Machine learning and knowledge extraction最新文献_第8页

Evaluating the Coverage and Depth of Latent Dirichlet Allocation Topic Model in Comparison with Human Coding of Qualitative Data: The Case of Education Research 评价潜在狄利克雷分配主题模型与人类定性数据编码的覆盖范围和深度——以教育研究为例

Machine learning and knowledge extraction Pub Date : 2023-05-14 DOI: 10.3390/make5020029

Gaurav Nanda, A. Jaiswal, Hugo Castellanos, Yuzhe Zhou, Alex Choi, Alejandra J. Magana

{"title":"Evaluating the Coverage and Depth of Latent Dirichlet Allocation Topic Model in Comparison with Human Coding of Qualitative Data: The Case of Education Research","authors":"Gaurav Nanda, A. Jaiswal, Hugo Castellanos, Yuzhe Zhou, Alex Choi, Alejandra J. Magana","doi":"10.3390/make5020029","DOIUrl":"https://doi.org/10.3390/make5020029","url":null,"abstract":"Fields in the social sciences, such as education research, have started to expand the use of computer-based research methods to supplement traditional research approaches. Natural language processing techniques, such as topic modeling, may support qualitative data analysis by providing early categories that researchers may interpret and refine. This study contributes to this body of research and answers the following research questions: (RQ1) What is the relative coverage of the latent Dirichlet allocation (LDA) topic model and human coding in terms of the breadth of the topics/themes extracted from the text collection? (RQ2) What is the relative depth or level of detail among identified topics using LDA topic models and human coding approaches? A dataset of student reflections was qualitatively analyzed using LDA topic modeling and human coding approaches, and the results were compared. The findings suggest that topic models can provide reliable coverage and depth of themes present in a textual collection comparable to human coding but require manual interpretation of topics. The breadth and depth of human coding output is heavily dependent on the expertise of coders and the size of the collection; these factors are better handled in the topic modeling approach.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"91 ","pages":"473-490"},"PeriodicalIF":0.0,"publicationDate":"2023-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72422652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Multi-Input Machine Learning Approach to Classifying Sex Trafficking from Online Escort Advertisements 一种多输入机器学习方法对在线应召广告中的性交易进行分类

Machine learning and knowledge extraction Pub Date : 2023-05-10 DOI: 10.3390/make5020028

L. Summers, Alyssa N. Shallenberger, John Cruz, Lawrence V. Fulton

{"title":"A Multi-Input Machine Learning Approach to Classifying Sex Trafficking from Online Escort Advertisements","authors":"L. Summers, Alyssa N. Shallenberger, John Cruz, Lawrence V. Fulton","doi":"10.3390/make5020028","DOIUrl":"https://doi.org/10.3390/make5020028","url":null,"abstract":"Sex trafficking victims are often advertised through online escort sites. These ads can be publicly accessed, but law enforcement lacks the resources to comb through hundreds of ads to identify those that may feature sex-trafficked individuals. The purpose of this study was to implement and test multi-input, deep learning (DL) binary classification models to predict the probability of an online escort ad being associated with sex trafficking (ST) activity and aid in the detection and investigation of ST. Data from 12,350 scraped and classified ads were split into training and test sets (80% and 20%, respectively). Multi-input models that included recurrent neural networks (RNN) for text classification, convolutional neural networks (CNN, specifically EfficientNetB6 or ENET) for image/emoji classification, and neural networks (NN) for feature classification were trained and used to classify the 20% test set. The best-performing DL model included text and imagery inputs, resulting in an accuracy of 0.82 and an F1 score of 0.70. More importantly, the best classifier (RNN + ENET) correctly identified 14 of 14 sites that had classification probability estimates of 0.845 or greater (1.0 precision); precision was 96% for the multi-input model (NN + RNN + ENET) when only the ads associated with the highest positive classification probabilities (>0.90) were considered (n = 202 ads). The models developed could be productionalized and piloted with criminal investigators, as they could potentially increase their efficiency in identifying potential ST victims.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"101 1","pages":"460-472"},"PeriodicalIF":0.0,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80783006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tree-Structured Model with Unbiased Variable Selection and Interaction Detection for Ranking Data 排序数据的无偏变量选择和交互检测树结构模型

Machine learning and knowledge extraction Pub Date : 2023-05-09 DOI: 10.3390/make5020027

Yu-Shan Shih, Yi-Hung Kung

引用次数: 0

Artificial Intelligence-Based Prediction of Spanish Energy Pricing and Its Impact on Electric Consumption 基于人工智能的西班牙能源价格预测及其对电力消费的影响

Machine learning and knowledge extraction Pub Date : 2023-05-02 DOI: 10.3390/make5020026

Marcos Hernández Rodríguez, Luis Gonzaga Baca Ruiz, D. Criado-Ramón, Maria del Carmen Pegalajar Jiménez

{"title":"Artificial Intelligence-Based Prediction of Spanish Energy Pricing and Its Impact on Electric Consumption","authors":"Marcos Hernández Rodríguez, Luis Gonzaga Baca Ruiz, D. Criado-Ramón, Maria del Carmen Pegalajar Jiménez","doi":"10.3390/make5020026","DOIUrl":"https://doi.org/10.3390/make5020026","url":null,"abstract":"The energy supply sector faces significant challenges, such as the ongoing COVID-19 pandemic and the ongoing conflict in Ukraine, which affect the stability and efficiency of the energy system. In this study, we highlight the importance of electricity pricing and the need for accurate models to estimate electricity consumption and prices, with a focus on Spain. Using hourly data, we implemented various machine learning models, including linear regression, random forest, XGBoost, LSTM, and GRU, to forecast electricity consumption and prices. Our findings have important policy implications. Firstly, our study demonstrates the potential of using advanced analytics to enhance the accuracy of electricity price and consumption forecasts, helping policymakers anticipate changes in energy demand and supply and ensure grid stability. Secondly, we emphasize the importance of having access to high-quality data for electricity demand and price modeling. Finally, we provide insights into the strengths and weaknesses of different machine learning algorithms for electricity price and consumption modeling. Our results show that the LSTM and GRU artificial neural networks are the best models for price and consumption modeling with no significant difference.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"5 1","pages":"431-447"},"PeriodicalIF":0.0,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69616788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Reinforcement Learning Approach for Scheduling Problems with Improved Generalization through Order Swapping 基于顺序交换的改进泛化调度问题的强化学习方法

Machine learning and knowledge extraction Pub Date : 2023-04-29 DOI: 10.3390/make5020025

Deepak Vivekanandan, Samuel Wirth, Patrick Karlbauer, Noah Klarmann

{"title":"A Reinforcement Learning Approach for Scheduling Problems with Improved Generalization through Order Swapping","authors":"Deepak Vivekanandan, Samuel Wirth, Patrick Karlbauer, Noah Klarmann","doi":"10.3390/make5020025","DOIUrl":"https://doi.org/10.3390/make5020025","url":null,"abstract":"The scheduling of production resources (such as associating jobs to machines) plays a vital role for the manufacturing industry not only for saving energy, but also for increasing the overall efficiency. Among the different job scheduling problems, the Job Shop Scheduling Problem (JSSP) is addressed in this work. JSSP falls into the category of NP-hard Combinatorial Optimization Problem (COP), in which solving the problem through exhaustive search becomes unfeasible. Simple heuristics such as First-In, First-Out, Largest Processing Time First and metaheuristics such as taboo search are often adopted to solve the problem by truncating the search space. The viability of the methods becomes inefficient for large problem sizes as it is either far from the optimum or time consuming. In recent years, the research towards using Deep Reinforcement Learning (DRL) to solve COPs has gained interest and has shown promising results in terms of solution quality and computational efficiency. In this work, we provide an novel approach to solve the JSSP examining the objectives generalization and solution effectiveness using DRL. In particular, we employ the Proximal Policy Optimization (PPO) algorithm that adopts the policy-gradient paradigm that is found to perform well in the constrained dispatching of jobs. We incorporated a new method called Order Swapping Mechanism (OSM) in the environment to achieve better generalized learning of the problem. The performance of the presented approach is analyzed in depth by using a set of available benchmark instances and comparing our results with the work of other groups.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135802826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Lottery Ticket Search on Untrained Models with Applied Lottery Sample Selection 应用彩票样本选择的未训练模型的彩票搜索

Machine learning and knowledge extraction Pub Date : 2023-04-18 DOI: 10.3390/make5020024

Ryan Bluteau, R. Gras

{"title":"Lottery Ticket Search on Untrained Models with Applied Lottery Sample Selection","authors":"Ryan Bluteau, R. Gras","doi":"10.3390/make5020024","DOIUrl":"https://doi.org/10.3390/make5020024","url":null,"abstract":"In this paper, we present a new approach to improve tabular datasets by applying the lottery ticket hypothesis to tabular neural networks. Prior approaches were required to train the original large-sized model to find these lottery tickets. In this paper we eliminate the need to train the original model and discover lottery tickets using networks a fraction of the model’s size. Moreover, we show that we can remove up to 95% of the training dataset to discover lottery tickets, while still maintaining similar accuracy. The approach uses a genetic algorithm (GA) to train candidate pruned models by encoding the nodes of the original model for selection measured by performance and weight metrics. We found that the search process does not require a large portion of the training data, but when the final pruned model is selected it can be retrained on the full dataset, even if it is often not required. We propose a lottery sample hypothesis similar to the lottery ticket hypotheses where a subsample of lottery samples of the training set can train a model with equivalent performance to the original dataset. We show that the combination of finding lottery samples alongside lottery tickets can allow for faster searches and greater accuracy.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"3 1","pages":"400-417"},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89078265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Diabetes Prediction System Based on Incomplete Fused Data Sources 基于不完全融合数据源的糖尿病预测系统

Machine learning and knowledge extraction Pub Date : 2023-04-10 DOI: 10.3390/make5020023

Zhaoyi Yuan, Hao Ding, Guoqing Chao, Mingqi Song, Lei Wang, Weiping Ding, Dianhui Chu

{"title":"A Diabetes Prediction System Based on Incomplete Fused Data Sources","authors":"Zhaoyi Yuan, Hao Ding, Guoqing Chao, Mingqi Song, Lei Wang, Weiping Ding, Dianhui Chu","doi":"10.3390/make5020023","DOIUrl":"https://doi.org/10.3390/make5020023","url":null,"abstract":"In recent years, the diabetes population has grown younger. Therefore, it has become a key problem to make a timely and effective prediction of diabetes, especially given a single data source. Meanwhile, there are many data sources of diabetes patients collected around the world, and it is extremely important to integrate these heterogeneous data sources to accurately predict diabetes. For the different data sources used to predict diabetes, the predictors may be different. In other words, some special features exist only in certain data sources, which leads to the problem of missing values. Considering the uncertainty of the missing values within the fused dataset, multiple imputation and a method based on graph representation is used to impute the missing values within the fused dataset. The logistic regression model and stacking strategy are applied for diabetes training and prediction on the fused dataset. It is proved that the idea of combining heterogeneous datasets and imputing the missing values produced in the fusion process can effectively improve the performance of diabetes prediction. In addition, the proposed diabetes prediction method can be further extended to any scenarios where heterogeneous datasets with the same label types and different feature attributes exist.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"30 1","pages":"384-399"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85633400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

3t2FTS: A Novel Feature Transform Strategy to Classify 3D MRI Voxels and Its Application on HGG/LGG Classification t2fts:一种新的MRI三维体素分类特征变换策略及其在HGG/LGG分类中的应用

Machine learning and knowledge extraction Pub Date : 2023-04-06 DOI: 10.3390/make5020022

Abdulsalam Hajmohamad, Hasan Koyuncu

{"title":"3t2FTS: A Novel Feature Transform Strategy to Classify 3D MRI Voxels and Its Application on HGG/LGG Classification","authors":"Abdulsalam Hajmohamad, Hasan Koyuncu","doi":"10.3390/make5020022","DOIUrl":"https://doi.org/10.3390/make5020022","url":null,"abstract":"The distinction between high-grade glioma (HGG) and low-grade glioma (LGG) is generally performed with two-dimensional (2D) image analyses that constitute semi-automated tumor classification. However, a fully automated computer-aided diagnosis (CAD) can only be realized using an adaptive classification framework based on three-dimensional (3D) segmented tumors. In this paper, we handle the classification section of a fully automated CAD related to the aforementioned requirement. For this purpose, a 3D to 2D feature transform strategy (3t2FTS) is presented operating first-order statistics (FOS) in order to form the input data by considering every phase (T1, T2, T1c, and FLAIR) of information on 3D magnetic resonance imaging (3D MRI). Herein, the main aim is the transformation of 3D data analyses into 2D data analyses so as to applicate the information to be fed to the efficient deep learning methods. In other words, 2D identification (2D-ID) of 3D voxels is produced. In our experiments, eight transfer learning models (DenseNet201, InceptionResNetV2, InceptionV3, ResNet50, ResNet101, SqueezeNet, VGG19, and Xception) were evaluated to reveal the appropriate one for the output of 3t2FTS and to design the proposed framework categorizing the 210 HGG–75 LGG instances in the BraTS 2017/2018 challenge dataset. The hyperparameters of the models were examined in a comprehensive manner to reveal the highest performance of the models to be reached. In our trails, two-fold cross-validation was considered as the test method to assess system performance. Consequently, the highest performance was observed with the framework including the 3t2FTS and ResNet50 models by achieving 80% classification accuracy for the 3D-based classification of brain tumors.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"12 1","pages":"359-383"},"PeriodicalIF":0.0,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74298614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Generalized Persistence for Equivariant Operators in Machine Learning 机器学习中等变算子的广义持久性

Machine learning and knowledge extraction Pub Date : 2023-03-24 DOI: 10.3390/make5020021

Mattia G. Bergomi, M. Ferri, A. Mella, Pietro Vertechi

引用次数: 0

Human Action Recognition-Based IoT Services for Emergency Response Management 基于人类行为识别的物联网应急响应管理服务

Machine learning and knowledge extraction Pub Date : 2023-03-13 DOI: 10.3390/make5010020

Talal H. Noor

{"title":"Human Action Recognition-Based IoT Services for Emergency Response Management","authors":"Talal H. Noor","doi":"10.3390/make5010020","DOIUrl":"https://doi.org/10.3390/make5010020","url":null,"abstract":"Emergency incidents can appear anytime and any place, which makes it very challenging for emergency medical services practitioners to predict the location and the time of such emergencies. The dynamic nature of the appearance of emergency incidents can cause delays in emergency medical services, which can sometimes lead to vital injury complications or even death, in some cases. The delay of emergency medical services may occur as a result of a call that was made too late or because no one was present to make the call. With the emergence of smart cities and promising technologies, such as the Internet of Things (IoT) and computer vision techniques, such issues can be tackled. This article proposes a human action recognition-based IoT services architecture for emergency response management. In particular, the architecture exploits IoT devices (e.g., surveillance cameras) that are distributed in public areas to detect emergency incidents, make a request for the nearest emergency medical services, and send emergency location information. Moreover, this article proposes an emergency incidents detection model, based on human action recognition and object tracking, using image processing and classifying the collected images, based on action modeling. The primary notion of the proposed model is to classify human activity, whether it is an emergency incident or other daily activities, using a Convolutional Neural Network (CNN) and Support Vector Machine (SVM). To demonstrate the feasibility of the proposed emergency detection model, several experiments were conducted using the UR fall detection dataset, which consists of emergency and other daily activities footage. The results of the conducted experiments were promising, with the proposed model scoring 0.99, 0.97, 0.97, and 0.98 in terms of sensitivity, specificity, precision, and accuracy, respectively.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"83 1","pages":"330-345"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82150575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0