Temporal variation in the detection of endangered birds in the Northern Atlantic Forest by Diego M. Lima, Antônio E.B. Alves de Sousa, Helder F. Pereira de Araujo

“…We identified distinct patterns of variation in bird detection. While 10 bird taxa were detected throughout the year, others were detected more frequently during the dry season, or at the onset of the rainy season. …”
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XGBoost-Based Detection of DDoS Attacks in Named Data Networking by Liang Liu, Weiqing Yu, Zhijun Wu, Silin Peng

“…In this paper, an attack detection method based on an improved XGBoost is proposed and applied to the hybrid attack pattern of IFA and CPA. …”
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EFFECT OF VIEWING ANGLE OF IR THERMOGRAPHY CAMERA FOR THE DETECTION OF LANDMINE by Moustafa M. Kurdi

“…In recent years, a number of studies have tried to overcome these limitations and improve the reliability of this method, using filtering and automatic pattern recognition techniques, specific for the detection of buried objects. …”
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Detection-Oriented Evaluation of SAR Dexterous Barrage Jamming Effectiveness by Hai Zhu, Sinong Quan, Shiqi Xing, Haoyu Zhang, Yun Ren

“…The assessment of the jamming effect of Synthetic Aperture Radar (SAR) is the primary means to measure the reliability of the jamming, which can provide important guidance for the use of jamming strategies and patterns. This paper proposes a detection-oriented evaluation of the effect of SAR dexterous barrage jamming. …”
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Smart deep learning model for enhanced IoT intrusion detection by Faisal S. Alsubaei

“…This paper addresses these limitations with large preprocessing steps followed by hyperparameter tuning of machine learning XGBoost and deep learning Sequential Neural Network (OSNN) algorithms through Grid Search for their best values to improve multiclass intrusion detection across varied datasets. These deep models were then augmented with a variety of various filters, kernels, activation functions, and regularization techniques in an attempt to boost them in detecting complex, multiclass intrusion patterns. …”
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Review of artificial intelligence-based applications for money laundering detection by Seyedmohammad Mousavian, Shah J Miah

“…Since studies of pattern recognition for detecting money laundering have overflowed with various outcomes, effective applications of artificial intelligence (AI) for delivering précised outcomes are still emerging. …”
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Sleep Spindles as an Electrographic Element: Description and Automatic Detection Methods by Dorothée Coppieters ’t Wallant, Pierre Maquet, Christophe Phillips

“…Sleep spindle is a peculiar oscillatory brain pattern which has been associated with a number of sleep (isolation from exteroceptive stimuli, memory consolidation) and individual characteristics (intellectual quotient). …”
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Metric-based learning approach to botnet detection with small samples by Honggang LIN, Junjing ZHU, Lin CHEN

“…Botnets pose a great threat to the Internet, and early detection is crucial for maintaining cybersecurity.However, in the early stages of botnet discovery, obtaining a small number of labeled samples restricts the training of current detection models based on deep learning, leading to poor detection results.To address this issue, a botnet detection method called BT-RN, based on metric learning, was proposed for small sample backgrounds.The task-based meta-learning training strategy was used to optimize the model.The verification set was introduced into the task and the similarity between the verification sample and the training sample feature representation was measured to quickly accumulate experience, thereby reducing the model’s dependence on the labeled sample space.The feature-level attention mechanism was introduced.By calculating the attention coefficients of each dimension in the feature, the feature representation was re-integrated and the importance attention was assigned to optimize the feature representation, thereby reducing the feature sparseness of the deep neural network in small samples.The residual network design pattern was introduced, and the skip link was used to avoid the risk of model degradation and gradient disappearance caused by the deeper network after increasing the feature-level attention mechanism module.…”
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Detecting synchronization in spatially extended discrete systems by complexity measurements by Juan R. Sánchez, Ricardo López-Ruiz

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Exploring NAS for anomaly detection in superconducting cavities of particle accelerators by Lynda Boukela, Julien Branlard, Annika Eichler, Annika Eichler

“…This paper presents the solution in which neural architecture search is applied, and elaborates on how visualizing and analyzing the anomaly detection results can provide critical insights for both short-term diagnostics and long-term pattern identification.…”
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Defect detection based on extreme edge of defective region histogram by Zouhir Wakaf, Hamid A. Jalab

“…Automatic thresholding has been used by many applications in image processing and pattern recognition systems. Specific attention was given during inspection for quality control purposes in various industries like steel processing and textile manufacturing. …”
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Comparison of Various Feature Extractors and Classifiers in Wood Defect Detection by Kenan Kiliç, Kazım Kiliç, İbrahim Alper Doğru, Uğur Özcan

“…The findings show that the most effective features in detecting defective wood are extracted by the Local Binary Pattern (LBP) method and the most effective classifier is the Random Forest Algorithm. …”
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Detecting Phantom Data Usage on Smartphones with Analysis of Contextual Information by Shiqi Jiang, Pengfei Zhou, Mo Li

“…Based on the observations that each user preserves specific data usage patterns under particular environmental context, we present PDS, a PDU detection system, which automatically detects whether the current data usage is consumed as expected. …”
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Sentinel‐1 Detects Firn Aquifers in the Greenland Ice Sheet by I. Brangers, H. Lievens, C. Miège, M. Demuzere, L. Brucker, G. J. M. De Lannoy

“…Here, we show the ability of satellite radar measurements from Sentinel‐1 to map firn aquifers across all of Greenland at 1 km 2 resolution. The detection of aquifers relies on a delay in the freezing of meltwater within the firn above the water table, causing a distinctive pattern in the radar backscatter. …”
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The Extreme Value Support Measure Machine for Group Anomaly Detection by Lixuan An, Bernard De Baets, Stijn Luca

“…Group anomaly detection is a subfield of pattern recognition that aims at detecting anomalous groups rather than individual anomalous points. …”
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Modeling an Image Clustering Algorithm For Detecting Overheated Railway Axle by Oleksandr Gertsiy, Serhii Karnatov, Vitaliy Gladish, Valentyna Tkachenko

“…The principles of the proposed algorithm are described in the context of image segmentation and compression, as well as pattern recognition in the transportation domain. …”
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Detection Of Sentence Modality On French Automatic Speech-to-text Transcriptions by Luisa Orosanu, Denis Jouvet

“… This article analyzes the detection of sentence modality in French when it is applied on automatic speech-to-text transcriptions. …”
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Transforming Weakness into Strength: Improving Unreliable Malware Detection Methods by Pavel Novak, Vaclav Oujezsky

“…Unlike traditional methods reliant on predefined signatures or behavior analysis, this approach dynamically assesses system behaviors, focusing on suspicious actions and interaction patterns. Key contributions include a novel combination of unreliable IoCs with sequence alignment methods, an extensive mapping study of detection techniques, and initial experiments on a dataset of over 19,000 malware samples. …”
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A SVM Approach of Aircraft Conflict Detection in Free Flight by Xu-rui Jiang, Xiang-xi Wen, Ming-gong Wu, Ze-kun Wang, Xi Qiu

“…In this article, aircraft conflict detection is considered as a binary classification problem; therefore, it can be solved by a pattern recognition method. …”
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A Novel Vision Sensing System for Tomato Quality Detection by Satyam Srivastava, Sachin Boyat, Shashikant Sadistap

“…Various pattern recognition and soft computing techniques have been implemented for data analysis as well as different parameters prediction like shelf life of the tomato, quality index based on disease detection and classification, freshness detection, maturity index detection, and different suggestions for detected diseases. …”
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