Efficient Hybrid Methods Research Articles

Strategies for biological treatment of waste water: A critical review

Global water demand is continuously growing due to rising population and associated industrial and agricultural activities. However, these factors are also polluting the existing water bodies and therefore, limiting the water supply. Increasing level of contaminants poses risk to human, wildlife and the environment. Widening gap between water demand and supply creates water stress. Therefore, treatment of waste water becomes imperative in order to ensure sustainable development of planet earth. Conventional methods utilizing physical, chemical and biological processes are generally adopted for this purpose. These methods are further modified so as to enhance the efficiency and economy of the treatment process as well as to make them more eco-friendly. None of the conventional or advance technology is individually sufficient for appropriate treatment of wastewater. Therefore, more efficient hybrid methods are required. The development of such hybrid methods needs detailed information about various treatment methods. This paper critically reviews the conventional and advanced biological methods used for waste water treatment. The main objective of this review is to present vital information about various biological treatment methods forming the basis for the development of sustainable hybrid methods for waste water treatment.

Prior knowledge and active learning enable hybrid method for estimating leaf chlorophyll content from multi-scale canopy reflectance

Real-time and accurate assessment of leaf chlorophyll content (Cab) will be significant for monitoring plant physiological status. Development of hybrid methods advances the assessment of Cab with the advantages of both physical mechanism and calculation capability, while the estimation accuracies were constrained by the lacking of prior knowledge and representative real samples. This study explored the use of prior knowledge and active learning to enable the development of efficient hybrid methods for assessing Cab from multi-scale canopy reflectance. The hybrid method was established by using the simulated dataset from the PROSAIL model to train the Gaussian process regression (GPR) model. We used two measured crop datasets and three publicly-available grassland, herbaceous, and tree species datasets collected from near-ground, unmanned aerial vehicle, and airborne platforms to examine the model performances. Prior knowledge for model parameters was obtained from ground measurements, and an active learning method, namely improved Greedy sampling (iGS), was employed to select new representative samples. The results showed that performance of the hybrid method in Cab estimations varied with leaf structure parameter, carotenoid content, anthocyanin content, carbon-based constituents, average leaf angle, leaf area index, and hot-spot size parameter. When prior knowledge for the ranges of model parameters was available, the hybrid method better estimated Cab with the root mean square error (RMSE) reduced by 7.59%–65.96%. Compared with the random selection, iGS was able to select representative samples to update the estimation models with only 10% of the measured samples, and a higher ratio (e.g., 20%) may be needed for small datasets. The hybrid method coupled with prior knowledge and active learning obtained satisfied estimations of Cab across different datasets with the relative RMSE of 12.30%–27.24%. The proposed method will contribute to improving the applicability and robustness of hybrid modeling for multi-scale monitoring of leaf biochemical traits.

Aspect-based Text Classification for Sentimental Analysis using Attention mechanism with RU-BiLSTM

Sentiment analysis has gained increasing attention from an educational and social perspective with the huge expansion of user interactions due to the Web’s significant improvement. The connection between an opinion target’s polarity scores and other aspects of the content is defined by aspect-based sentiment analysis. Identifying aspects and determining their different polarities is quite complicated because they are frequently implicit. To overcome these difficulties, efficient hybrid methods are used in aspect-based text classification in sentiment analysis. The existing process evaluates the aspects of polarity by using a Convolutional neural network, and it does not work with Big data. In this work, aspect-based text classification and attention mechanisms are used to assist in filtering out irrelevant information and quickly locating the essential features in big data. Initially, the data is collected, and then the data is preprocessed by using Tokenization, Stop word removal, Stemming, and Lemmatization. After preprocessing, the features are vectorized and extracted using Bag-of-Words and TF-IDF. Then, the extracted features are given into word embeddings by GloVe and Word2vec. It uses Deep Recurrent based Bidirectional Long Short Term Memory (RUBiLSTM) for aspect-based sentiment analysis. The RU-Bi-LSTM method integrates aspect-based embeddings and an attention mechanism for text classification. The attention mechanism focuses on more crucial aspects and the bidirectional LSTM to maintain context in both ways. Finally, the binary and ternary classification outcomes are obtained using the final dense softmax output layer. The proposed RU-BiLSTM uses four reviews and two Twitter datasets. The results of the studies demonstrate the efficacy of the RU-BiLSTM model, which outperformed aspect-based classifications on lengthy reviews and short tweets in terms of evaluation.

Study of Time-Fractional Nonlinear Model Governing Unsteady Flow of Polytropic Gas

The present study is concerned with studying the dynamical behavior of two space-dimensional nonlinear time-fractional models governing the unsteady-flow of polytropic-gas (in brief, pGas) that occurred in cosmology and astronomy. For this purpose, two efficient hybrid methods so-called optimal homotopy analysis J-transform method (OHAJTM) and J-variational iteration transform method (J-VITM) have been adopted. The OHAJTM is the hybrid method, where optimal-homotopy analysis method (OHAM) is utilized after implementing the properties of J-transform (JT), and in J-VITM is the J-transform-based variational iteration method. Banach’s fixed point approach is adopted to analyze the convergence of these methods. It is demonstrated that J-VITM is T-stable, and the evaluated dynamics of pGas are described in terms of Mittag–Leffler functions. The proposed evaluation confirms that the implemented methods perform better for the referred model equation of pGas. In addition, for a given iteration, the proposed behavior via OHAJTM performs better in producing more accurate behavior in comparison to J-VITM and the methods introduced recently.

Linear optimal weighting estimator (LOWE) for efficient parallel hybridization of load forecasts

PurposeImproving the accuracy and reducing computational costs of predictions, especially the prediction of time series, is one of the most critical parts of the decision-making processes and management in different areas and organizations. One of the best solutions to achieve high accuracy and low computational costs in time series forecasting is to develop and use efficient hybrid methods. Among the combined methods, parallel hybrid approaches are more welcomed by scholars and often have better performance than sequence ones. However, the necessary condition of using parallel combinational approaches is to estimate the appropriate weight of components. This weighting stage of parallel hybrid models is the most effective factor in forecasting accuracy as well as computational costs. In the literature, meta-heuristic algorithms have often been applied to weight components of parallel hybrid models. However, such that algorithms, despite all unique advantages, have two serious disadvantages of local optima and iterative time-consuming optimization processes. The purpose of this paper is to develop a linear optimal weighting estimator (LOWE) algorithm for finding the desired weight of components in the global non-iterative universal manner.Design/methodology/approachIn this paper, a LOWE algorithm is developed to find the desired weight of components in the global non-iterative universal manner.FindingsEmpirical results indicate that the accuracy of the LOWE-based parallel hybrid model is significantly better than meta-heuristic and simple average (SA) based models. The proposed weighting approach can improve 13/96%, 11/64%, 9/35%, 25/05% the performance of the differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO) and SA-based parallel hybrid models in electricity load forecasting. While, its computational costs are considerably lower than GA, PSO and DE-based parallel hybrid models. Therefore, it can be considered as an appropriate and effective alternative weighing technique for efficient parallel hybridization for time series forecasting.Originality/valueIn this paper, a LOWE algorithm is developed to find the desired weight of components in the global non-iterative universal manner. Although it can be generally demonstrated that the performance of the proposed weighting technique will not be worse than the meta-heuristic algorithm, its performance is also practically evaluated in real-world data sets.

A Survey on Pavement Sectioning in Network Level and an Intelligent Homogeneous Method by Hybrid PSO and GA

Pavement homogenous sectioning is a vital step in pavement management system (PMS) analysis. Sections can be created using either fixed, dynamic or static sectioning principles. Each of these has various implications with regard to the data collection and decision making strategy of management. Pavement management with true homogeneous section selection has great importance for cost minimization over a specified time period when modifying the pavement deterioration based on correct decisions in the PMS. This issue was proposed for cost reduction, minimization of sectioning errors, and accuracy improvement of pavement network analysis. Thus, the focus of this research is to investigate efficient hybrid methods applied for reducing complexity involved in this problem. Results show that various combinations of hybrid particle swarm optimization (PSO) and genetic algorithm (GA) were used for analysis of a given pavement network that play a better role as section makers than single GA or PSO in terms of network sectioning error, computation time (CPU time), and number of sections as well as convergence diagrams for network, project, and section management levels. Results indicated that hybrid approaches provide a highly suitable solution in a short time for each pavement branch in massive networks with big data and minimize the costs involved in the sectioning process.

Two Efficient Hybrid Methods for Enhancing Pan-Sharpening of Multi-spectral Images Transmitted from Satellite to Ground Stations

Pan-sharpening is one of the most important tasks performed on satellite images as it enhances spectral and spatial information of the images. Empirical mode decomposition (EMD) is one of the powerful methods for pan-sharpening. It first decomposes the image into a set of intrinsic mode functions and a residual component. These panchromatic and multi-spectral components are then fused to create an enhanced pan-sharpened image. This paper presents two efficient hybrid methods to enhance pan-sharpening of multi-spectral images. The new proposals combine the EMD with the two pan-sharpening methods: high-pass filtering and discrete wavelet transform to maximize the pan-sharpened image quality. The two methods are evaluated using satellite images of Cairo and Suez Canal region, Egypt, captured by Spot-4 and Landsat-8 satellites, respectively. The results imply that the proposed hybrid methods provide better qualitative and quantitative results compared to the individual and the common pan-sharpening methods.

Methodolo- gical Aspects of Semantic Relationship Extraction for Automatic Thesaurus Generation

The paper is devoted to analysis of methods for automatic generation of a specialized thesaurus. The main algorithm of generation consists of three stages: selection and preprocessing of a text corpus, recognition of thesaurus terms, and extraction of relations among terms. Our work is focused on exploring methods for semantic relation extraction. We developed a test bench that allow to test well-known algorithms for extraction of synonyms and hypernyms. These algorithms are based on different relation extraction techniques: lexico-syntactic patterns, morpho-syntactic rules, measurement of term information quantity, general-purpose thesaurus WordNet, and Levenstein distance. For analysis of the result thesaurus we proposed a complex assessment that includes the following metrics: precision of extracted terms, precision and recall of hierarchical and synonym relations, and characteristics of the thesaurus graph (the number of extracted terms and semantic relationships of different types, the number of connected components, and the number of vertices in the largest component). The proposed set of metrics allows to evaluate the quality of the thesaurus as a whole, reveal some drawbacks of standard relation extraction methods, and create more efficient hybrid methods that can generate thesauri with better characteristics than thesauri generated by using separate methods. In order to illustrate this fact, one of such hybrid methods is considered in the paper. It combines the best standard algorithms for hypernym and synonym extraction and generates a specialized medical thesaurus. The hybrid method leaves the thesaurus quality on the same level and finds more relations between terms than well-known algorithms.

IdDock+: Integrating Machine Learning in Probabilistic Search for Protein-Protein Docking.

Predicting the three-dimensional native structures of protein dimers, a problem known as protein-protein docking, is key to understanding molecular interactions. Docking is a computationally challenging problem due to the diversity of interactions and the high dimensionality of the configuration space. Existing methods draw configurations systematically or at random from the configuration space. The inaccuracy of scoring functions used to evaluate drawn configurations presents additional challenges. Evidence is growing that optimization of a scoring function is an effective technique only once the drawn configuration is sufficiently similar to the native structure. Therefore, in this article we present a method that employs optimization of a sophisticated energy function, FoldX, only to locally improve a promising configuration. The main question of how promising configurations are identified is addressed through a machine learning method trained a priori on an extensive dataset of functionally diverse protein dimers. To deal with the vast configuration space, a probabilistic search algorithm operates on top of the learner, feeding to it configurations drawn at random. We refer to our method as idDock+, for informatics-driven Docking. idDock+is tested on 15 dimers of different sizes and functional classes. Analysis shows that on all systems idDock+finds a near-native structure and is comparable in accuracy to other state-of-the-art methods. idDock+ represents one of the first highly efficient hybrid methods that combines fast machine learning models with demanding optimization of sophisticated energy scoring functions. Our results indicate that this is a promising direction to improve both efficiency and accuracy in docking.

Solving a robotic assembly line balancing problem using efficient hybrid methods

In this paper we are studying a robotic assembly line balancing problem. The goal is to maximize the efficiency of the line and to balance the different tasks between the robots by defining the sui...

Unsteady Reynolds-Averaged Navier-Stokes-Based Hybrid Methodologies for Rotor-Fuselage Interaction

L IFTING bodies produce wakes that interact with other bodies immersed in the same fluid. In particular for rotorcraft, the problem becomes significantly more complicated since the rotor wake remains near the vehicle in hover, descent, and low-speed forward flight. The proximity of the wake alters the inflow distribution at the rotor andmodifies the helicopter thrust.Moreover, since the main rotor wake may impinge on the fuselage, such interactions are an important consideration in modern rotorcraft design. For example, empennage impingement may result in undesirable handling qualities such as low-speed pitchup and tail buffet. Moreover, the wake can also generate unsteady impulsive loads on the fuselage, resulting in vibrations, thus negatively impacting the crew and passenger flight experience. Given the complexity of rotorcraft interactional aerodynamics problems, it is common for tail and empennage designs to be modified significantly after first flight [1]. Development of many aerospace technologies, not limited to helicopter rotor–fuselage applications, requires accurate resolution of both nearand far-field flow phenomena. Numerical prediction of wakes involve a tradeoff between accuracy, turnaround time, and computational expense [2]. Current grid-based computational fluid dynamics (CFD) codes can theoretically model the entire flowfield, but resolution and preservation of wake features become difficult since typical grid sizes used in industrial simulations are susceptible to numerical dissipation. The artificial diffusion of vorticity that results can be mitigated using grid adaptation techniques and higherorder methods [2–4], but this may not be practical for all applications since computational cost increases significantly. For this reason, computationally efficient hybrid methods may be more attractive, especially during design and for flight-test support. Traditional Lagrangian free-wake methods are inexpensive but become less accurate when vortex elements in the wake become distorted and tangled due to interactions with other vortices and solid bodies (i.e., rotor blades and the fuselage) [5]. These interactions typically occur in the rotor near field, which motivates coupling to a CFD solver to resolve the highly viscous and possibly compressible flow near the rotor. In such an approach, the CFD code does not have to resolve the entire wake region; thus, the size of the CFD domain can be greatly reduced and computational efficiency maximized. Additional challenges associated with surface interactions arise when modeling problems such as rotor–fuselage interactions. The ability of two approaches to hybridize a Reynolds-averaged Navier–Stokes (RANS) CFD solver and a free-wake method for the rotor–fuselage interaction problem is investigated. Predictions are compared with experimental data, as well as prior numerical predictions made with individual code simulations.

Computation strategies of orthogonal image moments: A comparative study

This paper discusses possible computation schemes that have been introduced in the past and cope with the efficient computation of the orthogonal image moments. An exhaustive comparative study of these alternatives is performed in order to investigate the conditions under which each scheme ensures high computation rates, for several test images. The present study aims to discover the properties and the behaviour of the different methodologies and it serves as a reference point in the field of moment's computation. Some useful conclusions are drawn regarding the applicability and the usefulness of the computation strategies in comparison and efficient hybrid methods are proposed to better utilize their advantages.

Hybrid conjugate gradient methods for unconstrained optimization

In this paper we propose two kinds of conjugate gradient methods for unconstrained optimization, based on the combinations of the presented conjugate gradient methods. The methods can be regarded as the modifications of the efficient hybrid methods proposed by Touati-Ahmed-Storey and Dai-Yuan. Under mild conditions, globally convergent results are proved. Primary numerical results of the new methods are encouraging.

Efficient hybrid methods for portfolio credit derivatives

In this paper we discuss the computation of basket credit default swaps and collateralized debt obligation squared transactions. We suggest two hybrid algorithms for these two portfolio credit derivatives. The method combines the analytic approximation to the loss distribution of conditionally independent heterogeneous portfolios with the Monte Carlo simulation. The efficiency and accuracy of the algorithms are illustrated with examples.

Chan Tony F, Shen Jianhong (Jackie): Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods.

Chan Tony F, Shen Jianhong (Jackie): Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods.

Efficient hybrid methods for global continuous optimization based on simulated annealing

We introduce several hybrid methods for global continuous optimization. They combine simulated annealing and a local proximal bundle method. Traditionally, the simplest hybrid of a global and a local solver is to call the local solver after the global one, but this does not necessarily produce good results. Besides, using efficient gradient-based local solvers implies that the hybrid can only be applied to differentiable problems. We show several ways how to integrate the local solver as a genuine part of simulated annealing to enable both efficient and reliable solution processes. When using the proximal bundle method as a local solver, it is possible to solve even nondifferentiable problems. The numerical tests show that the hybridization can improve both the efficiency and the reliability of simulated annealing.

Large-scale reliability-based structural optimization

A robust and efficient methodology is presented for treating large-scale reliability-based structural optimization problems. The optimization is performed with evolution strategies, while the reliability analysis is carried out with the Monte Carlo simulation method incorporating the importance sampling technique to reduce the sample size. Efficient hybrid methods are implemented to solve the reanalysis-type problems that arise in the optimization phase with evolution strategies and in the reliability analysis with Monte Carlo simulations. These hybrid solution methods are based on the preconditioned conjugate gradient algorithm using efficient preconditioning schemes. The numerical tests presented demonstrate the computational advantages of the proposed methods, which become more pronounced for large-scale optimization problems.

Various hybrid methods based on genetic algorithm with fuzzy logic controller

In this paper we propose several efficient hybrid methods based on genetic algorithms and fuzzy logic. The proposed hybridization methods combine a rough search technique, a fuzzy logic controller, and a local search technique. The rough search technique is used to initialize the population of the genetic algorithm (GA), its strategy is to make large jumps in the search space in order to avoid being trapped in local optima. The fuzzy logic controller is applied to dynamically regulate the fine-tuning structure of the genetic algorithm parameters (crossover ratio and mutation ratio). The local search technique is applied to find a better solution in the convergence region after the GA loop or within the GA loop. Five algorithms including one plain GA and four hybrid GAs along with some conventional heuristics are applied to three complex optimization problems. The results are analyzed and the best hybrid algorithm is recommended.

Efficient parallel multivalue hybrid methods for stiff differential equations

A class of efficient parallel multivalue hybrid methods for stiff differential equations are presented, which are all extremely stable at infinity, A-stable for orders 1—3 and A(α)-stable for orders 4—8. Each method of the class can be performed parallelly using two processors with each processor having almost the same computational amount per integration step as a backward differentiation formula (BDF) of the same order with the same stepsize performed in serial, whereas the former has not only much better stability properties but also a computational accuracy higher than the corresponding BDF. Theoretical analysis and numerical experiments show that the methods constructed in this paper are superior in many respects not only to BDFs but also to some other commonly used methods.

Hybrid methods for large sparse nonlinear least squares

Hybrid methods are developed for improving the Gauss-Newton method in the case of large residual or ill-conditioned nonlinear least-square problems. These methods are used usually in a form suitable for dense problems. But some standard approaches are unsuitable, and some new possibilities appear in the sparse case. We propose efficient hybrid methods for various representations of the sparse problems. After describing the basic ideas that help deriving new hybrid methods, we are concerned with designing hybrid methods for sparse Jacobian and sparse Hessian representations of the least-square problems. The efficiency of hybrid methods is demonstrated by extensive numerical experiments.