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machine learning and deep learning based on the small FT-MIR dataset for fine-grained sampling site recognition of boletus tomentipes

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FOOD RESEARCH INTERNATIONAL 2023年第1期167卷 112679页

作者： Dong, Jian-E Li, Jieqing Liu, Honggao Wang, Yuan-Zhong Southwest Forestry Univ Coll Big Data & Intelligence Engn Kunming 650224 Peoples R China Yunnan Agr Univ Coll Agron & Biotechnol Kunming 650201 Peoples R China Zhaotong Univ Zhaotong 657000 Peoples R China Yunnan Acad Agr Sci Med Plants Res Inst Kunming 650200 Peoples R China Yunnan Acad Agr Sci Med Plants Res Inst 2238 Beijing Rd Kunming 650200 Peoples R China

This study proposed the necessity of identifying the sampling sites for Boletus tomentipes (***) in combination with cadmium content and environmental factors. Based on fourier transform mid-infrared spectroscopy (FT-MIR) preprocessing by 1st, 2nd, MSC, SNV and SG, five machine learning (ML) algorithms (NB, DT, KNN, RF, SVM) and three Gradient Boosting machine (GBM) algorithms (XGBoost, LightGBM, CatBoost) were built. To avoid complex preprocessing, we construct BoletusResnet model, propose the concepts of 3DCOS, 3DCOS projected images, index images in addition to 2DCOS, and combine them with deep learning (DL) for classification for the first time. It shows that GBM has higher accuracy than ML and DL has better accuracy than GBM. The four DL models presented in this paper achieve fine-grained sampling sites recognition based on small samples with 100 % accuracy, and a computer application system was developed on them. Therefore, spectral image processing combined with DL is a rapid and efficient classification method which can be widely used in food identification.

关键词： Cadmium Sampling site machine learning algorithm Gradient Boosting machine algorithm Image processing Deep learning

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A wind speed forecasting model based on multi-objective algorithm and interpretability learning

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ENERGY 2023年第1期269卷

作者： Li, Min Yang, Yi He, Zhaoshuang Guo, Xinbo Zhang, Ruisheng Huang, Bingqing Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Xian Univ Posts & Telecommun Sch Telecommun & Informat Engn Xian 710121 Peoples R China Univ Technol Sydney Sydney NSW Australia

More accurate and reliable wind speed forecasting results can provide an effective assessment of wind energy resources and improve the efficiency of wind energy utilization. Therefore, this paper is devoted to constructing accurate wind speed forecasting models and quantitatively analyzing the models by using interpretable analysis. Firstly, a multivariate wind speed forecasting model (PMI-CMOGSA-RELM) is proposed based on machine learning methods and a clustering-based multi-objective gravity search algorithm (CMOGSA). Among them, wavelet packet decomposition (WPD) is used for noise reduction, candidate input feature pool and partial mutual information (PMI) are used for feature selection, and CMOGSA is used to optimize the final combined weights. Secondly, a new evaluation metric, Absolute Error Coverage Probability (AECP), is proposed to better evaluate forecasting accuracy. Then, post-hoc attribution analysis methods and visualization tools are used to analyze the forecasting model interpretability to help us better analyze the robustness and reliability of the forecasted results. Finally, this paper validates and evaluates the proposed model with two data sets of different resolutions. The experimental results not only prove the rationality of the AECP evaluation criteria, but also demonstrate that the proposed model has smaller errors, higher estimation accuracy, and is easier to understand.

关键词： Wind speed forecasting Partial mutual information machine learning algorithm Perturbation Interpretable learning

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Mining the synergistic effect in hydrothermal co-liquefaction of real feedstocks through machine learning approaches

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FUEL 2023年第Part1期334卷

作者： Yu, Jie Zhong, Xiaomei Huang, Zhilin Lin, Xiaoyu Weng, Haiyong Yee, Dapeng He, Quan Sophia Yang, Jie Minjiang Univ Inst Oceanog Coll Geog & Oceanog Fuzhou 350108 Peoples R China Fuzhou Univ Mech & Elect Engn Practice Ctr Fuzhou 350108 Peoples R China Dalhousie Univ Fac Engn Dept Civil & Resource Engn Halifax NS Canada Fujian Agr & Forest Univ Coll Landscape Architecture & Art Fuzhou 310002 Peoples R China Fujian Agr & Forest Univ Coll Mech & Elect Engn Fuzhou 310002 Peoples R China Dalhousie Univ Fac Agr Dept Engn Truro NS Canada Minjiang Univ Fujian Key Lab Conservat & Sustainable Utilizat Ma Fuzhou 350108 Peoples R China

Hydrothermal co-liquefaction (co-HTL) of different feedstocks has received much research attention, not only because its significant importance in real industrial applications, but also due to the potential synergy in biocrude yield by tuning mixed feedstock's biochemical composition and reaction conditions. Although some attempts have been made to search for the synergy from co-liquefying various feedstocks, these processes were remarkably time and labor consuming, and often with low rate of success. Therefore, this study for the first time employed machine learning algorithms to mine the synergistic effect in co-HTL. Started with single task prediction, three machine learning algorithms, including Adaboost, Gradient Boosting Regression and Random Forest, were trained and tested for predicting co-HTL biocrude yield and relative co-liquefaction effect (CE). It was found that their prediction performances were favorable over traditional mathematical equations, in which Gradient Boosting Regression exhibited the best performance for co-HTL biocrude yield prediction (training and testing R2 of 0.976 and 0.812 respectively), and Adaboost better estimated relative CE. Feature importance analysis further revealed that co-HTL biocrude yield was mainly influenced by the reaction temperature, but relative CE was closely related to mixed feedstock's lipid and carbohydrate content, implying that the synergism/antagonism from co-HTL was more dependent on the biochemical composition of mixed feedstock than reaction conditions. Multitask predictions, estimating biocrude yield and relative CE simultaneously that are usually required in real co-HTL practices, suggested Adaboost was the most satisfying algorithm (training R2 of 0.922) among studied ones. An optimal relative CE of 22.07 % along with 36.31 wt% (daf) biocrude yield could be obtained when the mixed feedstock contained 42.93 % protein, 50.49 % carbohydrate, 6.58 % lipid at a temperature of 320 degrees C, which were in we

关键词： Synergy Hydrothermal co -liquefaction machine learning algorithm Application software Biocrude yield

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A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023年第PartB期326卷 116813页

作者： Adnan, Mohammed Sarfaraz Gani Siam, Zakaria Shams Kabir, Irfat Kabir, Zobaidul Ahmed, M. Razu Hassan, Quazi K. Rahman, Rashedur M. Dewan, Ashraf Chittagong Univ Engn & Technol CUET Dept Urban & Reg Planning Chattogram 4349 Bangladesh North South Univ Dept Elect & Comp Engn Dhaka Bangladesh Curtin Univ Sch Earth & Planetary Sci Spatial Sci Discipline Perth 6102 Australia Presidency Univ Dept Elect & Comp Engn Dhaka Bangladesh Univ Newcastle Sch Environm & Life Sci Callaghan NSW 2258 Australia Univ Calgary Dept Geomat Engn 2500 Univ Drive NW Calgary AB T2N 1N4 Canada Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia

Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flood susceptibility models (FSMs) may produce varying spatial predictions. How-ever, there have not been many attempts to address these uncertainties because identifying spatial agreement in flood projections is a complex process. This study presents a framework for reducing spatial disagreement among four standalone and hybridized ML-based FSMs: random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and hybridized genetic algorithm-gaussian radial basis function-support vector regression (GA-RBF-SVR). Besides, an optimized model was developed combining the outcomes of those four models. The southwest coastal region of Bangladesh was selected as the case area. A comparable percentage of flood potential area (approximately 60% of the total land areas) was produced by all ML-based models. Despite achieving high prediction accuracy, spatial discrepancy in the model outcomes was observed, with pixel-wise correlation co-efficients across different models ranging from 0.62 to 0.91. The optimized model exhibited high prediction accuracy and improved spatial agreement by reducing the number of classification errors. The framework pre-sented in this study might aid in the formulation of risk-based development plans and enhancement of current early warning systems.

关键词： Flood susceptibility machine learning algorithm Uncertainty analysis GIS Remote sensing

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A real-world pharmacovigilance study on cardiovascular adverse events of tisagenlecleucel using machine learning approach

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SCIENTIFIC REPORTS 2024年第1期14卷 1-8页

作者： Jung, Juhong Kim, Ju Hwan Bae, Ji-Hwan Woo, Simon S. Lee, Hyesung Shin, Ju-Young Sungkyunkwan Univ Dept Biohlth Regulatory Sci Suwon South Korea Sungkyunkwan Univ Sch Pharm 2066 Seobu Ro Suwon 16419 Gyeonggi Do South Korea Sungkyunkwan Univ Coll Comp & Informat Dept Artificial Intelligence Suwon South Korea Sungkyunkwan Univ Samsung Adv Inst Hlth Sci & Technol Seoul South Korea

Chimeric antigen receptor T-cell (CAR-T) therapies are a paradigm-shifting therapeutic in patients with hematological malignancies. However, some concerns remain that they may cause serious cardiovascular adverse events (AEs), for which data are scarce. In this study, gradient boosting machine algorithm-based model was fitted to identify safety signals of serious cardiovascular AEs reported for tisagenlecleucel in the World Health Organization Vigibase up until February 2024. Input dataset, comprised of positive and negative controls of tisagenlecleucel based on its labeling information and literature search, was used to train the model. Then, we implemented the model to calculate the predicted probability of serious cardiovascular AEs defined by preferred terms included in the important medical event list from European Medicine Agency. There were 467 distinct AEs from 3,280 safety cases reports for tisagenlecleucel, of which 363 (77.7%) were classified as positive controls, 66 (14.2%) as negative controls, and 37 (7.9%) as unknown AEs. The prediction model had area under the receiver operating characteristic curve of 0.76 in the test dataset application. Of the unknown AEs, six cardiovascular AEs were predicted as the safety signals: bradycardia (predicted probability 0.99), pleural effusion (0.98), pulseless electrical activity (0.89), cardiotoxicity (0.83), cardio-respiratory arrest (0.69), and acute myocardial infarction (0.58). Our findings underscore vigilant monitoring of acute cardiotoxicities with tisagenlecleucel therapy.

关键词： Chimeric antigen receptor T-cell Cardiotoxicity machine learning algorithm Pharmacovigilance

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Understanding Influencing Factors of Travel Mode Choice in Urban-Suburban Travel: A Case Study in Shanghai

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URBAN RAIL TRANSIT 2023年第2期9卷 127-146页

作者： Le, Jiankun Teng, Jing Tongji Univ Urban Mobil Inst 4800 Caoan Highway Shanghai Peoples R China Tongji Univ Coll Transportat Engn Key Lab Rd & Traff Engn Minist Educ 4800 Caoan Highway Shanghai Peoples R China Tongji Univ Shanghai Collaborat Innovat Res Ctr Multinetwork & Shanghai 201804 Peoples R China

After the rapid expansion of the subway system over the past two decades, some cities are preparing to build more suburban railways. The emergence of suburban railways is bound to change the choice of suburban passenger transportation. This paper studies the factors that affect the choice of travel mode at the construction stage of suburban railways, aiming to design a more rational suburban railway network and urban public transport service system. Taking Shanghai as an example, this study first surveyed revealed preference (RP) and stated preference (SP) among urban-suburban travelers. Then, we used discrete choice models (DCM) and machine learning algorithms to build a travel mode choice model based on data collection and analysis. Furthermore, the importance of each factor was analyzed, and the effects were predicted under several traffic demand management schemes. Finally, this study proposed some strategies for increasing the share of public transport. On the one hand, it is suggested that Shanghai should continue to develop suburban railways and maintain low pricing of public transport services. Considering the construction and operation costs, the government needs to provide certain subsidies to stabilize prices. On the other hand, as passengers are very sensitive to the "last mile" trips in their suburban railway travel, transport planners should strengthen the connection from and to the suburban railway stations by developing services such as shared bikes and shuttle buses. In addition, the results indicated that some traffic demand management measures can also contribute to a larger share of public transport.

关键词： Travel mode choice Urban-suburban transportation corridors Discrete choice models Influencing factors machine learning algorithm RP and SP survey

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Liquid Level Intelligent Detection for Oil Tank Based on Empirical Mode Decomposition and Deep Belief Network

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SENSORS AND MATERIALS 2022年第7期34卷 2897-2909页

作者： Zhao, Qiwei Ningbo Madelt Semicond Co Ltd Ningbo 315000 Peoples R China

The liquid level of different types of oil in an oil tank can be judged on the basis of the amplitude of time-domain signals, but the characteristic information obtained by this signal is relatively simple. In addition, the liquid level intelligent detection for oil tanks based on machine learning techniques, such decision tree algorithm, artificial neural network algorithm, and support vector machine, has stimulated considerable research interest. To obtain more characteristic information and improve the liquid level detection rate of different medium signals in an oil tank, a liquid level intelligent detection method based on empirical mode decomposition (EMD) and deep belief network (DBN) for a steel oil tank of 5 mm thickness is provided in this paper. Firstly, the ipsilateral phase detection method of the air-coupled ultrasonic Lamb wave was adopted to detect the oil tank with the help of the AO model. Then, the intrinsic mode function (IMF) of each order was obtained by analyzing the signals of different media in the oil tank by EMD, and the correlation characteristics of the time/frequency domain signals of each order IMF component were analyzed. Finally, the time/frequency domain signals of the IMF component served as the input signals of the DBN model. The liquid level is divided into 15 sections as the output of DBN. The experimental results of the combination of EMD and DBN show that the liquid levels of different media in the oil tank can be accurately identified and further classified within the range of 10 mm, the detection rate can reach 99%, and the detection range meets the actual testing requirements of the oil tank.

关键词： machine learning algorithm intelligent detection ultrasonic Lamb wave empirical mode decomposition (EMD) deep belief network (DBN)

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Prediction of prunoideae fruit quality characteristics based on machine learning and spectral characteristic acquisition optimization

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FOOD CONTROL 2024年 165卷

作者： Liu, Xuan Wang, Juan Wang, Hao Huang, Yirui Ren, Zhenhui Hebei Agr Univ Coll Mech & Elect Engn Baoding Peoples R China Hebei GEO Univ Coll Informat & Engn Shijiazhuang Peoples R China

machine learning algorithms have been widely used in the estimation and prediction of various quality characteristics of food and agricultural products. The purpose of this study is to study the availability and accuracy of the prediction model of Prunoideae fruit quality characteristics based on machine learning algorithms. XGBoost, LightGBM, CatBoost, and Random Forest (RF) are the four machine learning algorithms used in this work to create prediction models for the soluble solids content (SSC) and titratable acidity (TA) of peach, apricot, and cherry. The study investigated three hyperspectral denoising methods and used two feature extraction techniques to lower the dimensionality of hyperspectral data. The models ' accuracy was further improved by adding multiple types of spectral data. Based on opposite spectral data input, the MLP-SG-XGBoost model produced the best SSC predictions for peach and apricot, with R 2 values of 0.9162 and 0.9251, respectively, according to experimental results. When inputting opposite spectral data, the LGR-SG-LightGBM model showed the highest accuracy in predicting TA for peach and apricot, with R 2 values of 0.9193 and 0.9206, respectively. The outcomes imply that the models can accurately forecast the qualities of Prunoideae fruit.

关键词： Prunoideae fruit Quality prediction machine learning algorithm Spectral data in different forms

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Smart doors - A machine learning Approach to monitor body temperature and detect face mask 3

Smart doors - A Machine Learning Approach to monitor body te...

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3rd International Conference on Communication, Computing and Industry 4.0, C2I4 2022

作者： Karthikeyan, S. Moses, M. Leeban Swetha, M. Srinidhi, S. Thrisha, E. Bannari Amman Institute of Technology ECE Department Sathyamangalam India

ISBN: (纸本)9781665474979

In recent years Internet of Things(IoT) plays a vital role in automation. Nearly millions of people have been affected by the threatening disease COVID-19 (coronavirus), who are either sick or being killed due to the spread of the disease. The densely populated world possesses a threat of spreading such infectious diseases rapidly. So, there is a need for supervision of people's health status working in large organizations/institutions. This paper emphasizes the automation in monitoring the temperature of human beings and face mask detection so that spreading of infectious disease like COVID-19 can be brought down. The proposed solution aids the institutions/organizations to find out the infected person and take necessary precaution at an earlier stage to avoid spreading of the disease to the other healthy persons. This prototype overcomes the drawback of existing ideas in which affected individuals are frequently exposed to high radiation devices. The idea comes with the provision of ensuring the operation of the system only in the presence of human beings and also it paves the way to install a low cost set-up. The system makes use of sensor technology to spot the common symptoms of the disease and machine learning algorithm to ensure people are wearing masks. The obtained data gets stored on the cloud and analyzed by the organizations/institution's authorities. The lid present in the entrance is opened for the people with normal constraints. The whole scheme helps the larger organizations/institutions to avoid spreading of infectious diseases. © 2022 IEEE.

关键词： COVID-19 high radiation devices Internet of Things(IoT) machine learning algorithm temperature

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Mechanical ventilation intervention based on machine learning from vital signs monitoring: a scoping review

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第6期34卷 062001-062001页

作者： Baidillah, Marlin Ramadhan Busono, Pratondo Riyanto, Riyanto Natl Res & Innovat Agcy Res Ctr Elect Bandung 40135 Indonesia

Asynchronous breathing (AB) during mechanical ventilation (MV) may lead to a detrimental effect on the patient's condition. Due to the massive amount of data displayed in a large ICU, a machine learning algorithm (MLA) was proposed extensively to extract the patterns within the multiple continuous-in-time vital signs, to determine which are the variables that will predict the AB, to intervene in the MV as an early warning system, and finally to replace a highly demand of clinician's cognition. This study reviews the MLA for prediction and detection models from vital signs monitoring data for MV intervention. Publication on MLA development on MV intervention based on vital signs monitoring to support clinicians' decision-making process was extracted from the three electronic academic research databases Web of Science Core Collection (WoSCC), ScienceDirect, and PUBMED Central to February 2023. 838 papers from the electronic academic research databases are extracted. There are 14 review papers, while 25 related papers that pass with the quality assessments (QA). Few studies have been published that considered VS monitoring data along with the MV parameters waveforms for MV intervention. Vital signs monitoring data is not the only predictor in the developed MLA. Most studies suggested that developing the MLA for direct MV intervention requires more concern in the pre-processing of real-time data to avoid false positive and false detection than developing MLA itself.

关键词： mechanical ventilation multiple continuous-in-time vital signs machine learning algorithm asynchronous breathing

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