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An expert system for redesigning software for cloud applications

EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期219卷

作者： Yedida, Rahul Krishna, Rahul Kalia, Anup Menzies, Tim Xiao, Jin Vukovic, Maja North Carolina State Univ Dept Comp Sci Raleigh NC 27606 USA IBM Res Cambridge MA USA

Cloud-based software has many advantages. When services are divided into many independent components, they are easier to update. Also, during peak demand, it is easier to scale cloud services (just hire more CPUs). Hence, many organizations are partitioning their monolithic enterprise applications into cloud-based *** there has been much work using machine learning to simplify this partitioning task. Despite much research, no single partitioning method can be recommended as generally useful. More specifically, those prior solutions are "brittle";i.e. if they work well for one kind of goal in one dataset, then they can be sub-optimal if applied to many datasets and multiple *** work extends prior work and proposes DEEPLY to fix the brittleness problem. Specifically, we use (a) hyper-parameter optimization to sample from the Pareto frontier of configurations (b) a weighted loss to choose optimally from this Pareto frontier (c) the 1cycle learning rate policy to avoid local minima with Adam and (d) spectral clustering over k-means. Our work shows that DEEPLY outperforms other algorithms in this space across different metrics. Moreover, our ablation study reveals that of the changes, the weighted loss is the most important, followed by hyper-parameter optimization (contrary to prior belief).To enable the reuse of this research, DEEPLY is available on-line at https://***/2WhfFlB.

关键词： Software engineering Microservices Deep learning hyper-parameter optimization Refactoring

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Inversion of Soil Moisture on Farmland Areas Based on SSA-CNN Using Multi-Source Remote Sensing Data

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REMOTE SENSING 2023年第10期15卷 2515-2515页

作者： Wang, Ran Zhao, Jianhui Yang, Huijin Li, Ning Henan Univ Coll Comp & Informat Engn Kaifeng 475004 Peoples R China Henan Univ Henan Key Lab Big Data Anal & Proc Kaifeng 475004 Peoples R China Henan Univ Henan Prov Engn Res Ctr Spatial Informat Proc Kaifeng 475004 Peoples R China

Soil moisture is a crucial factor in the field of meteorology, hydrology, and agricultural sciences. In agricultural production, surface soil moisture (SSM) is crucial for crop yield estimation and drought monitoring. For SSM inversion, a synthetic aperture radar (SAR) offers a trustworthy data source. However, for agricultural fields, the use of SAR data alone to invert SSM is susceptible to the influence of vegetation cover. In this paper, based on Sentinel-1 microwave remote sensing data and Sentinel-2 optical remote sensing data, a convolution neural network optimized by sparrow search algorithm (SSA-CNN) was suggested to invert farmland SSM. The feature parameters were first extracted from pre-processed remote sensing data. Then, the correlation analysis between the extracted feature parameters and field measured SSM data was carried out, and the optimal combination of feature parameters for SSM inversion was selected as the input data of the subsequent models. To enhance the performance of the CNN, the hyper-parameters of CNN were optimized using SSA, and the SSA-CNN model was built for SSM inversion based on the obtained optimal hyper-parameter combination. Three typical machine learning approaches, including generalized regression neural network, random forest, and CNN, were used for comparison to show the efficacy of the suggested method. With an average coefficient of determination of 0.80, an average root mean square error of 2.17 vol.%, and an average mean absolute error of 1.68 vol.%, the findings demonstrated that the SSA-CNN model with the optimal feature combination had a better accuracy among the 4 models. In the end, the SSM of the study region was inverted throughout four phenological periods using the SSA-CNN model. The inversion results indicated that the suggested method performed well in local situations.

关键词： soil moisture remote sensing convolution neural network sparrow search algorithm hyper-parameter optimization

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Modelling of pollutants and particulate matter in air using auto-tuned deep recurrent networks

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IFAC-PapersOnLine 2020年第1期53卷 530-535页

作者： Ravi kiran Inapakurthi Srinivas Soumitri Miriyala Kishalay Mitra Department of Chemical Engineering Indian Institute of Technology Hyderabad Telangana 502285 India

Atmospheric pollutants and Particulate Matter of size less than 10µm (PM10) are becoming dominant in the atmosphere due to human activities and natural calamities. To address their associated problems on human health, the interactions between pollutants and PM10 have to be envisaged. Machine learning techniques like Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) were successfully employed in establishing the interactions between various factors at play. However, these techniques are denounced for following a heuristic approach for determining network hyper-parameters. We propose a novel evolutionary multi-objective optimization algorithm which can optimally determine the hyper-parameters in deep recurrent neural networks. We test the algorithm to build optimal RNNs and LSTMs for modelling and forecasting the pollutants and PM10 data generated in northern Taiwan region during the year 2015. A state-of-the-art network training algorithm, Truncated Back Propagation Through Time was used in our study and single variable regression was done for CO, NOx, SO2, and PM10. Except for SO2 with RNN, model developed with the proposed algorithm gave high R2 values. LSTM was found to be superior than RNN in all the cases with R2 going as high as 0.9584 for PM10, while that attained by RNN is 0.93.

关键词： Particulate matter Machine learning hyper-parameter optimization Pareto Air quality monitoring Auto tuning of deep neural networks

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Integrated Simulation-Based optimization of Operational Decisions at Container Terminals

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ALGORITHMS 2021年第2期14卷 42页

作者： Kastner, Marvin Nellen, Nicole Schwientek, Anne Jahn, Carlos Hamburg Univ Technol Inst Maritime Logist D-21073 Hamburg Germany

At container terminals, many cargo handling processes are interconnected and occur in parallel. Within short time windows, many operational decisions need to be made and should consider both time efficiency and equipment utilization. During operation, many sources of disturbance and, thus, uncertainty exist. For these reasons, perfectly coordinated processes can potentially unravel. This study analyzes simulation-based optimization, an approach that considers uncertainty by means of simulation while optimizing a given objective. The developed procedure simultaneously scales the amount of utilized equipment and adjusts the selection and tuning of operational policies. Thus, the benefits of a simulation study and an integrated optimization framework are combined in a new way. Four meta-heuristics-Tree-structured Parzen Estimator, Bayesian optimization, Simulated Annealing, and Random Search-guide the simulation-based optimization process. Thus, this study aims to determine a favorable configuration of equipment quantity and operational policies for container terminals using a small number of experiments and, simultaneously, to empirically compare the chosen meta-heuristics including the reproducibility of the optimization runs. The results show that simulation-based optimization is suitable for identifying the amount of required equipment and well-performing policies. Among the presented scenarios, no clear ranking between meta-heuristics regarding the solution quality exists. The approximated optima suggest that pooling yard trucks and a yard block assignment that is close to the quay crane are preferable.

关键词： container terminal simulation simulation-based optimization meta-heuristic horizontal transportation hyper-parameter optimization

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Optimizing Activity Recognition in Video Using Evolutionary Computation

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ENGINEERING LETTERS 2021年第4期29卷 1452页

作者： Yagnik, Niraj Sharma, Chethan Manipal Acad Higher Educ Manipal Inst Technol Dept Informat & Commun Technol Manipal Karnataka India

We live in an era where the internet is flourishing with image and video data. Several algorithms and architecture have been devised, making most of such data and have been used to solve crucial problems. The number of features in image and video data can be extremely high, and such data can reach a dimensionality of thousands making the pre-processing step of feature selection extremely important. This work proposes the use of Evolutionary Computations to optimize the problem of Video Action Recognition or Classification. The VGG-16 architecture is used for extracting features from the images. The Binary Particle Swarm optimization algorithm is devised to perform feature selection on the image frames extracted from the video. Two separate experiments are then performed to optimize hyper-parameter selections, using Particle Swarm optimization and another Evolution Strategy. The robustness and consistency of the proposed methodology are tested on two popular datasets. The results obtained show that the optimized implementations using Evolutionary Algorithms perform much better than the traditional technique with no optimization.

关键词： Evolutionary Algorithms Particle Swarm optimization Deep Learning Feature Selection hyper-parameter optimization

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Machine learning approaches to predict the 1-year-after-initial-AMI survival of elderly patients

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2022年第1期22卷 115-115页

作者： Lee, Jisoo Lee, Sulyun Street, W. Nick Polgreen, Linnea A. Univ Iowa Dept Business Analyt Iowa City IA USA Univ Iowa Interdisciplinary Grad Program Informat Iowa City IA USA Univ Iowa Dept Pharm Practice & Sci Iowa City IA 52242 USA

Background While multiple randomized controlled trials (RCTs) are available, their results may not be generalizable to older, unhealthier or less-adherent patients. Observational data can be used to predict outcomes and evaluate treatments;however, exactly which strategy should be used to analyze the outcomes of treatment using observational data is currently unclear. This study aimed to determine the most accurate machine learning technique to predict 1-year-after-initial-acute-myocardial-infarction (AMI) survival of elderly patients and to identify the association of angiotensin-converting- enzyme inhibitors and angiotensin-receptor blockers (ACEi/ARBs) with survival. Methods We built a cohort of 124,031 Medicare beneficiaries who experienced an AMI in 2007 or 2008. For analytical purposes, all variables were categorized into nine different groups: ACEi/ARB use, demographics, cardiac events, comorbidities, complications, procedures, medications, insurance, and healthcare utilization. Our outcome of interest was 1-year-post-AMI survival. To solve this classification task, we used lasso logistic regression (LLR) and random forest (RF), and compared their performance depending on category selection, sampling methods, and hyper-parameter selection. Nested 10-fold cross-validation was implemented to obtain an unbiased estimate of performance evaluation. We used the area under the receiver operating curve (AUC) as our primary measure for evaluating the performance of predictive algorithms. Results LLR consistently showed best AUC results throughout the experiments, closely followed by RF. The best prediction was yielded with LLR based on the combination of demographics, comorbidities, procedures, and utilization. The coefficients from the final LLR model showed that AMI patients with many comorbidities, older ages, or living in a low-income area have a higher risk of mortality 1-year after an AMI. In addition, treating the AMI patients with ACEi/ARBs increases the 1-yea

关键词： Acute myocardial infarction (AMI heart attack) Machine learning Lasso logistic regression (LLR) Random forest (RF) Sampling methods hyper-parameter optimization Nested cross-validation (CV)

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Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning

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SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS 2023年第11期21卷 e2023SW003474-e2023SW003474页

作者： Conde, D. Castillo, F. L. Escobar, C. Garcia, C. Garcia, J. E. Sanz, V. Zaldivar, B. Curto, J. J. Marsal, S. Torta, J. M. Univ Valencia CSIC Ctr Mixto Inst Fis Corpuscular IFIC Valencia Spain Univ Savoie Mont Blanc Univ Grenoble Alpes CNRS IN2P3Lab Annecy Phys Particules LAPP Annecy France Univ Sussex Dept Phys & Astron Brighton England Univ Ramon Llull Observ Ebre OE CSIC Roquetes Spain

Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high-latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground-based conductors, such as power networks, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against GICs is to forecast them. This is a challenging task given the highly non-linear dependencies of the response of the magnetosphere to these perturbations. In the last few years, modern machine-learning models have shown to be very good at predicting magnetic activity indices. However, such complex models are on the one hand difficult to tune, and on the other hand they are known to bring along potentially large prediction uncertainties which are generally difficult to estimate. In this work we aim at predicting the SYM-H index characterizing geomagnetic storms multiple-hour ahead, using public interplanetary magnetic field (IMF) data from the Sun-Earth L1 Lagrange point and SYM-H data. We implement a type of machine-learning model called long short-term memory (LSTM) network. Our scope is to estimate the prediction uncertainties coming from a deep-learning model in the context of forecasting the SYM-H index. These uncertainties will be essential to set reliable alarm thresholds. The resulting uncertainties turn out to be sizable at the critical stages of the geomagnetic storms. Our methodology includes as well an efficient optimization of important hyper-parameters of the LSTM network and robustness tests.

关键词： geomagnetic storms deep learning forecasting SYM-H uncertainties hyper-parameter optimization

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Meta-knowledge guided Bayesian optimization framework for robust crop yield estimation

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2024年第1期36卷

作者： Tunio, Muhammad Hanif Li, Jian Ping Zeng, Xiaoyang Akhtar, Faijan Shah, Syed Attique Ahmed, Awais Yang, Yu Heyat, Md Belal Bin Univ Elect Sci & Technol China UESTC Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China Birmingham City Univ Sch Comp & Digital Technol STEAMhouse Birmingham B4 7RQ England Westlake Univ Sch Engn CenBRAIN Neurotech Ctr Excellence Hangzhou 310030 Zhejiang Peoples R China

Accurate pre-harvest crop yield estimation is vital for agricultural sustainability and economic stability. The existing yield estimating models exhibit deficiencies in insufficient examination of hyperparameters, lack of robustness, restricted transferability of meta-models, and uncertain generalizability when applied to agricultural data. This study presents a novel meta-knowledge-guided framework that leverages three diverse agricultural datasets and explores meta-knowledge transfer in frequent hyperparameter optimization scenarios. The framework's approach involves base tasks using LightGBM and Bayesian optimization, which automates hyperparameter optimization by eliminating the need for manual adjustments. Conducted rigorous experiments to analyze the meta-knowledge transformation of RGPE, SGPR, and TransBO algorithms, achieving impressive 𝑅�2 values (0.8415, 0.9865, 0.9708) using rgpe_prf meta-knowledge transfer on diverse datasets. Furthermore, the framework yielded excellent results for mean squared error (MSE), mean absolute error (MAE), scaled MSE, and scaled MAE. These results emphasize the method's significance, offering valuable insights for crop yield estimation, benefiting farmers and the agricultural sector.

关键词： Crop yield estimation Agricultural datasets Meta-knowledge hyper-parameter optimization Knowledge transfer

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Scalable hyperparameter optimization with Lazy Gaussian Processes

Scalable Hyperparameter Optimization with Lazy Gaussian Proc...

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IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC)

作者： Raju Ram Sabine Müller Franz-Josef Pfreundt Nicolas R. Gauger Janis Keuper Competence Center High Performance Computing Fraunhofer ITWM Kaiserslautern Germany Scientific Computing Group TU Kaiserslautern Germany Fraunhofer ITWM Kaiserslautern Germany Fraunhofer Center Machine Learning Germany Institute for Machine Learning and Analytics Offenburg University Germany

ISBN: (纸本)9781728159867

Most machine learning methods require careful selection of hyper-parameters in order to train a high performing model with good generalization abilities. Hence, several automatic selection algorithms have been introduced to overcome tedious manual (try and error) tuning of these parameters. Due to its very high sample efficiency, Bayesian optimization over a Gaussian Processes modeling of the parameter space has become the method of choice. Unfortunately, this approach suffers from a cubic compute complexity due to underlying Cholesky factorization, which makes it very hard to be scaled beyond a small number of sampling steps. In this paper, we present a novel, highly accurate approximation of the underlying Gaussian Process. Reducing its computational complexity from cubic to quadratic allows an efficient strong scaling of Bayesian optimization while outperforming the previous approach regarding optimization accuracy. First experiments show speedups of a factor of 162 in single node and further speed up by a factor of 5 in a parallel environment.

关键词： hyper-parameter optimization Neural architecture search Bayesian optimization Gaussian processes Scalability Gaussian processes Scalability hyperparameter parameter space methods Cholesky factorisation Cubic Speeding

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A predictive analytics model with Bayesian-Optimized Ensemble Decision Trees for enhanced crop recommendation

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Decision Analytics Journal 2024年 12卷

作者： Motamedi, Behnaz Villányi, Balázs Department of Electronics Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

Researchers have been working on developing new effective, reliable, and environmentally friendly crop recommendation systems. This study introduces a thorough framework for predicting crop recommendations by combining sophisticated machine learning (ML) classifiers with a multi-class classification approach. The study is intended to (1) comprehensively assess the importance of environmental alterations and soil nutrient characteristics across a variety of crop classes, (2) develop effective predictive analytics models using the fine Gaussian support vector machine (FGSVM) and coarse k-nearest neighbors (Coa-KNN) algorithms, (3) reduce the dimension of feature vectors and minimize training time (FGPCASVM-CRP) approach through principal component analysis (PCA), (4) explore and analyze a Bayesian optimized ensemble decision tree for crop recommendation prediction (BOEDT-CRP) model based on assessment specifications, and (5) compare the proposed approach with multiple ML classifiers with various hyperparameter optimization, including FGSVM, coarse Gaussian SVM (Coa-GSVM), wide neural network (WNN), trilayered neural network (TNN), Fine k-nearest neighbors (FKNN), cosine k-nearest neighbors (Cos-KNN), bagged tree ensemble (BTE), and subspace discriminant ensemble (SDE). The proposed models throughout the training and testing stages reveal outstanding results, with comparable accuracy rates of 99.5%, precision rates of 99.49% and 99.55%, recall rates of 99.49% and 98.59%, and f1-scores of 99.5% and 99.54%. The findings support the conclusion that the proposed models can significantly support farmers in intelligent crop management and harvesting, leading to enhanced production and decreased reliance on human labor. © 2024 The Author(s)

关键词： Bayesian-Optimized Ensemble Decision Trees hyper-parameter optimization Machine learning classifiers Multi-class classification Principal component analysis

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