Dissociable effects of fatigue on performance and metacognition from automatic target cuing in undersea threat detection by Max Kailler Smith, Amelia R. Kracinovich, Brandon J. Schrom, Timothy L. Dunn

“…Results showed that ATC did not enhance performance when participants were alert, though detection accuracy maintained despite increased fatigue. …”
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Analysis of the status of leprosy symptom surveillance in Guangdong province: an exploring effort to promote early detection by Lei Chen, Quan Luo, Haoze Xue, Jing Wang, Xiaohua Wang, Cheng Wang

“…The delayed discovery time decreased from (36.2 ± 2.0) months to (31.8 ± 3.1) months, and the patients delayed discovery time decreased from (27.7 ± 1.8) months to (20.8 ± 2.5) months, both showing a statistically significant difference (p < 0.05). Discussion Our findings indicate that the leprosy symptom surveillance program in Guangdong Province has effectively reduced delayed discovery time, particularly patients delayed discovery time in seeking medical care, thereby facilitating early detection of leprosy cases.…”
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Classification of oil palm tree conditions from UAV imagery using the YOLO object detector by Aakash Thapa, Teerayut Horanont, Bipul Neupane, Jirawan Klaylee, Apichon Witayangkurn

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A Hybrid STL-Deep Learning Framework for Behavioral-Based Intrusion Detection in IoT Environments by Abdullah AlHayan, Jalal Al-Muhtadi

“…These results represent a substantial improvement over standalone deep learning models (standalone LSTM FNR = 0.302, FPR = 0.185) and compare favorably to state-of-the-art benchmarks reported in the literature, particularly in minimizing critical detection errors. The findings indicate that the proposed hybrid STL-LSTM framework presents a robust and viable solution for high-stakes IoT network security, effectively balancing high detection accuracy with exceptionally low error rates, making it well-suited for real-time deployment in protecting critical IoT infrastructure.…”
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Real-Time Multi-Task Deep Learning Model for Polyp Detection, Characterization, and Size Estimation by Phanukorn Sunthornwetchapong, Kasichon Hombubpha, Kasenee Tiankanon, Satimai Aniwan, Pasit Jakkrawankul, Natawut Nupairoj, Peerapon Vateekul, Rungsun Rerknimitr

“…All these three tasks can have an intrapersonal error, which varies among endoscopists. A proven method for enhancing performance is computer-aided detection and a diagnosis system for endoscopists, which tends to be a real-time system. …”
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Lost in Translation? A Critical Review of Economics Research Using Nighttime Lights Data by John Gibson, Omoniyi Alimi, Geua Boe-Gibson

“…In the three decades since a digital archive of satellite-detected night-time lights (NTL) data was created, thousands of scholarly articles have been published using these data. …”
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Predicting communities with high tuberculosis case-finding efficiency to optimise resource allocation in Pakistan: comparing the performance of a negative binomial spatial lag mode... by Hasan Tahir, Frank Cobelens, Christina Mergenthaler, Mirjam I Bakker, Tanveer Ahmed, Jake D Mathewson, Daniella Brals, Abdullah Latif, Stephanie Lako, Andreas Werle van der Merwe, Matthys Potgieter, Vincent Meurrens, Zia Samad, Ente Rood

“…The NBR and BML models were compared on their respective predictive precisions for the identification of TB hotspots, based on Root Mean Square Error values, k-fold cross-validation and tehsil-level (sub-district) prediction rankings.Results 407 (1.9%) bacteriologically confirmed cases among 21 227 visitors were detected in 414 ACF events between September 2020 and January 2022. …”
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Dual-Model Synergy for Fingerprint Spoof Detection Using VGG16 and ResNet50 by Mohamed Cheniti, Zahid Akhtar, Praveen Kumar Chandaliya

“…In this paper, we address the challenge of fingerprint liveness detection by proposing a dual pre-trained model approach that combines VGG16 and ResNet50 architectures. …”
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Novel Fault Detection Method for Rolling Bearings Based on Improved Variational Modal Decomposition Method by Xiaoli Huang, Haifeng Xu, Junying Cui

“…Finally, the wavelet packet decomposition is adopted to filter noise and yield the Hilbert envelope spectrum of the de-noised signal to ascertain the bearing&#x2019;s health status based on the extraction of characteristic fault frequencies and harmonics. The experimental findings illustrate that the enhanced variational mode decomposition technique not only escalates the inner ring signal&#x2019;s signal-to-noise ratio from &#x2212;10.844 dB to 8.4471 dB and the outer ring signal&#x2019;s ratio from &#x2212;4.5852 dB to 3.0997 dB but also reduces the error of outer ring fault detection from 3.14&#x0025; to 0.37&#x0025;, and improves the frequency of inner ring fault detection from a feature extraction inability to an error frequency of 0.45&#x0025;.…”
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Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context. by Thomas W Willis, Chris Wallace

“…We show with simulation studies and real data from GWAS of 18 phenotypes from the UK Biobank that the test is to be preferred for use with small sample sizes, particularly when genetic effects are few and large, outperforming the genetic correlation and another nonparametric statistical test of independence. We find the test suitable for the detection of genetic similarity in the rare disease context.…”
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Detecting Exercise-Induced Temperature Distribution Changes at the Knee Using a Wearable Array of Thermistors by Benito L. Pugliese, Virginia Civeriati, Adam S. Tenforde, Danilo Demarchi, Paolo Bonato

“…The mean HDI error for the thermistor array was 0.20&#x00B1;0.36&#x2218;C. …”
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The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection by Tarek Berghout

“…Despite these advancements, comprehensive reviews synthesizing recent findings remain scarce. By analyzing over 100 research papers over past half-decade (2019–2024), this review fills that gap, exploring the latest methods and paradigms, summarizing key concepts, challenges, datasets, and offering insights into future directions for brain tumor detection using deep learning. …”
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A novel small-scale wind-turbine blade failure detection according to monitored-data by A. Aranizadeh, H. Shad, B. Vahidi, A. Khorsandi

“…Additionally, strain gauge signals demonstrate superior accuracy in identifying failure modes over accelerometer signals. These findings provide valuable insights into mode-specific impacts and the effectiveness of monitoring techniques for accurate fault detection.…”
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A robust dataset for surgical instrument detection to aid autonomous robotic surgeryMendeley Data by Ganesh Kumar Chellamani, Aishwarya N, Bhavesh Kumar P, Palle Sravan Kumar Reddy, Rakesh Thoppaen Suresh Babu

“…On an average, the models have attained 99.3% of mean Average Precision (mAP) and 99.2% F1-score, demonstrating the quality of SID-RAS dataset for surgical tool detection. These findings contribute to the preliminary development of AI-driven robotic surgical assistance systems, which can be extended to various types of surgeries.…”
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A Novel Machine Learning-based Diagnostic Algorithm for Detection of Onychomycosis through Nail Appearance by Serkan Düzayak, Muhammed Kürşad Uçar

“…Today, new technologies are needed to detect onychomycosis via AI-based ML to reduce the clinician and laboratory-induced error rate and increase diagnostic sensitivity and reliability. …”
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ChaMTeC: CHAnnel Mixing and TEmporal Convolution Network for Time-Series Anomaly Detection by Ibrahim Delibasoglu, Deniz Balta, Musa Balta

“…Our findings highlight the effectiveness of combining channel-mixing techniques with temporal convolutional networks and dynamic thresholding for detecting anomalies in complex industrial environments. …”
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A Comparative Study of Deep Audio Models for Spectrogram- and Waveform-Based SingFake Detection by Minh Nguyen-Duc, Luong Vuong Nguyen, Huy Nguyen-Ho-Nhat, Tri-Hai Nguyen, O-Joun Lee

“…Our findings contribute to developing more effective deepfake singing detection methods, with implications for security, media authentication, and digital content protection.…”
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Advancing Wildlife Protection: Mask R-CNN for Rail Track Identification and Unwanted Object Detection by Istiak Mahmud, Md. Mohsin Kabir, Jungpil Shin, Chayan Mistry, Yoichi Tomioka, M. F. Mridha

“…Since the detection depends on the driver or human, there is a possibility of occasionally making an error in honking the horn at the right moment, leading to the accident. …”
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Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning by Muhammad Umair, Jawad Ahmad, Nada Alasbali, Oumaima Saidani, Muhammad Hanif, Aizaz Ahmad Khattak, Muhammad Shahbaz Khan

“…However, the inherent complexity of EEG signals along with the human error in interpreting these readings requires the need for more reliable, automated methods of detection.MethodsThis study utilizes EEG signals to classify MDD and healthy individuals through a combination of machine learning, deep learning, and split learning approaches. …”
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Early detection of Alzheimer’s disease progression stages using hybrid of CNN and transformer encoder models by Hassan Almalki, Alaa O. Khadidos, Nawaf Alhebaishi, Ebrahim Mohammed Senan

“…Manual diagnosis is prone to human error, often leading to misdiagnosis or delayed detection. …”
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