Mapping the spatio-temporal characteristics of wetland inundation has an important significance to the study of wetland environment and associated flora and fauna. High temporal remote sensing ...imagery is widely used for this purpose with the limitations of relatively low spatial resolutions. In this study, a novel method based on integration of back-propagation neural network (BP) and genetic algorithm (GA), so-called IBPGA, is proposed for super-resolution mapping of wetland inundation (SMWI) from multispectral remote sensing imagery. The IBPGA-SMWI algorithm is developed, including the fitness function and integration search strategy. IBPGA-SMWI was evaluated using Landsat TM/ETM+ imagery from the Poyanghu wetland in China and the Macquarie Marshes in Australia. Compared with traditional SMWI methods, IBPGA-SMWI consistently achieved more accurate super-resolution mapping results in terms of visual and quantitative evaluations. In comparison with GA-SMWI, IBPGA-SMWI not only improved the accuracy of SMWI, but also accelerated the convergence speed of the algorithm. The sensitivity analysis of IBPGA-SMWI in relation to standard crossover rate, BP crossover rate and mutation rate was also carried out to discuss the algorithm performance. It is hoped that the results of this study will enhance the application of median-low resolution remote sensing imagery in wetland inundation mapping and monitoring, and ultimately support the studies of wetland environment.
There are challenges for cloud detection over bright surfaces, especially for Sentinel-2 image without thermal band. In this paper, we developed a new cloud detection algorithm for Sentinel-2 data ...designed to tackle the challenges of cloud detection on bright targets. Our algorithm (called RF-NDTeI-SNIC) was built through the integration of Random Forest (RF) model, Normalized Difference Temporal Index (NDTeI) cloud index and Simple Non-Iterative Clustering (SNIC) image segmentation algorithm. First, RF and NDTeI were used to derive the initial and second stage's cloud detection results, respectively. Then, the RF- and NDTeI-based results were fused to remove the bright surfaces and land cover changes. Finally, the fusion results were further refined using SNIC for morphological processing. The proposed RF-NDTeI-SNIC algorithm was evaluated using nine challenging cloud-covered Sentinel-2 scenes with substantial bright surfaces. Results indicated that the average overall accuracy of our algorithm was 95.01%, with average commission rate of 10.19% for cloud. In addition, five commonly used Sentinel-2 cloud mask algorithms including s2cloudless, QA, Fmask, CDI and Tmask were selected for comparative analysis. Results suggested that our algorithm outperformed others with overall accuracies of 89.60-94.17% and cloud commission rate of 9.46-37.50%, and have significant advantages in terms of bright surfaces. In summary, the RF-NDTeI-SNIC algorithm we developed was capable of yielding accurate Sentinel-2 cloud masks, and the novel NDTeI cloud index we proposed gave a new and effective approach for improving the separability of clouds and bright surfaces.
Based on big data information technology, this paper analyzes the opportunities and challenges of business development strategies. The clustering algorithm and tree model algorithm in data mining is ...analyzed. In order to effectively solve the problem of big data classification in consumer-oriented enterprises, the Kmeans clustering algorithm and XGBoost algorithm in the two previous models are integrated to effectively avoid the problem of over-fitting when the models are used alone. The opportunities and challenges in the current stage of business development strategy are analyzed separately. The Kmeans-XGBoost algorithm is used to analyze the pricing and output of the enterprise for prediction. It is shown that the prediction curves of the Kmeans-XGBoost model basically match the actual values, and the confidence interval range is expanding from 3694.879,7202.897 to 2211.819,8406.462. Meanwhile, the errors of enterprise output prediction under different algorithms are analyzed. The error rate weighted by Kmeans-XGBoost mean is 42.63, which is lower than the traditional model prediction error in 4.
The present work introduces a newly-developed mathematical-based Multi-Algorithm Integration (MAI) technique, GIS weighted overlay model, and field investigations for delineating the potentially ...mineralized alteration zones. The introduced technique was tested on Semna area in Egypt as a case study. Three different algorithms were utilized to map the abundance of six alteration indicator minerals. The introduced MAI technique enabled combining the outputs of the utilized mapping algorithms allowing the delineation of only the localities that were successfully mapped as the potential for alteration zones occurrence by all the three algorithms together. Localities delineated by only one or even two techniques are not included in the output of the MAI technique, giving rise to a higher accuracy than that of individual techniques. The structural linear features were automatically depicted from a hill-shade image generated from a high-accuracy Digital Elevation Model (DEM). A Geographic Information System (GIS) Weighted Overlay Model (WOM) was then implemented to integrate the alteration maps resulting from the MAI technique with the density map of lineaments. A final hydrothermal-related mineralization potentiality map was obtained and validated through fieldwork and spectral analyses for confirming the occurrence of alteration indicator minerals. Samples were collected from the delineated potentially mineralized localities. The spectral signatures of the collected samples were measured, and spectral analyses were performed to reveal the identity of the existing alteration minerals. Various index alteration minerals were recorded in the measured samples including; kaolinite, montmorillonite, sericite, chlorite, carbonates (dolomite), and iron oxides/hydroxide minerals.
There have been many survey papers in the area of project scheduling in recent years. These papers have primarily emphasized modeling and algorithmic contributions for specific classes of project ...scheduling problems, such as net present value (NPV) maximization and makespan minimization, with and without resource constraints. Paralleling these developments has been the research in the area of project scheduling decision support, with its emphasis on data sets, data generation methods, and so on, that are essential to benchmark, evaluate, and compare the new models, algorithms and heuristic techniques. These investigations have extended the frontiers of research and application in all areas of project scheduling and management. In this paper, we survey the vast literature in this area with a perspective that integrates models, data, and optimal and heuristic algorithms, for the major classes of project scheduling problems. We also include recent surveys that have compared commercial project scheduling systems. Finally, we present an overview of web-based decision support systems and discuss the potential of this technology in enabling and facilitating researchers and practitioners in identifying new areas of inquiry and application.
Industrial Internet of Things (IIoT) applications are being used more and more frequently. Data collected by various sensors can be used to provide innovative digital services supporting increasing ...efficiency or cost reduction. The implementation of such applications requires the integration and analysis of heterogeneous data coming from a broad variety of sensors. To support these steps, this paper introduces OPAL, a software toolbox consolidating several software components for the semantically annotated integration and analysis of IoT-data. Data storage is realized in a standardized and INSPIRE-compliant way utilizing the SensorThings API. Supporting a broad variety of use cases, OPAL provides several import adapters to access data sources with various protocols (e.g., the OPC UA protocol, which is often used in industrial environments). In addition, a unified management and execution environment, called PERMA, is introduced to allow the programming language independent integration of algorithms.
New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional ...elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies.
•The methods for protein function prediction in the last five years are summarized.•Discussing methods apply multi-algorithm combinations to predict protein function.•Methods based on multi-information source prediction are discussed.•Difficulties are pointed out and breakthrough developments are emphasized.
Spectral unmixing is an important technique in hyperspectral image exploitation. It comprises the extraction of a set of pure spectral signatures (called endmembers in hyperspectral jargon) and their ...corresponding fractional abundances in each pixel of the scene. Over the last few years, many approaches have been proposed to automatically extract endmembers, which is a critical step of the spectral unmixing chain. Recently, ant colony optimization (ACO) techniques have reformulated the endmember extraction issue as a combinatorial optimization problem. Due to the huge computation load involved, how to provide suitable candidate endmembers for ACO is particularly important, but this aspect has never been discussed before in the literature. In this paper, we illustrate the capacity of ACO techniques for integrating the results obtained by different endmember extraction algorithms. Our experimental results, conducted using several state-of-the-art endmember extraction approaches using both simulated and a real hyperspectral scene (cuprite), indicate that the proposed ACO-based strategy can provide endmembers which are robust against noise and outliers.
Person re-identification(RE-ID) has played a significant role in the fields of image processing and computer vision because of its potential value in practical applications. Researchers are striving ...to design new algorithms to improve the performance of RE-ID but ignore the advantages of existing approaches. In this paper, motivated by deep reinforcement learning, we propose a Deep Agent which can integrate existing algorithms and enable them to complement each other. Two Deep Agents are designed to integrate algorithms for data augmentation and feature extraction parts separately for RE-ID. Experiment results demonstrate that the integrated algorithms can achieve a better accuracy than using each one of them alone.
To understand and solve various natural environmental problems, geoscience research activities are becoming increasingly dependent on the integration of knowledge, data, and algorithms from ...scientists at different institutes and with multiple perspectives. However, the facilitation of these integrations remains a challenge because such scientific activities require gathering numerous geoscience researchers to provide data, knowledge, algorithms, and tools from different institutes and geographically distributed locations. The pivotal issue that needs to be addressed is the identification of a method to effectively combine geoscience algorithms in a distributed environment to promote cooperation. To address this issue, in this paper, a scheme for building a distributed geoscience algorithm integration based on the Open Geospatial Consortium web service (OWS) specifications is proposed. The architecture of the geoscience algorithm integration, algorithm service management mechanism, XML description method for algorithm integration, and integrated model execution strategy are designed and implemented. The experiment implements the integration of geoscience algorithms in a distributed cloud environment and evaluates the feasibility and efficiency of the integrated geoscience model. The proposed method provides a theoretical basis and practical guidance for promoting the integration of distributed geoscience algorithms; this approach can help to aggregate the distributed geoscience capabilities to address natural challenges.
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