Extracapsular extension risk assessment using an artificial intelligence prostate cancer mapping algorithm by Alan Priester, Sakina Mohammed Mota, Kyla P. Grunden, Joshua Shubert, Shannon Richardson, Anthony Sisk, Ely R. Felker, James Sayre, Leonard S. Marks, Shyam Natarajan, Wayne G. Brisbane

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Fast Processing of Massive Hyperspectral Image Anomaly Detection Based on Cloud-Edge Collaboration by Yunchang Wang, Jin Sun, Yang Xu, Zhihui Wei, Shangdong Zheng, Zebin Wu

“…With the improvement of hyperspectral image resolution, existing anomaly detection algorithms find it challenging to quickly process large volumes of hyperspectral data while fully exploiting spectral information. …”
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Optimization of IMU-based bending strain solving algorithm and full-scale experimental validation by Tong SHI, Xiaoben LIU, Lin ZHANG, Jun WANG, Rui LI, Ting Xie, Qingshan FENG, Qiyu HUANG

“…Furthermore, the full-scale pulling experiments conducted to validate the algorithm revealed substantial reductions in errors between bending strains computed by the optimized solving algorithm and those detected by a strain gauge at 79.6%, 79.4%, and 76.0%, respectively. …”
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Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers by S. B. G. Tilak Babu, Ch Srinivasa Rao

“…The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors, such as local ternary pattern, local phase quantization, local Gabor binary pattern histogram sequence, Weber local descriptor, and local monotonic pattern, and classifiers such as optimized support vector machine and optimized NBC. …”
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Detection of GNSS spoofed signals based on the weighted moving average bias correction method by Chuanyu Wu, Yuanfa Ji, Xiyan Sun

“…In addition, this paper proposes a Weighted Moving Average Bias Correction (WMA-BC) algorithm for metric post-processing. Comparative experiments with Moving Average (MA) and Moving Variance (MV) based SQM methods demonstrate that the WMA-BC algorithm achieves substantial advantages in Detection Rate enhancement and significantly improves the Area Under Curve (AUC) of the Receiver Operating Characteristic (ROC). …”
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Application of artificial intelligence technologies for the detection of early childhood caries by Priyanka A, Rishi Sreekumar, S Namasivaya Naveen

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Detecting and Mitigating Wind Turbine Clutter for Airspace Radar Systems by Wen-Qin Wang

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Constructing Real-Life Benchmarks for Community Detection by Rewiring Edges by Jing Xiao, Hong-Fei Ren, Xiao-Ke Xu

“…In order to make the performance evaluation of community detection algorithms more accurate and deepen our analysis of community structures and functional characteristics of real-life networks, a new benchmark constructing method is designed from the perspective of directly rewiring edges in a real-life network instead of building a model. …”
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Improved Adaptive Constant False Alarm Rate Detector Based on Fuzzy Theory for Multiple-Target Scenario by Xudong Yang, Chunbo Xiu

“…The Kaigh–Lachenbruch quantile detection algorithm (KLQ) exhibits robust detection performance in multiple-target environments. …”
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Automatic Data Race Error Detection in SystemC Models by A. V. Zakharov, M. J. Moiseev

“…These algorithms can detect all the errors that exist in the program. …”
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A survey of streaming data anomaly detection in network security by Pengju Zhou

“…This article provides a comprehensive review and a multi-faceted analysis of recent algorithms for anomaly detection in network security. …”
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Application of machine learning methods for automated detection of network intrusions by M. V. Babicheva, I. A. Tretyakov

“…Development of automated network attack detection systems capable of adapting to the ever-changing nature of network attacks and new types of threats. …”
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Detection and Analysis of Malicious Software Using Machine Learning Models by Selman Hızal, Ahmet Öztürk

“…In this study, we evaluate the performance of ML algorithms for obfuscated malware detection using the CIC-MalMem-2022 dataset. …”
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Theoretical approaches to detecting anomalies in meter readings in scientific literature by D.V. Furikhata, T.A. Vakalyuk

“…Classification methods of supervised learning are analysed, which require pre-labelled data for model training but provide high accuracy in detecting known types of anomalies. The prospects for further development of anomaly detection technologies in the context of integrating IoT devices, technology development and implementing more complex deep learning algorithms are considered.…”
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Self‐Learning e‐Skin Respirometer for Pulmonary Disease Detection by Anand Babu, Getnet Kassahun, Isabelle Dufour, Dipankar Mandal, Damien Thuau

“…To empower the eSR with early diagnosis functionality, self‐learning capability is further added by integrating the respirometer with the machine learning algorithms. Among various tested algorithms, gradient boosting regression emerges as the most suitable, leveraging sequential model refinement to achieve an accuracy exceeding 95% in detection of chronic obstructive pulmonary diseases (COPD). …”
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A Comparative Study on Detection and Recognition of Nonuniform License Plates by Mehak Arshid, Muhammad Raees Azam, Zahid Mahmood

“…Both detection algorithms yielded a mean detection accuracy of 98.41%. …”
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Correction: The OPS-SAT benchmark for detecting anomalies in satellite telemetry by Bogdan Ruszczak, Krzysztof Kotowski, David Evans, Jakub Nalepa

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Detection of Fake News Using Deep Learning and Machine Learning by Gabriela CHIRIAC, Ada Maria CATINA

“…Machine Learning and Deep Learning algorithms for detection demand continuous adaptation as disinformation tactics evolve. …”
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Radar Detection Simulation by Digital Twins of Target and Antenna System by A. S. Grigoriev, A. A. Kazantsev, A. M. Terentyev, B. S. Stavtsev

“…Development of an algorithmic support for simulating the radar detection process using digital twins of the antenna system and the observation object.Materials and methods. …”
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Target Detection and Characterization of Multi-Platform Remote Sensing Data by Koushikey Chhapariya, Emmett Ientilucci, Krishna Mohan Buddhiraju, Anil Kumar

“…The experiment took place on 23 July 2021, at the Rochester Institute of Technology’s Tait Preserve in Penfield, NY, USA. We validated the detection outcomes through receiver operating characteristic (ROC) curves and spectral similarity metrics across various detection algorithms and imaging platforms. …”
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