3D-CNN detection of systemic symptoms induced by different Potexvirus infections in four Nicotiana benthamiana genotypes using leaf hyperspectral imaging by Rizos-Theodoros Chadoulis, Ioannis Livieratos, Ioannis Manakos, Theodore Spanos, Zeinab Marouni, Christos Kalogeropoulos, Constantine Kotropoulos

“…The timing of disease detection was also assessed, finding that accuracies approached 0.8 as early as $$6$$ 6 – $$8$$ 8  DPI depending on the virus. …”
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Correlation of Calibration Parameters for HPGe Detector Efficiency Based on Monte Carlo Simulation by LI Yan1, SHAN Chenyu2, GU Weiguo1, WANG Dezhong1

“…A key finding is that the distance between the detector and the waste drum, as well as the eccentricity, has little effect on the detection efficiency ratio between different crystal diameters. …”
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Fine-tuned YOLO-based deep learning model for detecting malaria parasites and leukocytes in thick smear images: A Tanzanian case study by Beston Lufyagila, Bonny Mgawe, Anael Sam

“…Reliable and timely detection of malaria parasites and leukocytes is essential for precise parasitemia quantification. …”
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Mango (Mangifera indica) tree detection and counting in mango orchard with satellite images using deep learning model YOLO: A comparative analysis by LALIT BIRLA, ANSHU BHARADWAJ, RAJNI JAIN, CHANDAN KUMAR DEB, VINAY KUMAR SEHGAL, RAMASUBRAMANIAN V

“…These findings highlight the potential of deep learning models for scalable orchard monitoring, precision agriculture, and sustainable fruit production. …”
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Robust outdoor trajectory mapping using CNN features and loop closure optimization by Kamran Kazi, Arbab Nighat Kalhoro, Farida Memon, Tarique Rafique Memon, Azam Rafique Memon

“…To find a suitable layer to obtain features from the CNN model, 313,746 filters were checked to find a filter that has the least odometry error from three pre-trained CNN models, the ConvNeXtXLarge is leveraged to extract high-level semantic features from monocular images, enabling resilient optical flow estimation even in scenes with transient objects and lighting variations. …”
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Precise Performance Analysis of Dual-Hop Mixed RF/Unified-FSO DF Relaying With Heterodyne Detection and Two IM-DD Channel Models by Omer Mahmoud Salih Al-Ebraheemy, Anas M. Salhab, Anas Chaaban, Salam A. Zummo, Mohamed-Slim Alouini

“…This paper provides precise performance analysis of the dual-hop mixed radio frequency (RF)/unified free space optical (FSO) decode-and-forward (DF) relaying system, in which the heterodyne detection and the intensity modulation-direct detection (IM-DD) are taken into account for FSO detection. …”
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Integrated Lower Limb Robotic Orthosis with Embedded Highly Oriented Electrospinning Sensors by Fuzzy Logic-Based Gait Phase Detection and Motion Control by Ming-Chan Lee, Cheng-Tang Pan, Jhih-Syuan Huang, Zheng-Yu Hoe, Yeong-Maw Hwang

“…Results demonstrate that the exoskeleton accurately detects gait phases, achieving a maximum tracking error of 4.23% in an 8-s gait cycle under no-load conditions and 4.34% when tested with a 68 kg user. …”
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A Hybrid Deep Learning Model for Enhanced Structural Damage Detection: Integrating ResNet50, GoogLeNet, and Attention Mechanisms by Vikash Singh, Anuj Baral, Roshan Kumar, Sudhakar Tummala, Mohammad Noori, Swati Varun Yadav, Shuai Kang, Wei Zhao

“…Traditional methods of damage assessment, which rely on manual inspections, can be labor-intensive and subject to human error. This paper introduces a hybrid deep learning model that combines the capabilities of ResNet50 and GoogLeNet, further enhanced by a convolutional block attention module (CBAM), proposed to improve both the accuracy and performance in detecting structural damage. …”
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Model updating method for detect and localize structural damage using generalized flexibility matrix and improved grey wolf optimizer algorithm (I-GWO) by Sina Sadraei, Majid Gholhaki, Omid Rezaifar

“…A key objective faced by this system is the automatic identification and damage detection at the appropriate moment. Employing optimization algorithms in structural model updating is one approach to achieve this objective. …”
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Research on Change Point Detection during Periods of Sharp Fluctuations in Stock Prices–Based on Bayes Method <i>β</i>-ARCH Models by Fenglin Tian, Yong Wang, Qi Qin, Boping Tian

“…By detecting the change points of the price of eight stocks with a high number of limit up and limit down changes occurring in the observation period, the following conclusions are obtained: (1) Change point detection using the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>-ARCH model based on the Bayes method is effective. (2) For different values of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>, this research study finds that based on the classical ARCH model (i.e., <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>β</mi><mo>=</mo><mn>1</mn></mrow></semantics></math></inline-formula>) of the change point parameter, the results are relatively optimal. (3) The accuracy of change point detection can be improved by correcting stock short-term effects by using the Kalman filtering method.…”
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A case study of fractional-order varicella virus model to nonlinear dynamics strategy for control and prevalence by Nisar Kottakkaran Sooppy, Farman Muhammad, Ghannam Manal, Hincal Evren, Sambas Aceng

“…Several findings have been discussed by considering various fractal dimensions and arbitrary order. …”
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Respiratory Rate Estimation from Thermal Video Data Using Spatio-Temporal Deep Learning by Mohsen Mozafari, Andrew J. Law, Rafik A. Goubran, James R. Green

“…This paper introduces an end-to-end deep learning approach to RR measurement using thermal video data. A detection transformer (DeTr) first finds the subject’s facial region of interest in each thermal frame. …”
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Automated Coronary Artery Identification in CT Angiography: A Deep Learning Approach Using Bounding Boxes by Marin Sakamoto, Takaaki Yoshimura, Hiroyuki Sugimori

“…Discussion: These findings demonstrate the feasibility of automated coronary artery detection, potentially reducing observer variability and expediting CCTA analysis. …”
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Improve unsupervised Learning-based landslides detection by band ratio processing of RGB optical images: a case study on rainfall-induced landslide clusters by Lichuan Chen, Xuekun Xiang, Haijia Wen, Jiafeng Xiao, Chenhao Song, Xinzhi Zhou, Jingyuan Yu

“…These findings can provide a reference for landslide detection using optical remote-sensing images.…”
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The evolution of eavesdropping on heterospecific alarm calls: Relevance, reliability, and personal information by Cameron Rouse Turner, Matt Spike, Robert D. Magrath

“…This is because senders trade‐off false alarms and missed predator detections in a way that is also favorable for the eavesdropper, by producing less of the costlier error. …”
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Uniform Quantization for Multi-Antenna Amplify&#x2013;Quantize&#x2013;Forward Relay by Gangsan Jeong, Xianglan Jin

“…To address this, we evaluate error performance at the destination for the entire AQF relay communication system by introducing a linear detection method with significantly reduced complexity in the MIMO AQF relay channel. …”
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HyQ2:&#x2009;A&#x2009;Hybrid&#x2009;Quantum&#x2009;Neural&#x2009;Network for&#x2009;NextG&#x2009;Vulnerability&#x2009;Detection by Yifeng Peng, Xinyi Li, Zhiding Liang, Ying Wang

“…As fifth-generation (5G) and next-generation communication systems advance and find widespread application in critical infrastructures, the importance of vulnerability detection becomes increasingly critical. …”
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Fractal Algorithm for Multiple-lens Analyses by F. Abe

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Power Assessment and Performance Comparison of Wind Turbines Driven by Multivariate Environmental Factors by Bubin Wang, Bin Zhou, Denghao Zhu, Mingheng Zou, Zhao Rao, Haoxuan Luo, Weihao Ji

“…The proposed method achieves substantial improvements in predictive accuracy, with decreases of 9.39% in mean absolute error (MAE) and 11.75% in root mean square error (RMSE), compared to conventional binning approaches. …”
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Grapevine inflorescence segmentation and flower estimation based on Computer Vision techniques for early yield assessment by Germano Moreira, Filipe Neves dos Santos, Mário Cunha

“…The models demonstrated a strong correlation (R2 > 90.0%) between detected and visible flowers in inflorescences. A statistical analysis confirmed the robustness of the framework, with the YOLOv8 model once again standing out, showing no significant differences in error rates across diverse grapevine morphologies and varieties, ensuring wide applicability. …”
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