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Due to the initial value sensitivity and high pseudo-randomness of the chaotic system. Chaotic systems are often applied as a Pseudo-Random Number Generator (PRNG) in related fields. Especially in cryptography based on chaos, the Pseudo-Random Numbers (PRNs) produced by PRNG are often used as an interference in the design of encryption algorithm. However, due to lower chaos performance of PRNG such as chaotic output is discontinuous and lower complexity, it may bring the negative impact to its application in related field. In this study, a novel model of 2D sine–cosine-logistic coupling (2D-SCLC) hyperchaotic map is proposed. In comparison with several existing chaos map, the proposed system has better chaotic performance. It can effectively improve the security performance of encryption algorithm, if the proposed chaos map is applied in the design of image encryption algorithm. Further, the PRNs generated by 2D-SCLC is applied to the new image encryption algorithms. By using the security test tools, we can obtain the values of NPCR and UACI have reach 99.61% and 33.44%, they are close to the ideal value. In particular, the information entropy is 7.9996. The security test shows that the algorithms has higher security. This research provides a theoretical guidance for related fields based on chaotic cryptography. [ABSTRACT FROM AUTHOR]

With the ever-increasing intertwining of social networks and daily existence, the accumulation of personal privacy information is steadily mounting. However, the exposure of such data could lead to disastrous consequences. Current graph data protection algorithms lack sufficient research on the characteristics of social users, while simultaneously incurring significant time and space overhead. Additionally, the strategies lack adaptability in incorporating noise, often resulting in a subsequent decrease in data availability. To address these issues, a novel approach called the Information Entropy-driven Adaptive Differential Privacy Protection Scheme (IEA-DP) is presented for the release of social data in this study. The proposed solution initially designs the InfomapMerge algorithm to divide the data into communities based on the characteristics of social networks, thereby enabling parallel processing, and mitigating the time and space overhead. Subsequently, the Adaptive Edge Modification Algorithm (AEMA) is proposed to optimize the noise addition process by adaptively adding noise based on the score of node importance. This effectively reduces the amount of added noise, increasing data availability. Finally, the experimental results conducted on six public datasets demonstrate that the IEA-DP scheme strikes a desirable balance between data availability and privacy. [ABSTRACT FROM AUTHOR]

With the development of point cloud-based telemetry technology in recent years, the point cloud data of large field scenes acquired by various sensors have been applied to farmland boundary division, crop growth monitor, area surveying, etc. However, the large field point cloud will cost huge amounts of computational resources in the following transmission, storage and processing, which make it more important to simplify the field point cloud appropriately. In light of limitation of existing algorithms in point cloud simplification of large field scenes, we propose a novel feature-preserved simplification algorithm for large field point cloud data. By introducing the average local entropy as the threshold for area division, our algorithm effectively solves the problem of fuzzy boundary division, as well as preserving the field features and reducing the simplification errors. In view of the problem that the evaluation of current simplification algorithm is mainly focused on qualitative assessment, a quantitative evaluation index for the point cloud simplification is proposed by employing the point-average local entropy, which takes both model retention and simplification efficiency into account. Finally, comparable experiments are performed on four sets of point clouds. The results show that, compared with the statistics of six typical algorithms, the proposed algorithm increases the local entropy by 0.029%, 0.146% and 0.088% on our datasets, and increases 0.029% on the public dataset. The method accurately evaluates the simplification effect. Additionally, the surface area change rate is also used to further evaluate the performance of proposed algorithm, and the quantitative evaluation index is lower than others, which verify the advantages of proposed algorithm in feature protection and large field simplification. [ABSTRACT FROM AUTHOR]

The available studies on vulnerability evaluation of Supply Chain Networks (SCNs) oversimplify the availability of entities during disruptive events by classifying them into only two categories of "operational" and "not operational." Yet, the literature has remained relatively silent on vulnerability analysis of SCNs in the case of capacity reduction. To sidestep this shortcoming, this paper develops an entropy-based method to quantify the vulnerability of SCNs to capacity reduction. This is achieved by measuring the flexibility inherent within the layout of SCNs, both at local and global levels. By performing simulation and sensitivity analyses, this paper predicts the impact of capacity reductions on the performance of SCNs. The results of our study favor the fact that incorporating more backups and increasing the capacity of various entities in the network reduces vulnerability and enhances the robustness of SCNs. The findings of this research may assist operations and supply chain managers in identifying the sensitive areas of SCNs, in turn facilitating the development of an effective risk management strategy to respond to disruptive events. [ABSTRACT FROM AUTHOR]

Information fusion enables the integration and transformation of complimentary data from different sources, providing a unified representation for centralized knowledge discovery, which can contribute to effective decision-making, classification, prediction, and more. Multi-source interval-valued data, represented in the form of intervals to capture uncertainty phenomenons, is a common type of symbolic data that finds extensive applications in the real world. This paper aims to investigate the effective fusion of multi-source interval-valued data and to design dynamic updating algorithms for the situations involving multiple dimensions. The objective is to enhance the efficiency of fusion processes. Firstly, this paper use the Kullback–Leibler divergence to measure the dissimilarity between interval distributions, and construct fuzzy similarity relation. Furthermore, we define a fuzzy information granule structure of interval-valued. Secondly, the concept of fuzzy similarity relations is utilized to construct fuzzy decision-making for objects. Subsequently, based on the aforementioned fuzzy information granule structure and fuzzy decision-making, we propose a novel measure called fuzzy approximate conditional entropy and design a corresponding entropy fusion model. Finally, we discuss various scenarios where dynamic changes occur simultaneously in the attributes and information sources of dynamic multi-source interval-valued data. We design corresponding dynamic update algorithms for these situations. Numerical experiments are conducted on nine UCI datasets to validate our proposed fusion method. The experimental results indicate that our fusion approach exhibits improved classification performance compared to the common fusion methods. The designed dynamic update algorithms are also capable of reducing computation time and enhancing fusion efficiency. [ABSTRACT FROM AUTHOR]

Information entropy is widely used in discrete mathematical chemistry as the descriptor to quantify the structural complexity of molecules. This review covers opportunities to use the information entropy parameters to evaluate changes in the complexity of molecules in both simple and multi-step chemical reactions. [ABSTRACT FROM AUTHOR]

Local planning is a critical and difficult task for intelligent vehicles in dynamic transportation environments. In this paper, a new method Suppress Q Deep Q Network (SQDQN) combining traditional deep reinforcement learning Deep Q Network (DQN) with information entropy is proposed for local planning in automatic driving. In the proposed approach, local planning strategy in complex traffic environment established by the actor–critic network based on DQN, the method adopts the way of execution action-evaluation action-update network to explore the optimal local planning strategy. Proposed strategy does not rely on accurate modeling of the scene, so it is suitable for complex and changeable traffic scenes. At the same time, evaluate the update process and determine the update range by using information entropy to solve a common problem in the network that over expectation of actions damage the performance of strategies. Use this approach to improve strategic performance. The trained local planning strategy is evaluated in three simulation scenarios: overtaking, following, driving in hazardous situations. The results illustrate the advantages of the proposed SQDQN method in solving local planning problem. [ABSTRACT FROM AUTHOR]

In a recent paper (Found Phys 54:14, 2024), Carcassi, Oldofredi and Aidala concluded that the ψ -ontic models defined by Harrigan and Spekkens cannot be consistent with quantum mechanics, since the information entropy of a mixture of non-orthogonal states are different in these two theories according to their information theoretic analysis. In this paper, I argue that this no-go theorem for ψ -ontic models is false by explaining the physical origin of the von Neumann entropy in quantum mechanics. [ABSTRACT FROM AUTHOR]

Infectious diseases risk is directly related to human life safety. After the COVID-19 pandemic, people have paid unprecedented attention to the risk of infectious diseases. Compared with treatment after the outbreak of the epidemic, identifying the influencing factors of infectious disease risk and quantitatively analyzing and assessing infectious disease risk before the outbreak of the epidemic plays an equally important role. This article focuses on the risk of irregular outbreaks of infectious diseases. On the one hand, a method based on information gain is proposed to calculate the weight of environmental factors directly related to infectious disease risk, to clarify the correlation between environmental factors and infectious disease risk. On the other hand, the risk calculation method based on risk weight number is proposed to calculate the risk level of different infectious diseases under the influence of specific environmental factors. Finally, the effectiveness and feasibility of the proposed method are verified through case analysis and discussion. By comparing it with other risk assessment methods, the advantages and disadvantages of the proposed method are demonstrated. [ABSTRACT FROM AUTHOR]

The use of computer vision relies heavily on the accuracy of the images obtained by the sensors. However, the frequent occurrence of haze, stemming from severe environmental problems, significantly hampers the acquisition of high-precision images, consequently impacting the computer vision industry. Therefore, image defogging becomes an imperative task. Currently, the image defogging process combines physical models with image enhancement techniques, often relying on filtering methods. The selected filter should usually satisfy the ability to preserve the original image and enhance image details. In this regard, the homomorphic filter has excellent performance and is frequently preferred. In the process of homomorphic filtering, four parameters are involved: high-frequency gain( γ H ), low-frequency gain( γ L ), cutoff frequency (D0), and slope sharpening control parameter (C). Notably, determining parameters D0 and C are usually determined by empirical formulation, making it difficult to achieve optimal timeliness and quality in image defogging. Therefore, this paper proposes an optimal selection method for key parameters in homomorphic filtering based on information entropy. This method aims to establish an information entropy-based model, referred to as C-D0 information entropy model, that captures the relationship between slope sharpening and cutoff frequency parameters. This model utilizes a priori image information and employs the least squares method to swiftly determine the optimized parameters C and D0. By utilizing these optimized parameters, the information entropy of the defogged image can be maintained at a high level. Experimental results show that the proposed method enables rapid determination of parameter values, significantly enhancing the timeliness of homomorphic filtering. The parameters quickly selected by this model can result in a 92.12% improvement in the information entropy value of the defogged image compared to the original image. The average improvement rate of information entropy reaches 80.62%. Furthermore, the optimized image exhibits a substantial reduction in the fog effect, leading to a significant improvement in the defogging outcome. [ABSTRACT FROM AUTHOR]

As a new form of tourism, forest health has gradually attracted the attention of scholars and the public, becoming a new focus of research. There have been many qualitative studies on the product forms and development models of forest health tourism, but there is still a gap in the evaluation indicators and development systems for the development potential of forest health tourism. Using the research results of forest health tourism at home and abroad for reference, we will build an evaluation system for the development potential of forest health tourism, providing new research perspectives and operational practical support for the development of forest health tourism. The development potential evaluation of forest health tourism is a classical MAGDM problems. Recently, the TODIM and VIKOR technique has been employed to cope with MAGDM issues. The single-valued neutrosophic sets (SVNSs) are employed as a tool for characterizing uncertain information during the development potential evaluation of forest health tourism. In this paper, the single-valued neutrosophic number TODIM–VIKOR (SVNN–TODIM–VIKOR) technique is built to solve the MAGDM under SVNSs. In the end, a numerical case study for development potential evaluation of forest health tourism is given to validate the proposed technique. The prime contribution of this paper is managed: (1) the information entropy based on score values are constructed to obtain weight values under SVNSs; (2) an integrated SVNN–TODIM–VIKOR technique is constructed to address the MAGDM issue; and (3) an illustrative example for development potential evaluation of forest health tourism has accomplished to verify the SVNN–TODIM–VIKOR technique. [ABSTRACT FROM AUTHOR]

The present study proposes a new fuzzy finite element method for dynamic multibody interaction with consideration for structural damage. Here, fuzzy parameters are equivalently transformed into stochastic parameters using information entropy, and the fuzzy response of the structure is obtained by fuzzy calculation combined with the new point estimation method. Numerical examples are used to illustrate the accuracy and efficiency of the presented methods, and scanning method simulations are implemented to validate the computational results. Considering that the damage degree of the pier is uncertain, namely fuzzy uncertainty, stiffness reduction is used to simulate the damage of the pier. The fuzzy dynamic response of the train–bridge system is investigated when the pier structure and the mass of the train are fuzzy parameters. The response of the train–bridge interaction considering damage far exceeds that obtained from conventional deterministic parameter calculations. To ensure running safety, studying the response of the vehicle-system coupled vibration with fuzzy parameters is of great significance. [ABSTRACT FROM AUTHOR]

One of the current research gaps in multi-scale data analysis is studying information systems characterized by attributes with interval sets as attribute values and multiple scales. To address this gap, we first introduce the concepts of a multi-scale interval-set information system (MISIS) and a multi-scale interval-set decision table (MISDT). We then define the similarity relation between objects in an MISIS and the corresponding rough approximations. We further propose the positive region optimal scale, the modified conditional entropy optimal scale, and the positive complementary conditional entropy optimal scale in an MISDT. We examine the relationships among these optimal scales in consistent and inconsistent MISDTs and show that the positive region optimal scale and the modified conditional entropy optimal scale are equivalent in a consistent MISDT, while in an inconsistent MISDT, the positive region optimal scale is the same as the positive complementary conditional entropy optimal scale, and the modified conditional entropy optimal scale is not greater than the positive complementary conditional entropy optimal scale. Based on the optimal scale, we also develop attribute reduction approaches in MISDTs. Finally, through experimental analysis of data on the UCI dataset, we verify the effectiveness and reasonableness of our proposed methods. [ABSTRACT FROM AUTHOR]

The paper is devoted to the study of information characteristics which play an important role in solving problems of detection of weak signals and their classification. We formulate and prove lemmas on estimating upper and lower boundaries in statistical and spectral complexity diagrams for different signal-noise mixtures. The obtained theoretical results are verified by numerical experiments, which have confirmed the efficiency of the theoretical estimates. We establish some important laws of the behavior of noise-like and weak signals and the possibilities of their detection in white and blue noise conditions, formulate and prove the corresponding lemmas. Also, we obtain estimates on conditions for the possibility of classifying weak signals by means of information characteristics. [ABSTRACT FROM AUTHOR]

In this study, the authors combine the acoustic emission (AE) technique with the Brazilian splitting test and the angled die shear test to monitor the development of cracks in red sandstone specimens under compression–tension and compression–shear conditions to identify the mechanism of propagation of tensile and shear failures during the process of damage to rock. We use this to propose a method to analyze the comprehensive index of fracture-induced AEs of red sandstone based on information entropy. A comprehensive index to characterize the tensile and shear failures of rock was obtained by quantifying the entropy weights of the characteristic parameters of AE and their laws of evolution were analyzed. Areas for the classification of different cracking mechanisms of rock were established based on the Box–Cox transform, and the fractal features of crack development under compression–tension and compression–shear conditions were analyzed by using the Grassberger and Procaccia (G–P) algorithm. The results showed differences in the entropy of the characteristic parameters of red sandstone in cases of tensile and shear damage, with the rise time and energy recording the maximum entropies, respectively, and the amplitude yielding the minimum entropy. The comprehensive evaluation indices for tensile failure ξ t and shear failure ξ τ confirmed the existence of at least two mechanisms of deformation-induced damage—tensile fracture and shear slip—in the process of damage to the rock. The curve of evolution of the fractal dimensions D t and D τ of compression–tension and compression–shear, respectively, followed the law of continuously decreasing dimensions, and their turning points reflected the change in crack development in the rock. [ABSTRACT FROM AUTHOR]

Graph neural networks have received increased attention over the past years due to their promising ability to handle graph-structured data, which can be found in many real-world problems such as recommender systems and drug synthesis. Most existing research focuses on using graph neural networks to solve homophilous problems, and not much attention has been paid to heterophily-type problems. In this paper, we propose a graph network model for graph coloring, which is a class of representative heterophilous problems. Different from the message passing in conventional graph networks, we introduce negative message passing into a physics-inspired graph neural network for more effective information exchange in handling graph coloring problems. Moreover, a new term in the objective function taking into account the information entropy of nodes is suggested to increase the uniformity of the color assignment of each node, giving the neural network more chance to choose suitable colors for each node. Therefore, it could avoid the final solution getting stuck into the local optimum. Experimental studies are carried out to compare the proposed graph model with five state-of-the-art algorithms on ten publicly available graph coloring problems and d-regular graphs with up to 10 4 nodes, demonstrating the effectiveness of the proposed graph neural network. [ABSTRACT FROM AUTHOR]

To solve the problems of density peaks clustering (DPC) algorithm sensitive to cutoff distance and subjectivity of clustering center selection, we propose an improved density peaks algorithm based on information entropy and merging strategy (DPC-IEMS) for realizing power load curve clustering. First, a cutoff distance optimization method based on information entropy is proposed. This method uses sparrow search algorithm (SSA) to find the minimum value of information entropy about the product of local density and relative distance to calculate the optimal cutoff distance suitable for the load datasets. Then, a merging strategy is proposed to realize the adaptive selection of clustering centers. This strategy first generates a large number of initial sub-clusters by DPC, and then merges the sub-clusters using the fusion condition until the final iteration condition is satisfied. The performance of DPC-IEMS algorithm is evaluated on the U.S. load datasets and the Chinese load datasets, and the effectiveness and practicality of DPC-IEMS algorithm for power load curve clustering are fully demonstrated. [ABSTRACT FROM AUTHOR]

The topological photonics plays an important role in the fields of fundamental physics and photonic devices. The traditional method of designing topological system is based on the momentum space, which is not a direct and convenient way to grasp the topological properties, especially for the perturbative structures or coupled systems. Here, we propose an interdisciplinary approach to study the topological systems in real space through combining the information entropy and topological photonics. As a proof of concept, the Kagome model has been analyzed with information entropy. We reveal that the bandgap closing does not correspond to the topological edge state disappearing. This method can be used to identify the topological phase conveniently and directly, even the systems with perturbations or couplings. As a promotional validation, Su–Schrieffer–Heeger model and the valley-Hall photonic crystal have also been studied based on the information entropy method. This work provides a method to study topological photonic phase based on information theory, and brings inspiration to analyze the physical properties by taking advantage of interdisciplinarity. [ABSTRACT FROM AUTHOR]

The exponential growth of social networks has resulted in the generation of vast amounts of graph data containing sensitive information. However, the exposure of such data could lead to disastrous consequences. Current graph data protection algorithms lack sufficient research on the attribute characteristics of social users, which results in a failure to safeguard potentially vulnerable private data effectively. To address these issues, an entropy-driven differential privacy protection scheme based on social graphlet attributes (EDP-SGA) is proposed. Firstly, a matrix-based algorithm is proposed for constructing an attribute intimacy matrix, which can quantify the strength of links among social users' attributes. Secondly, an influence algorithm based on user node attributes and information entropy is proposed, which can divide social networks into communities and select seed nodes. Thirdly, a privacy-preserving social network data publishing algorithm is proposed, which can combine graph modification techniques and differential privacy to convert sensitive graph data into an uncertain graph. Finally, experimental results demonstrate that the EDP-SGA can keep the balance between the privacy and the utility of social graph data. [ABSTRACT FROM AUTHOR]

In an unknown network environment, satellites are easily disturbed by various noises, and the interference signals change sharply, resulting in a decline in the accuracy of the cooperative spectrum sensing. Thus, a hybrid two-stage cooperative spectrum sensing scheme is proposed in this paper. The geostationary earth orbit (GEO)/low earth orbit (LEO) two-layer constellation network architecture is adopted, secondary LEO satellites randomly form several coalitions. In the coalition, the Dempster–Shafer (D–S) evidence theory distributed cooperative spectrum sensing algorithm based on information entropy is used to perform a first-level fusion of sensing information, and a 1bit fusion result is generated. A centralized fusion method is adopted between coalitions, the 1bit fusion result is sent to the GEO fusion center, and the secondary fusion is performed through K-out-of-N (K-N) fusion rules, and the final perception result is obtained and broadcast to all secondary users in the coalition. The simulation results show that the proposed method is superior to the traditional fusion rules in terms of detection probability and total error rate in a low-Signal-to-noise ratio (low-SNR) environment, and improves the cooperative sensing performance. [ABSTRACT FROM AUTHOR]

In mathematical chemistry, a chemical reaction is represented as a transformation of one molecular ensemble into another one, and information entropy is used for quantitative describing changes in the molecular complexity. The information entropy of a chemical reaction is the difference between the values of the ensembles of products and reagents. As is known, the information entropy of molecular ensemble depends on the information entropies of individual molecules and, additionally, on the cooperative entropy, an emergent parameter that reflects uniting the molecules into the ensemble. Accounting this parameter determines the peculiarities of calculating the information entropy for interdependent chemical reactions. In the present study, we have derived a general formula that connects the information entropy of the complex chemical process with the parameters of its elementary stages and demonstrated its work on typical examples of successive, parallel, and conjugated chemical reactions. Notably, the view of the derived formula differs from the equations used when Hess' law is applied to the thermodynamic parameters of interdependent reactions. The only case when the Hess' law has the same analytical expression for both information-entropy and thermodynamic parameters is the isomegethic set of chemical reactions, viz. the system of the successive reactions, in which the size of the molecular ensemble remains constant. [ABSTRACT FROM AUTHOR]

Practical engineering problems often involve stochastic uncertainty, which can cause substantial variations in the response of engineering products or even lead to failure. The coupling and propagation of uncertainty play a crucial role in this process. Hence, it is imperative to quantify, propagate and control stochastic uncertainty. Different from most traditional uncertainty propagation methods, the proposed method employs Gaussian splitting method to divide the input random variables into Gaussian mixture models. These GMMs have a limited number of components with very small variances. As a result, the input Gaussian components can be conveniently propagated to the response and remain Gaussian distributions after nonlinear uncertainty propagation, which is able to provide an effective method for high-precision nonlinear uncertainty propagation. Firstly, the probability density function of input random variable is reconstructed by Gaussian mixture models. Secondly, the K-value criterion is proposed for selecting split direction, taking into account both the nonlinearity and variance. The components of input random variables are then divided into a Gaussian mixture model with small variance along the direction determined by the K-value. Thirdly, the individual components of the Gaussian mixture model are propagated one by one to obtain the probability density function of the response. Finally, the convergence criterion based on Shannon entropy is developed to ensure the accuracy of uncertainty propagation. The efficacy of the method is verified using three numerical examples and two engineering examples. [ABSTRACT FROM AUTHOR]

Outlier detection aims to find data points that are significantly different from other observed values. It has been widely used in fraud detection, network security, and medical fields. Most of the existing outlier detection methods do not fully consider the problem of missing values in data sets. This paper studies outlier detection for incomplete real-valued data via information entropy and rough set theory (RST). First, a tolerance relation based on class-consistent technology is introduced to describe the similarity between information values in an incomplete real-valued information system (IRVIS). Then, the tolerance classes are formed according to the tolerance relation, and are used to calculate information entropy and other metrics. Next, an outlier factor is defined for each object in an IRVIS to describe its uncertainty and degree of outlier. Finally, an outlier detection method for an IRVIS is proposed, and the corresponding algorithm (CIEOD) is designed. The proposed method is compared with five other detection methods by numerical experiments based on UCI data. The experimental results show that the CIEOD algorithm is more efficient. It is worth mentioning that in order to make comprehensive comparison, Precision, Recall, F1-measure and ROC curve are used to describe the strengths of the proposed method. [ABSTRACT FROM AUTHOR]

As the initial stage of the sewage treatment system, the degradation of pollutants inevitably involves an entropy change process. Microorganisms play a vital role, where they interact with pollutants and constantly adjust own ecosystem. However, there is a lack of research on the entropy change and external dissipation processes within the sewer system. In this study, considering the characteristics of microbial population changes in the biofilm within the urban sewage pipe network, entropy theory is applied to characterize the attributes of different microorganisms. Through revealing the entropy change of the microbial population and chemical composition, a coupling relationship between the functional bacteria diversity, organic substances composition, and external dissipation in the pipeline network is proposed. The results show that the changes of nutrient availability, microbial community structure, and environmental conditions all affect the changes of information entropy in the sewer network. This study is critical for assessing the understanding of ecological dynamics and energy flows within these systems and can help researchers and operation managers develop strategies to optimize wastewater treatment processes, mitigate environmental impacts, and promote sustainable management practices. [ABSTRACT FROM AUTHOR]

With the rapid development of convolutional neural networks (CNNs), higher accuracy is usually accompanied by huge parameters and calculations. In embedded devices with limited computing resources, huge network models are often difficult to deploy. It has been shown in previous researches that there are a large number of invalid filters in the CNNs network, and removing these invalid filters has little effect on network accuracy. Therefore, compressing CNNs by pruning these redundant filters can be done while maintaining accuracy. Model pruning is one of the main compression techniques for models. In the existing model pruning work, the filter with a small ℓ 1 -norm value is pruned by calculating the ℓ 1 -norm of the filter, but some researches have pointed out that the ℓ 1 -norm is not always effective. This paper proposes a kernel density estimation-based entropy algorithm to prune filters with low information content. We experimentally demonstrate the effectiveness of the algorithm. Pruning VGG-16 model on CIFAR-10 datasets achieves an accuracy of 93.36% even after removing 91.97% of the original parameter count and 64.87% of the FLOPs. After pruning 75.84% of the network parameters and 52.35% of the FLOPs of the ResNet-110 model on the CIFAR-100 datasets, the accuracy remains almost the same. [ABSTRACT FROM AUTHOR]

Tourism is vital to national economic growth and fulfilling individuals' spiritual pursuits. However, traditional scenic recommendation algorithms must improve accuracy, susceprecommended route personalizationtion of recommended tour routes, and suboptimal tour value. This study presents a UGC text mining-based travel recommendation method to address these problems. To begin with, the proposed method utilizes text mining techniques to enrich scenic tag data and introduces the recommendation algorithm based on fuzzy label-matching user-item characteristics (FLMC). Additionally, we incorporate scenic attributes and travel time between scenics to identify four key attributes that impact scenic selection in tour routes. Users' subjective preferences for these attributes are calculated, and objective weights derived from information entropy are used to determine combination weights for scenic selection. This ensures a more personalized and optimized tour route. Lastly, the proposed method features a tour route ranking method that selects the highest-scoring route as the recommended option. This approach enhances the overall quality of the recommended tour routes. Through experimentation with 240,194 reviews of 369 scenics in Beijing, in the attraction recommendation scenario, the proposed FLMC algorithm achieves up to 75.40% accuracy, which is higher than the other three compared algorithms (46.48%, 35.90%, and 32.98%). In the travel route recommendation scenario, the proposed method has a route gain value of up to 1.57 and a user experience value of recommended route personalizationd compared to the other three compared algorithms (1.03, 0.9; 0.95, 0.53; 1.12, 0.85). These results highlight the significant potential of the proposed method in improving the personalization of travel recommendations and enhancing tourist experiences. [ABSTRACT FROM AUTHOR]

In this paper, a quantitative detection method of the weak ultrasonic guided wave (UGW) signal based on information entropy of the Duffing chaotic system is proposed. It can simultaneously identify the location and size of small defects in pipelines. First, by adjusting the driving force amplitude of the Duffing system, the system is in a critical state from period to chaos. When the weak UGW is added on the driving force, the state of the Duffing system will change. It can be used to detect weak signal. To realize the quantitative detection of UGW, a method based on information entropy is presented in this paper. By analyzing the influence of UGW on the driving force amplitude of the Duffing equation, a sensitive damage index is constructed to indicate the signal strength. Furthermore, a time-moving window function is introduced. By scanning the recording signal, the defect location can be identified using the damage index. The experimental studies show that the proposed method is validity for detecting small defects in pipeline, and simultaneously identify the size and location of the defects. This method has a strong noise immunity and significantly improves the sensitivity of small defects detection in pipelines. Highlights: A method to calculate the information entropy of Duffing system is proposed. A method for detecting small defects in pipe is proposed based on the sensitive dependence on initial conditions and information entropy of chaotic system. The method is successfully used to detect small defect detection in a pipe. [ABSTRACT FROM AUTHOR]

Aimed at image distortion caused in process of defogging images and interfered by bright objects, a single adaptive image defogging method based on sky segmentation is proposed in this paper. Firstly, the combination of the dark channels and bright channels is used to estimate the atmospheric light. And then the sky region and the accurate atmospheric light are obtained by the information entropy and the dark channel prior. Next, the initial transmittance is estimated by the dark channel prior, and then the fast guiding filter is utilized to estimate the transmittance accurately. Further, an adaptive weight factor and an error amplification compensation factor of bright objects are introduced to optimize the transmittance mapping constraint and to correct the sky region and the non-sky region, respectively. Finally, bright adjustments are performed on the fog-free image obtained from the atmospheric scattering model by a nonlinear mapping method, which causes a clearer and more natural defogging image to be got. The experimental results show that adaptive single image defogging algorithms proposed in this paper has the higher value of peak signal-to-noise ratio, the higher value of structural similarity index measure, the higher value of naturalness image quality evaluator, the higher value of fog aware density evaluator, and the less time consumed, which illustrates that the adaptive single image defogging algorithm proposed in this paper has a better defogging effect on haze images with sky areas and bright objects. [ABSTRACT FROM AUTHOR]

As structural descriptors in chemistry, indices based of information entropy are widely used. The change in information entropy in a chemical reaction is calculated as the difference of the values for the ensemble of products and the ensemble of reactants. In this work, for a generalized scheme of a catalytic reaction, analytical expressions were derived that relate the information entropy change in it with the parameters of individual steps and the corresponding overall equation. It was found that the sum of the parameters of individual steps is proportional to the information entropy change in the formal noncatalytic reaction, and the coefficient of proportionality is the fraction of atoms contained in reacting (forming) molecules of the ensemble of reactants (or products). [ABSTRACT FROM AUTHOR]

In order to make up for the shortcomings of traditional discontinuity occurrence clustering algorithms, such as narrow application scope, difficulty in effectively identifying noise points, and susceptibility to initial conditions and hyperparameters. In this work, the dominant partitioning of rock masses discontinuities based on information entropy selective heterogeneous ensemble (DRIE) model is proposed. Firstly, the base cluster set is generated by four base-clustering algorithms. Secondly, the comprehensive quality evaluation index is obtained by evaluating the quality and difference of the base cluster, and the information entropy weighting matrix is integrated. Finally, the number of integrated base clusters and the optimal number of groups are determined, and the noise points are eliminated. In addition, this work lists four base-clustering algorithm models, establishes nine comparison models, and applies all models to the dominant partitioning of an adit discontinuity of Songta Hydropower Station in Tibet, China. The results show that the DRIE model and the above 13 models are evaluated by combining the silhouette index and the ability to identify and eliminate noise. Compared with other models, the DRIE model has wide applicability and objective grouping results. It provides a new idea for the study of the dominant occurrence grouping of discontinuity. [ABSTRACT FROM AUTHOR]

Imbalanced data constitute a significant challenge in practical applications, as standard classifiers are usually designed to work on data with balanced class label distributions. One of effective methods to solve the imbalanced problem is boundary oversampling method, which only focuses on the classification of boundary samples. However, most boundary oversampling methods roughly select boundary samples for oversampling without considering the potentially useful boundary characteristics inherent in majority (negative) class. To overcome this limitation, we propose a novel boundary-degree-based oversampling method (BDO) in this paper. The originality of BDO stemps from quantifying the degree to which each negative sample can be regarded as a boundary sample in terms of probability using information entropy. Applying the sigma rule on the quantified boundary degree, negative boundary samples are determined to indirectly select minority (positive) boundary samples for oversampling. In this way, a substantial amount of information hidden in the negative class can be mined. To further transfer the mined information to help oversample, BDO iteratively synthesizes aided boundary points along a fraudulent gradient. Oversampling finally is performed on both positive boundary samples and the aided boundary points. Experimental results completed on 15 benchmark imbalanced datasets, two multi-label datasets and one large-scale dataset in terms of G-mean, F-measure, AUC, accuracy, TPR and TNR show that BDO exhibits better performance, which is competitive with some commonly considered methods. [ABSTRACT FROM AUTHOR]

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In mathematical chemistry problems, a chemical reaction is represented as a transformation of one molecular ensemble into another, and information entropy and related parameters are often used to quantify changes in the complexity of molecules. The information entropy of a chemical reaction is calculated as the difference between the values corresponding to an ensemble of products and an ensemble of reagents. Previously, we have shown that the information entropy of molecular ensembles depends not only on the information entropy of individual molecules, but also on cooperative entropy—an emergent parameter that arises when molecules are combined into an ensemble. Inclusion of this parameter in calculation determines the peculiarities of calculating the information entropy for interrelated chemical reactions. The article considers systems of independent and parallel chemical reactions and gives an analytical dependence that correlates the information entropy of the total process with the parameters of individual reactions. [ABSTRACT FROM AUTHOR]

The network, with some or all characteristics of scale-free, self-similarity, self-organization, attractor and small world, is defined as a complex network. The identification of significant spreaders is an indispensable research direction in complex networks, which aims to discover nodes that play a crucial role in the structure and function of the network. Since influencers are essential for studying the security of the network and controlling the propagation process of the network, their assessment methods are of great significance and practical value to solve many problems. However, how to effectively combine global information with local information is still an open problem. To solve this problem, the generalized mechanics model is further improved in this paper. A generalized mechanics model based on information entropy is proposed to discover crucial spreaders in complex networks. The influence of each neighbor node on local information is quantified by information entropy, and the interaction between each node on global information is considered by calculating the shortest distance. Extensive tests on eleven real networks indicate the proposed approach is much faster and more precise than traditional ways and state-of-the-art benchmarks. At the same time, it is effective to use our approach to identify influencers in complex networks. [ABSTRACT FROM AUTHOR]

Feature selection, which is a commonly used data prepossessing technique, focuses on improving model performance and efficiency by removing redundant or irrelevant features. However, an implicit assumption made by traditional feature selection approaches is that data are independent and identically distributed (IID). To further obtain more complex and significant information, an effective feature selection construction should consider the couplings (non-IIDness) contained within feature values and relevance between features. Hence, referring to rough set theory, this paper first introduces a new coupled similarity measure to discover the value-to-feature-to-class coupling information, which can be used to calculate object neighbor and update feature weights. Second, using mutual information, a new coupled relevance measure is defined to capture the feature-to-feature coupling relationships. On this basis, an effective feature-selection algorithm based on coupling learning is developed for categorical data. To demonstrate the proposed algorithm, four common classifiers and 12 UCI data sets are employed in the experiments. The experimental results confirm the feasibility of the new algorithm and its effectiveness. [ABSTRACT FROM AUTHOR]

In the process of multi-label learning, feature selection methods are often adopted to solve the high-dimensionality problem in feature spaces. Most existing multi-label feature selection algorithms focus on exploring the correlation between features and labels and then obtain the target feature subset by importance ranking. These algorithms commonly use single-channel structure to obtain important features, which induces the excessive reliance on the ranking results and causes the loss of important features. However, the correlation between label-specific feature and label-instance is ignored. Therefore, this paper proposes Parallel Dual-channel Multi-label Feature Selection algorithm (PDMFS). We first introduce the concept of dual channel and design the algorithm model as two independent modules. The algorithm obtained different feature correlation sequences, thus avoided relevant feature loss. And then, the proposed algorithm uses the subspace model to select the feature subset with the maximum correlation and minimum redundancy for each sequence, thus obtaining feature subsets under respective correlations. Finally, the subsets are cross-merged to reduce the important feature loss caused by the serial structure processing single feature correlation. The experimental results on eight datasets and statistical hypothesis testing indicate that the proposed algorithm is effective. [ABSTRACT FROM AUTHOR]

In order to effectively solve the parameter selection problem of variational mode decomposition (VMD) and accurately extract the bearing fault features, a bearing fault diagnosis method based on multi-feature optimized VMD and fusion index is proposed. Considering the multiple features of fault pulse when the bearing fails, the objective functions and fusion index of information entropy, correlation coefficient, and kurtosis are established, and the parameter optimization problem of VMD is transformed into a multi-objective optimization problem. Firstly, the multi-objective particle swarm optimization (MOPSO) algorithm is used to optimize the three objective functions, and the optimal Pareto frontier solution set of VMD parameter combination is obtained. Secondly, the fusion index is used to evaluate the Pareto frontier solution set, from which the optimal parameter combination of VMD is determined. The bearing fault signal is decomposed by VMD based on the optimal parameter combination, and several intrinsic mode functions (IMFs) are obtained. Then, the fusion index is used to select the optimal IMF, and fault features are extracted. Finally, the analysis results of the simulation signal and actual bearing vibration signals show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, an entropy-based grey wolf optimizer (IEGWO) algorithm is proposed for solving global optimization problems. This improvement is proposed to alleviate the lack of population diversity, the imbalance between exploitation and exploration, and the premature convergence of grey wolf optimizer algorithm and consists of three aspects: Firstly, we proposed an information entropy-based population generation strategy to optimize the distribution of initial grey wolf pack. Secondly, a modified dynamic position update equation based on information entropy is introduced to maintain the population diversity in the process of iteration, thus avoiding premature convergence. Thirdly, a nonlinear convergence strategy is proposed to balance the exploration and exploitation. The performance of the proposed IEGWO algorithm is assessed on the CEC2014 and CEC2017 test suites and compared with other meta-heuristic algorithms. Furthermore, two engineering design problems and one real-world problem are also solved using the IEGWO algorithm. The experimental and statistical results indicate that the IEGWO algorithm has better solution accuracy and robustness than the compared algorithms in solving global optimization problems. [ABSTRACT FROM AUTHOR]

Histogram equalization plays an important role in digital image preprocessing. However, traditional histogram equalization tends to have technical defects such as over-enhancement and artifacts. This can lead to a loss of detail and make the target image look unnatural. To resolve this issue, this paper presents an approach to image enhancement that uses a limited wavelet integer coefficient histogram to maintain high information entropy. First, a single-layer wavelet transform is performed on the input image to obtain a low-frequency sub-image and three high-frequency sub-images. Then, the low-frequency sub-image is subjected to a histogram-limitation technique to acquire the wavelet integer coefficients. After this, the processed low-frequency and three high-frequency sub-images are reconstructed to output a single high-information enhanced image. An experiment was conducted that shows that the proposed method performs very well in terms of the amount of detailed information captured when compared to an existing improved method based on histogram equalization. In addition, the method can handle the enhancement of images across different dynamic ranges, especially images with narrow a dynamic range, thus improving the amount of detailed information in the output image and maximizing the visual effect for human observers. [ABSTRACT FROM AUTHOR]

The calculation results of marine environmental design parameters obtained from different data sampling methods, model distributions, and parameter estimation methods often vary greatly. To better analyze the uncertainties in the calculation of marine environmental design parameters, a general model uncertainty assessment method is necessary. We proposed a new multivariate model uncertainty assessment method for the calculation of marine environmental design parameters. The method divides the overall model uncertainty into two categories: aleatory uncertainty and epistemic uncertainty. The aleatory uncertainty of the model is obtained by analyzing the influence of the number and the dispersion degree of samples on the information entropy of the model. The epistemic uncertainty of the model is calculated using the information entropy of the model itself and the prediction error. The advantages of this method are that it does not require many-year-observation data for the marine environmental elements, and the method can be used to analyze any specific factors that cause model uncertainty. Results show that by applying the method to the South China Sea, the aleatory uncertainty of the model increases with the number of samples and then stabilizes. A positive correlation was revealed between the dispersion of the samples and the aleatory uncertainty of the model. Both the distribution of the model and the parameter estimation results of the model have significant effects on the epistemic uncertainty of the model. When the goodness-of-fit of the model is relatively close, the best model can be selected according to the criterion of the lowest overall uncertainty of the models, which can both ensure a better model fit and avoid too much uncertainty in the model calculation results. The presented multivariate model uncertainty assessment method provides a criterion to measure the advantages and disadvantages of the marine environmental design parameter calculation model from the aspect of uncertainty, which is of great significance to analyze the uncertainties in the calculation of marine environmental design parameters and improve the accuracy of the calculation results. [ABSTRACT FROM AUTHOR]

With the development of data mining, artificial intelligence, neural network, expert system and machine learning, information system (i-system) becomes more and more important. If the objects, attributes and information values in an i-system are replaced by cells, genes and gene expression values, respectively, then the i-system is said to be a gene space. Because gene expression data is characterized by small samples, high dimension and noise, there is considerable uncertainty in a gene space. Traditional machine learning and statistical methods are often powerless to a gene space. Granular computing (GrC) can effectively deal with various uncertainties. This paper studies the uncertainty measurement of gene space based on the class-consistent technology and discusses its application in gene selection from the perspective of GrC. A class-consistent relation between cells in a gene space is first established by the gene expression values of cells on the basis of class-consistent technology. Then, the information granules (i-granules) are obtained from a gene space by using the class-consistent relation. Next, two metrics (information granularity and information entropy) to measure the uncertainty of gene space are defined and their properties are also investigated. The results of numerical experiments and statistical tests verify their effectiveness. Furthermore, as their application to gene space, two gene selection algorithms are proposed. Finally, the clustering experiments and statistical tests on 16 gene spaces show that the designed gene selection algorithms outperform some state-of-the-art feature selection algorithms in terms of three clustering performance indicators. [ABSTRACT FROM AUTHOR]

In recent years, the energy consumption and associated carbon emissions from household consumption are increasing rapidly. It is an essential indicator to evaluate the extent of building a low-carbon society in China under the background of carbon peaking and carbon neutrality. Thus, we firstly calculate the information entropy of direct household consumption-induced carbon emission structure (IDHCES) in China during 2005–2019. Secondly, the spatial association network of the IDHCES is constructed by using the modified gravity model. Finally, we apply the social network analysis (SNA) to investigate spatial association characteristics of the spatial association network and explore influential factors by constructing the quadratic assignment procedure (QAP) model. There are four primary discoveries: (1) The balance of inter-provincial direct carbon emission structure from residential consumption is quite different. And the spatial linkage of the IDHCES is not just geographical proximity, but shows the complex network pattern. The extent of this network linkage is getting higher over time. (2) The spatial association network of the IDHCES presents an evident core-edge distribution. Most of the eastern provinces situated at the core of this network, such as Shanghai, Beijing and Tianjin, play essential roles, while most of the central and western provinces such as Qinghai, Guizhou, Xiangjiang and Ningxia are on the edge and have slight influence to this network. (3) The spatial association network for the IDHCES can be divided into four blocks, which are strongly related to each other and have obvious stepwise spillover effects. (4) The expansion of differences in per capita GDP, energy consumption per unit of GDP, family size and government investment in science and technology promotes the formation of the spatial association network of the IDHCES. While, the expansion of differences in geographical distance, population density and engel coefficient acts as a barrier. Based on the above analysis, we put forward some related suggestions for optimizing the information entropy of the direct carbon emission structure from Chinese residents’ consumption. [ABSTRACT FROM AUTHOR]

There are various information theoretic measures to study many complex systems. Most of the attention has been confined to the study of the free and confined hydrogen atom for the ground state. The Shannon entropy, Fisher entropy, Renyi entropy, and Tsalli entropy have been investigated extensively in the literature. These entropies are non-extensive and employ only the first moment of probability distribution functions. In the present work, we have presented results of the confined Hydrogen atom in a hard and a soft wall, in which higher moments of probability distribution functions are used that have a close relationship with entropic moments. These moments are the average values of nth power of probability distribution functions. The n = 2 moments in (r -) and (p -) spaces are specifically known as Onicescu energies and are denoted by E r and E p . Since the position (r -) and the momentum (p -) wavefunctions are connected through Fourier transformation, all the moments in (r -) and (p -) spaces exhibit contrasting behaviour. For large confining radius, these moments attain their asymptotic values of the free Hydrogen atom. In confining environments, the (r -) and (p -) curves cross each other. The product of each moment in (r -) and (p -) spaces remain almost constant, in conformity with the Uncertainty product. The effect of confinement is noticeable below the crossing points. We have also evaluated E r , for a confined attractive short-range spherical Gaussian type potential. [ABSTRACT FROM AUTHOR]

The open interconnection of information networks, the inherent vulnerabilities and design deficiencies of information system assets have brought great difficulties to security and management of information systems, posing potential security risks to the transmission, storage and processing of data. In this study, in order to work out a decision-making plan about risk assessment of information system assets, Euclidean distance is firstly used to calculate the deviation degree of expert evaluation data, referring to the deviation between index evaluation data and evaluation average value of different experts, in combination with the fact that the evaluation data is interval-valued intuitionistic fuzzy number (IVIFN) and the index weight is unknown, and then the index weight vector is determined by the index importance calculation method based on information entropy. Next, in the decision matrix processing, interval intuitionistic fuzzy ideal points (IIFIP) and related theories are used to aggregate interval-valued intuitionistic fuzzy information, calculate the score vector of each scheme, and a projection model is constructed to measure the similarity between each scheme and IIFIP. The proposed projection model not only can describe the uncertainties of the problems and the hesitancy of the decision makers, but also can decrease subjective and increase objectives of the decision making results. Finally, it is verified through the case analysis that the proposed method has good practicability and effectiveness in the security risk assessment. [ABSTRACT FROM AUTHOR]

The projection pursuit model is an important tool for processing high-dimensional nonnormal and nonlinear data, and it has a wide range of applications. In this paper, clustering analysis and information entropy theory are simultaneously introduced into the projection pursuit model. The K-means clustering method is used to cluster high-dimensional data, and information entropy is used to measure the overall dispersion and local aggregation of projection data. A projection pursuit model based on clustering and information entropy is proposed. The new model has both the classification advantages of clustering analysis and the evaluation advantages of the projection pursuit model. In the case test, different cases are tested by using the Shapiro-Wilk test, Kolmogorov-Smirnov test, Epps-Pulley test, etc. The results show that in most cases, the new model is better than the original model, and the advantage is clearer when the data dimension is higher. Finally, the new model is applied to the agricultural drought risk assessment of Qiqihar, a major grain-producing area in China that is prone to drought. The regional agricultural drought risk shows a downward trend over time. Spatially, the risk of the central and southern regions is low, while the risk of the northern and western regions is high. Regional ability to resist disasters is the main reason for the spatial and temporal differences. This paper extends projection pursuit model theory, analyzes the characteristics of the spatiotemporal variation in regional agricultural drought risk, and provides a reference for identifying the hidden dangers of drought disasters and disaster reduction. [ABSTRACT FROM AUTHOR]

Randomized machine learning focuses on problems with considerable uncertainty in data and models. Machine learning algorithms are formulated in terms of a functional entropy-linear programming problem. We adapt these algorithms to forecasting problems on an example of the evolution of thermokarst lakes area in permafrost zones. Thermokarst lakes generate methane, a greenhouse gas affecting climate change. We propose randomized machine learning procedures using dynamic regression models with random parameters and retrospective data (climatic parameters and remote sensing of the Earth's surface). The randomized machine learning algorithm developed below estimates the probability density functions of model parameters and measurement noises. Randomized forecasting is implemented as algorithms transforming the optimal distributions into the corresponding random sequences (sampling algorithms). The randomized forecasting procedures and technologies are trained, tested, and then applied to forecast the evolution of thermokarst lakes area in Western Siberia. [ABSTRACT FROM AUTHOR]

In this paper, the indoor loading and unloading tests and acoustic emission (AE) monitoring tests of marble under different stress paths are carried out. Based on the AE events in the process of rock failure, this paper introduces the concept of information entropy of Shannon, and defines a new cumulative state information entropy which can characterize the degree of disorder and accumulation of rock microcracks. This paper also offers a comparative analysis on the evolution law of rock entropy under different paths and different loading and unloading conditions. The results show that the newly defined cumulative state information entropy can well reflect the order change of the loading and unloading process system, and there are significant differences in entropy evolution under different stress paths, but the entropy generally evolves with the development of the system from "quasi-equilibrium" to "far away from equilibrium". The higher the unloading level is, the greater the confining pressure is, the stronger the unloading disturbance is, and the entropy value drops sharply at the starting point of unloading. The faster the unloading rate is, the later the rock fracture instability is, and the later the entropy drop point of the system appears. The change rate of entropy value ∣ΔH∣ = 0.1 can be used as the precursory signal threshold of rock confining pressure unloading disturbance damage and peak rupture instability. [ABSTRACT FROM AUTHOR]

Unrefined and highly corrosive upstream petroleum resources and complex operating environments pose a significant threat to the integrity and safety of gathering pipelines. The present study proposed a novelty method to perform a risk assessment for gathering pipelines. The use of historical failure data developed a fishbone diagram model of hazard factors. The risk index system was developed based on the KENT method, including failure likelihood and failure consequence coefficient models. Information entropy theory was used to determine the weight of each indicator. Combined with the area-level safety design coefficient, The welding institute (TWI) method was improved to perform risk classification for different areas. The proposed method was applied to 81 gathering pipelines. Results demonstrated that the proposed method could meet the actual conditions of gathering pipelines, improving upstream energy security. [ABSTRACT FROM AUTHOR]

In today's information age, information is gathered from text and more complex media, such as images, audio, and video. Among these data sources, the rapid growth of video information has led to it to gradually become the main source of information in people's lives. Video information is characterized by many kinds of information, complex forms, and a low degree of structure. Therefore, effectively classifying, managing and retrieving video information has become a difficult problem to solve. In this paper, an improved crow search algorithm is used to process video images, and the information entropy is used to extract the key frames, which reduces the computation burden of each frame feature calculation and feature contrast process, thus shortening the key frame detection time. Then, all the feature sets are extracted and used as input for an HMM according to the observed sequence O = O 1 , O 2 , O 3 , · · · , O T of the input image or video data and the initial model parameters λ = (π , A , B) . According to the training rules, the model parameters are repeatedly adjusted and modified, and the new model λ ¯ is constructed step by step to realize the retrieval of multimedia images and videos. The experimental results show that the method has obvious advantages in terms of the retrieval time and retrieval effect and provides new ideas for multimedia image and video retrieval. [ABSTRACT FROM AUTHOR]

Water serves numerous purposes besides drinking, such as irrigation and industrial usage. Most water quality indices developed have primarily focused on drinking water quality. However, assessing other functionalities of water bodies is also equally essential. The present study proposes a novel technique to measure water quality for two highly specific water use, i.e., assessing heavy metal contamination and irrigation suitability. The ambiguities in the current practice of entropy weights were identified, and a novel method was proposed, considering a three-dimensional approach instead of the conventional two-dimensional procedure. Weights to different parameters were assigned based on the probability estimates obtained from the frequency of observed values within acceptable limits. The proposed method's reliability, correctness, and applicability were tested using Deepor Beel's water quality dataset. Results were highly consistent with the experimental values and correlated well with other established methods. The efficacy of the method was determined by employing sensitivity analyses. Both indices showed high reliability and correctness, as no single parameter was found to be highly sensitive compared to others. Therefore, the proposed methodology proved to be the most reasonable, incorporating all the factors required for a reliable water quality monitoring program. [ABSTRACT FROM AUTHOR]