Preliminary analysis of acoustic detection of the Red-throated Caracara in northern Costa Rica by Roberto Vargas-Masís, Diego Quesada

“…Advances in automatic acoustic detection have transformed bird ecology, allowing researchers to analyze bird populations using pattern matching algorithms, machine learning, and random forest models. …”
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Cyber–Physical System Attack Detection and Isolation: A Takagi–Sugeno Approach by Angel R. Guadarrama-Estrada, Gloria L. Osorio-Gordillo, Rodolfo A. Vargas-Méndez, Juan Reyes-Reyes, Carlos M. Astorga-Zaragoza

“…This paper presents an approach for designing a generalized dynamic observer (GDO) aimed at detecting and isolating attack patterns that compromise the functionality of cyber–physical systems. …”
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An Efficient Algorithm for Small Livestock Object Detection in Unmanned Aerial Vehicle Imagery by Wenbo Chen, Dongliang Wang, Xiaowei Xie

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Beat-aligned motor synergies and kinematic beat detection in street dance movements by Keli Shen, Jun-ichiro Hirayama

“…Unlike existing methods, our technique accounts for the temporal variability induced by music beats, enabling an accurate representation of dance motion patterns. The extracted motor synergies, capturing both spatial and temporal patterns across motion segments and beat durations, were analyzed to gain insights into motor coordination, consistency, similarity, and variability across different dance genres. …”
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Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network by Fatema A. Albalooshi

“…This study explores the potential of a multi-scale convolutional neural network (MSCNN) design for object classification, specifically focusing on vegetation detection. The MSCNN is designed to integrate multi-scale feature extraction and attention mechanisms, enabling the model to capture both fine and coarse vegetation patterns effectively. …”
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AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review by Nusrat Jahan Sarna, Farzana Ahmed Rithen, Umme Salma Jui, Sayma Belal, Al Amin, Tasnim Kabir Oishee, A. K. M. Muzahidul Islam

“…By analyzing vast datasets, AI can uncover hidden fraud patterns and dynamically adapt to emerging threats. …”
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Harnessing Large Language Models and Deep Neural Networks for Fake News Detection by Eleftheria Papageorgiou, Iraklis Varlamis, Christos Chronis

“…The spread of fake news threatens trust in both traditional and digital media. Early detection methods, based on linguistic patterns and handcrafted features, struggle to identify more sophisticated misinformation. …”
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Detection of Soybean Pod Formation Stage Using Sentinel-1 SAR Data by Sakshi Jain, Unmesh Khati, Vineet Kumar, Rakesh Kumar Verma

“…Previous five year data over the same fields showed same pattern of growth and dip, with more accuracy for those years having enough dataset. …”
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Novel Spatiotemporal Filter for Dim Point Targets Detection in Infrared Image Sequences by Zhaohui Li, Xiaorui Wang, Jianqi Zhang, Delian Liu

“…Dim point target detection is of great importance in both civil and military fields. …”
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A Novel Approach to Detect Malware Based on API Call Sequence Analysis by Youngjoon Ki, Eunjin Kim, Huy Kang Kim

“…From checking the existence of certain functions or API call sequence patterns matched, we can even detect new unknown malware. …”
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Anomaly detection of smart grid stealing network attacks based on deep autoencoder by Huang Yan, Li Jincan, Yang Xiaqin, Li Pei, Li Zi

“…Existing anomaly detectors in AMIs suffer from shallow architectures, which impede their ability to capture temporal correlations and complex patterns in electricity consumption data, thus impact detection performance adversely. …”
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Smart Contract Security in Decentralized Finance: Enhancing Vulnerability Detection with Reinforcement Learning by Jose Juan de Leon, Cenchuan Zhang, Christos-Spyridon Koulouris, Francesca Medda, Rahul

“…The PPO model exhibits more stable and consistent learning patterns and achieves higher overall rewards. This research introduces a machine learning method for enhancing smart contract security, reducing financial risks for users, and contributing to future developments in reinforcement learning applications.…”
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Evaluating Robotic Walker Performance: Stability, Responsiveness, and Accuracy in User Movement Detection by Larisa Dunai, Isabel Seguí Verdú, Sui Liang, Ismael Lengua Lengua

“…Findings reveal that the walker successfully distinguishes individual gait patterns and adapts its behavior accordingly, demonstrating its potential for personalized mobility support.…”
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Adaptive Toeplitz convolution- enhanced classifier for anomaly detection in ECG big data by Lili Wu, Tao Li, Majid Khan Majahar Ali, Chenmin Ni, Ying Tian, Xiaojie Zhou

“…Abstract The anomaly detection of electrocardiogram (ECG) data is crucial for identifying deviations from normal heart rhythm patterns and providing timely interventions for high-risk patients. …”
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Unobtrusive stress detection using wearables: application and challenges in a university setting by Peter Neigel, Andrew Vargo, Benjamin Tag, Koichi Kise

“…We identify potential stress patterns by observing elevated waking heart rate (HR) and maximum waking HR, supported by related metrics such as sleep HR, sleep heart rate variability (HRV), activity patterns, and sleep phases.ResultsThe physiological changes align with significant academic and societal events, indicating a strong link to stress.DiscussionOur findings demonstrate the potential of consumer wearables to detect collective changes in stress biomarkers within a cohort using in-the-wild data, i.e., data that is noisy and has gaps. …”
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An Adaptive Intrusion Detection System for Evolving IoT Threats: An Autoencoder-FNN Fusion by J. Jasmine Shirley, M. Priya

“…Key metrics such as precision, recall, F1-score, and high ROC-AUC values validate its effectiveness in detecting diverse intrusion patterns and handling various attack scenarios. …”
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Graph neural network approach with spatial structure to anomaly detection of network data by Hao Zhang, Yun Zhou, Huahu Xu, Jiangang Shi, Xinhua Lin, Yiqin Gao

“…Abstract Network anomaly detection using graph-structured data is a critical task in data mining and cybersecurity, involving the identification of unusual patterns within a network by analyzing its structure as a graph. …”
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Temporal Logical Attention Network for Log-Based Anomaly Detection in Distributed Systems by Yang Liu, Shaochen Ren, Xuran Wang, Mengjie Zhou

“…This paper introduces a TLAN (Temporal Logical Attention Network), a novel deep learning framework that integrates temporal sequence modeling with logical dependency analysis for robust anomaly detection in distributed system logs. Our approach makes three key contributions: (1) a temporal logical attention mechanism that explicitly models both time-series patterns and logical dependencies between log events across distributed components, (2) a multi-scale feature extraction module that captures system behaviors at different temporal granularities while preserving causal relationships, and (3) an adaptive threshold strategy that dynamically adjusts detection sensitivity based on system load and component interactions. …”
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Data augmentation based multi-view contrastive learning graph anomaly detection by LI Yifan, LI Jiayin, LIN Xingpeng, DAI Yuanfei, XU Li

“…However, most existing contrast-based graph anomaly detection methods focused only on node-subgraph contrast patterns, ignoring the fact that the sampled node-subgraph instance pairs contained only the local information of the target node, and at the same time did not take into account the importance of each subgraph to the target node, which led to the lack of global information about the node and the emergence of the problem that the contrast patterns were too generalized. …”
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Detecting Keratoconus by Using SVM and Decision Tree Classifiers with the Aid of Image Processing by Mosa et al.

“…This is done by using image processing techniques and pattern classification methods. Pentacam is the device that is used to detect the cornea’s health; it provides four maps that can distinguish the changes on the surface of the cornea which can be used for Keratoconus detection. …”
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