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The traditional "forecast-response" paradigm is facing significant practical challenges. For example, when different scenarios require different reaction mechanisms, the applicability of this method is weak since this paradigm describes the crisis itself from a macroperspective, neglecting analyzing emergency response measures from a microperspecti...
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... Furthermore, the incident response's evolutionary precision was examined by contrasting scenario parallels. Thus, the proposed approach can offer a theoretical foundation for deploying a "scenario-response" paradigm [21]. ...
... Smiti noted that if a data point has a significantly higher score than the rest of the data, it is considered an outlier [21]. Once outliers are identified, they can be further analyzed to determine what type of malicious activity is taking place [23]. ...
With the increase of cyber-attacks and security threats in the recent decade, it is necessary to safeguard sensitive data and provide robust protection to information systems and computer networks. In this paper, an anomaly-based network outlier detection system (NODS) is proposed and optimized to check and classify the incoming network traffic stream’s behaviours that affect the computer networks. The proposed NODS has high classification efficiency. Network connection events classified as outliers are reported to the network admin to drop and block its packets. The NSL-KDD and CICIDS2017 intrusion datasets were employed to build the proposed system and test its detection capabilities. Sequential scenarios were implemented to optimize the system’s effectiveness. Network features were normalized by min-max and Z-Score approaches, while the relevant features were selected individually by the principal component analysis (PCA) and correlated features selection (CFS) techniques. Support vector machine (SVM) and Gaussian Naive Bayes (GNB) algorithms are used to build the detection model, while the Genetic algorithm (GA) was employed to tune their control parameters. The obtained evaluation results proved that the proposed SVM based NODS is characterized by low false alarms and detection time as well as high classification accuracy. Furthermore, a comparative analysis was conducted with other existing techniques, and the results obtained demonstrate the effectiveness of the proposed SVM-IDS.
... On the other hand, the stated measure can be utilized to solve other real-life problems under different environment to address the ambiguity in the process. The suggested approach has tremendous potential in fields including flow-shop scheduling problem (Lu et al. 2023), evidence theory (Xie et al. 2023), Supply Chain with Uncertain Demands (Li et al. 2020), unmanned aerial vehicle with uncertainties where decisions under unpredictability and inaccurate facts are frequent. Finally, we shall extend our approach to analyze the different applications related to different tools of artificial intelligence such as multi-objective decision-making or neural network (Cao et al. 2020;Ma et al. 2023). ...
The intuitionistic fuzzy rough set (IFRS) is a robust framework that plays a vital role in reducing the uncertainty from the extracted information from real-life scenarios. In this article, we proposed some aggregation operators (AOs) based on Schweizer-Sklar t-norm (SSTNrM) and Schweizer-Sklar t-conorm (SSTCNrM). These AOs include intuitionistic fuzzy rough weighted averaging (IFRSSWA) and intuitionistic fuzzy rough weighted geometric (IFRSSWG) operators for intuitionistic fuzzy rough values (IFRVs). The basic properties of the developed AOs are investigated and then applied to the multi-attribute group decision-making (MAGDM) problem. The variation of the obtained results is obtained by changing the values of the involved parameter in SSTNrM and SSTCNrM. Additionally, the obtained results are compared with those obtained by existing AOs. Furthermore, all the observations and results are presented graphically.
... In recent years, the papers including various methods such as variational iteration method, 6-10 homotopy perturbation method, 11,12 Adomian decomposition method, [13][14][15] Lucas polynomial method, 16 Taylor matrix and collocation method, 17 Bessel function method, 18,19 Keller-Segel method, 20,21 Mathieu-Duffing system 22 and Bayesian network [23][24][25] have been published for solving the Riccati differential equations. ...
In order to solve the Riccati type functional differential equations under mixed condition, this research suggests a collocation approach based on shifted Legendre polynomials. These equations are seen in physics and electronic sciences. By means of the evenly spaced collocation points, the shifted Legendre polynomials, their derivatives and matrix forms, the method is constructed. It reduces the problem into a system nonlinear algebraic equations. Error analysis is made. The method is applied to numerical examples and their results are compared with other methods from literature. And also, the method is applied to nonlinear RL electrical circuit model. The results show that the method is effective.
... In future work, the stated approach can be extended to other types of ambiguous and vague environments to address ambiguity in the decision-making approach. The researchers can also utilize the method in a wide range of research fields such as COVID analysis (Li et al., 2020a), robust optimization (Li et al., 2021), Monte carlo method (Xie et al., 2021), evidence theory (Xie et al., 2023), supply chain system (Li et al., 2020b). Finally, we shall extend our approach to analyze the different application related to different tools of artificial intelligence such as classification (Lu et al., 2023), multi-model fusion (Liu et al., 2023b) etc. ...
A R T I C L E I N F O Keywords: CPyFEWG operator CPyFEOWG operator CPyFEHG operator I-CPyFEOWG Operator I-CPyFEHG operator A B S T R A C T Since the start of COVID-19, a fair amount of work has been undertaken by scholars around the world to model its progression. It became clear from the start of pandemic that its progression is affected by various factors within different communities. Subsequently, the necessary means and the range of measures used to effectively control the virus would vary from place to place. And we have been witness to different approaches adopted around the world to maintain the virus under check both in the short term and the long term. So, in this unexpected situation, it is a great challenge for the world health organization (WHO) to save the lives of COVID-19 patients. For this, several mathematical models have been made for better understanding the coronavirus contagion. Mostly, these models are based on classical integer-order derivative using real numbers which cannot capture the fading memory. Thus, in this unexpected situation, fuzzy sets (FSs) are considered due to their inherent capability to deal with uncertainty. Fuzzy sets (FSs) theory has the ability to manage uncertain situations. Thus, the goal of this research is to present newly mathematical methods based on complex Pythagorean fuzzy sets (CPyFSs) and their operators, namely complex Pythagorean fuzzy Einstein weighted geometric operator, and induced complex Pythagorean fuzzy Einstein hybrid geometric operator to reduce the spreading rate of COVID-19. At the end of the paper an illustrative example is constructed to show the effectiveness, reliability of the new techniques.
... Zhang and Zhou proposed in 2022 that feature pyramid networks (FPN), an essential component of general-purpose object detectors, can greatly improve detection performance for objects of various scales. Bayesian learning is used by Xie et al. 39 , and Cheng et al. 40 www.nature.com/scientificreports/ the made predictions. Due to their exceptional ability to marginalize uncertainty associated with the parameter estimates, they can more effectively utilize relevant prior information and organically add robustness to the model. ...
Security threats posed by Ponzi schemes present a considerably higher risk compared to many other online crimes. These fraudulent online businesses, including Ponzi schemes, have witnessed rapid growth and emerged as major threats in societies like Nigeria, particularly due to the high poverty rate. Many individuals have fallen victim to these scams, resulting in significant financial losses. Despite efforts to detect Ponzi schemes using various methods, including machine learning (ML), current techniques still face challenges, such as deficient datasets, reliance on transaction records, and limited accuracy. To address the negative impact of Ponzi schemes, this paper proposes a novel approach focusing on detecting Ponzi schemes on Ethereum using ML algorithms like random forest (RF), neural network (NN), and K-nearest neighbor (KNN). Over 20,000 datasets related to Ethereum transaction networks were gathered from Kaggle and preprocessed for training the ML models. After evaluating and comparing the three models, RF demonstrated the best performance with an accuracy of 0.94, a class-score of 0.8833, and an overall-score of 0.96667. Comparative evaluations with previous models indicate that our model achieves high accuracy. Moreover, this innovative work successfully detects key fraud features within the Ponzi scheme dataset, reducing the number of features from 70 to only 10 while maintaining a high level of accuracy. The main strength of this proposed method lies in its ability to detect clever Ponzi schemes from their inception, offering valuable insights to combat these financial threats effectively.
... Optimal resource utilization is a crucial aspect in various fields, including computer science, engineering, medical science, and others. In computer science, efficient algorithms and data structures facilitate the maximum utilization of computational resources, ensuring speedy and accurate data processing [1][2][3]. In engineering, the judicious use of materials and energy is paramount, enabling sustainable designs and minimizing waste production [4][5][6]. ...
... The specifics of the IFHSE-Set with characteristics are described in Sect. 3. The definition of the CC and its key features are covered in Sect. ...
Human resource management is the process of making a company’s human resources decisions. In general, these decisions include hiring, firing, training, and developing people according to their positions and the needs of the organization. It includes a variety of policies and strategies designed to recognize the contribution that people make to an organization. The core goal of this article is to depict a novel fuzzy multi-criteria decision-making methodology for selecting employees. The purpose behind selecting employees is to identify and hire individuals who possess the required skills, qualifications, and attributes that align with the organization’s goals and job requirements. To reflect an inadequate assessment, ambiguity, and anxiety in making choices, the intuitionistic fuzzy hypersoft expert set is an extension of the intuitionistic fuzzy soft expert and hypersoft sets. It is a novel approach to decisions and intelligent computing in the face of uncertainty. The intuitionistic fuzzy hypersoft expert set has a better ability to handle ambiguous and unclear data. In the research that follows, the ideas and characteristics of the correlation coefficient and the weighted correlation coefficient of the intuitionistic fuzzy hypersoft expert sets are proposed. Under the aegis of intuitionistic fuzzy hypersoft expert sets, a TOPSIS based on correlation coefficients and weighted correlation coefficients is introduced. Aside from that, we also covered aggregation operators, including intuitionistic fuzzy hypersoft weighted geometric operators. The decision-making process is suggested in an intuitionistic fuzzy hypersoft expert environment to resolve uncertain and ambiguous information, relying on the well-established TOPSIS approach and aggregation operators. An illustration of decision-making challenges shows how the suggested algorithm can be used. The efficacy of this strategy is lastly demonstrated by comparing its benefits, effectiveness, flexibility, and numerous current studies.
... In recent years, the decrease in the annual rainfall rate, the lack of proper soil moisture, and as a result, the decrease in the amount of water stored in aquatic ecosystems have been known as factors that threaten the quantity and quality of products (Guo et al. 2022;Xie et al. 2023). One of the most important challenges in this regard is human food security and as a result social and economic problems. ...
Dry and humid climates have different potentials for providing soil moisture. Agricultural drought is a confirmed criterion for evaluating production potential in agriculture, which is discussed in this research. Therefore, this research aims to investigate drought using meteorological and agricultural drought indicator data in four climatic regions of China (humid, semi-humid, semi-arid and dry). For this purpose, climatic information was collected in the last 20 years, and the values of the standard precipitation index (SPI) and reconnaissance drought index (RDI) were determined. Examining the indicators indicates that the indicators are high in all the years under review in dry areas. In the semi-arid region, there was a significant decrease in the average value of the indices in July and August in the years 2017–2022. Drought indicators did not show a critical situation in humid and semi-humid areas, and there was sufficient moisture for plants throughout the year. The results showed that there was a high correlation between the SPI and the RDI in all the identified areas. In addition to rainfall, the RDI also includes transpiration and is more sensitive, especially in dry areas where transpiration is higher than rainfall.
... In the future, on the one hand, we can extend the presented work to overcome such shortcoming by using extensions of the fuzzy sets. Also, we can extend the application of the work to the diverse areas related to Clustering and classification features [56,57], Bayesian network [58], deep learning [59], and its application to the different sectors of optimalization data-driven approaches [60,61,62]. ...
The main aim of this paper is to present a new multi-attribute decision-making (MADM) approach for solving the problems under the uncertain and complex environment. The key challenges during any MADM problem are how to quantify the objective uncertainty information in the data and how to aggregate such collective information. To answer this, in this paper, we utilize the concept of the bipolar fuzzy information to mark the information in terms of the positive and negative support. To aggregate this different information, we propose some power aggregation operators based on the Aczel-Alsina operators (AAO). The AAOs are the generalized t-norm based operations with an additional parameter to analyze the influence of the expert preferences. Based on these AAO and bipolar fuzzy information, we stated bipolar fuzzy AA power weighted averaging and geometric operators and investigate their features. Later, based on these operators, we establish a MADM algorithm to solve the decision-making problems. The applicability of the stated algorithm is demonstrated through a case study related to quantum computing. The comparative studies and advantages of the study are also analyzed with the various prevailing theories.
... Besides, some local derivatives are conformable derivative [28,19], beta-derivative [4], M-derivative [44,1,5] and so on. Non linear partial differential equations (NPDEs) give a central part for the learning of nonlinear substantial models as well as used to describe complex phenomenon such as mechanical systems [20], electronics [55,31,48,49,29,30,52,51,12], biological models [25,7], optics [23,22,26,21]. ...
... In future work, the stated approach can be extended to other types of ambiguous and vague environments to address ambiguity in the decision-making approach. The researchers can also utilize the method in a wide range of research fields such as data-driven learning (Song et al. 2022), evidence theory (Xie et al. 2023), stock intelligent investment (Li and Sun 2020). Finally, we shall extend our approach to analyze the different application related to different tools of artificial intelligence such as multi-optimization (Cao et al. 2020a, b) and neural network (Li and Sun 2021). ...
The paper aims is to present a multi-criteria decision-making algorithm for solving decision-making problems with the utilization of the C-IFSs (circular intuitionistic fuzzy sets) features. In it, the uncertainties present in the data are handled with the help of C-IFSs in which we considers the circular rating of each object within a certain radius. Later on, we propose a novel algebraic framework for C-IFSs based on Archimedean t-norm operations, including addition, multiplication, subtraction, and division. These operations enable the aggregation of preferences from multiple experts into a single ranking. Also, we propose an extended EDAS (Evaluation Based on Distance from Average Solution) method, which utilizes weighted aggregation operators and defuzzification techniques to rank alternatives. To validate our approach, we provide a numerical example and compare the results with existing methods. Additionally, we discuss the time complexity of the algorithm. The proposed methodology offers decision-makers the flexibility to analyze the influence of different ratings on the final decision and select suitable parameters.
