Comparative analysis of single-view and multiple-view data collection strategies for detecting partially-occluded grape bunches: Field trials by Mar Ariza-Sentís, Hilmy Baja, Sergio Vélez, Rick van Essen, João Valente

“…While most research concentrates on data processing algorithms, this study focused on the preceding step: collecting reliable data. …”
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Enhanced Superpixel-Guided ResNet Framework with Optimized Deep-Weighted Averaging-Based Feature Fusion for Lung Cancer Detection in Histopathological Images by Karthikeyan Shanmugam, Harikumar Rajaguru

“…<b>Methods:</b> The study begins with image preprocessing using an adaptive fuzzy filter, followed by segmentation with a modified simple linear iterative clustering (SLIC) algorithm. The segmented images are input into deep learning architectures, specifically ResNet-50 (RN-50), ResNet-101 (RN-101), and ResNet-152 (RN-152), for feature extraction. …”
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Estimating Complex Signals With a Fuzzy-Enhanced Kalman Filter: A Note on &#x201C;the Output Regulation and the Kalman Filter as the Signal Generator&#x201D; by Jesus Alberto Meda-Campana, Juan Carlos Garcia-Hernandez, Rodolfo Daniel Velazquez-Sanchez, Luis Alberto Paramo-Carranza, Tonatiuh Hernandez-Cortes, Ricardo Tapia-Herrera

“…Furthermore, the result shows that incorporating simple fuzzy logic concepts may substantially enhance the performance of some established algorithms.…”
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Meat analogues: The relationship between mechanical anisotropy, macrostructure, and microstructure by Miek Schlangen, Iris van der Doef, Atze Jan van der Goot, Mathias P. Clausen, Thomas E. Kodger

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Machine learning-based prediction of postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy by Qianchang Wang, Zhe Wang, Fangfeng Liu, Zhengjian Wang, Qingqiang Ni, Hong Chang

“…Multivariate analysis identified soft pancreatic texture as an independent predictor (OR = 4.99, 95% CI: 1.93–12.86). …”
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Comparison of dose calculation approaches and clinical dose–response in image-guided head&neck brachytherapy of the oral cavity by Sabrina Schaller, Vratislav Strnad, Claudia Schweizer, Dorota Lubgan, Ricarda Merten, Rainer Fietkau, Christoph Bert, Andre Karius

“…Purpose: Model-based dose calculation algorithms (MBDCAs) are increasingly applied in brachytherapy, but their considerations in dose–response analyses is still lacking. …”
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MODERN APPROACHES TO CLINICAL AND LABORATORY DIAGNOSTICS OF RHEUMATOID ARTHRITIS EARLY ONSET by D. G. Rekalov, S. Y. Dotsenko, A. V. Kylinich

“…The data on the sensitivity and specificity of the diagnostic classification and clinical criteria of eRA and an algorithm for the identification of the disease were presented. …”
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Comparative Analysis of Computed Tomography and Laparoscopy Data in Preoperative Staging of Gastric Cancer by N. K. Silanteva, T. A. Аgababyan, A. A. Kholeva, V. Yu. Skoropad, Z. N. Shavladze, S. A. Ivanov, L. P. Zhavoronkov

“…The combined use of CT and DL is a current algorithm for examining patients with gastric cancer. …”
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Plasma cell tumors of the head and neck. Analysis of diagnostic difficulties based on literature and own data by F. Sh. Kamolova, L. Yu. Grivtsova, S. M. Samborskiy, V. B. Larionova, Yu. E. Ryabukhina, P. A. Zeynalova, A. M. Mudunov

“…Extramedullary plasmacytoma (EP) of soft tissues, which in most cases affects the organs of the head and neck region, is a relatively rare malignant tumor. …”
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Non-Destructive Early Detection of Drosophila Suzukii Infestation in Sweet Cherries (c.v. <i>Sweet Heart</i>) Based on Innovative Management of Spectrophotometric Multilinear Corre... by Giuseppe Altieri, Mahdi Rashvand Avaei, Attilio Matera, Francesco Genovese, Vincenzo Verrastro, Naouel Admane, Orkhan Mammadov, Sabina Laveglia, Giovanni Carlo Di Renzo

“…The identified procedure of management of regression algorithms allowed the selection of a very performant and robust model using the partial least squares regression algorithm: its false negative rate and false positive rate, after 500 Monte Carlo runs, were 0.004% +/− 0.003 and 0.02% +/− 0.01, respectively, and, in addition, the 50% of samples were used for the external cross-validation set.…”
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Research on the optimization of optimal blasting parameters and fragmentation control based on coal seam geological conditions by Zhouquan Liao, Xiaohua Ding, Mingzhi Liang, Jiangfeng Li, Zhongao Yang, Xin Liu, Bin Li, Yan Zhang

“…Using the LS-DYNA software’s fluid–solid coupling algorithm, the effects of charge structure, explosive type, intermediate medium, and hole spacing parameters on blasting results are analyzed. …”
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Optimizing DD-SIMCA modeling for accurate classification of rice varieties via raman spectroscopy by Somaye Vali Zade, Hamed Sahebi, Adel Mirza Alizadeh, Behrooz Jannat, Hossein Rastegar, Solmaz Abedinzadeh, Fataneh Hashempour-Baltork, Amin Mousavi Khaneghah

“…The rice samples were then separated into training and validation sets using the Kennard-Stone (70–30) algorithm. The smoothing and differential pretreatment algorithms were applied to prepare the spectra for analysis. …”
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Research on Apple Recognition and Localization Method Based on Deep Learning by Zhipeng Zhao, Chengkai Yin, Ziliang Guo, Jian Zhang, Qing Chen, Ziyuan Gu

“…Secondly, the CBAM attention mechanism is introduced to improve the network’s attention to the target region of interest of the input image. Finally, the Soft-NMS algorithm is adopted, which can effectively prevent high-density overlapping targets from being suppressed at one time and thus prevent the occurrence of missed detection. …”
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Improving Performance of Motor Imagery-Based Brain&#x2013;Computer Interface in Poorly Performing Subjects Using a Hybrid-Imagery Method Utilizing Combined Motor and Somatosensory... by Sangin Park, Jihyeon Ha, Laehyun Kim

“…Twenty healthy subjects participated in these experiments involving the following three paradigms: (1) Control-condition: motor imagery only, (2) Hybrid-condition I: combined motor and somatosensory stimuli (same stimulus: rough ball), and (3) Hybrid-condition II: combined motor and somatosensory stimuli (different stimulus: hard and rough, soft and smooth, and hard and rough ball). The three paradigms for all participants, achieved an average accuracy of 63.60&#x00B1; 21.62&#x0025;, 71.25&#x00B1; 19.53&#x0025;, and 84.09&#x00B1; 12.79&#x0025; using the filter bank common spatial pattern algorithm (5-fold cross-validation), respectively. …”
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Thermal-Aware Test Schedule and TAM Co-Optimization for Three-Dimensional IC by Chi-Jih Shih, Chih-Yao Hsu, Chun-Yi Kuo, James Li, Jiann-Chyi Rau, Krishnendu Chakrabarty

“…We used both greedy and simulated annealing algorithms to solve this optimization problem. We compare the results of two assumptions: soft-die mode and hard-die mode. …”
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Analysis of anomalous behaviour in network systems using deep reinforcement learning with convolutional neural network architecture by Mohammad Hossein Modirrousta, Parisa Forghani Arani, Reza Kazemi, Mahdi Aliyari‐Shoorehdeli

“…Deep Q‐Learning (DQL), Soft DQL, Double DQL, and Soft double DQL are examined from two perspectives. …”
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A Review of Control Strategies for Four-Switch Buck–Boost Converters by Guanzheng Lin, Yan Li, Zhaoyun Zhang

“…For control strategies based on controlling the inductor current to achieve soft-switching, the discussion mainly focuses on optimizing the implementation of soft-switching, reducing overall system losses, and improving the computation speed. …”
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Interdisciplinary management of skull base surgery by R. Rothweiler, M.C. Metzger, P.J. Voss, J. Beck, R. Schmelzeisen

“…Current techniques and new approaches will be discussed with emphasize on hard and soft tissue reconstruction.…”
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Efficient identification of maximum independent sets in stochastic multilayer graphs with learning automata by Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi, Alireza Rezvanian

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Developing and validating a machine learning-based model for predicting in-hospital mortality among ICU-admitted heart failure patients: A study utilizing the MIMIC-III database by De Su, Jie Zheng, Yue-kai Shao, Jun-ya Liu, Xin-xin Liu, Kun Yu, Bang-hai Feng, Hong Mei, Song Qin

“…Results In five-fold cross-validation, the soft voting ensemble learning model demonstrated the best overall performance, with accuracy and AUC values both at 0.86. …”
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