Pointer Meter Reading Recognition Based on YOLOv11-OBB Rotated Object Detection by Xing Xu, Liming Wang, Chunhua Deng, Bi He

“…The average relative error of readings is 0.41568%, with a maximum relative error of less than 1.1468%. …”
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Harnessing multimodal approaches for depression detection using large language models and facial expressions by Misha Sadeghi, Robert Richer, Bernhard Egger, Lena Schindler-Gmelch, Lydia Helene Rupp, Farnaz Rahimi, Matthias Berking, Bjoern M. Eskofier

“…We evaluate three approaches: text-based features, facial features, and a combination of both. Our findings show the best results are achieved by enhancing text data with speech quality assessment, with a mean absolute error of 2.85 and root mean square error of 4.02. …”
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Cooperative Control for Multi-Agent Systems with Deception Attack Based on an Attack Detection Mechanism by Shuhan Zhang, Kai Zhang, Zhijian Hu

“…The analytical results reveal that observer errors grow unbounded under DAs but converge to zero in attack-free scenarios, enabling effective attack identification. …”
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Early detection of Wheat Stripe Mosaic Virus using multispectral imaging with deep-learning by Malithi De Silva, Dane Brown

“…This result suggests filters that capture visible and near-infrared spectrum ranges perform better in identifying WhSMV. These findings show that multispectral images combined with deep-learning models are viable for WhSMV detection in wheat fields, especially for identifying early-stage infections.…”
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The effect of estrus detection by progesterone test kits and clinical examination on conception rates in cows by Mustafa Sönmez, Gaffari Türk, Eşref Demirci

“…In conclusion, the use of practice progesterone tests can be useful lor preventing errors in heat detection and for increasing conception rate in cows which not be detected in estrus by clinical examination.…”
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Glaucoma detection in myopic eyes using deep learning autoencoder-based regions of interest by Christopher Bowd, Akram Belghith, Mark Christopher, Makoto Araie, Aiko Iwase, Goji Tomita, Kyoko Ohno-Matsui, Hitomi Saito, Hiroshi Murata, Tsutomu Kikawa, Kazuhisa Sugiyama, Tomomi Higashide, Atsuya Miki, Atsuya Miki, Toru Nakazawa, Makoto Aihara, Tae-Woo Kim, Christopher Kai Shun Leung, Robert N. Weinreb, Linda M. Zangwill

“…These findings suggest that deep learning models leveraging ROI-based reconstruction error from texture enface images may enhance glaucoma classification in myopic eyes, providing a robust alternative to conventional structural thickness metrics.…”
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The Scalable Detection and Resolution of Data Clumps Using a Modular Pipeline with ChatGPT by Nils Baumgartner, Padma Iyenghar, Timo Schoemaker, Elke Pulvermüller

“…This paper explores a modular pipeline architecture that integrates ChatGPT, a Large Language Model (LLM), to automate the detection and refactoring of data clumps—a prevalent type of code smell that complicates software maintainability. …”
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Optimized Multi-Scale Detection and Numbering of Teeth in Panoramic Radiographs Using DentifyNet by Salih Taha Alperen Ozcelik, Huseyin Uzen, Abdulkadir Sengur, Muammer Turkoglu, Adalet Celebi, Nebras M. Sobahi

“…Manual tooth detection and numbering in panoramic radiographs are time-consuming and prone to human errors, negatively impacting diagnostic accuracy and treatment outcomes. …”
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Detection of external defects of tomato crop using appearance parameters by convolutional neural networks by Nima Noorali, Ali Rajabipour, Hamed Sardari, Soleiman Hosseinpour

“…Training the YOLOv7 model necessitated 11 minutes and 60 epochs, culminating in an error rating of 0.017 with satisfactory outcomes. …”
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Benchmarking and optimization of methods for the detection of identity-by-descent in high-recombining Plasmodium falciparum genomes by Bing Guo, Shannon Takala-Harrison, Timothy D O'Connor

“…Notably, IBD detected with optimized parameters allows for more accurate capture of selection signals and population structure; IBD-based Ne inference is very sensitive to IBD detection errors, with IBD called from hmmIBD uniquely providing less biased estimates of Ne in this context. …”
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FedDL: personalized federated deep learning for enhanced detection and classification of diabetic retinopathy by Dasari Bhulakshmi, Dharmendra Singh Rajput

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Research on Link Alignment and Signal Detection Technologies for Cross-water Visible Light Communication by YAN Hongqiang, JIANG Ming

“…This scheme can achieve a high accuracy of WE and a Bit Error Rate (BER) performance close to that achieved under ideal channel state conditions.…”
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Research on the Rapid Detection of Formaldehyde Emission From Wood-Based Panels Based on the AMSHKELM by Yinuo Wang, Huanqi Zheng, Hua Wang, Yucheng Zhou

“…Experimental results indicate that the AMSHKELM model achieves the coefficient of determination of up to 0.9767 and the root mean square error of 2.7141e-03, demonstrating higher fitting accuracy and stronger robustness compared to various other models. …”
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Enhancing Facial Feature Detection: Hybrid Active Shape and Active Appearance Model (HASAAM) by Musab Iqtait, Jafar Ababneh, Mohammad Rasmi, Amer Abu-Jassar, Suhaila Abuowaida

“…The aim of the HASAAM integrated fitting model is to find new solutions for the feature identification issue by combining the strengths of the Active Shape Model (ASM) and Active Appearance Model (AAM) to provide unique findings on the feature detection problem. …”
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An end-to-end deep learning solution for automated LiDAR tree detection in the urban environment by Julian R. Rice, G. Andrew Fricker, Jonathan Ventura

“…We compare this model to a number of high-performing baselines on a large and varied dataset in the Southern California region, and find that our method outperforms all baselines in terms of tree detection ability (75.5% F-score) and positional accuracy (2.28 meter root mean squared error), while being highly efficient. …”
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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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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

“…On Livedet2015, our method achieves an average accuracy of 96.32%, outperforming several state-of-the-art models, including CNN (95.27%) and LivDet 2015 (95.39%). Error rate analysis reveals consistently low Bonafide Presentation Classification Error Rate (BPCER) scores with 0.28% on LivDet 2013 and 1.45% on LivDet 2015. …”
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