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–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± 21.62%, 71.25± 19.53%, and 84.09± 12.79% 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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An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form by Zaher Salah, Hamza Abu Owida, Esraa Abu Elsoud, Esraa Alhenawi, Suhaila Abuowaida, Nawaf Alshdaifat

“…Our empirical experiments demonstrates that using ensemble learning to merge attributes in the evolution of phishing emails showcases the competitive performance of ensemble learning over other machine learning algorithms. This superiority is underscored by achieving an F1-score of 0.90 in the weighted ensemble method and 0.85 in the soft voting method, showcasing the effectiveness of this approach.…”
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COMPARATIVE EVALUATION OF INDICATORS OF STRUCTURAL AND MECHANICAL PROPERTIES OF WHEAT GRAIN by P. V. Medvedev, V. A. Fedotov, E. S. Lukyanova

“…The following hard and soft wheat varieties of different districts of the Orenburg region harvest of the last 5 years were studied: 10 Orenburgskaya, 200 Bezenchukskaya, 21 Orenburgskaya, Bezenchuksky amber, 3 Kharkovskaya, 3 Step, 42 Saratovskaya, Uchitel, 13 Orenburgskaya, 3 Yugo-vostochnaya, Varyag, Prokhorovka, L-503.The virtuosity of grain was determined in the traditional way, the hardness of the grain – using the developed fractographic analysis, using computer (technical) vision algorithms to describe the geometric characteristics of the size and shape of the particles of ground grain.The article presents the results of a comparative assessment of the virtuosity and hardness of grain of hard and soft wheat varieties. …”
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TAL-SRX: an intelligent typing evaluation method for KASP primers based on multi-model fusion by Xiaojing Chen, Xiaojing Chen, Jingchao Fan, Jingchao Fan, Shen Yan, Longyu Huang, Longyu Huang, Longyu Huang, Guomin Zhou, Guomin Zhou, Jianhua Zhang, Jianhua Zhang

“…Finally, the two machine learning algorithms are fused through a soft voting integration strategy to output the KASP marker typing effect scores. …”
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Study on the inversion and spatiotemporal variation mechanism of soil salinization at multiple depths in typical oases in arid areas: A case study of Wei-Ku Oasis by Jinming Zhang, Jianli Ding, Zihan Zhang, Jinjie Wang, Xu Zeng, Xiangyu Ge

“…Taking the Wei-Ku Oasis, a typical arid region oasis, as an example, this study uses Landsat remote sensing imagery as the data source, incorporating soil salinity field measurements over a decade, employing the Bootstrap Soft Shrinkage(BOSS) algorithm to select feature variables, and building soil salinity inversion models at various depths through a Convolutional Neural Networks and Long Short-Term Memory networks (CNN-LSTM) framework. …”
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Integrating Machine Learning and Deep Learning for Predicting Non-Surgical Root Canal Treatment Outcomes Using Two-Dimensional Periapical Radiographs by Catalina Bennasar, Antonio Nadal-Martínez, Sebastiana Arroyo, Yolanda Gonzalez-Cid, Ángel Arturo López-González, Pedro Juan Tárraga

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Relationship of transforming growth factor β1 with diabetic retinopathy in type 2 diabetes by A.S. Hudz, V.A. Serhiyenko, I.V. Kudryl, V.G. Guryanov, M.I. Kovtun, S.V. Ziablitsev

“…Using the genetic selection algorithm, 3 features were identified that were associated with DR: diabetes compensation and TGF-β1 content in blood and IOF. …”
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Are Electromyography Data a Fingerprint for Patients with Cerebral Palsy (CP)? by Mehrdad Davoudi, Firooz Salami, Robert Reisig, Dimitrios A. Patikas, Nicholas A. Beckmann, Katharina S. Gather, Sebastian I. Wolf

“…The patients underwent at least one soft tissue surgery on their shank and foot muscles. …”
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Proposing a New Classification for Managing Prostaglandin-Induced Enophthalmos in Glaucoma Patients by Ferraro V, Gaeta A, Barone G, Confalonieri F, Tredici C, Vinciguerra P, Di Maria A

“…The underlying mechanism is believed to involve adipocyte apoptosis, altered lipid metabolism, and changes in soft tissue dynamics. Currently, there is no standardized grading for enophthalmos, complicating its management. …”
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FPGA Implementation for 24.576-Gbit/s Optical PAM4 Signal Transmission with MLP-Based Digital Pre-Distortion by Sheng Hu, Tianqi Zheng, Chengzhen Bian, Xiongwei Yang, Xinda Sun, Zonghui Zhu, Yumeng Gou, Yuanxiao Meng, Jie Zhang, Jingtao Ge, Yichen Li, Kaihui Wang

“…Lookup table (LUT) algorithms are commonly employed for pre-distortion in intensity modulation and direct detection (IM/DD) systems. …”
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Geoinformatics and Machine Learning for Shoreline Change Monitoring: A 35-Year Analysis of Coastal Erosion in the Upper Gulf of Thailand by Chakrit Chawalit, Wuttichai Boonpook, Asamaporn Sitthi, Kritanai Torsri, Daroonwan Kamthonkiat, Yumin Tan, Apised Suwansaard, Attawut Nardkulpat

“…This study analyzes 35 years (1988–2023) of shoreline changes using geoinformatics, machine learning algorithms (Random Forest, Support Vector Machine, Maximum Likelihood, Minimum Distance), and the Digital Shoreline Analysis System (DSAS). …”
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LR-FHSS Transceiver for Direct-to-Satellite IoT Communications: Design, Implementation, and Verification by Sooyeob Jung, Seongah Jeong, Jinkyu Kang, Gyeongrae Im, Sangjae Lee, Mi-Kyung Oh, Joon Gyu Ryu, Joonhyuk Kang

“…Moreover, we apply a robust synchronization scheme against the Doppler effect and co-channel interference (CCI) caused by LEO satellite channel environments, including signal detection for the simultaneous reception of numerous frequency hopping signals and an enhanced soft-output-Viterbi-algorithm (SOVA) for the header and payload receptions. …”
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Comparison of the effectiveness of various conservative treatment options for trophic ulcers and varicose eczema by E. V. Ivanov, E. P. Burleva

“…Based on the obtained data, therapeutic algorithms were proposed for the management of patients with venous TU and VE in outpatient settings.Conclusions. …”
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