Penetrating Barriers: Microwave-Based Remote Sensing and Reconstruction of Audio Signals Through Walls by Kobi Aflalo, Zeev Zalevsky

“…This study investigate the remote detection and reconstruction of audio signals using Radio Frequency (RF) emissions, focusing on the implications for eavesdropping detection and prevention. …”
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Precise detection of differential RNA editing sites across varied biological conditions using the CADRES pipeline by Jun Sun, Chi Zhang, Xiuling Li

“…Abstract RNA editing is an important post-transcriptional modification for gene regulation and protein diversity. Detecting these modifications, especially Differential Variants on RNA (DVRs), presents significant challenges due to interference from sequencing errors and genetic variants. …”
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A novel ensemble model for fall detection: leveraging CNN and BiLSTM with channel and temporal attention by Sarita Sahni, Sweta Jain, Sri Khetwat Saritha

“…When using models that utilize multiple sensors, giving equal importance to each sensor can lead to errors because some activities may appear similar. …”
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Deep learning-based dual monitoring system for power forecasting and fault detection in nuclear power applications by Mingzhe Lyu, Helin Gong, Zhang Chen, Jiangyu Wang, Mingxiao Zhong, Zhiyong Wang, Qing Li, Zefei Pan

“…Monitoring key parameters in nuclear power plant control rooms is critical, as human errors can result in severe safety and operational consequences. …”
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Detecting Artificial Intelligence–Generated Versus Human-Written Medical Student Essays: Semirandomized Controlled Study by Berin Doru, Christoph Maier, Johanna Sophie Busse, Thomas Lücke, Judith Schönhoff, Elena Enax- Krumova, Steffen Hessler, Maria Berger, Marianne Tokic

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Fuzzy Hypothesis Testing for Radar Detection: A Statistical Approach for Reducing False Alarm and Miss Probabilities by Ahmed K. Elsherif, Hanan Haj Ahmad, Mohamed Aboshady, Basma Mostafa

“…The proposed methods are validated through both real and synthetic simulations of radar measurements, demonstrating their ability to enhance detection reliability across diverse conditions. The findings confirm the applicability of fuzzy hypothesis testing for modern radar systems in both civilian and military contexts, providing a statistically sound and operationally applicable approach for reducing detection errors and optimizing system performance.…”
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Stuck Pipe Detection in Oil and Gas Drilling Operations Using Deep Learning Autoencoder for Anomaly Diagnosis by Hasan N. Al-Mamoori, Jialin Tian, Haifeng Ma

“…The proposed model leverages reconstruction error as an anomaly detection metric, effectively distinguishing between normal and stuck cases. …”
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Synergistic detection of E. coli using ultrathin film of functionalized graphene with impedance spectroscopy and machine learning by Amrit Kumar, Shweta Mishra, R. K. Gupta, V. Manjuladevi

“…These findings establish a robust framework for rapid and selective E. coli detection, crucial for ensuring food and water safety. …”
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Impact of Chromatic Dispersion Compensation in Single Carrier Two-Dimensional Stokes Vector Direct Detection System by Mohammed Y. S. Sowailem, Thang M. Hoang, Mohamed Morsy-Osman, Mathieu Chagnon, Meng Qiu, Stephane Paquet, Carl Paquet, Ian Woods, Odile Liboiron-Ladouceur, David V. Plant

“…We experimentally demonstrate, aided with mathematical analysis, two methods for chromatic dispersion (CD) compensation for two-dimensional Stokes vector direct detection (2D-SV-DD) system. Results show that the better CD compensation method is dependent on transmission distance and operating symbol rate relative to the digital-to-analog converter (DAC) sampling rate. …”
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Evaluation of an acne lesion detection and severity grading model for Chinese population in online and offline healthcare scenarios by Na Gao, Jiaping Wang, Zheng Zhao, Xiao Chu, Bin Lv, Gangwen Han, Yuan Ni, Guotong Xie

“…For follow-up visits in online scenarios, the accuracy for detecting the changing trends reached 87.8%, with a total counting error of 1.91 ± 3.28 for all acne lesions. …”
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An Advanced Generative AI-Based Anomaly Detection in IEC61850-Based Communication Messages in Smart Grids by Aydin Zaboli, Yong-Hwa Kim, Junho Hong

“…To address these issues, implementing defense and mitigation strategies is essential. Identifying and detecting irregularities in information and communication technology (ICT) is vital to maintaining secure interactions between devices in digital substations. …”
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A hybrid approach for cervical cancer detection: Combining D-CNN, transfer learning, and ensemble models by Abu Hanzala, Tanjila Akter, Md. Sadekur Rahman

“…Cervical cancer is the leading cause of cancer-related death among women worldwide, although it is easily preventable through early detection and treatment. This paper proposed deep learning techniques, specifically transfer learning, deep convolutional neural networks (D-CNNs), and ensemble learning for automating cervical cancer detection and classification. …”
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An Ensemble Fuzzy-Based Deep Learning Framework for Automatic Detection of Children with ADHD From EEG Signal by Jalil Manafian, Mehdi Fazli, Onur Ilhan, Subhiya Zeynalli, Sukaina Tuama Ghafel

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Detecting Anomalies in Attributed Networks Through Sparse Canonical Correlation Analysis Combined With Random Masking and Padding by Wasim Khan, Mohammad Ishrat, Ahmad Neyaz Khan, Mohammad Arif, Anwar Ahamed Shaikh, Mousa Mohammed Khubrani, Shadab Alam, Mohammed Shuaib, Rajan John

“…Attributed networks are prevalent in the current information infrastructure, where node attributes enhance knowledge discovery. Anomaly detection in attributed networks is gaining attention for its potential uses in cybersecurity, finance, and healthcare. …”
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State-of-the-Art Deep Learning Algorithms for Internet of Things-Based Detection of Crop Pests and Diseases: A Comprehensive Review by Jean Pierre Nyakuri, Celestin Nkundineza, Omar Gatera, Kizito Nkurikiyeyezu

“…Convolutional Neural Network (CNN) architectures for image recognition, object detection, and their integration with IoT, embedded into mobile devices and unmanned aerial vehicles (UAV) are explored. …”
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A recurrent neural network and parallel hidden Markov model algorithm to segment and detect heart murmurs in phonocardiograms. by Andrew McDonald, Mark J F Gales, Anurag Agarwal

“…Segmentation errors then propagate to the subsequent disease classifier steps. …”
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Research on injection molded parts defect detection algorithm based on multiplicative feature fusion and improved attention mechanism by Rongnan Zhang, Yang Li, Zhiguang Guan

“…However, traditional defect detection methods for these parts often rely on manual visual inspection, which is inefficient, expensive, and prone to errors. …”
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Federated learning with LSTM for intrusion detection in IoT-based wireless sensor networks: a multi-dataset analysis by Raja Waseem Anwar, Mohammad Abrar, Abdu Salam, Faizan Ullah

“…The evaluation metrics for its performance included accuracy, F1 score, FPR, and root mean square error (RMSE). We evaluated the performance of the FL-based LSTM model against traditional centralized models, finding significant improvements in intrusion detection. …”
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Enhancing Log-Likelihood Ratios with Mutual Information on Three-Reader One-Track Detection in Staggered BPMR Systems by Natthakan Rueangnetr, Santi Koonkarnkhai, Piya Kovintavewat, Simon John Greaves, Chanon Warisarn

“…Next, using these mutual data sequences, four novel techniques are presented to enhance bit-error rate (BER) performance and detection accuracy: hard-information flipping, maximum soft-information finding, bit-summation detection, and multilayer perceptron (MLP). …”
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Vision Transformer-Based Unhealthy Tree Crown Detection in Mixed Northeastern US Forests and Evaluation of Annotation Uncertainty by Durga Joshi, Chandi Witharana

“…Our findings underline the considerable influence of human annotation errors on model performance, emphasizing the need for standardized annotation guidelines and quality control measures.…”
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