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8th international conference on Signal and Information Processing, Network and Computers, ICSINC 2021

作者： Chen, Guoen Xiang, Li Mao, Zhiquan Sun, Fenggang Lan, Peng College of Information Science and Engineering Shandong Agricultural University Tai’an271000 China State Key Laboratory of Crop Biology College of Horticultural Science and Engineering Shandong Agricultural University Tai’an271000 China

ISBN: (纸本)9789811933868

Apple replant disease (ARD) has become the limiting factor of the sustainable development of apple industry in China. The dry weight inhibition rate of apple tree rootstocks is one of the important indicators for judging the degree of apple replant disease. The calculation of dry weight inhibition rate needs to detect the dry weight of planted fruit tree rootstocks before and after soil sterilization. The specific operation is more troublesome. In response to this problem, this article proposes a method of using machine learning to predict the dry weight inhibition rate of fruit tree rootstocks. Using the existing measured data of the chemical composition, microbial community, and apple tree rhizosphere exudates in the soil in the orchard, the data is first preprocessed, including the division of the data set and the data normalization, and then the feature vectors are screened by the Distance Correlation Coefficient, and finally, using three models of Multiple Linear Regression, Support Vector Regression and Random Forest to predict the dry weight suppression rate respectively. The study found that the three machine learning methods are more suitable for the analysis of the experimental data. All three methods have obtained more accurate analysis and prediction results, and their R2_score is above 0.8. This study is of great significance for evaluating the severity of apple replant disease. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Analysis of machine learning Algorithms in Predicting Chronic Kidney Diseases Based on Performance: A Systematic Literature Review 4

Analysis of Machine Learning Algorithms in Predicting Chroni...

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4th international conference on Artificial Intelligence and data sciences, AiDAS 2023

作者： Alpaullivarez, Angelique Aurielle Aurelia, Miracle Monica, Sheila Suri, Puti Andam School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Chronic Kidney Disease (CKD) has been increasing nowadays from 750 million cases in 2019 to 850 million in 2023. Lack of access and late detection are one of the reasons why CKD cases have been increasing for all these years. Various machine learning (ML) algorithms have been studied to detect CKD and this paper will discuss and compare different types of algorithms to detect CKD early before progressing to advanced stages based on its performance. Although the use of ML to predict chronic kidney disease in the medical world is still hard to find, in other literature reviews with similar topic, it is found that various ML algorithms have been developed with the goal of assisting medical professionals in predicting CKD. Highly accurate ML algorithms have also been discovered using Artificial Neural Network, Random Forest, and XGBoost, despite needing further improvement in quality to be able to be applied on a large scale. In this paper, 5 commonly used ML algorithms namely XGBoost, Random Forest, Support Vector machine (SVM), Artificial Neural Networks (ANN), and logistic regression were analyzed. Using Systematic Literature Review as this paper's methodology, this paper provides detailed explanation on how the algorithms work, implemented, and their performance were also analyzed by using 5 parameters: AUC, accuracy, precision, recall or sensitivity, and F1-Score. It was found that XGBoost achieved the highest score among all parameters except for AUC, which highest score was possessed by Artificial Neural Network. © 2023 IEEE.

关键词： Forecasting

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An ensemble learning-based machine learning with voting mechanism for chronic disease prediction

An ensemble learning-based machine learning with voting mech...

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2024 international conference on Artificial Intelligence, Computer, data sciences, and Applications, ACDSA 2024

作者： Soualihou, Soualihou Ngnamsie Kim, Jeong-Dong Zonyfar, Candra Lee, Hyun Lee, Taek Lee, Jung-Been Sun Moon University Dept. of Computer Science and Electronic Convergence Asan Korea Republic of Sun Moon University Dept. of Computer Science and Engineering Asan Korea Republic of Sun Moon University Division of Computer Science and Engineering Asan Korea Republic of

ISBN: (纸本)9798350394528

Chronic diseases present a significant challenge in healthcare, often requiring ongoing medical attention and posing limitations on patients' daily activities. Diagnosis of such diseases is hindered by the absence of specific symptoms making them hard to detect and or prevent. Addressing this issue, researchers have turned to computational approaches, analyzing patients' medical records to predict the presence or absence of chronic diseases with promising results. However, there is potential for further improvement. This paper introduces an ensemble classifier, ELVot-CroDiP, designed to enhance chronic disease prediction. ELVot-CroDiP harnesses the collective strengths of various machine learning algorithms through a majority voting mechanism, thereby boosting predictive accuracy. The model's performance was evaluated using precision, recall, f1-score, and accuracy across five medical datasets, including heart disease, chronic kidney disease, diabetes, and heart stroke. Comparative analysis shows that ELVot-CroDiP offers superior performance in predicting chronic diseases compared to existing models, as evidenced by its higher scores in the mentioned evaluation metrics for each dataset tested. © 2024 IEEE.

关键词： Forecasting

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ARGUS: Rapid Wildfire Tracking using Satellite data Collections 16

ARGUS: Rapid Wildfire Tracking using Satellite Data Collecti...

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IEEE 16th international conference on Cloud Computing (IEEE CLOUD)

作者： Mitra, Saptashwa Khandelwal, Paahuni Pallickara, Shrideep Pallickara, Sangmi Lee Colorado State Univ Dept Comp Sci Ft Collins CO 80523 USA

ISBN: (纸本)9798350304817

Interactive visual analytics over distributed systems housing voluminous datasets is hindered by three main factors disk and network I/O, and data processing overhead. Requests over geospatial data are prone to erratic query load and hotspots due to users' simultaneous interest over a small sub-domain of the overall data space at a time. Interactive analytics in a distributed setting is further hindered in cases of voluminous datasets with large/high-dimensional data objects, such as multi-spectral satellite imagery. The size of the data objects prohibits efficient caching mechanisms that could significantly reduce response latencies. Additionally, extracting information from these large data objects incurs significant data processing overheads and they often entail resource-intensive computational methods. Here, we present our framework, ARGUS, that extracts low-dimensional representation (embeddings) of high-dimensional satellite images during ingestion and houses them in the cache for use in model-driven analysis relating to wildfire detection. These embeddings are versatile and are used to perform model-based extraction of analytical information for a set of different scenarios, to reduce the high computational costs that are involved with typical transformations over high-dimensional datasets. The models for each such analytical process are trained in a distributed manner in a connected, multi-task learning fashion, along with the encoder network that generates the original embeddings.

关键词： embedding multi-task learning science-guided machine learning visual analytics

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Predicting N2O Emissions in Full-Scale Activated Sludge Systems Through Mechanistic Approach and machine learning: Heading Toward Generalized Model Structure Development

Predicting N2O Emissions in Full-Scale Activated Sludge Syst...

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international conference on Wider-Uptake of Water Resource Recovery from Wastewater Treatment, ICWRR 2024

作者： Szeląg, Bartosz Zaborowska, Ewa Mąkinia, Jacek Institute of Environmental Engineering University of Natural Sciences Warsaw Poland Faculty of Civil and Environmental Engineering Gdańsk University of Technology Gdańsk Poland

ISBN: (纸本)9783031633522

This study focuses on the assessments of N2O emissions during the operation of full-scale wastewater treatment plants (WWTPs). Using comprehensive measurements from two large WWTPs, simulation models were developed, employing both mechanistic (MCM) and machine learning (ML) approaches. The feasibility and limitations were explored for developing a generalized model with a transferable structure across different WWTPs. Three scenarios of input data were studied, including routine measurements, extended data set, and extended data set with a delay term. A hybrid approach was applied to support ML models with data generated by MCM. High consistency between measurements and ML model predictions was achieved, confirmed by the coefficient of determination equal to 0.71 - 0.76, 0.80 - 0.84, and 0.90 – 0.92 in the three scenarios, respectively. A key finding was that the extended data set improved the accuracy of multilayer perceptron model predictions while the number of neurons was insignificant. In the case of the K-nearest neighbors model, the number of neighbors did not affect results of simulation significantly regardless of the input data scenario. The results confirmed the potential for applying the same model structure at another WWTP but recalibration of model parameters was necessary. The ML models based on the hybrid approach can be further used to develop N2O mitigation strategies in full-scale WWTPs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Carbon footprint

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Improvement of Particle Swarm Optimization Algorithm and Its Application in Power data Mining 4

Improvement of Particle Swarm Optimization Algorithm and Its...

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4th international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2023

作者： Hu, Hongbin Zhang, Yifan Han, Junfei Wang, Yuqiang Zhong, Ming He, Qianjin Zhang, Longlong Inner Mongolia Power Research Institute Inner Mongolia Enterprise Key Laboratory of Smart Grid Simulation of Electrical Power System Hohhot China Inner Mongolia Tianda Tiancai Information Technology Co. Ltd. Hohhot China

ISBN: (纸本)9798350308082

In power data mining, in order to cope with the rapidly growing data volume, particle swarm optimization algorithms should be improved and applied to power systems to improve performance and efficiency. In the process of particle search, the size of the particle is changed to affect the size of the ELM network structure. Such operation can not only expand the solution space of the problem, but also solve the shortcoming that traditional Particle Swarm Optimization-Extreme learning machine (PSO-ELM) can only find the optimal network structure in the network structure of the same scale. Experiments show that the PSO algorithm with mutation operator can converge to the optimal value more quickly. In addition, compared with the traditional ELM algorithm, Improved Particle Swarm Optimization-Extreme learning machine (IPSO-ELM) can obtain higher generalization performance and prediction progress in short-term power load prediction. The 4 curves respectively represent the MAPE value obtained by IPSO-ELM on the training set 1-4, and the fluctuation range is all within 4%, which proves that IPSOELM has good stability. By improving the particle swarm optimization algorithm and applying it to power data mining, the efficiency of power system can be enhanced, energy waste can be reduced, and power supply quality and reliability can be improved. © 2023 IEEE.

关键词： data mining

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Qualitative and quantitative analysis of mixtures using terahertz spectroscopy combined with machine learning 3

Qualitative and quantitative analysis of mixtures using tera...

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3rd international conference on Laser, Optics, and Optoelectronic Technology, LOPET 2023

作者： Wang, Ying Ma, Huifang Ren, Hao Guo, Wenyue School of Materials Science and Engineering China University of Petroleum Shandong Qingdao266580 China

ISBN: (纸本)9781510667624

Terahertz spectroscopy has many excellent characteristics. This paper uses terahertz spectroscopy to test glucose, fructose, mannose, three kinds of monosaccharides and three binary mixtures according to different mixing ratios. The results show that terahertz spectroscopy is effective in characterizing in the field of similar substances. Based on the experimental data of fructose and mannose binary mixture components, machine learning methods are used to predict the content of each component in the mixture. The prediction accuracy reaches 99.91%, which proves that our model is reasonable. It further proves that the machine learning method combined with the terahertz spectrum of the mixture has an important application in predicting the content of each component of the mixture. © 2023 SPIE.

关键词： Glucose

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Recent results on a machine learning approach to event position reconstruction in the DEAP-3600 Dark Matter Search Experiment

Recent results on a machine learning approach to event posit...

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2023 international conference on Particle Physics and Cosmology, ICPPCRubakov 2023

作者： Ilyasov, Aidar National Research Center "Kurchatov Institute" Kurchatov sq. 1 Moscow Russia National Research Nuclear University MEPhI Kashirskoe sh. 31 Moscow Russia

machine learning is increasingly being applied in elementary particle physics, and the DEAP-3600 dark matter detector is no exception. One application of the new algorithm is the event position reconstruction in the detector. Here, we present updated results on the application of a fully-connected neural network for quality improvement. We also describe the structure of the neural network, its changes from the previous version, and a comparison with existing event position reconstruction algorithms. © Copyright owned by the author(s).

关键词： machine learning

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data science during COVID-19 CORONA VIRUS in Sustainable Teaching and learning in Architecture Engineering 6

Data Science during COVID-19 CORONA VIRUS in Sustainable Tea...

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6th international conference of Women in data science at Prince Sultan University, WiDS-PSU 2023

作者： Khogali, Hind Dar Al Uloom Universityline College of Architecture Engineering Department of Architecture Riyadh Saudi Arabia

ISBN: (纸本)9781665477239

On 11 March, the World Health Organization (WHO) announced that the COVID-19 outbreak became a global pandemic. The governments have been implementing measures to limit the number of people congregating in public places. Therefore, the Ministry of Education stated that all educational institutes should complete the 2019-2020-2 semester using online video conferences and virtual classes. This research aims to study the effect of COVID-19 on teaching and learning during the last three months of lockdown after shifting to virtual classes. The research studies the procedures applied by the College of Architecture Engineering at Dar Al Uloom University. Adding value is improving the E-learning process for the upcoming semesters and solving the negative points for a better education. To achieve this objective the researcher, distribute a survey to the students to scale their experience and record the positive points, and find a solution to the negative points to solve these problems. The outcome of the research showed a good experience and many recommendations to be applied in the coming future. © 2023 IEEE.

关键词： COVID-19

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Risk prediction for cut-ins using multi-driver simulation data and machine learning algorithms: A comparison among decision tree, GBDT and LSTM

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international Journal of Transportation science and Technology 2023年第3期12卷 862-877页

作者： Tianyang Luo Junhua Wang Ting Fu Qiangqiang Shangguan Shou’en Fang The Key Laboratory of Road and Traffic Engineering Ministry of EducationTongji UniversityShanghai 201804China College of Transportation Engineering Tongji University4800 Cao’an HighwayShanghai 201804China

The cut-ins(one kind of lane-changing behaviors)have result in severe safety issues,especially at the entrances and exits of urban *** prediction and characteristics analysis of cut-ins are part of the essential research for advanced in-vehicle technologies which can reduce crash *** paper makes some efforts on these *** this paper,twenty-four participants were recruited to conduct the experiments of multi-driver simulation for risky driving data *** surrogate measures,Time Exposure Time-to-Collision(TET)and Time Integrated Time-to-collision(TIT)were employed to quantify the risk of cut-ins,then k-means clustering was applied for risk classification of 3 *** candidate variables of two kinds were extracted including 10 behavioral variables and 7 driver trait *** on these variables,three prediction models including decision tree(DT),gradient boosting decision tree(GBDT)and long shortterm memory(LSTM)are used for predicting the risks of *** from data validity verification show that the data collected from multi-driver simulation experiments is valid compared with real-world *** results of risk prediction models,the LSTM,with an overall accuracy of 87%,outperforms the GBDT(80.67%)and DT(76.9%).Despite this,this paper also concludes the merits of the DT over the GBDT and LSTM in variable explanation and the results of DT suggest that controlling the proper lane-changing gap and short duration of cut-ins can help reduce risks of cut-ins.

关键词： Multi-driver simulation platform Cut-in events Risk prediction Urban expressway machine learning approach

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