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Differentiation of Sinonasal NKT From Diffuse Large B-Cell Lymphoma Using machine learning and MRI-Based Radiomics

JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY 2023年第6期47卷 973-981页

作者： Zhang, Yiyin Lin, Naier Xiao, Hanyu Xin, Enhui Sha, Yan Fudan Univ Eye & ENT Hosp Dept Radiol Shanghai Peoples R China Shanghai United Imaging Intelligence Co Ltd Shanghai Peoples R China Fudan Univ Eye & ENT Hosp Dept Radiol 83 Fenyang Rd Shanghai 200031 Peoples R China

Purpose The aim of this study was to construct and validate a noninvasive radiomics method based on magnetic resonance imaging to differentiate sinonasal extranodal natural killer/T-cell lymphoma from diffuse large B-cell *** We collected magnetic resonance imaging scans, including contrast-enhanced T1-weighted imaging and T2-weighted imaging, from 133 patients with non-Hodgkin lymphoma (103 sinonasal extranodal natural killer/T-cell lymphoma and 30 diffuse large B-cell lymphoma) and randomly split them into training and testing cohorts at a ratio of 7:3. Clinical characteristics and image performance were analyzed to build a logistic regression clinical-image model. The radiomics features were extracted on contrast-enhanced T1-weighted imaging and T2-weighted imaging images. Maximum relevance minimum redundancy, selectKbest, and the least absolute shrinkage and selection operator algorithms (LASSO) were applied for feature selection after balancing the training set. Five machine learning classifiers were used to construct the single and combined sequences radiomics models. Sensitivity, specificity, accuracy, precision, F1score, the area under receiver operating characteristic curve, and the area under precision-recall curve were compared between the 15 models and the clinical-image model. The diagnostic results of the best model were compared with those of 2 *** The combined sequence model using support vector machine proves to be the best, incorporating 7 features and providing the highest values of specificity (0.903), accuracy (0.900), precision (0.727), F1score (0.800), and area under precision-recall curve (0.919) with relatively high sensitivity (0.889) in the testing set, along with a minimum Brier score. The diagnostic results differed significantly (P < 0.05) from those of radiology residents, but not significantly (P > 0.05) from those of experienced *** Magnetic resonance imaging based on machine learnin

关键词： radiomics DLBCL NKT magnetic resonance imaging machine learning algorithm

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machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer

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World Journal of Gastrointestinal Surgery 2024年第6期16卷 1571-1581页

作者： Kai-Feng Yang Sheng-Jie Li Jun Xu Yong-Bin Zheng Department of Gastrointestinal Surgery Renmin Hospital of Wuhan UniversityWuhan 430030Hubei ProvinceChina Department of Gastrointestinal Surgery The First College of Clinical Medical ScienceChina Three Gorges UniversityYichang Central People’s HospitalYichang 443008Hubei ProvinceChina

BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of *** To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May *** were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate *** prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network *** operating characteristic curves and decision curves were used to evaluate the prediction *** Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction *** difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),*** A predictive model combining GLCM image features with machine learning can predict SLM in *** model can assist clinicians in making timely and personalized clinical decisions.

关键词： Colorectal cancer Synchronous liver metastasis Gray-level co-occurrence matrix machine learning algorithm Prediction model

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Neural Degeneration in Normal-Aging Human Cochleas: machine-learning Counts and 3D Mapping in Archival Sections

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JARO-JOURNAL OF THE ASSOCIATION FOR RESEARCH IN OTOLARYNGOLOGY 2023年第5期24卷 499-511页

作者： Wu, Pei-zhe O'Malley, Jennifer T. Liberman, M. Charles Massachusetts Eye & Ear Infirm Eaton Peabody Labs 243 Charles St Boston MA 02114 USA Harvard Med Sch Dept Otolaryngol Boston MA 02115 USA

Quantifying the survival patterns of spiral ganglion cells (SGCs), the cell bodies of auditory-nerve fibers, is critical to studies of sensorineural hearing loss, especially in human temporal bones. The classic method of manual counting is tedious, and, although stereology approaches can be faster, they can only be used to estimate total cell numbers per cochlea. Here, a machine-learning algorithm that automatically identifies, counts, and maps the SGCs in digitized images of semi-serial human temporal-bone sections not only speeds the analysis, with no loss of accuracy, but also allows 3D visualization of the SGCs and fine-grained mapping to cochlear frequency. Applying the algorithm to 62 normal-aging human ears shows significantly faster degeneration of SGCs in the basal than the apical half of the cochlea. Comparison to fiber counts in the same ears shows that the fraction of surviving SGCs lacking a peripheral axon steadily increases with age, reaching more than 50% in the apical cochlea and almost 66% in basal regions.

关键词： machine learning algorithm Spiral ganglion cells Human temporal bone Age-related hearing loss Normal aging

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A machine learning Approach and Methodology for Solar Radiation Assessment Using Multispectral Satellite Images

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Annals of Data Science 2023年第4期10卷 907-932页

作者： Verma, Preeti Patil, Sunil Department of Computer Science Engineering Vedica Institute of Technology RKDF University Bhopal India

In this paper, machine learning based method for the estimation of solar radiation in earth surface is presented. To design the machine learning model, multispectral (visible and infrared) satellite images of the very high-resolution from multiple locations are considered as primary data. The satellite images in visible and infrared bands, altitude, latitude, longitude, month, day, time, solar zenith angle, solar azimuth angle, viewing zenith angle, and viewing azimuth angle are used as input to the machine learning, while the solar radiation is taken as output variable. The paper specifics the entire procedure, including data collection, pre-processing, and feature selection, as well as the selection of the best machine learning algorithm, measurements, and validation. The impact of each input feature in estimating the solar radiation is also analyzed using correlation methods. SOLCAST datasets are used for Carcassonne city in the France. The analysis of correlations provides how variables are connected or linked. The Pearson correlation, Kendall rank correlation, Spearman correlation, and Phi K correlations are used in the present study and useful correlations exist because they allow us to anticipate future behaviour by relating the relevant parameters (such as azimuth angle, cloud capacity, dew point temp, air temp, DHI, DNI, horizontal component of beam radiation (Ebh), GHI, precipitable water, relative humidity, surface pressure, wind direction, wind speed, zenith, albedo daily). From the correlation results, neural network algorithm has been adopted using most relevant parameters to validate the results. Researchers and scientists may use the method to build high-efficiency solar devices like solar power plants and photovoltaic cells. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Solar radiation Solar power plant machine learning algorithm Multispectral satellite images

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Predicting pulse wave age from cardiovascular characteristics using machine learning algorithms

Predicting pulse wave age from cardiovascular characteristic...

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作者： Ruihan Wang University of California

Arterial pulse waves are an essential and informative source of data for measuring cardiovascular health and are currently available on various devices,such as *** addition,most experiments have focused on pulse wave velocity(PWV) and arterial pulse waveform(APW).However,in recent years,due to many factors,such as high work pressure among young people,cardiovascular diseases have become more *** a result,we need more research on pulse age to help patients focus on the consistency of their biological age with their legal ***,this paper introduces a machine learning framework to predict the age of patients according to the HR and SV data in the pulse wave of existing *** the beginning of the experiment,we attempted to use K-nearest neighbours(KNN),logistic regression(LR),and random forest(RF) *** we compared the accuracy of patient age estimates;RF had the best performance and was used as the final *** addition,HR and SV were selected as the main features to predict patients' age according to the feature's *** final experimental results indicate that the RF model can predict the results with up to 1 accuracy only with two features,i.e.,HR and *** particular,this is the point that we need to continue to pay attention to in the future to determine whether the prediction accuracy of this model can reach *** it because the model is overly optimistic or because the selected HR and SV features strongly correlate with age? In the future,we can introduce the concept of actual body age and attract young people's attention to their body *** example,more people will be urged to take a quick and efficient pulse diagnosis,complete a simple physical examination,and give rapid feedback by placing devices in places where young people gather,such as shopping *** this case,users can immediately know what part of their body may have *** general,our experiment demonstrates that patients' pulse wave ages co

关键词： pulse wave age machine learning algorithm cardiovascular characteristics

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Empirical study on understanding online buying behaviour through machine learning algorithms

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Model Assisted Statistics and Applications 2022年第1期17卷 59-68页

作者： Mukherjee, Sayantan Jason, A. Prince Selvakumar, Angeline V.I.T. Bhopal University Bhopal India College of The Rockies Canada

The research study tries to understand teenagers’ online engagement and the behavioral transformation in buying stuff online. The study also tries to ideate the stability of spike in online buying (if any) and its sustainability. Statistical tools like the K-S test, M.L.R. test, Pearson Correlation has been used to justify the study and the usage of machine learning algorithms to construct a predictive model of behaviour and its efficiency. The study will help online retailers understand their sales figures’ stability. It will allow them to strategize their marketing functionalities to make the space more attractive even after the world comes out of the pandemic. The increasing usage of intelligent android devices and relatively cheap data has surged the penetration of online engagements among all the age group peoples. The youngsters are engaging in online stuff hence bringing down a considerable transformation in buying behaviour, pattern, and a collective change in marketers’ approach to strategizing according to the ever-evolving market forces.

关键词： Consumer data analysis COVID-19 online buying behaviour machine learning algorithm teenager consumer behaviour

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Optimizing warehouse logistics scheduling strategy using soft computing and advanced machine learning techniques

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SOFT COMPUTING 2023年第23期27卷 18077-18092页

作者： Li, Kuigang Anyang Normal Univ Sch Econ Anyang 455000 Henan Peoples R China

In recent years, with the improvement of people's living standards, online shopping has become an indispensable part of people's lives. The rapid development of e-commerce has brought unprecedented opportunities to the express delivery industry. Therefore, modern manufacturing enterprises must shorten the cycle from order to delivery to be successful. The study of machine learning (ML), which integrates computer science, statistics, pattern recognition, data mining, and predictive analytics, has become one of the most significant areas of research in the last few decades. It has also established itself as a cornerstone in terms of applications, making significant progress in modern information technology and practice. This paper used the capabilities of one of the powerful paradigms of ML called reinforcement learning (RL) and soft computing to improve the warehouse automation process while taking market demands into account. Since stackers and Automatic Guided Vehicles (AGV) are the main participants in this automation process, we focused on these two in our research to enhance the warehouse logistic scheduling process as a whole. To accomplish this, we collected historical data related to warehouse operation from the warehouse environment, such as AGV and stacker moments, inventory level, job execution time, and other pertinent factors. We first created an RF-based model using the Q-learning technique, one of the RF approaches, before using these data for the model training. The model designing is accomplished by first formulating the logistic scheduling problem as a Markov Decision Process (MDP), where the warehouse system changes between states and takes actions to maximize a cumulative reward over time. After that, we performed a number of operations, including state representation, action space definition, and reward design, to transform the problem into a format that the Q-learning approach can handle. In four experiments, the design model is trained using th

关键词： machine learning algorithm Reinforcement learning Warehousing logistics Scheduling strategy System simulation

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Intelligent prediction and evaluation models for the seismic risk and vulnerability of reinforced concrete girder bridges in large-scale zones

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 256卷

作者： Li, Si-Qi Han, Jia-Cheng Li, Yi-Ru Qin, Peng-Fei Heilongjiang Univ Sch Civil Engn 74 Xuefu Rd Harbin Peoples R China Minist Emergency Management China Earthquake Adm Inst Engn Mech Key Lab Earthquake Engn & Engn Vibrat Beijing Peoples R China

The prediction of the seismic risk and vulnerability of bridge clusters can contribute positively to the development of large-scale regional earthquake resilience and loss models. Using artificial intelligence and machine learning techniques, a quantitative model that considers automated intelligence algorithms for predicting the seismic risk of reinforced concrete (RC) girder bridges was developed. Using earthquake risk theory and artificial intelligence methods, 1,198 RC girder bridges and 2428,407 seismic accelerations monitored by 15 typical seismic stations were processed during the Wenchuan earthquake in Sichuan Province, China, on May 12, 2008. Multiple intelligent models (support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF)) and nonlinear dynamic curves were developed on the basis of the influence of different intensity measures, and predictive parameter identification and comparative analysis were performed. Using the actual seismic risk theory of bridges and the developed automation model, an intelligent comparison confusion matrix and curve considering multidimensional prediction parameters were generated. The rationality of the developed intelligent prediction model for RC girder bridges was compared and validated via empirical seismic damage datasets.

关键词： Intelligent automation model RC girder bridge portfolios Seismic risk and vulnerability Empirical seismic fragility machine learning algorithm

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Layer-Wise Adaptive Weighting for Faster Convergence in Federated learning 25

Layer-Wise Adaptive Weighting for Faster Convergence in Fede...

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25th IEEE International Conference on Information Reuse and Integration for Data Science (IEEE IRI)

作者： Lanjewar, Vedant S. Tran, Hai-Anh Tran, Truong X. Penn State Univ Sch Sci Engn & Technol Penn State Harrisburg Middletown PA 17057 USA Hanoi Univ Sci & Technol HOST Sch Informat & Commun Technol Hanoi Vietnam

ISBN: (纸本)9798350351194;9798350351187

Federated learning (FL), a decentralized approach utilizing multiple devices for training machine learning algorithms, presents an innovative solution to the challenges associated with handling sensitive personal data in neural network training. This research explores the application of FL for training neural network models, emphasizing its benefits in safeguarding privacy, optimizing efficiency, and addressing communication costs. While FL offers advantages such as enhanced data efficiency, promotion of heterogeneity, and scalability, challenges arise from the presence of non-independent and non-identically distributed data in the real world. This research introduces the FedLayerWise algorithm, a novel approach that dynamically assigns weights to each layer of clients' models based on their contributions to the global model. The algorithm leverages the relationship between gradients of the client model and the global model to adaptively modify weights post-training, expediting convergence towards optimal loss. Experimental results on the MNIST dataset with non-identical distributions demonstrate the algorithm's effectiveness. FedLayerWise outperforms existing algorithms (FedAdp and FedAvg) under highly skewed non-IID data distribution, achieving faster convergence. The proposed modifications showcase improvements and converge faster to non-IID data distributions, reducing computation rounds by about 40% compared to FedAdp and about 50% compared to FedAvg in such scenarios.

关键词： Federated learning Neural Networks Fast Convergence Rate machine learning algorithm

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Monitoring Sea Surface Temperature and Sea Surface Salinity Around the Maltese Islands Using Sentinel-2 Imagery and the Random Forest algorithm

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APPLIED SCIENCES-BASEL 2025年第2期15卷 929-929页

作者： Darmanin, Gareth Craig Gauci, Adam Bucci, Monica Giona Deidun, Alan Univ Malta Dept Geosci Msida 2080 Msd Malta

Marine regions are undergoing rapid evolution, primarily driven by natural and anthropogenic activities. Safeguarding these ecosystems necessitates the ability to observe their physical features and control processes with precision in both space and time. This demands the acquisition of precise and up-to-date information regarding several marine parameters. Thus, to gain a comprehensive understanding of these ecosystems, this study employs remote sensing techniques, machine learning algorithms and traditional in situ approaches. Together, these serve as valuable tools to help comprehend the distinctive parametric characteristics and mechanisms occurring within these regions of the Maltese archipelago. An empirical workflow was implemented to predict the spatial and temporal variations in sea surface salinity and sea surface temperature from 2022 to 2024. This was achieved by leveraging Sentinel-2 satellite platforms, the random forest machine learning algorithm, and in situ data collected from sea gliders and floats. Subsequently, the numerical data generated by the random forest algorithm were validated with different error metrics and converted into visual representations to illustrate the sea surface salinity and sea surface temperature variations across the Maltese Islands. The random forest algorithm demonstrated strong performance in predicting sea surface salinity and sea surface temperature, indicating its capability to handle dynamic parameters effectively. Additionally, the parametric maps generated for all three years provided a clear understanding of both the spatial and temporal changes for these two parameters.

关键词： remote sensing in situ measurements machine learning algorithm random forest sea surface salinity sea surface temperature

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