Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis by Zishen Zhang, Hong Cheng, Meiyu Chen, Lixin Zhang, Yudou Cheng, Wenjuan Geng, Junfeng Guan

“…The Backpropagation Neural Network (BPNN) model could accurately distinguish six distinct pear varieties, achieving prediction accuracies of above 99% for both the calibration and test sets. In summary, the combination of HSI and machine learning models enabled an efficient, rapid, and non-destructive detection of pear quality and provided a practical value for quality control and the commercial processing of pears.…”
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Application of different edge detection methods for faults identification in Anza Basin, Kenya by Gege Li, Qing Chen, Bingqiang Yuan, Xianfeng Tan, Hao Chen, Xiaoyu Huang, Lang Pan

“…However, the traditional potential field boundary identification method has limited ability to detect the boundaries of buried deep geological bodies. …”
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Enhancing IoT Network Security Through Deep Learning-Based Intrusion Detection by MOHAMMED FAWWAZ ALI MOHAMMED FAWWAZ ALI

“…Certain of the machine learning and deep learning methods, and benchmark data sets, used for IoT security enhancement are described in this paper. …”
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Detection of wave activity in measurements of thermospheric vertical winds and temperatures at subauroral latitudes by Anneliese L. Schmidt, John W. Meriwether, Matthew B. Cooper, Andrew J. Gerrard, Lindsay V. Goodwin, Shun-Rong Zhang, Ying Ye

“…The heightened sensitivity of HODI enables analysis of observations with uncertainties of approximately 3–5 m s−1 for vertical wind speeds and 10–15 K for temperatures for 2-min exposures. Data was collected during periods of both geomagnetically quiet and active conditions, and GW structures were seen in both data sets. …”
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Whispers in the air: Designing acoustic classifiers to detect fruit flies from afar by Alia Khalid, Muhammad Latif Anjum, Salman Naveed, Wajahat Hussain

“…Detecting weak wingbeats of a flying bug is a challenging problem in uncontrolled outdoor settings. …”
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Detection of Structural Damage After an Earthquake Using GIS and Remote Sensing Methods by Aşır Yüksel Kaya

“…Considering this, high quality images of Malatya city before and after the earthquake were analyzed and data sets were created by masking using Mask RCNN deep learning method through ArcGIS Pro 3.4.0 software. …”
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Moving Healthcare AI Support Systems for Visually Detectable Diseases to Constrained Devices by Tess Watt, Christos Chrysoulas, Peter J. Barclay, Brahim El Boudani, Grigorios Kalliatakis

“…This study explores the use of tinyML to provide healthcare support with low-spec devices in low-connectivity environments, focusing on the diagnosis of skin diseases and the ethical use of AI assistants in a healthcare setting. To investigate this, images of skin lesions were used to train a model for classifying visually detectable diseases (VDDs). …”
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Detecting Tumor-Associated Intracranial Hemorrhage Using Proton Magnetic Resonance Spectroscopy by Hye Bin Yoo, Hyeong Hun Lee, Vincent Diong Weng Nga, Yoon Seong Choi, Jeong Hoon Lim

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Microwave Tomography System for Methodical Testing of Human Brain Stroke Detection Approaches by Ilja Merunka, Andrea Massa, David Vrba, Ondrej Fiser, Marco Salucci, Jan Vrba

“…It is therefore suitable for large-scale measurements with high variability of measured data for stroke detection and classification based on machine learning methods. …”
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RGB-D salient object detection based on BC2 FNet network by WANG Feng, CHENG Yongmei

“…Simulation results on five standard RGB-D data sets show that the proposed model can achieve better performance.…”
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Lightweight construction safety behavior detection model based on improved YOLOv8 by Kan Huang, Mideth B. Abisado

“…Traditional YOLO models often have problems of missed detection and insufficient feature processing when dealing with complex scenes, especially when facing large-scale data sets. …”
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Mobility difference index: a quantitative method for detecting human mobility difference by Zhaohui Liu, Rui Li, Jing Cai, Qiushi Hu, Huayi Wu

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Fresh or Rotten? Enhancing Rotten Fruit Detection With Deep Learning and Gaussian Filtering by Leopold Fischer-Brandies, Lucas Muller, Justus Johannes Riegger, Ricardo Buettner

“…We achieve high accuracies beyond 99% on unseen test data, setting a new benchmark and outperforming previous efforts. …”
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Capsule network approach for monkeypox (CAPSMON) detection and subclassification in medical imaging system by M. Nuthal Srinivasan, Mohamed Yacin Sikkandar, Maryam Alhashim, M. Chinnadurai

“…Our model’s superior performance, demonstrated through rigorous evaluation, exhibits significant improvements in accuracy, precision, recall, and F1 score, even with limited data. The results highlight the potential of ESACN as a reliable tool for enhancing diagnostic accuracy in medical settings. …”
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Signals of propaganda-Detecting and estimating political influences in information spread in social networks. by Alon Sela, Omer Neter, Václav Lohr, Petr Cihelka, Fan Wang, Moti Zwilling, John Phillip Sabou, Miloš Ulman

“…We use 16 data sets of a total size of 13 GB, 10 related to political topics and 6 related to non-political ones (large-scale disasters), each ranging from tens of thousands to a few million of tweets. …”
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Ensemble based high performance deep learning models for fake news detection by Mohammed E.Almandouh, Mohammed F. Alrahmawy, Mohamed Eisa, Mohamed Elhoseny, A. S. Tolba

“…It significantly aids the global fight against false information by setting the stage for future research to expand fake news detection to multiple languages.…”
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Optimization of Complex Cancer Morphology Detection Using the SIVQ Pattern Recognition Algorithm by Jason Hipp, Steven Christopher Smith, Jerome Cheng, Scott Arthur Tomlins, James Monaco, Anant Madabhushi, Lakshmi Priya Kunju, Ulysses J. Balis

“…Especially important is the determination of relative contributions of each key SIVQ matching parameter with respect to the algorithm’s overall detection performance. Herein, by combinatorial testing of SIVQ ring size, sub-ring number, and inter-ring wobble parameters, in the setting of a morphologically complex bladder cancer use case, we ascertain the relative contributions of each of these parameters towards overall detection optimization using urothelial carcinoma as a use case, providing an exemplar by which this algorithm and future histology-oriented pattern matching tools may be validated and subsequently, implemented broadly in other appropriate microscopic classification settings.…”
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YOLO-NeRFSLAM: underwater object detection for the visual NeRF-SLAM by Zhe Wang, Zhibin Yu, Zhibin Yu, Bing Zheng, Bing Zheng

“…Through a Marine Motion Fusion (MMF) strategy—leveraging YOLO to detect known marine organisms and integrating optical flow for pixel-level motion analysis—we effectively screen out all dynamic objects, thus maintaining stable camera pose estimation and pixel-level dense reconstruction even without relying on depth data. …”
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A Comprehensive Approach for Detecting and Handling MitM-ARP Spoofing Attacks by Standy Oei, Yohanes Suyanto, Reza Pulungan

“…In the host, we employ a combination of ping Round-Trip Time (RTT) anomaly detection, the SendARP function, static entry, and ping confirmation to detect and mitigate attacks. …”
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Artificial Intelligence in Laryngeal Cancer Detection: A Systematic Review and Meta-Analysis by Ali Alabdalhussein, Mohammed Hasan Al-Khafaji, Rusul Al-Busairi, Shahad Al-Dabbagh, Waleed Khan, Fahid Anwar, Taghreed Sami Raheem, Mohammed Elkrim, Raguwinder Bindy Sahota, Manish Mair

“…The subset analysis revealed that CNN-based AI models are superior to non-CNN-based models in image analysis and lesion detection. (4) Conclusions: AI can be used in real-world settings due to its diagnostic accuracy, high sensitivity, and specificity.…”
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