Implementation of Machine Learning in Flat Die Extrusion of Polymers by Nickolas D. Polychronopoulos, Ioannis Sarris, John Vlachopoulos

“…This ML-based methodology has the potential to reduce or even eliminate the use of trial and error procedures.…”
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CapSurv: Capsule Network for Survival Analysis With Whole Slide Pathological Images by Bo Tang, Ao Li, Bin Li, Minghui Wang

“…A novel capsule network named CapSurv is introduced in this paper, with a new loss function named survival loss to make survival analysis with whole slide pathological images. In addition, to train CapSurv preferably, semantic-level features extracted by VGG16, are used to distinguish discriminative patches from whole slide pathological images. …”
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Detecting Adversarial Examples Using Cross-Modal Semantic Embeddings From Images and Text by Sohee Park, Gwonsang Ryu, Daeseon Choi

“…We employed image and text encoders trained to maximize the similarity between the embeddings extracted from images and labels, optimized these cross-modal semantic embeddings and mapped them to a common semantic space to extract detailed semantic features. …”
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Volumetric evolution of supraglacial lakes in southwestern Greenland using ICESat-2 and Sentinel-2 by T. Feng, T. Feng, X. Ma, X. Ma, X. Liu, X. Liu, X. Liu

“…First, the area of SGLs is extracted using a random forest (RF) model based on spectral features from Sentinel-2 imagery, achieving an intersection over union (IoU) of 90.20 % compared to manually delineated lake extents. …”
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A fully automated, expert-perceptive image quality assessment system for whole-body [18F]FDG PET/CT by Cong Zhang, Xin Gao, Xuebin Zheng, Jun Xie, Gang Feng, Yunchao Bao, Pengchen Gu, Chuan He, Ruimin Wang, Jiahe Tian

“…Abstract Background The quality of clinical PET/CT images is critical for both accurate diagnosis and image-based research. However, current image quality assessment (IQA) methods predominantly rely on handcrafted features and region-specific analyses, thereby limiting automation in whole-body and multicenter evaluations. …”
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Predicting pediatric patient rehabilitation outcomes after spinal deformity surgery with artificial intelligence by Wenqi Shi, Felipe O. Giuste, Yuanda Zhu, Ben J. Tamo, Micky C. Nnamdi, Andrew Hornback, Ashley M. Carpenter, Coleman Hilton, Henry J. Iwinski, J. Michael Wattenbarger, May D. Wang

“…In total, 171 pre-operative clinical features are used to train six machine-learning models for post-operative outcomes prediction. …”
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An Intrusion Detection System over the IoT Data Streams Using eXplainable Artificial Intelligence (XAI) by Adel Alabbadi, Fuad Bajaber

“…DL models are needed to train enormous amounts of data and produce promising results. …”
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Using high angular resolution diffusion imaging data to discriminate cortical regions. by Zoltan Nagy, Daniel C Alexander, David L Thomas, Nikolaus Weiskopf, Martin I Sereno

“…We then derived several features of the HARDI profile with respect to the local cortical normal direction, as well as several fully orientationally invariant features. …”
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Quantifying Lenition as a Diagnostic Marker for Parkinson’s Disease and Atypical Parkinsonism by Ratree Wayland, Rachel Meyer, Ruhi Reddy, Kevin Tang, Karen W. Hegland

“…<b>Methods:</b> Using Phonet, a machine learning model trained to detect phonological features, we analyzed the posterior probabilities of continuant and sonorant features from the speech of 142 participants (108 PD, 34 APD). …”
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Network Pseudohealth Information Recognition Model: An Integrated Architecture of Latent Dirichlet Allocation and Data Block Update by Jie Zhang, Pingping Sun, Feng Zhao, Qianru Guo, Yue Zou

“…The research results show that (1) the LDA model can deeply mine the semantic information of network pseudohealth information, obtain the features of document-topic distribution, and classify and train topic features as input variables; (2) the dataset partitioning method can effectively block data according to the text attributes and class labels of network pseudohealth information and can accurately classify and integrate the block data through the data block reintegration method; and (3) considering that the combination model has certain limitations on the detection of network pseudohealth information, the support vector machine (SVM) model can extract the granularity content of data blocks in pseudohealth information in real time, thus greatly improving the recognition performance of the combination model.…”
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Automatic Liver segmentation Using Vector Field Convolution and Artificial Neural Network in MRI Images by Hassan Masoumi, Ahad Salimi, Hamidreza Sadeghi Madavani

“…By convolving the edge map generated from the image with the user-defined vector field kernel, VFC is calculated. We use trained neural networks to extract some features from liver region. …”
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Integrating Landsat, Sentinel-2 and Sentinel-1 time series for mapping intermediate crops by Kassandra Jensch, Gohar Ghazaryan, Stefan Ernst, Patrick Hostert, Claas Nendel

“…The best classification was based on a combination of all input features and achieved an overall accuracy of 92.9%. …”
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Augmented Envelope Neural Networks on RF-SoC for Digital Self-Interference Cancellation by Udara De Silva, Hiruni Silva, Arjuna Madanayake

“…These networks model non-linear artifacts arising from both the transmit power amplifier and the receiver&#x2019;s low-noise amplifier. Trained model parameters are subsequently applied in real-time via a neural network-based digital signal processor to mitigate self-interference. …”
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Estimating a User's Internal State before the First Input Utterance by Yuya Chiba, Akinori Ito

“…Finally, we conducted a discrimination experiment with the trained user model using combined features. As a three-class discrimination result, we obtained about 85% accuracy in an open test.…”
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Empowering entity synonym set generation using flexible perceptual field and multi-layer contextual information. by Subin Huang, Daoyu Li, Chengzhen Yu, Junjie Chen, Qing Zhou, Sanmin Liu

“…Within the classifier, the approach builds a three-layer interactive network, and connects the entity layer, set layer, and sentence layer to the same embedding space to extract synonym features. Secondly, we introduce a dynamic-weight-based algorithm for synthesizing entity synonym sets, leveraging a neural network classifier trained to generate entity synonym sets from the candidate entity vocabulary. …”
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Vivid Face Perception as a Constructive Component of Multimodal Affective States by V.A. Barabanschikov, E.V. Suvorova

“… <p>The features of expression and perception of vivid facial expressions in the system of multimodal affective states of a person are studied. …”
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Adjusting Xception Neural Network by Improved Human Evolutionary Optimizer for Fine Art Sorting by Tianfu Liu

“…In this paper, a new approach is presented based on the combination of the Xception Neural Network and an Improved version of Human Evolutionary Optimizer (IHEO) to overcome these limitations. …”
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Performance Evaluation of Hybrid Machine Learning Algorithms for Online Lending Credit Risk Prediction by Tesfahun Berhane, Tamiru Melese, Abdu Mohammed Seid

“…The experimental results show that the hybrid CNN-kNN model outperforms the CNN-GBDT and CNN-LR models based on the performance metrics accuracy, recall, F1-score, and area under the curve for both all input and important features. …”
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Symbiotic star candidates in Gaia Data Release 3 by Samantha E. Ball, Benjamin C Bromley, Scott J. Kenyon

“…A machine-learning algorithm, trained on known symbiotic stars, produced a new catalog of 1,674 sources. …”
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Prediction of virus-host associations using protein language models and multiple instance learning. by Dan Liu, Francesca Young, Kieran D Lamb, David L Robertson, Ke Yuan

“…The method combines a pre-trained large protein language model (ESM) and attention-based multiple instance learning to allow protein-orientated predictions. …”
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