Identification of recurrences in women diagnosed with early invasive breast cancer using routinely collected data in England by Jake Probert, David Dodwell, John Broggio, Robert Coleman, Helen Marshall, Sarah C. Darby, Gurdeep S. Mannu

“…Results The sensitivity of the RCD to detect distant recurrences recorded in the AZURE trial during the ten years following randomisation was 95.6% and its sensitivity to detect any recurrence was 96.6%. …”
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An Automatic Movement Monitoring Method for Group-Housed Pigs by Ziyuan Liang, Aijun Xu, Junhua Ye, Suyin Zhou, Xiaoxing Weng, Sian Bao

“…The instance segmentation model YOLOv8m-seg was applied to detect the presence of pigs. We then applied a spatial moment algorithm to quantitatively summarize each detected pig’s contour as a corresponding center point. …”
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Design of integrated radar and communication system based on solvable chaotic signal by Jun-Liang Yao, Mei-Xin He, Yu-Jie Ma, Yun-Fan Yang, Hui-Ping Yin, Hui Hui

“…Second, the system and frame structures of IRCS using chaos as baseband signal and the corresponding signal processing algorithms are given. Third, the ambiguity function, radar detection errors and communication decoding errors of the proposed scheme are analyzed. …”
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Dose distribution comparison using image registration principles by Meryeme Bellahsaouia, Ibtissam Zidouh, Ouadie Kabach, Wafae Chfeq, Assia Arctout, Taher Elkhoukhi, Elmahjoub Chakir

“…This study introduces a novel algorithm based on image registration principles to address these limitations. …”
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Beam Discovery Signal-Based Beam Selection in Millimeter Wave Heterogeneous Networks by Bo Yin, Yanbo Chen, Zufan Zhang, Mengjun Wang, Shaohui Sun

“…This paper proposes a mmWave massive MIMO beam selection algorithm based on the beam discovery signal (BDS), where each beam transmitted by the mmWave access point is identified by a specific BDS, and the best beam for the user equipment is then determined by detecting the BDS. …”
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Swarm learning network for privacy-preserving and collaborative deep learning assisted diagnosis of fracture: a multi-center diagnostic study by Yi Xie, Yi Xie, Xinmeng Wang, Huiwen Yang, Huiwen Yang, Jiayao Zhang, Honglin Wang, Zineng Yan, Jiaming Yang, Zhiyuan Yan, Zhiwei Hao, Pengran Liu, Yijie Kuang, Zhewei Ye, Zhewei Ye

“…An explainable object detection algorithm was proposed for the identification of fractures. …”
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Newborn Screening for Metachromatic Leukodystrophy in Tuscany: The Paradigm of a Successful Preventive Medicine Program by Sabrina Malvagia, Alessandra Bettiol, Margherita Porcaro, Massimo Mura, Silvia Funghini, Daniela Ombrone, Giulia Forni, Emanuela Scolamiero, Filippo Coppi, Roberta Damiano, Cristina Cereda, Simonetta Simonetti, Annalisa Lonetti, Marta Daniotti, Anna Caciotti, Amelia Morrone, Valeria Calbi, Francesca Fumagalli, Alessandro Aiuti, Elena Procopio, Renzo Guerrini, Giancarlo la Marca

“…Retrospective analysis of eight neonatal and fifteen non-neonatal DBS from patients with genetically confirmed MLD showed that the algorithm accurately identified MLD patients. This diagnostic algorithm proved feasible and accurate for early detection of MLD in prospective NBS.…”
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Development and validation of a carotid plaque risk prediction model for coal miners by Yi-Chun Li, Yi-Chun Li, Yi-Chun Li, Tie-Ru Zhang, Tie-Ru Zhang, Tie-Ru Zhang, Fan Zhang, Fan Zhang, Fan Zhang, Chao-Qun Cui, Chao-Qun Cui, Chao-Qun Cui, Yu-Tong Yang, Yu-Tong Yang, Yu-Tong Yang, Jian-Guang Hao, Jian-Ru Wang, Jiao Wu, Hai-Wang Gao, Ying-Bo Liu, Ming-Zhong Luo, Li-Jian Lei, Li-Jian Lei, Li-Jian Lei

“…The area under the curve (AUC), sensitivity, and specificity of the model constructed based on the XGBoost algorithm were 0.846, 0.867, and 0.702, respectively.ConclusionsIt is possible to predict the presence of carotid plaque using machine learning. …”
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Análisis y modelado de una estructura sensorial ultrasónica MIMO basado en M-CSS y técnicas de correlación by Alberto Ochoa, Jesús Ureña, Álvaro Hernández, Apolinar González, Walter Mata, Ramón A. Félix

“…Once the system model of ultrasonic transmission MIMO is developed and correlation algorithms are implemented for the detection of macro-sequences; the mathematical model, the results obtained in the simulation as well as experimental evidence are presented in this paper. …”
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A Two-Phase Embedding Approach for Secure Distributed Steganography by Kamil Woźniak, Marek R. Ogiela, Lidia Ogiela

“…Additionally, robustness tests with StegoExpose and Aletheia confirm the algorithm’s stealthiness, as no detections are made by these advanced steganalysis tools. …”
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Circulating Tumor Cells Count and Morphological Features in Breast, Colorectal and Prostate Cancer. by Sjoerd T Ligthart, Frank A W Coumans, Francois-Clement Bidard, Lieke H J Simkens, Cornelis J A Punt, Marco R de Groot, Gerhardt Attard, Johann S de Bono, Jean-Yves Pierga, Leon W M M Terstappen

“…In one patient with benign tumor 2 and another 9 aCTC were detected.<h4>Significance of the study</h4>CTC can be identified and morphological features extracted by an algorithm on images stored by the CellSearch system and strongly correlate with clinical outcome in metastatic breast, colorectal and prostate cancer.…”
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Research on the Integration of Sensing and Communication Based on Fractional-Order Fourier Transform by Mingyan Qi, Yuelong Su, Zhaoyi Wang, Kun Lu

“…Second, the three search algorithms and the performance of the integrated sensing and communication waveform are simulated. …”
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A Study of Deep Learning Models for Audio Classification of Infant Crying in a Baby Monitoring System by Denisa Maria Herlea, Bogdan Iancu, Eugen-Richard Ardelean

“…This study investigates the ability of well-known deep learning models, such as ResNet and EfficientNet, to perform audio-based infant cry detection. By comparing the performance of different machine learning algorithms, this study seeks to determine the most effective approach for the detection of infant crying, enhancing the functionality of baby monitoring systems and contributing to a more advanced understanding of audio-based deep learning applications. …”
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Failure Management Overview in Optical Networks by Sergio Cruzes

“…This study surveys ML techniques for early-warning and failure prediction, failure detection, identification, localization, magnitude estimation, and soft failure detection and prediction. …”
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Machine vision approach for monitoring and quantifying fish school migration by Feng Lin, Jicheng Zhu, Aiju You, Lei Hua

“…Evaluation on an extensive fish migration dataset demonstrates that DVE-YOLO outperforms YOLOv8 and other mainstream detection algorithms, showcasing superior detection accuracy with higher AP50 and AP50−95 metrics. …”
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An automated approach to identify sarcasm in low-resource language. by Shumaila Khan, Iqbal Qasim, Wahab Khan, Aurangzeb Khan, Javed Ali Khan, Ayman Qahmash, Yazeed Yasin Ghadi

“…Sarcasm detection has emerged due to its applicability in natural language processing (NLP) but lacks substantial exploration in low-resource languages like Urdu, Arabic, Pashto, and Roman-Urdu. …”
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Efficient IDS for IoT Networks Using Host-Based Data Aggregation and Multi-Entropy Analysis by Yusei Katsura, Arata Endo, Ismail Arai, Kazutoshi Fujikawa

“…This enables the reduction of computational resources during detection processing while maintaining detection accuracy, even when using fewer features and lightweight machine learning algorithms. …”
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Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipel... by Nalinda D. Liyanagedera, Corinne A. Bareham, Heather Kempton, Hans W. Guesgen

“…While testing different feature extraction algorithms, a common neural network structure was used as the classification algorithm to compare the performance of the feature extraction algorithms. …”
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Removing Cardiac Artifacts From Single-Channel Respiratory Electromyograms by Eike Petersen, Julia Sauer, Jan GraBhoff, Philipp Rostalski

“…No algorithm completely removes the cardiac interference, but the characteristic errors introduced by the considered algorithms differ. …”
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A VLBI Software Correlator for Fast Radio Transients by Calvin Leung, Shion Andrew, Kiyoshi W. Masui, Charanjot Brar, Tomas Cassanelli, Shami Chatterjee, Victoria Kaspi, Kholoud Khairy, Adam E. Lanman, Mattias Lazda, Juan Mena-Parra, Gavin Noble, Aaron B. Pearlman, Mubdi Rahman, Pranav Sanghavi, Vishwangi Shah, (CHIME/FRB Collaboration)

“…Our correlation algorithm recovers this sensitivity on both simulated and real data. …”
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