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Forecasting personal learning performance in virtual reality-based construction safety training using biometric responses

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AUTOMATION IN CONSTRUCTION 2023年 156卷

作者： Choi, Dajeong Seo, Seungwon Park, Hyunsoo Hong, Taehoon Koo, Choongwan Incheon Natl Univ Div Architecture & Urban Design Incheon 22012 South Korea Yonsei Univ Dept Architecture & Architectural Engn Seoul 03722 South Korea

During virtual reality-based safety training, it is necessary to immediately and objectively evaluate personal learning performance. In light of this, this study proposed an interpretable machine learning approach for forecasting personal learning performance in VR-based construction safety training using real-time biometric responses. During VR-based safety training ('fall accidents on scaffolding'), eye-tracking and EEG data were collected in real time from 30 participants (i.e., construction workers). The main findings can be summarized as follows. Compared to the full forecast model (FM), the support vector regression algorithm of the simplified forecast model (SM), which considers only principal features as independent variables, demonstrated better prediction performance (i.e., accuracy improvement: 0.087 of mean absolute error, overfitting: one-third level of the FM). This study creates new ground in the field of personalized safety training by enabling real-time monitoring and diagnosis for the cognitive states (i.e., learning performance) of construction workers during VR-based construction safety training.

关键词： VR-based construction safety training learning performance Forecast model Biometric response machine learning algorithm Construction worker

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A comparative study of machine learning methods for gas hydrate identification

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2023年 223卷

作者： Tian, Dongmei Yang, Shengxiong Gong, Yuehua Geng, Minghui Li, Yuanheng Hu, Guang Southern Marine Sci & Engn Guangdong Lab Guangzhou Guangzhou 511458 Peoples R China China Geol Survey Guangzhou Marine Geol Survey Guangzhou 510760 Peoples R China

Gas hydrates are a kind of efficient and clean energy that is recognized as the ideal alternative energy for fossil fuels in the future. Accurate identification of gas hydrate reservoirs is a prerequisite for the application of gas hydrate resources. Artificial intelligence algorithms have been widely applied to solve geological research problems and have obtained good results. Therefore, we use machine learning algorithms to analyze whether sediments contain gas hydrates in the Oregon Hydrate Ridge. In this paper, several commonly used machine learning algorithms are selected, including the random forest, bagging, decision tree, AdaBoost, support vector machine (SVM), k-nearest neighbor (KNN) and gradient boosting decision tree (GBDT). The P- and S-wave velocities (Vp and Vs, respectively) that have high sensitivities to hydrate changes are analyzed, the parameters of different algorithm models are optimized through training, and the classification effects of different algorithm models are compared in detail. Finally, the results show that these algorithms can better distinguish whether there are hydrates in the sediments. Compared with other algorithms, the random forest has the highest accuracy, precision and f1 score in the results of testing hydrate identification models;the AdaBoost has the highest recall;and the KNN has the closest area under curve (AUC) value to 1. The combination of artificial intelligence and resource exploration greatly improves the efficiency and accuracy of hydrate identification, which provides strong support for the subsequent development and utilization of gas hydrate resources.

关键词： Gas hydrate machine learning algorithm Classification

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Flood susceptibility modeling of the Karnali river basin of Nepal using different machine learning approaches

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GEOMATICS NATURAL HAZARDS & RISK 2023年第1期14卷

作者： Duwal, Sunil Liu, Dedi Pradhan, Prachand Man Kathmandu Univ Sch Sci & Engn Dept Civil Engn Dhulikhel Nepal Wuhan Univ Sch Water Resources & Hydropower Engn Wuhan Peoples R China

The Karnali River Basin (KRB) comprises the longest river in Nepal, located south of the Himalayas. Despite its high susceptibility to floods, the basin lacks detailed studies. Proper floodplain management is essential to reduce the impacts due to rising flood frequency, magnitude, and severity aggravated by climate change. This research applies three machine learning techniques, Support Vector machine (SVM), Random Forest (RF), and Artificial Neural Networks (ANN), to flood event data from the KRB. Ten flood conditioning factors;Aspect, curvature, distance to a river (DTR), normalized difference vegetation index (NDVI), elevation, slope, rainfall, soil, stream power index (SPI), and topographical wetness index (TWI) were selected based on the multicollinearity test. The parameter performance was evaluated using the Cohen Kappa Score, with NDVI having the greatest influence, followed by elevation, DTR, curvature, and TWI. Based on the Area Under the Curve of Receiver Operating Characteristics (AUROC), SVM outperformed RF, ANN, and ANN for FSM. The area of very high flood susceptible areas ranges from 0.8 to 2.5% of the basin area, most of them located in the south with low slopes and elevations. The results of this study suggest the use of SVM for FSM to help with proper floodplain management.

关键词： Flood susceptibility modeling machine learning algorithm Karnali River Basin Nepal

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Detecting memory content in firing rate signals using a machine learning approach: A fractal analysis

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第1期85卷

作者： Mehrabbeik, Mahtab Shams-Ahmar, Mohammad Sabourin, Carina Jafari, Sajad Lomber, Stephen G. Merrikhi, Yaser Tehran Polytech Dept Biomed Engn Amirkabir Univ Technol Tehran Iran Glendon Coll Dept Psychol Toronto ON Canada York Univ Ctr Vis Res Toronto ON Canada McGill Univ Fac Med Dept Biol & Biomed Engn Montreal PQ H3G 1Y6 Canada Tehran Polytech Hlth Technol Res Inst Amirkabir Univ Technol Tehran Iran McGill Univ Fac Med Dept Physiol Montreal PQ H3G 1Y6 Canada McGill Univ Dept Physiol McIntyre Med Sci BldgRm 12233655 Promenade Sir W Montreal PQ H3G 1Y6 Canada

While top-down modulation can be observed by increases in neuronal firing in some brain areas, modulation in other areas demonstrates complex nonlinear dynamics. When these signal modifications cannot be identified by classical methods, fractal analysis can be used to better describe the resulting changes. It was previously shown that the Higuchi dimension could detect such modification. However, these changes likely extend to multiple fractal dimensions. In this paper, machine learning algorithms were employed to extract the fractal features of neuronal firing and detect the occurrence of top-down modulation. We assessed the performance of classifiers trained on different fractal features as well as the Fourier transform and wavelet transform-based features obtained from the firing rate signals during a spatial memory task to measure the ability of individual neurons in the middle temporal cortex to hold memory information. By comparing the results of the different classifiers, Higuchi fractal dimension was confirmed as the strongest indicator of working memory content in cortical firing patterns compared to other fractal features. We demonstrated the utility of this approach and observed that the middle temporal cortex likely encodes spatial working memory based on linear and nonlinear temporal features rather than simply increasing the average firing rate.

关键词： Working memory Fractal dimension machine learning algorithm Higuchi fractal dimension

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Predicting seismic response of SMRFs founded on different soil types using machine learning techniques

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ENGINEERING STRUCTURES 2023年 274卷 1页

作者： Kazemi, F. Asgarkhani, N. Jankowski, R. Gdansk Univ Technol Fac Civil & Environm Engn Ul Narutowicza 11-12 PL-80233 Gdansk Poland

Predicting the Maximum Interstory Drift Ratio (M-IDR) of Steel Moment-Resisting Frames (SMRFs) is a useful tool for designers to approximately evaluate the vulnerability of SMRFs. This study aims to explore supervised machine learning (ML) algorithms to build a surrogate prediction model for SMRFs to reduce the need for complex modeling. For this purpose, twenty well-known ML algorithms implemented in Python software are trained based on the dataset achieved from Incremental Dynamic Analyses (IDAs) performed on the 2-Story, 3Story, 4-Story, 5-Story, 6-Story, 7-Story, 8-Story, and 9-Story SMRFs modeled in Opensees. Then, important features of weight, fundamental period of structure (T1), the RSN number of record subsets, the direction of the record subsets, soil types, and Sa(T1) of analysis, which affect the results of predictions, were selected by trial and error. Having these important features, data-driven techniques developed in Python software were compared for predicting the M-IDR of SMRFs as target. Results showed that ML algorithms of GPReg, PLSReg, SReg, LReg, GReg, MLPReg, SVM, and LSVR had lower values of coefficient of determination (R2 lower than 0.5) in both train and test datasets. In addition, XGBoost, BReg, HistGBR, and ERTReg algorithms achieved higher values of R2 (i.e. upper than 0.95 in the 5-Story SMRF) with low Mean Squared Error (MSE) for prediction of M-IDR. Therefore, using these algorithms mitigates the need for computationally expensive, time-consuming, and complex analysis, while preliminary prediction of M-IDR can be considered a low computational and efficient tool for seismic vulnerability assessment.

关键词： machine learning algorithm Supervised learning Maximum interstory drift ratio Data-driven techniques Seismic vulnerability assessment

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A Collaborative and Early Detection of Email Spam using Multitask learning 1

A Collaborative and Early Detection of Email Spam using Mult...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Chelliah, Balika J. Sasidharan, Anand Singh, Dharmesh Kumar Dangi, Nilesh SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India

ISBN: (纸本)9781607685395

Email spam has become a huge nuisance since the last couple of years. It not only wastes valuable time but is also extremely dangerous as well. The various solutions that exist to detect spam do so by manual input of keywords or filtering particular domains. But no matter the amount of filtering, it is quite tedious and difficult to check for spam. This paper includes a unique solution that attempts to use deep neural network, a machine learning technique which detects any pattern of recurrent words which may have been classified as spam. Every other parameter of the email is examined as a feature and applied accordingly to the machine learning algorithm. Deep neural network is quite advanced and can easily differentiate between a proper and an improper output. © The Electrochemical Society

关键词： Deep neural network Early Detection Email Spam machine learning algorithm Multitask learning

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FunlncModel: integrating multi-omic features from upstream and downstream regulatory networks into a machine learning framework to identify functional lncRNAs

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BRIEFINGS IN BIOINFORMATICS 2024年第1期26卷 bbae623页

作者： Li, Yan-Yu Qian, Feng-Cui Zhang, Guo-Rui Li, Xue-Cang Zhou, Li-Wei Yu, Zheng-Min Liu, Wei Wang, Qiu-Yu Li, Chun-Quan Univ South China Affiliated Hosp 1 Hengyang Med Sch Hengyang 421001 Hunan Peoples R China Univ South China Hengyang Med Sch Natl Hlth Commiss Key Lab Birth Defect Res & Preve Hengyang 421001 Hunan Peoples R China Univ South China Hunan Prov Key Lab Multi & Artificial Intelligence Hengyang 421001 Hunan Peoples R China Univ South China Key Lab Rare Pediat Dis Minist Educ Hengyang 421001 Hunan Peoples R China Univ South China Inst Biochem & Mol Biol Hengyang 421001 Hunan Peoples R China Univ South China Inst Biochem & Mol Biol Hengyang Med Coll Hengyang 421001 Hunan Peoples R China Harbin Med Univ Sch Med Informat Daqing Campus Daqing 163000 Peoples R China Inst Zool Chinese Acad Sci State Key Lab Stem Cell & Reprod Biol Beijing 100101 Peoples R China Heilongjiang Inst Technol Coll Sci Harbin 150000 Heilongjiang Peoples R China

Accumulating evidence indicates that long noncoding RNAs (lncRNAs) play important roles in molecular and cellular biology. Although many algorithms have been developed to reveal their associations with complex diseases by using downstream targets, the upstream (epi)genetic regulatory information has not been sufficiently leveraged to predict the function of lncRNAs in various biological processes. Therefore, we present FunlncModel, a machine learning-based interpretable computational framework, which aims to screen out functional lncRNAs by integrating a large number of (epi)genetic features and functional genomic features from their upstream/downstream multi-omic regulatory networks. We adopted the random forest method to mine nearly 60 features in three categories from >2000 datasets across 11 data types, including transcription factors (TFs), histone modifications, typical enhancers, super-enhancers, methylation sites, and mRNAs. FunlncModel outperformed alternative methods for classification performance in human embryonic stem cell (hESC) (0.95 Area Under Curve (AUROC) and 0.97 Area Under the Precision-Recall Curve (AUPRC)). It could not only infer the most known lncRNAs that influence the states of stem cells, but also discover novel high-confidence functional lncRNAs. We extensively validated FunlncModel's efficacy by up to 27 cancer-related functional prediction tasks, which involved multiple cancer cell growth processes and cancer hallmarks. Meanwhile, we have also found that (epi)genetic regulatory features, such as TFs and histone modifications, serve as strong predictors for revealing the function of lncRNAs. Overall, FunlncModel is a strong and stable prediction model for identifying functional lncRNAs in specific cellular contexts. FunlncModel is available as a web server at https://***/FunlncModel/.

关键词： multi-omics analysis functional lncRNA upstream/downstream regulatory network analysis machine learning algorithm systems biology

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Supervised classifiers with TF-IDF features for sentiment analysis of Marathi tweets

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SOCIAL NETWORK ANALYSIS AND MINING 2022年第1期12卷 1-16页

作者： Patil, Rupali S. Kolhe, Satish R. Kavayitri Bahinabai Chaudhari North Maharashtra U Sch Comp Sci Jalgaon 425001 Maharashtra India

In today's digital era, the proliferation of vernacular languages such as Hindi, Marathi, Bengali, Tamil, and Malayalam cannot be overlooked. The social media sites like Facebook and Twitter are great sources of opinionated content for these languages. The work to analyze public opinions has been concentrated on English, with very few Sentiment Analysis studies of minor or morphologically rich languages like Marathi. Moreover, it is a challenge to investigate the results of Sentiment Analysis with the local context for the under-resourced languages. This paper presents the sentiment prediction work over Twitter for the Marathi language using supervised machine learning techniques. The first-ever attempt experiments on the dataset for the Marathi political tweets. The benchmark dataset of 4248 tweets for the four major political parties of Maharashtra (India) is created. The Multinomial Naive Bayes, Support Vector machines with both linear and RBF kernel, Logistic Regression, and Random Forest are used to train classifiers considering the Term Frequency vs. Inverse Document Frequency (TF-IDF) as features to classify the tweets as positive or negative. The performance of the Sentiment Analysis model is evaluated using the standard measures viz., accuracy, precision, recall, and f1-score. The experimental results evidence that the Multinomial Naive Bayes outperforms among all the classifiers with the maximum accuracy of 87.29% in the prediction of the Indian State Assembly Election 2019. The proposed model ranks first in the list of Naive Bayes classifiers employed for the current state-of-the-art sentiment analysis of Indian text.

关键词： Sentiment analysis Twitter machine learning algorithm Marathi

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Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake

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NATURAL HAZARDS 2022年第1期113卷 403-421页

作者： Contreras, Diana Wilkinson, Sean Alterman, Evangeline Hervas, Javier Cardiff Univ Coll Phys Sci & Engn Ctr Resilience & Environm Change CHANGING Sch Earth & Environm Sci Main BldgPk Pl Cardiff CF10 3AT Wales Newcastle Univ Fac Sci Agr & Engn Sch Engn Learning Earthquakes LfE 2nd Floor Drummond Bldg Newcastle Upon Tyne NE1 7RU Tyne & Wear England Auckland Univ Fac Engn Dept Civil Engn Private Bag 92019 Auckland 1142 New Zealand 77 Landmark Pl Churchill Way Cardiff CF10 2HS Wales

Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection;however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. We used these data to test the accuracy of the pre-trained SA classification model developed by MonkeyLearn to identify polarity in text data. This test explores the feasibility to automate the classification process to extract meaningful information from text data from SM in real-time in the future. We tested the no-code machine learning platform's performance using a confusion matrix. We obtained an overall accuracy (ACC) of 63% and a misclassification rate of 37%. We conclude that the ACC of the unsupervised classification is sufficient for a preliminary assessment, but further research is needed to determine if the accuracy is improved by customising the training model of the machine learning platform.

关键词： Earthquakes Social media (SM) Twitter Sentiment analysis (SA) machine learning algorithm Accuracy (ACC)

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machine learning-based prediction of residual drift and seismic risk assessment of steel moment-resisting frames considering soil-structure interaction

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COMPUTERS & STRUCTURES 2023年 289卷

作者： Asgarkhani, N. Kazemi, F. Jankowski, R. Gdansk Univ Technol Fac Civil & Environm Engn Ul Narutowicza 11-12 PL-80233 Gdansk Poland UCL Dept Civil Environm & Geomat Engn London England

Nowadays, due to improvements in seismic codes and computational devices, retrofitting buildings is an important topic, in which, permanent deformation of buildings, known as Residual Interstory Drift Ratio (RIDR), plays a crucial role. To provide an accurate yet reliable prediction model, 32 improved machine learning (ML) algorithms were considered using the Python software to investigate the best method for estimating Maximum Interstory Drift Ratio (IDRmax) and RIDR of 384 Steel Moment-Resisting Frames (SMRFs). In addition, the curve plot ability of methods was investigated to provide an estimation of Median of IDA curve (IDAMed) and Seismic Failure Probability curve (SFPCurve) considering Soil-Structure Interaction (SSI) effects. It is noteworthy that ML algorithms were improved with a pipeline-based hyper-parameters Fine-Tuning (FT) method followed by for-ward and backward feature selection methodologies to avoid overfitting and data leakage issues. The improved methods were evaluated to find the best prediction model regarding seismic demands. The results show that proposed methods have higher prediction accuracy and curve fitting ability (i.e. more than 95%) that can be used to estimate IDAMed and SFPCurve of a structure to accelerate the seismic risk assessment. A prediction tool is introduced to use the methods of this study for estimating abovementioned seismic demands.

关键词： machine learning algorithm Soil-structure interaction Seismic risk assessment Residual interstory drift Seismic demand Seismic failure probability

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