Outlier Analysis

Intelligent Production and Detection Template of Outlier Dataset Using Clustering

Kiani, R., Montazeri, M., & Bidgoli, B. M. (2019) · International Conference on Knowledge-Based Engineering and Innovation · doi:10.1109/kbei.2019.8734928 · Cited by 3

Self-Learning and Efficient Health-Status Analysis for a Core Router System

Jin, S., Zhang, Z., Chakrabarty, K., & Gu, X. (2019) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2019.2926506 · Cited by 4

Web-Based Financial Disclosures by Using Machine Learning Analysis: Evidence from Bahrain

Sarea, A. M. Y., Subramanian, S., & Alareeni, B. A. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-62796-6_21 · Cited by 3

Approximate Distance-based Anomaly Detection at Massive Scale

Pasupathipillai, S., & Valle, E. D. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9377962 · Cited by 3

Data pre-processing methods for NPP equipment diagnostics algorithms: an overview

Katser, I. D., Kozitsin, V., Maksimov, I., Larionov, D., & Kotsoev, K. I. (2021) · Nuclear Energy and Technology · doi:10.3897/nucet.7.63675 · Cited by 4

Anomaly Detection on ADS-B Flight Data Using Machine Learning Techniques

Taşdelen, O., Çarkacıoğlu, L., & Töreyın, B. U. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88081-1_58 · Cited by 3

Detecting Malicious Gradients from Asynchronous SGD on Variational Autoencoder

Gu, Z., Yang, Y., & Shi, H. (2021) · IEEE Symposium on Reliable Distributed Systems (SRDS) · doi:10.1109/srds53918.2021.00039 · Cited by 3

FDNN: Feature-based Deep Neural Network Model for Anomaly Detection of KPIs

Lan, Z., Xu, L., & Fang, W. (2019) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess47205.2019.9040841 · Cited by 3

“Education 4.0” in the Era of Digital Transformation: Ways to Improve Its Efficiency

Aghayev, F., Mammadova, G., & Malikova, R. T. (2023) · Open Education · doi:10.21686/1818-4243-2023-4-4-16 · Cited by 3

A Fast Outlier Detection Algorithm for Big Datasets

Hiếu, D. V., & Meesad, P. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-40415-8_16 · Cited by 3

The Problem of Correction Diagnostic Errors in the Target Attribute With the Function of Rival Similarity

Borisova, I. A., & Андреевна, К. О. (2018) · Mathematical Biology and Bioinformatics · doi:10.17537/2018.13.38 · Cited by 3

The comparison network model for cyber anomaly detection

Qu, H., Qin, J., & Chen, H. (2019) · Intelligent Data Analysis · doi:10.3233/ida-184391 · Cited by 3

RECol: Reconstruction Error Columns for Outlier Detection

Herurkar, D., Meier, M., & Hees, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42608-7_6 · Cited by 4

An Ensemble Learning-Based Architecture for Security Detection in IoT Infrastructures

Hemmer, A., Abderrahim, M., Badonnel, R., & Chrisment, I. (2021) · International Conference on Network and Service Management (CNSM) · doi:10.23919/cnsm52442.2021.9615588 · Cited by 3

CBOWRA: A Representation Learning Approach for Medication Anomaly Detection

Zhao, L., Ma, Z., Zhou, Y., Wang, K., Liu, S., Gao, J., & Du, W. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983111 · Cited by 3

Outliers Detection in Multi-label Datasets

Bello, M., Nápoles, G., Morera, R., Vanhoof, K., & Bello, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60884-2_5 · Cited by 3

Stages and Methods for Cleaning Large Secondary Data Using R

Jena, M. K., & Kar, B. (2019) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-78973-973-220191018 · Cited by 2

Temporal Characterization and Filtering of Sensor Data to Support Anomaly Detection

Grosskopf, M. J., Myers, K., Lawrence, E., & Bingham, D. (2022) · Technometrics · doi:10.1080/00401706.2022.2124308 · Cited by 2

Detecting Fake Co-visitation Injection Attack in Graph-based Recommendation Systems

Mahmood, T., & Adnan, M. A. (2022) · International Conference on Networking, Systems and Security (NSysS) · doi:10.1145/3569551.3569556 · Cited by 2

A Survey on Anomaly Detection in IoT and Cloud Computing Security

Gagan, P., Varghese, J. E., & Aarella, S. G. (2024) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac61858.2024.10714750 · Cited by 3

Comprehensive Functional ETL Testing Methodologies for Real-World Data

Homayouni, H., Pourebadi, M., Nguyen, S. T., Hashemi, M., & Shirazi, H. M. (2024) · IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) · doi:10.1109/qrs-c63300.2024.00013 · Cited by 2

Real-Time Anomaly Detection over ECG Data Stream Based on Component Spectrum

Wu, M., Qiu, Z., Hong, S., & Li, H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45817-5_5 · Cited by 2

Online Detection of Outliers in Clusters of Continuous Data Streaming

Pereira, M. A., Faria, E. R., & Naldi, M. C. (2017) · Brazilian Conference on Intelligent Systems · doi:10.1109/bracis.2017.54 · Cited by 2

Unified Clustering and Outlier Detection on Specialized Hardware

Cohen, E., Ushijima‐Mwesigwa, H., Mandal, A., & Roy, A. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413754 · Cited by 2

Log-based Anomaly Detection from Multi-view by Associating Anomaly Scores with User Trust

Wang, L., Zhang, K., Li, C., & Tu, B. (2021) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom53373.2021.00096 · Cited by 2

Spatial Data Science

Li, Y., Xie, Y., & Shekhar, S. (2023) · Springer eBooks · doi:10.1007/978-3-031-24628-9_18 · Cited by 2

Evaluation of Dynamic Incentive Pricing for Congestion Management in Transit System: An Agent-Based Simulation

Tang, Y., & Zhao, B. (2024) · Lecture notes in civil engineering · doi:10.1007/978-3-031-34027-7_43 · Cited by 2

Community Outlier Based Fraudster Detection

Sun, C., Li, Q., Li, H., Zhang, S., & Zheng, Y. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-63558-3_35 · Cited by 2

Volcanic Anomalies Detection Through Recursive Density Estimation

Gomez, J. E., Corrales, D. C., Lasso, E., Iglesias, J. A., & Corrales, J. C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04491-6_23 · Cited by 1

Novel criteria for elimination of the outliers in QSPR studies, when the ‘forward stepwise’ procedure is used

Tarko, L. (2019) · Journal of Mathematical Chemistry · doi:10.1007/s10910-019-01036-x · Cited by 1

Abnormal Behavior Detection in Farming Stream Data

Moso, J. C., Cormier, S., Fouchal, H., de Runz, C., & Wandeto, J. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-88259-4_4 · Cited by 1

Similarity Distribution Density: An Optimized Approach to Outlier Detection

Quan, L., Gong, T., & Jiang, K. (2023) · Electronics · doi:10.3390/electronics12204227 · Cited by 1

PyExplore 2.0: Explainable, Approximate and Combined Clustering Based SQL Query Recommendations

Glenis, A. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-51643-6_7 · Cited by 1

Predictive Modeling Of Work Accidents Using Lagging Safety Indicators: Perspectives On Social Security Transformation In The 21st Century

Tuna, G. (2025) · Sosyal Guvence · doi:10.21441/sosyalguvence.1682358 · Cited by 1

Multi-View Cluster Structure Guided One-Class BLS-Autoencoder for Intrusion Detection

Yang, Q., Chen, Y., & Shi, Y. (2025) · Applied Sciences · doi:10.3390/app15148094 · Cited by 1

Assessing urban morphological dynamics and landscape fragmentation in Kuwait using integrated fractal analysis from 1985 to 2100

Al-Shaar, W., Bonin, O., Lehmann, X., Al-Shaar, M., & Youssef, D. (2026) · Discover Cities · doi:10.1007/s44327-026-00229-9 · Cited by 1

Big Data Analytics in Cloud—A Streaming Approach

Gandhi, R. (2017) · Springer eBooks · doi:10.1007/978-981-10-5026-8_3 · Cited by 1

Viewpoints in bioerosion research—are we really disagreeing? A reply to the comment by Silbiger and DeCarlo (2017)

Schönberg, C. H. L., Tribollet, A. D., Fang, J. K., Carreiro‐Silva, M., & Wisshak, M. (2017) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsx167 · Cited by 1

FROD: Fast and Robust Distance-Based Outlier Detection with Active-Inliers-Patterns in Data Streams

Li, Z., Wang, Y., Zhao, G., Cheng, L., & Ma, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01418-6_62 · Cited by 1

Early Anomalous Vehicular Traffic Detection Through Spectral Techniques and Unsupervised Learning Models

Nava, R. C. V., González-Mendoza, M., Herrera-Alcántara, O., & Hernández-Gress, N. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33749-0_14 · Cited by 1

VGG Based Unsupervised Anomaly Detection in Multivariate Time Series

Jabłoński, G. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-50936-1_107 · Cited by 1

Detecting Anomalies in Production Quality Data Using a Method Based on the Chi-Square Test Statistic

Mayr, M., & Himmelbauer, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59065-9_27 · Cited by 1

Modal parameter identification of a curved cable-stayed model bridge based on EDA and DATA-SSI

Zhou, X., Shan, D., & Li, Q. (2017) · Report · doi:10.2749/vancouver.2017.0253 · Cited by 1

Pristopi k transformaciji modela poslovnega procesa v pametne pogodbe

Баггиа, А., Hriberšek, J., & Leskovar, R. (2021) · University of Maribor, University Press eBooks · doi:10.18690/978-961-286-509-2.5 · Cited by 1

Machine Learning-Based Model Selection for Anomalous Wireless Link Detection

Cerar, G., Yetgin, H., & Fortuna, C. (2021) · International Conference on Software, Telecommunications and Computer Networks (SoftCOM) · doi:10.23919/softcom52868.2021.9559082 · Cited by 1

Convolutional One-Dimensional Variational Autoencoder Based Intrusion detection

Wu, K., Cao, M., Wang, P., & Wang, Z. (2021) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc-picom-cbdcom-cyberscitech52372.2021.00087 · Cited by 1

Behavior Pattern Mining from Traffic and Its Application to Network Anomaly Detection

Meng, Y., Qin, T., Li, S., & Wang, P. (2022) · Security and Communication Networks · doi:10.1155/2022/9139321 · Cited by 1

Identifying Anomalous Flight Trajectories by leveraging ensembled outlier detection framework

Forney, M., Yan, X., Gupta, K. D., Nabil, M., & Homaifar, A. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9891907 · Cited by 1

Robust Physics Guided Data-Driven Fleet Battery Pack Fault Detection Under Dynamic Operating Conditions

Peng, X., Duan, S., Sankvaram, C., & Jin, X. (2022) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case49997.2022.9926643 · Cited by 1

SAFER: Safety Assurances for Emergent Behavior

de Melo, C. B., Ashrafiamiri, M., Seo, M., Kurdahi, F. J., & Dutt, N. D. (2023) · IEEE Design and Test · doi:10.1109/mdat.2023.3324887 · Cited by 1

Outlier-based Anomaly Detection in Firewall Logs

Liang, X.-R., Li, H.-T., Huang, C.-Y., Chen, W.-A., Chen, Y.-F., Gao, Z.-J., Sun, M.-W., & Chia, H.-C. (2023) · International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) · doi:10.1109/ccci58712.2023.10290797 · Cited by 1

ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network Approach

Sotiropoulos, K., Zhao, L., Liang, P. J., & Akoglu, L. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386245 · Cited by 4

Automating the Analysis of Negative Test Verdicts: A Future-Forward Approach Supported by Augmented Intelligence Algorithms

Gnacy–Gajdzik, A., & Przystałka, P. (2024) · Applied Sciences · doi:10.3390/app14062304 · Cited by 1

Defending against Adversarial Attacks in Federated Learning on Metric Learning Model

Gu, Z., Shi, J., Yang, Y., & He, L. (2023) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom60117.2023.00047 · Cited by 1

K-Nearest Neighbor-Based Video Anomaly Detection Approaches

Wang, X. (2024) · Cognitive intelligence and robotics · doi:10.1007/978-981-97-3023-0_4 · Cited by 1

Generalized Low-Computational Cost Laplacian Eigenmaps

Salazar-Castro, J. A., Peña, D. F., Basante-Villota, C. K., Ortega, C., Cruz-Cruz, L. D., Revelo-Fuelagán, J., Valencia, X. P. B., Castellanos-Dominguez, G., & Peluffo-Ordóñez, D. H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03493-1_69 · Cited by 1

A Robust Embedding for Attributed Networks with Outliers

Zhang, C., Zhang, L., He, Y., & Zha, D. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-36808-1_38 · Cited by 1

Implementation of Interpolation in Credit Card Fraud Detection

Shenvi, P., Samant, N., Kumar, S., & Kulkarni, V. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-2475-2_12 · Cited by 1

Performance Evaluation of Geometric Area Analysis Technique for Anomaly Detection Using Trapezoidal Area Estimation

Pawar, Y., Amayri, M., & Bouguila, N. (2020) · International Symposium on Networks, Computers and Communications (ISNCC) · doi:10.1109/isncc49221.2020.9297250 · Cited by 1

Simulation of sports action monitoring based on feature similarity model

Hu, X. R., Zong, B., & Pang, B. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03046-7 · Cited by 1

NOCOL - Nonnegative Orthogonal Constraint Outlier Learning

Balasubramaniam, T., Mohotti, W. A., Nayak, R., & Yuen, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91560-5_27 · Cited by 1

Tree-based Self-adaptive Anomaly Detection by Human-Machine Interaction

Li, Q., Yu, Z., Xu, H., & Guo, B. (2021) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms53169.2021.9582631 · Cited by 1

Detecting Online Outlier for Data Streams using Recursive Residual

Dani, Y., Gunawan, A. Y., & Indratno, S. W. (2022) · International Conference on Informatics and Computing (ICIC) · doi:10.1109/icic56845.2022.10006916 · Cited by 1

Ensemble Learning for Extremely Imbalced Data Flows

Fréry, J. (2019) · theses.fr (ABES) · doi:10.70675/6ed0cd83zff35z4d39zbf8az5eec0d3ba1b3 · Cited by 1

Détection d’anomalies multiples par apprentissage automatique de règles dans les séries temporelles

Kraiem, I. B. (2021) · theses.fr (ABES) · doi:10.70675/9e7c2d29z85c8z48aczae33ze7b849ed9582 · Cited by 1

Activity monitoring using topic models

Nabaei, B., & Ester, M. (2016) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi.2016.7745453

Anomaly detection for virtualized data center via outlier analysis

Li, Z., Zhu, C., Liu, X., & Sui, X. (2017) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc.2017.8000085

Technological Opportunity Analysis for the Telehealth Industry

Wang, J., & Chen, Y. (2017) · IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) · doi:10.1109/iiai-aai.2017.97

Mixture Initialization Based on Prior Data Visual Analysis

Suzdaleva, E., & Nagy, I. (2018) · Studies in computational intelligence · doi:10.1007/978-3-319-78931-6_3

Deep Learning Analytics

Passalis, N., & Tefas, A. (2018) · Intelligent systems reference library · doi:10.1007/978-3-319-94030-4_13

Algoritmos rápidos para estimativas de densidade hierárquicas e suas aplicações em mineração de dados

dos Santos, J. A. (2018) · doi:10.11606/d.55.2018.tde-25102018-174244

Self-learning and Efficient Health-Status Analysis

Jin, S., Zhang, Z., Chakrabarty, K., & Gu, X. (2019) · Springer eBooks · doi:10.1007/978-3-030-33664-6_5

Prognostication of Neurological Recovery by Analyzing Structural Breaks in EEG Data

Bethge, D., Chen, J., Grothe, O., Elmer, J., & Dubrawski, A. W. (2019) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2019.00136

SocialCCF: Graph-text Collaborative Cleaning Framework Based on Social Networks

Zhang, Y., Jin, Z., Liu, F., Zhu, W., Mu, W., & Wang, W. (2020) · International Conference on Artificial Intelligence and Information Systems (ICAIIS) · doi:10.1109/icaiis49377.2020.9194844

An Approach to Cloud Computing for Medical Image Analysis

Chitra, M. P., Ponmagal, R. S., Bhavani, N. P. G., & Srividhya, V. (2021) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-7998-3092-4.ch010

HDQGF:Heterogeneous Data Quality Guarantee Framework Based on Deep Learning

Zhang, Y., Jin, Z., Zhu, W., Chi, L., & Wang, W. (2021) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd49262.2021.9437684

Vpliv pandemije COVID-19 na kibernetsko varnost: analiza stanja s priporočili za mala podjetja

Brezavšček, A. (2021) · University of Maribor, University Press eBooks · doi:10.18690/978-961-286-509-2.6

Sistemi za podporo odločanju: metodologija razvoja

Rajkovič, U. (2021) · University of Maribor, University Press eBooks · doi:10.18690/978-961-286-509-2.3

An Outlier Detection Algorithm Based on Spectral Clustering

Li, X., & Liu, T. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-0572-8_94

Affine invariant integrated rank-weighted statistical depth: properties and finite sample analysis

Clémençon, S., Mozharovskyi, P., & Staerman, G. (2023) · Electronic Journal of Statistics · doi:10.1214/23-ejs2189 · Cited by 13

ViCubeLab-An Integrated Platform Using VR to Visualise and Analyse Road Traffic Conditions

Shiratuddin, M. F., Wong, K. W., & Rai, S. (2024) · Journal of Advanced Research in Applied Sciences and Engineering Technology · doi:10.37934/araset.49.2.176186

A forensic statistical analysis of fraud in the federal food stamp program

Woody, J., Zhao, Z., Lund, R., & Wu, T.-L. (2024) · The Annals of Applied Statistics · doi:10.1214/24-aoas1891

Attend2trend: Attention-Based LSTM Model for Detecting and Forecasting of Trending Topics

Saleh, A. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0847-8_10

MVOD: A Multi-View Outlier Detection Method with Single-Feature View Augmentation

Zhu, Z., Zhu, C., Gui, N., Xu, J., Lei, Y., & Li, D. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831892

Negative Feedback Matters: Exploring Positive and Negative Correlations for Time Series Anomaly Detection

Jin, Y., Liu, X., Hu, B., Walker, J., Wang, K., Wu, W., & Zhong, T. (2025) · Electronics · doi:10.3390/electronics14102068

Anomaly Detection for Small Hydropower Based on Deep Spatio-Temporal Modeling

Zhan, Y., Yang, B., & Ma, Y. (2025) · International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM) · doi:10.1109/icmtim65484.2025.11040954

Multi-label document classification for Urdu: corpus and methods

Shafi, J., Hussain, A., Nawab, R. M. A., & Rasool, M. (2025) · Language Resources and Evaluation · doi:10.1007/s10579-025-09861-0

Anomaly detection in multivariate human activity time series using graph neural networks

M, R. D. S., Nandini, P., Venkateshulu, B., Ranjini, P. R., Sanjeeva, P., Aradhanna, C., & Al-Fatlawi, M. (2025) · AIP conference proceedings · doi:10.1063/5.0298246

CLASSIFICAÇÃO DE NÍVEIS DE OBESIDADE POR MODELOS DE MACHINE LEARNING: COMPARAÇÃO ENTRE RANDOM FOREST, SVM E REGRESSÃO LOGÍSTICA SOB UMA PERSPECTIVA DE INTELIGÊNCIA ARTIFICIAL CLÍNICA

Machado, V., Oliveira, W. J. S. D., Ganzaroli, C. A., Franco, E. L. P., Cabral, G. D. S., Júnior, W. M. L. D. S., Leão, H. L. S. L., & França, H. F. D. C. (2025) · Aracê. · doi:10.56238/arev7n12-107

Identifying Personal Information in Textual Data

Kayem, A. V. D. M. (2025) · Advances in information security · doi:10.1007/978-3-031-93906-8_7

The Impact of AI-Driven Recommendation Systems on Patients’ Value Co-Creation Behavior in Online Health Communities: A Moderated Mediation ModelPreprint

Shabbir, S., Rouzhen, Q., Khan, N., & Arshad, M. U. (2025) · Research Square · doi:10.21203/rs.3.rs-8413621/v1

Gecikmeli Güvenlik Göstergeleriyle İş Kazalarının Tahminlemesi: 21. Yüzyılda Sosyal Güvenlik Dönüşümüne Yönelik Perspektifler

Tuna, G. (2025) · DergiPark (Istanbul University)

Fouille de motifs : entre accessibilité et robustesse

Abboud, Y. (2018) · doi:10.70675/7e44c2d5zbc70z447dzbe36za1d3babac934

Contributions statistiques à l'analyse de mégadonnées publiques

Sainct, B. (2018) · theses.fr (ABES) · doi:10.70675/c68eba40z5046z434bz86d9zf6ded8a88562

Détection des modifications de l’organisation circadienne des activités des animaux en relation avec des états pré-pathologiques, un stress, ou un événement de reproduction

Wagner, N. (2020) · theses.fr (ABES) · doi:10.70675/170664d0za7f1z4264zb5a9z6755a4164b9b

Outlier Detection as a Tool for Reinforcing Data Analysis and Prediction in Education

Novoseltseva, D. (2022) · doi:10.70675/3943ba18z7db8z4aafza410z7509388782bf

Estimation et prédiction des productions d’énergies renouvelables et des consommations d’un réseau de distribution d’électricité

Tribak, M. (2021) · theses.fr (ABES) · doi:10.70675/bf913277z3b21z4dcbza8b9z6acc1a50cb7f

Learning-based representations and methods for 3D shape analysis, manipulation and reconstruction

Rakotosaona, M. (2021) · theses.fr (ABES) · doi:10.70675/9f9943a6z9cc3z47afz802dzd6887d387ca3

Deep Learning for anomaly detection in industry 4.0

Keleko, A. T. (2022) · theses.fr (ABES) · doi:10.70675/c5883623za980z4e72z8275z2b478cb8b270

A Comparative Deep Learning Framework for Multivariate Time Series Anomaly Detection in Satellite Telemetry

Rustemli, A. C., Şahin, G., Akin, E., Adewole, K. S., & Rüstemli, S. (2026) · Applied Sciences · doi:10.3390/app16115694

Introduction

Hawkins, D. M. (1980) · Springer eBooks · doi:10.1007/978-94-015-3994-4_1 · Cited by 28

An Introduction to Outlier Analysis

Aggarwal, C. C. (2016) · Springer eBooks · doi:10.1007/978-3-319-47578-3_1 · Cited by 296

Novelty Detection

Sanguinetti, G., & Hawkins, D. (2009)

Outlier Detection, Influential Observations and Robust Estimation of Principal Components

Jolliffe, I. T. (1986) · Springer series in statistics · doi:10.1007/978-1-4757-1904-8_10 · Cited by 1

Anomaly Detection

Cleophas, T. J., & Zwinderman, A. H. (2013) · Springer eBooks · doi:10.1007/978-94-007-6886-4_10