Gait Perception via Actual and Estimated Pneumatic Physical Reservoir Output by Junyi Shen, Tetsuro Miyazaki, Swaninda Ghosh, Toshihiro Kawase, Kenji Kawashima

“…This enhanced clustering performance is subsequently leveraged in gait perception by incorporating Takagi–Sugeno fuzzy logic for joint angle estimation and a softmax activation function for walking condition recognition. …”
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An interaction relational inference method for a coal-mining equipment system by Xiangang Cao, Jiajun Gao, Xin Yang, Fuyuan Zhao, Boyang Cheng

“…The interaction constructor of the CIRI interaction inference model in this method introduces Gumbel-softmax technology, which autonomously generates multiple types of interaction relations based on several probability matrices. …”
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Dual Generative Network with Discriminative Information for Generalized Zero-Shot Learning by Tingting Xu, Ye Zhao, Xueliang Liu

“…Specifically, the model uses the discrimination information of visual features, according to the relevant semantic embedding, synthesizes the visual features of unseen categories by using the learned generator, and then trains the final softmax classifier by using the generated visual features, thus realizing the recognition of unseen categories. …”
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Adolescent and adult mice use both incremental reinforcement learning and short term memory when learning concurrent stimulus-action associations. by Juliana Chase, Liyu Xia, Lung-Hao Tai, Wan Chen Lin, Anne G E Collins, Linda Wilbrecht

“…Adolescent and adult mice also showed comparable performance, with no change in learning rate or softmax beta parameters with adolescent development and task experience. …”
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Unsupervised and Semisupervised Machine Learning Frameworks for Multiclass Tool Wear Recognition by Maryam Assafo, Peter Langendoerfer

“…The underlying methods include Laplacian score, sparse autoencoder (SAE), stacked SAE (SSAE), self-organizing map, Softmax, support vector machine, and random forest. …”
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Advanced retinal disease detection from OCT images using a hybrid squeeze and excitation enhanced model. by Gülcan Gencer, Kerem Gencer

“…<h4>Results</h4>The combined features from EfficientNetB0 and Xception are processed via fully connected layers and categorized using the Softmax algorithm. The methodology was tested on UCSD and Duke's OCT datasets and produced excellent results. …”
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Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets by Dinesh Chellappan, Harikumar Rajaguru

“…Evaluated the performance of a system by using the following classifiers as Non-Linear Regression—NLR, Linear Regression—LR, Gaussian Mixture Model—GMM, Expectation Maximization—EM, Bayesian Linear Discriminant Analysis—BLDA, Softmax Discriminant Classifier—SDC, and Support Vector Machine with Radial Basis Function kernel—SVM-RBF classifier on two publicly available datasets namely the Nordic Islet Transplant Program (NITP) and the PIMA Indian Diabetes Dataset (PIDD). …”
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Zaposlovanje vrhunskih športnikov v javni upravi na preizkušnji by Darko Repenšek

“…Članek ob pomenu vrhunskega športa za državo predstavlja pravne dileme in težave pri dosedanji realizaciji sporazuma in skozi proučevanje zaposlovanja vrhunskih športnikov v državni upravi nakaže potrebne rešitve, ki bi nedvomno dobremu ukrepu podpore države vrhunskemu športu dal stabilno in trajno sistemsko rešitev, ki ne bi bila odvisna od volje aktualne vlade ali ekonomskih razmer v državi. …”
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Multiscale wildfire and smoke detection in complex drone forest environments based on YOLOv8 by Wenyu Zhu, Shanwei Niu, Jixiang Yue, Yangli Zhou

“…This module combines Softmax and linear attention to optimize feature extraction, improving the model’s accuracy and robustness. …”
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Automatic Sleep Stage Classification Based on Convolutional Neural Network and Fine-Grained Segments by Zhihong Cui, Xiangwei Zheng, Xuexiao Shao, Lizhen Cui

“…Finally, the results from the full-connected layer of each segment in the input time sequence are put into the softmax classifier together to get a single most likely sleep stage. …”
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Topic-aware neural attention network for malicious social media spam detection by Maged Nasser, Faisal Saeed, Aminu Da’u, Abdulaziz Alblwi, Mohammed Al-Sarem

“…Second, to further learn the contextualized features of texts, the model was further integrated with the BERT technique. Last, the Softmax function was then applied at the output layer for the final spam classification. …”
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Infrared thermography based fault diagnosis of diesel engines using convolutional neural network and image enhancement by Wang Rongcai, Yan Hao, Dong Enzhi, Cheng Zhonghua, Li Yuan, Jia Xisheng

“…The proposed method involves conducting adaptive histogram equalization for image enhancement, followed by employing Softmax regression for pattern recognition. Finally, two sets of self-made experimental data are used to investigate the impact of temperature variations on fault diagnosis performance and to validate the efficacy of the proposed method in comparison with three DL methods. …”
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Semantic Segmentation Method for High-Resolution Tomato Seedling Point Clouds Based on Sparse Convolution by Shizhao Li, Zhichao Yan, Boxiang Ma, Shaoru Guo, Hongxia Song

“…Finally, to solve model training class bias caused by the uneven distribution of point cloud classes, the composite loss function of Lovász-Softmax and weighted cross-entropy is introduced to supervise the model training and improve its performance. …”
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Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images by Miao Wu, Chuanbo Yan, Gan Sen

“…The proven classifier models, k - nearest neighbor (KNN) and support vecter machine (SVM) models, are integrated to classify the extracted deep CNN features. 3 distinct experiments with the same deep CNN features but different classifier models (softmax, KNN, SVM) are performed. The experiments followed 10 runs of the five-fold cross-validation process on a total of 1820 ultrasound images and the results were compared using Wilcoxon signed-rank test. …”
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Advanced sleep disorder detection using multi-layered ensemble learning and advanced data balancing techniques by Muhammad Mostafa Monowar, S. M. Nuruzzaman Nobel, Maharin Afroj, Md Abdul Hamid, Md Zia Uddin, Md Mohsin Kabir, M. F. Mridha

“…Techniques such as thresholding, predictive scoring, and the conversion of Softmax labels into multidimensional feature vectors improve interpretability. …”
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Violence Detection From Industrial Surveillance Videos Using Deep Learning by Hamza Khan, Xiaohong Yuan, Letu Qingge, Kaushik Roy

“…Unlike traditional methods that process all frames indiscriminately, this targeted filtering mechanism allows computational resources to be allocated more effectively. Next, SoftMax classifier processes the extracted features to categorize frame sequences as violent or non-violent. …”
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Mer enn konsepter: Utforsking av sammenheng mellom lærerstudenters praksiserfaringer og lærerforventninger med metrisk og ikke-metrisk analyse by Sigve Høgheim, Eirik S. Jenssen

“… Denne studien undersøker om tradisjonelle metoder for å analysere tallmateriale i utdanningsforskning, som måling, signifikanstesting og bruk av aggregerte estimater, er nødvendige for meningsfull tallanalyse. Vi stiller spørsmål ved om disse metodene, som ikke tester grunnleggende empiriske antakelser og ikke gir innsikt om personer eller teorier, virkelig er fordelaktige. …”
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Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases by Mosleh Hmoud Al-Adhaileh, Ebrahim Mohammed Senan, Waselallah Alsaade, Theyazn H. H Aldhyani, Nizar Alsharif, Ahmed Abdullah Alqarni, M. Irfan Uddin, Mohammed Y. Alzahrani, Elham D. Alzain, Mukti E. Jadhav

“…In the classification stage, pretrained convolutional neural network (CNN) models are tuned by transferring learning to perform new tasks. The softmax activation function receives the deep feature vector and classifies the input images into five classes. …”
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Intensive home visiting for adolescent mothers in the Family Nurse Partnership in England 2010–2019: a population-based data linkage cohort study using propensity score matching by Ruth Gilbert, Jan van der Meulen, Katie Harron, Eilis Kennedy, Sally Kendall, Francesca Cavallaro

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“I can see a lady with a curly brown hair” - A Corpus-Based Investigation of Article Use in the Language of Young Norwegian EFL Learners by Sofie Larsen, Kristian A. Rusten

“…Våre kvantitative data fører til at vi tillater oss å stille spørsmål ved Bækkens påstand om at norske elever med engelsk som fremmedspråk har spesielle problemer med overbruk av bestemt artikkel og utelatelse av ubestemt artikkel. …”
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