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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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

“…Also, we can achieve 300G transmission for distances below 320 km using 84 GBd 16&#x00A0;QAM signal with BER below hard decision forward error correcting threshold of <inline-formula><tex-math notation="LaTeX">$\sim3.8\times 10^{-3}$</tex-math></inline-formula>.…”
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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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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

“…The proposed ToD model demonstrates significant advantages over the human-in-the-loop (HITL) approach, particularly in error rate, adaptability, and scalability. Specifically, compared to HITL, the ToD model achieves a reduction in false positives (FPs) of up to 20% and enhances the accuracy of AD by up to 17.5%, resulting in a general accuracy of 97.5%. …”
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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

“…For this, we performed error level analysis (ELA) on the dataset to ensure that no patterns were missed within each image. …”
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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 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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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

“…Approaches utilizing the reconstruction error rely on straightforward, simple mappings, which introduce a substantial risk of overfitting in high-dimensional data, wherein the model acquires patterns that are exclusive to the training data and fails to generalize to new data. …”
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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

“…Traditional inspection methods are time-consuming and prone to errors due to limited labor expertise. Therefore, to tackle these challenges, harnessing advanced technologies such as artificial intelligence (AI), Machine Learning/Deep Learning (ML/DL), and Internet of Things (IoT) is essential for managing and mitigating agriculture hazards. …”
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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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Signal Detection in Intelligent Reflecting Surface-Assisted NOMA Network Using LSTM Model: A ML Approach by Haleema Sadia, Hafsa Iqbal, Syed Fawad Hussain, Nasir Saeed

“…In this article, we propose a machine learning (ML)-based approach to perform joint channel estimation and signal detection in an IRS-enabled uplink NOMA network under efficient mitigation of SIC error propagation. …”
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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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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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Using the optimal combined index weight ratio to improve the probability of anomaly detection in big area additive manufacturing by Monique S. McClain, Dhrubajit Chowdhury, Omar Eldaghar, Kris Villez

“…Approach 2 systematically varies the ratio of the weights to find the optimum. We show that approach 1 delivers optimal anomaly detection performance in select cases while approach 2 fares better in practice. …”
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Amplified Sensitivity in SERS Detection of L1CAM With Silver Plasmonic Mesoporous Silica Capsules on an Imprinted Films by Yuselis Castaño‐Guerrero, Belén Arjones‐Fernández, Felismina T.C. Moreira, Ramon A. Alvarez‐Puebla, Miguel A. Correa‐Duarte, H. Águas, M. Goreti F. Sales

“…Thus, the combination of dual detection and recognition capabilities presents promising potential for detecting diverse biomarkers, especially in critical scenarios where reducing false‐positive or false‐negative errors is crucial.…”
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Efficacy of two radiographic algorithms for detection of peri-implant bone defects on cone-beam computed tomography scans by Faezeh Yousefi, Ali Heidari, Azita Ehsani, Maryam Farhadian, Marzieh Ehsani

“…The most common diagnostic error was related to misdiagnosis of control group with full defect with ANR filter, such that the existing bone was not detected. …”
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