Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients. by Yulin Lai, Peiyuan Huang

“…<h4>Methods</h4>Machine learning components, including ridge regression, XGBoost, k-nearest neighbor, light gradient boosting machine, logistic regression, support vector machine, neural network, and random forest, were used to construct a predictive model and identify the risk factors for SPMs with data from the Surveillance, Epidemiology and End Results. …”
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Edge-AI Enabled Wearable Device for Non-Invasive Type 1 Diabetes Detection Using ECG Signals by Maria Gragnaniello, Vincenzo Romano Marrazzo, Alessandro Borghese, Luca Maresca, Giovanni Breglio, Michele Riccio

“…A spectrogram-based preprocessing method is combined with a 1-Dimensional Convolutional Neural Network (1D-CNN) to analyze the ECG signals directly on the device. …”
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Optimization of dried garlic physicochemical properties using a self-organizing map and the development of an artificial intelligence prediction model by Hany S. El-Mesery, Mohamed Qenawy, Mona Ali, Merit Rostom, Ahmed Elbeltagi, Ali Salem, Abdallah Elshawadfy Elwakeel

“…The relationships between the input process factors and response factors’ physicochemical properties of dried garlic were optimized by a self-organizing map (SOM), and the model was developed using machine learning. Artificial Neural Network (ANN) with 99% predicting accuracy and Self-Organizing Maps (SOM) with 97% clustering accuracy were used to determine the quality characteristics of garlic. …”
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Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader Clustering by Shuhui Yi, Yinglong Diao, Junjie Liu, Tian Fang, Xiaodong Yin

“…The results indicate that the model proposed can effectively achieve the nonintrusive industrial load identification, and least unified residue (LUR) is about 10−16, which is much better than the factorial hidden Markov model (FHMM) and the artificial neural network (ANN) model.…”
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Screening of multi deep learning-based de novo molecular generation models and their application for specific target molecular generation by Yishu Wang, Mengyao Guo, Xiaomin Chen, Dongmei Ai

“…Moreover, we propose an integrated end-to-end neural network learning framework based on one complete encoder-decoder architecture transformer model: Transfer Text-to-Text Transformer (T5), by learning the embedding vector representation space of conditional molecular properties to encode and guide the vector representation of SMILES sequences, resulting in the output of the final decoder block with a softmax output (maximum likelihood objective). …”
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Cluster analysis of social determinants of health and HIV/AIDS knowledge among Peruvian youths using Kohonen’s self-organized maps: a data-exploration study based on a Demographic... by Alejandro Aybar-Flores, Alvaro Talavera, Elizabeth Espinoza-Portilla

“…Conclusions Kohonen’s neural networks allowed the identification of patterns and behaviors among youths in Peru, quantifying and characterizing the four social clusters regarding HIV/AIDS and their social determinants. …”
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Benchmarking human face similarity using identical twins by Shoaib Meraj Sami, John McCauley, Sobhan Soleymani, Nasser Nasrabadi, Jeremy Dawson

“…The facial similarity measure is determined via a deep convolutional neural network. This network is trained on a tailored verification task designed to encourage the network to group together highly similar face pairs in the embedding space and achieves a test AUC of 0.9799. …”
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A Denoising Based Autoassociative Model for Robust Sensor Monitoring in Nuclear Power Plants by Ahmad Shaheryar, Xu-Cheng Yin, Hong-Wei Hao, Hazrat Ali, Khalid Iqbal

“…Sensors health monitoring is essentially important for reliable functioning of safety-critical chemical and nuclear power plants. Autoassociative neural network (AANN) based empirical sensor models have widely been reported for sensor calibration monitoring. …”
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Soft computing paradigm for climate change adaptation and mitigation in Iran, Pakistan, and Turkey: A systematic review by Muhammad Talha, A. Pouyan Nejadhashemi, Kieron Moller

“…Although some articles utilized multiple techniques, classical ML methods appeared in approximately 37.3 % of the studies, neural network paradigms in about 57.5 %, and optimization or meta-heuristic algorithms in around 5.0 %. …”
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Estimating traffic flow at urban intersections using low occupancy floating vehicle data by Lili Zhang, Kang Yang, Ke Zhang, Wei Wei, Jing Li, Hongxin Tan

“…These estimated flow rates are then refined using the proposed Radial Basis Function (RBF) neural network approximation method to achieve higher accuracy. …”
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An Automatic Emergency Braking Model considering Driver’s Intention Recognition of the Front Vehicle by Wei Yang, Jiajun Liu, Kaixia Zhou, Zhiwei Zhang, Xiaolei Qu

“…Therefore, we propose a driver’s intention recognition model for the front vehicle, which is based on the backpropagation (BP) neural network and hidden Markov model (HMM). The brake pedal, accelerator pedal, and vehicle speed data are used as the input of the proposed BP-HMM model to recognize the driver’s intention, which includes uniform driving, normal braking, and emergency braking. …”
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Fixed-Time Sliding Mode Control for Vehicle Platoon With Input Dead-Zone and Prescribed Performance by Yongqiang Jiang, Yiguang Wang, Xiaojie Li, Xiaoyan Zhan, Xubing Tang

“…Furthermore, Chebyshev neural network (CNN) is adopted to approximate unknown nonlinearities, and new adaptive mechanisms are designed to estimate IDZ slope and external disturbances. …”
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Machine learning prediction of combat basic training injury from 3D body shape images. by Steven Morse, Kevin Talty, Patrick Kuiper, Michael Scioletti, Steven B Heymsfield, Richard L Atkinson, Diana M Thomas

“…Predictions were made using logistic regression, random forest, and artificial neural network (ANN) models. Model comparison was done using the area under the curve (AUC) of a ROC curve.…”
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Application of Multiattention Mechanism in Power System Branch Parameter Identification by Zhiwei Wang, Liguo Weng, Min Lu, Jun Liu, Lingling Pan

“…To overcome these limitations, we propose a novel multitask Graph Transformer Network (GTN), which combines a graph neural network and a multiattention mechanism to construct our model. …”
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A Joint Network of 3D-2D CNN Feature Hierarchy and Pyramidal Residual Model for Hyperspectral Image Classification by Hongwei Wei, Yufan Wang, Yu Sun, Jianfeng Zheng, Xiaodong Yu

“…Since convolutional neural networks (CNN) can extract deeper features from hyperspectral images, they show good classification performance in the hyperspectral image (HSI) classification task. …”
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Predicting the hub interactome of COVID-19 and oral squamous cell carcinoma: uncovering ALDH-mediated Wnt/β-catenin pathway activation via salivary inflammatory proteins by Pradeep Kumar Yadalam, Deepavalli Arumuganainar, Prabhu Manickam Natarajan, Carlos M. Ardila

“…Hub proteins were identified using Cytoscape and Cytohubba, and machine learning algorithms including naïve Bayes, neural networks, gradient boosting, and random forest were used to predict hub genes. …”
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Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data by Ísak Valsson, Matthew T. Warren, Charlotte M. Deane, Aniket Magarkar, Garrett M. Morris, Philip C. Biggin

“…Here, we address these issues by first introducing a novel attention-based graph neural network model called AEV-PLIG (atomic environment vector–protein ligand interaction graph). …”
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Adaptive CNN Ensemble for Complex Multispectral Image Analysis by Syed Muslim Jameel, Manzoor Ahmed Hashmani, Mobashar Rehman, Arif Budiman

“…Secondly, an adaptive convolutional neural network (CNN) ensemble framework is proposed and evaluated for a new spectral band adaptation. …”
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BucketAugment: Reinforced Domain Generalisation in Abdominal CT Segmentation by David Jozef Hresko, Peter Drotar

“…<italic>Goal:</italic> In recent years, deep neural networks have consistently outperformed previously proposed methods in the domain of medical segmentation. …”
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Design, Sensing and Control of a Robotic Prosthetic Eye for Natural Eye Movement by J. J. Gu, M. Meng, A. Cook, P. X. Liu

“…Theoretical issues on sensor failure detection and recovery, and signal processing techniques used in sensor data fusion, are studied using statistical methods and artificial neural network based techniques. In addition, practical control system design and implementation using micro-controllers are studied and implemented to carry out the natural eye movement detection and artificial robotic eye control tasks. …”
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