1,622 results on '"Hybrid Models"'
Search Results
252. AVO Analysis of Bottom Simulating Reflector (BSR) for Hybrid Model of Gas Hydrate Distribution
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Hoda Yari, Majid Nabi-Bidhendi, Naser Keshavarz, and Reza Heidari
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gas hydrate distribution ,effective medium theory (emt) ,hybrid models ,amplitude variation vs offset (avo) ,Petroleum refining. Petroleum products ,TP690-692.5 - Abstract
Due to the substantial effect of the gas hydrate distribution model (cement, un-cement, or hybrid of both models) on the elastic properties (such as shear modulus, bulk modulus, Poisson’s ratio, etc.), determining the distribution model in the hydrate-bearing sediments is a requirement for decreasing uncertainty in quantitative studies based on seismic velocities. Many different empirical and theoretical rock physics theories cover different ranges of rock properties. Among them, the Effective Medium Theory (EMT) is the most appropriate in quantitative studies of gas hydrate resources. Four types of hydrate distributions have been considered and divided into two cemented and un-cemented categories. EMT is one of the advanced rock physics modeling tools. This theory has been modified by introducing hybrid distribution models of gas hydrate instead of having assumptions about single models of hydrate distribution. Moreover, when a scientific manuscript is written, using dangling and misplaced modifiers are not suggested. On the other hand, one method to determine the gas hydrate distribution model can be performed by identifying AVO’s class on the bottom simulating reflector (BSR); caused by the contrast between an overlying gas hydrate and underlying free gas sediments. This reflector mimics seafloor topography, cross-cuts stratigraphic reflections, and is controlled by thermodynamic conditions. The results of this study on conceptual models showed that in hybrid approach for hydrate distribution, AVO’s class on BSR shows sensitivity to (1) the combination type of gas hydrate distributions models, (2) the total saturation of the gas hydrate and free-gas across the BSR.
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- 2022
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253. AVQBits—Adaptive Video Quality Model Based on Bitstream Information for Various Video Applications
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Rakesh Rao Ramachandra Rao, Steve Goring, and Alexander Raake
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Bitstream video quality models ,quality of experience (QoE) ,quality assessment ,HTTP-based adaptive streaming (HAS) ,hybrid models ,video quality ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
The paper presents $AVQBits$ , a versatile, bitstream-based video quality model. It can be applied in several contexts such as video service monitoring, evaluation of video encoding quality, of gaming video QoE, and even of omnidirectional video quality. In the paper, it is shown that $AVQBits$ predictions closely match video quality ratings obained in various subjective tests with human viewers, for videos up to 4K-UHD resolution (Ultra-High Definition, 3840 x 2180 pixels) and framerates up 120 fps. With the different variants of $AVQBits$ presented in the paper, video quality can be monitored either at the client side, in the network or directly after encoding. The no-reference $AVQBits$ model was developed for different video services and types of input data, reflecting the increasing popularity of Video-on-Demand services and widespread use of HTTP-based adaptive streaming. At its core, $AVQBits$ encompasses the standardized ITU-T P.1204.3 model, with further model instances that can either have restricted or extended input information, depending on the application context. Four different instances of $AVQBits$ are presented, that is, a Mode 3 model with full access to the bitstream, a Mode 0 variant using only metadata such as codec type, framerate, resoution and bitrate as input, a Mode 1 model using Mode 0 information and frame-type and -size information, and a Hybrid Mode 0 model that is based on Mode 0 metadata and the decoded video pixel information. The models are trained on the authors’ own AVT-PNATS-UHD-1 dataset described in the paper. All models show a highly competitive performance by using AVT-VQDB-UHD-1 as validation dataset, e.g., with the Mode 0 variant yielding a value of 0.890 Pearson Correlation, the Mode 1 model of 0.901, the hybrid no-reference mode 0 model of 0.928 and the model with full bitstream access of 0.942. In addition, all four $AVQBits$ variants are evaluated when applying them out-of-the-box to different media formats such as 360° video, high framerate (HFR) content, or gaming videos. The analysis shows that the ITU-T P.1204.3 and Hybrid Mode 0 instances of $AVQBits$ for the considered use-cases either perform on par with or better than even state-of-the-art full reference, pixel-based models. Furthermore, it is shown that the proposed Mode 0 and Mode 1 variants outperform commonly used no-reference models for the different application scopes. Also, a long-term integration model based on the standardized ITU-T P.1203.3 is presented to estimate ratings of overall audiovisual streaming Quality of Experience (QoE) for sessions of 30 s up to 5 min duration. In the paper, the $AVQBits$ instances with their per-1-sec score output are evaluated as the video quality component of the proposed long-term integration model. All $AVQBits$ variants as well as the long-term integration module are made publicly available for the community for further research.
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- 2022
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254. Water Quality Prediction for Smart Aquaculture Using Hybrid Deep Learning Models
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K. P. Rasheed Abdul Haq and V. P. Harigovindan
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Aquaculture ,CNN ,deep learning ,GRU ,hybrid models ,LSTM ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Water quality prediction (WQP) plays an essential role in water quality management for aquaculture to make aquaculture production profitable and sustainable. In this work, we propose hybrid deep learning (DL) models, convolutional neural network (CNN) with the long short-term memory (LSTM) and gated recurrent unit (GRU) for aquaculture WQP. CNN can effectively fetch the aquaculture water quality characteristics, whereas GRU and LSTM can learn long-term dependencies in the time series data. We conduct experiments using the two different water quality datasets and present an extensive study on the impact of hyperparameters on the performance of the proposed hybrid DL models. Furthermore, the performance of hybrid CNN-LSTM and CNN-GRU models are compared with different baseline LSTM, GRU and CNN DL models and also with attention-based LSTM and attention-based GRU DL models. The results show that the hybrid CNN-LSTM outperformed all other models in terms of prediction accuracy and computation time.
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- 2022
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255. A state of art review on estimation of solar radiation with various models
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Ali Etem Gürel, Ümit Ağbulut, Hüseyin Bakır, Alper Ergün, and Gökhan Yıldız
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Solar radiation estimation ,Empirical methods ,Time series models ,Artificial neural networks ,Hybrid models ,Science (General) ,Q1-390 ,Social sciences (General) ,H1-99 - Abstract
Solar radiation is free, and very useful input for most sectors such as heat, health, tourism, agriculture, and energy production, and it plays a critical role in the sustainability of biological, and chemical processes in nature. In this framework, the knowledge of solar radiation data or estimating it as accurately as possible is vital to get the maximum benefit from the sun. From this point of view, many sectors have revised their future investments/plans to enhance their profit margins for sustainable development according to the knowledge/estimation of solar radiation. This case has noteworthy attracted the attention of researchers for the estimation of solar radiation with low errors. Accordingly, it is noticed that various types of models have been continuously developed in the literature. The present review paper has mainly centered on the solar radiation works estimated by the empirical models, time series, artificial intelligence algorithms, and hybrid models. In general, these models have needed the atmospheric, geographic, climatic, and historical solar radiation data of a given region for the estimation of solar radiation. It is seen from the literature review that each model has its advantages and disadvantages in the estimation of solar radiation, and a model that gives the best results for one region may give the worst results for the other region. Furthermore, it is noticed that an input parameter that strongly improves the performance success of the models for a region may worsen the performance success of another region. In this direction, the estimation of solar radiation has been separately detailed in terms of empirical models, time series, artificial intelligence algorithms, and hybrid algorithms. Accordingly, the research gaps, challenges, and future directions for the estimation of solar radiation have been drawn in the present study. In the results, it is well-observed that the hybrid models have exhibited more accurate and reliable results in most studies due to their ability to merge between different models for the benefit of the advantages of each model, but the empirical models have come to the fore in terms of ease of use, and low computational costs.
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- 2023
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256. Diagnosing heart disease by a novel hybrid method: Effective learning approach
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Aymen A. Altae and Abdolvahab Ehsani Rad
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Heart disease (HD) ,Classification ,Extreme learning machine (ELM) ,Particle swarm Optimization algorithm (PSO) ,Hybrid models ,Enhanced fast learning network (EFLN) ,Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
In this paper, the effective classifier Extreme Learning Machines (ELM), Enhanced Fast Learning Networks (EFLN), Support Vector Machines (SVM), and Decision Trees (DT) for early Heart Disease (HD) diagnostics have been investigated. Using the optimal parameters for the proposed method, ELM, EFLN, SVM, and DT are considered for early diagnosis of heart disease. The performance of the proposed method has been evaluated versus the Accuracy (ACC), Sensitivity (SE), and Specificity (SP) HD dataset. This study also proposed a new effective Hybrid Model, by combining the Particle Swarm Optimization Algorithm (PSOA) with the most effective Learning classifiers. The proposed hybrid diagnostic method records ACC through ten runs for a 10-fold cross-validation (CV). According to the results, the proposed method shows an ACC of 93% for (PSO-ELM), 96% (PSO_EFLN), 91% (PSO-SVM) and 93% (PSO-DT).
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- 2023
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257. Personalizing Hybrid-Based Dialogue Agents.
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Matveev, Yuri, Makhnytkina, Olesia, Posokhov, Pavel, Matveev, Anton, and Skrylnikov, Stepan
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MULTIPURPOSE buildings - Abstract
In this paper, we present a continuation of our work on the personification of dialogue agents. We expand upon the previously demonstrated models—the ranking and generative models—and propose new hybrid models. Because there is no single definitive way to build a hybrid model, we explore various architectures where the components adopt different roles, sequentially and in parallel. Applying the perplexity and BLEU performance metrics, we discover that the Retrieve and Refine and KG model—a modification of the Retrieve and Refine model where the ranking and generative components work in parallel and compete based on the proximity of the candidate found by the ranking model with a knowledge-grounded generation block—achieves the best performance, with values of 1.64 for perplexity and 0.231 for BLEU scores. [ABSTRACT FROM AUTHOR]
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- 2022
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258. Recommendation of Project Management Practices: A Contribution to Hybrid Models.
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Bianchi, Michael J., Conforto, Edivandro C., Rebentisch, Eric, Amaral, Daniel C., Rezende, Solange O., and de Padua, Renan
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AGILE software development , *PROJECT management , *COMPUTER software development , *CLUSTER analysis (Statistics) , *GREEN movement - Abstract
Dealing with an uncertain and dynamic environment when facing multiple and fast-paced challenges forces professionals to adopt agile practices in different environments, resulting in the use of hybrid project management models. However, identifying the right practice to adopt can be challenging, given the variety of project types and environmental factors. In this article, a recommendation method that would allow for the identification of patterns of project management practices for different environments—using an agility indicator—is proposed. The proposed method is tested with a dataset of 856 projects. A cluster analysis is applied to divide the projects into three groups according to environmental characteristics, called scenarios: waterfall, agile, and hybrid. Then, we apply the association rule technique for each group separately, identifying specific patterns of practice for each group. Through a comparative analysis, we verify the consistency between the recommendations of the proposed method for each scenario and the literature on project management. The results indicate the feasibility of the proposed method, thus opening up new research opportunities for hybrid models that can be customized for different projects. This article can help project management professionals apply the agile method beyond its use in software development and improve the process of combining project management practices. We also suggest directions for new research to advance the knowledge of useful decision support tools for hybrid model customization. [ABSTRACT FROM AUTHOR]
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- 2022
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259. Diagnosis of Parkinson's disease from hand drawing utilizing hybrid models.
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Varalakshmi, P, Tharani Priya, B, Anu Rithiga, B, Bhuvaneaswari, R, and Sakthi Jaya Sundar, Rajasekar
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Parkinson's disease is a nervous system abnormality marked by decreased dopamine levels in the brain. Parkinson's disease inhibits one's ability to move. Speech difficulty, changes in movement and handwriting, and other symptoms are common with Parkinson's disease. A collection of hand drawings is employed to predict Parkinson's disease. There are 102 spiral images in the hand drawing dataset. Due to the minimal size of the dataset, augmentation is utilized to increase it. After that, the augmented images are utilized to train several machine learning and deep learning models, as well as pre-trained networks like RESNET50, VGG16, AlexNet, and VGG19. The performance metrics of hybrid models of deep learning with machine learning and hybrid models of deep learning (for feature extraction) with deep learning (for classification) are then compared. It was observed that the hybrid model of RESNET-50 and SVM performed well with better performance measures compared to other Machine Learning, Deep Learning and Hybrid Models with an accuracy score of 98.45%, sensitivity score of 0.99 and specificity score of 0.98. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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260. Performance improvement of machine learning models via wavelet theory in estimating monthly river streamflow.
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Wang, Kegang, Band, Shahab S., Ameri, Rasoul, Biyari, Meghdad, Hai, Tao, Hsu, Chung-Chian, Hadjouni, Myriam, Elmannai, Hela, Chau, Kwok-Wing, and Mosavi, Amir
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WAVELETS (Mathematics) , *STREAMFLOW , *WATER management , *DECISION trees , *MACHINE performance , *STANDARD deviations , *MACHINE learning - Abstract
River streamflow is an essential hydrological parameters for optimal water resource management. This study investigates models used to estimate monthly time-series river streamflow data at two hydrological stations in the USA (Heise and Irwin on Snake River, Idaho). Five diverse types of machine learning (ML) model were tested, support vector machine-radial basis function (SVM-RBF), SVM-Polynomial (SVM-Poly), decision tree (DT), gradient boosting (GB), random forest (RF), and long short-term memory (LSTM). These were trained and tested alongside a conventional multiple linear regression (MLR). To improve the estimation and model performance, hybrid models were designed by coupling the models with wavelet theory (W). The models performance was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), and Willmott's index (WI). A side-by-side performance assessment of the stand-alone and hybrid models revealed that the coupled models exhibit better estimates of monthly river streamflow relative to the stand-alone ones. The statistical parameter values for the best model (W-LSTM4) during the test phase was RMSE = 36.533 m3/s, MAE = 26.912 m3/s, R2 = 0.947, NSE = 0.946, WI = 0.986 (Heise station), and RMSE = 33.378 m3/s, MAE = 24.562 m3/s, R2 = 0.952, NSE = 0.951, WI = 0.987 (Irwin station). [ABSTRACT FROM AUTHOR]
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- 2022
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261. Hybrid learning model for spatio-temporal forecasting of PM2.5 using aerosol optical depth.
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Nath, Pritthijit, Roy, Biparnak, Saha, Pratik, Middya, Asif Iqbal, and Roy, Sarbani
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BLENDED learning , *DEEP learning , *AEROSOLS , *STATISTICAL learning , *URBAN pollution , *RANDOM forest algorithms - Abstract
Existence of several challenges and high cost in the development of monitoring infrastructure have become major reasons for data sparsity by statutory government agencies tasked to study pollution exposure in urban areas. As an effort to mitigate this problem, the recent usage of satellite aerosol optical depth data along with the usage of learning algorithms have become popular in recent times. This paper presents a novel four-staged approach using different machine learning, deep learning and statistical methods to develop a spatio-temporal hybrid model for temporal forecasting using data from existing stations along with satellite aerosol optical depth data for spatial interpolation. Experiments conducted on real-world data belonging to the cities of Kolkata, Bengaluru and Mumbai show that a consistent pattern is not followed in all the cities in all stages except in spatial interpolation where Random Forest Regression is found to surpass all other models used. While a long short-term memory network (LSTM Auto-Encoder) when employed in temporal forecasting inside the hybrid method outperforms others in Mumbai, a random forest regression-based method and a multi-layer perceptron-based method outperform others similarly in Kolkata and Bengaluru, respectively. [ABSTRACT FROM AUTHOR]
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- 2022
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262. A Hybrid Discrete–Continuum Modelling Approach to Explore the Impact of T-Cell Infiltration on Anti-tumour Immune Response.
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Almeida, Luis, Audebert, Chloe, Leschiera, Emma, and Lorenzi, Tommaso
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We present a spatial hybrid discrete–continuum modelling framework for the interaction dynamics between tumour cells and cytotoxic T cells, which play a pivotal role in the immune response against tumours. In this framework, tumour cells and T cells are modelled as individual agents while chemokines that drive the chemotactic movement of T cells towards the tumour are modelled as a continuum. We formally derive the continuum counterpart of this model, which is given by a coupled system that comprises an integro-differential equation for the density of tumour cells, a partial differential equation for the density of T cells and a partial differential equation for the concentration of chemokines. We report on computational results of the hybrid model and show that there is an excellent quantitative agreement between them and numerical solutions of the corresponding continuum model. These results shed light on the mechanisms that underlie the emergence of different levels of infiltration of T cells into the tumour and elucidate how T-cell infiltration shapes anti-tumour immune response. Moreover, to present a proof of concept for the idea that, exploiting the computational efficiency of the continuum model, extensive numerical simulations could be carried out, we investigate the impact of T-cell infiltration on the response of tumour cells to different types of anti-cancer immunotherapy. [ABSTRACT FROM AUTHOR]
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- 2022
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263. Hybrid Modeling of ARIMA, ANN, And SYM for Macro Variables Forecasting in Pakistan.
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Ali, Rizwan, Hina, Hafsa, and Urooj, Amena
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SUPPORT vector machines ,ARTIFICIAL neural networks ,BOX-Jenkins forecasting ,FOREIGN exchange rates ,RATE of return on stocks - Abstract
Time series forecasting remains a challenging task owing to its nonlinear, complex, and chaotic behaviour. The purpose of the current paper was to analyze the forecast performance of different models to determine Pakistan's macroeconomic variables, such as inflation, exchange rate, and stock return. These models included Linear Autoregressive Integrated Moving Average (ARIMA) model as well as nonlinear models, such as Artificial Neural Networks (ANN), and Support Vector Machine (SVM). Afterwards, a hybrid methodology was used to combine the linear ARIMA with nonlinear models of ANN and SVM. The forecasting performance of all the models, that is, ARIMA, ANN, SVM, ARIMA-ANN, and ARIMASVM was compared on the basis of RMSE and MAE. The results indicated that the best forecasting model to achieve high forecast accuracy was the hybrid ARIMA-SVM. [ABSTRACT FROM AUTHOR]
- Published
- 2022
264. A novel PM2.5 concentrations probability density prediction model combines the least absolute shrinkage and selection operator with quantile regression.
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Yang, Shaomei and Wu, Haoyue
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QUANTILE regression ,PREDICTION models ,PROBABILITY density function ,PROBABILITY theory - Abstract
PM2.5 has a significant negative impact on human health and atmospheric quality, and accurate prediction of its concentration is necessary. When using common point prediction models for PM2.5 concentration prediction, the influence of various uncertainties on PM2.5 concentrations makes the prediction results suffer from poor accuracy. To address this issue, this paper proposes the quantile regression neural network (QRNN) model based on the least absolute shrinkage and selection operator (LASSO), combined with kernel density estimation (KDE) for probabilistic density prediction of PM2.5 concentrations. The model uses LASSO regression to select the influential factors, and then the quartiles of daily PM2.5 concentrations calculated by the QRNN model are imported into the KDE model to obtain the probability density predictions of PM2.5 concentrations. In the paper, empirical analyses are carried out with the cities of Beijing and Jinan in China as well as six other datasets, and the prediction performance of the model is assessed by using evaluation criteria in both point prediction and interval prediction. The simulation reveals that the predictive performance of the LASSO-QRNN-KDE model is well, and the model is not only effective in filtering high-dimensional data, but also has a higher accuracy compared to common research models. In addition, the model is able to describe the uncertainty of PM2.5 concentration fluctuations and carry more information on the variation of PM2.5 concentrations, which can provide a novel and excellent PM2.5 concentration prediction tool for relevant policy makers. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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265. On the Modeling of Energy-Multisource Networks by the Thermostatted Kinetic Theory Approach: A Review with Research Perspectives.
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Bianca, Carlo
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DISTRIBUTION (Probability theory) , *RENEWABLE energy sources , *MODEL theory , *INTEGRO-differential equations - Abstract
Recently, different mathematical frameworks of the thermostatted kinetic theory approach have been proposed for the modeling of complex systems. In particular, thermostatted kinetic frameworks have been employed for the modeling and time evolution of a hybrid energy-multisource network composed of renewable and nonrenewable energy sources, for the construction of the energy storage and for open networks. In the frameworks of the thermostatted kinetic theory approach, the evolution of an energy source and the interactions with other energy sources are modeled by introducing a distribution function and interaction rates. This paper is a survey of the recent proposed frameworks of the thermostatted kinetic theory for the modeling of a hybrid energy-multisource network and reviews the recent proposed models. The paper is not limited to review the existing frameworks, but it also generalizes the mathematical structures proposed in the pertinent literature and outlines future research perspectives and applications of this new approach proposed in 2012. [ABSTRACT FROM AUTHOR]
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- 2022
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266. Gait Events Prediction Using Hybrid CNN-RNN-Based Deep Learning Models through a Single Waist-Worn Wearable Sensor.
- Author
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Arshad, Muhammad Zeeshan, Jamsrandorj, Ankhzaya, Kim, Jinwook, and Mun, Kyung-Ryoul
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WEARABLE technology , *DEEP learning , *GAIT in humans - Abstract
Elderly gait is a source of rich information about their physical and mental health condition. As an alternative to the multiple sensors on the lower body parts, a single sensor on the pelvis has a positional advantage and an abundance of information acquirable. This study aimed to improve the accuracy of gait event detection in the elderly using a single sensor on the waist and deep learning models. Data were gathered from elderly subjects equipped with three IMU sensors while they walked. The input taken only from the waist sensor was used to train 16 deep-learning models including a CNN, RNN, and CNN-RNN hybrid with or without the Bidirectional and Attention mechanism. The groundtruth was extracted from foot IMU sensors. A fairly high accuracy of 99.73% and 93.89% was achieved by the CNN-BiGRU-Att model at the tolerance window of ±6 TS (±6 ms) and ±1 TS (±1 ms), respectively. Advancing from the previous studies exploring gait event detection, the model demonstrated a great improvement in terms of its prediction error having an MAE of 6.239 ms and 5.24 ms for HS and TO events, respectively, at the tolerance window of ±1 TS. The results demonstrated that the use of CNN-RNN hybrid models with Attention and Bidirectional mechanisms is promising for accurate gait event detection using a single waist sensor. The study can contribute to reducing the burden of gait detection and increase its applicability in future wearable devices that can be used for remote health monitoring (RHM) or diagnosis based thereon. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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267. K-Means Clustering And Two-Level Classification For Vessel Segmentation In Detection Of Diabetic Retinopathy.
- Author
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Mohod, Sudhir W. and Devida, Malpe Kalpana
- Abstract
A frequent and sometimes sight-threatening consequence of diabetes is diabetic retinopathy. In order to diagnose and track diabetic retinopathy, retinal blood vessels must be accurately identified early on and segmented. The improvements in vessel segmentation methods for the identification of diabetic retinopathy are summarised in this abstract. Due to the intricate structure and diversity of blood vessels, as well as the presence of noise and artefacts, vascular segmentation in retinal pictures is a difficult process. In order to overcome these difficulties and increase the precision of vessel segmentation, a number of approaches have been developed. Feature extraction, classification, post-processing, and picture pre-processing are frequently combined in these methods. Recent research have demonstrated encouraging outcomes in vascular segmentation using a suggested framework utilising hybrid models. To extract pertinent information from retinal pictures, the framework uses pre-processing techniques such cropping, colour space conversion, and contrast augmentation. Gabor filtering and texture analysis are two techniques for feature extraction that effectively capture specific vessel properties. Vessel and non-vessel pixels are distinguished using classification techniques like K-means clustering and ensemble classifiers. The vessel segmentation findings are refined using post-processing techniques including morphological operations and linked component analysis. The STARE dataset and other benchmark datasets used for this approach evaluation showed great accuracy, specificity, and sensitivity. However, there are still issues with establishing high specificity and good sensitivity in vessel segmentation. More investigation is required to enhance vascular abnormality identification and lessen false-positive and false-negative mistakes. The improvements in vascular segmentation techniques help to identify and monitor diabetic retinopathy early, allowing for prompt therapies to avert vision loss. The suggested framework and hybrid models have the potential to improve vessel segmentation's precision and effectiveness. The integration of deep learning methodologies and the creation of reliable, automated systems for healthcare applications may be future research priorities. [ABSTRACT FROM AUTHOR]
- Published
- 2022
268. Power prediction of wind turbine in the wake using hybrid physical process and machine learning models.
- Author
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Zhou, Huanyu, Qiu, Yingning, Feng, Yanhui, and Liu, Jing
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WIND turbines , *ARTIFICIAL neural networks , *WIND power , *MACHINE learning , *WIND power plants - Abstract
Precise power prediction for wind turbines under wake effects is requisite for wind farm wake control to increase the energy production and economic benefits. Wind farm wake effects that have spatial and temporal characteristics significantly complicate the physical modelling. To overcome the modelling difficulties, this paper proposes two new coupled structures of physical process and machine learning to predict wind turbine output power under wake effects. Their structures are fully presented and verified by comparisons to the pure physical model, neural network model alone and existing physical-guided neural network models. One of the new models that couples physical model and transfer learning approach shows the best prediction performance. It uses data generated from physical model and small size real data to establish the model, which makes it adaptive to data with different distribution. The results confirm that a robust hybrid physical and machine learning model can simultaneously inherit the advantages from developments of physical models and machine learning approaches. Important insights into HPML models for output power prediction of WTs under wake effects are provided. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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269. hybrid approach based on complete ensemble empirical mode decomposition with adaptive noise for multi-step-ahead solar radiation forecasting.
- Author
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Ferkous, Khaled, Boulmaiz, Tayeb, Ziari, Fahd Abdelmouiz, and Bekkar, Belgacem
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SOLAR radiation ,SOLAR radio emission ,HILBERT-Huang transform ,FORECASTING ,STANDARD deviations ,GLOBAL radiation - Abstract
Accurate measurements of solar radiation are required to ensure that power and energy systems continue to function effectively and securely. On the other hand, estimating it is extremely challenging due to the non-stationary behaviour and randomness of its components. In this research, a novel hybrid forecasting model, namely complete ensemble empirical mode decomposition with adaptive noise–Gaussian process regression (CEEMDAN–GPR), has been developed for daily global solar radiation prediction. The non-stationary global solar radiation series is transformed by CEEMDAN into regular subsets. After that, the GPR model uses these subsets as inputs to perform its prediction. According to the results of this research, the performance of the developed hybrid model is superior to two widely used hybrid models for solar radiation forecasting, namely wavelet–GPR and wavelet packet–GPR, in terms of mean square error, root mean square error, coefficient of determination and relative root mean square error values, which reached 3.23 MJ/m
2 /day, 1.80 MJ/m2 /day, 95.56%, and 8.80%, respectively (for one-step forward forecasting). The proposed hybrid model can be used to ensure the safe and reliable operation of the electricity system. [ABSTRACT FROM AUTHOR]- Published
- 2022
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270. Hybrid Wavelet Scattering Network-Based Model for Failure Identification of Reinforced Concrete Members.
- Author
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Barkhordari, Mohammad Sadegh, Barkhordari, Mohammad Mahdi, Armaghani, Danial Jahed, Rashid, Ahmad Safuan A., and Ulrikh, Dmitrii Vladimirovich
- Abstract
After earthquakes, qualified inspectors typically conduct a semisystematic information gathering, physical inspection, and visual examination of the nation's public facilities, buildings, and structures. Manual examinations, however, take a lot of time and frequently demand too much work. In addition, there are not enough professionals qualified to assess such structural damage. As a result, in this paper, the efficiency of computer-vision hybrid models was investigated for automatically detecting damage to reinforced concrete elements. Data-driven hybrid models are generated by combining wavelet scattering network (WSN) with bagged trees (BT), random subspace ensembles (RSE), artificial neural networks (ANN), and quadratic support vector machines (SVM), named "BT-WSN", "RSE-WSN", "ANN-WSN", and "SVM-WSN". The hybrid models were trained on an image database containing 4585 images. In total, 15% of images with different sorts of damage were used to test the trained models' robustness and adaptability; these images were not utilized in the training or validation phase. The WSN-SVM algorithm performed best in classifying the damage. It had the highest accuracy of the hybrid models, with a value of 99.1% in the testing phase. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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271. From time-series to hybrid models: advancements in short-term load forecasting embracing smart grid paradigm
- Author
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Universitat Politècnica de Catalunya. Doctorat en Enginyeria Elèctrica, Universitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica, Universitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group, Ali, Salman, Bogarra Rodríguez, Santiago, Riaz, Muhammad Naveed, Phyo, Pyae Pyae, Flynn, Damian, Taha, Ahmad, Universitat Politècnica de Catalunya. Doctorat en Enginyeria Elèctrica, Universitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica, Universitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group, Ali, Salman, Bogarra Rodríguez, Santiago, Riaz, Muhammad Naveed, Phyo, Pyae Pyae, Flynn, Damian, and Taha, Ahmad
- Abstract
This review paper is a foundational resource for power distribution and management decisions, thoroughly examining short-term load forecasting (STLF) models within power systems. The study categorizes these models into three groups: statistical approaches, intelligent-computing-based methods, and hybrid models. Performance indicators are compared, revealing the superiority of heuristic search and population-based optimization learning algorithms integrated with artificial neural networks (ANNs) for STLF. However, challenges persist in ANN models, particularly in weight initialization and susceptibility to local minima. The investigation underscores the necessity for sophisticated predictive models to enhance forecasting accuracy, advocating for the efficacy of hybrid models incorporating multiple predictive approaches. Acknowledging the changing landscape, the focus shifts to STLF in smart grids, exploring the transformative potential of advanced power networks. Smart measurement devices and storage systems are pivotal in boosting STLF accuracy, enabling more efficient energy management and resource allocation in evolving smart grid technologies. In summary, this review provides a comprehensive analysis of contemporary predictive models and suggests that ANNs and hybrid models could be the most suitable methods to attain reliable and accurate STLF. However, further research is required, including considerations of network complexity, improved training techniques, convergence rates, and highly correlated inputs to enhance STLF model performance in modern power systems., Peer Reviewed, Postprint (published version)
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- 2024
272. Inferring global terrestrial carbon fluxes from the synergy of Sentinel 3 & 5P with Gaussian process hybrid models
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Universidad de Alicante. Departamento de Matemática Aplicada, Reyes-Muñoz, Pablo, Kovács, Dávid D., Berger, Katja, Pipia, Luca, Belda, Santiago, Rivera-Caicedo, Juan Pablo, Verrelst, Jochem, Universidad de Alicante. Departamento de Matemática Aplicada, Reyes-Muñoz, Pablo, Kovács, Dávid D., Berger, Katja, Pipia, Luca, Belda, Santiago, Rivera-Caicedo, Juan Pablo, and Verrelst, Jochem
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The ongoing monitoring of terrestrial carbon fluxes (TCF) goes hand in hand with progress in technical capacities, such as the next-generation Earth observation missions of the Copernicus initiative and advanced machine learning algorithms. Proceeding along this line, we present a physically-based data-driven workflow for quantifying gross primary productivity (GPP) and net primary productivity (NPP) at a global scale from the synergy of Copernicus’ Sentinel-3 (S3) Ocean and Land Color Instrument (OLCI) and the TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5 Precursor (S5P), along with meteorological variables from Copernicus ERA5-Land. Specifically, we created generic hybrid Gaussian process regression (GPR) retrieval models combining S3-OLCI-derived vegetation products with the TROPOMI solar-induced fluorescence (SIF) product to capture global GPP and NPP. First, the GPR algorithms were trained on theoretical simulations through the Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE) model, with the final retrieval models termed SCOPE-GPR-TCF. Second, the SCOPE-GPR-TCF models were integrated in Google Earth Engine (GEE) and fed with satellite data and products (coming from Sentinel 3 & 5P and ERA5-Land), producing global and regional (Iberian Peninsula) maps at spatial resolutions of 5 km and 300 m during the year 2019. Moderate relative uncertainties in the range between 10%–40% of the GPP and NPP estimates were achieved by the SCOPE-GPR-TCF models. Analysis of the driving variables revealed that the S3-OLCI vegetation products, i.e., leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (FAPAR), and SIF provided the highest prediction strengths. Validation of GPP temporal estimates from GPR against partitioned GPP estimates at 113 flux towers located in America and Europe highlighted a good overall consistency at the local scale, with performances varying depending on the site and vegetation type. The
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- 2024
273. VERY SHORT-TERM LOAD FORECAST (VSTLF) FORMULATION FOR NETWORK CONTROL SYSTEMS : A comprehensive evaluation of existing algorithms for VSTLF
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Al Madani, Mhd Rami and Al Madani, Mhd Rami
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This degree project undertakes a detailed examination of various algorithms used in Very Short-Term Load Forecasting (VSTLF) within network control systems, prioritizing forecasting accuracy and computational efficiency as critical evaluation criteria. The research comprehensively assesses a range of forecasting methods, including statistical models, machine learning algorithms, and advanced deep learning techniques, aiming to highlight their respective advantages, limitations, and suitability for different operational contexts. The study conducts a detailed analysis by comparing essential performance metrics such as Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and execution time, before and after implementing adjustments to the formulations. This approach highlights how optimization strategies enhance the effectiveness of the models. Notably, the study identifies Support Vector Machine (SVM) and Multiple Linear Regression as frontrunners in terms of balancing accuracy with computational demand, making them particularly suitable for real-time forecasting needs. Meanwhile, Long Short-Term Memory (LSTM) networks demonstrate a commendable ability to capture complex, non-linear data patterns, albeit at a higher computational cost. The degree project further explores the sensitivity of these forecasting models to parameter adjustments, revealing a nuanced landscape where strategic modifications can significantly enhance model performance. This degree project not only contributes to the ongoing discourse on optimizing VSTLF algorithms but also provides actionable insights for stakeholders in the energy sector, aiming to facilitate the development of more reliable, efficient, and sustainable power system operations.
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- 2024
274. A Comparison of Categorical, Image, and Hybrid-Based Machine Learning for Classification of Breast Cancer
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Abdi, Johan, Hansson Huber, Felix, Abdi, Johan, and Hansson Huber, Felix
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One of the leading global health issues is cancer. For women, by far the most pervasive variant is breast cancer. It was also one of the cancers with the highest number of new cases in 2020. To give individuals suffering from breast cancer the best chance possible to survive it is vital to detect the disease early. To do so, computer-aided diagnostics such as a machine learning model can be used to classify mammography images of the breast abnormality as either benign or malignant. In the patient’s journal, more data is available regarding the abnormality and the context of how the mammography scans were taken. This paper aims to examine how that additional data might affect the performance of a model classifying mammography images. Three different image model architectures were used, one quite simple and two utilizing transfer learning. They were then combined with a rather simple architecture used for the additional categorical data. The categorical data was divided into three classes: features describing how the images were taken (class 1), features describing the abnormality (class 2), and a combination of the two aforementioned classes (class 3). The results show that a significant increase was achieved when comparing the hybrid models to the image-only models. However, this increase seems to occur as a result of the image model’s poor performance, and the categorical data being favored by the models combining the two modalities. A slight synergy effect was observed for the hybrid models using the class 3 data. This was, however, too small for us to be able to make any general claims., En av de ledande globala hälsoproblemen är cancer. För kvinnor är den absolut mest förekommande varianten bröstcancer. Det var även en av varianterna av cancer med det högsta antalet nya fall 2020. För att ge individer drabbade av bröstcancer den bästa möjliga chansen att överleva är det avgörande att att upptäcka sjukdomen i ett tidigt stadie. För att göra det kan datorstödd diagnostik, som exempelvis en maskininlärningsmodel, användas för att classificera mammografibilder av bröstabnormiteter som elak- eller godartad. I patientjournalen finns det givetvis mer data som beskriver abnormiteten och den kontext i vilken bilderna togs. Den här rapporten har som mål att undersöka hur ytterligare data kan påverka prestandan hos en model som klassificerar mammografibilder. Tre olika arkitekturer för bildmodeller användes, en rätt så enkel och två som nyttjade transfer learning. Vardera arkitektur kombinerades med en ganska enkel arkitektur som användes för den ytterligare kategoriska datan. Den kategoriska datan delades in i tre klasser: attribut som beskriver hur bilderna togs (klass 1), attribut som beskriver abnormiteten (klass 2), och en kombination av de två redan nämnda klasserna (klass 3). Resultaten visar att en markant ökning uppnåddes när man jämför hybridmodellerna med modellerna som endast använder bilddata. Denna ökning verkar dock uppstå till följd av bildmodellernas låga prestanda och att den kategoriska datan föredras av modellerna som kombinerar de två modaliteterna. En viss synergieffekt observerades för kombinationsmodellerna som använde klass 3-data. Den var dock för liten för att vi ska kunna göra några generella påståenden.
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- 2024
275. Climate warming effects in stratified reservoirs: Thorough assessment for opportunities and limits of machine learning techniques versus process-based models in thermal structure projections
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Mi, Chenxi, Tilahun, A.B., Flörke, M., Dürr, H.H., Rinke, Karsten, Mi, Chenxi, Tilahun, A.B., Flörke, M., Dürr, H.H., and Rinke, Karsten
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It is nowadays a hot topic to apply machine learning (ML) algorithms to illustrate water temperature dynamics in lentic waters. Due to the limited amount of in-situ temperature measurements from traditional sampling programmes, most of the related studies, however, restricted their analysis within the surface and rarely checked the results for whole depth profiles. Moreover, capability of such methods in projecting thermal dynamics under future climate conditions is even less illustrated. To fill the gap, we collected an unparalleled huge database including 9 million water temperature measurements, from 2013 to 2022, in Rappbode Reservoir, Germany as well as the corresponding climatic and hydrological observations, to train three commonly used ML models (Random Forest, XGBoost, and Long Short-Term Memory) and comprehensively evaluate their performance in reproducing thremal structure within the whole water column. We also systemically compared such simulation results with a well-established process-driven model (CE-QUAL-W2). Going beyond a pure reproduction of observatiosn, we further evaluated the ability of CE-QUAL-W2 and ML models in projecting thermal dynamics under RCP8.5 climate scenario up to 2100. Our results suggested three ML methods yielded high accuracy in capturing water temperature dynamics from the surface (epilimnion) to bottom (hypolimnion), and also satisfactorily reproduced temporal development of the stratification pattern, with the results corresponding well with those from CE-QUAL-W2. By contrast, the projections by ML models were rather insensitive to future climate warming under RCP8.5 comparing with those by CE-QUAL-W2, pointing to an important risk whenever ML-based models are extrapolated beyond their training data range. Supported by millions of in situ measurements, our research clearly illustrated the opportunities (limits) of ML models in projecting thermal dynamics in deep waters, and the conclusion also provides key guidance for scie
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- 2024
276. Clustering techniques performance comparison for predicting the battery state of charge: A hybrid model approach
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García-Ordás, María Teresa, Marcos del Blanco, David Yeregui, Aveleira Mata, Jose Antonio, Zayas-Gato, Francisco, Jove, Esteban, Casteleiro-Roca, José-Luis, Quintián, Héctor, Calvo-Rolle, José Luis, Alaiz Moretón, Héctor, García-Ordás, María Teresa, Marcos del Blanco, David Yeregui, Aveleira Mata, Jose Antonio, Zayas-Gato, Francisco, Jove, Esteban, Casteleiro-Roca, José-Luis, Quintián, Héctor, Calvo-Rolle, José Luis, and Alaiz Moretón, Héctor
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[Abstract] Batteries are a fundamental storage component due to its various applications in mobility, renewable energies and consumer electronics among others. Regardless of the battery typology, one key variable from a user’s perspective is the remaining energy in the battery. It is usually presented as the percentage of remaining energy compared to the total energy that can be stored and is labeled State Of Charge (SOC). This work addresses the development of a hybrid model based on a Lithium Iron Phosphate (LiFePO4) power cell, due to its broad implementation. The proposed model calculates the SOC, by means of voltage and electric current as inputs and the latter as the output. Therefore, four models based on k-Means, Agglomerative Clustering, Gaussian Mixture and Spectral Clustering techniques have been tested in order to obtain an optimal solution.
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- 2024
277. An interpretable hybrid framework combining convolution latent vectors with transformer based attention mechanism for rolling element fault detection and classification.
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Saeed A, Usman Akram M, Khattak M, and Belal Khan M
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Failure of industrial assets can cause financial, operational and safety hazards across different industries. Monitoring their condition is crucial for successful and smooth operations. The colossal volume of sensory data generated and acquired throughout industrial operations supports real-time condition monitoring of these assets. Leveraging digital technologies to analyze acquired data creates an ideal environment for applying advanced data-driven machine learning techniques, such as convolutional neural networks (CNNs) and vision transformer (ViT) to detect faults and classify, enabling accurate prediction and timely maintenance of industrial assets. In this paper, we present a novel hybrid framework based on the local feature extraction ability of CNN with comprehensive understanding of transformer within a global context. The proposed method leverages the complex weight-sharing properties of CNNs and ability of transformers to understand the larger context of spatial relationships in large-scale patterns, making it applicable to datasets of varying sizes. Preprocessing methods such as data augmentation are used to train the model on the Case Western Reserve University (CWRU) dataset in order to increase generalization through computational efficiency. An average fault classification accuracy of 99.62% is accomplished over all three fault classes with an average time-to-fault detection of 38.4 ms. MFPT fault dataset is used to further validate the method with an accuracy of 99.17% for outer race and 99.26% for inner race. Moreover, the proposed framework can be modified to accommodate alternative convolutional models., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)
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- 2024
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278. Hybrid Model for Parkinson’s Disease Prediction
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Guimarães, Augusto Junio, de Campos Souza, Paulo Vitor, Lughofer, Edwin, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Lesot, Marie-Jeanne, editor, Vieira, Susana, editor, Reformat, Marek Z., editor, Carvalho, João Paulo, editor, Wilbik, Anna, editor, Bouchon-Meunier, Bernadette, editor, and Yager, Ronald R., editor
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- 2020
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279. Container Demand Forecasting at Border Posts of Ports: A Hybrid SARIMA-SOM-SVR Approach
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Ruiz-Aguilar, Juan Jesús, Urda, Daniel, Moscoso-López, José Antonio, González-Enrique, Javier, Turias, Ignacio J., Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Dorronsoro, Bernabé, editor, Ruiz, Patricia, editor, de la Torre, Juan Carlos, editor, Urda, Daniel, editor, and Talbi, El-Ghazali, editor
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- 2020
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280. DDES and OES Simulations of a Morphing Airbus A320 Wing and Flap in Different Scales at High Reynolds
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Marouf, A., Simiriotis, N., Tô, J. B., Bmegaptche, Y., Hoarau, Y., Braza, M., Hirschel, Ernst Heinrich, Founding Editor, Schröder, Wolfgang, Series Editor, Boersma, Bendiks Jan, Series Editor, Fujii, Kozo, Series Editor, Haase, Werner, Series Editor, Leschziner, Michael A., Series Editor, Periaux, Jacques, Series Editor, Pirozzoli, Sergio, Series Editor, Rizzi, Arthur, Series Editor, Roux, Bernard, Series Editor, Shokin, Yurii I., Series Editor, Hoarau, Yannick, editor, Peng, Shia-Hui, editor, Schwamborn, Dieter, editor, Revell, Alistair, editor, and Mockett, Charles, editor
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- 2020
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281. Train Overtaking Prediction in Railway Networks: A Big Data Perspective
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Oneto, Luca, Buselli, Irene, Lulli, Alessandro, Canepa, Renzo, Petralli, Simone, Anguita, Davide, Angelov, Plamen, Series Editor, Kozma, Robert, Series Editor, Oneto, Luca, editor, Navarin, Nicolò, editor, Sperduti, Alessandro, editor, and Anguita, Davide, editor
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- 2020
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282. Cavitation Noise Spectra Prediction with Hybrid Models
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Cipollini, Francesca, Miglianti, Fabiana, Oneto, Luca, Tani, Giorgio, Viviani, Michele, Anguita, Davide, Angelov, Plamen, Series Editor, Kozma, Robert, Series Editor, Oneto, Luca, editor, Navarin, Nicolò, editor, Sperduti, Alessandro, editor, and Anguita, Davide, editor
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- 2020
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283. Improving Classification Accuracy of Ensemble Learning for Symbolic Data Trough Neural Networks’ Feature Extraction
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Pełka, Marcin, Gaul, Wolfgang, Managing Editor, Vichi, Maurizio, Managing Editor, Weihs, Claus, Managing Editor, Baier, Daniel, Editorial Board Member, Critchley, Frank, Editorial Board Member, Decker, Reinhold, Editorial Board Member, Diday, Edwin, Editorial Board Member, Greenacre, Michael, Editorial Board Member, Lauro, Carlo Natale, Editorial Board Member, Meulman, Jacqueline, Editorial Board Member, Monari, Paola, Editorial Board Member, Nishisato, Shizuhiko, Editorial Board Member, Ohsumi, Noboru, Editorial Board Member, Opitz, Otto, Editorial Board Member, Ritter, Gunter, Editorial Board Member, Schader, Martin, Editorial Board Member, Jajuga, Krzysztof, editor, Batóg, Jacek, editor, and Walesiak, Marek, editor
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- 2020
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284. An Interpretable Machine Learning Model for Human Fall Detection Systems Using Hybrid Intelligent Models
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Souza, Paulo Vitor C., Guimaraes, Augusto J., Araujo, Vanessa S., Batista, Lucas O., Rezende, Thiago S., Kacprzyk, Janusz, Series Editor, Ponce, Hiram, editor, Martínez-Villaseñor, Lourdes, editor, Brieva, Jorge, editor, and Moya-Albor, Ernesto, editor
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- 2020
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285. A Hybrid Approach for Short-Term NO2 Forecasting: Case Study of Bay of Algeciras (Spain)
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Van Roode, Steffanie, Ruiz-Aguilar, Juan Jesus, González-Enrique, Javier, Turias, Ignacio J., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Martínez Álvarez, Francisco, editor, Troncoso Lora, Alicia, editor, Sáez Muñoz, José António, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor
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- 2020
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286. Hybrid Approaches for Time Series Prediction
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Fontes, Xavier, Castro Silva, Daniel, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Madureira, Ana Maria, editor, Abraham, Ajith, editor, Gandhi, Niketa, editor, and Varela, Maria Leonilde, editor
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- 2020
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287. Applying AI in Practice: Key Challenges and Lessons Learned
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Fischer, Lukas, Ehrlinger, Lisa, Geist, Verena, Ramler, Rudolf, Sobieczky, Florian, Zellinger, Werner, Moser, Bernhard, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Holzinger, Andreas, editor, Kieseberg, Peter, editor, Tjoa, A Min, editor, and Weippl, Edgar, editor
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- 2020
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288. OD Mobility Estimation Using Artificial Neural Networks
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Acosta Sánchez, Luis E., González-Enrique, Javier, Ruiz-Aguilar, J. J., Moscoso-López, J. A., Turias, I. J., Monteiro, Jânio, editor, João Silva, António, editor, Mortal, António, editor, Aníbal, Jaime, editor, Moreira da Silva, Manuela, editor, Oliveira, Miguel, editor, and Sousa, Nelson, editor
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- 2020
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289. Beyond the agile methods: a diagnostic tool to support the development of hybrid models
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Bianchi, Michael Jordan, Conforto, Edivandro Carlos, and Amaral, Daniel Capaldo
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- 2021
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290. Deep Learning Based Hybrid Classifier for Analyzing Hepatitis C in Ultrasound Images
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hussein al-ogaili
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Deep Learning ,Hybrid Models ,Hepatitis ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Although liver biopsy is the gold standard for identifying diffuse liver disorders, it is an intrusive procedure with a host of negative side effects. Physician subjectivity may affect the ultrasonography diagnosis of diffuse liver disease. As a result, there is still a clear need for an appropriate classification of liver illnesses. In this article, an unique deep classifier made up of deep convolutional neural networks (CNNs) that have already been trained is proposed to categories the liver condition. The variants of ResNet and AlexNet are a few networks that are combined with fully connected networks (FCNs). Transfer learning can be used to extract deep features that can offer adequate categorization data. Then, an FCN can depict images of the disease in its many stages, including tissue, liver hepatitis, and hepatitis. To discriminate between these liver images, three different (normal/cirrhosis, perfectly natural, and cirrhosis/hepatitis) and 3 (normal/cirrhosis/hepatitis) models were trained. A hybrid classifier is presented in order to integrate the graded odds of the classes produced by each individual classifier since two-class classifiers performed better than three-class classifiers. The class with the highest score is then chosen using a majority voting technique. The experimental results demonstrate an high accuracy when liver images were divided into three classes using ResNet50 and a hybrid classifier.
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- 2022
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291. Traffic flow prediction models – A review of deep learning techniques
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Anirudh Ameya Kashyap, Shravan Raviraj, Ananya Devarakonda, Shamanth R Nayak K, Santhosh K V, and Soumya J Bhat
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deep learning ,deep neural networks ,hybrid models ,intelligent transport system ,traffic flow prediction ,unsupervised learning ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
Traffic flow prediction is an essential part of the intelligent transport system. This is the accurate estimation of traffic flow in a given region at a particular interval of time in the future. The study of traffic forecasting is useful in mitigating congestion and make safer and cost-efficient travel. While traditional models use shallow networks, there has been an exponential growth in the number of vehicles in recent times and these traditional machine learning models fail to work in current scenarios. In our paper, we review some of the latest works in deep learning for traffic flow prediction. Many deep learning architectures include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Restricted Boltzmann Machines (RBM), and Stacked Auto Encoder (SAE). These deep learning models use multiple layers to extract higher level of features from raw input progressively. The latest deep learning models developed to tackle this very problem are reviewed and due to the complexity of transport networks, this review gives the reader information about how various factors influence these models and what models work best in different scenarios.
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- 2022
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292. Short-Term Forecasting of Ozone Concentration in Metropolitan Lima Using Hybrid Combinations of Time Series Models
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Natalí Carbo-Bustinza, Hasnain Iftikhar, Marisol Belmonte, Rita Jaqueline Cabello-Torres, Alex Rubén Huamán De La Cruz, and Javier Linkolk López-Gonzales
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short-term ozone concentration forecasting ,seasonal trend decomposition method ,time series models ,hybrid models ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
In the modern era, air pollution is one of the most harmful environmental issues on the local, regional, and global stages. Its negative impacts go far beyond ecosystems and the economy, harming human health and environmental sustainability. Given these facts, efficient and accurate modeling and forecasting for the concentration of ozone are vital. Thus, this study explores an in-depth analysis of forecasting the concentration of ozone by comparing many hybrid combinations of time series models. To this end, in the first phase, the hourly ozone time series is decomposed into three new sub-series, including the long-term trend, the seasonal trend, and the stochastic series, by applying the seasonal trend decomposition method. In the second phase, we forecast every sub-series with three popular time series models and all their combinations In the final phase, the results of each sub-series forecast are combined to achieve the results of the final forecast. The proposed hybrid time series forecasting models were applied to four Metropolitan Lima monitoring stations—ATE, Campo de Marte, San Borja, and Santa Anita—for the years 2017, 2018, and 2019 in the winter season. Thus, the combinations of the considered time series models generated 27 combinations for each sampling station. They demonstrated significant forecasts of the sample based on highly accurate and efficient descriptive, statistical, and graphic analysis tests, as a lower mean error occurred in the optimized forecast models compared to baseline models. The most effective hybrid models for the ATE, Campo de Marte, San Borja, and Santa Anita stations were identified based on their superior out-of-sample forecast results, as measured by RMSE (4.611, 3.637, 1.495, and 1.969), RMSPE (4.464, 11.846, 1.864, and 15.924), MAE (1.711, 2.356, 1.078, and 1.462), and MAPE (14.862, 20.441, 7.668, and 76.261) errors. These models significantly outperformed other models due to their lower error values. In addition, the best models are statistically significant (p < 0.05) and superior to the rest of the combination models. Furthermore, the final proposed models show significant performance with the least mean error, which is comparatively better than the considered baseline models. Finally, the authors also recommend using the proposed hybrid time series combination forecasting models to predict ozone concentrations in other districts of Lima and other parts of Peru.
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- 2023
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293. Hybrid Methods for Fundus Image Analysis for Diagnosis of Diabetic Retinopathy Development Stages Based on Fusion Features
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Mohammed Alshahrani, Mohammed Al-Jabbar, Ebrahim Mohammed Senan, Ibrahim Abdulrab Ahmed, and Jamil Abdulhamid Mohammed Saif
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CNN ,FFNN ,hybrid models ,hybrid features ,diabetic retinopathy ,handcrafted ,Medicine (General) ,R5-920 - Abstract
Diabetic retinopathy (DR) is a complication of diabetes that damages the delicate blood vessels of the retina and leads to blindness. Ophthalmologists rely on diagnosing the retina by imaging the fundus. The process takes a long time and needs skilled doctors to diagnose and determine the stage of DR. Therefore, automatic techniques using artificial intelligence play an important role in analyzing fundus images for the detection of the stages of DR development. However, diagnosis using artificial intelligence techniques is a difficult task and passes through many stages, and the extraction of representative features is important in reaching satisfactory results. Convolutional Neural Network (CNN) models play an important and distinct role in extracting features with high accuracy. In this study, fundus images were used for the detection of the developmental stages of DR by two proposed methods, each with two systems. The first proposed method uses GoogLeNet with SVM and ResNet-18 with SVM. The second method uses Feed-Forward Neural Networks (FFNN) based on the hybrid features extracted by first using GoogLeNet, Fuzzy color histogram (FCH), Gray Level Co-occurrence Matrix (GLCM), and Local Binary Pattern (LBP); followed by ResNet-18, FCH, GLCM and LBP. All the proposed methods obtained superior results. The FFNN network with hybrid features of ResNet-18, FCH, GLCM, and LBP obtained 99.7% accuracy, 99.6% precision, 99.6% sensitivity, 100% specificity, and 99.86% AUC.
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- 2023
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294. A Physics-Informed Generative Car-Following Model for Connected Autonomous Vehicles
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Lijing Ma, Shiru Qu, Lijun Song, Zhiteng Zhang, and Jie Ren
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car-following modeling ,hybrid models ,generative model ,deep learning ,connected and autonomous vehicles ,mixed traffic flow ,Science ,Astrophysics ,QB460-466 ,Physics ,QC1-999 - Abstract
This paper proposes a novel hybrid car-following model: the physics-informed conditional generative adversarial network (PICGAN), designed to enhance multi-step car-following modeling in mixed traffic flow scenarios. This hybrid model leverages the strengths of both physics-based and deep-learning-based models. By taking advantage of the inherent structure of GAN, the PICGAN eliminates the need for an explicit weighting parameter typically used in the combination of traditional physics-based and data-driven models. The effectiveness of the proposed model is substantiated through case studies using the NGSIM I-80 dataset. These studies demonstrate the model’s superior trajectory reproduction, suggesting its potential as a strong contender to replace conventional models in trajectory prediction tasks. Furthermore, the deployment of PICGAN significantly enhances the stability and efficiency in mixed traffic flow environments. Given its reliable and stable results, the PICGAN framework contributes substantially to the development of efficient longitudinal control strategies for connected autonomous vehicles (CAVs) in real-world mixed traffic conditions.
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- 2023
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295. Rethinking commerce education in South Africa
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Loots, Elsabé, Oberholster, Johan, Steyn, Adriana, Antonites, Alex, van der Merwe, Alta, Merino, Andres, Callaghan, Chris, Herman, Dinko, Marques, Gary, van der Merwe, Herman, Surujlal, Jhalukpreya, Plant, Kato, van den Berg, Liandi, Wentzel, Mandie, Kanyane, Modimowabarwa, Lamberti, Pia, Musundwa, Sedzani, Eybers, Sunet, Benvenuti, Susan, Onaji-Benson, Theresa, Leendertz, Verona, Moyo, Vusani, Maroun, Warren, Rosslyn-Smith, Wesley, Chinyamurindi, Willie T., Loots, Elsabé, and Oberholster, Johan
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Business education ,hybrid models ,hi-flex models ,student preparedness ,interdisciplinary programmes ,future-fit curriculum ,societal impact ,thema EDItEUR::K Economics, Finance, Business and Management::KJ Business and Management - Abstract
This book aims to advance the knowledge on the future of business education in South Africa and to allow all relevant role players (universities and industry) the opportunity to debate and share ideas on how to best position business education to optimally serve the interest of students and the dynamic changes taking place in the world of work. Although some of these changes have taken shape before 2020, the COVID-19 pandemic has accelerated the need and pace for change. If business schools (in our context, faculties of economic and management sciences) do not adapt rapidly, they will be left behind by other up-and-coming industry providers. The research scope covers all business-related undergraduate and postgraduate economics, management and accounting programmes, excluding MBA programmes.
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- 2024
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296. Improving Air Pollution Prediction Modelling Using Wrapper Feature Selection.
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Ul-Saufie, Ahmad Zia, Hamzan, Nurul Haziqah, Zahari, Zulaika, Shaziayani, Wan Nur, Noor, Norazian Mohamad, Zainol, Mohd Remy Rozainy Mohd Arif, Sandu, Andrei Victor, Deak, Gyorgy, and Vizureanu, Petrica
- Abstract
Feature selection is considered as one of the essential steps in data pre-processing. However, all of the previous studies on predicting PM
10 concentration in Malaysia have been limited to statistical method feature selection, and none of these studies used machine-learning approaches. Therefore, the objective of this research is to investigate the influence variables of the PM10 prediction model by using wrapper feature selection to compare the prediction model performance of different wrapper feature selection and to predict the concentration of PM10 for the next day. This research uses 10 years of daily data on pollutant concentrations from two stations (Klang and Shah Alam) obtained from the Department of Environment Malaysia (DOE) from 2009 until 2018. Six wrapper methods (forward selection, backward elimination, stepwise, brute-force, weight-guided and genetic algorithm evolution and the predictive analytics multiple linear regression (MLR) and artificial neural network (ANN)) were implemented in this study. This study found that brute-force is the dominant wrapper method in most of the best models in selecting important features for MLR. Moreover, compared to MLR, ANN provides more advantages regarding model accuracy and permits feature selection in predicting PM10 . The overall results revealed that the RMSE value for next day prediction in Klang is 20.728, while the AE value is 15.69. Furthermore, the RMSE value for next day prediction in Shah Alam is 10.004, while the AE value is 7.982. Finally, all of the predicted models in Klang and Shah Alam can be used to predict the PM10 concentrations. This proposed model can be used as a tool for an early warning system in giving air quality information to local authorities in order to formulate air-quality-improvement strategies. [ABSTRACT FROM AUTHOR]- Published
- 2022
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297. Integrating climate, ecophysiology, and forest cover to estimate the vulnerability of sloths to climate change.
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Tourinho, Luara, Sinervo, Barry, Oliveira Caetano, Gabriel Henrique de, Fernandez Giné, Gastón Andrés, dos Santos, Cinthya Chiva, Cruz-Neto, Ariovaldo Pereira, and Vale, Mariana M.
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LAZINESS , *ECOPHYSIOLOGY , *SPECIES distribution , *LAND cover , *PLANT transpiration , *TREE growth - Abstract
Global change imposes multiple challenges on species and, thus, a reliable prediction of current and future vulnerability of species must consider multiple stressors and intrinsic traits of species. Climate, physiology, and forest cover, for example, are required to evaluate threat to thermolabile forest-dependent species, such as sloths (Bradypus spp.; Mammalia: Xenarthra). Here, we estimated future changes in the distribution of three sloth species using a metabolic-hybrid model focused on climate (climatic only, i.e., CO approach) and adding forest cover constraints to distribution of species (climate plus land cover, i.e., CL approach). We used an innovative method to generate estimates of physiological parameters for endotherms, validated with field data. The CF approach predicted a future net expansion of distribution of B. torquatus and B. variegatus, and a future net contraction of distribution of B. tridactylus. The inclusion of forest cover constraints, however, reversed the predictions for B. torquatus, with a predicted net distribution contraction. It also reduced expansion of B. variegatus, although still showing a large net expansion. Thus, B. variegatus is not predicted to be threatened in the future; B. tridactylus emerges as the species most vulnerable to climate change, but with no considerable forest losses, while B. torquatus shows the opposite pattern. Our study highlights the importance of incorporating multiple stressors in predictive models in general. To increase resilience of species to climate change, it is key to control deforestation in the Amazon for B. tridactylus, and to promote reforestation in the Atlantic Forest for B. torquatus. As mudanças globais impõem vários desafios à biodiversidade e uma previsão confiável da vulnerabilidade atual e futura das espécies deve considerar vários estressores e características intrínsecas das mesmas. Clima, fisiologia e cobertura florestal, por exemplo, são necessários para avaliar a ameaça às espécies dependentes da floresta e termolábeis, como as preguiças (Bradypus spp.; Mammalia: Xenarthra). Aqui, estimamos as mudanças futuras na distribuição de três espécies de preguiças usando um modelo híbrido metabólico focado no clima (abordagem apenas climática, CO) e adicionando restrições de cobertura florestal à distribuição das espécies (abordagem de clima mais cobertura florestal, CL). Utilizamos um método inovador para gerar estimativas de parâmetros fisiológicos para endotérmicos, validado com dados de campo. A abordagem CO previu uma expansão líquida futura da distribuição de B. torquatus e B. variegatus, e uma contração líquida futura da distribuição de B. tridactylus. A inclusão de restrições de cobertura florestal, no entanto, reverteu as previsões de B. torquatus, com uma contração da distribuição líquida prevista. A restrição de cobertura florestal também reduziu a expansão de B. variegatus, embora ainda exiba uma grande expansão líquida. Assim, B. variegatus não está previsto para ser ameaçado no futuro; B. tridactylus emerge como a espécie mais vulnerável às mudanças climáticas, mas sem perdas florestais consideráveis, enquanto B. torquatus mostra o padrão oposto. Nosso estudo destaca a importância de incorporar múltiplos estressores em modelos preditivos em geral. Para aumentar a resiliência das espécies às mudanças climáticas, é fundamental controlar o desmatamento na Amazônia para B. tridactylus e promover o reflorestamento na Mata Atlântica para B. torquatus. [ABSTRACT FROM AUTHOR]
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- 2022
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298. Improving the performance of random forest for estimating monthly reservoir inflow via complete ensemble empirical mode decomposition and wavelet analysis.
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Ahmadi, Farshad, Mehdizadeh, Saeid, and Nourani, Vahid
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HILBERT-Huang transform , *RANDOM forest algorithms , *WAVELETS (Mathematics) , *WATER management , *STANDARD deviations , *WATER consumption - Abstract
Estimation of reservoir inflow is of particular importance in optimal planning and management of water resources, proper allocation of water to consumption sectors, hydrological studies, etc. This study aimed to estimate monthly inflow (Q) to the Maroon Dam reservoir located in Iran utilizing climatic data such as minimum, maximum, and mean air temperatures (Tmin, Tmax, T), reservoir evaporation (E), and rainfall (R). The impact of any of the mentioned variables was analyzed by the entropy-based pre-processing technique. The results of the pre-processing showed that the rainfall is the most important parameter affecting the reservoir inflow. Therefore, three types of input patterns were taken into consideration consisting the antecedent Q-based, antecedent R-based, and combined antecedent Q and R-based input combinations. To estimate the monthly reservoir inflow, a random forest (RF) was firstly employed as the standalone model. Then, two different types of hybrid models were proposed via coupling the RF on complete ensemble empirical mode decomposition (CEEMD) and wavelet analysis (W) in order to implement the coupled CEEMD-RF and W-RF models. It is worthwhile to mentioning that six mother wavelets were used in developing the hybrid W-RF models. Four error metrics including root mean square error (RMSE), mean absolute error (MAE), Kling-Gupta efficiency (KGE), and Willmott index (WI) were used to assess the accuracy of implemented models. The attained results indicated the superiority of proposed hybrid models over the classic RF for estimating the monthly reservoir inflow. The most precise model during the test phase was W-RF(3) utilizing the Sym(2) as the mother wavelet under a lagged Q-based pattern with error measures of RMSE = 15.011 m3/s, MAE = 10.439 m3/s, KGE = 0.832, WI = 0.773. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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299. Comprehensive CFD Aerodynamic Simulation of a Sport Motorcycle.
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Wiński, Krzysztof and Piechna, Adam
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COMPUTATIONAL fluid dynamics , *RAILROAD tunnels , *MOTORCYCLES , *MOTORCYCLISTS , *MOTORCYCLING , *AERODYNAMICS , *TURBULENCE - Abstract
Nowadays, aerodynamics is a key focal point in the vehicle design process. Beyond its direct impact on the performance of a vehicle, it also has significant effects on economics and safety. In the last decade numerical methods, mainly Computational Fluid Dynamics (CFD), have established themselves as a reliable tool that assists in the design process and complements classical tunnel tests. However, questions remain about the possible obtained accuracy, best practices and applied turbulence models. In this paper, we present a comprehensive study of motorcycle aerodynamics using CFD methods which, compared to the most common car aerodynamics analysis, has many specific features. The motorcycle, along with its rider, constitutes a shape with very complex aerodynamic properties. A detailed insight into the flow features is presented with detailed commentary. The front fairing, the front wheel and its suspension were identified as the main contributors to the aerodynamic drag of the motorcycle and its rider. The influence of rider position was also studied and identified as one of the most important elements when considering motorcycle aerodynamics. An extensive turbulence models study was performed to evaluate the accuracy of the most common Reynolds-averaged Navier–Stokes models and novel hybrid models, such as the Scale Adaptive Simulation and the Delayed Detached Eddy Simulation. Similar values of drag coefficients were obtained for different turbulence models with noticeable differences found for k − ϵ models. It was also observed that near-wall treatment affects the flow behaviour near the wheels and windshield but has no impact on the global aerodynamic parameters. In the summary, a discussion about the obtained results was set forth and a number of questions related to specifics of motorcycle CFD simulations were addressed. [ABSTRACT FROM AUTHOR]
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- 2022
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300. Wind speed forecasting based on hybrid model with model selection and wind energy conversion.
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Wang, Chen, Zhang, Shenghui, Liao, Peng, and Fu, Tonglin
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WIND speed , *WIND forecasting , *WIND power , *ENERGY conversion , *WIND power plants , *FORECASTING , *MATHEMATICAL optimization , *PERCENTILES - Abstract
As an important part of a power system, the usage of wind power is increasing rapidly and playing an indispensable role in energy planning. Therefore, efforts are needed to find and improve the accuracy of wind speed forecasting and the reliability of wind energy conversion, which play a vital role in the development of wind farms. In this paper, a novel multi-objective optimization algorithm is proposed to optimize the parameters of different models, a model selection strategy is used to select the optimal hybrid models for different datasets, to improve the accuracy and stability of the forecasting model. Wind power conversion is examined based on the wind speed forecasting, and found to be a feasible method for wind farms. The numerical results show that compared with the mean absolute percentage error values of the multi-hybrid models, that of the optimal model is reduced about 3%. Moreover, the standard deviation of the absolute percentage error is decreased about 3% for wind speed forecasting. In addition, the effectiveness of the model selection is verified using the onsite wind speed data of four wind farms, and the selected model is shown to be more reliable and accurate than other models. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
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