Pheromone‐binding proteins based phylogenetics and phylogeography of Maruca spp. from Asia, Africa, Oceania, and South America by Periasamy Malini, Srinivasan Ramasamy, Roland Schafleitner, Krishnan Muthukalingan

“…The raw‐sequence data were assembled using homologous genes from related organisms in GenBank to detect M. vitrata PBPs (MvitPBPs). …”
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Human Activity Detection Events Through Human Eye Reflection using Bystander Analyzer by P. Nagalakshmi

“…The main barrier to the accurate detection of human movement remains to the perspective problem, which results from the fact that video sequences are frequently shot from random camera angles. …”
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Improving UAV-Based Detection of Low-Emission Smoke with an Advanced Dataset Generation Pipeline by Marek Szczepański

“…This study improves the UAV-based detection of low-emission smoke by enhancing the dataset generation pipeline, addressing a key limitation of previous approaches, namely the lack of high-quality, automatically annotated datasets for small-scale chimney emissions. …”
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Enhanced lightweight infrared object detection algorithm for assistive navigation in visually impaired individuals by Zhimin Bai, Yang Yang, Jian Wang, Zhengyang Li, Jiajun Wang, Chunxin Liu

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A deep learning‐based approach for software vulnerability detection using code metrics by Fazli Subhan, Xiaoxue Wu, Lili Bo, Xiaobing Sun, Muhammad Rahman

“…Abstract Vulnerabilities can have devastating effects on information security, affecting the economy, social stability, and national security. The idea of automatic vulnerability detection has always attracted researchers. …”
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Development of a Novel Recombinant Full-Length IgY Monoclonal Antibody against Human Thymidine Kinase 1 for Automatic Chemiluminescence Analysis on a Sandwich Biotin-Streptavidin P... by Xiangbin Wang, Shan Li, Rui Zhao, Huijun Li, Peng Gao, Cuicui Jin, Cong Fang, Yongping Liu, Ailian Hei, Ji Zhou, Jin Li, Ellen He, Sven Skog

“…Finally, a recombinant full-length IgY monoclonal antibody (hTK1-IgY-rmAb#5) with high-affinity binding with human recombinant TK1 (rhTK1) (3.95×10−10 mol/L), high sensitivity with hTK1 calibrators (slope of linear curve: 89.98), and high specificity with low/elevated STK1p (r≈0.92-0.963) was identified. hTK1-IgY-rmAb#5 showed a specific immune response with thymidine kinase 1 (TK1) in TK1-positive/negative cell lysates by Western blotting and immunohistochemistry (IHC) in normal and cancer tissues. In particular, the detection of TK1 serum samples from health centres showed a high coincidence rate (r=0.988,n=90) between hTK1-IgY-rmAb#5 and hTK1-IgY-pAb and between the semiautomatic ECL dot blot BSA platform and the novel automatic chemiluminescence sandwich-BSA platform (r=0.857,n=292). hTK1-IgY-rmAb#5 is stable and highly sensitive for detecting the lowest STK1p value at 0.01 pmol/L (pM). …”
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Morphological Background-Subtraction Modeling for Analyzing Traffic Flow by Erik-Josué Moreno-Mejía, Daniel Canton-Enriquez, Ana-Marcela Herrera-Navarro, Hugo Jiménez-Hernández

“…They analyze data sequences from these photo sensors over time to detect moving objects or other relevant information. …”
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The medial prefrontal cortex encodes procedural rules as sequential neuronal activity dynamics by Shuntaro Ohno, Masanori Nomoto, Kaoru Inokuchi

“…We utilized a method based on convolutional negative matrix factorization, iSeq, to automatically detect temporal neuronal sequences in the recorded data. …”
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Deep learning vulnerability detection method based on optimized inter-procedural semantics of programs by Yan LI, Weizhong QIANG, Zhen LI, Deqing ZOU, Hai JIN

“…In recent years, software vulnerabilities have been causing a multitude of security incidents, and the early discovery and patching of vulnerabilities can effectively reduce losses.Traditional rule-based vulnerability detection methods, relying upon rules defined by experts, suffer from a high false negative rate.Deep learning-based methods have the capability to automatically learn potential features of vulnerable programs.However, as software complexity increases, the precision of these methods decreases.On one hand, current methods mostly operate at the function level, thus unable to handle inter-procedural vulnerability samples.On the other hand, models such as BGRU and BLSTM exhibit performance degradation when confronted with long input sequences, and are not adept at capturing long-term dependencies in program statements.To address the aforementioned issues, the existing program slicing method has been optimized, enabling a comprehensive contextual analysis of vulnerabilities triggered across functions through the combination of intra-procedural and inter-procedural slicing.This facilitated the capture of the complete causal relationship of vulnerability triggers.Furthermore, a vulnerability detection task was conducted using a Transformer neural network architecture equipped with a multi-head attention mechanism.This architecture collectively focused on information from different representation subspaces, allowing for the extraction of deep features from nodes.Unlike recurrent neural networks, this approach resolved the issue of information decay and effectively learned the syntax and semantic information of the source program.Experimental results demonstrate that this method achieves an F1 score of 73.4% on a real software dataset.Compared to the comparative methods, it shows an improvement of 13.6% to 40.8%.Furthermore, it successfully detects several vulnerabilities in open-source software, confirming its effectiveness and applicability.…”
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HAD-YOLO: An Accurate and Effective Weed Detection Model Based on Improved YOLOV5 Network by Long Deng, Zhonghua Miao, Xueguan Zhao, Shuo Yang, Yuanyuan Gao, Changyuan Zhai, Chunjiang Zhao

“…With the purpose of improving the model’s capacity to extract features and making it more lightweight, this algorithm employs the HGNetV2 as its backbone network. The Scale Sequence Feature Fusion Module (SSFF) and Triple Feature Encoding Module (TFE) from the ASF-YOLO are introduced to improve the model’s capacity to extract features across various scales, and on this basis, to improve the model’s capacity to detect small targets, a P2 feature layer is included. …”
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Deep learning-based debris flow hazard detection and recognition system: a case study by Fei Wu, Jianlin Zhang, Dunlong Liu, Andreas Maier, Vincent Christlein

“…Inspired by recent developments of anomaly detection in the field of computer vision, in this paper, we propose a novel automatic debris flow detection and recognition system based on deep learning. …”
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U-SCAD: An Unsupervised Method of System Call-Driven Anomaly Detection for Containerized Edge Clouds by Jiawei Ye, Ming Yan, Shenglin Wu, Jingxuan Tan, Jie Wu

“…To increase the accuracy of anomaly detection and reduce the false positive rates, this method embeds system calls into sequences using the proposed Syscall2vec and processes the remain sequences in favor of the anomaly detection model’s analysis. …”
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LT-YOLO: long-term temporal enhanced YOLO for stenosis detection on invasive coronary angiography by Jiaxin Li, Xiang Tang, Xuesong Wang

“…To address these limitations, this paper proposes a long-term temporal enhanced You Only Look Once (YOLO) method for automatic stenosis detection and assessment in invasive coronary angiography. …”
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Two-Stage Target Detection for Compact HFSWR With Space-to-Depth YOLOv8 and Multiframe ViT by Tong Wu, Ming Li, Jiong Niu, Ling Zhang, Wandong Zhang, Qing Ming Jonathan Wu

“…Moreover, the dataset for training and validating the two-stage network is automatically generated based on the automatic identification system ship data and constant false alarm rate detection results to identify all true and visible targets, ensuring the dataset's credibility. …”
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Detection of Student Engagement via Transformer-Enhanced Feature Pyramid Networks on Channel-Spatial Attention by A. Naveen, I. Jeena Jacob, Ajay Kumar Mandava

“…One of the most important aspects of contemporary educational systems is student engagement detection, which involves determining how involved, attentive, and active students are in class activities. …”
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Traffic Scene Analysis using Hierarchical Sparse Topical Coding by P. Ahmadi, I. Gholampour, M. Tabandeh

“…Such descriptions may further be employed in different traffic applications such as traffic phase detection and abnormal event detection. One of the most recent and successful unsupervised methods for complex traffic scene analysis is based on topic models. …”
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An abnormal traffic detection method for chain information management system network based on convolutional neural network by Chao Liu, Chunxiang Liu, Changrong Liu

“…Finally, the conditional random field (CRF) determines the optimal label sequence based on the conditional probability distribution and applies the Viterbi algorithm to complete the sequence labeling of network traffic in chain information management system. …”
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DNA barcoding of Culicoides biting midges (Diptera: Ceratopogonidae) and detection of Leishmania and other trypanosomatids in southern Thailand by Piyapat Tepboonrueng, Thanapat Pataradool, Rungfar Boonserm, Luke W. Rimmer, Kanok Preativatanyou, Sakone Sunantaraporn, Padet Siriyasatien

“…Detection of Leishmania and other trypanosomatid parasites was performed by polymerase chain reaction (PCR) based on the ITS1 region and small subunit SSU ribosomal RNA (rRNA) gene, followed by Sanger sequencing and haplotype diversity analysis. …”
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A toolbox for the identification of foot-floor contact sequences to analyze atypical gait cycles in a real-life scenario: application on patients after proximal femur fracture and... by Marco Ghislieri, Nicolas Leo, Marco Caruso, Clemens Becker, Andrea Cereatti, Valentina Agostini

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Multimodal sleep staging network based on obstructive sleep apnea by Jingxin Fan, Jingxin Fan, Jingxin Fan, Mingfu Zhao, Li Huang, Li Huang, Bin Tang, Bin Tang, Lurui Wang, Zhong He, Zhong He, Xiaoling Peng

“…Therefore, a more widely applicable network is needed for sleep staging.MethodsThis paper introduces MSDC-SSNet, a novel deep learning network for automatic sleep stage classification. MSDC-SSNet transforms two channels of electroencephalogram (EEG) and one channel of electrooculogram (EOG) signals into time-frequency representations to obtain feature sequences at different temporal and frequency scales. …”
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