A Bootstrapping Convolutional Neural Network Technique for Optimizing Automated Detection of Equatorial Plasma Bubbles by Optical All‐Sky Imagers by Daniel Okoh, Claudio Cesaroni, Babatunde Rabiu, Kazuo Shiokawa, Yuichi Otsuka, Samuel Ogunjo, Aderonke Akerele, John Bosco Habarulema, Bruno Nava, Yenca Migoya‐Orué, Punyawi Jamjareegulgarn, Adeniran Seun, Ogechi Adama, George Ochieng, James Ameh, Adero Awuor, Paul Baki

“…This study presents a novel bootstrapping convolutional neural network (CNN) approach to optimize automated EPB detection on ASI images for operational space weather monitoring applications, and overcoming challenges related to image variability and imbalanced data sets. …”
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BWFER-YOLOv8: An Enhanced Cascaded Framework for Concealed Object Detection by Khalid Ijaz, Ikramullah Khosa, Ejaz A. Ansari, Syed Farooq Ali, Asif Hussain, Faran Awais Butt

“…Several concealed object detection models have demonstrated outstanding performance but failed to combat the above-mentioned challenges concurrently. …”
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Bootstrap Confidence Intervals for Multiple Change Points Based on Two-Stage Procedures by Li Hou, Baisuo Jin, Yuehua Wu, Fangwei Wang

“…This paper investigates the construction of confidence intervals for multiple change points in linear regression models. First, we detect multiple change points by performing variable selection on blocks of the input sequence; second, we re-estimate their exact locations in a refinement step. …”
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A Variance Shift Model for Detection of Outliers in the Linear Measurement Error Model by Babak Babadi, Abdolrahman Rasekh, Ali Akbar Rasekhi, Karim Zare, Mohammad Reza Zadkarami

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You Only Look Once v5 and Multi-Template Matching for Small-Crack Defect Detection on Metal Surfaces by Pallavi Dubey, Seth Miller, Elif Elçin Günay, John Jackman, Gül E. Kremer, Paul A. Kremer

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Radar target detector based on banded sample autocovariance matrices by Chang Qu, Xiaoying Wang, Jing Chen, Junping Yin, Jiang Hu, Zhigen Gao

“…We propose novel CFAR detectors based on time series analysis and statistical foundations. We model radar echo data within a coherent processing interval as stationary time series governed by linear random processes, enabling the application of a time series resampling approach to establish the autoregressive sieve bootstrap consistency of the banded sample autocovariance matrix (SACM) in the spectral norm. …”
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Detection of Outliers and Influential Observations in Linear Ridge Measurement Error Models with Stochastic Linear Restrictions by F. Ghapani, A. R. Rasekh, M. R. Akhoond, B. Babadi

“…In addition, we derive the corrected score test statistic for outliers detection based on mean shift outlier models. The analogues of Cook's distance and likelihood distance are proposed to determine influential observations based on case deletion model. …”
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Climate change induced extreme meteorological conditions over sub-tropical environment: pathways towards achieving Sustainable Development Goals (SDGs) by Asish Saha, Subodh Chandra Pal, Aznarul Islam, Abu Reza Md. Towfiqul Islam, Chaitanya Baliram Pande, Edris Alam, Md Kamrul Islam

“…This study investigates the long-term rainfall pattern and its effects on achieving the “Sustainable Development Goals (SDGs)” in Birbhum district, West Bengal, India, part of the red and lateritic agroclimatic zone over the sub-tropical environment. Trend detection methods like “innovative trend analysis (ITA)”, “Mann Kendall (MK)”, “Modified Mann Kendall” and “Bootstrapped Mann Kendall (BMK)” were used to assess the problems. …”
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Predicting glycated hemoglobin levels in the non-diabetic general population: Development and validation of the DIRECT-DETECT prediction model - a DIRECT study. by Simone P Rauh, Martijn W Heymans, Anitra D M Koopman, Giel Nijpels, Coen D Stehouwer, Barbara Thorand, Wolfgang Rathmann, Christa Meisinger, Annette Peters, Tonia de Las Heras Gala, Charlotte Glümer, Oluf Pedersen, Henna Cederberg, Johanna Kuusisto, Markku Laakso, Ewan R Pearson, Paul W Franks, Femke Rutters, Jacqueline M Dekker

“…Using backward selection, age, BMI, waist circumference, use of anti-hypertensive medication, current smoking and parental history of diabetes remained in sex-specific linear regression models. To minimize overfitting of coefficients, we performed internal validation using bootstrapping techniques. …”
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Comparative analysis of machine learning models for malaria detection using validated synthetic data: a cost-sensitive approach with clinical domain knowledge integration by Gudi V. Chandra Sekhar, Chekol Alemu

“…We systematically compared five machine learning models—Naive Bayes, Logistic Regression, Random Forest, XGBoost, and Enhanced Bayesian Logistic Regression—for malaria detection using a rigorously validated synthetic dataset ( $$N=10,100$$ ) representing Sub-Saharan African epidemiological conditions. …”
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Prediabetes detection in unconstrained conditions using wearable sensors by Dimitra Tatli, Vasileios Papapanagiotou, Aris Liakos, Apostolos Tsapas, Anastasios Delopoulos

“…Features are aggregated per individual using bootstrap. Support Vector Machines are used to classify normoglycemic vs. prediabetic individuals. …”
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Transformer Oil Acid Value Prediction Method Based on Infrared Spectroscopy and Deep Neural Network by Linjie Fang, Chuanshuai Zong, Zhenguo Pang, Ye Tian, Xuezeng Huang, Yining Zhang, Xiaolong Wang, Shiji Zhang

“…The traditional detection method of transformer oil acid value has limitations, such as long detection period and toxicity of reagents; while, with the traditional spectral analysis, it is difficult to realize the efficient extraction of key features related to the acid value content. …”
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SARS-CoV-2 Detection From Voice by Gadi Pinkas, Yarden Karny, Aviad Malachi, Galia Barkai, Gideon Bachar, Vered Aharonson

“…Automated voice-based detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could facilitate the screening for COVID19. …”
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Detecting and analyzing explosive bubbles and their relationship with volatility: evidence from Tunisia by Sirine Ben Yaala, Jamel Eddine Henchiri

“…Design/methodology/approach – The research uses the Supremum Augmented Dickey-Fuller (SADF) and Generalized Supremum Augmented Dickey-Fuller (GSADF) tests, alongside Monte Carlo and bootstrap simulations (Sieve-bootstrap and Wild-bootstrap), to detect speculative bubbles. …”
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Anomalous Node Detection in Blockchain Networks Based on Graph Neural Networks by Ze Chang, Yunfei Cai, Xiao Fan Liu, Zhenping Xie, Yuan Liu, Qianyi Zhan

“…In recent years, the mainstream method for detecting anomalous nodes in graphs has been the use of graph data mining techniques. …”
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Spiking Residual ShuffleNet-Based Intrusion Detection in IoT Environment by Sneha Leela Jacob, H. Parveen Sultana

“…In this context, a new model called Spiking Residual ShuffleNet (SR-ShuffleNet) is introduced for intrusion detection (ID) in an IoT environment. …”
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Leveraging Bias in Pre-trained Word Embeddings for Unsupervised Microaggression Detection by Tolúlọpẹ́ Ògúnrẹ̀mí, Valerio Basile, Tommaso Caselli

“…The algorithm relies on pre-trained word-embeddings, leveraging the bias encoded in the model in order to detect microaggressions in unseen textual instances. …”
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Detecting Malicious URLs Using Classification Algorithms in Machine Learning and Deep Learning by Sira Astour, Ahmad Hasan

“…This study presents an improvement in the accuracy and speed of detecting malicious URLs through ensemble learning techniques, specifically Bagging (Bootstrap) and Stacking. …”
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Performance Evaluation of Different Speech-Based Emotional Stress Level Detection Approaches by Jan Stas, Stanislav Ondas, Jozef Juhar

“…Among the deep learning approaches, we fine-tuned self-supervised models such as Wav2Vec 2.0 and BYOL-S (Bootstrap Your Own Latent for Speech), as well as transfer learning models including VGGish and YAMNet. …”
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Security-Enhanced Decentralized Content Sharing in Publish/Subscribe System by Wang-Seok Park, Chang-Seop Park, Samuel Woo

“…Regarding security issues, we consider a malicious-broker model and authorized-but-malicious-node model, under which a couple of security primitives are designed to provide an end-to-end security and a detection mechanism against malicious content modification. …”
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