the model’s mean Average Precision (mAP). …”
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Classification of SERS spectra for agrochemical detection using a neural network with engineered features by Mateo Frausto-Avila, Monserrat Ochoa-Elias, Jose Pablo Manriquez-Amavizca, María del Carmen González-López, Gonzalo Ramírez-García, Mario Alan Quiroz-Juárez

“…Surface-Enhanced Raman Spectroscopy (SERS) substrates offer a promising solution for the sensitive and specific detection of agrochemicals, enabling timely interventions to mitigate their harmful effects on humans and ecosystems. …”
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