Relevance Weights Research Articles

Construction of the China financial pressure index measurement model based under the AHP-EWM-TOPSIS model

With the complexity of the internal structure of the financial market, systemic financial risks occur frequently. The cross cycle of the real economy and the financial system makes the bad problems in the economic development enter into the financial system with the transmission mechanism, thus leading to the reduction of the macroeconomic stability. Therefore, the foundation for preventing systemic financial risks and economic crises in the financial market is the accurate detection of pertinent potential threats. Based on relevant literature, combined with the characteristics of China’s financial system, this paper selects indicators from four dominant markets: banking market, stock market, bond market and foreign exchange market. Analytic hierarchy process (AHP) and entropy weight method (EWM) are used to calculate the relevant weights of each index, and the AHP-EWM coupling weight is calculated based on the Lagrange multiplier method. Then, the coupling weight of each index is substituted into the TOPSIS model to measure the comprehensive influence degree of multi-evaluation objects, and the pressure threshold and abnormal risk situation of China’s financial market are analyzed accordingly, so as to realize the construction of CFSI measurement model.The results show that:(1)The fluctuation of CFSI over the years is within the controllable range of 0.2~0.5, with few serious outliers;(2)The whole trend has a development trend to the medium and low regions, accompanied by the characteristics of rapid fluctuation and smaller amplitude;(3)The high-risk time zone identified by the model presents a strong inertial effect, with a large fluctuation span and a long duration;(4)The stability of financial pressure in the low-risk area is stronger than in the-high risk area, and it can quickly recover to the stable stage and develop towards the trend of low pressure.

Depth-Adaptive Deep Neural Network Based on Learning Layer Relevance Weights

In this paper, we propose two novel Adaptive Neural Network Approaches (ANNAs), which are intended to automatically learn the optimal network depth. In particular, the proposed class-independent and class-dependent ANNAs address two main challenges faced by typical deep learning paradigms. Namely, they overcome the problems of setting the optimal network depth and improving the model interpretability. Specifically, ANNA approaches simultaneously train the network model, learn the network depth in an unsupervised manner, and assign fuzzy relevance weights to each network layer to better decipher the model behavior. In addition, two novel cost functions were designed in order to optimize the layer fuzzy relevance weights along with the model hyper-parameters. The proposed ANNA approaches were assessed using standard benchmarking datasets and performance measures. The experiments proved their effectiveness compared to typical deep learning approaches, which rely on empirical tuning and scaling of the network depth. Moreover, the experimental findings demonstrated the ability of the proposed class-independent and class-dependent ANNAs to decrease the network complexity and build lightweight models for less overfitting risk and better generalization.

Machine learning and ontology-based novel semantic document indexing for information retrieval

The goal of information retrieval (IR) systems is to find the contents most closely related to the user's information needs from a pool of information. However, conventional IR methods neglect semantic descriptions of document contents and index documents based on the words that they include. When users and indexing systems use different terms to express the same subject, a vocabulary gap emerges. To overcome this limitation and to enhance the effectiveness of the IR systems, this paper introduced a novel hybrid semantic document indexing employing machine learning and domain ontology. The presented technique uses a skip-gram with negative sampling-based machine learning model and a domain ontology to determine the concepts for annotating unstructured documents. The proposed work also introduced multiple feature based novel concept ranking algorithm where statistical, semantic, and scientific named entity features of the concept were used to assign relevance weight to the annotations. The fuzzy analytical hierarchy process was used to derive the parameters of these feature weights. The final step is to rank the concepts according to their relevance to the document. Five benchmark publicly accessible datasets from the computer science domain were used in a series of experiments to validate the results of presented method. Experiment findings showed that the proposed method performs better than state-of-the-art techniques on these datasets, by improving average accuracy by 29%, while an improvement of 25% was recorded in F-measure. The improvement in average accuracy demonstrates that the performance of the proposed approach is better than the state-of-the-art methods in extracting document concepts accurately even when the same concept is referred to by distinct terms in the document and domain ontologies. The proposed system's ability to find similar concepts when the documents possess no concept from domain ontology is demonstrated by the improvement in F-measure, which is attributed to high recall rates of the proposed indexing scheme while maintaining high accuracy.

Selecting the Best-Performing Low-Cost Carrier (LCC) Airlines Using Analytical Hierarchy Process (AHP) and Elimination et Choix Traduisant la Realite (ELECTRE)

Background: Low-cost carrier (LCC) is a popular air transportation service as it offers affordable fares. Many airlines have adopted the LCC system because they need to adapt to the changes in the airline industry. The competition is tight. Despite the low cost, consumers demand quality services. Therefore, LCC airlines need to find their competitive edge. Objective: This study aims to determine the best-performing LCC airlines, the criteria, and the sub-criteria to improve the performance. Methods: This study uses two methods from multi-criteria decision-making (MCDM), namely the analytical hierarchy process (AHP) and elimination et choix traduisant la realite (ELECTRE) II. The MCDM is selected for this study because there are four criteria and 21 sub-criteria to evaluate airline performance. The AHP method selects subcriteria that affect airline customer satisfaction. It solves complex problems by establishing a hierarchy. After being assessed by relevant parties, weights or priorities are developed. The results are used to determine the best-performing airline. Meanwhile, the ELECTRE II method ranks the airline’s alternatives. This method is straightforward and widely used in the MCDM. Results: The results indicate that four criteria and 18 sub-criteria affect the performance of LCC airlines in Indonesia. The LCC airline with the best performance is AirAsia, followed by Citilink, Wings Air, and Lion Air. Conclusion: This research integrates the AHP and ELECTRE II methods in evaluating the performance of LCC airlines. This research also provides information about the criteria and sub-criteria to improve airline performance, hence, the customer experience.

Vector batch SOM algorithms for multi-view dissimilarity data

Multi-view data has become fairly important since large amounts of information are constantly generated from different sources. So far, most multi-view research on unsupervised learning has focused on clustering algorithms suitable for analyzing vector data. Instead, our study presents two batch SOM algorithms for multi-view dissimilarity data, namely a batch SOM with each cluster representative as a vector of a subset of objects (vector of set-medoids) and another batch SOM for multi-view dissimilarity data with each cluster representative as a matrix whose elements are the weights of its objects regarding the dissimilarity matrices (weighted medoids). These algorithms are suitable for clustering and visualizing multi-view dissimilarity data. In addition, they provide the relevance weights on the importance of each dissimilarity matrix (view) concerning each cluster related to the neurons in the map. Furthermore, the weight can be computed both locally, for each cluster, or globally, for the whole partition. Experiments with 12 datasets showed the importance of multi-view algorithms with respect to single-view algorithms in some contexts. Among the multi-view algorithms, the results showed the need to consider the relevance weights of the dissimilarity matrices. Moreover, an application study on the FOREST TYPE dataset found that some of the proposed methods provide better-quality maps.

Reliability Evaluation Method of New Energy Distribution Network Based on Improved Correlation Weight Method

The reliability of the distribution system includes adequacy and security. Adequacy refers to the degree to which the system capacity meets the power and energy demand of the load. Security is the evaluation of the system’s ability to withstand sudden failures. In view of the uncertainty of decision factors and the difficulty to quantify the weights in the process of reliability evaluation of distribution network, an evaluation method for load restoration of a smart distribution network based on the improved relevance weight with index correlation and improved analytic hierarchy process (AHP) is proposed. The paper also proposes an evaluation system including distribution network structure level indexes. The planning scheme of the distribution network is comprehensively evaluated, and the effectiveness of the method is analyzed through the comparison of actual cases combined with the indicators of the grid structure level. Experimental analysis shows that compared with the traditional power supply reliability or system average outage time, the global index considering LRP is more refined in describing the impact of outage, and the evaluation results are more accurate.

Vulnerability Mapping of Groundwater Resources of Mekelle City and Surroundings, Tigray Region, Ethiopia

The management and monitoring of the quality of water resources in the Mekelle area are challenging, due to both geogenic and anthropogenic impacts. The extent of these impacts and the sources of pollution in this area have not been thoroughly investigated. In this article, a mapping of water resources vulnerability was carried out using the DRASTIC method and a modified DRASTIC vulnerability map was produced. Single-parameter and map-removal sensitivity analyses were performed on the relevant rates and weights. A final DRASTIC vulnerability index, varying from 54 to 140, was divided into four vulnerability classes: low (225.7 km2), medium (302.8 km2), high (307.2 km2), and very high (187.6 km2); the values in the parentheses indicate the corresponding areal coverage of each class. Similarly, a modified DRASTIC vulnerability index, ranging from 91 to 192, was divided into four vulnerability classes: low (166.4 km2), medium (266.8 km2), high (338.0 km2), and very high (252.2 km2) Nitrates were used to validate both models. in which moderate positive correlations (with Pearson’s correlation coefficient, r) of 0.681 and 0.702 were calculated for the DRASTIC and modified DRASTIC indices, respectively. A comparison of the two maps showed that significant sources of pollution are located in areas with high to very high vulnerability. The results of this research work can be used for the protection and monitoring of groundwater resources in the Mekelle area.

The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning

Endometrial cancer (EC) diagnostics is evolving into a system in which molecular aspects are increasingly important. The traditional histological subtype-driven classification has shifted to a molecular-based classification that stratifies EC into DNA polymerase epsilon mutated (POLEmut), mismatch repair deficient (MMRd), and p53 abnormal (p53abn), and the remaining EC as no specific molecular profile (NSMP). The molecular EC classification has been implemented in the World Health Organization 2020 classification and the 2021 European treatment guidelines, as it serves as a better basis for patient management. As a result, the integration of the molecular class with histopathological variables has become a critical focus of recent EC research. Pathologists have observed and described several morphological characteristics in association with specific genomic alterations, but these appear insufficient to accurately classify patients according to molecular subgroups. This requires pathologists to rely on molecular ancillary tests in routine workup. In this new era, it has become increasingly challenging to assign clinically relevant weights to histological and molecular features on an individual patient basis. Deep learning (DL) technology opens new options for the integrative analysis of multi-modal image and molecular datasets with clinical outcomes. Proof-of-concept studies in other cancers showed promising accuracy in predicting molecular alterations from H&E-stained tumor slide images. This suggests that some morphological characteristics that are associated with molecular alterations could be identified in EC, too, expanding the current understanding of the molecular-driven EC classification. Here in this review, we report the morphological characteristics of the molecular EC classification currently identified in the literature. Given the new challenges in EC diagnostics, this review discusses, therefore, the potential supportive role that DL could have, by providing an outlook on all relevant studies using DL on histopathology images in various cancer types with a focus on EC. Finally, we touch upon how DL might shape the management of future EC patients.

Grid adaptive reduced-order model of fluid flow based on graph convolutional neural network

In the interdisciplinary field of data-driven models and computational fluid mechanics, the reduced-order model for flow field prediction is mainly constructed by a convolutional neural network (CNN) in recent years. However, the standard CNN is only applicable to data with Euclidean spatial structure, while data with non-Euclidean properties can only be convolved after pixelization, which usually leads to decreased data accuracy. In this work, a novel data-driven framework based on graph convolution network (GCN) is proposed to allow the convolution operator to predict fluid dynamics on non-uniform structured or unstructured mesh data. This is achieved by the fact that the graph data inherit the spatial characteristics of the mesh and by the message passing mechanism of GCN. The conversion method from the form of mesh data to graph data and the operation mechanism of GCN are clarified. Moreover, additional relevance features and weight loss function of the dataset are also investigated to improve the model performance. The model learns an end-to-end mapping between the mesh spatial features and the physical flow field. Through our studies of various cases of internal flow, it is shown that the proposed GCN-based model offers excellent adaptability to non-uniformly distributed mesh data, while also achieving a high accuracy and three-order speedup compared with numerical simulation. Our framework generalizes the graph convolution network to flow field prediction and opens the door to further extending GCN to most existing data-driven architectures of fluid dynamics in the future.

System Model of Shipping Enterprise Safety Culture Based on Dynamic Calculation Matrix Model

Abstract This paper establishes shipping enterprises’ safety culture system model based on the dynamic calculation matrix model. This paper calculates the deep learning theory’s shipping safety early warning system. At the same time, we use the fuzzy evaluation method to establish the relevant weights of the safety culture index system. The method model mentioned in this paper effectively avoids the subjectivity of the pure expert evaluation method. We use experimental simulations to verify the effectiveness of our model. Experiments show that the system in this paper has the advantages of reliable prediction results and high precision.

The Integrated Development of Tourism and Cultural and Creative Industries Based on Smart City Cloud Service Information System

In response to the integration of tourism and media, this paper proposes research on the integration of tourism and its practice, leadership, and creative business based on smart city weather information. Based on this research, a systematic investigation and evaluation of smart city tourism competition models are conducted to accurately measure and influence the growth and competitiveness of smart cities, build smart tourism cities, and bring health benefits. Build China’s smart tourism city and enhance the international competitiveness of China’s tourism industry. Second, develop a measurement process. As for the evaluation criteria of tourism competitiveness of smart tourism cities, this paper mainly adopts the methods of single index evaluation and expert experience scoring. Select single indicators, especially quantitative indicators, and evaluate about relevant national, local, and industrial standard values. For qualitative indicators, based on the determination of relevant weights, five grades I, II, III, IV, and V are adopted, with a total score of 100, and the corresponding scores of the five grades are ≤50, 50–59, 60–69, 70–89, and ≥90, so as to determine the evaluation standard of smart Tourism city. The research shows that the research on the integration of tourism industry and cultural and creative industry based on smart city weather data is effective.

Gesture Recognition in Robotic Surgery With Multimodal Attention.

Automatically recognising surgical gestures from surgical data is an important building block of automated activity recognition and analytics, technical skill assessment, intra-operative assistance and eventually robotic automation. The complexity of articulated instrument trajectories and the inherent variability due to surgical style and patient anatomy make analysis and fine-grained segmentation of surgical motion patterns from robot kinematics alone very difficult. Surgical video provides crucial information from the surgical site with context for the kinematic data and the interaction between the instruments and tissue. Yet sensor fusion between the robot data and surgical video stream is non-trivial because the data have different frequency, dimensions and discriminative capability. In this paper, we integrate multimodal attention mechanisms in a two-stream temporal convolutional network to compute relevance scores and weight kinematic and visual feature representations dynamically in time, aiming to aid multimodal network training and achieve effective sensor fusion. We report the results of our system on the JIGSAWS benchmark dataset and on a new in vivo dataset of suturing segments from robotic prostatectomy procedures. Our results are promising and obtain multimodal prediction sequences with higher accuracy and better temporal structure than corresponding unimodal solutions. Visualization of attention scores also gives physically interpretable insights on network understanding of strengths and weaknesses of each sensor.

MULTI-CRITERIA ANALYSIS IN THE MANAGEMENT OF TEMPORARY REUSE OF SPACES

Abstract. Urban change has always marked the way of rethinking cities, bringing continuous studies aimed at urban regeneration. In contrast to traditional approaches, centered on the duality of demolition and construction, the temporary reuse of spaces addresses the issue of urban transformation with a multi-criteria approach involving the participation of community representatives.Analytic-hierarchical methods are support systems based on multiple criteria able to process a large number of different factors. Analytic-Hierarchy Process (AHP) is a specific instance of the general methods by means of which weights and quantities of an element, respect to the category taken into consideration, are assigned.The multi-criteria fuzzy criterion, applied in a study-case to three different alternatives (hypotheses) of reusing industrial ex-spaces abandoned in a municipality of the Milan province (Italy,) allows one to select the best alternatives in all cases in which many selection choices (spaces typology, required functions, benefits, timing) are possible and the optimal choice can be achieved by analyzing the different scopes of each criterion and sub-criterion as well as the relevant weights. Criteria are mutually compared in terms of the global objective while the alternatives are compared as function of the criterion. As outcome of this operative sequence the most sustainable solution, among the three different alternatives of the temporary re-use of the urban spaces addressed in the study-case, measures the relative priority of the alternatives existing at the lower level with respect to the achievement of the main objective and it is implemented according to the lowest economic cost.

Diversified and Sustainable Business Strategy of Smallholder Farmers in the Suburbs of Taiwan

Taiwan’s smallholder farmers are transforming into a diversified business model to respond to the transformation of economic structures and the demand for sightseeing and recreation. On that account, it is urgent and valuable at the research level to develop diversified and sustainable business strategies and models for smallholder farmers. There are two goals in this study. First of all, the study formulated the indicators of diversified and sustainable business strategies for smallholder farmers in Taiwan’s suburbs. Second, the study measured the relevant weights of these indicators from the perspectives of experts. The qualitative and quantitative Analytic Hierarchy Process (AHP) was applied in the study, with Taiwan’s agricultural experts being our main subjects to establish the indicators and models for diversified and sustainable business strategies based on their opinions. According to the research results, there were 6 criteria and 25 sub-criteria for smallholder farmers in terms of diversified and sustainable business strategies, among which, catering services topped other criteria; food health and safety outweighed other sub-criteria of diversified and sustainable business strategies for smallholder farmers. The greatest contribution of the study is the establishment of the indicators and models of diversified and sustainable strategies for smallholder farmers in Taiwan’s suburbs and the discovery that catering service can be an important orientation of diversified and sustainable business strategies for smallholder farmers in Taiwan’s suburbs.

Clustering interval-valued data with adaptive Euclidean and City-Block distances

In several applications, data information is obtained in the form of intervals, such as the monthly temperature in a meteorological station or daily pollution levels in different locations. This paper proposes partitioning clustering algorithms for interval-valued data based on adaptive Euclidean and City-Block distances. Since some boundary variables may be more relevant for the clustering process, the proposals consider the joint weights of the relevance of the lower and upper boundaries of the interval-valued variables. Consequently, clusters of different shapes and sizes in some subspaces of the variables, even in specific boundaries of the interval-valued data, can be recognized. In addition, robust dissimilarity functions were introduced to reduce the influence of outliers in the data. The adaptive distances change at each iteration of the algorithms and can be different from one cluster to another. The methods optimize an objective function by alternating three steps for obtaining the representatives of each group, the cluster partition, and the relevance weights for the interval-valued variables. Experiments on synthetic and real data sets corroborate the robustness and usefulness of the proposed adaptive clustering methods.

DEHFF – A hybrid approach based on distinctively encoded fingerprint features for live fingerprint detection

Fingerprint-based biometric systems have elevated industrial standards worldwide through rapid personal verification. The array of identity frauds and cyber-attacks have also jeopardized the security countermeasures of biometric authentication systems. In this paper, an improved feature descriptor named Distinctively Encoded Histogram of Fingerprint Features (DEHFF) is proposed for live fingerprint detection. The DEHFF incorporates the two fundamental features i.e., ridge-valley contrast (ridge contours) and phase (ridge orientation) of fingerprints for liveness detection. The perceived response balances the abrupt intensity variations in the initial pixel information. Further, the Gabor filters are used to extract ridge contour information in the transform domain. Moreover, the objective function based on the Twin Confidence Value is proposed for linear integration of ridge contours extracted by Gabor and perceived spatial stimuli. The Local Phase Quantization utilizing the Point-wise Covariance of Gradients (PCG-LPQri) is proposed to determine the ridge orientation. Both feature sets are exclusively quantized into pre-defined intervals. The quantized features with relevant weights are then registered into a single feature set and represented through a 2-D histogram. Experimental evaluations indicate that the proposed DEHFF is able to reduce the average classification error to 4.01, 1.72, and 4.58% on LivDet 2011, 2013, and 2015, respectively.

Weighted composite time to event endpoints with recurrent events: comparison of three analytical approaches

BackgroundIn clinical trials the study interest often lies in the comparison of a treatment to a control regarding a time to event endpoint. A composite endpoint allows to consider several time to event endpoints at once. Usually, only the time to the first occurring event for a patient is thereby analyzed. However, an individual may experience more than one non-fatal event. Including all observed events in the analysis can increase the power and provides a more complete picture of the disease. Thus, analytical methods for recurrent events are required. A challenge is that the different event types belonging to the composite often are of different clinical relevance. In this case, weighting the event types according to their clinical relevance is an option. Different weight-based methods for composite time to event endpoints were proposed. So far, there exists no systematic comparison of these methods.MethodsWithin this work we provide a systematic comparison of three methods proposed for weighted composite endpoints in a recurrent event setting combining non-fatal and fatal events of different clinical relevance. We consider an extension of an approach proposed by Wei and Lachin, an approach by Rauch et al., and an approach by Bakal et al.. Comparison is done based on a simulation study and based on a clinical study example.ResultsFor all three approaches closed formula test statistics are available. The Wei-Lachin approach and the approach by Rauch et al. show similar results in mean squared error. For the approach by Wei and Lachin confidence intervals are provided. The approach by Bakal et al. is not related to a quantifiable estimand. The relevance weights of the different approaches work on different level, i.e. either on cause-specific hazard ratios or on event count.ConclusionThe provided comparison and simulations can help to guide applied researchers to choose an adequate method for the analysis of composite endpoints combining (recurrent) events of different clinical relevance. The approach by Wei and Lachin and Rauch et al. can be recommended in scenarios where the composite effect is time-independent. The approach by Bakal et al. should be applied carefully.

Bearing Fault Diagnosis Under Small Data Set Condition: A Bayesian Network Method With Transfer Learning for Parameter Estimation

Bearings are broadly applied in various types of industrial systems. Fault diagnosis, as a promising way for reliability of modern industrial internet of thing applications, has attracted increasing attention from both academia and industry fields. Being ideal modeling and inference tool in uncertainty situations, Bayesian network (BN) is becoming increasingly popular in many systems. However, in practical uncertain and complicated engineering surroundings, it’s difficult or expensive to collect massive labeled fault data for the sake of fault diagnosis model learning. To address the issue of BN parameter learning under small data set conditions, this paper proposes a Varying Coefficient Transfer Learning (VCTL) algorithm based on aggregation and transfer learning, that considers both knowledge from the resource domain and the resource relevance contributions. The balancing weight function is designed to determine whether the learning task in the resource domain is activated. Relevance weight factors are proposed to measure the relevance of resource and target parameters quantitatively, by combing parameter information from resource domains with those obtained from the target domain, using maximum a posterior (MAP) or maximum likelihood estimation (MLE). Finally, the target parameters are aggregated with both the target initial parameters and the parameter knowledge from the resource domain. Based on VCTL, a bearing fault diagnosis approach is proposed and verified. The experimental results show that, under the condition of the small data set, learning accuracy of VCTL algorithm with varying coefficient aggregation is better than MLE algorithm, MAP algorithm or state-of-the-art parameter transfer method, local linear pooling transfer learning (LoLP) algorithm. Under the condition of sufficient data set, learning accuracy of VCTL algorithm approaches the classical MLE or MAP, and the correctness of the proposed algorithm is verified. Moreover, we illustrate the successful application to real-world bearing fault diagnosis case with VCTL, where we had access to expert-provided resource knowledge and real fault diagnosis data.

ОСОБЛИВОСТІ УПРАВЛІННЯ ЛОЯЛЬНІСТЮ КЛІЄНТІВ НА ТОРГІВЕЛЬНИХ ПІДПРИЄМСТВАХ МАЛОГО БІЗНЕСУ

The article deals with the study of the formation of consumer loyalty of goods and services of small businesses and identify features of the management of this process. The essence of the definition of loyalty is determined, the peculiarities of consumer loyalty formation at small and micro business enterprises are analyzed, the degree of influence of the trade environment on the level of consumer loyalty is substantiated. Peculiarities of structuring small organizational forms are considered and the main factors that should ensure the formation of loyalty of customers (potential consumers) in the system organization of small trade enterprises are singled out. Trade enterprises occupy a special place in the consumer sector of the country’s economy and are characterized by a number of advantages and opportunities that help small businesses survive in difficult economic conditions. In particular, this business format is more flexible and easily adapts to changing business conditions. Along with this, small businesses are constantly looking for universal forms of consumer loyalty and pay considerable attention to this compared to large businesses. In particular, the influence of the trade environment on the formation of consumer loyalty with the determi- nation of the relevant weights is updated. The characteristic requirements put forward to small business enterprises by consum- ers of goods or services are conditioned. The subjective approach and its possibilities used in the formation of customer loyalty of small businesses are analyzed. Thus, the authors substantiate the methodological approach, according to which there are at least three groups of important factors that to some extent affect the formation of customer loyalty and consideration of which is mandatory for the formation of a permanent customer base. The authors provide recommendations for the effective operation of commercial enterprises based on a series of evaluation procedures using an expert approach, which will ultimately provide a stable customer base for the company and the foundation for effective marketing activities.

Weighted differential evolution algorithm based pansharpening

ABSTRACT Imaging Satellites acquire multispectral images, MIs, in low resolution, LR, and panchromatic images, PANs, in high resolution, HR, due to some advantages provided in satellite design. The pansharpening, PS, is a super resolution image synthesis method that is used to generate the pansharpened image, PI, by fusion of PAN and MI. The use of PS process is unavoidable in applications such as efficient use of communication-bandwidth of imaging satellites and fusion of images derived from various image sensors. The PS process is a multi-step process consisting of various complex image processing stages, such as registration, resampling, synthesis, and fusion. The Weighted Differential Evolution Algorithm-based PS method, WDEPS, has been proposed in this paper. The WDEPS uses WDE to synthesize the intensity image, which is the blended-image of MI -bands. WDE has been used to compute the relevant image-blending weights, efficiently. In the experiments, several satellite images (QuickBird-2, Ikonos-2, and GeoEye-1) with different spatial resolutions were used. The WDEPS’s experimental results have been compared with 17 well-known PS methods by using 9 full-reference and 3 blind image quality assessment metrics. The experimental results exposed that WDEPS generates high-quality PIs than traditional PS methods used in the experimental aspect of qualitative and quantitative assessment.