DRBD-YOLOv8: A Lightweight and Efficient Anti-UAV Detection Model by Panpan Jiang, Xiaohua Yang, Yaping Wan, Tiejun Zeng, Mingxing Nie, Zhenghai Liu

“…Furthermore, DN-ShapeIoU, a novel loss function, has been established to enhance detection accuracy, and depthwise separable convolutions have been included to decrease computational complexity. The experimental results showed that the proposed model outperformed YOLOV8n in terms of mAP50, mAP95, precision, and FPS while reducing GFLOPs and parameter count. …”
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Real-time freshness prediction for Apples and Lettuces using imaging recognition and advanced algorithms in a user-friendly mobile application by Chrysanthos Maraveas, George Kalitsios, Marianna I. Kotzabasaki, Dimitrios V. Giannopoulos, Kosmas Dimitropoulos, Anna Vatsanidou

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Effects of Microsatellite Instability on the Clinical and Pathological Characteristics of Colon Cancer and the Diagnostic Accuracy of Preoperative Abdominal CT Scans by Rıdvan Yavuz, Orhan Aras, Hüseyin Çiyiltepe, Onur İlkay Dinçer, Ahmet Şükrü Alparslan, Tebessüm Çakır

“…The accuracy in the determination of the T and N statuses was not affected by the parameters examined. <b>Conclusions</b>: dMMR CC has specific characteristic features. …”
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YOLO-Tryppa: A Novel YOLO-Based Approach for Rapid and Accurate Detection of Small Trypanosoma Parasites by Davide Antonio Mura, Luca Zedda, Andrea Loddo, Cecilia Di Ruberto

“…YOLO-Tryppa incorporates ghost convolutions to reduce computational complexity while maintaining robust feature extraction and introduces a dedicated P2 prediction head to improve the localization of small objects. …”
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A discrete extension of the Lindley distribution for health and sustainability data: Theoretical insights and decision-making applications by Mahmoud El-Morshedy, Mohamed S. Eliwa, Abhishek Tyagi, Hend S. Shahen

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PlutoNet: An efficient polyp segmentation network with modified partial decoder and decoder consistency training by Tugberk Erol, Duygu Sarikaya

“…Although state‐of‐the‐art models are proposed, it remains a challenge to define representations that are able to generalize well and that mediate between capturing low‐level features and higher‐level semantic details without being redundant. …”
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Unitary and entangling solutions to the parametric Yang–Baxter equation in all dimensions by Arash Pourkia

“…We present a new class of solutions to the parameter-dependent Yang–Baxter equation across all dimensions, which includes a significant subclass of unitary and entangling solutions. …”
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BCI‐control and monitoring system for smart home automation using wavelet classifiers by Amer Al‐Canaan, Hicham Chakib, Muhammad Uzair, Shuja‐uRehman Toor, Amer Al‐Khatib, Majid Sultan

“…In this study, the design and implementation of the BCI control and monitoring system for smart home automation using wavelet features, which is based upon a dual‐channel analogue EEG signal acquisition module is reported. …”
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Device Modeling Based on Cost-Sensitive Densely Connected Deep Neural Networks by Xiaoying Tang, Zhiqiang Li, Lang Zeng, Hongwei Zhou, Xiaoxu Cheng, Zhenjie Yao

“…Therefore, this work proposes a machine learning-based device modeling algorithm to capture the complex nonlinear relationship between parameters and electrical characteristics of gate-all-around (GAA) nanowire field-effect transistors (NWFETs) from technology computer-aided design (TCAD) simulation results. …”
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Modelling and verification of parameterized architectures: A functional approach by Salah Merniz, Saad Harous

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Optimized Machine Learning-Augmented Hybrid Empirical Models for AlGaN/GaN HEMTs: A Comprehensive Analysis by Ahmad Khusro, Saddam Husain, Mohammad Hashmi

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Role of pre-procedure CCTA in predicting failed percutaneous coronary intervention for chronic total occlusions by Hua Zhou, Xiaojun Fan, Mingyuan Yuan, Wei Wang, Qiyuan Wu

“…Conclusion: Our study demonstrated that combining CCTA and CCA morphologic characteristics could improve PCI outcome prediction in patients with CTO compared to CCTA morphologic features alone.…”
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CBCT based investigation of furcation groove’s prevalence, depth, length and associated dentin thickness in Maxillary First Permanent Premolars in Saudi Sub-population by Safia S. Shaikh, Muhammad Qasim Javed, Ahmed Ali, Zaina Ahmad, Khawlah Almohimeed, Ayman M. Abulhamael, Ra’fat I. Farah

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From Image to Sequence: Exploring Vision Transformers for Optical Coherence Tomography Classification by Amirali Arbab, Aref Habibi, Hossein Rabbani, Mahnoosh Tajmirriahi

“…Results: While our model achieves an accuracy of 99.80% on the OCT2017 dataset, its standout feature is its parameter efficiency–requiring only 6.9 million parameters, significantly fewer than larger, more complex models such as Xception and OpticNet-71. …”
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Straightness monitoring of scraper conveyor based on CUDA-accelerated dynamic programming and optimized panoramic stitching by LI Bo, SHI Shouyi, ZHANG Jianjun, XIA Rui, WANG Xuewen, CUI Weixiu, NI Qiang

“…Feature point matches were calculated using K-nearest neighbors (KNN), with incorrect matches filtered using a threshold ratio. …”
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GLS-YOLO: A Lightweight Tea Bud Detection Model in Complex Scenarios by Shanshan Li, Zhe Zhang, Shijun Li

“…The model leverages GhostNetV2 as its backbone network, replacing standard convolutions with depthwise separable convolutions, resulting in substantial reductions in computational load and memory consumption. Additionally, the C2f-LC module is integrated into the improved model, combining cross-covariance fusion with a lightweight contextual attention mechanism to enhance feature recognition and extraction quality. …”
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Using explainable machine learning and eye-tracking for diagnosing autism spectrum and developmental language disorders in social attention tasks by Adoración Antolí, Adoración Antolí, Francisco Javier Rodriguez-Lozano, José Juan Cañas, Julia Vacas, Julia Vacas, Fátima Cuadrado, Fátima Cuadrado, Araceli Sánchez-Raya, Araceli Sánchez-Raya, Carolina Pérez-Dueñas, Carolina Pérez-Dueñas, Juan Carlos Gámez-Granados

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Evaluation of Variations in Morphology and Morphometry of the Hard Palate Using CBCT Images: A Radio-Surgical Perspective by Melda Pelin Akkitap

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Integrating deep learning and machine learning for improved CKD-related cortical bone assessment in HRpQCT images: A pilot study by Youngjun Lee, Wikum R. Bandara, Sangjun Park, Miran Lee, Choongboem Seo, Sunwoo Yang, Kenneth J. Lim, Sharon M. Moe, Stuart J. Warden, Rachel K. Surowiec

“…Manually annotated cortical bone was used to train each segmentation deep-learning model. Textural features were extracted via Gray-Level Co-occurrence Matrix (GLCM) and classified as CKD or non-CKD using XGBoost with each segmentation model. …”
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Dynamic Rupture Simulation Reproduces Spontaneous Multifault Rupture and Arrest During the 2016 Mw 7.9 Kaikoura Earthquake by Ryosuke Ando, Yoshihiro Kaneko

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